Structural and functional neural correlates of developmental dyspraxia in the mirror neuron system

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STRUCTURAL
AND
FUNCTIONAL
NEURAL
CORRELATES
OF
DEVELOPMENTAL

DYSPRAXIA
IN
THE
MIRROR
NEURON
SYSTEM

by

Julie
Marie
Werner

A
Dissertation
Presented
to
the

FACULTY
OF
THE
USC
GRADUATE
SCHOOL

UNIVERSITY
OF
SOUTHERN
CALIFORNIA

In
Partial
Fulfillment
of
the

Requirements
for
the
Degree

DOCTOR
OF
PHILOSOPHY

(OCCUPATIONAL
SCIENCE)

December
2013

Copyright
2013

Julie
Marie
Werner

ii
ACKNOWLEDGEMENTS

I
would
like
to
thank
those
who
have
supported
me
throughout
my
journey

in
completing
this
dissertation.

This
work
would
have
been
extremely
difficult,

unimpassioned,
and
maybe
even
impossible
were
it
not
for
my
family,
friends,

colleagues,
and
mentors
encouraging
me
and
inspiring
me
along
the
way.

I
would
like
to
first
thank
my
mentor,
Dr.
Lisa
Aziz-‐Zadeh,
for
giving
me

numerous
opportunities
to
develop
as
a
scientist
and
for
challenging
me
to
stretch

my
potential.

Thank
you
for
your
enthusiasm,
your
contagious
passion
for
cognitive

neuroscience,
and
your
always-‐open
door.

Together
with
Dr.
Sharon
Cermak,
your

thought-‐provoking
perspectives
enabled
me
to
bridge
neuroscience
research
with

my
background
in
occupational
therapy,
two
things
that
I
love.

Sharon,
thank
you

also
for
your
support,
advocacy,
and
knowledge.

I
also
want
to
thank
Dr.
Erna

Blanche
for
assistence
in
the
development
of
my
dissertation
topic
and
for
providing

resources
and
helpful
discussion
throughout
the
research.

In
addition
to
my
formal
mentors,
I
am
grateful
to
the
numerous
instructors

in
the
Division
of
Occupational
Science
and
Occupational
Therapy
who
taught
me

clinical
skills,
critical
thinking,
and
who
helped
me
develop
myself
in
various

capacities
as
a
person,
an
occupational
therapist,
and
a
research
scientist.

I
would

especially
like
to
thank
Dr.
Nancy
Bagatell
for
her
incredible
skill
as
an
instructor

and
as
a
model
of
how
introductory
neuroscience
should
be
taught.

Thank
you
to

Dr.
Linda
Fazio
for
her
ever
wise
advice
and
wonderfully
calming
voice.

Thank
you

iii
to
Kate
Crowley
for
providing
such
a
positive
experience
in
my
very
first

undergraduate
OT
course
at
USC.

Dr.
Gelya
Frank,
thank
you
for
challenging
me
to

think
differently
and
for
helping
me
keep
a
larger
context
for
my
research
in
mind.

I

would
like
to
thank
Corey
Fitzgerald
for
imparting
her
knowledge
and
(hopefully
at

least
a
little
of)
her
skill
to
me,
and
also
for
her
friendship.

I
also
want
to
thank
my

OT
classmates,
especially
those
who
lived
in
the
OT
House.

You
guys
were
such
an

essential
part
of
my
education-‐-‐you
kept
me
going
with
your
encouragement
and

viewpoints
and
my
friendships
with
you
mean
so
much
to
me.

Thank
you
to
all
of
my
labmates,
past
and
present.

I
am
so
grateful
to
have

met
each
one
of
you
and
have
benefitted
immensely
from
your
cheerful
presence

and
friendship.

I
have
especially
appreciated
your
comeraderie,
open-‐mindedness,

creativity,
and
continual
willingness
to
lend
your
particular
expertise.

In
particular,

Tong
Sheng,
thank
you
for
getting
me
started
on
my
very
first
research
projects,
for

your
expert
statistical
advice,
for
our
lively
discussions,
and
for
always
keeping
it

real.

Lei
Liew,
thank
you
for
just
getting
it
(enough
said).

I
want
to
thank
Mona

Sobhani
for
this
last
year,
especially.

Thank
you
to
Katie
Garrison
for
sitting
by
me

during
my
first
scan.

I
would
like
to
thank
Panthea
Heydari
for
being
such
a
joy
to

be
around,
both
in
the
lab
and
out.

Thank
you
to
Vesna
Gamez-‐Djokic
for
sharing

your
perspective
and
for
your
kind
and
calming
nature.

A
most
sincere
thank
you
to
all
of
the
BCI
grad
students:
Helder
Felipe,

Kingson
Man,
Fei
Yang,
Glenn
Fox,
Ryan
Essex,
Vanessa
Singh,
and
Kevin
Crimi.

Being
around
all
of
you
has
been
a
blast.

Helder,
thank
you
for
keeping
me
laughing.

iv
Kingson,
thanks
for
being
my
wingman.

Fei,
thank
you
for
your
kindness
and

willingness
to
help
out.

Glenn,
thanks
for
teaching
me
through
your
work
that

writing
this
acknowledgments
section
is
actually
an
expression
of
a
human
emotion

that
can
be
studied
with
fMRI,
and
for
car
talk.

Ryan,
thank
you
for
our
chats
and

debates
and
for
drawing
things
on
my
white
board
and
for
always
sending
me

amusing
messages.

Vanessa,
thank
you
for
being
so
enthusiastic
and
happy
all
the

time.

Kevin,
thanks
for
putting
up
with
all
of
us
calling
you
k-‐space
and
for
the

hand-‐me-‐downs
when
you
moved.

I
owe
a
huge
thank
you
to
Drs.
Hanna
and
Antonio
Damasio
for
hosting
me
at

the
Brain
and
Creativity
Institute,
for
encouraging
me
to
grow
and
stretch,
and
for

being
so
open
and
kind.

The
BCI
is
truly
a
special
place
and
the
two
of
you
really
do

make
it
feel
like
a
family.

I
would
also
like
to
thank
the
BCI
post-‐docs
for
your

wisdom
and
advice:
Sarah
Gimbel,
Assal
Habibi,
and
Gil
Carvalho.

Thank
you
to

Jonas
Kaplan
for
knowing
the
answer
to
every
technical
question
I
ever
asked
you

and
a
wealth
of
other
information.

Thank
you
to
Morteza
Dehghani
for
frequently

checking
in
on
me,
up
to
the
very
end.

Special
thanks
to
Susan,
Pamela,
Denise,

Faith,
and
Cinthya
for
keeping
everything
running.

To
my
OS
PhD
cohort,
a
big
thank
you
for
your
diverse
opinions,
your

company
at
conferences,
and
for
sticking
together
despite
our
widely
different

pursuits.

Thank
you
to
Aaron
Bonsall,
Leah
Stein,
and
Amanda
Foran.

I
would

especially
like
to
thank
Amanda
for
being
more
than
just
a
member
of
the
cohort.

I

am
so
glad
to
count
you
as
a
good
friend.

There
is
no
one
with
whom
I
would
rather

v
throw
cheese
off
a
parking
structure
or
who
would
be
so
passionate
about
salmon

(the
lifeblood
of
the
Pacific).

Whenever
I
needed
someone
who
would
understand
it

all,
I
knew
I
could
count
on
you
for
indulging
in
passive
coping
with
me.

I
want
to
thank
my
wonderful
friends
for
believing
in
me,
cheering
me
on,

and
for
all
of
our
fun
and
silly
adventures.

Our
conversations
and
fun
times
together

were
just
the
right
amount
of
occasional
distraction
to
keep
me
on
an
even
keel

through
this
process.

Thank
you
to
Kirsten
Muncy,
my
good
friend
from
way
back

when.

I
can't
even
begin
to
describe
everything
about
our
friendship
that
makes
me

smile
(and
all
the
memories
that
make
me
laugh).

The
constancy
of
knowing
I
can

always
count
on
your
support
and
likemindedness
has
been
a
real
support.

Thank

you
to
Renee
Moreno
for
everything
Renee-‐like
that
you
do.

Your
confidence,
spirit,

and
sense
of
humor
are
inspiring
to
me.

I
want
to
thank
Arlene
Hsu
for
so
many

good
times
and
so
many
more
to
come.

Arlene,
you
are
one
of
the
kindess,
most
fun,

and
most
generous
people
I
am
honored
to
know.

Thank
you
to
the
W.I.
trivia
team

for
frequently
being
too
educated
for
trivia.

Thank
you
to
all
the
friends
I
made
at

Casa
Colina
among
the
rehab
staff.

Thank
you
especially
to
Claire
Malawy
for
being

a
fantastic
role
model
for
so
many
things,
both
professionally
and
personally,
and

for
being
such
a
fantastic
hostess
of
gatherings.

Thank
you
to
the
rehab
staff
at

California
Hospital
for
good
times
at
work
and
beyond.

Thank
you
to
Ivo
Krka
for
so
much
support
and
encouragement,
and
for
truly

believing
in
me.

I
only
wish
I
could
better
put
into
words
my
gratitude
toward
you.

vi
I
would
like
to
thank
Dr.
Jerry
Walker
("Dr.
JW,
Sr.").

I
strongly
value
the

friendship
we
have
built
over
the
past
ten
years
and
I
can't
thank
you
and
Lora

enough
for
taking
me
in
as
a
part
of
your
family.

Thank
you
for
keeping
me
well-‐fed,

for
always
sharing
your
umbrella
when
it
is
raining,
laughing
with
me,
singing

barbershop
chorus
songs
to
me,
and
never
being
disinclined
to
help
me
with
just

about
anything.

Thank
you
especially
for
your
genuine
encouragement
to
take
my

graduate
studies
further
and
for
being
truly
invested
in
me.

I
wish
to
extend
a
very
important
thank
you
to
my
family
for
your

unconditional
love,
for
always
believing
in
me,
and
for
wanting
the
best
for
me.

To

my
sister,
Katie
Parks,
I
know
I
can
count
on
you
for
anything
and
I
could
not
ask
for

a
better
sister.

Thank
you
for
your
listening
ear,
for
inspiring
me
by
your
example,

and
especially
for
reading
and
editing
my
dissertation!

Danny,
thank
you
for
taking

a
genuine
interest
in
my
work
and
for
all
of
our
thought-‐provoking
conversations.

Thank
you,
Mom,
for
encouraging
me
to
try
my
best,
raising
me
to
value
education,

and
being
my
biggest
fan.

Thank
you
to
Grandma
and
Grandpa
Esch
for
always

taking
the
time
to
talk
with
me.

Thank
you
to
Grandma
Ginnie
for
making
sure
I
was

having
fun
in
between
working
hard.

To
my
Dad,
thank
you
for
your
example
of

boldness
and
perseverance,
and
your
willingness
to
lend
advice
at
any
time.

Finally,
I
want
to
acknowledge
the
Division
of
Occupational
Science
and

Occupational
Therapy
and
the
Brain
and
Creativity
Institute
for
all
of
the
training,

resources,
and
support
throughout
my
time
in
graduate
school.

I
would
also
like
to

the
University
of
Southern
California,
in
general,
for
the
excellent,
well-‐rounded

vii
education
I
have
received
beginning
with
my
undergraduate
studies
and

culminating
in
the
Doctor
of
Philosophy
degree.

Fight
on!

viii
TABLE
OF
CONTENTS

ACKNOWLEDGEMENTS

iv

LIST
OF
TABLES

x

LIST
OF
FIGURES

xi

ABSTRACT

xiii

CHAPTER
ONE:
Introduction

1

The
Mirror
Neuron
System
(MNS)

6

Properties
of
mirror
neurons
in
monkeys

8

The
human
mirror
neuron
system
and
imitation

10

Coding
of
biologic
actions
in
the
superior
temporal
sulcus

12

Nuances
of
mirror
neuron
system
activation

13

Imitation
learning

18

Imitation,
empathy,
and
the
mirror
neuron
system

21

Evidence
of
brain-‐based
differences
in
developmental
dyspraxia

22

White
Matter
Structural
Connections
Within
the
MNS

25

Cortical
Thickness
in
MNS
regions

29

Theoretical
Models

32

CHAPTER
TWO:

Functional
Brain
Differences
in
the
Mirror
Neuron

36

System
During
Imitation
and
Motor
Planning
in
Young
Adults

with
Developmental
Dyspraxia

Introduction

38

Methods

42

Participants

42

Design

43

Image
acquisition

46

Image
processing

47

Conjunction
analysis

48

ROI
analyses

49

Results

50

Shared
regions
for
execution
and
observation

50

Region
of
interest
analyses
for
imitation
and
motor
planning

53

Whole
brain
results
for
individual
conditions
compared
to
rest

58

Discussion

61

Conclusion

68

ix
CHAPTER
THREE:

Microstructural
Differences
in
Left
Arcuate
Fasciculus

70

in
Young
Adults
with
Developmental
Dyspraxia

Introduction

71

Methods

76

Participants

76

Image
acquisition

77

Data
pre-‐processing

78

Tensor
estimation

78

Registration

79

ROI
and
seed
definition

79

Tractography

81

Tract
statistics

82

Results

83

Tractography

83

Tract
statistics

83

Discussion

85

Conclusion

89

CHAPTER
FOUR:
Imitation
Skill
Predicts
Cortical
Thickness
in
Mirror

91

Neuron
System
Areas
in
Individuals
with
Developmental
Dyspraxia
and

Their
Typically
Developing
Peers

Introduction

93

Methods

96

Participants

96

Image
acquisition

98

Cortical
surface
reconstruction
and
parcellation
of
ROIs

98

Statistical
analyses

100

Results

101

Discussion

117

Conclusion

122

CHAPTER
FIVE:
Conclusions

123

Summary
of
the
Research

124

Implications
and
Future
Directions

128

REFERENCES

134

APPENDIX
A:

Background
on
Methods
Employed

167

APPENDIX
B:

Recruitment
Information

173

APPENDIX
C:

Instruments

176

APPENDIX
D:

SIPT
Postural
Praxis
Test
Pilot
Data

185

APPENDIX
E:

Additional
Descriptive
Measures
of
Participants

191

x
LIST
OF
TABLES

Table
2.1.
Participant
demographics

44

Table
2.2.
Co-‐activations
during
execution
and
observation

51

for
the
contrast
TD-‐DD

Table
2.3.
Whole
brain
activations
for
individual
conditions

59

compared
to
rest

Table
3.1.
Arcuate
fasciculus
tract
metrics

85

Table
4.1.
Mean
cortical
thickness
in
regions
of
interest

103

Table
4.2.
Correlations
between
predictor
variables

103

Table
4.3.
Predictors
of
cortical
thickness
by
region
of
interest

104

Table
C.1.
Instruments

184

Table
E.1.
Additional
descriptive
measures
of
participants

191

xi
LIST
OF
FIGURES

Figure
1.1.
Lateral
view
of
brain
with
MNS
regions

7

Figure
1.2.
Tractography
reconstruction
of
the
arcuate
fasciculus

26

Figure
2.1.
Design
and
conditions
for
fMRI
study

45

Figure
2.2.
Regions
of
interest
in
fMRI
study

49

Figure
2.3.
fMRI
results
of
conjunction
analysis

52

Figure
2.4.
ROI
results
of
imitation
minus
execution
to
static
control

54

Figure
2.5.
Localizations
of
imitation
minus
execution
to
static
control

55

Figure
2.6.
ROI
results
of
imitation
motor
planning
minus
observation

56

Figure
2.7.
Localizations
of
imitation
motor
planning
minus
observation

57

Figure
2.8.
Localizations
of
whole
brain
activations
for
TD
>
DD

60

Figure
3.1.
Cortical
ROIs
used
for
seeding
tractography

80

Figure
3.2.
Arcuate
fasciculus
tractography
results

84

Figure
4.1.
Regions
of
interest
for
cortical
thickness
study

102

Figure
4.2.
Partial
regression
leverage
plot
in
left
POp

106

Figure
4.3.
Partial
regression
leverage
plot
in
right
POp

107

Figure
4.4.
Partial
regression
leverage
plot
in
left
PTr

108

Figure
4.5.
Partial
regression
leverage
plot
in
right
PTr

109

Figure
4.6.
Partial
regression
leverage
plot
in
left
SMG

111

Figure
4.7.
Partial
regression
leverage
plot
in
right
SMG

112

Figure
4.8.
Partial
regression
leverage
plot
in
left
AG

113

Figure
4.9.
Partial
regression
leverage
plot
in
right
AG

114

xii
Figure
4.10.
Partial
regression
leverage
plot
in
left
pSTS

115

Figure
4.11.
Partial
regression
leverage
plot
in
right
pSTS

116

Figure
5.1.
Potential
additional
brain
regions
involved
in
DD

130

Figure
5.2.
Schematic
of
functional-‐structural
developmental
model

132

Figure
D.1.
Frequecy
distribution
of
17-‐item
SIPT
Postural
Praxis
test

188

Figure
D.2.
Correlation
plot
between
Postural
Praxis
test
and
questionnaire

189

xiii
ABSTRACT

Developmental
dyspraxia
is
a
disorder
of
impaired
imitation
and
motor

planning.

Although
believed
to
be
of
neurological
origin,
the
neural
correlates
have

not
been
investigated.

One
neural
system
believed
to
be
essential
to
imitation
is
the

human
mirror
neuron
system.

This
work
postulates
that
differences
in
the
structure

and
function
of
the
mirror
neuron
system
may
underlie
imitation
and
motor

planning
impairments,
or
developmental
dyspraxia.

First,
the
current
work

explored
function
in
the
mirror
neuron
system
by
comparing
brain
activity
in
a

group
with
developmental
dyspraxia
to
a
typically
developing
group
using

functional
magnetic
resonance
imaging.

Second,
microanatomical
properties
of
the

structural
connections
between
nodes
of
the
mirror
neuron
system
were
explored

using
diffusion
tensor
imaging.

Third,
thickness
of
the
cortical
gray
matter
in
mirror

neuron
system
regions
was
measured
and
compared
to
imitation
skill.

Finally,
a

model
is
postulated
to
explain
how
all
of
these
neural
properties
may
relate
to
one

another
and
suggestions
for
future
research
and
implications
for
treatment
are

discussed.
The
current
work
is
the
first
to
comprehensively
address,
in
an

hypothesis-‐driven
manner,
multiple
structural
and
functional
neural
components
of

the
mirror
neuron
system
that
may
underlie
developmental
dyspraxia.

1
CHAPTER
ONE:

Introduction

Developmental
dyspraxia
(DD)
is
a
neurologically
based
disorder

characterized
by
impaired
imitation
and
motor
planning
and
coordination,
and

associated
with
decreased
performance
of
daily
activities
requiring
motor
skill

(Ayres,
1989/2004;
Dewey,
1995;
Dewey,
Cantell,
&
Crawford,
2007;
Gibbs,

Appleton,
&
Appleton,
2007;
Lalanne,
Falissard,
Golse,
&
Vaivre-‐Douret,
2012;

O’Brien,
Spencer,
Atkinson,
Braddick,
&
Wattam-‐Bell,
2002;
Reeves
&
Cermak,
2002;

Sinani,
Sugden,
Hill,
2011;
Vaivre-‐Douret
et
al.,
2011).

Developmental
dyspraxia
is

sometimes
categorized
as
a
subgroup
of
developmental
coordination
disorder

(DCD),
a
broader
diagnostic
term
for
describing
motor
coordination
difficulties
that

are
significantly
below
a
developmentally
appropriate
level
for
the
individual
and

not
otherwise
explained
by
obvious
neurological
damage
(Cermak,
Gubbay,
&

Larkin,
2002;
Dewey
et
al.,
2007;
Gibbs,
et
al.,
2007;
Sanger,
2003;
Sanger,
Delgado,

Gaebler-‐Spira,
Hallett,
&
Mink,
2003;
Sanger
et
al.,
2006).

Individuals
with
DD/DCD

may
have
difficulty
learning
and
imitating
skilled
or
sequenced
movements,

including
object
manipulation
and
tool
use,
assuming
body
postures,
gesturing,
and

carrying
out
multi-‐step
or
goal-‐directed
actions
(Ayres,
1965;
Ayres,
1989/2004;

Biancotto,
Skabar,
Bulgheroni,
Carrozzi,
&
Zoia,
2011;
Dewey,
1995;
Dewey
et
al.,

2007;
Lalanne
et
al.,
2012;
Poole,
Gallagher,
Janosky,
&
Qualls,
1997;
Sanger,
2003;

Sanger
et
al.,
2003;
Sanger
et
al.,
2006;
Sinani,
Sugden,
&
Hill,
2011;
Wilmut,
Wann,

2
&
Brown,
2006).

Functionally,
individuals
with
DD/DCD
have
trouble
coordinating

their
movements
to
learn
new
fine
and
gross
motor
tasks
required
to
perform

instrumental
daily
activities,
such
as
tying
shoelaces,
dressing,
handwriting,
playing

sports,
and
using
playground
equipment
(Cermak
et
al.,
2002;
Gheysen,
Van

Waelvelde,
&
Fias,
2011;
Missiuna
&
Polatajko,
1995;
Reeves
&
Cermak,
2002).

In

addition,
decreased
educational
performance
and
poor
social
emotional
adjustment,

perhaps
a
result
of
participation
limitations,
have
been
found
in
children,

adolescents,
and
adults
with
developmental
motor
difficulties
(Chen
et
al.,
2009;

Cantell
&
Kooistra,
2002;
Cousins
&
Smyth,
2003;
Kirby,
Sugden,
Beveridge,
&

Edwards,
2008).

Historically,
a
number
of
labels
have
been
used
in
describing
specific

developmental
disorders
of
motor
function,
such
as
clumsiness
(Gubbay,
Ellis,

Walton,
&
Court,
1969;
Dare
&
Gordon,
1970),
minimal
cerebral
dysfunction
(Dare
&

Gordon,
1970),
physical
awkwardness
(Wall,
Reid,
and
Paton,
1990),
and
congenital

maladroitness
(cited
by
Dewey,
1995;
cited
by
Cermak
et
al.,
2002).

In
addition,

dyspraxia
is
sometimes
incorrectly
used
interchangeably
with
apraxia,
a
condition

in
which
normal
function
is
disrupted
in
adults
or
in
previously
typically
developing

children
due
to
an
upper
motor
neuron
lesion
(e.g.,
after
a
stroke;
Goodgold-‐
Edwards
&
Cermak,
1990;
Koski,
Iacoboni,
&
Mazziotta,
2002;
Poole
et
al.,
1997;

Sanger
et
al.,
2003;
Sanger
et
al.,
2006).

Although
terminology
varies,
developmental

dyspraxia
is
a
term
common
to
the
fields
of
occupational
therapy
and
neurology

while
developmental
coordination
disorder
is
a
term
frequently
used
by
allied
health

3
professionals,
movement
scientists,
and
related
researchers
(Steinman,
Mostofsky,

&
Denckla,
2010).

DCD
is
recognized
as
a
diagnosable
disorder
by
the
American

Psychiatric
Association
(2013)
when
specific
criteria
are
met:
(a)
motor

coordination
substantially
below
what
is
expected
for
chronological
age
and

intelligence;
(b)
motor
difficulties
interfere
with
activities
of
daily
living
or
academic

performance;
(c)
motor
difficulties
are
not
due
to
a
general
medical
condition,
such

as
cerebral
palsy;
and
(d)
motor
difficulties
are
in
excess
of
any
mental
retardation,

if
present.

While
the
research
studies
in
this
dissertation
involved
a
participant
pool

of
individuals
with
developmental
dyspraxia,
literature
regarding
both
DD
and
DCD

was
reviewed
with
the
assumption
that
the
two
are
likely
overlapping
with
similar

features
and
functional
outcomes.

Henceforth,
DD
and
DCD
will
collectively
be

referred
to
as
DD/DCD,
except
when
distinction
is
noted
in
primary
cited
sources,
or

when
it
is
necessary
to
precisely
specify
between
the
two
for
accuracy.

Although
DD/DCD
is
most
apparent
in
childhood
when
many
new
skills
are

acquired
and
motor
milestones
are
tracked,
an
increasing
body
of
evidence
reveals

that
many
with
DD/DCD
continue
to
have
difficulties
with
coordination
and
have

reduced
participation
in
motor
tasks
during
adolescence
and
adulthood
(Cantell
&

Kooistra,
2002;
Cantell,
Smyth,
&
Ahonen,
2003;
Cousins
&
Smyth,
2003;
Kirby
et
al.,

2008;
Kirby,
Edwards,
&
Sugden,
2011;
Kirby,
Edwards,
Sugden,
&
Rosenblum,

2010;
Missiuna,
Moll,
King,
Stewart,
&
Macdonald,
2008).

In
a
longitudinal
study
of

children
with
DCD
beginning
when
the
children
were
5
years
old,
Cantell
et
al.

(2003)
found
that
a
subset
of
participants
with
relatively
more
severe
motor

4
impairments
compared
to
other
participants
continued
to
have
motor
difficulties
at

age
17.

Cousins
and
Smyth
(2003)
documented
poor
performance
on
a
variety
of

standardized
motor
tasks
in
19
adults
who
qualified
for
a
diagnosis
of
DCD
as
adults

or
were
previously
diagnosed
as
having
DD
or
DCD
as
children.

Anecdotal

descriptions
by
adolescents,
young
adults,
and
parents
of
young
adults
with

probable
DD/DCD
corroborate
this
data
with
reported
sustained
motor
difficulties

and
lower
performance
in
other
life
domains
related
to
their
motor
impairments

(Kirby
et
al.,
2011;
Missiuna
et
al.,
2008).

There
is
increasingly
more
research
being

conducted
with
adolescents
and
adults
with
DD/DCD;
however,
only
children
have

participated
in
the
few
available
neuroimaging
studies
on
the
disorder.

Conversely,

the
majority
of
neuroscience
investigations
using
brain
imaging
methods
in
typically

developing
individuals
have
been
performed
with
adult
or
young
adult
participants.

There
is
somewhat
limited
data
available
for
comparing
the
results
of
any

neuroimaging
study
in
children
with
DD/DCD
to
typically
developing
children
as

well
as
a
lack
of
comprehensive
exploration
of
the
neural
correlates
of
DD/DCD
in

adults
and
children.

Despite
a
number
of
recognizable
impairments
identified
in
this
population

(Van
Waelvelde,
De
Weerdt,
&
De
Cock,
2005;
Wilson
&
McKenzie,
1998),
DD/DCD
is

a
disorder
of
largely
unknown
etiology.

Furthermore,
the
neural
correlates
of

DD/DCD
have
not
been
investigated
with
advanced
imaging
methods
to
the
extent

that
they
have
in
adult
apraxia
(Buxbaum,
2001;
Goldenberg,
2009;
Haaland,

Harrington,
&
Knight,
2000;
Koski,
Iacoboni,
&
Mazziotta,
2002;
Pazzaglia,
Smania,

5
Corato,
&
Aglioti,
2008)
and
other
developmental
disorders
with
motor

impairments
(Dewey
et
al.,
2007;
MacNeil
&
Mostofsky;
2012;
Martin,
Piek,
Baynam,

Levy,
&
Hay,
2010;
Sanger
et
al.,
2006),
such
as
cerebral
palsy
(Bax,
Tydeman,
&

Flodmark,
2006),
attention-‐deficit/hyperactivity
disorder
(ADHD;
Lee,
Chen,
&
Tsai,

2013;
Loh,
Piek,
&
Barrett,
2011;
Schoemaker,
Ketelaars,
Zonneveld,
Minderaa,
&

Mulder,
2005),
and
autism
spectrum
disorder
(ASD;
Mostofsky
et
al.,
2009).

Because
impairments
in
imitation
are
a
hallmark
feature
of
DD,
it
has
been
proposed

that
a
neural
correlate
of
the
disorder
may
be
found
in
a
fronto-‐parietal
system,

known
as
the
mirror
neuron
system,
which
is
thought
to
be
highly
involved
in

imitation
in
humans
(Iacoboni
et
al.,
1999;
Iacoboni,
2005).

The
putative
human
mirror
neuron
system
(MNS),
located
in
the
inferior

frontal
gyrus
(IFG),
adjacent
ventral
premotor
cortex
(vPMC),
and
inferior
parietal

lobule
(IPL),
is
active
during
both
action
execution
and
action
observation

(Rizzolatti
&
Craighero,
2004).

This
system
is
thought
to
be
highly
involved
in

action
imitation
and
emulation
(Iacoboni
et
al.,
1999;
Iacoboni,
2005).

In
fact,

evidence
from
a
repetitive
transcranial
magnetic
stimulation
(rTMS)
study
indicated

that
disruption
of
the
IFG
during
an
imitation
task
significantly
interfered
with

imitation
ability
(Heiser,
Iacoboni,
Maeda,
Marcus,
&
Mazziotta,
2003).

Therefore,

hypothetically,
individuals
with
developmental
motor
impairments
may
be
limited

in
their
ability
to
imitate,
learn
new
movements,
imagine
movements,
and
produce

goal-‐directed
and
object-‐oriented
actions
because
they
do
not
accurately
perceive
or

match
such
actions
with
neural
motor
representations
the
way
individuals
without

6
coordination
disorders
can.

Mechanisms
recruited
in
addition
to
mirror
neuron

system
areas
may
be
important
for
understanding
the
coding
of
visual
input,

imitation
learning,
and
action
goal
understanding,
all
of
which
may
contribute
to

imitative
ability.

The
putative
human
MNS
is
reviewed
here
as
it
is
involved
in

imitation,
and
it
has
been
theorized
that
its
dysfunction
may
be
related
to
motor

impairments,
specifically
imitation
impairments,
in
DD/DCD
(Werner,
Cermak,
&

Aziz-‐Zadeh,
2012).

The
Mirror
Neuron
System
Researchers
have
argued
that
the
mirror
neuron
system
is
a
key
element
of

human
imitation
(Arbib,
2002;
Iacoboni
et
al.,
1999;
Iacoboni,
2005;
Rizzolatti,

Fogassi,
&
Gallese,
2001;
Rizzolatti,
Fadiga,
Fogassi,
&
Gallese,
2002).

Mirror

neurons,
originally
discovered
in
macaque
monkeys
using
single-‐cell
recording
from

surgically
implanted
microelectrodes,
are
a
particular
type
of
neuron
that
fire
both

when
a
monkey
performs
an
action
as
well
as
when
the
monkey
observes
another

individual
performing
a
similar
action
(Di
Pelligrino,
Fadiga,
Fogassi,
&
Gallese,

1992;
Gallese,
Fadiga,
Fogassi,
&
Rizzolatti,
1996;
Rizzolatti,
Fadiga,
Gallese,
&

Fogassi,
1996).

In
other
words,
the
same
neuron
has
both
motor
and
sensory

representations.

In
monkeys,
mirror
neurons
have
been
found
in
F5
and
PF
brain
regions

(Gallese
et
al.,
1996;
Gallese
et
al.,
2002;
Rizzolatti
et
al.,
1996).

Based
on
gyri
and

sulci
topography
and
evidence
from
cytoarchitecture,
it
is
thought
that
the

7
homologous
region
to
F5
in
humans
is
the
pars
opercularis
of
the
inferior
frontal

gyrus
(Petrides
&
Pandya,
1997;
Rizzolatti
&
Matelli,
2003).

Single-‐neuron

recording
studies
are
typically
not
conducted
in
humans
for
experimental
purposes.

Therefore,
there
is
little
direct
evidence
of
the
existence
of
mirror
neurons
at
the

Figure
1.1

Lateral
view
of
brain
with
frontal
(IFG
&
vPMC)
and
parietal
(IPL)

mirror
neuron
system
regions
highlighted.

The
posterior
STS
is
also
labeled.

(From

Werner,
Cermak,
&
Aziz-‐Zadeh,
2012.)

[IFG
=
inferior
frontal
gyrus;
vPMC/PMv
=

ventral
premotor
cortex;
IPL
=
inferior
parietal
lobule;
STS
=
superior
temporal

sulcus]

8
cellular
level
(Rizzolatti,
2005;
Rizzolatti
&
Craighero,
2004)
with
the
exception
of

Mukamel
et
al.
(2010),
who
reported
mirror
neurons
in
humans
in
the

supplementary
motor
area,
hippocampus,
parahippocampal
gyrus,
and
entorhinal

cortex
when
recording
at
sites
in
the
medial
frontal
and
temporal
cortices.

Brain

imaging
and
neurophysiological
studies
have
provided
indirect
evidence
for
a

putative
mirror
neuron
system
in
human
frontal
and
parietal
brain
regions.

Specifically,
these
areas
include
the
IFG
and
adjacent
vPMC,
and
the
IPL
(see
Figure

1.1;
Rizzolatti
&
Craighero,
2004).

Properties
of
mirror
neurons
in
monkeys
Properties
of
mirror
neurons
in
the
macaque
brain
are
described
here,
as
this

data
provides
a
framework
for
our
understanding
of
the
MNS.

To
elicit
mirror

neuron
activity
in
the
monkey,
both
observed
and
executed
actions
must
be
goal-‐
directed
(i.e.,
hand-‐object
interaction
or
aiming
for
a
target)
(di
Pellegrino
et
al.,

1992;
Gallese
et
al.,
1996;
Rizzolatti
et
al.,
1996;
Tkach,
Reimer,
&
Hatsopoulos,

2007).

This
implies
that
monkey
mirror
neurons
are
not
merely
responsive
for
a

body
part
or
an
object
alone,
but
code
the
conceptual
goal
of
the
action.

Second,
the

majority
of
mirror
neurons
in
F5
are
broadly
visuo-‐motor
congruent;
they
respond

to
visually
similar
or
conceptually
related
observed
and
executed
actions.

About
a

third
of
mirror
neurons
are
strictly
congruent
in
that
the
observed
and
executed

actions
have
to
match
exactly,
such
as
in
the
type
of
grasp
used
to
hold
an
object
(di

Pellegrino
et
al.,
1992;
Gallese
et
al.,
1996;
Rizzolatti
et
al.,
1996).

Third,
partial

9
action
sequences
activate
mirror
neurons
in
monkeys
who
have
previously
seen
the

full
action
sequence,
presumably
because
the
animal
infers
the
missing
end

sequence
or
goal
of
the
action
(Umilta
et
al.,
2001).

This
property
speaks
to
the

point
that
mirror
neurons
are
involved
in
action
understanding
through
an
internal

motor
representation
of
a
full
action,
even
when
complete
visual
information
is
not

immediately
present
(Umilta
et
al.,
2001).

Finally,
many
neurons
in
the
monkey

frontal
mirror
area
resonate
action
information
from
audio
as
well
as
visual

representation
of
object-‐related
actions
(Keysers
et
al.,
2003;
Kohler
et
al.,
2002).

Together
these
findings
in
monkeys
indicate
that
mirror
neurons
may
aid
in
the

integrated
understanding
of
action
information
at
a
sensorimotor
level
rather
than

relying
on
conceptual
interpretation
through
a
semantic
transformation
process.

Although
a
number
of
distinct
properties
indicate
that
mirror
neurons
in

monkeys
function
as
a
means
of
understanding
the
complex
actions
of
others,
there

is
conflicting
evidence
regarding
the
ability
of
monkeys
to
imitate
(Ferrari
et
al.,

2005;
Subiaul
et
al.,
2004;
Visalberghi
&
Fragaszy,
1990,
2002;
Voelkl
&
Huber,

2000;
Whiten,
2002;
Whiten
&
Ham,
1992).

Thus,
imitation
capability
in
humans

supported
by
the
MNS,
as
well
as
possible
additional
neural
regions,
probably

represents
an
evolved
mechanism
from
one
that
served
more
basic
action

understanding,
such
as
that
in
monkeys.

More
evolved
neural
systems
for
learning

may
have
facilitated
imitation
ability
while
the
evolution
of
tool
use
may
have

helped
select
for
imitation
ability.

10
The
human
mirror
neuron
system
and
imitation
In
recent
years,
a
body
of
research
primarily
utilizing
functional
magnetic

resonance
imaging
(fMRI)
has
provided
evidence
that
the
human
MNS
is
active

during
action
observation,
execution,
and
imitation
tasks
(Arbib,
2002;
Buccino
et

al.,
2001;
Gazzola
&
Keysers,
2009;
Iacoboni
et
al.,
1999;
Iacoboni,
2005;
Jackson,

Meltzoff,
&
Decety,
2006;
Koski,
Iacoboni,
Dubeau,
Woods,
&
Mazziotta,
2003;

Rizzolatti
et
al.,
2002).

In
a
seminal
study,
Iacoboni
et
al.
(1999)
monitored
brain

function
in
human
frontal
and
parietal
mirror
regions
while
participants
in
an
fMRI

scanner
were
shown
finger
tapping
actions
or
control
stimuli.

Participants

passively
observed
the
actions,
imitated
them,
or
executed
a
finger
movement
to
a

given
cue.

The
researchers
predicted
that
the
IFG
and
IPL
would
follow
a
pattern
of

increasing
signal
activity
as
conditions
approached
imitation.

That
is,
these
regions

would
be
active
during
action
observation,
more
for
action
execution,
and
when

observation
and
execution
are
combined,
as
in
the
case
of
imitation,
the
highest

signal
intensity
would
be
observed.

The
data
validated
these
hypotheses
(Iacoboni

et
al.,
1999).

Research
using
similar
imitation
tasks
or
tasks
including
object
interactions

have
supported
the
previous
findings
that
the
fronto-‐parietal
mirror
network
is

most
active
during
imitation
when
compared
to
observation
and
execution
(Buccino

et
al.,
2004;
Jackson
et
al.,
2006;
Koski
et
al.,
2002;
Koski
et
al.,
2003;
Nishitani
&

Hari,
2000).

Furthermore,
a
re-‐analysis
of
data
from
Iacoboni
et
al.’s
(1999)
study

and
six
others
from
the
same
laboratory
revealed
additional
details
regarding

11
functional
segregation
within
the
inferior
frontal
gyrus
for
action
observation
and

imitation
(Molnar-‐Szakacs,
Iacoboni,
Koski,
&
Mazziotta,
2005).

This
re-‐analysis

indicated
that
the
dorsal
pars
opercularis
within
the
IFG
was
specifically
active

during
action
observation
and
imitation,
with
the
most
activation
during
imitation

(Molnar-‐Szakacs
et
al.,
2005).

In
addition,
disruption
of
the
inferior
frontal
gyrus
in

either
hemisphere
with
rTMS
resulted
in
a
transient
impairment
of
imitation

compared
to
a
control
movement
task,
indicating
that
this
region
is
essential
for

imitation
processing
(Heiser
et
al.,
2003).
Together,
this
evidence
strongly
indicates
that
these
regions
have
a

prominent
role
in
typical
human
imitation,
rendering
the
fronto-‐parietal
mirror

network
a
likely
location
of
differences
in
neural
activation
between
individuals

with
and
without
DD/DCD.

In
particular,
one
may
expect
decreased
signal
intensity

to
correspond
with
imitation
impairments.

Although
the
vast
majority
of
the
MNS

and
imitation
literature
includes
only
adult
participants,
a
few
investigations
have

included
typically
developing
children
and
have
reported
MNS
functioning
similar
to

what
has
been
described
in
typical
adults
(Dapretto
et
al.,
2006;
Martineau,
Cochin,

Magne,
&
Barthelemy,
2008;
Nishitani,
Avikainen,
&
Hari,
2004;
Oberman
et
al.,

2005;
Théoret
et
al.,
2005).

Furthermore,
children
with
ASD—a
disorder

characterized
by
both
motor
and
social
deficits
(Werner,
Aziz-‐Zadeh,
&
Cermak,

2011)—showed
less
activity
in
components
of
the
MNS
when
imitating
emotional

faces
in
an
fMRI
study
conducted
by
Dapretto
and
colleagues
(2006),
despite
similar

behavioral
performance
on
the
imitation
task.

Such
evidence
supports
the
view
that

12
a
neural
mirroring
mechanism
is
present
from
childhood
and
that
dysfunction
in
the

MNS
may
contribute
to
developmental
disorders
of
imitation,
motor,
and
social

skills
(Iacoboni
&
Mazziotta,
2007;
Lepage
&
Théoret,
2007).

Coding
of
biologic
actions
in
the
superior
temporal
sulcus

Although
not
considered
a
mirror
area
because
it
is
not
active
during
action

execution,
the
superior
temporal
sulcus
(STS;
see
Figure
1.1)
has
been
implicated
in

studies
assessing
the
neural
mechanisms
of
imitation
(Chaminade,
Meltzoff,
&

Decety,
2002
&
2005;
Decety,
Chaminade,
Grezes,
&
Meltzoff,
2002;
Iacoboni,
1999).

The
posterior
portion
of
the
STS
is
thought
to
code
visual
stimuli
for
meaningful
and

goal-‐directed
biological
actions
(Jellema,
Baker,
Wicker,
&
Perrett,
2000;
Perrett
et

al.,
1989).

In
addition,
the
STS
and
the
fronto-‐parietal
mirror
circuit
are
thought
to

be
connected
via
the
arcuate
fasciculus
and
local
connecting
white
matter
fibers

(Catani,
Jones,
&
Ffytche,
2005;
Frey,
Campbell,
Pike,
&
Petrides,
2008;
Glasser
&

Rilling,
2008;
Halwani,
Loui,
Ruber,
&
Schlaug,
2011;
Iacoboni,
2005;
Kaplan,
Naeser,

Martin,
Ho,
Wang,
et
al.,
2010;
Makris,
Kennedy,
McInerney,
Sorensen,
Wang,
et
al.,

2005l
Rizzolatti
et
al.,
2001;
Thiebaut
de
Schotten,
et
al.,
2008).

Thus,
it
has
been

proposed
that
information
from
the
visual
cortices
undergoes
further
processing
for

the
visual
aspects
of
observed
action
by
the
STS
and
is
then
sent
to
the
parietal

cortex,
which
codes
the
affordances
of
the
action
and
its
kinesthetic
qualities

(Iacoboni,
2005)
via
the
posterior
lateral
segment
of
the
arcuate
fasciculus

terminating
in
the
IPL
(Catani
et
al.,
2005).

Next,
it
is
relayed
to
the
IFG
where

13
action
goal
coding
occurs
(Iacoboni,
2005)
via
the
anterior
lateral
segment
of
the

arcuate
fasciculus
(Catani
et
al.,
2005).

Nuances
of
mirror
neuron
system
activation
The
MNS
is
thought
to
be
bilateral
(Aziz-‐Zadeh
et
al.,
2006),
and
hemispheric

differences
have
been
found
when
comparing
the
experience
of
being
imitated
as

opposed
to
the
experience
of
imitating
another
person.

In
a
study
to
investigate

these
differences
using
positron
emission
tomography
(PET),
Decety
et
al.
(2002)

found
the
left
inferior
parietal
cortex,
which
is
thought
to
be
involved
with

integrating
visual
and
motor
information
for
sequential
processing
of
goal-‐directed

actions
(Mutha,
Sainburg,
&
Haaland,
2011;
Torres,
Raymer,
Rothi,
Heilman,
&

Poizner,
2010),
correlated
with
imitating
others.

The
right
inferior
parietal
cortex,

thought
to
be
involved
in
body
awareness
(Berlucchi
&
Aglioti,
1997
&
2010;

Fotopoulou,
Rudd,
Holmes,
&
Kopelman,
2009),
correlated
with
being
imitated.

Furthermore,
in
an
experiment
in
which
researchers
applied
rTMS
to
the
inferior

parietal
lobule,
participants
were
less
accurate
on
a
self-‐other
discrimination
task

when
a
virtual
lesion
was
created
on
the
right
as
compared
to
the
left
(Uddin,

Molnar-‐Szakacs,
Zaidel,
&
Iacoboni,
2006).

These
results
point
to
a
lateralization
of

the
parietal
mirror
neuron
area
in
particular
and
general
differences
between

processing
self-‐
and
other-‐related
visual
information.

In
addition,
some
have

suggested
that
the
fronto-‐parietal
MNS,
especially
in
the
right
hemisphere
and
the

cortical
midline
structures
(such
as
the
medial
prefrontal
cortex,
anterior
cingulate

14
cortex,
and
precuneus)
work
together
to
process
self-‐other
distinctions
(Decety
&

Sommerville,
2003;
Uddin,
Iacoboni,
Lange,
&
Keenan,
2007;
Uddin,
Kaplan,
Molnar-‐
Szakacs,
Zaidel,
&
Iacoboni,
2005).

Coupled
with
evidence
that
the
experience
of

being
imitated
is
an
important
aspect
of
learning
to
imitate
(Nadel,
2002),
an

underlying
deficit
in
self-‐other
distinction
may
preclude
the
development
of

imitation
skill.

If,
compared
to
typically
developing
individuals,
differences
in
inferior

parietal
regions
exist
in
individuals
with
DD/DCD
during
imitation
or
while
being

imitated,
such
differences
could
have
implications
for
understanding
the
neural

etiology
of
the
disorder.

Dysfunction
in
the
left
inferior
parietal
cortex
may
indicate

that
motor
or
imitation
impairments
are
related
to
a
specific
deficit
in
visual-‐motor

integration
(Mutha,
Sainburg,
&
Haaland,
2010;
2011;
Torres,
Raymer,
Rothi,

Heilman,
&
Poizner,
2010),
whereas
dysfunction
in
the
right
inferior
parietal
cortex

may
indicate
that
impairments
are
due
to
body
awareness
and
difficulty

distinguishing
self
from
others
(Uddin,
Molnar-‐Szakacs,
Zaidel,
&
Iacoboni,
2006).

Hemispheric
differences
in
the
MNS,
particularly
in
the
posterior
parietal
cortex,

bring
to
light
only
weakly
supported
theories
about
cerebral
laterality
in
DD/DCD.

It
has
been
suggested
that
left
parietal
lobe
dysfunction
may
underlie
DD/DCD

based
on
the
known
lesion
location
usually
responsible
for
acquired
apraxia

(Morris,
1997;
Zwicker,
Missiuna,
&
Boyd
,
2009).

Although
it
cannot
be
assumed

that
the
mechanisms
of
acquired
apraxia
equate
to
those
of
developmental
motor

impairments
(in
which
there
is
no
notable
brain
lesion),
Zwicker
and
colleagues

15
(2010)
found
differential
activation
in
the
left
IPL
in
the
direction
of
higher
blood-‐
oxygenation-‐level
dependent
(BOLD)
signal
in
children
with
DCD
compared
with

typically
developing
peers
on
a
simple
motor
task.

In
a
follow-‐up
investigation

(2011a),
these
same
researchers
found
greater
signal
in
the
right
IPL
in
typically

developing
children
compared
with
peers
with
DCD
during
a
motor
learning
task.

More
evidence
is
needed
to
elucidate
the
particularities
of
potential
laterality

differences
in
DD/DCD,
especially
in
the
context
of
imitation
and
imitation
learning.
In
light
of
results
from
Decety
et
al.
(2002),
Jackson
et
al.
(2006)
predicted

that
the
visual
perspective
of
an
imitator
would
modulate
neural
activation
involved

in
imitation.

Increased
activation
in
the
IFG
was
found
when
participants
imitated

simple
hand
or
foot
actions
viewed
from
the
first-‐person
perspective
compared
to

the
third-‐person
perspective.

In
addition,
areas
outside
the
fronto-‐parietal
mirror

areas
were
found
when
first-‐person
and
third-‐person
conditions
were
contrasted,

suggesting
that
additional
brain
areas,
such
as
the
frontal
poles,
medial
orbitofrontal

cortex,
and
left
pre-‐
and
post-‐central
gyri,
are
used
for
processing
perspective

(Jackson
et
al.,
2006).

This
implies
that
perspective
moderates
neural
activity
of

imitation
and
is,
therefore,
an
important
consideration
in
measuring
imitation

impairments
and
designing
experiments
to
investigate
the
neural
correlates
of

DD/DCD.

Drawing
on
evidence
from
monkey
studies
of
action
observation
indicating

heightened
activity
in
the
mirror
neuron
system
for
goal-‐directed
actions
(di

Pellegrino
et
al.,
1992;
Gallese
et
al.,
1996;
Rizzolatti
et
al.,
1996)
and
behavioral

16
studies
in
humans
indicating
better
accuracy
and
higher
speed
when
imitating
an

action
for
its
goal
(Bekkering,
Wohlschlager,
&
Gattis,
2000;
Gleissner,
Meltzoff,
&

Bekkering,
2000;
Wohlschlager
&
Bekkering,
2002),
Koski
et
al.
(2002)
conducted

an
imitation
experiment
using
fMRI
to
test
whether
heightened
activity
in
the
MNS

for
goal
directed
actions
could
be
observed
in
humans.

Participants
observed
and

imitated
fingers
pointing
to
a
target
(goal)
or
to
the
same
location
with
no
marked

target.

Each
condition
was
presented
with
the
ipsilateral
or
contralateral
hand,

making
it
possible
for
participants
to
match
their
own
actions
with
the
same

movement
or
same
goal
of
the
action.

Data
indicated
that
goal-‐directed
actions

activated
the
IFG
more
strongly
relative
to
actions
without
a
goal
(Koski
et
al.,

2002).

In
another
study,
neural
activation
in
the
mirror
neuron
system
was
found
to

differ
in
strength
depending
on
whether
a
research
participant
was
imitating
means,

goal,
or
whole
actions
(Chaminade
et
al.,
2002).

Interestingly,
inferior
frontal,

inferior
parietal,
and
superior
temporal
regions
were
activated
even
when
the
goal

was
not
immediately
apparent
during
action
component
conditions
if
sufficient

information
was
available
to
infer
the
goal
(e.g.,
gripping
a
bottle
infers
drinking,

touching
it
does
not).

However,
activation
was
greater
in
complete
actions
with

visible
goals.

A
similar
finding
comes
from
a
study
by
Iacoboni
et
al.
(2005)
in
which

participants
saw
video
clips
of
actions
free
from
context,
contexts
free
from
action,

and
actions
embedded
in
context
which
allowed
attribution
of
intention.

Results

indicated
that
observing
actions
in
a
particular
context
induced
the
strongest

17
activation
in
the
inferior
frontal
cortex,
meaning
that
the
mirror
neuron
system

codes
the
intention
(the
“why”)
of
action,
not
just
the
contents
(the
“how”)
of
the

action
(Iacoboni
et
al.,
2005).

Further
evidence
of
inferior
frontal
mirror
activity
relative
to
the
presence
of

a
goal
has
been
provided
by
Johnson-‐Frey
et
al.
(2003)
and
Jackson
et
al.
(2006).

Johnson-‐Frey
et
al.
had
participants
view
hand-‐object
interactions
under
three

conditions:
(a)
grasp
of
an
object
which
inferred
an
obvious
intention;
(b)
unusual

grasp
of
an
object
in
which
intention
was
not
immediately
inferred;
and
(c)
mere

touch
of
an
object
without
grasp
or
clear
intention
(e.g.,
a
touch
with
one
finger).

Their
results
indicated
that
the
inferior
frontal
regions
were
activated
the
most

when
participants
viewed
hand-‐object
interactions
in
which
the
goal
was
apparent,

as
compared
to
when
they
viewed
other
hand-‐object
interactions
(Johnson-‐Frey
et

al.,
2003).

Jackson
et
al.
(2006)
conducted
a
study
indicating
that
significant
mirror

activation
occurs
with
imitation
relative
to
action
observation,
especially
when

actions
are
intransitive
(i.e.,
not
directed
toward
an
object).

The
researchers
did
not

find
IFG/vPMC
activation
when
participants
passively
observed
intransitive
actions.

However,
active
imitation
of
intransitive
actions
produced
activation
in
these
mirror

neuron
areas
as
well
as
other
neural
regions,
including
the
left
ventral
premotor

area,
supplementary
motor
area,
cerebellum,
and
primary
somatosensory
and

motor
cortices.

18
With
respect
to
the
presence
of
a
goal,
the
above
investigations
of
action

perception
and
imitation
emphasize
a
stronger
role
for
action
conceptions
over

movement
kinematics
for
representation
in
the
mirror
neuron
system.

However,

this
does
not
imply
that
the
imitation
of
meaningless
or
intransitive
movements
are

not
represented
at
all
in
the
MNS.

The
relatively
greater
activation
of
mirror
neuron

regions
when
an
action
intention
or
meaning
is
readily
available
to
the
observer

(Decety
et
al.,
1997;
Rumiati
et
al.,
2005)
may
be
due
to
the
presence
of
a
relatively

greater
number
of
goal-‐specific
to
effector-‐specific
mirror
neurons,
such
as
the
case

in
monkeys
(di
Pellegrino
et
al.,
1992;
Gallese
et
al.,
1996;
Rizzolatti
et
al.,
1996).

The
presence
of
both
types
of
neurons
would
permit
activation
for
non-‐meaningful

and
intransitive
actions,
such
as
those
used
in
the
pivotal
experiment
conducted
by

Iacoboni
et
al.
(1999).

Furthermore,
Press
et
al.
(2008)
concluded
that
the
human

MNS
is
involved
in
the
imitation
of
intransitive
actions
as
evidenced
by
their

findings
that
automatic
imitation,
thought
to
be
an
index
of
mirror
system

functioning
in
humans,
occurs
even
with
non-‐object-‐directed
actions.

Imitation
learning
An
evident
benefit
of
imitation
is
the
potential
for
learning
novel
actions
by

copying
the
actions
of
others.

Indeed,
humans
imitate
more
than
any
other
animal,

and
much
of
how
humans
learn
is
through
imitation
(Prinz
&
Meltzoff,
2002).

To

investigate
the
neural
correlates
of
imitation
learning,
Buccino
et
al.
(2004)
asked

non-‐musician
participants
to
observe
guitar
chords
played
by
a
guitarist
and,
after
a

19
pause,
imitate
each
chord.

The
results
revealed
the
IPL,
posterior
portion
of
the
IFG,

and
adjacent
vPMC
were
more
active
during
imitation
compared
to
observation
or

non-‐imitative
execution.

During
the
pause,
however,
the
middle
frontal
gyrus,

thought
to
be
involved
in
the
spatial
aspects
of
working
memory,
and
other
motor

preparation
areas
became
active
in
addition
to
the
MNS.

In
a
followup
study,
Vogt
et

al.
(2007)
found
that
the
MNS
regions
were
more
active
during
imitation
of

unlearned
actions
than
practiced
actions.

In
addition,
they
found
that
the

dorsolateral
prefrontal
cortex
(DLPFC)
was
also
active
during
learning
of
novel

hand
actions
(Vogt
et
al.,
2007).

Based
on
these
results,
Buccino
et
al.
and
Vogt
et
al.

concluded
that
the
MNS
plays
a
prominent
role
in
learning
by
imitation,
but
other

regions,
such
as
the
middle
frontal
gyrus
and
DLPFC,
may
be
needed
as
well
to

orchestrate
the
selection,
organization,
and
monitoring
of
motor
representations

needed
to
learn
and
imitate
a
new
motor
program.

In
a
study
by
Cross,
Hamilton,
and
Grafton
(2006),
expert
dancers
learned
a

complex,
whole-‐body
dance
sequence
over
a
period
of
five
weeks,
and
researchers

recorded
their
brain
activity
while
watching
the
rehearsed
sequences
and
control

sequences
(not
previously
rehearsed)
at
the
end
of
each
week.

Participants
were

asked
to
imagine
themselves
performing
the
movement
sequences
they
observed

and
rate
their
perceived
ability
to
perform
each.
Greater
activation
was
found
in
the

MNS,
namely
the
left
inferior
parietal
lobule
and
ventral
premotor
area,
when

participants
observed
rehearsed
movement
sequences
they
judged
they
could

perform
well,
compared
to
control
movement
sequences
for
which
they
deemed

20
their
performance
ability
would
be
poor
(Cross
et
al.,
2006).

Although
the

participants
in
this
study
could
not
physically
imitate
the
dance
sequences
while

inside
the
fMRI
scanner,
the
use
of
imagined
movement
is
commonly
used
in
brain

imaging
experiments
to
induce
internal
representation
of
motor
plans
without
overt

movement.

Motor
imagery
is
thought
to
be
functionally
equivalent
to
motor

planning
for
real
motor
actions
(Jeannerod,
1994).

In
a
related
study,
Cross
et
al.
(2009)
demonstrated
that
neural

representations
of
movement
sequences
could
be
acquired
by
non-‐dancer

participants
either
physically
rehearsing
or,
to
a
lesser
degree,
passively
observing

actions.

The
experiment
yielded
activity
in
the
premotor
and
inferior
parietal

regions
after
physical
or
observational
training,
with
the
dorsolateral
prefrontal

cortex
additionally
active
for
both
types
of
rehearsal
and
the
cerebellum
involved

during
observation.

These
results,
together
with
those
of
Buccino
et
al.
(2004)
and

Cross
et
al.
(2006),
convey
a
consistent
pattern
of
MNS
activity
when
participants

learned
and
imitated,
or
mentally
simulated,
new
movements.

However,
additional

brain
regions
related
to
a
broad
range
of
sensorimotor
tasks
appear
to
be
necessary

supplements
to
the
core
imitation
circuit
when
learning
new
actions.

Because

DD/DCD
entails
impairment
in
learning
new
motor
programs,
dysfunction
in
these

additional
structures
(i.e.,
prefrontal
cortex,
middle
frontal
cortex,
anterior
medial

cortex,
supplementary
motor
area,
and
superior
parietal
lobule)
could
accompany

MNS
dysfunction
during
imitation
learning.

21
Imitation,
empathy,
and
the
mirror
neuron
system
The
shared
neural
representation
for
action
and
perception
that
is
thought
to

underlie
imitation
and
action
understanding,
the
MNS,
is
also
thought
to
be
involved

in
empathy
(Gallese,
2001;
Gallese,
Keyers,
&
Rizzolatti,
2004;
Iacoboni,
2005
&

2009).

Convincing
evidence
for
this
hypothesis
comes
from
experiments
showing

that
self-‐reported
trait
empathy
and
perspective-‐taking
ability
(usually
measured

with
a
widely-‐used
empathy
scale,
the
Interpersonal
Reactivity
Index;
Davis,
1983)

correlates
with
activity
in
the
MNS
(Aziz-‐Zadeh
et
al.,
2010;
Carr,
Iacoboni,
Dubeau,

Mazziotta,
&
Lenzi,
2003;
Gazzola,
Aziz-‐Zadeh,
&
Keysers,
2006;
Kaplan
&
Iacoboni,

2006;
Lamm,
Batson,
&
Decety,
2007;
Pfeifer,
Iacoboni,
Mazziotta,
&
Dapretto,
2008;

Schulte-‐Ruther
et
al.,
2007).

In
addition,
in
conditions
in
which
empathy
is
known

to
be
impaired,
such
as
ASD,
decreased
activity
in
MNS
regions
has
been
found
to

correlate
with
social
symptom
severity
(Dapretto
et
al.,
2006).

Interestingly,

individuals
with
ASD
have
also
been
found
to
have
impairments
in
imitation

(Dowell,
Mahone,
&
Mostofsky,
2009;
MacNeil
&
Mostofsky,
2012;
Mostofsky
et
al.,

2006;
Rogers,
Hepburn,
Stackhouse,
&
Wehner,
2003;
Smith
&
Bryson,
1994;

Steinman
et
al.,
2010;
Werner,
Aziz-‐Zadeh,
&
Cermak,
2011;
Williams,
Whiten,
&

Singh,
2004;
Williams
et
al.,
2006).
Some
evidence
points
to
higher
rates
of
poorer
psychosocial
adjustment
and

decreased
social
skills
and
empathy
in
children
and
adults
with
DD/DCD
compared

to
their
peers
without
motor
coordination
problems
(Cantell
et
al.,
2003;
Chen
&

Cohn,
2003;
Chen
et
al
,
2009;
Cummins
et
al.,
2005;
Dewey,
Kaplan,
Crawford,
&

22
Wilson,
2002;
Kaplan,
Crawford,
Cantell,
Kooistra,
&
Dewey,
2006;
Skinner
&
Piek,

2001;
Visser,
2003).

If
social
skills
or
empathy
impairments
exist
in
DD/DCD,
these

may
be
modulated
by
MNS
dysfunction.

Existing
evidence
of
brain-based
differences
in
developmental
dyspraxia

To
date,
no
fMRI
studies
have
been
conducted
which
examined
imitation
and

mirror
neuron
system
functioning
in
children
or
adults
with
developmental

dyspraxia,
DCD,
clumsiness,
or
other
similarly
labeled
developmental
motor

coordination
disorder.

However,
a
limited
number
of
brain
imaging
studies
have

broadly
investigated
group-‐level
differences
in
brain
activation
on
motor
tasks

between
individuals
with
DCD
and
typically
developing
individuals
(Kashiwagi,

Iwaki,
Narumi,
Tamai,
&
Suzuki,
2009;
Querne
et
al.,
2008;
Zwicker
et
al.,
2010;

2011)
and
are
fairly
encouraging
that
a
mirror
system
hypothesis
may
be

supported.

Kashiwagi
et
al.
(2009)
examined
direct
perceptual-‐motor
mechanisms
of

DCD
via
a
study
of
brain
activation
during
a
visuomotor
tracking
task
in
which

participants
followed
an
on-‐screen
moving
target
with
a
joystick.

The
authors

report
less
posterior
parietal
activation
in
the
DCD
group;
however,
these
results

appear
to
be
undermined
by
a
skewed
results
distribution
as
a
consequence
of
one

extreme
outlier
in
the
DCD
group.

Querne
et
al.
(2008)
examined
attention,
a
common
corollary
of

developmental
coordination
disorder,
by
examining
the
attentional
brain
network

23
in
children
with
DCD
and
a
control
group
using
a
go/no-‐go
task
in
which

participants
responded
when
consecutive
letters
were
presented
(go)
with
the

exception
of
“X”
(no
go).

Using
structural
equation
modeling
to
determine
effective

connectivity,
the
authors
concluded
that
middle
frontal
and
anterior
cingulate

cortex
to
inferior
parietal
cortex
connectivity
was
increased
in
children
with
DCD,

indicating
less
effective
switching
between
go
and
no-‐go
tasks
in
children
with
DCD

and
the
need
for
additional
recruitment
of
inhibitory
brain
responses
to

compensate.

Of
more
potential
use
in
understanding
the
neurological
etiology
of
DD/DCD

are
a
series
of
brain
imaging
experiments
conducted
by
Zwicker
and
colleagues

(Zwicker
et
al.,
2010;
2011;
2012).

Using
fMRI
to
measure
whole-‐brain
activation

patterns
in
seven
children
with
DCD
(aged
8-‐12
years)
and
7
age-‐matched
typically

developing
(TD)
peers,
Zwicker
et
al.
(2010)
predicted
group-‐wise
differences
in

cerebellar
activity
when
participants
performed
a
fine
motor
task
adapted
from
the

Movement
Assessment
Battery
for
Children
2
(M-‐ABC
2;
Henderson
&
Sugden,

2007)
consisting
of
trail
tracing
using
a
joystick.

Although
these
researchers
did
not

find
any
significant
difference
between
groups
on
task
performance
in
the
scanner,

whole
brain
exploratory
analysis
revealed
diffuse
between-‐groups
differences
in
a

number
of
brain
regions.

Significant
differences
were
observed
in
the
left
IPL
and

right
supramarginal
gyrus
(DCD
>
TD)
and
left
IFG
and
left
precuneus
(TD
>
DCD).

In
addition,
a
small
cluster
of
activity
was
found
in
the
right
cerebellar
lobule
VI
that

was
significantly
greater
in
the
DCD
than
TD
group.

The
authors
interpreted
these

24
results
in
light
of
a
theory
that
children
with
DCD
may
rely
on
visuospatial
feedback

in
lieu
of
somatosensory
feedback
to
guide
their
movements.

Because
the

experimental
task
did
not
include
imitation,
action
observation,
or
even
the

presence
of
another
person,
no
conclusions
can
be
drawn
that
these
differences

relate
to
a
mirroring
mechanism.

In
fact,
the
authors’
conclusion
that
the
DCD
group

utilizes
more
of
a
visuospatial
mechanism
during
a
motor
task
than
the
typically

developing
group
is
incongruent
with
the
MNS
hypothesis
(Werner,
Cermak,
&
Aziz-‐
Zadeh,
2012).

However,
the
results
are
noteworthy
because
they
include
key
MNS

regions
and
may
be
indicative
of
different
functioning
in
these
regions.

In
a
follow-‐up
study
with
the
same
participants
as
their
previous

investigation,
Zwicker
et
al.
(2011)
broadly
hypothesized
that
the
cerebellum,

prefrontal
cortex,
and
posterior
parietal
cortex
would
differ
between
DCD
and
TD

groups
on
a
retention
test
following
practice
of
a
fine
motor
task.

Presumably,
their

predictions
were
derived
from
a
broad
array
of
hypotheses
implying
large-‐scale

motor
and
motor-‐related
regional
differences
in
DCD
(Zwicker
et
al.,
2009).

The

same
trail
tracing
task
used
in
their
previous
study
was
employed,
and
brain
activity

was
measured
at
baseline
and
after
three
days
of
practice
outside
the
scanner.

In

this
study,
between
group
differences
from
the
initial
learning
task
to
retention

were
found
in
the
bilateral
IPL
and
other
regions
(DCD
>
TD).

As
in
the
previous

study
conducted
by
Zwicker
and
colleagues,
the
procedures
of
this
investigation
do

not
provide
grounds
for
making
any
assumptions
about
a
mirroring
mechanism,
but

25
still
support
the
potential
for
broad
functional
differences
in
the
primary
mirror

neuron
system
regions
when
comparing
individuals
with
DD/DCD
and
TD.

White
Matter
Structural
Connections
within
the
MNS
The
use
of
diffusion
tensor
imaging
(DTI)
in
recent
years
has
improved
white
matter

imaging
techniques,
allowing
for
the
quantifiable
measurement
of
white
matter
and

fiber
path
orientation
even
in
the
absence,
or
prior
to
the
development
of

myelination
(Mukherjee
&
McKinstry,
2006).

One
interesting
finding
revealed
by

DTI
is
the
existence
of
two
local
tracts,
previously
not
identified
with
brain
imaging,

that
commonly
track
together
with
the
arcuate
fasciculus.

These
appear
to
course

from
the
STS
to
the
IPL
and
from
the
IPL
to
the
IFG
(see
Figure
1.2;
Catani
et
al.,

2005).

In
addition,
evidence
from
research
combining
DTI
and
fMRI
demonstrates

functional
connectivity
between
bilateral
superior
parietal
areas
and
the

contralateral
inferior
frontal
areas
via
the
posterior
corpus
callosum
(Baird
et
al.,

2005).

Given
these
strong
links
between
the
frontal
and
parietal
nodes
of
the
mirror

neuron
system
and
superior
temporal
region,
it
is
reasonable
to
expect
that
any

decreased
functional
activity
in
the
MNS
regions
(as
has
been
hypothesized
in
DD)

would
likely
be
accompanied
by
abnormal
white
matter
structure
in
the
arcuate

fasciculus.

In
the
only
known
investigation
of
white
matter
structure
comparing

children
with
DD/DCD
and
typically
developing
children,
Zwicker
et
al.
(2012)

piloted
a
DTI
study
in
order
to
ascertain
if
functional
brain
activity
differences
in

26

Figure
1.2.

Tractography
reconstruction
of
the
arcuate
fasciculus.

IFG
(Broca’s

area)
and
IPL
(Wernicke’s
territory)
are
connected
through
direct
and
indirect

pathways
in
the
average
brain.

The
direct
pathway
(long
segment
shown
in
red)

runs
medially
and
corresponds
to
classical
descriptions
of
the
arcuate
fasciculus.

The
indirect
pathway
runs
laterally
and
medially
and
is
composed
of
an
anterior

segment
(green)
connecting
the
IFG
and
IPL
and
a
posterior
segment
(yellow)

connecting
IPL
and
STS.

(From
Catani,
Jones,
&
Ffytche,
2005.)

[IFG
=
inferior

frontal
gyrus;
IPL
=
inferior
parietal
lobule;
STS
=
superior
temporal
sulcus]

children
with
DCD
(as
found
in
their
previous
work)
is
accompanied
by
differences

in
white
matter
motor
pathway
integrity.

These
researchers
did
not
propose
any

hypotheses
specific
to
MNS
regions
and
instead
focused
on
primary
motor
pathways

(corticospinal
and
corticobulbar
tracts).

No
significant
differences
in
fractional

27
anisotropy
values
were
found
in
any
region,
but
children
with
DCD
had
significantly

lower
apparent
diffusion
coefficient
values
in
the
corticospinal
tract
when
compared

to
TD
children.

In
addition,
the
children’s
scores
on
the
M-‐ABC
2
moderately

correlated
with
the
apparent
diffusion
coefficient
of
the
corticospinal
tract.

This

study
provides
support
for
the
hypothesis
that
structural
white
matter
differences

may
be
related
to
DCD;
however,
no
conclusions
can
be
drawn
regarding
tracts

connecting
the
fronto-‐parietal
network
as
this
was
not
specifically
investigated.

Furthermore,
a
small
sample
size
(n
=
12)
limits
generalizability
of
the
results.

Despite
a
lack
of
gross
structural
pathology
on
neuroimaging
scans,
white

matter
abnormalities
have
been
found
to
correlate
with
a
number
of
neurological

conditions,
including
developmental
disorders
related
to
or
co-‐occurring
with
DCD,

such
as
ADHD
(Ashtari
et
al.,
2005;
Silk
et
al.,
2009),
preterm
birth
and
low
birth

weight
(Counsell
et
al.,
2006;
Counsell
et
al.,
2008;
Dudink
et
al.,
2007;
Nagy
et
al.,

2009;
Skranes
et
al.,
2007;
Yung
et
al.,
2007),
and
ASD
(Alexander
et
al.,
2007;

Barnea-‐Goraly
et
al.,
2004;
Sundaram
et
al.,
2008).

The
majority
of
this
literature

concerns
children
with
a
few
researchers
reporting
continued
differences
in

adolescence
(Nagy
et
al.,
2009;
Skranes
et
al.,
2007).

Although
the
results
of
such

investigations
indicate
diffuse
abnormalities
with
sometimes
conflicting
results

among
studies,
in
general
the
following
results
have
been
found:
(a)
fractional

anisotropy
(FA)
differences
in
internal
capsule,
tracts
extending
from
the
premotor

area,
cerebral
peduncles,
and
striatum,
parieto-‐occipital
tracts,
and
white
matter

underlying
the
inferior
frontal
and
inferior
parietal
cortices
in
individuals
with

28
ADHD
(Ashtari
et
al.,
2005;
Silk
et
al.,
2009);
(b)
FA
differences
in
corpus
callosum,

internal
capsule,
uncinate
fasciculus,
and
superior
and
inferior
longitudinal
fasciculi

correlated
with
motor
performance
in
individuals
with
pre-‐term
birth
or
low
birth

weight
(Counsell
et
al.,
2006;
Counsell
et
al.,
2008;
Dudink
et
al.,
2007;
Nagy
et
al.,

2009;
Skranes
et
al.,
2007;
Yung
et
al.,
2007);
and
(c)
abnormalities
in
short-‐range

frontal
lobe
association
fibers
and
corpus
callosum
correlated
with
motor

performance
in
ASD
(Alexander
et
al.,
2007;
Barnea-‐Goraly
et
al.,
2004;
Sundaram
et

al.,
2008).

In
children
with
sensory
processing
disorders
(SPD)—a
disorder

frequently
associated
with
DD/DCD,
ASD,
and
ADHD—increased
FA
coupled
with

decreased
mean
diffusivity
(MD)
and
radial
diffusivity
(RD)
has
been
found
in
the

superior
longitudinal
fasciculus,
a
pathway
linking
anterior
and
posterior
regions
of

the
brain
to
one
another
(Owen
et
al.,
2013).

In
addition,
disordered
functional

connectivity
has
been
found
in
those
with
ASD
between
the
primary
visual
cortex

and
inferior
frontal
cortex,
correlating
with
poorer
visuomotor
performance

(Villalobos
et
al.,
2005),
and
between
frontal
and
parietal
regions
(Just
et
al.,
2007).

Collectively,
this
evidence
suggests
that
dysmyelination,
contributing
to
abnormal

motor
development,
may
underlie
developmental
disorders
with
an
associated

motor
component.

Considering
the
evidence
that
white
matter
is
abnormal
in
developmental

disabilities
related
to
DD/DCD,
it
is
likely
that
white
matter
differences
exist
in

individuals
with
DD/DCD
with
some
evidence
supporting
this
(Zwicker
et
al.,
2012).

As
it
has
been
hypothesized
that
functional
differences
in
the
fronto-‐parietal
MNS

29
underlie
imitation
impairments
in
DD/DCD,
it
is
reasonable
to
suspect
that
white

matter
abnormalities
connecting
these
regions
would
co-‐occur
in
this
population.

Cortical
Thickness
in
MNS
Regions
The
radial
unit
hypothesis
postulates
that
the
population
of
neurons
forming

the
neocortex
are
generated
from
progenitor
cells
lining
the
embryonic
cerebral

ventricle,
which
then
migrate
via
glial
scaffolding
to
the
cortex
during
middle

gestation
in
primates
(Rakic,
1988;
2000).

At
the
level
of
the
germinal
epithelium,

these
cells
are
already
arranged
in
columns
representing
their
prospective

cytoarchitechtonic
areas
(Kornack
&
Rakic,
1995;
Mountcastle,
1997;
Rakic,
1988).

Regulatory
genes
that
control
cell
division
and
apoptosis
in
the
ventricular
zone
are

thought
to
determine
interspecies
and
interindividual
variations
in
the
number
of

columns
and
number
of
cells
within
a
column
which
migrate
to
the
cortex
(Rakic,

2000).

The
number
of
cells
within
columns
(or
radial
units)
determines
cortical

thickness,
and
this
amount
is
thought
to
be
similar
between
individuals
in
a
species

(Rakic,
2000).

Measurements
from
postmortem
investigations
confirm
fairly

consistent
cortical
thickness
among
humans,
averaging
at
approximately
2.5
mm
for

the
entire
cortical
surface,
with
relatively
higher
measurements
on
the
lateral

compared
to
the
medial
surfaces
(von
Economo,
1929).

Using
T1-‐weighted
MRI

images
to
generate
3-‐dimensional
surface
reconstruction,
it
is
now
possible
to

measure
cortical
thickness
in
vivo
(Fischl
&
Dale,
2000).

Furthermore,
cortical

thickness
measurements
gained
from
neuroimaging
have
demonstrated
excellent

30
agreement
with
published
values
from
postmortem
data
of
regional
cortical

thickness
(Fischl
&
Dale,
2000).

Although
increasing
evidence
in
recent
years
points
to
the
possibility
of

neurogenesis
and
cell
migration
in
adult
primates
(Gould,
Reeves,
Graziano,
&
Gross,

1999),
a
process
likely
to
affect
cortical
thickness,
this
claim
is
still
largely
disputed

(Rakic,
2002).

Thus,
interindividual
differences
in
cortical
thickness
are
more

frequently
attributed
to
innate
genetic
(Narr,
Woods,
Thompson,
Szeszko,
Robinson,

et
al.,
2007,
Panizzon,
et
al.,
2009;
Pontious,
Kowalczyk,
Englund,
&
Hevner,
2007)

or
environmental
factors
in
utero,
such
as
fetal
alcohol
exposure
(Sowell,
Mattson,

Kan,
Thompson,
Riley,
&
Toga,
2008)
or
prenatal
stress
(Fleming,
Anderson,
&

Rhees,
1986).

With
typical
development,
longitudinal
mapping
of
cortical
thickness

in
children
between
5
and
11
years
of
age
reveals
a
pattern
of
gray
matter
thinning

in
right
frontal
and
bilateral
parieto-‐occipital
regions
coupled
with
thickening
in
left

Broca’s
area
and
bilateral
posterior
perisylvian
regions
(Sowell,
Thompson,

Leonard,
Welcome,
Kan,
&
Toga,
2004).

In
healthy
adults,
regional
cortical

thickness,
especially
in
prefrontal
and
posterior
temporal
areas,
has
been
found
to

correlate
with
intelligence,
and
this
relationship
is
moderated
by
sex
and
age
(Narr,

et
al.,
2007).

Deviations
from
the
typical
pattern
of
develop
of
cortical
thickness
are

thought
to
exist
in
individuals
with
developmental
disorders
related
to
DD/DCD

such
as
ASD
and
ADHD.

Evidence
of
increased
whole-‐brain
(Hardan,
Muddasani,

Vemulapalli,
Keshavan,
&
Minshew,
2006)
and
regional
cortical
thickness
in
IFG,
IPL,

31
and
STS
(Hyde,
Samson,
Evans,
&
Mottron,
2010)
has
been
found
in
ASD.

Conversely,
Hadjikhani
et
al.
(2006)
found
decreased
cortical
thickness,
correlating

with
social
and
communication
symptom
severity
(the
investigators
did
not

measure
motor
or
imitation
skills),
in
MNS
regions
in
high-‐functioning
adults
with

ASD.

Studies
in
children
with
ADHD
show
decreases
in
global
and
regional
thickness

measurements
in
prefrontal
and
precentral
areas
(Shaw,
Lerch,
Greenstein,
Sharp,

Clasen,
et
al.,
2006)
and
delayed
maturation
of
peak
thickness,
especially
in

prefrontal
areas
(Shaw,
Eckstrand,
Sharp,
Blumenthal,
Lerch,
et
al.,
2007).

At
least

one
investigation
(Wolosin,
Richardson,
Hennessey,
Denckla,
&
Mostofsky,
2009)

found
no
significant
differences
in
cortical
thickness
in
children
with
ADHD,

although
ADHD
was
found
to
be
associated
with
reduced
cortical
folding,
surface

area,
and
gray
matter
volume.

Incidentally,
preterm
birth
alone
is
associated
with

decreased
cortical
surface
area
and
total
gray
matter
volume,
but
no
change
in

cortical
thickness
(Kapellou,
Counsell,
Kennea,
Dyet,
Saeed,
et
al.,
2006;

Martinussen,
Fischl,
Larsson,
Skranes,
Kulseng,
et
al.,
2005),
a
finding
that
may
be

attributed
to
the
completion
of
neurogenesis
in
the
ventricular
zone
prior
to
viable

birth
(Kapellou
et
al.,
2006).

However,
there
is
somewhat
conflicting
evidence

regarding
cortical
thickness
differences
in
this
population
(Nagy,
Lagercrantz,
&

Hutton,
2011).

Given
the
similar
presentation
of
motor
symptoms
among
ASD,

ADHD,
and
DD/DCD,
as
well
as
evidence
that
cortical
thickness
differences
exist
in

motor-‐related
brain
regions
in
these
disorders,
including
MNS
regions,
it
is

reasonable
to
expect
underlying
differences
in
cortical
thickness
in
MNS
regions
in

32
DD/DCD.

The
directionality
of
any
potential
differences,
however,
is
difficult
to

predict.

Theoretical
Models
Although
it
has
not
been
hypothesized
before
that
dysfunction
in
the
mirror

neuron
system
regions
underlies
imitation
impairments
in
DD/DCD,
other
theories

of
neural
mechanisms
of
the
disorder,
and
of
imitation
and
praxis
generally,
have

been
put
forward.

These
theories
are
fairly
consistent
with
the
MNS
hypothesis
and

what
is
known
about
white
matter
tracts
connecting
these
regions
but
have
some

theoretical
limitations.

They
are
briefly
reviewed
here.

First,
Rothi,
Ochipa,
and
Heilman
(1997)
have
proposed
a
model
of
nonlexical

(i.e.,
novel
and/or
meaningless)
gesture
imitation.

In
contrast
to
symbolic,
or

lexical,
gestures
(those
that
can
be
named),
imitation
of
nonlexical
actions
is

specifically
proposed
to
be
mediated
by
“a
direct
route
between
visual
systems
and

motor
systems”
(p.
39).

Although
basic
in
concept,
this
model
parallels
the
idea
that

the
shared
representation
of
action
and
perception
may
exist
through
direct

connections,
such
as
white
matter
tracts
like
the
arcuate
fasciculus
(Catani
et
al.

2005).

Next,
Ayres’
sensory
integration
theory
was
developed
to
explain
difficulties

in
motor
learning
thought
to
be
a
result
of
deficits
in
interpreting
somatosensation

(Ayres,
1965;
1972;
1985/2011;
Bundy
&
Murray,
2002).

This
theory
was

formulated
prior
to
the
invention
of
fMRI
and
other
technologies
now
available
for

33
studying
in
vivo
brain
function.

Nonetheless,
based
on
a
review
of
available

neuroscience
literature
at
the
time,
Ayres
posited
that
primarily
left
cerebral

dysfunction
may
be
responsible
for
developmental
dyspraxia
based
on
the
role
of

that
hemisphere
in
causing
lesion-‐based
apraxia
(Ayres,
1985/2011).

Specifically,

she
outlined
a
3-‐step
process
for
motor
acts.

First,
ideation,
or
cognitive

conceptualization,
occurs
broadly
in
the
left
hemisphere
(Ayres,
1985/2011).

This

is
followed
by
motor
planning,
or
praxis,
which
is
supported
by
parietal
regions

such
as
supramarginal
gyrus,
the
secondary
somatosensory
area
(SII),
premotor

area,
connections
between
parietal
and
frontal
regions,
and
the
parietal-‐occipital-‐
temporal
junction
(Ayres,
1985/2011).

Finally,
motor
execution
is
carried
out
via

the
pyramidal
(for
unilateral,
skilled,
and
gestural
movements)
and
non-‐pyramidal

(for
midline
and
postural
movements)
tracts
(Ayres,
1985/2011).

Ayres’
outline
of

the
neural
circuitry
of
motor
acts
is
broad,
but
similar,
to
the
regions
of
the
MNS.

Ayres’
conception
of
how
motor
acts
are
conceptualized,
planned,
and

executed
by
the
brain
is
similar
to
the
sensory-‐motor
(or
stimulus-‐response)

translation
model
of
action
and
imitation
(Iacoboni,
2009;
Proctor
&
Reeve,
1990)

and
shares
a
challenge
of
that
paradigm—it
does
not
account
for
how
exactly

sensory
input
from
the
external
environment
is
transformed
into
a
matching
motor

output
by
the
imitator
(Brass
&
Heyes,
2005),
nor
does
it
account
for
automatic

imitation
(Chartrand
&
Bargh,
1999).

The
MNS
theory
of
imitation
(or
ideomotor

framework),
however,
assumes
that
shared
representation
of
action
and
perception

allows
for
imitation
without
the
need
for
translation
(Brass
&
Heyes,
2005;
Iacoboni,

34
2009).

In
addition,
this
representation
is
thought
to
be
formed
through
experience-‐
based
learning
from
birth
(Heyes,
2010),
a
notion
Ayres
hypothesized
as
well

(Ayres,
1972
&
1985/2011),
and
has
been
forwarded
as
the
basis
for
sensory

integration
treatment
by
occupational
therapists
(Bundy,
Lane,
&
Murray,
2002;

Roley,
Blanche,
Schaaf,
2001).

At
the
cellular,
there
is
evidence
of
the
necessity
of

early
sensory
input
for
the
development
of
multisensory
integration
(Wallace,

Perrault,
Hairston,
&
Stein,
2004).

Another
proposed
theory
of
neural
dysfunction
in
DD/DCD
is
that
of

impaired
internal
representation
of
movement
resulting
from
a
deficit
in
processing

the
efference
copy
of
motor
commands
(Maruff,
Wilson,
Trebilcock,
&
Currie,
1999).

‘Efference
copy’
refers
to
a
copy
of
the
motor
command
signal
that
is
sent
to
the

posterior
parietal
cortex
from
the
primary
motor
cortex
as
a
prediction
of
the

sensory
consequences
of
an
action
to
be
compared
to
actual
sensations
of
the
action

arriving
upstream
via
the
spinal
cord
(Jeannerod,
1997).

The
existence
of
an

efference
copy
accounts
for
the
existence
of
motor
imagery
in
situations
where

actual
movement
is
suppressed,
and
this
idea
is
supported
by
the
demonstration

that
imagined
movements
maintain
the
same
speed-‐for-‐accuracy
trade-‐off
(Fitts’

law)
of
real
movements
(Decety,
1996).

In
addition,
the
MNS
regions
are
thought
to

facilitate
motor
imagery
(Binkofski
et
al.,
2000;
Buccino,
Solodkin,
&
Small,
2006;

Gerardin
et
al.,
2000;
Rizzolatti,
Fogassi,
&
Gallese,
2002).

Following
parietal
cortex

damage,
imagined
movements
often
do
not
conform
to
the
principle
of
Fitts’
law,

indicating
an
impairment
of
the
mental
representation
of
movement
(Sirigu
et
al.,

35
1995;
1996).

Evidence
in
support
of
impaired
internal
movement
representation
in

DD/DCD
comes
from
studies
that
have
found
that
these
children
are
not
able
to

accurately
time
imagined
movement
sequences
according
to
Fitts’
law
as
with
real

movements
(Lewis,
Vance,
Maruff,
Wilson,
&
Cairney,
2008;
Maruff
et
al.,
1999;

Williams;
Omizzolo,
Galea,
&
Vance,
2013;
Wilson,
Maruff,
Ives,
&
Currie,
2001)
and

were
less
accurate
in
using
mental
imagery
in
mental
rotation
tasks
(Williams,

Thomas,
Maruff,
Butson,
&
Wilson,
2006;
Williams
et
al.,
2013;
Wilson
et
al.,
2004).

Evidence
that
motor
imagery—a
skill
thought
to
be
supported
by
the
MNS
and

functionally
similar
to
actual
motor
planning
(Jeannerod,
1994;
Wilson,
Thomas,
&

Maruff,
2002)—is
impaired
in
DD/DCD
(Williams,
Thomas,
Maruff,
&
Wilson,
2008;

Wilson
et
al.,
2004)
is
congruent
with
the
dysfunctional
MNS
hypothesis
of

developmental
dyspraxia
(Werner,
Cermak,
&
Aziz-‐Zadeh,
2012).

In
the
following
chapters,
I
will
focus
on
a
model
of
developmental
dyspraxia

that
centers
on
hypothesized
dysfunction
in
the
fronto-‐parietal
mirror
neuron

system.

Comparing
DD
to
TD
individuals,
both
structural
and
functional

neuroimaging
studies
to
test
this
model
are
described
and
discussed
in
terms
of

differences
in
this
neural
network
that
may
underlie
DD.

Finally,
the
research
is

summarized
and
implications
for
the
results
and
future
directions
are
suggested.

36
CHAPTER
TWO:

Functional
Brain
Differences
in
the
Mirror
Neuron
System
During
Imitation

and
Motor
Planning
in
Young
Adults
with
Developmental
Dyspraxia

Abstract

Individuals
with
developmental
dyspraxia
exhibit
impairments
in
imitation,

motor
learning,
and
motor
planning.

The
human
mirror
neuron
system
(MNS)
is

thought
to
support
imitation
and
has
been
shown
to
function
differently
in

individuals
with
developmental
disorders
in
which
a
motor
impairment
exists,
such

as
autism
spectrum
disorder.

In
the
current
fMRI
study,
we
sought
to
determine
if

shared
representations
of
observation
and
execution
differ
between
young
adults

with
developmental
dyspraxia
and
a
group
of
their
typically
developing
peers
and

how
activity
in
the
MNS
regions
differs
during
motor
planning
and
imitation.

Participants
observed,
executed,
and
imitated
novel,
meaningless,
bimanual

gestures,
and
fMRI
data
was
collected
during
a
prolonged
motor
planning
phase
and

during
the
response
phase
of
each
condition.

We
found
areas
of
shared
observation-‐
execution
representation
in
bilateral
ventral
premotor
cortex
(vPMC)
and
right
pars

triangularis
in
the
typically
developing
group
compared
with
the
developmental

dyspraxia
group.

Imitation
minus
motor
execution
yielded
greater
activity
in
left

inferior
parietal
lobule
(IPL)
and
right
vPMC/inferior
frontal
gyrus
in
the
typically

developing
group
compared
to
the
developmental
dyspraxia
group;
whereas
motor

planning
resulted
in
bilateral
increased
activity
in
vPMC/IFG
and
left
IPL
for
the

37
typically
developing
group.

An
exploration
of
individual
imitation,
execution,
and

observation
conditions
at
the
whole
brain
level
revealed
no
differences
during

execution
but
several
areas
of
between
group
differences
during
imitation
and

observation.

These
results
indicate
that
a
deficit
in
the
observation-‐execution

matching
system
may
underlie
impaired
imitation
and
motor
planning
in

individuals
with
developmental
dyspraxia.

Results
are
discussed
in
light
of

potential
underlying
dysfunction,
such
as
differences
in
the
cognitive
aspects
of

motor
planning,
visual
perception
of
human
bodies,
or
error
monitoring.

This

knowledge
could
potentially
inform
the
development
of
intervention
strategies,

such
as
treatments
aimed
at
training
sensorimotor
integration,
but
more
research
is

needed.

38
Introduction

Individuals
with
developmental
dyspraxia
(DD)
show
impairments
in

imitation,
motor
planning,
and
learning
new
motor
skills,
although
they
do
not
have

primary
social
or
communication
impairments
(Ayres,
1989/2004;
O’Brien
et
al.,

2002;
Reeves
&
Cermak,
2002).

These
motor
skills
difficulties
are
thought
to
affect

5-‐15%
of
people
(Cermak,
Gubbay,
&
Larkin,
2002;
Dewey,
2002;
Gibbs,
Appleton,
&

Appleton,
2007),
are
not
known
to
have
obvious
structural
brain
abnormalities,
and

have
documented
negative
consequences
carrying
over
to
daily
living
tasks,

educational
performance,
and
activity
participation
(Cermak
et
al.,
2002;
Gheysen,

Van
Waelvelde,
&
Fias,
2011;
Reeves
&
Cermak,
2002;
Chen
et
al.,
2009;
Kirby,

Sugden,
Beveridge,
&
Edwards,
2008).

Although
developmental
in
nature,
motor

skill
performance
typically
remains
poor
into
adolescence
and
adulthood
(Cantell
&

Kooistra,
2002;
Cantell,
Smyth,
&
Ahonen,
2003;
Cousins
&
Smyth,
2003;
Kirby,

Edwards,
&
Sugden,
2011;
Kirby,
Edwards,
Sugden,
&
Rosenblum,
2010;
Kirby,

Sugden,
Beveridge,
&
Edwards,
2008;
Missiuna,
Moll,
King,
Stewart,
&
MacDonald,

2008).

Because
imitation
is
thought
to
play
a
role
in
motor
and
social
learning

(Meltzoff
&
Decety,
2003;
Piaget,
1962),
individuals
with
DD
who
have
imitation

impairments
may
have
more
difficulty
acquiring
new
skills.

Matching
an
observed
action
with
the
motor
representation
of
that
action
is

thought
to
serve
as
a
neural
precursor
mechanism
of
human
imitation,
and
this
has

consistently
been
shown
to
occur
in
a
network
of
frontal
and
parietal
regions

commonly
called
the
mirror
neuron
system
(MNS;
Buccino
et
al.,
2004;
Iacoboni
et

39
al.,
1999;
Koski
et
al.,
2003;
Rizzolatti
&
Craighero,
2004).

In
particular,
activity
in

the
inferior
frontal
gyrus
(IFG)
has
been
shown
to
be
essential
to
imitative
ability

(Heiser
et
al.,
2003).

Because
imitation
is
impaired
in
DD
(Ayres
1989/2004;

Wilson,
2005),
we
hypothesize
that,
relative
to
a
typically
developing
(TD)
group,

activity
elicited
by
imitation
compared
to
non-‐imitative
execution
(referred
to
from

here
on
simply
as
“execution”)
might
be
decreased
in
those
with
DD
in
this
frontal-‐
parietal
network
(Werner
et
al.,
2012).

Specifically,
we
expect
this
IFG/pars

opercularis
and
the
adjacent
ventral
premotor
cortex
(vPMC)
and
in
the
inferior

parietal
lobule
(IPL).

In
addition,
the
posterior
portion
of
the
superior
temporal

sulcus
(pSTS)
has
been
consistently
implicated
in
imitation
and
action

understanding
(Buccino
et
al.,
2001;
Iacoboni
et
al.,
2001),
likely
due
to
its
role
in

biological
motion
processing
(Grossman
&
Blake,
2002;
Calvo-‐Merino
et
al.,
2005;

2006).

Activity
in
this
region
may
also
be
compromised
in
DD.

Previous
research
has
revealed
differences
in
MNS
activity
in
developmental

disorders
which
are
sometimes
co-‐morbid
with
DD,
such
as
autism
spectrum

disorders
(ASD;
Dapretto
et
al.,
2006);
however,
there
is
no
prior
existing

investigation
of
the
MNS
as
it
relates
to
DD.

Furthermore,
motor
imitation,
but
not

necessarily
social
functioning,
is
impaired
in
DD
whereas
both
are
impaired
in
ASD

(Reeves
&
Cermak,
2002).

In
light
of
evidence
for
a
putative
mirror
neuron
system

in
humans—a
network
of
shared
neural
representation
for
observation
and

execution
of
actions—we
would
expect
that
shared
representation
in
DD
would
be

less
robust
compared
to
TD
individuals
(Werner
et
al.,
2012).

40
In
addition
to
imitation
impairments,
developmental
dyspraxia
is
often

described
as
a
disorder
of
motor
planning
(Ayres,
1989/2004;
Dewey,
1995;
O’Brien

et
al.,
2002;
Gibbs
et
al.,
2007;
Lalanne,
Falissard,
Golse,
&
Vaivre-‐Douret,
2012;

Reeves
&
Cermak,
2002;
Sinani,
Sugden,
Hill,
2011;
Vaivre-‐Douret
et
al.,
2011).

Behavioral
investigations
have
revealed
that
individuals
with
dyspraxia
and

developmental
coordination
disorder
(DCD;
a
syndrome
of
impaired
motor
skill
that

may
include
dyspraxia)
complete
motor
imagery
tasks
with
less
accuracy
than
TD

individuals
(Maruff,
Wilson,
Trebilcock,
&
Currie,
1999;
Wilson,
Maruff,
Ives,
&

Currie,
2001;
Wilson
et
al.,
2004),
but
there
is
currently
little
insight
on
the
neural

correlates
of
motor
planning
in
DD.

In
two
related
functional
magnetic
resonance

imaging
(fMRI)
studies
conducted
by
Zwicker
and
colleagues
(2010;
2011),
children

with
DCD
exhibited
numerous,
diffuse
cortical
differences
compared
to
a
control

group
during
a
simple
motor
task
(trail
tracing
with
a
joystick)
and
after
skilled

motor
practice
that
required
planning
and
online
error
monitoring.

Although
their

results
included
between-‐group
differences
in
MNS
regions
(e.g.,
in
bilateral
IPL
and

left
IFG),
neither
study
was
designed
to
investigate
the
MNS
and
their
results
could

reflect
general
differences
in
sensory
and
motor
processes.

The
investigators

concluded
that
their
results
could
reflect
between
groups
differences
broadly

related
to
cognitive
processing.

To
directly
examine
potential
differences
in
brain
function
in
a
DD
group

compared
to
a
TD
group,
we
designed
a
protocol
to
isolate
motor
planning
and

imitation.

Because
we
expect
stronger
activity
in
the
TD
group
compared
to
the
DD

41
group
during
imitation,
and
because
motor
planning
precedes
imitation,
we
expect

that
motor
planning
will
show
a
similar
pattern
to
that
of
imitation.

It
is
our

hypothesis
that
individuals
with
DD
will
display
decreased
levels
of
activity
in
MNS

regions
during
a
motor
planning
phase
prior
to
imitation.

We
further
predict
that

they
will
show
less
activity
than
the
TD
group
in
MNS
regions
during
imitation
as

compared
to
executing
a
non-‐imitative
control
action.

It
is
unlikely
that
DD
is
the
result
of
only
one
specific
neural
mechanism,
but

probably
encompasses
systemic
differences
in
the
functioning
of
numerous
sensory

and
motor
brain
areas.

Given
that
there
have
been
very
few
fMRI
investigations

which
have
explored
DD
in
any
comprehensive
manner,
we
also
ran
more

exploratory
whole
brain
whole
brain
analysis
to
examine
potential
differences

between
groups.

We
predicted
differences
in
core
regions
related
to
imitation

processing
and
praxis
[IFG,
PMC,
posterior
parietal
cortex
(PPC),
supplemental

motor
area
(SMA);
Heiser
et
al,
2003;
Iacoboni
et
al.,
1999;
Krainik
et
al.,
2001;

Wheaton,
Shibasaki,
&
Hallett,
2005;
Wheaton,
Yakota,
&
Hallett,
2005].

In
summary,
the
aims
of
the
current
fMRI
study
were:
(a)
to
determine
if

individuals
with
developmental
dyspraxia
exhibit
differences
in
mirror-‐like
activity,

or
observation-‐execution
matching;
(b)
to
understand
the
contributions
of
regions

commonly
considered
part
of
the
mirror
neuron
system
to
imitation
and
motor

planning
between
DD
and
TD
groups;
and
(c)
to
characterize
whole
brain
neural

correlates
of
imitation,
execution,
and
observation
in
DD
compared
to
TD
groups.

42
Methods

Participants

Inclusion
criteria
for
all
participants
were
the
following:
18-‐30
years
of
age,

right-‐handed
as
assessed
by
a
Modified
Edinburgh
Handedness
Inventory
(Oldfield,

1971),
safe
to
undergo
MRI,
normal
or
corrected-‐to-‐normal
vision,
and
no

neurological
or
psychiatric
history
(except
DD
for
participants
in
the
DD
group).

In

addition,
all
potential
participants
were
screened
for
autism
spectrum
disorder

symptoms
according
to
the
Ritvo
Autism
Asperger
Diagnostic
Scale-‐Revised
(Ritvo

et
al.,
2011)
using
that
measure’s
published
cutoff
scores
to
determine
if
ASD

symptoms
were
likely.

Participants
were
excluded
from
the
study
if
they
were

suspected
to
have
ASD
as
this
would
confound
the
data
given
previous
findings
that

indicate
MNS
differences
in
ASD
(Dapretto
et
al.,
2006).

For
the
DD
group,

additional
inclusion
criteria
were:
(a)
a
stated
history
and
current
difficulty
with

motor
coordination,
imitation,
and/or
learning
new
motor
skills
in
an
initial

screening
interview;
(b)
a
combined
score
within
the
probable
DCD
range
on
the

Adult
DCD/Dyspraxia
Checklist
(ADC;
Kirby,
Edwards,
Sugden,
&
Rosenblum,
2010;

Kirby
&
Rosenblum,
2011)
and
modified
DCD-‐Q’07
(re-‐worded
to
be
appropriate
for

adults;
Wilson,
Kaplan,
Crawford,
&
Roberts,
2007);
(c)
a
score
below
15
th

percentile

for
age
and
gender
on
the
Bruininks-‐Oseretsky
Test
of
Motor
Proficiency
Second

Edition
(BOT-‐2)
Short
Form
(Bruininks
&
Bruininks,
2005);
and
(d)
raw
score
below

28
on
the
Sensory
Integration
and
Praxis
Tests
(SIPT)
Postural
Praxis
test
(Ayres,

1989/2004).

Because
the
SIPT
Postural
Praxis
test
has
been
standardized
only
in

43
children,
we
collected
data
on
100
young
adults
meeting
the
same
age
and

handedness
criteria
as
our
study
participants
and
determined
a
cutoff
score
of
2

standard
deviations
below
the
mean
(see
Appendix
D).

Written
informed
consent
was
obtained
from
all
participants
before

inclusion
in
the
study.

Recruitment
procedures
(see
Appendix
B)
and
study

protocol
were
approved
by
the
Institutional
Review
Board
at
the
University
of

Southern
California
prior
to
recruitment
and
data
collection.

All
procedures
were

conducted
with
regard
to
ethical
standards
for
human
subjects
research
in

accordance
with
the
1964
Declaration
of
Helsinki.

A
total
of
30
young
adults
(range:
18-‐28
years
old)
were
recruited
in
two
age-‐

and
gender-‐matched
groups
of
15
each
(DD
and
TD).

All
participants
completed
the

questionnaires,
motor
and
imitation
testing,
and
tasks
inside
the
scanner.

One

participant
in
the
DD
group
and
one
participant
in
the
TD
group
were
excluded
from

analysis
due
to
excessive
head
motion
artifact
of
>
3
mm
translational
motion

detected
during
the
motion
correction
pre-‐processing
step,
with
a
resulting
total
of

28
participants
(14
in
each
group)
included
in
analysis.

Participant
characteristics

are
listed
in
Table
2.1.

Design

Task
Design
and
Conditions.
The
visual
stimuli
consisted
of
2-‐s
movie
clips
of

meaningless,
bilateral
hand
gestures
and
still
control
images
of
the
same
actor
with

44
TABLE
2.1.

Participant
demographics

Typically

developing

N
=
14

Developmental

dyspraxia

N
=
14
p

Age
22.3
(3.6)
21.8
(2.7)
.679

Gender
8
male,
6
female
8
male,
6
female
-‐

Questionnaire
composite
84.3
(15.2)
100.4
(15.7)
<
.01

BOT-‐2
Short
Form
(%ile)
45.81
11.42
<
.001

SIPT
Postural
Praxis
30.9
(3.4)
25.8
(1.6)
<
.01

Note.

Questionnaires
included
in
composite
were
the
ADC,
items
adapted
from
the
DCDQ’07,
and

additional
similar
items
generated
for
this
investigation.

A
higher
composite
questionnaire
score

indicates
greater
perceived
impairment
in
motor
skill
or
coordination
(possible
range:
0-‐160).

BOT-‐
2
Short
Form
percentile
ranks
were
derived
from
published
normative
standards
for
gender
and
age.

SIPT
Postural
Praxis
group
are
reported,
with
higher
score
indicating
greater
imitation
skill
(possible

range:
0-‐38).

hands
resting.

Movie
clips
were
repeated
within
events
to
allow
for
jittered

duration
events.

Three
experimental
conditions
were
used:
imitation,
execution,

and
observation.

Four
trials
of
each
condition
were
presented
per
run,
with
four

runs
total.

Each
trial
was
preceded
by
a
color-‐coded
cue
and
corresponding
colored

frames
were
used
around
videos
to
indicate
the
trial
condition.

All
conditions
were

presented
as
part
of
a
nested
event-‐related
design
such
that
stimuli
were
initially

presented,
followed
by
a
delay
period
to
allow
for
separate
analysis
of
motor

planning,
and
finally
by
a
response
period
during
which
videos
or
still
photos
were

shown
a
second
time
while
participants
either
imitated,
executed,
or
observed
(see

Figure
2.1
for
schematic
of
stimuli
presentation).

Although
the
instructed
delay

representing
the
motor
planning
phase
is
unnaturally
prolonged
in
the

45
experimental
setting,
this
type
of
task
has
been
used
in
previous
research
with

evidence
supporting
the
validity
of
the
task
(Bohlhalter
et
al.,
2009;
Buccino
et
al.,

2004;
Georgopoulos,
Crutcher,
&
Schwartz,
1989;
Godschalk,
Lemon,
Kuypers,
&
van

der
Steen,
1985;
Menz
et
al.
2009;
Weinrich
&
Wise,
1982)
and
evidence
indicating

that
motor
planning
may
occur
up
to
3
seconds
prior
to
self-‐paced
praxis

movements
(Wheaton,
Shibasaki,
&
Hallett,
2005;
Wheaton,
Yakota,
&
Hallett,

2005).

Figure
2.1.

For
all
conditions,
participants
viewed
a
series
of
5
events:
(a)
with
a

color-‐coded
cue
to
indicate
trial
type;
(b)
video
or
still
image
of
an
actor;
(c)

instructed
delay
for
motor
planning
during
which
participants
were
trained
to

prepare
for
the
appropriate
response
following;
(d)
response
period
to
perform

imitation,
execution,
or
observation;
and
(e)
jittered
inter-‐stimulus
rest
after
each

trial.

“Catch”
trials
(not
shown)
were
identical
to
imitation
trials
with
the
exception

of
a
colored
background
only
during
the
response
period
(d).

46
In
addition
to
the
three
experimental
conditions,
“catch”
trials,
in
which
only

the
colored
frame
were
shown
during
the
response
period,
were
included
to
ensure

participants
were
actively
attending
and
preparing
to
respond
in
advance,
rather

than
imitating
online.

A
6-‐12
second
jittered
rest
period
followed
each
trial.

Participants
were
trained
with
sham
stimuli
in
a
mock
scanner
prior
to
actual

scanning
until
they
were
able
to
identify
color-‐coded
trial
type
cues
and
successfully

demonstrate
the
task.

Stimuli
Presentation.
Visual
stimuli
were
presented
via
a
projector
onto
a

rear-‐projection
screen
located
at
the
head
of
the
scanner
bore.

Participants
viewed

the
screen
through
a
mirror
mounted
to
the
head
coil.

Because
portions
of
the
task

required
participants
to
move
their
upper
extremities,
participants
were
trained
to

keep
their
heads
still
throughout
the
scan.

Firm
foam
cushions
placed
around
the

head
and
neck
and
medical-‐grade
tape
was
used
to
help
keep
participants’
heads

still.

A
large,
MRI-‐safe
mirror
(24
in
2
)
on
a
floor-‐mount
stand
was
positioned
so
that

participants’
movements
could
be
monitored
in
the
scanner
by
experimenters

through
a
window
between
the
scanning
room
and
control
room,
and
participants

were
reminded
between
scanning
runs
to
keep
their
heads
as
still
as
possible
during

all
portions
of
the
task.

Image
Acquisition

Scanning
was
conducted
with
a
Siemens
MAGNETOM
Trio
3T
scanner
at
the

Dornsife
Cognitive
Neuroscience
Imaging
Center
at
the
University
of
Southern

47
California.

A
high-‐resolution
T1-‐weighted
structural
scan
was
acquired
with
a

magnetization-‐prepared
rapid
gradient
echo
(MPRAGE)
sequence
(TR
=
1950
ms;

TE
=
2.26
ms;
224
x
256
voxel
matrix;
176
sagittal
slices;
1
mm
3

voxels).

Two

hundred
seven
functional
volumes
were
acquired
with
a
T2*-‐weighted
echo
planar

imaging
(EPI)
sequence
(TR
=
2000
ms;
TE
=
30
ms;
flip
angle
=
90°;
64
x
64
voxel

matrix;
37
interleaved
axial
slices
per
volume;
3.5
mm
3

voxels).

In
addition,
a
T2-‐
weighted
structural
scan
was
acquired
of
each
participant
and
screened
for

neurological
disorders
by
a
qualified
neuroradiologist
in
accordance
with
the
local

IRB
guidelines
and
scanning
center’s
incidental
findings
policy.

Image
Processing

Imaging
data
were
analyzed
using
FSL
(FMRIB’s
Software
Library;

www.fmrib.ox.ac.uk/fsl; Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012).

The
following
pre-‐processing
procedures
were
applied
to
the
functional
data:

motion
correction
using
MCFLIRT
(Jenkinson
et
al.,
2002);
slice-‐timing
correction

using
Fourier-‐space
time-‐series
phase-‐shifting;
non-‐brain
removal
using
BET

(Smith,
2002);
spatial
smoothing
using
a
Gaussian
kernel
of
FWHM
5
mm;
grand-‐
mean
intensity
normalization
of
the
entire
4D
dataset
by
a
single
multiplicative

factor;
and
high-‐pass
temporal
filtering
with
a
cutoff
of
100
s.

Functional
images

were
aligned
and
co-‐registered
to
the
high-‐resolution
T
1

structural
image
using
12-‐
degrees
of
freedom
linear
registration
with
FLIRT
(FMRIB’s
Linear
Registration

Tool;
Jenkinson
&
Smith,
2001;
Jenkinson
et
al.,
2002)
and
all
images
were

48
normalized
to
a
2
mm
3

Montreal
Neurological
Institute
(MNI-‐152)
template
image.

Functional
data
processing
was
carried
out
using
FEAT
(fMRI
Expert
Analysis
Tool)

Version
6.0,
part
of
FSL.

Event-‐related
neural
activity
was
convolved
to
a
double-‐
gamma
hemodynamic
response
function
(HRF).

Effects
at
each
voxel
were

estimated
using
general
linear
modeling
(GLM).

First-‐level
fixed
effects
estimations

from
individuals
were
entered
into
higher-‐level
random
effects
models
for
across-‐
sessions
and
across-‐subjects
analyses
using
FLAME
(FMRIB’s
Local
Analysis
of

Mixed
Effects;
Beckmann,
Smith,
&
Jenkinson,
2001).

Conjunction
analysis

Mirror
neurons
are
active
for
both
execution
and
observation,
and
BOLD

signal
should
reflect
this
underlying
neural
activity.

Thus,
to
determine
which

voxels
were
active
for
both
execution
and
observation,
we
performed
conjunction

analysis
testing
for
significant
activation
against
the
conjunction
null
based
on
the

minimum
statistic
(Nichols
et
al.,
2005).

For
each
subject,
execution
minus
rest
was

masked
with
observation
minus
rest.

Gaussianized
statistic
images
were

thresholded
at
p
<
.05
FDR
corrected
for
multiple
comparisons
at
the
voxel
level

(Worsley,
2001).

Surviving
clusters
are
representative
of
active
clusters
for
both

conditions,
and
between
groups
comparisons
were
then
carried
out
with
a

significance
threshold
of
p
<
.05.

49
ROI
analyses

To
compare
activation
patterns
between
groups
in
the
MNS,
regions
of

interest
(ROIs)
were
defined
independently
of
the
current
dataset
to
avoid

circularity
(Krigeskorte
et
al.,
2009).

ROIs
were
selected
on
the
basis
of
those

consistently
reported
in
the
literature
(Koski
et
al.,
2003;
Rizzolatti
&
Craighero,

2004;
Van
Overwalle
&
Baetens,
2009)
as
being
part
of
the
MNS
and
active
during

Figure
2.2.

ROIs
were:
IFG
pars
opercularis
and
pars
triangularis,
and
IPL
(top),

and
pSTS
(bottom).

ROIs
anatomically
defined
using
50%
probability
thresholds
in

the
Harvard-‐Oxford
probabilistic
structural
atlas
(distributed
with
the
FSL
software

package).

X
=
-‐56

X
=
56

X
=
-‐48

X
=
50

50
imitation:
(a)
a
region
encompassing
the
ventral
premotor
cortex,
inferior
frontal

gyrus
(IFG)
pars
opercularis,
and
posterior
portion
of
pars
triangularis,
and
(b)
the

inferior
parietal
lobule
(IPL).

In
addition,
(c)
the
posterior
superior
temporal
sulcus

(pSTS),
although
not
typically
considered
part
of
the
MNS
as
it
is
not
a
motor
region,

was
included
because
it
is
often
concurrently
active
with
the
MNS
and
presumed
to

underlie
biological
motion
processing
in
visual
tasks
(Puce
&
Perrett,
2003)
and

may
underlie
a
comparison
mechanism
for
observed
actions
and
reafferent
motor-‐
related
actions
during
imitation
(Iacoboni
et
al.,
2001).

ROI
masks
were
generated

from
50%
probability
thresholds
in
the
Harvard-‐Oxford
probabilistic
structural

atlas
(distributed
with
the
FSL
software
package;
www.fmrib.ox.ac.uk/fsl),
co-‐
registered
to
each
participant’s
structural
image,
and
adjusted
manually
if
needed

based
on
individual
anatomy.

ROIs
are
shown
in
Figure
2.2.

Gaussianized
statistic

images
within
ROIs
were
thresholded
at
p
<
.05
at
the
voxel
level
(Worsley,
2001)

and
between
groups
comparisons
were
then
carried
out
with
a
significance

threshold
of
p
<
.05.

Results

Shared
regions
for
execution
and
observation

In
the
TD
group,
conjunction
analysis
resulted
in
significant
activation
(p
<

.05,
corrected)
in
pars
opercularis
bordering
vPMC
on
the
left,
and
in
vPMC
and
pars

triangularis
on
the
right.

In
addition,
activation
in
bilateral
IPL
was
active
for
both

51
execution
and
observation
in
the
TD
group.

In
the
DD
group,
the
execution
plus

observation
conjunction
yielded
no
significant
voxels.

A
conjunction
of
voxels
active
for
both
execution
and
observation
for
the

contrast
of
TD
minus
DD
yielded
significant
clusters
in
the
left
ventral
premotor

cortex
(vPMC)
adjacent
to
pars
opercularis,
and
another
slightly
dorsal
to
the
first
in

the
left
vPMC.

In
the
right
hemisphere
the
analysis
yielded
two
activation
peaks:

one
in
the
vPMC
and
a
second
in
pars
triangularis
(see
Table
2.2
for
peak
activations

and
Figure
2.3
for
fMRI
results).

No
significant
clusters
emerged
for
the
contrast
of

DD
minus
TD.

TABLE
2.2.

Co-activations
during
execution
and
observation
for
the
contrast

TD-DD

Anatomical
region
BA
Z-‐value
Voxels
MNI
Coordinates

L

ventral
PMC/pars
opercularis
44
2.29
52
-‐62,
6,
16

L

ventral
PMC
6
2.10
25
-‐62,
-‐2,
26

R

ventral
PMC
6
1.78
37
64,
6,
26

R

pars
triangularis
45
1.94
15
58,
32,
2

Note.

Results
reported
at
p
<
.05,
FDR
corrected
for
multiple
comparisons.
There
were
no
significant

findings
for
the
contrast
DD-‐TD.

52

Figure
2.3.

fMRI
results
of
conjunction
(execution
and
observation;
p
<
.05,

corrected)
for
TD
(A),
DD
(B),
and
TD-‐DD
(C).

A

B

C

53
Region
of
interest
analyses
for
imitation
and
motor
planning

Imitation.

In
the
contrast
of
imitation
of
gestures
minus
execution
while

viewing
a
static
control,
the
TD
group
yielded
significant
activations
in
bilateral

vPMC/IFG,
IPL,
and
pSTS
(p
<
.05,
FDR
corrected).

For
the
same
contrast,
the
DD

group
produced
significant
activations
in
left
vPMC/IFG,
and
in
bilateral
IPL
and

pSTS
(p
<
.05,
FDR
corrected).

The
between
groups
comparison
during
imitation
of
gestures
minus

execution
while
viewing
a
static
control
yielded
significant
differences
in
two

regions
of
interest:
left
IPL
and
right
ventral
premotor
cortex/IFG
in
the
direction
of

TD
>
DD
(p
<
.05,
FDR
corrected).

No
significant
clusters
emerged
in
the

remaining
ROIs
or
for
any
ROI
in
the
direction
of
DD
>
TD.

ROI
results
are
presented

in
Figure
2.4
and
localizations
of
significant
clusters
in
ROIs
for
this
contrast
are

shown
in
Figure
2.5.

Motor
planning.
In
the
contrast
of
motor
planning
prior
to
imitation
minus

the
same
event
prior
to
observation,
bilateral
vPMC/IFG,
IPL,
and
pSTS
were

significantly
active
in
the
TD
group
(p
<
.05,
FDR
corrected).

There
were
no

significant
activations
in
any
ROIs
in
the
DD
group
for
this
contrast.

During
motor
planning
prior
to
imitation
compared
with
the
same
time

period
prior
to
observation,
significant
differences
emerged
in
the
direction
of
TD
>

DD
in
three
regions
of
interest:
left
ventral
premotor
cortex/IFG,
left
IPL,
and
right

ventral
premotor
cortex/IFG.

No
significant
clusters
emerged
in
the
remaining
ROIs

54
or
for
any
ROI
in
the
direction
of
DD
>
TD.

ROI
results
are
presented
in
Figure
2.6

and
localizations
of
significant
clusters
in
ROIs
are
shown
in
Figure
2.7.

Figure
2.4.

Mean
percentage
signal
change
in
regions
of
interest
for
TD
and
DD

groups
during
imitation
minus
execution
while
viewing
static
control.

Significant

differences
between
groups
(p
<
.05,
corrected)
were
found
in
the
left
IPL
and
right

vPMC/IFG.

Error
bars
reflect
sample
standard
deviation
of
the
mean
calculated
for

each
ROI
separately.

[L
=
left;
R
=
right;
vPMC/IFG
=
ventral
premotor

cortex/inferior
frontal
gyrus
pars
opercularis;
IPL
=
inferior
parietal
lobule;
pSTS
=

posterior
superior
temporal
sulcus].

55

Figure
2.5.

Localization
of
fMRI
activations
are
displayed,
showing
significant

differences
for
TD>DD
in
right
IFG
(A)
and
left
IPL
(B)
during
imitation
minus

execution
to
static
control.

!!!!!!!!"#$ ! !!!!!"%$!
!
!
!
!
!
!
!
!
!
!
&!
!
!
!
!
!
!
!
!
!
!
!
&!
'()* !!!!!!!!!!'(+,-!
!
!
!
!
!
!
!
!
!
.(/0 !!!!!!!!!!.(+)-!
!
!
!
!
!
!
!
!
!
!
!
1(2, !!!!!!!!!!!1(/,!
!
!
!
!
!
!
!
!
!
!
3!
!
!
!
!
!
!
!
!
!
!
!
3!

56

Figure
2.6.

Mean
percentage
signal
change
in
regions
of
interest
for
TD
and
DD

groups
during
motor
planning
delay
before
imitation
minus
delay
before

observation.

Significant
differences
between
groups
(p
<
.05,
corrected)
were
found

in
the
left
vPMC/IFG
and
IPL
and
right
vPMC/IFG.

Error
bars
reflect
sample

standard
deviation
of
the
mean
calculated
for
each
ROI
separately.

[L
=
left;
R
=

right;
vPMC/IFG
=
ventral
premotor
cortex/inferior
frontal
gyrus
pars
opercularis;

IPL
=
inferior
parietal
lobule;
pSTS
=
posterior
superior
temporal
sulcus].

57

Figure
2.7.

Localization
of
fMRI
activations
are
displayed,
showing
significant

differences
for
TD>DD
in
right
and
left
IFG
(A)
and
left
IPL
(B)
during
motor

planning
prior
to
imitation
minus
a
control
delay
prior
to
observation.

!
"#$% ! !!!!!!!
!
!
!
!
!
!
!
!
&#'( ! !!!!!!&#)*(!
!
!
!
!
!
!
!
!
!
!
!
+#', ! !!!!!!!!+#%-!
"#)$%!
!
!
!
!
!
!
!
!
!
!!!!!!.!
!
!
!
!
!
!
!
!
!
!
!
!!!!!!.!
!!!!!!!!/01 ! !!!!!!!!!!!!!/21!
!
"#)%,!
!
!
!
!
!
!
!
!
!!!!3!
!
!
!
!
!
!
!
!
!
!
!
!!!!3!

58
Whole
brain
results
for
individual
conditions
compared
to
rest

Imitation,
observation,
and
execution
of
gestures
were
each
separately

contrasted
with
rest,
and
a
whole-‐brain
analysis
was
carried
out
at
the
p
<
.01

uncorrected
level
with
a
cluster
extent
threshold
of
k
>7
voxels.

Table
2.3
presents
a

complete
list
of
activations
by
condition
and
Figure
2.8
indicates
localizations
of

results.

During
imitation,
a
broad
array
of
significant
clusters
were
more
active
for

TD
>
DD
(p
<
.01,
uncorrected).

These
were
mostly
in
frontal,
temporal,
and
midline

areas
as
well
as
primary
sensory
and
motor
cortex,
and
premotor
and

supplementary
motor
areas.

No
significant
clusters
were
revealed
for
DD
>
TD

during
imitation.

Several
clusters
were
active
during
observation
for
the
comparison
of
TD
>

DD,
including
primary
and
secondary
visual
areas,
dorsal
ACC,
primary
sensory

cortex,
and
superior
parietal
lobule
(p
<
.01,
uncorrected).

During
observation,
two

significant
clusters
in
the
comparison
of
DD
>
TD
were
found,
one
in
an
MNS
region

(right
IPL),
and
one
in
right
middle
temporal
gyrus
(p
<
.01,
uncorrected).

59
TABLE
2.3.

Whole
brain
activations
for
individual
conditions

compared
to
rest

Anatomical
Region
BA
Z-‐value
Voxels
MNI
Coordinates

Imitation

TD
>
DD

R
dorsolateral
PFC
46
3.00
158
38,
40,
18

L
precentral
gyrus
4
2.56
96
-‐30,
-‐22,
56

R
rostral
ACC
24
3.35
83
6,
34,
-‐2

L
middle
frontal
gyrus
6
2.77
70
-‐36,
12,
56

L
postcentral
gyrus
3/1/2
2.50
68
-‐52,
-‐18,
38

L
middle
temporal
gyrus
21
2.74
62
-‐70,
-‐22,
-‐14

L
premotor
cortex
6
2.49
31
-‐30,
-‐2,
58

R
dorsal
ACC
24
2.48
31
14,
44,
14

L
temporal
pole
24/38
2.79
26
-‐60,
10,
-‐8

R
inferior
temporal
gyrus
20
3.02
26
66,
-‐16,
-‐30

R
lateral
occipital
cortex
39
2.71
23
54,
-‐74,
16

L

supplementary
motor
cortex
6
2.41
22
-‐6,
8,
60

R
precentral
gyrus
4
2.46
22
44,
-‐18,
62

Observation

TD
>
DD

R
primary
visual
cortex
18
2.65
154
26,
-‐92,
0

L
superior
parietal
lobule
7
2.96
120
-‐16,
-‐56,
68

L
lateral
occipital
cortex
39
2.96
87
-‐26,
-‐88,
30

R
superior
parietal
lobule
7
2.78
69
18,
-‐56,
62

R
fusiform
gyrus
37
2.82
58
42,
-‐58,
-‐12

L
fusiform
gyrus
37
2.69
55
-‐42,
-‐56,
-‐14

L
precentral
gyrus
4
2.79
48
-‐32,
-‐20,
54

R
medial
precentral
gyrus
4/6
2.62
39
6,
-‐26,
56

R
fusiform
gyrus
20
2.84
37
46,
-‐42,
-‐16

L
lateral
occipital
cortex
19
2.69
35
-‐52,
-‐80,
-‐2

R
postcentral
gyrus
3/1/2
2.56
33
14,
-‐34,
60

L
postcentral
gyrus
3/1/2
2.63
30
-‐28,
-‐32,
66

R
lateral
occipital
cortex
39
2.43
30
46,
-‐72,
12

L
dorsal
ACC
24
2.67
26
-‐8,
0,
44

DD
>
TD

R
IPL/angular
gyrus
39
2.66
58
52,
-‐50,
52

R
middle
temporal
gyrus
21
2.45
14
68,
-‐46,
-‐6

Note.

Results
reported
at
p
<
0.01
(uncorrected)
and
cluster
extent
threshold
k
>
7.
There
were

clusters
above
threshold
for
the
execution
condition
and
none
for
the
contrast
DD
>
TD
during
the

imitation
condition.

60

Figure
2.8.

Localizations
of
whole
brain
activations
for
TD
>
DD
for
individual

conditions
(z
>
2.3,
uncorrected).

Hot
=
observation
compared
to
rest;
Cool
=

imitation
compared
to
rest.

No
significant
between-‐group
differences
were
found

for
execution.

Slices
shown
are
X=(-‐42,
-‐20,
4,
8,
16,
40,
46)
and
Z=(-‐16,
-‐14,
-‐4,
16,

44,
56,
64).

61
Discussion

Our
main
focus
in
this
study
was
to
explore
whether
imitation
deficits

observed
in
developmental
dyspraxia
are
related
to
reduced
activity
in
brain

regions
known
to
be
important
for
imitation
processing,
namely
the
MNS
(Heiser
et

al.,
2003).

Previous
research
in
typically
developing
healthy
adults
has

demonstrated
that
the
putative
MNS
regions
(IFG/vPMC,
IPL)
are
active
during

action
imitation
(Iacoboni
et
al.,
1999;
Buccino
et
al.,
2004),
observation,
and

execution
and
that
the
IFG
in
particular
may
be
essential
for
imitation
(Heiser
et
al.,

2003).

In
the
present
study,
we
compared
activity
in
the
putative
MNS
in

individuals
with
developmental
dyspraxia,
who
have
imitation
impairments,
as

compared
to
TD
controls.

Supporting
our
hypothesis
of
greater
shared

representation
of
observation
and
execution
(i.e.,
mirror-‐like
activity)
in
the
TD

group,
the
conjunction
analysis
showed
a
greater
pattern
of
overlapping
activity
for

action
observation
and
execution
in
bilateral
IFG/vPMC
in
typically
developing

individuals
compared
with
their
peers
with
developmental
dyspraxia.

Furthermore,

our
ROI
analyses
also
showed
less
activity
in
the
DD
group
compared
to
the
TD

group
in
components
of
the
putative
MNS
(right
vPMC/IFG
and
left
IPL)
during

imitation
(contrasted
with
execution
to
static
control)
and
in
bilateral
vPMC/IFG

and
left
IPL
during
motor
planning
(contrasted
with
a
delay
before
observation).

Individuals
with
developmental
dyspraxia
are
not
completely
lacking
in

ability
to
imitate
but
are
generally
described
as
slower
or
less
accurate
at
imitating

novel
movements
(including
participants
in
the
current
investigation,
who
scored

62
lower
in
behavioral
motor
and
imitation
testing
outside
the
scanner).

This
is
similar

to
the
findings
by
Heiser
et
al.
(2003),
who
showed
that
disruption
using
repetitive

transcranial
magnetic
stimulation
(rTMS)
to
a
component
of
the
putative
MNS,
pars

opercularis,
was
associated
with
increased
errors
during
imitation.

Thus,
we

suggest
that
our
results
may
indicate
that
an
underlying
neural
deficit
in
the
shared

representation
of
observation
and
execution
could
contribute
to
imitation

impairments
in
DD.

Of
course,
imitation
is
a
complex
behavior
that
likely
requires

numerous
brain
regions
working
together
and
may
be
accomplished
with
more
than

one
cognitive
mechanism
utilizing
visual,
perceptual,
and
motor
components.

For

instance,
the
present
results
could
indicate
that
individuals
with
dyspraxia
are
using

a
different
(presumably
less
effective)
cognitive
strategy
such
as
a
top-‐down
or

semantic
approach.

This
is
evidenced
by
our
finding
that
those
with
imitation

impairments—the
DD
group—do
not
recruit
MNS
regions
thought
to
be
essential

for
imitation
through
an
observation-‐execution
matching
mechanism
to
the
same

degree
that
the
TD
group
does
(Heiser
et
al.,
2003;
Iacoboni
et
al.,
1999;
Koski
et
al.,

2003).

This
interpretation
would
also
predict
that
the
DD
group
would
show
more

activity
than
the
TD
group
in
other
regions,
such
as
the
prefrontal
cortex
or

language
related
regions.

However,
there
does
not
appear
to
be
any
indication
of

greater
prefrontal
activation
in
results
from
the
whole
brain
analysis,
although
a

cluster
in
right
middle
temporal
gyrus
(DD
>
TD)
during
observation
may
suggest

some
involvement
of
language
pathways.

63
It
is
interesting
to
note
that
our
findings
from
conjunction
analysis

comparing
TD
participants
to
DD
participants
include
two
neighboring
but
distinct

peaks
in
each
hemisphere:
one
in
vPMC
in
each
hemisphere,
one
bordering
vPMC

and
pars
opercularis
on
the
left,
and
one
in
pars
triangularis
on
the
right.

Other

investigations
have
suggested
that
activity
during
observation-‐execution
matching

tasks
may
be
functionally
segregated
and
that
this
segregation
represents
multiple

functions
of
the
vPMC/IFG.

In
a
re-‐analysis
of
their
seven
previous
fMRI
studies,

Molnar-‐Szakacs
et
al.
(2005)
concluded
that
activity
in
a
more
dorsal
portion
of
pars

opercularis
may
underlie
the
mirroring
mechanism
analogous
to
activity
recorded

from
single
cells
in
monkey
F5,
whereas
activity
in
the
ventral
sector
of
pars

opercularis
is
the
source
of
an
efference
copy
of
motor
planning
sent
to
the
STS
via

the
IPL.

In
a
comparison
between
vPMC,
pars
opercularis,
and
pars
triangularis

during
hand
action
observation,
Johnson-‐Frey
et
al.
(2003)
found
that
pars

triangularis
was
more
frequently
active
than
pars
opercularis.

Although
pars

triangularis
is
not
typically
considered
to
be
a
motor
region
because
it
is
lacking
in

granule
cell
layer
VI,
it
may
represent
an
important
aspect
of
the
observation-‐
execution
matching
system
as
it
has
been
found
to
be
active
during
mental

simulation
of
actions,
action
observation,
and
silent
generation
of
action
verbs

(Grezes
&
Decety,
2001).

The
multiple
peaks
found
in
the
present
study,
including

one
in
pars
triangularis
on
the
right,
may
be
representative
of
differences
between

groups
in
these
multiple
functional
areas
within
the
IFG.
This
perhaps
indicates
that

imitation
impairments
in
DD
could
be
due
to
underlying
mental
simulation
or

64
imagery
impairment,
abstraction,
or
to
a
difference
in
action
observation

processing.

Evidence
suggests
that
activity
in
the
mirror
neuron
system
is
bilateral,
as
is

typically
the
case
for
non-‐primary
motor
systems
(Aziz-‐Zadeh
et
al.,
2006).

Region

of
interest
analysis
in
the
vPMC/IFG
during
motor
planning
revealed
bilateral

significant
differences
but
was
only
found
on
the
right
vPMC/IFG
region
during

imitation.

During
both
motor
planning
and
imitation,
we
found
a
consistent
pattern

of
decreased
activity
in
the
dyspraxia
group
in
left
IPL.

Left
IPL
differences

observed
in
the
present
investigation
are
consistent
with
the
literature
and

numerous
clinical
observations
of
acquired
ideomotor
apraxia
following
left

hemisphere
lesions
(Geschwind,
1975;
Mutha,
Sainburg,
&
Haaland,
2010;
2011).

Thus,
these
results
confirmed
our
hypothesis
regarding
the
fronto-‐parietal
network

in
general
(i.e.,
we
did
obtain
significant
results
in
frontal
and
parietal
ROIs),
but

they
raise
the
question
of
whether
laterality
differences
could
modulate
imitation

skill
in
developmental
dyspraxia.

Without
a
direct
comparison
between

hemispheres,
we
are
somewhat
limited
in
speculating
on
laterality.

Future
work

should
specifically
address
the
question
of
laterality
in
developmental
dyspraxia
by

using
a
protocol
appropriate
for
making
such
inferences.

In
addition,
an

investigation
involving
a
DD
group
and
a
group
with
acquired
apraxia—a
disorder

with
known
left
hemisphere
involvement—may
be
a
useful
comparison.

Region
of
interest
analysis
revealed
no
significant
differences
in
either

hemisphere
in
the
pSTS
during
motor
planning
or
imitation
nor
did
whole
brain

65
analysis
for
the
observation
condition
reveal
any
significant
clusters
in
pSTS.

This

suggests
that
biological
motion
processing
per
se
may
not
be
a
likely
source
of

imitation
impairments
in
this
disorder.

However,
this
does
not
rule
out
the

possibility
that
some
other
aspect
of
biological
or
general
visual
perception
may
be

disordered
in
developmental
dyspraxia.

Along
this
line,
whole
brain
analysis
during
observation
compared
with
rest

revealed
clusters
in
bilateral
fusiform
gyrus
consistent
with
areas
known
as

extrastriate
body
area
(EBA;
Downing,
Jiang,
Shuman,
&
Kanwisher,
2001)
and

fusiform
body
area
(FBA;
Peelen
&
Downing,
2005;
Schwarzlose,
Baker
&

Kanwisher,
2005).

While
the
EBA
has
been
found
to
respond
to
dynamic

representations
of
biological
forms,
such
as
point-‐light
animations
(Michels,
Lappe,

&
Vaina,
2005),
more
generally
it
seems
selective
to
human
bodies
and
body
parts

(Downing
et
al.,
2001).

Our
findings
in
regard
to
EBA
during
observation
were
not

predicted
a
priori,
and
thus
we
are
limited
in
speculating
whether
these
activations

would
have
occurred
if
a
static
control
had
been
compared
to
dynamic
observation.

It
is
possible
that
between
groups
differences
in
EBA
during
observation
are

attributable
to
the
presence
of
a
human
figure
rather
than
some
higher
deficit
of

biological
motion
processing
and
could
conceivably
make
a
difference
in
imitation

skill
between
groups.

Supporting
this
interpretation,
we
also
found
decreased

activity
in
the
DD
group
in
primary
and
secondary
visual
cortices
during

observation
(in
whole
brain
analysis).

It
is
also
possible
that
such
differences
in

perceiving
human
figures
could
contribute
to
impairment
in
biological
motion

66
processing
as
well,
but
this
was
not
reflected
in
significant
pSTS
activation
in
the

current
investigation.

In
support
of
this
interpretation,
behavioral
evidence

suggests
children
with
dyspraxia
are
poorer
at
form
coherence
(O’Brien
et
al.,
2002)

and
visual
closure
tasks
(Schoemaker
et
al.,
2001).

Interestingly,
clusters
in
the
anterior
cingulate
cortex
(ACC)
and
lateral

prefrontal
cortex
(specifically,
right
middle
frontal
gyrus
and
right
dorsolateral

prefrontal
cortex
[dlPFC],
all
TD
>
DD)
emerged
during
imitation
compared
to
rest

in
the
whole
brain
analysis.

In
addition,
a
cluster
in
the
dorsal
ACC
was
found

during
observation.

These
results
are
somewhat
similar
to
the
findings
of
Zwicker

and
colleagues,
who
found
that
a
TD
group,
compared
to
a
group
of
children
with

DCD,
activated
lateral
PFC
during
a
joystick
trail
tracing
task
(2010)
and
after
motor

practice
of
the
same
task
(2011).

The
ACC
(dorsal
aspect)
has
been
associated
with
the
cognitive,
or
executive,

aspects
of
motor
control—in
particular,
selecting
action
plans
and
error
monitoring

(Botvinick,
Cohen,
&
Carter,
2004;
Holroyd,
Nieuwenhuis,
Mars,
&
Coles,
2004;

Matthews,
Paulus,
Simmons,
Nelesen,
&
Dimsdale,
2004,
Picard
&
Strick,
1996).

Evidence
for
this
comes
from
neuroimaging
and
EEG
studies
showing
that
this

region
is
responsive
to
incongruence
between
stimuli
and
motor
responses
and

evidence
that
ACC
maintains
strong
reciprocal
connections
with
premotor
and

supplementary
motor
areas,
parietal
cortex,
and
lateral
prefrontal
cortex
(Bush,

Luu,
&
Posner,
2000).
In
light
of
the
ACC
clusters
obtained
from
whole
brain
analysis

in
the
present
study,
these
between-‐group
differences
(TD
>
DD)
may
represent

67
brain
functioning
aligned
with
better
motor
planning
or
executive
function.

Regarding
the
rostral
aspect
of
ACC,
this
area
has
been
associated
with
emotion

processing
because
of
its
connections
with
limbic
structures
(i.e.,
insula,
amygdala,

hypothalamus,
nucleus
accumbens)
and
evidence
that
it
is
activated
during
induced

sadness
tasks
(Bush,
Luu,
&
Posner,
2000).

Activity
in
this
region
also
has
been

found
to
correlate
with
heart
rate
variability,
a
measure
of
parasympathetic

modulation
(Matthews
et
al.,
2004).

Individuals
with
sensory
modulation
disorder,

a
common
co-‐morbid
condition
in
those
with
DD,
have
been
found
to
have

parasympathetic
dysregulation
(Schaaf,
Miller,
Seawell,
&
O’Keefe,
2003;
Schaaf
et

al.,
2010).

Thus,
the
relative
lack
of
rostral
ACC
activity
in
the
DD
group
during

imitation
obtained
from
whole
brain
analysis
could
reflect
disordered
sensory

modulation,
or
indicate
that
these
disorders
share
similar
neurological

underpinnings.

With
regard
to
the
activations
in
the
lateral
PFC
during
the
imitation
task
for

the
TD
group
as
compared
to
the
DD
group,
various
functions
under
the
umbrella

term
executive
function
have
been
ascribed
to
the
lateral
PFC
including:
information

selection,
retention,
and
monitoring;
attention
regulation;
action
planning;
and

judgment,
inference,
and
speculation
(Tanji
&
Hoshi,
2008).

Collectively,
activations

during
imitation
in
the
ACC
and
lateral
PFC
in
the
current
study
could
indicate

impairments
in
an
array
of
behaviors
such
as
action
selection,
error
monitoring,

attention
modulation,
or
executive
function
and
behavioral
planning.

Furthermore,

it
is
possible
that
such
differences
could
be
related
to
parasympathetic
modulation

68
of
these
functions.

A
future
investigation
could
directly
address
these
topics
by
first

determining
if
those
with
DD
are
slower
or
less
accurate
at
stimulus-‐response

selection,
information
retention,
or
aspects
of
executive
function
such
as
judgment

or
inference,
and
then
determining
if
these
functions
relate
to
functional
brain

activity.

Some
evidence
for
behavioral
deficits
of
this
type
in
DCD
already
exist

(although
they
have
not
been
directly
linked
to
ACC/PFC
involvement),
indicating

that
these
individuals
may
be
impaired
in
an
array
of
information
processing

(Wilson
&
McKenzie,
1998),
attention
modulation
(Lingam
et
al.,
2010;
Querne
et
al.,

2008),
and
executive
function
abilities
(Piek
et
al.,
2004).

Conclusion

The
results
of
this
study
demonstrated
impaired
putative
mirror
neuron

system
activation
in
developmental
dyspraxia
as
evidenced
by
decreased
shared

representation
during
action
execution
and
action
observation
in
bilateral

IFG/vPMC.

In
addition,
compared
with
typically
developing
peers,
the
group
with

developmental
dyspraxia
had
significantly
less
activation
in
some
regions
of
the

MNS
during
imitation
compared
to
execution,
and
during
motor
planning
prior
to

imitation.

Furthermore,
we
found
diffuse
functional
brain
differences
in
individuals

with
developmental
dyspraxia
during
imitation
and
observation
at
the
whole
brain

level.

Together,
these
findings
suggest
an
MNS
deficit
in
developmental
dyspraxia,

which
may
reflect
underlying
impairments
in
visual
body
or
visual
form
processing,

69
stimulus-‐response
selection
for
actions,
error
monitoring,
attention
modulation,
or

executive
function.

70
CHAPTER
THREE:

Microstructural
Differences
in
Left
Arcuate
Fasciculus
in
Young
Adults
with

Developmental
Dyspraxia

Abstract

In
the
previous
chapter,
using
fMRI,
we
found
that
individuals
with

developmental
dyspraxia
(DD)—a
disorder
characterized
by
impairments
in

imitation
and
motor
skill—demonstrated
functional
differences
in
some
regions
of

the
human
mirror
neuron
system
(MNS)
during
imitation
and
motor
planning.

A

major
structural
path
thought
to
connect
these
regions
is
the
arcuate
fasciculus

(AF),
a
white
matter
(WM)
fiber
bundle.

Using
diffusion
tensor
imaging
(DTI),

tractography
between
cortical
MNS
regions
was
performed
and
used
to
constrain

measures
of
WM
microstructure
within
the
AF.

Compared
to
an
age-‐
and
gender-‐
matched
group
of
typically
developing
(TD)
peers,
the
DD
group
had
decreased

fractional
anisotropy
(FA),
increased
mean
diffusivity
(MD),
and
increased
radial

diffusivity
(RD),
and
no
difference
in
axial
diffusivity
(AD)
in
the
left
AF.

The
overall

pattern
of
results
suggests
dysmyelination
in
this
tract
in
the
DD
group.

Further

research
is
needed
to
characterize
WM
structural
differences
in
DD,
such
as
fiber

length
and
AF
spatial
distribution,
modulation
by
age
and
motor
task
experience,

and
to
compare
these
results
with
functional
connectivity.

71
Introduction
Individuals
with
developmental
dyspraxia
(DD)
are
impaired
in
imitation

(Ayres,
1989/2004;
O’Brien
et
al.,
2002;
Reeves
&
Cermak,
2002),
and
this
may

negatively
affect
their
participation
in
daily
activities
and
present
challenges
in

learning
new
motor
tasks.
A
network
of
brain
regions
consisting
of
the
superior

temporal
sulcus
(STS),
inferior
parietal
lobule
(IPL)
and
inferior
frontal
gyrus
(IFG)

are
thought
to
comprise
the
“core
circuit”
for
imitation
(Iacoboni
&
Dapretto,
2006).

Disruptions
in
part
of
this
circuit—the
IFG—are
associated
with
impaired
imitation

(Heiser
et
al.,
2003).

In
the
previous
chapter
we
proposed
that
functional

differences
in
this
network,
commonly
called
the
mirror
neuron
system
(MNS),
may

underlie
imitation
impairments
found
in
individuals
with
DD.

Results
from
our
fMRI

study
confirmed
that
those
with
DD
demonstrated
differences
in
some
of
these

regions
during
imitation
and
motor
planning
prior
to
imitation
(see
Chapter
2

Results).

Because
brain
function
is
predicated
on
brain
structure,
the
present

diffusion
tensor
imaging
(DTI)
investigation
sought
to
determine
if
fMRI
findings
in

the
previous
study
are
accompanied
by
underlying
differences
in
white
matter

fibers
connecting
the
nodes
of
the
MNS.

One
structure
that
connects
the
regions
of
the
MNS
is
the
arcuate
fasciculus

(AF),
a
fiber
bundle
originating
in
the
superior
temporal
gyrus,
coursing
around
the

Sylvian
fissure,
and
terminating
in
the
inferior
frontal
lobe
(Catani
&
Thiebaut
de

Schotten,
2008).

Local
association
fibers
and
portions
of
the
horizontal
segments
II

and
III
of
the
superior
longitudinal
fasciculus
(SLF
II
&
III),
which
are
mostly

72
inseparable
from
AF
in
humans
(Brauer,
Anwander,
&
Friederici,
2011;
Broser,

Groeschel,
Hauser,
Lidzba,
&
Wilke,
2012;
Makris
et
al.,
2005),
directly
connect
pars

opercularis
in
the
inferior
frontal
lobe
to
the
supramarginal
gyrus
in
the
inferior

parietal
lobule
in
both
the
right
and
left
hemispheres
(Catani
et
al.,
2005;
Glasser
&

Rilling,
2008;
Kaplan,
Naeser,
Martin,
Ho,
Wang,
et
al.,
2010).

It
should
be
noted
that

this
description
of
the
AF
and
SLF
is
rooted
largely
in
evidence
from
tractography,

and
there
is
ongoing
debate
in
the
literature
surrounding
the
distinction
between

tracts,
cortical
termination
points,
and
association
fiber
system
of
these
tracts
in

humans
(Catani,
Jones,
&
Ffytche,
2005;
Frey,
Campbell,
Pike,
&
Petrides,
2008;

Glasser
&
Rilling,
2008;
Kaplan,
Naeser,
Martin,
Ho,
Wang,
et
al.,
2010;
Makris,
et
al.,

2005;
Petrides
&
Pandya,
1984).

Recognizing
this
debate,
henceforward
this
tract

will
be
referred
to
as
the
AF.

Classically,
the
AF,
particularly
on
the
left,
has
been
implicated
in
language
as

it
is
thought
to
structurally
and
functionally
connect
Wernicke’s
and
Broca’s
areas

(Bernal
&
Ardila,
2009).

Lesions
in
this
tract
frequently
result
in
aphasia
(Damasio

&
Damasio,
1980;
Geschwind,
1965;
Yeatman
et
al.,
2011).

The
AF
likely
contributes

to
a
number
of
other
functions
relevant
to
the
present
investigation,
including

praxis
(Heilmann
&
Watson,
2008),
visuospatial
processing
(Doricchi
et
al.,
2008;

Thiebaut
de
Schotten
et
al.,
2008),
and
motor
planning
and
feedback
and

feedforward
of
motor
commands
(Guenther
et
al.,
2006).

Thus,
the
AF
is
a
likely

physical
pathway
between
frontal
and
parietal
nodes
of
the
MNS
and
may
function

73
as
a
relay
for
signals
between
cortical
regions
of
this
network
for
imitation
and

motor
planning
(Iacoboni
&
Dapretto,
2006;
Thiebaut
de
Schotten
et
al.,
2008).

Fractional
anisotropy
(FA)
is
a
sensitive,
though
nonspecific,
biomarker
of

microstructural
architecture
(Alexander,
Lee,
Lazar,
&
Field,
2007;
Jones,
Knosche,
&

Turner,
2012).

The
FA
metric
provides
a
quantification
of
the
directional
diffusion

of
water
protons,
with
higher
mean
values
thought
to
represent
more
alignment

between
fibers
within
a
tract
or
region
(Halwani,
Loui,
Ruber,
&
Schlaug,
2011).

Other
measures
of
diffusivity
(e.g.,
mean
diffusivity,
radial
diffusivity,
and
axial

diffusivity)
further
describe
microstructural
properties
that
may
be
useful
for

interpreting
FA
values,
and
these
should
be
examined
together
with
FA
(Alexander

et
al.,
2007).

In
addition,
volume
of
white
matter
tracts
is
thought
to
signify
the

number
of
fibers
or
amount
of
myelin
contained
within
an
identified
tract
(Mori
&

Zhang,
2006).

Thus,
as
arcuate
fasciculus
is
thought
to
connect
regions
supporting

imitation
and
motor
skill,
the
above
microstructural
properties
within
the
AF
would

likely
differ
between
individuals
with
different
levels
of
imitation
and
motor
skill.

Currently,
little
direct
evidence
exists
to
support
or
disprove
this
hypothesis.
In
the
only
known
investigation
of
white
matter
structure
comparing

individuals
with
developmental
coordination
disorder
(DCD;
a
diagnosis
related
to

and
possibly
encompassing
DD)
and
typically
developing
children,
Zwicker
et
al.

(2012)
collected
pilot
data
for
a
DTI
study
of
children
in
order
to
ascertain
if

functional
brain
activity
differences
in
children
with
DCD
found
in
their
previous

work
would
be
accompanied
by
differences
in
white
matter
motor
pathways.

These

74
researchers
did
not
propose
any
hypotheses
specific
to
MNS
regions
and
instead

focused
on
two
primary
motor
pathways:
the
corticospinal
and
corticobulbar
tracts.

No
significant
differences
in
FA
values
were
found
in
any
region,
although
children

with
DCD
had
significantly
lower
apparent
diffusion
coefficient
(ADC)
values
in
the

corticospinal
tract
when
compared
to
typically
developing
(TD)
children.

In

addition,
the
children’s
scores
on
the
Movement
ABC
2
(M-‐ABC
2),
a
common

pediatric
motor
skills
test
used
in
identifying
DCD,
moderately
correlated
with
ADC

of
the
corticospinal
tract.

This
study
provides
support
for
the
hypothesis
that
broad

structural
white
matter
differences
in
motor
pathways
may
be
related
to
DCD;

however,
no
conclusions
can
be
drawn
regarding
tracts
connecting
the
fronto-‐
parietal
network
as
this
was
not
specifically
investigated.

Furthermore,
a
nominal

sample
size
(DCD
n
=
7;
TD
n
=
9)
limits
generalizability
of
the
results.

Despite
a
lack
of
gross
structural
pathology
on
typical
neuroimaging
scans,

white
matter
abnormalities
identified
by
DTI
have
been
found
to
correlate
with
a

number
of
related
and
commonly
co-‐occurring
neurological
conditions,
such
as

attention
deficit
hyperactivity
disorder
(ADHD;
Ashtari
et
al.,
2005;
Silk
et
al.,
2009),

preterm
birth
and
low
birth
weight
(Counsell
et
al.,
2006;
Counsell
et
al.,
2008;

Dudink
et
al.,
2007;
Nagy
et
al.,
2009;
Skranes
et
al.,
2007;
Yung
et
al.,
2007),
and

autism
spectrum
disorder
(ASD;
Alexander
et
al.,
2007;
Barnea-‐Goraly
et
al.,
2004;

Fletcher
et
al.,
2010;
Sundaram
et
al.,
2008).

The
majority
of
this
literature
concerns

children,
with
a
few
researchers
reporting
differences
in
adolescents
with

developmental
disabilities
(Fletcher
et
al.,
2010;
Nagy
et
al.
2009;
Skranes
et
al.,

75
2007).

Although
the
results
of
such
investigations
indicate
diffuse
abnormalities,

with
sometimes
conflicting
data
among
studies,
results
of
such
studies
are
generally

consistent
with
microstructural
patterns
suggesting
dysmyelination.

Considering
the
evidence
that
white
matter
is
abnormal
in
individuals
with

developmental
disabilities
related
to
DD/DCD
and
that
some
evidence
already

indicates
broad
white
matter
differences
in
DCD
(Zwicker
et
al.,
2012),
it
is

reasonable
to
suspect
that
white
matter
differences
will
be
found
between

individuals
with
DD
and
TD
individuals.

Specifically,
we
hypothesized
that
white

matter
microstructural
properties
of
FA
and
mean
diffusivity
(MD)
would
differ

between
groups
in
the
right
and
left
AF.

In
the
DD
group
we
predicted
decreased
FA

and
increased
MD—a
pattern
broadly
indicative
of
white
matter
neuropathology

and
consistent
with
abnormal
brain
development
and
dysmyelination
(Alexander,

Lee,
Lazar,
&
Field,
2007).

However,
it
is
important
to
note
that
FA
and
MD
suggest

different,
and
not
necessarily
correlated,
information
about
white
matter

microstructure
and
thus
should
be
considered
separately
(Burzynska,
Preuschhof,

Backman,
et
al.,
2010).
In
addition,
the
predicted
decrease
in
FA
and
increase
in
MD
would
likely
be

accompanied
by
increases
in
radial
diffusivity
(RD)
and/or
axial
diffusivity
(AD),

two
orthogonal
measures
which
may
help
explain
which
components
of
the

diffusion
tensor
contribute
to
differences
in
FA
and
MD.

Because
fiber
bundles
may

increase
in
volume
as
connections
between
cortical
regions
are
strengthened,
it
was

also
predicted
that
the
DD
group
would
exhibit
decreased
volume
in
AF.

76
Methods

Participants

Young
adults
(age
18-‐28)
with
developmental
dyspraxia
and
their
age-‐
and

gender-‐matched
typically
developing
peers
were
recruited
for
this
study.

All

participants
were
right-‐handed
as
assessed
by
a
Modified
Edinburgh
Handedness

Inventory
(Oldfield,
1971),
had
no
known
neurological
or
psychiatric
impairment

(aside
from
motor
skill
impairment
in
the
DD
group),
and
were
deemed
appropriate

for
MRI
after
undergoing
safety
screening.

Because
dyspraxia
is
thought
to
co-‐occur

with
autism
spectrum
disorders
(ASD),
the
presence
of
ASD
diagnosis
or
symptoms

was
an
exclusion
criterion
for
this
study
as
determined
by
the
Ritvo
Autism

Asperger
Diagnostic
Scale-‐Revised
(Ritvo
et
al.,
2011).

Additional
inclusion
criteria
for
the
developmental
dyspraxia
group
were:
(a)

a
stated
history
and
current
difficulty
with
motor
coordination,
imitation,
and/or

learning
new
motor
skills;
(b)
combined
score
of
probable
DCD/DD
on
the
Adult

DCD/Dyspraxia
Checklist
(ADC;
Kirby,
Edwards,
Sugden,
&
Rosenblum,
2010;
Kirby

&
Rosenblum,
2011)
and
modified
DCD-‐Q’07
(re-‐worded
to
be
appropriate
for

adults);
(c)
score
of
below
15
th

percentile
for
age
and
gender
on
the
Bruininks-‐
Oseretsky
Test
of
Motor
Proficiency
Second
Edition
(BOT-‐2;
Bruininks
&
Bruininks,

2005)
Short
Form;
and
(d)
raw
score
below
28
on
the
Sensory
Integration
and

Praxis
Tests
(SIPT;
Ayres,
1989/2004)
Postural
Praxis
test.

Because
the
SIPT

Postural
Praxis
test
has
been
standardized
only
in
children,
we
collected
data

(unpublished)
on
100
young
adults
meeting
the
same
age
and
handedness
criteria

77
as
our
study
participants
and
determined
a
cutoff
score
of
2
standard
deviations

below
the
mean.

A
group
of
14
individuals
with
DD
and
14
TD
individuals
were
included
in
the

sample.

All
participants
met
the
inclusion
and
exclusion
criteria
for
their
respective

group.

For
a
full
description
of
the
participant
pool,
see
Chapter
2
Methods.

The
study
was
approved
by
the
Institutional
Review
Board
of
the
University

of
Southern
California
and
all
procedures
were
conducted
according
to
the

approved
protocol.

Participants
gave
their
written
informed
consent
prior
to

participation.

All
procedures
were
conducted
with
regard
to
ethical
standards
for

human
subjects
research
in
accordance
with
the
1964
Declaration
of
Helsinki.

Image
acquisition

Magnetic
resonance
(MR)
images
were
obtained
using
a
3-‐Tesla
Siemens

MAGNETOM
Trio
scanner.

High
Resolution
structural
T1-‐weighted
magnetization-‐
prepared
rapid
gradient
echo
(MPRAGE)
data
were
acquired
from
each
participant

with
the
following
parameters:
176
x
256
x
256
voxel
matrix
with
a
spatial

resolution
of
1
x
1
x
1
mm,
TR
=
1950
ms,
TE
=
2.26,
FOV
=
256
mm,
flip
angle
=
90°.

DTI
was
performed
using
a
diffusion-‐weighted,
spin
recoil
echo-‐planar
imaging

sequence
with
the
following
parameters:
128
x
128
x
60
voxel
matrix
with
a
spatial

resolution
of
2
x
2
x
2
mm,
interleaved
transverse
slices
TR
=
10000
ms,
TE
=
88
ms,

64
non-‐collinear
gradient
directions
with
a
b-‐value
of
1000
s/mm
2

and
5
volumes

with
a
b-‐value
of
0
s/mm
2
.

78

Data
pre-processing

Diffusion
data
were
pre-‐processed
using
FMRIB’s
Diffusion
Toolbox
(FDT)

version
3.0,
a
part
of
the
FMRIB
Software
Library
(FSL
version
5.0.2;

www.fmrib.ox/ac.uk/fsl;
Jenkinson
et
al.,
2012).

Diffusion
data
was
first
corrected

for
head
motion
artifacts
and
eddy
current
distortion
using
affine
registration
to
a

reference
volume,
followed
by
BET
for
brain
extraction
from
non-‐brain
tissue
in
the

image
(Smith,
2002).

Next,
individual
volumes
were
visually
inspected
to
ensure

there
were
no
slice-‐drop
outs
or
problems
with
motion
correction
in
the
dataset,
as

may
sometimes
occur
in
diffusion
weighted
imaging
(Jones
et
al.,
2012).

Tensor
estimation

DTIFIT
(part
of
FSL’s
FDT)
was
used
for
local
fitting
of
diffusion
tensors,

yielding
estimates
of
eigenvectors
and
eigenvalues
at
each
voxel.

The
eigenvalues

were
used
to
compute
FA,
MD
(mean
of
the
3
eigenvalues),
AD
(diffusivity
along
the

principle
axis),
and
RD
(mean
of
diffusivities
along
the
two
minor
axes)
at
each

voxel.

A
probability
distribution
for
fiber
direction
was
calculated
for
each
brain

voxel
using
Bayesian
modeling
via
BEDPOSTX
(Behrens
et
al.,
2003a;
2003b).

Estimates
of
two
directions
per
voxel
were
allowed
to
account
for
crossing
fibers

(Behrens
et
al.,
2007).

The
data
was
then
visually
inspected
to
ensure
no
gross

errors
in
modeling
of
crossing
fiber
architecture
(e.g.,
a
single
coherent
orientation

is
strongly
expected
in
corpus
callosum,
but
less
prominent
elsewhere).

79

Registration

Although
tractography
was
conducted
in
diffusion-‐weighted
imaging
space,

registration
to
structural
and
standard
space
allows
for
tractography
results
to
be

binned
in
high-‐resolution
structural
space
for
better
visualization
or
standard
space

for
comparisons
across
participants.

Transformation
matrices
were
created
to

register
each
subject’s
diffusion
data
to
their
structural
image
using
FLIRT

(Jenkinson
&
Smith,
2001;
Jenkinson
et
al.,
2002)
with
6
degrees
of
freedom,
the

correlation
ratio
cost
function
and
normal
search.

Non-‐linear
registration
of

structural
data
to
standard
space
was
carried
out
with
FNIRT
(Andersson,

Jenkinson,
&
Smith,
2007)
with
a
transformation
matrix
derived
using
12
degrees
of

freedom,
the
correlation
ratio
cost
function
and
normal
search.

ROI
and
seed
definition

Cortical
regions
of
interest
(ROIs)
were
defined
on
each
participant’s
T1-‐
weighted
scan
for
anatomical
precision
using
FreeSurfer
(Dale
et
al.,
1999;
Desikan

et
al.,
2006;
Fischl
et
al.,
1999;
Fischl
&
Dale,
2000;
Fischl
et
al.,
2004),
a
program
for

automated
parcellation
of
anatomical
locations
based
on
sulcal
and
gyral
anatomy.

Seeds
were
labeled
at
the
gray-‐white
matter
boundary
underlying
cortical
regions
of

interest.

This
automated
cortical
parcellation
method
has
demonstrated
high

validity
compared
to
manual
labeling
of
regions
of
interest
based
on
sulcal
and
gyral

anatomy
(Desikan
et
al.,
2006)
and
high
face
validity
in
the
present
dataset
based
on

visual
inspection
of
the
resulting
parcellation
in
each
subject.

Cortical
ROIs
for
the

80

Figure
3.1.

Cortical
ROIs
used
for
seeding
tractography
in
a
single
subject
pictured

on
T1
images.

Arcuate
fasciculus
was
tracked
at
the
gray-‐white
boundary
between

posterior
STS
(blue,
top
image)
and
pars
opercularis
(red,
top
image)
through
a

waypoint
mask
in
the
white
matter
beneath
SMG
(green,
bottom
image).

A
single

slice
midsaggital
exclusion
mask
was
also
applied
(not
pictured).

81
present
investigation
were:
(1)
posterior
aspect
of
the
STS
(origination
seed)
and

(2)
pars
opercularis
of
the
IFG
(termination
seed).

In
addition,
a
waypoint
mask

was
drawn
in
the
white
matter
underlying
supramarginal
gyrus
of
the
IPL
and
using

anatomical
guidelines
for
identifying
the
AF
as
described
in
Catani
and
Thiebaut
de

Schotten
(2008).

A
single
slice
at
the
midsaggital
line
was
used
as
an
exclusion

mask
to
preclude
fibers
crossing
hemispheres
from
being
included
in
either
the

right
or
left
AF
tracts.

Examples
of
seed
regions
in
a
single
subject
can
be
seen
in

Figure
3.1.

Tractography

Probabilistic
tractography
using
PROBTRACKX
implemented
in
FDT
was

applied
to
constrain
white
matter
tracts
of
interest
between
cortical
and
waypoint

ROIs.

PROBTRACKX
repetitively
samples
from
the
distributions
of
voxel-‐wise

principal
diffusion
directions
to
generate
a
probabilistic
streamline
on
the

distribution
in
orthograde
and
retrograde
directions
from
each
image
voxel

(Behrens,
Johansen-‐Berg,
Jbabdi,
Rushworth,
&
Woolrich,
2007).

Parameters
for

tractography
were
the
following:
modified
Euler
streamlining
with
5000
streamline

samples;
curvature
threshold
=
0.2;
step
length
0.5
mm;
2000
maximum
steps;

minimum
FA
threshold
=
0.1.

Resulting
tracts
were
then
masked
to
exclude
voxels

below
10%
of
the
maximum
intensity
value
of
each
tract.

82
Tract
statistics

For
the
AF
in
each
hemisphere,
tract
volume
was
computed
by
extracting
the

number
of
voxels
in
each
tract
multiplied
by
voxel
size
(voxels
=
2
mm
isotropic;
1

voxel
=
4
mm
3
).

Measures
of
mean
FA,
MD,
RD,
and
AD
within
each
voxel
of
the

delineated
AF
in
each
hemisphere
were
extracted
and
a
weighted
average
was

computed
across
the
entire
tract
using
the
probabilistic
distribution
from

tractography
results.

All
metrics
were
compared
separately
in
each
hemisphere

using
two-‐way
t-‐tests
between
groups
(TD,
DD)
and
a
significance
threshold
of
α
=

0.05
was
used
for
all
tests.

Although
directionality
was
predicted
a
priori,
no

comparable
DTI
investigation
has
been
previously
undertaken
in
this
population

and
two-‐way
tests
were
chosen
in
order
to
detect
possible
group-‐wise
differences
in

either
direction.

Effect
sizes
for
significant
results,
Cohen’s
d,
were
calculated
as
the

difference
between
means
divided
by
the
pooled
standard
deviation
(Cohen,
1988;

Rosnow
&
Rosenthal,
1996).

To
test
whether
whole-‐brain
differences
in
FA,
diffusivity,
and
volume
could

account
for
potential
differences
between
groups,
each
measure
was
extracted
at

the
whole-‐brain
level
and
compared
in
three
additional
two-‐way
t-‐tests
(FA,
MD,

volume)
between
groups
(TD,
DD).

Cortical
white
matter
volume
was
derived
from

segmentation
performed
with
FreeSurfer
and
consisted
of
total
volume
inside
the

white
surface
minus
non-‐white
matter
structures
(i.e.,
ventricles,
CSF,
subcortical

gray
matter
structures).

83
Results

Tractography

Probabilistic
tractography
between
cortical
ROIs
successfully
delineated
the

arcuate
fasciculus
in
all
participants.

Visual
comparison
shows
tracts
are

qualitatively
similar
between
groups
in
size,
but
bilateral
AF
appears
to
be
slightly

more
medial
in
the
DD
group
than
in
the
TD
group.

This
difference
is
most
apparent

in
the
axial
view
(see
Figure
3.2).

Tract
statistics

Table
3.1
lists
results
of
left
and
right
AF
metrics
for
each
group.

Tract

volume
was
not
statistically
significant
in
either
hemisphere
between
groups.

In
the

left
hemisphere
in
the
DD
group,
FA
was
significantly
decreased
[t(2,26)
=
2.482,
p
=

0.019,
d
=
0.95]
and
MD
was
increased
[t(2,26)
=
2.060,
p
=
0.049,
d
=
0.81].

In

addition,
left
hemisphere
RD
was
increased
in
the
DD
group
[t(2,26)
=
2.066,
p
=

0.048,
d
=
0.74].

No
differences
in
FA
or
diffusivity
were
found
between
groups
in

the
right
hemisphere.

Separate
tests
for
whole-‐brain
differences
in
volume,
FA,
and

diffusivity
showed
no
significant
between-‐groups
differences
(all
ps
>
0.05).

84

Figure
3.2.

Left
and
right
arcuate
fasciculus
(AF)
in
typically
developing
individuals

(red),
individuals
with
developmental
dyspraxia
(blue),
and
areas
common
to
both

groups
(purple).

Probabilistic
tractography
results
indicate
that
left
and
right
AF

could
be
identified
in
all
brains
in
the
dataset
by
tracking
between
seed
regions
STS,

IPL,
and
IFG.

Z =32 Y = -32

X = -38 X = 38

85

TABLE
3.1.

Arcuate
Fasciculus
Tract
Metrics

TD
(n
=
14)

M
(SD)

DD
(n
=
14)

M
(SD)

t

(df=26)
p

Volume
(mm
3
)
L
7758.77
(5046.43)
9162.86
(3005.82)
0.167
0.869

R
13523.08
(3308.13)
12737.14
(4729.89)
0.345
0.733

FA
L
0.463
(0.036)
0.433
(0.026)
2.482
0.019

R
0.420
(0.029)
0.411
(0.019)
0.975
0.339

MD
(10
-‐3

mm
2
/s)
L
0.702
(0.033)
0.726
(0.026)
2.060
0.049

R
0.735
(0.023)
0.744
(0.027)
0.924
0.364

AD
(10
-‐3

mm
2
/s)
L
1.089
(0.056)
1.092
(0.038)
0.071
0.944

R
1.075
(0.035)
1.092
(0.027)
1.678
0.105

RD
(10
-‐3

mm
2
/s)
L
0.522
(0.033)
0.546
(0.032)
2.066
0.048

R
0.562
(0.028)
0.574
(0.030)
1.026
0.314

Note.
FA
=
fractional
anisotropy;
MD
=
mean
diffusivity;
AD
=
axial
diffusivity;
RD
=
radial
diffusivity;

TD
=
typically
developing;
DD
=
developmental
dyspraxia.

Summary
statistics
for
volume
and
tensor

scalors
by
group
for
each
hemisphere
and
results
of
t-‐tests
between
groups
are
presented.

Discussion

In
this
DTI
investigation
a
comparison
between
individuals
with

developmental
dyspraxia
and
typically
developing
individuals
indicated
differences

in
microstructural
properties
in
left
arcuate
fasciculus,
a
white
matter
fiber
bundle

that
connects
regions
of
the
brain
involved
in
imitation
and
motor
planning.

In

addition,
one
notable
qualitative
difference
in
morphology
between
groups

indicates
a
slightly
more
medial
distribution
of
the
AF
in
both
hemispheres
in
the

DD
group.

The
AF
is
composed
of
both
long
and
short
fibers,
with
the
short
fibers

lying
in
a
more
lateral
region
than
the
long
fibers
(Catani
&
Thiebaut
de
Schotten,

86
2008).

One
explanation
for
this
observable
difference
in
tract
arrangement
could
be

found
in
the
distribution
of
fiber
length
(i.e.,
number
of
short-‐range
association

fibers
near
the
cortical
surface
versus
long-‐range
fibers
within
deeper
white
matter

tracts).

This
would
parallel
the
finding
of
abnormal
distribution
in
the
number
of

short-‐
and
long-‐range
white
matter
(WM)
fibers,
and
differences
in
FA
and
MD
by

fiber
length
in
the
frontal,
parietal,
and
temporal
lobes
of
children
with
ASD—
evidence
which
is
thought
to
be
related
to
local
underconnectivity
or
a
lack
of

inhibitory
interneurons
(Shukla,
Keehn,
Smylie,
&
Muller,
2011;
Sundaram,
Kumar,

Makki,
Behen,
Chugani,
&
Chugani,
2008).

A
direct
comparison
of
the
spatial

distribution
of
fibers
in
DD
and
TD
individuals
using
a
tractography
protocol

capable
of
discriminating
fibers
by
length
could
be
used
to
quantify
any
such

potential
difference
and
may
be
explored
in
a
future
investigation.

The
present
results
showed
that
the
developmental
dyspraxia
group
had

decreased
FA
and
increased
MD
in
the
left
AF.

Recognizing
the
potentially
differing

contributions
of
axial
and
radial
diffusivities,
RD
and
AD
calculations
revealed

increased
RD—but
not
AD—in
the
DD
group
compared
to
the
TD
group.

These

differences
cannot
be
accounted
for
by
global
differences
in
white
matter
volume
or

whole-‐brain
FA
or
diffusivity.

Results
confirm
the
predicted
group
differences
and
directionality
but
only
in

the
left
hemisphere.

This
is
somewhat
consistent
with
results
obtained
from
fMRI
in

this
sample.

In
the
previous
investigation,
left
IPL
and
right
IFG
were
significantly

different
between
groups
during
imitation,
whereas
left
IPL
and
bilateral
IFG

87
differed
during
motor
planning
(for
complete
details,
see
Chapter
2
Results).

The

only
condition
during
which
activity
in
both
IFG
and
IPL
on
the
same
side
(left)
was

decreased
in
the
DD
group
was
motor
planning.

Arcuate
fasciculus
has
been

implicated
in
motor
planning
and
praxis
(Catani
&
Thiebaut
de
Schotten,
2008;

Heilmann
&
Watson,
2008)
but
not
specifically
imitation.

One
possible
explanation

is
that
the
differences
in
left
AF
underlie
functional
differences
during
motor

planning
in
this
frontal-‐parietal
network.

Because
the
AF
is
strongly
associated
with

language
function
(Bernal
&
Ardila,
2009;
Damasio
&
Damasio,
1980;
Geschwind,

1965;
Yeatman
et
al.,
2011),
the
current
findings
should
be
explored
further
with

regard
to
language
in
DD.

No
explicit
data
on
language
skill
was
collected
in
this

study
sample
(although
participants
indicated
no
known
co-‐occurring
disorders,

such
as
dyslexia).

In
addition,
future
research
endeavors
could
utilize
functional

connectivity
methods
in
addition
to
DTI
to
explore
the
relationship
between

function
in
IFG
and
IPL
and
their
structural-‐functional
connectivity
profile.

The
finding
of
lower
FA
in
the
DD
group
compared
to
the
TD
group
is

congruent
with
general
DTI
findings
throughout
various
brain
regions
in
other

(related)
developmental
disorders
of
neurological
origin,
such
as
ASD
and
ADHD

(Alexander
et
al.,
2007;
Ashtari
et
al.,
2005;
Barnea-‐Goraly
et
al.,
2004;
Silk
et
al.,

2009),
as
well
as
a
broad
spectrum
of
other
CNS
diseases
with
a
motor
impairment

component
(Kubicki
et
al.,
2007;
Melzer,
et
al.,
2013;
Pagani,
Filippi,
Rocca,
&

Horsfield,
2005).

Decreased
FA,
on
its
own,
is
considered
a
fairly
sensitive
although

nonspecific
biomarker
of
neuropathology
with
many
previous
studies
equating
it
to

88
decreased
microstructural
integrity,
although
this
broad
interpretation
has
been

shown
to
be
problematic
(Jones
et
al.,
2012).

By
considering
the
present
finding

that
decreased
FA
was
accompanied
by
increased
MD
in
DD,
we
were
able
to
gain
a

more
comprehensive
picture
of
white
matter
differences
in
this
tract
between
those

with
DD
and
typically
developing
individuals.

Accompanying
differences
in
FA
and
MD
in
the
DD
group,
an
increase
in
RD

with
no
difference
in
AD
suggests
that
RD
accounts
for
the
overall
increase
in
MD.

Histological
studies
and
evidence
from
mouse
models
suggests
that
the
pattern
of

decreased
FA
and
increased
MD
and
RD,
without
an
increase
in
AD,
is
indicative
of

dysmyelination
rather
than
differences
in
axon
properties,
such
as
diameter
or

density,
Wallerian
degeneration,
or
gliosis
(Burzynska
et
al.,
2010;
Song,
et
al.,
2002;

Song
et
al.,
2005;
Tyszka
et
al.,
2006).

Considering
the
developmental
nature
of

dyspraxia
together
with
the
present
findings,
dysmyelination
or
a
failure
of
normal

myelin
development
is
the
most
likely
explanation
of
the
obtained
results.

While
it
is
assumed
that
imitation
and
motor
skill
are
predicated
on
brain

structure,
it
is
also
recognized
that
neural
pruning
occurs
throughout
the
lifespan

such
that
unused
connections
are
pruned
and
pertinent
ones
are
strengthened

(LaMantia
&
Rakic,
1994).

With
the
strengthening
of
synaptic
connections
come

increased
axon
diameter
and
myelin
thickness
(Muller,
Toni,
&
Buchs,
1999),
and

these
changes
influence
diffusion-‐weighted
metrics.

Therefore,
some
degree
of
the

observed
differences
in
DD
could
be
attributed
to
experience-‐dependent
pruning
of

axons
as
individuals
with
DD
may
have
less
comparable
experience
with
imitation

89
and
motor
tasks
through
behavioral
avoidance.

Indeed,
those
in
the
DD
group

reported
avoidance
of
motor
and
imitation-‐learning
tasks
during
initial
recruitment

screening
and
through
questionnaires
used
for
inclusion
criteria
(i.e.,
ADC
and

modified
DCD-‐Q’07).

Evidence
from
Missiuna
et
al.
(2008)
indicates
behavioral

avoidance
of
skilled
motor
tasks
is
the
most
frequently
reported
coping
strategy
for

managing
coordination
impairments
among
young
adults
with
DCD.

The
present

study
sample
indicated
that
they
had
such
difficulties
since
early
childhood
(as
did

the
sample
in
Missiuna
et
al.’s
investigation),
so
experience-‐dependent
axonal

pruning
could
contribute
to
an
increase
in
the
magnitude
of
effect
but
is
an
unlikely

primary
source
of
differences.

This
could
be
further
explored
with
a
longitudinal

design
to
determine
if
age
and
experience
negatively
modulate
WM
microstructural

properties
in
DD.

Finally,
it
is
important
to
stress
the
fact
that
DTI
cannot
determine
specific

characteristics
of
individual
neurons
or
WM
fibers
and
that
DTI
metrics
are
derived

of
a
number
of
biological
factors
including
axon
number,
caliber,
and
fiber
direction

coherence
as
well
as
the
amount
and
quality
of
myelination.

We
are,
therefore,

limited
to
a
somewhat
high
degree
of
speculation
in
interpreting
results
from
this
or

any
diffusion-‐weighted
imaging
investigation
(Jones
et
al.,
2012).

Conclusion

In
this
investigation
we
used
diffusion
tensor
imaging
to
study
the
structural

connections
between
nodes
of
the
human
mirror
neuron
system—the
arcuate

90
fasciculus—in
young
adults
with
developmental
dyspraxia
and
their
typically

developing
peers.

Results
indicated
a
pattern
of
differences
in
the
left
AF
of
the
DD

group
indicative
of
a
failure
of
normal
myelin
development,
or
dysmyelination.

While
these
results
may
have
been
modulated
by
neural
pruning
secondary
to

motor
task
behavioral
avoidance
in
this
group,
such
an
explanation
alone
cannot

account
for
the
obtained
results.

Further
research
is
needed
to
better
understand

and
explain
WM
differences
in
DD,
such
as
by
classifying
fiber
distribution
by
length

and
location,
comparing
results
with
functional
connectivity
data,
and
incorporating

a
longitudinal
design
to
investigate
the
role
of
age
and
experience.

91
CHAPTER
FOUR:

Imitation
Skill
Predicts
Cortical
Thickness
in
Mirror
Neuron
System
Areas
in

Individuals
with
Developmental
Dyspraxia
and
Their
Typically
Developing

Peers

Abstract

In
previous
chapters,
using
fMRI
and
DTI,
we
found
that
individuals
with

developmental
dyspraxia
(DD)—a
disorder
characterized
by
impaired
motor
and

imitation
skill—demonstrated
bilateral
functional
differences
in
the
human
mirror

neuron
system
(MNS)
and
in
structural
connections
between
MNS
regions
in
the
left

arcuate
fasciculus.

Underlying
these
functional
activity
and
structural
connectivity

differences,
it
was
hypothesized
that
gray
matter
structure—in
particular,
cortical

thickness—would
differ
as
well
in
individuals
with
imitation
impairments.

Automated
surface
reconstruction
and
cortical
parcellation
were
employed
to

measure
mean
cortical
thickness
in
five
MNS
subdivisions
based
on

cytoarchetectonics:
pars
opercularis
(POp),
pars
triangularis
(PTr),
supramarginal

gyrus
(SMG),
angular
gyrus
(AG),
and
posterior
superior
temporal
sulcus
(pSTS).

An

adapted
version
of
the
Sensory
Integration
and
Praxis
Tests
Postural
Praxis
test

(SIPT-‐PP)
was
used
to
measure
imitation
skill.

Multiple
regression
models
were

developed
for
predicting
cortical
thickness
in
MNS
regions
of
interest
from
the

predictor
variables
of
age,
gender,
and
SIPT-‐PP.

SIPT-‐PP
emerged
as
a
significant

zero-‐order
predictor
in
all
ROIs
except
right
POp
and
right
pSTS.

The
full
three-‐

92
predictor
model
significantly
predicted
cortical
thickness
in
bilateral
POp
and
PTr,

and
left
SMG,
AG,
and
pSTS,
accounting
for
21-‐47%
of
the
variance
in
these
regions.

The
results,
together
with
MNS
functional
and
white
matter
differences
in
DD

discussed
previously,
suggest
an
overall
pattern
of
atypical
brain
development.

Future
investigations
could
expand
and
integrate
these
findings
to
inform

interventions.

93
Introduction

Developmental
dyspraxia
(DD)
is
a
neurodevelopmental
disorder
of

impaired
imitation
and
motor
skills
(Ayres,
1989/2004;
O’Brien
et
al.,
2002;
Reeves

&
Cermak,
2002)
that
is
apparent
during
development
and
persists
into
adulthood

in
affected
individuals
(Cantell
&
Kooistra,
2002;
Cantell,
Smyth,
&
Ahonen,
2003;

Cousins
&
Smyth,
2003;
Kirby,
Edwards,
&
Sugden,
2011;
Kirby,
Edwards,
Sugden,
&

Rosenblum,
2010;
Kirby,
Sugden,
Beveridge,
&
Edwards,
2008;
Missiuna,
Moll,
King,

Stewart,
&
MacDonald,
2008).

Drawing
on
evidence
that
a
network
of
fronto-‐
parietal
brain
regions
known
as
the
putative
human
mirror
neuron
system
(MNS)

underlies
imitation
ability
(Heiser
et
al.,
2003;
Iacoboni
et
al.,
1999),
it
has
been

hypothesized
that
MNS
dysfunction
underlies
imitation
impairments
in
DD/DCD

(Werner,
Cermak,
&
Aziz-‐Zadeh,
2012).

Evidence
from
fMRI
and
DTI
investigations

(detailed
in
Chapters
2
and
3)
lend
credence
to
this
hypothesis.

Given
that

functional
activity
in
MNS
regions
and
microarchitecture
of
structural
connections

between
MNS
regions
differ
in
individuals
with
DD
compared
to
typically

developing
(TD)
peers,
it
was
hypothesized
in
this
investigation
that
cortical

structure
(in
particular,
gray
matter
thickness)
in
these
regions
also
differs.

Furthermore,
these
differences
were
expected
to
correlate
with
imitation
skill
after

accounting
for
age
and
gender
as
these
demographic
features
are
well-‐documented

predictors
of
global
and
regional
cortical
thickness
(Giorgio
et
al.,
2010;
Narr
et
al.,

94
2007;
Panizzon
et
al.,
2009;
Sowell
et
al.,
2001;
2003;
2004;
2007;
Sullivan
et
al.,

2004;
Thambisetty
et
al.,
2010;
Westlye
et
al.,
2010;
Witelson,
Glezer,
&
Kigar,
1995;

Xu
et
al.,
2000).

Supporting
the
hypothesis
of
differing
cortical
thickness
between
DD
and
TD

individuals,
evidence
of
altered
cortical
thickness
has
been
found
in
developmental

disorders
associated
with
DD,
namely
autism
spectrum
disorder
(ASD)
and

attention-‐deficit/hyperactivity
disorder
(ADHD).

Abnormal
cortical
thickness
has

been
found
globally
and
regionally
in
inferior
frontal
gyrus
(IFG),
inferior
parietal

lobule
(IPL),
and
posterior
superior
temporal
sulcus
(pSTS)
in
ASD
(Hadjikhani,

Joseph,
Snyder,
&
Tager-‐Flusberg,
2006;
Hardan,
Muddasani,
Vemulapalli,
Keshavan,

&
Minshew,
2006;
Hyde,
Samson,
Evans,
&
Mottron,
2010)
and
in
prefrontal
areas

and
precentral
gyrus
in
ADHD
(Almeida
et
al.,
2010;
Shaw,
et
al.,
2006;
Shaw,
et
al.,

2007).

At
least
one
study
(Wolosin,
Richardson,
Hennessey,
Denckla,
&
Mostofsky,

2009)
found
no
difference
in
cortical
thickness
in
children
with
ADHD
compared
to

their
TD
peers,
although
the
investigators
did
find
an
association
between
ADHD

and
reduced
cortical
folding,
surface
area,
and
gray
matter
volume.

Furthermore,

cortical
structure
was
analyzed
at
the
lobar
and
whole
brain
level
only
in
this

investigation,
which
may
have
masked
any
focal
differences
between
groups.

In
general,
ADHD
has
typically
been
associated
with
thinner
cortex
while
ASD

has
been
associated
with
both
thinner
and
thicker
cortex
(Almeida
et
al.,
2010;

Hardan,
et
al,
2006;
Shaw
et
al.,
2006).

For
instance,
decreased
cortical
thickness,

correlating
with
social
and
communication
symptom
severity,
has
been
found
in

95
MNS
regions
in
high-‐functioning
adults
with
ASD
(Hadjikhani
et
al.,
2006),
although

cortical
thickening
in
temporal
and
parietal
lobes
has
been
found
in
children
with

ASD
(Hardan
et
al.,
2006).

Thus,
it
is
reasonable
to
hypothesize
regional
cortical

thickness
differences
in
MNS
areas
associated
with
DD
and
impaired
imitation

ability,
but
it
is
difficult
to
predict
directionality
of
any
potential
differences
based

on
results
of
studies
involving
similar
developmental
disorders.

Cortical
cytoarchitectonics
are
thought
to
be
established
almost
entirely

prenatally
in
primates
(Rakic,
1988;
2002);
however,
this
claim
has
been
somewhat

disputed
(Gould,
Reeves,
Graziano,
&
Gross,
1999).

Nonetheless,
it
appears
that
the

innate
particulars
of
neurogenesis
affecting
cortical
thickness
in
an
individual
begin

very
early
in
life
(Rakic,
2000)
and
are
likely
to
affect
the
development
of
cognitive

traits,
such
as
intelligence
(Narr,
et
al.,
2007).

This
is
not
to
say
that

cytoarchitecture,
particularly
cortical
thickness,
results
in
a
fixed
developmental

trajectory.

Evidence
of
altered
cortical
thickness
in
adulthood
has
been
extensively

documented
in
relation
to
Alzheimer’s
disease
(Lerch,
et
al.,
2005;
Lerch,
et
al.,

2008),
mood
disorders
(Ducharme,
et
al.,
2013),
and
chronic
alcohol
use
(Durazzo,

et
al.,
2011).

These
examples
represent
incidences
in
which
cortical
thickness

changes
are
associated
with
disease,
although
there
is
increasing
evidence
from

rodent
and
human
investigations
that
behavioral
interventions
(e.g.,
motor
skill

practice)
along
with
functional
and
behavioral
changes
correlate
with
measured

alterations
in
cortical
thickness
(Anderson,
Eckburg,
&
Relucio,
2002;
Haier,

Karama,
Leyba,
&
Jung,
2009).

Thus,
it
is
important
to
characterize
cortical

96
thickness
in
DD
with
the
long-‐term
goal
that
this
information
could
potentially

inform
behavioral
interventions
or
validate
outcomes
as
a
measure
of
adaptive

plasticity
(Nudo,
2003).

In
this
investigation,
we
sought
to
determine
the
relationship
between

imitation
impairments—a
hallmark
characteristic
of
DD—and
cortical
thickness.

Because
individuals
with
DD
may
share
some
similar
features
with
ASD
and
ADHD,

we
modeled
imitation
skill
in
particular
as
a
potential
predictor
of
mean
cortical

thickness
in
MNS
regions,
after
controlling
for
age
and
gender.

Methods

Participants

Young
adults
(age
18-‐28)
with
developmental
dyspraxia
and
their
age-‐
and

gender-‐matched
typically
developing
peers
were
recruited
for
this
study.

All

participants
were
right-‐handed
as
assessed
by
a
Modified
Edinburgh
Handedness

Inventory
(Oldfield,
1971),
had
no
known
neurological
or
psychiatric
impairment

(aside
from
motor
skill
impairment
in
those
with
DD),
and
were
deemed

appropriate
for
MRI
after
undergoing
safety
screening.

The
presence
of
ASD

diagnosis
or
symptoms
was
an
exclusion
criterion
for
this
study
as
determined
by

the
Ritvo
Autism
Asperger
Diagnostic
Scale-‐Revised
(Ritvo
et
al.,
2011).

Data
was

collected
on
ADHD
symptoms
using
the
Conners’
Adult
ADHD
Rating
Scales—Self-‐
Report
Short
Version
(CAARS;
Conners,
Erhardt,
&
Sparrow,
1999);
however,
ADHD

97
was
not
used
as
an
exclusion
criterion.

(For
details
on
CAARS
data
in
the
participant

pool,
see
Appendix
E.)

Additional
inclusion
criteria
for
the
DD
group
were:
(a)
a
stated
history
and

current
difficulty
with
motor
coordination,
imitation,
and/or
learning
new
motor

skills;
(b)
combined
score
of
probable
DCD/DD
on
the
Adult
DCD/Dyspraxia

Checklist
(ADC;
Kirby,
et
al.,
2010;
Kirby
&
Rosenblum,
2011)
and
modified

Developmental
Coordination
Disorder
Questionnaire
(DCD-‐Q’07;
re-‐worded
to
be

appropriate
for
adults);
(c)
score
of
below
15
th

percentile
for
age
and
gender
on
the

Bruininks-‐Oseretsky
Test
of
Motor
Proficiency
Second
Edition
(BOT-‐2;
Bruininks
&

Bruininks,
2005)
Short
Form;
and
(d)
raw
score
below
28
on
the
Sensory

Integration
and
Praxis
Tests
Postural
Praxis
test
(SIPT-‐PP;
Ayres,
1989/2004).

Because
the
SIPT-‐PP
has
been
standardized
only
in
children,
we
collected
data
(see

Appendix
D)
on
100
young
adults
meeting
the
same
age
and
handedness
criteria
as

our
study
participants
and
determined
a
cutoff
score
for
inclusion
in
the
DD
group

of
2
standard
deviations
below
the
mean.

A
group
of
14
individuals
with
DD
(8
male,
6
female;
mean
age
21.8
+/-‐
2.7)

and
14
TD
individuals
(8
male,
6
female;
mean
age
22.3
+/-‐3.6)
were
included
in
the

sample.

All
participants
met
the
inclusion
and
exclusion
criteria
for
their
respective

group.

For
a
full
description
of
the
participant
pool,
see
Chapter
2
Methods;
for

descriptive
statistics,
see
Chapter
2
Table
2.1.

The
study
was
approved
by
the
Institutional
Review
Board
of
the
University

of
Southern
California
and
all
procedures
were
conducted
according
to
the

98
approved
protocol.

Participants
gave
their
written
informed
consent
prior
to

participation.

All
procedures
were
conducted
with
regard
to
ethical
standards
for

human
subjects
research
in
accordance
with
the
1964
Declaration
of
Helsinki.

Image
acquisition

Magnetic
resonance
(MR)
images
were
obtained
using
a
3-‐Tesla
Siemens

MAGNETOM
Trio
scanner.

High
Resolution
structural
T1-‐weighted
magnetization-‐
prepared
rapid
gradient
echo
(MPRAGE)
data
were
acquired
from
each
participant

with
the
following
parameters:
176
x
256
x
256
voxel
matrix
with
a
spatial

resolution
of
1
x
1
x
1
mm,
TR
=
1950
ms,
TE
=
2.26,
FOV
=
256
mm,
flip
angle
=
90°.

Cortical
surface
reconstruction
and
parcellation
of
ROIs

Cortical
regions
of
interest
(ROIs)
were
defined
on
each
participant's
T1-‐
weighted
scan
in
native
space
using
FreeSurfer,
a
set
of
automated
procedures
for

accurate
cortical
surface
reconstruction
and
parcellation
of
anatomical
locations

based
on
sulcal
and
gyral
anatomy
(Dale,
Fischl,
&
Sereno,
1999,
Desikan
et
al.,

2006;
Fischl
et
al.,
1999;
Fischl
et
al.,
2004;
Fischl
&
Dale,
2000;
Fischl,
Sereno,
&

Dale,
1999).

To
allow
for
later
co-‐registration
between
brains,
a
transformation

matrix
was
computed
using
the
automated
Talairach
registration
procedure

developed
and
distributed
by
the
Montreal
Neurological
Institute
(Dale,
Fischl,
&

Sereno,
1999;
Talairach
&
Tournoux,
1998;
Collins
et
al.,
1994).

Additional
image

pre-‐processing
included
the
following
steps:
(1)
intensity
normalization
to
correct

99
for
magnetic
field
inhomogeneities;
(2)
removal
of
skull
and
extra-‐cerebral
voxels;

(3)
assignment
of
cutting
planes
to
delineate
the
hemispheres
and
subcortical

structures,
with
any
interior
holes
filled
and
representing
white
matter;
(4)

polygonal
tessellation
and
deformation
of
the
resulting
volume
to
produce
an

accurate
and
smooth
representation
of
the
gray-‐white
interface
and
pial
surface,

allowing
for
accurate
measurement
of
thickness
of
the
cortical
surface
across
the

entire
brain;
and
(5)
visual
inspection
and
manual
editing
of
the
parcellation,
if

needed,
to
ensure
accuracy
(Dale,
Fischl,
&
Sereno,
1999;
Fischl,
Sereno,
&
Dale,

1999).

The
automated
cortical
parcellation
method
employed
here
has

demonstrated
high
validity
in
terms
of
surface
topology
and
geometry
for
labeling

regions
of
interest
based
on
sulcal
and
gyral
anatomy
(Desikan
et
al.,
2006),
high

within-‐subject
and
test-‐retest
reliability
of
thickness
measurements
(Dale,
Fischl,
&

Sereno,
1999;
Fischl
&
Dale,
2000),
and
high
face
validity
of
anatomical
accuracy

based
on
visual
inspection
in
the
present
dataset.

In
addition,
the
method
has

demonstrated
high
qualitative
validity
of
surface
reconstruction
geometry
when

compared
to
task-‐specific
functional
activation
in
primary
visual,
motor,

somatosensory,
and
auditory
cortices
(Dale,
Fischl,
&
Sereno,
1999)
and
excellent

overall
agreement
with
regionally
specific
cortical
thickness
measurements
from

postmortem
samples
(Fischl
&
Dale,
2000).

Descriptions
of
gyral
anatomy
used
to

generate
the
automated
parcellation
atlas
can
be
found
in
Desikan
et
al.
(2006).

100

Broad
ROIs
for
the
present
investigation
were
the
IFG,
IPL,
and
pSTS.

As
the

cerebral
cortex
is
known
to
be
organized
into
ontogenetic
columns
(Mountcastle,

1997,
Rakic,
1988)
and
cortical
thickness
may
be
influenced
by
the
number,
size,

and
type
of
cells
within
these
columns
(Panizzon
et
al.,
2009),
these
broad
ROIs

were
further
subdivided
to
delineate
more
precise
ROIs
based
on
cytoarchitecture.

The
IFG
and
IPL
can
each
be
thought
of
as
containing
cytoarchitecturally
distinct

subdivisions—Brodmann
areas
(BAs)
44
and
45,
and
39
and
40,
respectively—and

these
different
cytoarchitectural
properties
could
contribute
differently
to
cortical

thickness.

Neuroimaging
resolution
does
not
allow
differentiation
of
cortical

territories
by
cytoarchitecture.

Thus,
sulcal
and
gyral
anatomy
was
used
as
a
proxy

to
define
the
frontal
ROIs
[approximately
corresponding
to
pars
opercularis
(POp)

and
pars
triangularis
(PTr)]
and
the
parietal
ROIs
[approximately
corresponding
to

supramarginal
gyrus
(SMG)
and
angular
gyrus
(AG)].

Mean
cortical
thickness
within

each
of
the
five
ROIs
(POp,
PTr,
SMG,
AG,
pSTS)
in
each
hemisphere
was
then

extracted
from
the
resulting
parcellation.

Statistical
analyses

Multiple
regression
models
were
used
to
analyze
age,
gender,
and
SIPT-‐PP

score
at
the
zero-‐order
and
full
model
level
for
predicting
cortical
thickness
in
each

ROI.

A
significance
threshold
of
.05
was
adopted
for
zero-‐order
and
model
fit

statistics.

To
account
for
multiple
comparisons
in
computing
partial
effects
of
the

three
independent
variables
in
the
model,
a
Bonferroni
correction
was
applied
to

101
hypothesis
testing
of
the
regression
coefficients,
setting
α
=
.0167
(Cohen
&
Cohen,

2002;
Mundfrom,
Perrett,
Schaffer,
Piccone,
&
Roozeboom,
2006).

Results

The
automated
cortical
parcellation
method
successfully
delineated
ROIs
in

each
brain,
and
visual
inspection
confirmed
excellent
agreement
with
anatomical

landmarks.

ROIs
on
an
average
brain
are
shown
in
Figure
4.1.

Means
and
standard

deviations
of
cortical
thickness
in
each
ROI
are
reported
in
Table
4.1.

Correlation

coefficients
between
the
predictor
variables
(i.e.,
age,
gender,
SIPT-‐PP)
are
shown
in

Table
4.2.

Multiple
regression
models
were
developed
for
predicting
cortical
thickness

in
each
ROI
from
the
predictor
variables
of
age,
gender,
and
SIPT-‐PP
score.

For

gender,
positive
coefficients
represent
male
gender
and
negative
coefficients

represent
female
gender.

Regression
model
goodness
of
fit
is
reported
as
adjusted

multiple
R
2

to
account
for
a
nominal
sample
size
in
relation
to
the
number
of

predictor
variables
in
each
model.

Regression
coefficients
and
statistics
for
each

ROI
are
shown
in
Table
4.3.

102

Figure
4.1.

Regions
of
interest
are
pictured
on
an
average
brain.

Orange
=
pars

triangularis;
yellow
=
pars
opercularis;
green
=
supramarginal
gyrus;
pink
=
angular

gyrus;
blue
=
posterior
superior
temporal
sulcus.

103

TABLE
4.1.

Mean
Cortical
Thickness
in
Regions
of
Interest

Cortical
thickness
(N
=
28)

Left
Right

POp
2.51
(.29)
2.34
(.28)

PTr
2.38
(.36)
3.03
(.35)

SMG
2.94
(.35)
3.32
(.48)

AG
3.12
(.34)
2.98
(.56)

pSTS
2.59
(.30)
2.74
(.38)

Note.

POp
=
pars
opercularis,
PTr
=
pars
triangularis,
SMG
=

supramarginal
gyrus,
AG=
angular
gyrus,
pSTS
=
posterior

superior
temporal
sulcus.

Means
and
standard
deviations
of

cortical
thickness
(mm)
in
each
region
of
interest.

TABLE
4.2.

Correlations
Between
Predictor
Variables

Age
Gender
SIPT-‐PP

Age
—

Gender
.284
(n.s.)
—

SIPT-‐PP
-‐.180
(n.s.)
.154
(n.s.)
—

Note.

SIPT-‐PP
=
SIPT
Postural
Praxis
(imitation),
n.s.
=
not
significant.

104
TABLE
4.3.

Predictors
of
Cortical
Thickness
by
Region
of
Interest

Left
POp
thickness
Right
POp
thickness

(A)

r

β
t
p

(B)

r
β
t
p

.09

.05

.17***

.13

-‐.01

.20

2.90

-‐.32

4.61

.008

.755

<
.001

-‐.10

-‐.10

-‐.09

-‐.10

-‐.06

-‐.09

-‐1.84

-‐1.19

-‐1.86

.078

.247

.075

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.47,
p
<
.001

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.21,
p
=
.035

Left
PTr
thickness
Right
PTr
thickness

(C)

r
β
t
p

(D)

r
β
t
p

.01

.01

-‐.24***

-‐.05

.06

-‐.25

-‐.89

1.09

-‐4.42

.384

.286

<
.001

-‐.03

-‐.02

-‐.21***

-‐.09

.04

-‐.23

-‐1.43

.64

-‐3.97

.167

.531

<
.001

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.38,
p
=
.002

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.33,
p
=
.006

Left
SMG
thickness
Right
SMG
thickness

(E)

r
β
t
p

(F)

r
β
t
p

-‐.11

.12

.20**

-‐.11

.13

.16

-‐2.11

2.43

3.08

.045

.023

.005

-‐.01

-‐.01

.23*

.04

-‐.05

.24

.48

-‐.56

2.73

.637

.580

.012

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.44,
p
<
.001

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.14,
p
=
.085

Left
AG
thickness
Right
AG
thickness

(G)

r
β
t
p

(H)

r
β
t
p

.13*

-‐.02

-‐.20***

.10

-‐.02

-‐.18

1.72

-‐.31

-‐3.28

.098

.757

.003

-‐.08

-‐.09

-‐.26*

-‐.12

-‐.01

-‐.28

-‐1.17

-‐.08

-‐2.78

.254

.939

.010

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.35,
p
=
.004

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.18,
p
=
.054

Left
pSTS
thickness
Right
pSTS
thickness

(I)

r
β
t
p

(J)

r
β
t
p

-‐.02

-‐.12*

-‐.19***

-‐.02

-‐.09

-‐.18

-‐.50

-‐1.88

-‐3.93

.625

.072

<
.001

-‐.01

-‐.06

.18

.02

.03

.18

.34

.38

2.61

.740

.707

.015

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.43,
p
<
.001

Age

Gender

SIPT-‐PP

Adj.
R
2

=
.15,
p
=
.077

Note.

POp
=
pars
opercularis
(A,
B),
PTr
=
pars
triangularis
(C,
D),
SMG
=
supramarginal
gyrus
(E,
F),

AG=
angular
gyrus
(G,
H),
pSTS
=
posterior
superior
temporal
sulcus
(I,
J).

SIPT-‐PP
=
SIPT
Postural

Praxis.

Zero-‐order
correlations,
r,
of
predictor
variables
(age,
gender,
SIPT-‐PP)
against
criterion

variable
(cortical
thickness)
for
each
region
of
interest
are
reported.

Significance
levels
for
zero-‐
order
correlations
are
indicated
with
asterisks
(*p
<
.05,
**p
<
.01,
***
p
<
.001).

For
gender,
negative

coefficients
indicate
female.

Full
model
standardized
beta
coefficients,
t-‐values,
exact
p-‐values,
and

adjusted-‐R
2

are
reported
for
each
region
of
interest.

105

At
the
zero-‐order
level,
SIPT-‐PP
had
a
significant
positive
correlation
with

cortical
thickness
in
left
POp
(p
<
.001),
left
SMG
(p
=
.001),
and
right
SMG
(p
=
.011),

and
a
significant
negative
correlation
with
left
PTr
(p
<
.001),
right
PTr
(p
<
.001),

left
AG
(p
<
.001),
right
AG
(p
=
.012),
and
left
pSTS
(p
<
.001).

In
addition,
age
was
a

significant
zero-‐order
predictor
(p
=
.050)
of
cortical
thickness
in
left
AG,
and
female

gender
was
a
significant
zero-‐order
predictor
(p
=
.032)
of
cortical
thickness
in
left

pSTS.

In
left
POp,
both
age
and
SIPT-‐PP
had
significant
partial
effects
in
the
full

model
(see
Figure
4.2).

The
three-‐predictor
model
was
able
to
account
for
47%
of

the
variance
in
left
POp
cortical
thickness
(F3,
24
=
8.829,
p
<
.001).

In
right
POp,
age

and
SIPT-‐PP
both
trended
toward
significance
as
negative
predictors
of
cortical

thickness
(p
=
.078
and
p
=
.075,
respectively),
but
no
predictor
reached
the

significance
threshold
(Figure
4.3
shows
partial
leverage
plot
for
non-‐significant

SIPT-‐PP).

However,
the
overall
regression
model
significantly
accounted
for
21%
of

the
variance
in
right
POp
(F3,
24
=
3.369,
p
=
.035).

SIPT-‐PP
was
retained
as
a
strong

negative
predictor
in
both
left
and
right
PTr
(Figures
4.4
and
4.5).

In
left
PTr,
38%

of
the
variance
was
accounted
for
by
the
regression
model
(F3,
24
=
6.516,
p
=
.002),

and
in
right
PTr
33%
of
the
variance
was
accounted
for
by
the
regression
model
(F3,

24
=
5.382,
p
=
.006).

106

Figure
4.2.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
left
POp
cortical
thickness
residuals.

The
standardized
β-‐
coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate
95%
confidence

intervals.

107

Figure
4.3.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
right
POp
cortical
thickness
residuals.

SIPT-‐PP
did
not
reach

significance
in
right
POp
(p
=
.075),
although
the
3-‐predictor
regression
model
did.

The
standardized
β-‐coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate

95%
confidence
intervals.

108

Figure
4.4.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
left
PTr
cortical
thickness
residuals.

The
standardized
β-‐
coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate
95%
confidence

intervals.

109

Figure
4.5.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
right
PTr
cortical
thickness
residuals.

The
standardized
β-‐
coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate
95%
confidence

intervals.

110

Age
and
male
gender
approached
significance,
but
only
SIPT-‐PP
was
a

significant
positive
predictor
of
left
SMG
cortical
thickness
(Figure
4.6).

The
three-‐
predictor
model
accounted
for
44%
of
the
variance
in
left
SMG
(F3,
24
=
7.961,
p
<

.001).

In
right
SMG,
only
SIPT-‐PP
had
significant
partial
effects
(Figure
4.7),
and
the

full
regression
model
failed
to
reach
significance
(F3,
24
=
2.485,
p
=
.085).

In
left
AG,

SIPT-‐PP
remained
a
significant
negative
predictor
when
age
and
gender
were

accounted
for
(Figure
4.8),
with
35%
of
variance
explained
by
the
three-‐predictor

regression
(F3,
24
=
5.794,
p
=
.004).

In
right
AG,
only
SIPT-‐PP
had
significant
partial

effects
as
a
negative
predictor
(Figure
4.9),
and
the
full
regression
model
failed
to

the
reach
significance
threshold
(F3,
24
=
2.931,
p
=
.054).

SIPT-‐PP
positively
predicted
cortical
thickness
in
left
pSTS
(Figure
4.10),
and

female
gender
approached
significance
as
a
predictor
in
this
region
(p
=
.072).

The

three-‐predictor
model
was
able
to
account
for
43%
of
the
variance
in
cortical

thickness
in
left
pSTS
(F3,
24
=
7.812,
p
<
.001).

In
right
pSTS,
SIPT-‐PP
positively

predicted
cortical
thickness
as
a
partial
effect
(Figure
4.11),
but
the
full
model
did

not
reach
significance
(F3,
24
=
2.583,
p
=
.077).

111

Figure
4.6.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
left
SMG
cortical
thickness
residuals.

The
standardized
β-‐
coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate
95%
confidence

intervals.

112

Figure
4.7.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
right
SMG
cortical
thickness
residuals.

The
standardized
β-‐
coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate
95%
confidence

intervals.

113

Figure
4.8.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
left
AG
cortical
thickness
residuals.

The
standardized
β-‐
coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate
95%
confidence

intervals.

114

Figure
4.9.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
right
AG
cortical
thickness
residuals.

The
standardized
β-‐
coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate
95%
confidence

intervals.

115

Figure
4.10.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
left
pSTS
cortical
thickness
residuals.

The
standardized
β-‐
coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate
95%
confidence

intervals.

116

Figure
4.11.

Partial
regression
leverage
plot
for
SIPT-‐PP
adjusted
for
age
and

gender,
plotted
against
right
pSTS
cortical
thickness
residuals.

The
standardized
β-‐
coefficient
is
represented
by
the
solid
line;
dotted
lines
indicate
95%
confidence

intervals.

117
Discussion

The
results
show
that
SIPT-‐PP
score
had
partial
effects
in
bilateral
PTr

(higher
SIPT-‐PP
was
associated
with
decreased
thickness),
bilateral
SMG
(increased

thickness),
bilateral
AG
(decreased
thickness
on
the
left,
increased
thickness
on
the

right),
and
left
pSTS
(decreased
thickness).

In
left
POp,
both
SIPT-‐PP
and
age
had

significant
positive
partial
effects
in
predicting
cortical
thickness.

Furthermore,
the

three-‐predictor
model
(age,
gender,
SIPT-‐PP)
significantly
accounted
for
21-‐47%
of

the
variance
in
cortical
thickness
in
bilateral
POp
and
PTr,
and
in
left
SMG,
AG,
and

pSTS.

The
use
of
hierarchical
or
stepwise
regression
modeling
in
future
work
could

result
in
increased
power
in
predicting
regional
cortical
thickness
with
regard
to

demographic
and
imitation
skills
differences.

Nonetheless,
the
results
show
that

SIPT-‐PP
emerged
as
a
significant
predictor
of
cortical
thickness,
with
or
without

accounting
for
demographic
variables,
in
all
ROIs
in
the
left
hemisphere.

This
is

congruent
with
functional
differences
in
MNS
regions
and
white
matter
differences

in
the
arcuate
fasciculus
between
DD
and
TD
individuals
previously
found
in
the
left

hemisphere
(see
Chapters
2
and
3
Results).

SIPT-‐PP
score
as
a
zero-‐order
predictor

was
associated
with
cortical
thickness
in
three
of
the
right
hemisphere
ROIs

(excluding
POp
and
pSTS).

However,
the
full
model
was
significant
only
in
right
PTr,

indicating
that
the
inclusion
of
age
and
gender
may
have
actually
reduced
the

predictive
power
of
the
model
for
cortical
thickness.

Interestingly,
in
right
POp
none
of
the
predictors
reached
significance,

although
the
full
model
did.

This
can
occur
with
high
levels
of
multicollinearity.

118
However,
recruitment
efforts
for
this
study
intentionally
balanced
age
and
gender

with
relatively
higher
and
lower
SIPT-‐PP
scores
for
the
purpose
of
controlling
for

age
and
gender
in
between-‐groups
comparisons
for
functional
and
white
matter

structural
analyses
(see
Chapters
2
and
3).

Correlation
coefficients
between
each
of

the
three
predictor
variables
confirm
that
none
of
the
predictors
were
significantly

correlated
with
each
other
(see
Table
4.2).

More
likely,
the
presence
of
two

predictors
in
the
model
that
trend
toward
significance
(age,
p
=
.078,
and
SIPT-‐PP,
p

=
.075)
accounts
for
the
significant
full
model
result.

A
larger
sample
size
with
a

greater
range
of
ages
in
the
participant
pool
would
be
useful
to
clarify
the

relationship
between
age,
gender,
SIPT-‐PP,
and
cortical
thickness
in
right
POp.

In

addition,
thickness
may
both
increase
and
decrease
throughout
the
lifespan,
so
any

investigation
including
age
as
a
predictor
could
be
limited
by
the
general
linear

model
(as
opposed
to
a
quadratic
model).

In
general,
the
results
indicate
a
lack
of
age
and
gender
effects
with
the

exception
of
a
positive
partial
correlation
between
age
and
thickness
in
left
POp
and

a
trend
toward
partial
negative
correlations
between
age
and
male
gender
with

thickness
in
left
SMG.

It
is
possible
that
the
lack
of
predictive
power
of
age
and

gender
results
from
caveats
in
the
dataset,
namely,
a
relatively
small
age
range
was

included
in
the
participant
pool
(18-‐28)
and
this
age
range
(post-‐adolescent
and

prior
to
age
30)
represents
a
time
period
associated
with
relative
stability
in
brain

development
(Sowell
et
al.,
2003).

In
previous
investigations
in
which
age
and
sex

differences
were
compared
to
cortical
thickness
in
adults,
a
much
larger
sample
size

119
(e.g.,
N
=
176)
and
greater
age
span
(e.g.,
7-‐87
years)
has
been
used
(Sowell
et
al.,

2001;
2003;
2007;
Sullivan
et
al.,
2004).

In
regard
to
gender
differences,
previous
studies
have
indicated
that
regions

with
the
largest
gray
matter
thickness
differences
between
the
genders
are
typically

found
in
right
hemisphere
temporal
and
parietal
association
cortices—regions

generally
overlapping
with
the
ROIs
used
in
the
present
investigation
(Sowell
et
al.,

2007;
Sullivan
et
al.,
2004;
Witelson
et
al.,
1995;
Xu
et
al.,
2000).

However,
such

investigations
scaled
for
overall
body
size
and/or
brain
size
within
their
samples,
a

noteworthy
caveat
that
should
be
considered
in
future
work
when
modeling
gender

differences
as
a
predictor
of
thickness
(Luders
et
al.,
2005).

Together,
the
relatively

small
sample
size,
limited
age
range,
and
lack
of
size
scaling
in
the
present

investigation
presents
a
limitation
in
interpreting
age
and
gender
effects
along
with

imitation
in
modeling
cortical
thickness.

Compared
with
developmental
disorders
characterized
by
motor
or
imitation

impairment
or
with
documented
MNS
involvement
(such
as
ASD
or
ADHD),
the

results
of
the
present
investigation
are
fairly
consistent
in
that
general
differences
in

cortical
structure
correlating
with
imitation
skill
were
found
in
IFG,
IPL,
and
pSTS.

With
respect
to
directionality,
ASD
in
children
has
been
associated
with
increased

cortical
thickness,
particularly
in
right
IFG
and
IPL
and
bilateral
STS
(Hyde
et
al.,

2010).

In
comparable
brain
regions
in
the
present
study,
poor
imitation
was
only

associated
with
an
increase
in
thickness
(higher
SIPT-‐PP
associated
with
decreased

thickness)
in
right
IFG,
both
in
POp
and
PTr.

Somewhat
more
consistent
with
the

120
obtained
results,
social
symptom
severity
in
adults
with
ASD
has
been
associated

with
decreased
cortical
thickness
(higher
SIPT-‐PP
associated
with
increased

thickness)
in
bilateral
IFG
and
IPL
and
right
STS
(Hadjikhani
et
al.,
2006).

Compared

to
ADHD
in
which,
relative
to
TD
children,
diffuse
cortical
thinning
throughout
the

prefrontal
cortex
has
been
found
(Shaw
et
al.,
2006;
2007),
only
left
POp
(higher

SIPT-‐PP
associated
with
increased
cortical
thickness)
in
the
present
results
was

found
to
be
congruent.

Longitudinal
studies
suggest
that
cortical
thickness

developmental
trajectories
in
ADHD
converge
with
those
of
typically
developing

peers
over
time,
suggesting
that
structural
gray
matter
differences
in
ADHD

represent
a
delay
in
cortical
maturation
(Rubia,
2007;
Shaw
et
al.,
2006;
2007).

In

comparison,
the
participant
pool
of
the
present
investigation
consisted
of
young

adults
whose
self-‐reported
imitation
and
motor
impairments
have
persisted
since

childhood.

In
light
of
Wolosin
et
al.’s
(2009)
finding
that
cortical
surface
area
and

folding
is
decreased
in
children
with
ADHD,
as
well
as
evidence
that
there
are

distinct
genetic
influences
on
surface
area
and
thickness,
future
work
should

address
potential
differences
in
these
measures
in
DD
as
well.

In
conjunction
with
fMRI
and
DTI
results
in
the
previous
chapters,
these

results
are
evidence
of
generalized
differences
in
brain
structure
and
function
in

MNS
regions
in
individuals
with
DD,
a
disorder
characterized
by
imitation

impairments.

In
general,
these
differences
appear
to
be
left
lateralized,
reflecting
a

similarity
to
the
lateralization
of
lesions
typically
responsible
for
acquired
apraxia

(Morris,
1997;
Mutha,
Sainburg,
&
Haaland,
2011).

Presumably,
structural

121
differences,
such
as
abnormal
cortical
thickness
and
white
matter
microstructure,

underlie
functional
differences
in
the
MNS
in
this
population.

In
light
of
the
radial

unit
hypothesis
(Rakic,
1988)
and
the
typical
trajectory
of
white
matter

strengthening
and
pruning
of
synapses
during
the
first
months
of
life
(Kandel,

Schwartz,
&
Jessell,
2000),
functional
brain
and
behavioral
differences
in
individuals

with
DD
may
be
a
result
of
very
early
alterations
in
development.

This
can
also
help

explain
the
developmental
aspect
of
this
disorder.

One
important
question
is
to

what
degree,
if
any,
can
early
therapeutic
interventions
alter
these
brain-‐based

differences
and
improve
motor
and
imitation
skill.

Limited
evidence
suggests
this

may
be
possible
(Anderson
et
al.,
2002;
Haier
et
al.,
2009).

Furthermore,
it
has
yet

to
be
determined
if
neuroimaging
can
be
used
to
identify
DD
perhaps
for
the

purpose
of
implementing
early
intervention
prior
to
observed
behavioral

impairments.

In
future
work,
the
inclusion
of
additional
predictor
variables,
such
as
motor

skill
or
a
measure
of
ADHD,
could
further
elucidate
the
relationship
between
cortical

thickness
and
developmental
dyspraxia.

Entering
variables
into
each
model

hierarchically
or
in
a
forward-‐backward
stepwise
fashion
may
serve
to
increase

model
parsimony.

With
more
predictor
variables
and
more
complex
modeling,
a

much
higher
number
of
participants
will
certainly
be
needed
to
achieve
adequate

power.

122
Conclusions

This
investigation
identified
cortical
thickness
differences
in
areas
of
the

mirror
neuron
system
associated
with
imitation
skill
after
accounting
for
age
and

gender,
although
these
results
are
likely
driven
by
SIPT-‐PP
more
than
demographic

variables.

Small
to
moderate
effect
sizes
were
obtained.

Therefore,
individual
body

and
brain
size
scaling,
a
larger
sample
size,
inclusion
of
more
predictor
variables,

and
more
sophisticated
statistical
modeling
would
likely
improve
the
ability
to

predict
cortical
thickness.

Future
work
should
take
into
account
ways
in
which
this

information
could
apply
to
intervention.

123
CHAPTER
FIVE:

Conclusions

Overall,
this
research
suggests
that
multiple
aspects
(structural
and

functional)
of
the
putative
human
mirror
neuron
system
(MNS)
are
atypical
in

individuals
with
developmental
imitation
and
motor
coordination
impairments.

Previous
research
has
demonstrated
a
strong
association
between
the
MNS
and

imitation
(Heiser
et
al.,
2003;
Iacoboni
et
al.,
1999).

The
current
work
is
the
first
to

examine
this
neural
system
in
developmental
dyspraxia
(DD),
a
disorder

characterized
by
impaired
imitation
and
motor
coordination,
using
multiple

neuroimaging
techniques.

Although
evidence
of
general
brain-‐based
differences

exists
in
individuals
with
developmental
motor
coordination
disorders
(Kashiwagi,

et
al.,
2009;
Querne
et
al.,
2008;
Zwicker
et
al.,
2010;
2011;
2012),
the
neural

correlates
of
DD
have
not
previously
been
examined
in
a
comprehensive,

hypothesis-‐driven
manner
with
a
focus
on
the
etiology
of
the
cardinal
features
of
the

disorder,
namely
imitation
and
motor
planning.

In
addition,
the
majority
of

previous
research
in
this
population
has
focused
on
children,
despite
evidence
that

motor
coordination
difficulties
persist
into
adulthood
(Cantell
&
Kooistra,
2002;

Cantell,
et
al.,
2003;
Cousins
&
Smyth,
2003;
Kirby
et
al.,
2008;
2010;
2011;

Missiuna,
et
al.,
2008).

The
studies
described
in
this
dissertation
involved
young

adults
and
included
a
larger
sample
size
than
any
previous
neuroimaging

investigation
in
this
population.

124

The
central
goals
of
the
research
were:
(a)
to
determine
if
DD
is
associated

with
brain-‐based
differences
that
would
indicate
dysfunction
in
the
MNS
and
(b)
to

ascertain
whether
the
relationship
between
DD
and
potential
MNS
dysfunction

would
be
manifest
across
different
neural
properties,
including
gray
and
white

matter
structure
and
function.

The
implications
for
the
first
goal
would
be
to
better

understand
the
cardinal
impairments
of
DD
in
light
of
what
is
understood
about
the

human
MNS.

Addressing
the
second
goal
could
gain
a
more
comprehensive
picture

of
the
possible
etiology
of
DD,
and
a
better
description
of
the
underlying

neurobiological
features
of
the
disorder
could
be
described.

Results
support
the

theory
that
a
dysfunctional
MNS
underlies
imitation
impairments
found
in
DD.

Furthermore,
results
from
across
all
of
the
studies
bolsters
this
idea
by
providing

support
that
multiple
structural
features
of
the
MNS
are
abnormal
in
DD,
and
these

likely
underlie
the
observed
functional
differences.

Summary
of
the
Research

In
Chapter
2,
an
analysis
of
shared
regions
for
action
observation
and
action

execution
revealed
significant
activation
in
bilateral
inferior
fronal
gyrus
(IFG)/

ventral
premotor
cortex
(vPMC)
and
inferior
parietal
lobule
(IPL)—the
fronto-‐
parietal
MNS—in
the
typically
developing
(TD)
group,
and
no
significant
activation

in
the
DD
group.

A
comparison
between
groups
resulted
in
significant
bilateral

IFG/vPMC
activity
(TD
>
DD).

Within
anatomically-‐defined
MNS
regions
of
interest

(ROIs),
analysis
of
bimanual,
meaningless
gesture
imitation
(compared
to
non-‐

125
imitative
execution)
between
groups
yielded
significant
differences,
all
in
the

direction
of
TD
>
DD,
in
left
IPL
and
right
IFG/vPMC.

During
motor
planning
prior
to

imitation
(compared
to
the
analogous
period
prior
to
observation),
the
TD
group

demonstrated
significantly
greater
activity
in
left
IPL
and
bilateral
IFG/vPMC.

Finally,
in
a
whole-‐brain
analysis
of
the
three
experimental
conditions—imitation,

execution,
and
observation
(each
contrasted
with
rest)—diffuse
differences
across

multiple
brain
areas
were
shown,
including
in
the
prefrontal
and
cortical
midline

structures
during
imitation
(TD
>
DD),
in
primary
and
secondary
visual
processing

areas
during
observation
(TD
>
DD),
and
in
the
right
temporo-‐parietal
region
during

observation
(DD
>
TD).

There
were
no
significant
differences
between
groups
for

action
execution.

Altogether
these
results
support
the
notion
of
atypical
MNS

functioning
in
developmental
dyspraxia.

In
particular,
there
is
a
comparative
lack
of

shared
representation
for
observation
and
execution,
possibly
accompanied
by

underlying
error
monitoring,
attention,
and
visual
processing
differences,
which

together
may
be
responsible
for
a
visual-‐motor
incongruence
affecting
imitation

ability.

In
Chapter
3,
white
matter
(WM)
fiber
bundles
connecting
cortical
MNS

regions
were
defined
using
diffusion
tensor
imaging
(DTI)
tractography.

This

revealed
a
tract
consistent
with
the
arcuate
fasciculus
bilaterally
in
both
the
TD

group
and
the
DD
group.

Upon
visual
inspection,
it
was
noted
that
the
bilateral

tracts
identified
were
arranged
somewhat
more
medially
in
the
DD
group
compared

with
the
TD
group.

An
analysis
of
microstructural
properties
in
the
arcuate

126
fasciculus
indicated
that
the
DD
group
had
decreased
fractional
anisotropy
(FA),

increased
mean
diffusivity
(MD),
and
increased
radial
diffusivity
(RD)
in
the
left

arcuate
fasciculus.

There
were
no
significant
differences
between
groups
on
the

right.

This
pattern
is
consistent
with
developmental
dysmyelination
in
the
DD

group
on
the
left.

Although
functional
imaging
results
indicated
bilateral
differences

between
TD
and
DD
individuals,
it
should
be
noted
that
both
frontal
and
parietal

MNS
differences
were
found
only
on
the
left,
during
motor
planning.

These
DTI

results
suggest
abnormal
WM
structural
connections
between
left
frontal
and

parietal
MNS
regions
in
DD,
which
are
in
general
agreement
with
findings
from
the

functional
study.

However,
functional
connectivity
analysis
should
be
employed
in
a

future
investigation
to
better
understand
the
structure-‐function
relationship.

In
Chapter
4,
cortical
thickness,
a
metric
of
gray
matter
structure,
was

measured
in
MNS
regions
in
individuals
with
DD
and
their
typically
developing

peers.

It
was
hypothesized
that
gray
matter
structure
could
underlie
the
obtained

findings
from
both
functional
and
WM
structural
studies.

For
this
study,
frontal
and

parietal
MNS
regions
of
interest
were
subdivided
into
pars
opercularis
(POp)
and

pars
triangularis
(PTr),
and
supramarginal
gyrus
(SMG)
and
angular
gyrus
(AG),

respectively,
on
the
basis
of
differing
cytoarchitectonics
contributing
to
cortical

thickness
within
the
IFG
and
IPL.

Multiple
regression
models,
including
imitation

skill,
age,
and
gender,
were
developed
to
predict
cortical
thickness
in
each
of
the

above
four
ROIs
and
in
pSTS.

Imitation
(measured
with
the
SIPT
Postural
Praxis

test;
SIPT-‐PP)
significantly
predicted
thickness
at
the
zero-‐order
level
in
all
ROIs

127
except
right
POp
and
right
pSTS
and
was
retained
at
the
higher-‐order
level
in
all

ROIs
except
right
POp.

The
full
model,
including
demographic
variables
(age,

gender)
and
imitation
as
predictors,
reached
significance
in
bilateral
POp
and
PTr,

and
left
SMG,
AG,
and
pSTS,
accounting
for
21-‐47%
of
the
variance
in
these
regions.

The
strong
relationship
between
behavioral
skill
and
gray
matter
structure
in
the

left
hemisphere
found
here
parallels
functional
and
WM
structural
findings
in
the

previous
studies.

The
results
in
this
study
provide
evidence
of
structural

abnormalities
at
the
level
of
the
cortex
in
individuals
with
imitation
impairments
or

developmental
dyspraxia.

These
results
are
based
on
studies
involving
right
hand
dominant
young

adult
participants
with
developmental
dyspraxia,
that
is,
impairments
of
imitation

and
motor
coordination,
and
their
typically
developing
peers.

The
work
used

structural
MRI,
fMRI,
and
DTI
to
describe
brain
structure
and
function
as
the

participants
completed
observation,
execution,
and
imitation
of
meaningless

gestures
with
their
bilateral
upper
extremities.

The
major
findings—that
there
are

numerous
differences
in
the
structure
underlying
the
mirror
neuron
system,
and
in

MNS
function,
in
individuals
with
DD—support
the
notion
that
imitation

impairments
could
be
due
to
developmental
impairments
in
this
neural
system.

Future
work
is
needed
to
further
refine
this
theory
and
outline
more
particulars
of

the
MNS
in
DD
and
to
better
inform
therapeutic
intervention
strategies.

128
Implications
and
Future
Directions

In
general,
the
results
of
this
research
lend
support
to
the
notion
of
left

hemisphere
dominance
for
praxis
(Mutha
et
al.,
2011;
Torres
et
al.,
2010),

concurrent
with
findings
that
action
representation
in
the
brain
is
privileged
to
the

left
hemisphere,
and
this
lateralization
is
thought
to
have
supported
the

development
of
language
in
the
left
hemisphere
(Aziz-‐Zadeh,
2003).

Tasks
used
in

the
functional
imaging
portion
of
the
present
research,
however,
were
all
bimanual,

meaningless
gestures
presented
to
both
visual
fields
and
participants
were
not

specifically
assessed
for
language
abilities
(although
no
participants
had
frank

language
impairments).

Nonetheless,
findings
from
structural
imaging
support
a

leftward
inclination
in
differences
between
DD
and
TD
groups.

Furthermore,

evidence
suggests
there
may
be
a
relationship
between
developmental
perceptual-‐
motor,
speech,
and
language
disabilities
(Dewey,
2002)
and
that
left
hand

dominance
is
more
highly
represented
in
DD/DCD
than
in
the
general
population

(Cairney
et
al.,
2008;
Goez
&
Zelnik,
2008).

Future
work
should
address
these

details,
such
as
through
direct
comparisons
of
lateralized
stimuli
and
meaningless

versus
meaningful
(i.e.,
semantic)
gestures,
and
through
measurement
and

comparison
of
participants
with
and
without
co-‐morbid
speech
and
language

impairments
and
differing
hand
dominance.

Of
course,
the
inclusion
of
more

between-‐group
comparisons
would
necessitate
a
much
larger
sample
size,

presenting
a
challenge
to
study
recruitment
as
formal
diagnosis
and
identification
of

the
disorder
is
still
somewhat
limited
and
compounded
by
co-‐morbid
conditions.

129

Despite
evidence
of
impaired
attention,
executive
function,
and
even

empathy
and
social
skills
in
DD/DCD
(Dewey
et
al.,
2002),
supplemental
data
from

the
present
research
indicated
no
significant
differences
in
these
domains
(see

Appendix
E).

This
may
have
been
due
to
the
fact
that
participants
were
largely

recruited
at
a
high-‐ranking
academic
research
institution,
and
participants
can
be

assumed
to
be
relatively
high-‐functioning
with
above
average
intelligence
and

possibly
to
have
above
average
social-‐demographic
resources.

The
relationship

between
behavioral
factors
such
as
attention,
executive
function,
empathy,
and

social
skills,
and
brain-‐based
differences
associated
with
DD
needs
to
be
explored

further.

Although
functional
and
structural
imaging
components
were
included
in

this
body
of
work
and
can
be
interpreted
with
respect
to
each
other,
they
were
not

directly
compared
here.

Functional
connectivity
analysis
paired
with
a
replication

of
these
results
would
better
inform
the
structure-‐function
relationship
in
the
MNS

of
individuals
with
DD.

An
analysis
of
this
type
was
not
included
in
this
research

due
to
design
constraints,
namely,
concerns
with
the
validity
of
functional

connectivity
analysis
in
event-‐related
fMRI
designs
and
the
need
for
a
relatively
long

functional
imaging
acquisition
time
in
order
to
include
a
sufficient
number
of
trials

for
reaching
adequate
power.

In
light
of
the
functional
differences
observed
in

cortical
midline
structures
in
whole
brain
analysis
in
the
first
investigation,
resting

state
activity
and
functional
connectivity
also
might
reveal
differences
in
the
brains

130
of
individuals
with
DD.

Coupled
with
diffuse
structural
differences,
such
results
may

point
to
a
cohesive
picture
of
generalized
brain
abnormalities.

Evidence
from
this
research
supports
the
hypothesis
of
dysfunction
and

atypical
structural
features
in
the
MNS.

It
does
not,
however,
indicate
that
this
is
the

only
neural
circuit
involved.

It
is
highly
likely
that
a
number
of
brain
structures
and

regions
are
affected
in
DD.

These
potentially
include
numerous
primary
and

secondary
motor
and
sensory
areas,
such
as
primary
motor
cortex
(M1),
the
entire

premotor
area
and
supplementary
motor
area
(SMA),
primary
sensory
cortex
(S1)

and
secondary
sensory
cortex
(S2),
and
superior-‐posterior
parietal
cortex,
as
well
as

cortical
midline
structures,
visual
processing
areas,
the
basal
ganglia,
and

cerebellum
(see
Figure
5.1).

Figure
5.1.

Potential
additional
regions
that
may
underlie
functional
and
structural

differences
in
developmental
dyspraxia
are
shown
on
lateral
surface
(left)
and
a

coronal
slice
(right).

PPC
=
posterior
parietal
cortex,
S1
=
primary
somatosensory,

S2
=
secondary
somatosensory,
M1
=
primary
motor,
SMC
=
supplemental
motor

cortex,
PFC
=
prefrontal
cortex,
BG
=
basal
ganglia,
ACC
=
anterior
cingulate
cortex.

131

Despite
the
limitations
presented
above
and
an
apparent
need
for
expanded

investigation
into
this
topic,
it
is
possible
to
speculate
from
the
data
obtained
here

on
the
developmental
origin
of
imitation
and
motor
coordination
impairments
in

DD.

Figure
5.2
depicts
a
schematic
summarizing
this
interaction
model
of

developmental
functional
and
structural
differences
in
DD.

Beginning
with
findings
from
structural
imaging
and
in
light
of
the
radial
unit

hypothesis
of
cortical
development
(Rakic,
1988),
it
is
possible
that
genetic
or

prenatal
environmental
factors
during
mid-‐gestation—affecting
neurogenesis
and

apoptosis
of
progenitor
cells
in
the
embryonic
cerebral
ventricle—result
in
cortical

thickness
differences
in
MNS
regions,
affecting
imitation
and
motor
skill.

Because

neuron
migration
to
the
neocortex
is
also
dependent
on
glial
scaffolding,
it
is

possible
that
abnormal
glia
formation
or
functioning
prevents
proper
migration,

resulting
in
both
observable
cortical
gray
matter
and
subcortical
white
matter

structural
abnormalities.

Any
combination
of
these
events
would
likely
affect
brain

function.

Compounding
functional
differences
based
on
basic
structural
differences,

there
is
evidence
that
mirror
neurons
(or,
more
generally,
multisensory
integration

neurons)
develop
via
an
associative
learning
mechanism
through
the
consistent

pairing
of
visual
perceptual
input
and
motor
output
(Heyes,
2010);
and
a
decrease

in
the
quantity
or
quality
of
visual
perceptual
information
or
motor
execution

(Wallace
et
al.,
2004)
could
negatively
impact
this
development.

Thus,
it
is
possible

that
any
early
impairment
in
basic
sensory-‐motor
functions
serves
to
compound

132

Figure
5.2.

The
proposed
model
postulates
that
prenatal
and
postnatal

development
of
cortical
gray
matter
in
the
MNS
and
the
arcuate
fasciculus
interact

with
genetic
and
environmental
factors
affecting
brain
structure
and
function.

Associative
learning
may
further
modulate
imitation
and
motor
planning
skills.

Early
atypical
development
may
result
in
a
cascade
of
changes
leading
to

developmental
dyspraxia.

The
pattern
of
structural
and
functional
differences

found
in
the
present
work
is
pictured
in
the
context
of
this
model.

133
impairments,
diminishing
development
of
imitation
or
motor
coordination
skills

further.

Longitudinal
data
would
be
useful
to
support
this
model.

In
light
of
these
explanations,
there
is
a
need
for
a
better
understanding
of

prenatal
or
genetic
factors
that
may
cause
these
brain
abnormalities,
with
the

overarching
goal
of
prenatal
prevention
or
treatment
early
in
postnatal

development.

In
the
persisting
absence
of
in
vivo
imaging
techniques
with
spatial

resolution
at
the
cellular
level
that
can
safely
be
used
on
human
research

participants,
postmortem
neurobiological
investigations
would
be
required
to
fully

understand
cellular
development
in
this
population
as
it
pertains
to
their

impairments.

In
addition,
it
remains
to
be
demonstrated
to
what
degree
early

behavioral
or
motor
skills
interventions
can
alter
both
brain
structure
and
function

and
if
altering
brain
structure
definitively
results
in
improved
functional
outcomes

for
affected
individuals
(Liew,
Garrison,
Werner,
&
Aziz-‐Zadeh,
2012;
Orton,
Spittle,

Doyle,
Anderson,
&
Boyd,
2009).

Therapeutic
interventions
aimed
at
increasing
the

quantity
or
quality
of
early
sensory
input
paired
with
appropriate
motor
output,

such
as
the
sensory
integration
techniques
utilized
by
occupational
therapists

(Bundy,
Lane,
&
Murray,
2002;
Smith
Roley,
Blanche,
&
Schaaf,
2001)
or
reciprocal

imitation
training
(Ingersoll,
2010),
assume
a
model
of
associative
learning,

although
there
is
very
limited
convincing
evidence
on
the
effectiveness
of
such

interventions.

Randomized
clinical
trials
are
a
necessary
future
step.

134
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167
APPENDIX
A:

Background
on
Methods
Employed

Functional
Magnetic
Resonance
Imaging
(fMRI)

In
order
to
examine
brain
activity
during
functional
tasks
of
interest,

functional
magnetic
resonance
imaging
(fMRI)
was
used
as
an
indirect
measure
of

brain
activity
to
explore
the
research
questions
in
the
present
work.

fMRI
is
used
to

create
quantifiable
images
of
physiological
changes
at
spatially
mapped
locations
in

the
brain.

Specifically,
the
proportion
of
oxygenated
to
deoxygenated
hemoglobin
is

measured
at
a
set
number
of
locations
in
the
image
space,
and
this
contrast
is

referred
to
as
the
blood-‐oxygenation-‐level
dependent
(BOLD)
signal.

BOLD
signal
is

dependent
on
the
timing
of
hemodynamic
events
occurring
as
neural
activity

requires
energy
consumption,
but
gives
no
direct
measure
of
neural
activity
or

electrochemical
changes
occurring
within
neurons
or
interneuronal
space
(Huettel,

Song,
&
McCarthy,
2009).

To
derive
magnetic
resonance
(MR)
images,
the
MRI
scanner
depends
on
a

strong
static
magnetic
field
(a
3-‐Tesla
magnet
was
used
for
data
collection
in
the

present
work).

Within
the
static
magnetic
field,
positively
charged
hydrogen
nuclei

assume
a
parallel
spin
state,
aligning
with
the
magnetic
field.

Radiofrequency
pulses

are
applied
at
a
given
frequency
for
an
extended
period
of
time
in
order
to
invoke

spin
excitation
of
the
hydrogen
nuclei,
perturbing
their
spin
systems
into
a
net

magnetization
away
from
equilibrium.

The
time
interval
between
successive

168
excitation
pulses
is
known
as
repetition
time
(TR).

As
the
spin
of
hydrogen
nuclei

relax
back
to
equilibrium
(that
is,
in
parallel
alignment
with
the
static
magnetic

field),
a
detector
coil
measures
the
time
to
signal
decay
(until
they
return
to

equilibrium)
along
the
x-‐,
y-‐,
and
z-‐axes.

The
time
interval
between
an
excitation

pulse
and
data
acquisition
is
known
as
echo
time
(TE).

Acquired
raw
MR
signal
is

then
converted
into
spatially
informative
images
and
the
brain
space
is
divided
up

into
small
cubic
regions
called
voxels
(i.e.,
the
three-‐dimensional
equivalent
of

pixels),
or
volume
elements,
for
the
purpose
of
quantifying
signal
at
any
given
point

(Haacke,
Brown,
Thompson,
&
Venkatesan,
1999).

Different
MR
contrast
mechanisms
allow
for
image
construction
that

emphasizes
differences
in
the
relaxation
properties
of
atomic
nuclei.

T1-‐weighted

contrasts
are
typically
employed
for
anatomical
imaging
and
can
be
improved
with

the
use
of
an
inversion
pulse,
such
as
in
a
magnetization-‐prepared
rapid
gradient

echo
(MPRAGE)
sequence,
while
T2-‐weighted
contrasts
deemphasize
cerebrospinal

fluid
(CSF)
and
are
commonly
in
clinical
diagnosis
protocols.

In
functional
imaging,

a
T2*-‐weighted
contrast—most
commonly,
an
echo
planar
imaging
(EPI)

sequence—is
employed
since
this
contrast’s
long
TR
and
medium
TE
values
make
it

most
sensitive
to
the
amount
of
deoxygenated
hemoglobin
present
(Haacke
et
al.,

1999;
Twieg,
1983).

Raw
MR
data
is
noisy
and
must
undergo
a
series
of
preprocessing
steps

before
statistical
inference
can
be
made.

Prior
to
modeling
functional
data,
raw
data

(T2*-‐weighted)
must
be
slice-‐timing
corrected
to
adjust
for
delays
caused
by

169
differences
in
acquisition
time
between
slices
during
sequential
image
acquisition.

In
addition,
this
step
reassembles
images
to
account
for
the
interleaved
slice

acquisition
that
some
scanners
utilize.

The
functional
images
then
need
to
be

realigned
to
the
first
scan
volume
in
order
to
correct
for
between-‐scan
head
motion

along
six
movement
parameters
(x-‐,
y-‐,
and
z-‐translation
and
roll,
pitch,
and
yaw

rotation).

A
mean
functional
image
is
produced
from
the
realignment
process
and
is

used
to
co-‐register
functional
images
with
the
anatomical
image
(T1-‐weighted

MPRAGE
contrast)
and
segmented
into
tissue
classes
(white
matter,
gray
matter,

CSF)
for
normalization.

All
images
are
then
normalized
to
a
standard
template
to

allow
inter-‐individual
comparison.

Functional
images
are
then
spatially
smoothed

using
a
full-‐width
at
half-‐max
(FWHM)
Gaussian
kernel,
typically
of
5-‐8mm
in
order

to
increase
signal-‐to-‐noise
ratio
in
the
data.

It
is
important
to
note
that
a
large

smoothing
kernel
may
wash
out
the
signal
if
effect
sizes
in
the
data
are
small.

After

smoothing,
each
voxel
can
no
longer
be
assumed
to
be
independent
and
statistical

correction
for
multiple
comparisons
will
need
to
be
performed
later.

After
preprocessing,
functional
MR
data
is
in
a
form
that
can
used
as
the

dependent
variable
in
the
general
linear
model
for
hypothesis
testing
with
a
data

processing
and
statistical
package
such
as
FMRIB’s
Software
Library
(FSL)
for

neuroimaging
data
(www.fmrib.ox.ac.uk/fsl).

For
this,
a
design
matrix
in
which

each
condition
of
interest
is
assigned
to
the
time
point
at
which
it
occurred
is

constructed
along
with
nuisance
regressors
which
help
account
for
non-‐
experimental
sources
of
variability
(e.g.,
head
motion)
also
included
in
the
model.

170
Contrast
weights
accounting
for
time-‐dependent
conditions
are
then
multiplied
by

the
parameter
weights
(i.e.,
relative
signal
within
a
given
voxel)
and
statistical
tests

to
evaluate
for
significant
effects
are
conducted
using
fixed
effects
modeling
within

in
each
run.

Results
from
multiple
scanning
runs
within
each
participant
and

between
participants
are
combined
using
mixed
effects
models.

Diffusion
Tensor
Imaging
(DTI)

Diffusion
tensor
imaging
(DTI)
is
a
neuroimaging
method
which
is

particularly
useful
in
imaging
the
structure
of
white
matter.

This
type
of
imaging

relies
on
the
diffusion
of
water
molecules
within
and
across
cell
membranes
and

extracellular
space.

This
movement
of
water
molecules
in
brain
tissue
varies
as
a

function
of
tissue
type
and
limitations
by
obstacles
such
as
myelin
sheath,

axolemma,
and
neurofilaments
(Mukherjee
&
McKinstry,
2006).

The
directionally

dependent
property
of
molecules
is
called
anisotropy
(Huettel
et
al.,
2009).

Water

molecules
typically
diffuse
more
quickly
along
the
longitudinal
direction
of
white

matter
fibers,
rather
than
across
membranes,
and
this
dispersion
can
be
quantified

using
specific
MR
sequences
sensitive
to
different
gradient
directions.

Thus,
it
is

assumed
that
the
direction
of
the
fastest
diffusion
indicates
the
overall
orientation

of
white
matter
fibers
(Le
Bihan
et
al.,
1999).

However,
there
is
much
debate
in
the

literature
as
to
what,
exactly,
diffusion-‐weighted
imaging
is
measuring
due
to
the

fact
that
multiple
microanatomical
structures
exist
within
a
single
voxel
and
DTI

171
only
accounts
for
the
average
signal
derived
from
each
voxel
(Jones
et
al.,
2012).

Thus,
interpretations
of
DTI
data
should
be
approached
with
caution.

Diffusion
can
be
described,
quantified,
and
analyzed
using
a
diffusion
tensor,

accounting
for
each
of
the
three
principle
directions
(along
the
x-‐,
y-‐,
and
z-‐axes)
in

three-‐dimensional
space
(Basser,
Mattiello,
Turner,
&
Le
Bihan,
1993;
Le
Bihan
et

al.,
1999).

The
mean
of
these
three
eigenvalues
is
called
the
mean
diffusivity
(MD)

and
represents
one
metric
used
in
analyzing
DTI
data
(Mukherjee
&
McKinstry,

2006).

The
eigenvalue
of
principle
direction
of
the
diffusion
tensor
is
known
as

axial
diffusivity
(AD),
while
the
mean
of
the
remaining
two
eigenvalues
is
known
as

radial
diffusivity
(RD).

These
are
interpreted
as
representing
the
primary
within-‐

and
across-‐membrane
diffusion
directions.

A
scalar
quantity,
known
as
fractional

anisotropy
(FA),
can
be
computed
for
each
voxel
to
express
the
tendency
for
water

to
diffuse
in
a
given
direction,
and
vector
representations
of
this
value
at
every
voxel

are
commonly
used
to
construct
maps
of
white
matter
fiber
bundles
(Huettel
et
al.,

2009).

Tractography
is
one
application
of
DTI
and
is
used
to
track
the
likely
path
of

axonal
fibers
as
they
travel
between
cortical
brain
regions
(Huettel
et
al.,
2009).

Tractography
can
be
deterministic
or
probabilistic.

Deterministic
tractography

cannot
account
for
uncertainty
or
distributed
connectivity,
while
probabilistic

tractography
is
capable
of
estimating
the
most
likely
fiber
orientation
and
the

likelihood
distribution
of
each
other
orientation
along
the
fiber,
typically
using

thousands
of
samples
to
make
this
estimation
(Human
Connectome
Project,
2013;

172
Jones
et
al.,
2012).

Thus,
probabilistic
tractography
provides
a
more
accurate

estimate
of
the
likely
fiber
path
between
cortical
regions
or
passing
through
any

given
brain
area.

Cortical
Thickness
Measurement

Measurement
of
cortical
thickness
can
be
estimated
in
vivo
from
high-‐
resolution
T1-‐weighted
anatomical
scans
(Dale
&
Fischl,
2000).

This
is
done
through

smoothed
three-‐dimensional
cortical
surface
reconstruction
using
a
polygonal

tessellation
of
the
brain
volume
(Dale
et
al.,
1999;
Fischl
et
al.,
1999).

This
way,
an

accurate
measure
of
the
distance
between
the
gray-‐white
matter
boundary
and
the

cortical
surface
can
be
acquired
even
if
the
cortical
surface
does
not
lie

perpendicular
to
any
of
the
cardinal
axes.

173
APPENDIX
B:

Recruitment
Information

Institutional
Review

The
procedures
outlined
in
this
dissertation
were
approved
by
the

Institutional
Review
Board
(IRB)
at
the
University
of
Southern
California.

All

procedures
related
to
participant
recruitment
and
participation,
data
analysis,
and

reporting
were
conducted
in
accordance
with
IRB
policies
and
ethical
standards

pertaining
to
human
participants.

Participants
and
Recruitment

Participants
were
recruited
in
two
groups
for
the
present
research:

individuals
with
probable
developmental
dyspraxia
(DD)
and
typically
developing

(TD)
individuals.

For
the
DD
group,
participants
did
not
need
to
have
a
prior
formal

diagnosis
or
have
been
identified
as
having
DD.

Instead,
participants
in
the
DD

group
were
initially
recruited
based
on
self-‐identification
(or
because
they
believe

others
have
identified
them)
as
being
“clumsy,”
having
poor
coordination
or

difficulty
learning
new
motor
skills,
or
having
persistent
difficulty
with
motor
tasks

and
activities
of
daily
living.

Individuals
who
had
been
diagnosed
with
DCD
or
told

they
had
DD
as
children
or
adults
were
included
in
this
group
as
well.

TD

individuals
from
the
community
were
also
recruited
to
serve
as
control
group

participants.

174

In
order
to
be
eligible
for
the
study,
all
participants
had
to
(a)
be
18-‐28
years

old;
(b)
have
normal
or
corrected-‐to-‐normal
vision;
(c)
be
right-‐handed
as

determined
by
the
Edinburgh
Handedness
Inventory
(Oldfield,
1971),
(d)
be
not

taking
any
prescription
medications
(with
the
exception
of
oral
contraceptives),
(e)

be
safe
to
undergo
MRI
(e.g.,
no
ferrous
magnetic
implants,
no
claustrophobia,
not

pregnant,
etc.),
and
(f)
be
free
of
other
neurological
or
psychiatric
disease,
including

diagnosed
ASD.

To
be
included
in
the
DD
group,
participants
who
initially
self-‐
identified
as
having
motor
impairments
during
recruitment
screening
had
to

additionally
indicate
via
survey
that
they
had
significant
challenges
with
activities
of

daily
living
and/or
motor
tasks
(Adult
DCD/Dyspraxia
Checklist,
ADC,
Kirby
et
al.,

2010;
age-‐adapted
DCD
Questionnaire
’07
edition,
DCD-‐Q
’07,
Wilson
et
al.,
2007).

In
addition,
participants
recruited
for
the
DD
group
had
to
score
28
or
below
on
the

SIPT
Postural
Praxis
test
(Ayres,
1989/2004)
and
score
at
or
below
the
15
th

percentile
for
their
age
and
gender
on
the
BOT-‐2
Short
Form
(Bruininks
&

Bruininks,
2005).

Participants
in
the
TD
group
were
age-‐
and
gender-‐matched
to

the
DD
group
and
had
score
above
28
on
the
SIPT
Postural
Praxis
test
and
above
the

15
th

percentile
on
the
BOT-‐2,
as
well
as
indicate
no
significant
motor
impairment.

Recruitment
was
conducted
via
word
of
mouth,
flyers,
and
email
to
a
listserv

compiled
by
the
Aziz-‐Zadeh
laboratory
of
individuals
interested
in
participating
in

fMRI
research
studies.

Recruitment
sites
include
numerous
locations
on
campus

and
in
the
surrounding
community.

In
addition,
some
participants
were
recruited

via
the
USC
Department
of
Psychology
Human
Participant
Pool
and
in
classrooms

175
(as
permitted)
on
the
USC
campus.

Thirty
participants
(15
in
each
group)
were

recruited
and
scanned
for
the
study.

Two
participants
(1
from
each
group)
were

excluded
from
the
analysis
due
to
head
movement
in
excess
of
3
mm
translational
or

rotational
movement,
rendering
their
data
unusable.

176
APPENDIX
C:

Instruments

In
order
to
identify
individuals
with
DD
and
to
investigate
correlations

between
individual
differences
in
motor
and
imitation
skill
and
neural
structure
and

function,
questionnaires
and
behavioral
skill
measures
were
administered
to

participants
outside
the
scanner.

Investigator-‐administered
measures
were

collected
in
a
quiet
experiment
room.

Participants
completed
self-‐report
measures

with
the
experimenter
present.

Behavioral
measures
are
described
below
(in

alphabetical
order)
and
all
instruments
are
summarized
in
Table
C.1.

Adult
Developmental
Co-ordination
Disorders/Dyspraxia
Checklist
(ADC)

The
ADC
is
a
self-‐report
questionnaire
designed
to
distinguish
adults
with

DCD
or
dyspraxia
from
typically
developing
adults
(Kirby
et
al,
2010).

The
ADC
uses

a
4-‐point
Likert
scale
to
assess
perceived
motor
difficulties
along
3
dimensions:
(1)

difficulties
experienced
during
childhood;
(2)
the
individual’s
current
perception
of

their
performance
in
activities
requiring
good
motor
coordination;
and
(3)
current

perception
about
performance
as
reflected
upon
by
others.

The
ADC
is
not
norm-‐
referenced.

However,
with
a
modest
sample
size
(N
=
107),
standardization
analysis

has
shown
it
to
demonstrate
good
content
and
face
validity,
good
internal
reliability

of
each
subtest,
good
construct
validity
between
subtests,
and
the
ability
to

discriminate
between
groups
of
adults
with
DD
or
DCD
and
typical
development

177
(Kirby
et
al.,
2010).

In
addition,
a
moderate
significant
correlation
was
found

between
the
ADC
and
a
measure
of
handwriting
difficulty
(Kirby
et
al.,
2010),
a

common
correlate
of
DD/DCD
in
children
(Barnett,
2006).

Although
the
ADC
has

not
been
standardized
for
use
in
the
United
States,
Kirby
and
colleagues
note
that

the
measure
remained
sensitive
and
accurately
differentiated
groups
in
both

locations
in
which
it
was
tested—Wales,
United
Kingdom
and
Israel—despite

potential
cultural
differences
between
the
two.

Behavior
Rating
Inventory
of
Executive
Function,
Adult
Version
(BRIEF-A)—
Plan/Organize
scale

The
BRIEF-‐A
is
a
standardized
measure
for
adults
18-‐90
years
old
that

assesses
self-‐reported
executive
functioning.

Both
self-‐report
and
informant-‐report

versions
of
this
measure
exist.

In
the
present
investigation,
only
the
self-‐report

Plan/Organize
scale
(10
items)
from
this
measure
was
used
in
order
to
minimize

responder
burden
and
because
executive
functioning
difficulties,
particularly
in
the

planning
and
organization
domains,
are
frequently
reported
as
co-‐occurring
with

DD/DCD
(Alizadeh
&
Zahedipour,
2005;
Livesey
et
al.,
2006).

The
scale
was

administered
as
a
descriptor
of
the
study
sample.

The
Plan/Organize
scale
is
a

measure
of
the
individual’s
ability
to
“manage
current
and
future-‐oriented
task

demands
within
the
situational
context”
(Roth,
Isquith,
&
Gioia,
2005,
p.
22).

Specifically,
this
assessment
quantifies
the
ability
to
anticipate
future
events
and

organize
the
actions
necessary
to
carry
out
a
plan.

This
may
require
the
ability
to

178
sequence
a
string
of
actions
and
prepare
for
action,
such
as
by
gathering
the

necessary
information,
tools
and
materials
in
order
to
complete
a
task
in
a
timely

manner.

The
Plan/Organize
scale
captures
adults’
perception
of
their
ability
to
do

these
things
(Roth
et
al.,
2005).

Item
content
for
the
specific
domain
scales
of
the
BRIEF-‐A
was
developed

based
on
theoretical,
clinical,
and
research
descriptions
of
the
various
aspects
of

executive
functioning
and
confirmed
with
statistical
factor
analysis.

Following
this

procedure,
the
Plan/Organize
scale
has
demonstrated
acceptably
high
internal

consistency
in
both
a
normative
sample
(α=.85)
and
a
mixed
clinical
and
healthy

adult
sample
(α=.91)
and
acceptable
test-‐retest
reliability
in
a
normative
sample

(r=.82)
on
the
self-‐report
form.

In
addition,
the
Plan/Organize
scale
has
exhibit