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Family aggression exposure and community violence exposure associated with brain volume in late adolescence: a comparison of automated versus manual segmentation

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Family aggression exposure and community violence exposure associated with brain volume in late adolescence: a comparison of automated versus manual segmentation

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Content Aggression exposure and brain volume 1
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RUNNING HEAD: Aggression exposure and brain volume

Family Aggression Exposure and Community Violence Exposure Associated with Brain
Volume in Late Adolescence: A Comparison of Automated versus Manual Segmentation.
Hannah Lyden, M.Sc
(PSYCHOLOGY)
University of Southern California
August 2015

Aggression exposure and brain volume 2
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Table of Contents

List of Tables 3

List of Figures 4

Abstract 5

Introduction: Background and Significance 6
Early Life Stress 6
Community Violence 8
How Does ELS Shape the Brain? 11
The Adolescent Brain 13
Specific Brain Structures Linked with ELS 14
Hippocampus 14
Amygdala 16
Aim 2: Approaches Used To Estimate Brain Volume 18
Manual and Automated Segmentation Approaches 18
Total Brain Volume Estimation 20

Methods 22
Participants 22
Procedures 23
Measures 24
Analyses 26

Results 31
Automated Segmentation Performance 32
Associations Between Brain Volume and Family Aggression Exposure 32
Associations Between Brain Volume and Community Violence Exposure 33

Discussion 33
Manual Segmentations 34
Automated Segmentations 35
Community Violence Exposure 36
Manual versus Automated Segmentations 37

References 39

Appendix: Measures 67

Aggression exposure and brain volume 3
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List of Tables

Table 1: Summary"of"Intercorrelations"of"Age,"Gender,"SES,"" """ """"
" " Family"Aggression"Exposure,"Community"Violence"Exposure,""
" " and"All""Manual"and"Automated"Segmentations"Adjusted"by""
" " Total"Brain"Volume."
"
Table"2:"" Means,"Standard"Deviations"and"Comparison"of"Means"" "
" " between"Manual"and"Automated"Segmentations."
"
Table"3:"" Summary"of"Automated"Segmentation"Performance,"Percent""
" " Volume"Overlap,"Percent"Volume"Difference,"and"Pearson’s""
" " Correlations"between"Automated"and"Manual"Segmentations."
"
Table"4:"" Separate"Multivariate"Linear"Regression"Analyses"of"Family""
" " Aggression"Exposure"with"Manual"and"Automated"Bilateral""
" " Hippocampal"and"Amygdala"Segmentations,"Adjusting"for"Age,""
" " Gender,"and"Total"Brain"Volume."
!
Table"5:!! Separate"Multivariate"Linear"Regression"Analyses"of"" "
" " Community"Violence"Exposure"and"Bilateral"Hippocampal"and""
" " Amygdala"Volume"from"both"Automated"and"Manual"" "
" " Segmentations,"Adjusting"for"Age,""Gender,"and"Total"Brain""
" " Volume. !
!
"
"
"

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Aggression exposure and brain volume 4
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List of Figures

Figure 1: Select Pictures of Coronal Slices Depicting a Manually Traced Left
Hippocampus Mask.

Figure 2: Select Pictures of Coronal Slices Depicting a Manually Traced Right
Amygdala Mask.

Figure 3: Results of a Partial Correlation Representing the Relationship between
Family Aggression Exposure and Bilateral Hippocampal Volume
Measured by FSL Segmentation.

Figure 4: Results of a Partial Correlation Representing the Relationship
between Family Aggression Exposure and Right Amygdala Volume
Measured by Manual Segmentation.

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Aggression exposure and brain volume 5
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Abstract

The present study had two aims. First, early life stressors such as family and
community aggression are consistently associated with volumetric differences in
subcortical brain structures e.g. the hippocampus and amygdala. However, the
neuroanatomical signature of early aggression exposure may be different in adolescence
versus adulthood. Second, automated segmentation approaches to studying brain volume
have proliferated in the literature, but to date no studies have compared manual vs.
automated approaches in adolescents. Given that adolescence is a time of significant
brain growth and that certain brain areas have distinct rates of development, standardized
approaches to segmentation for adolescents may be limited. Therefore two aims were
proposed.
Aim 1. To investigate the associations between volume of subcortical structures and early
life aggression exposure. A positive association between family aggression exposure and
right amygdala volume was found using manual segmentations. A negative association
between family aggression exposure and bilateral hippocampal volume were found using
automated segmentations. No associations between community violence exposure and
brain volume were found using manual or automated segmentations.
Aim 2. Two different methods for estimating hippocampal and amygdala volume were
used: manual tracing onto MRI images of each individual’s brain, and automated
estimation of brain volumes using computer-generated algorithms. Additionally, three
different established methods for estimating total brain volume were compared. Results
from each method were contradictory, suggesting that manual tracing may be required for
accurate estimation of subcortical brain volumes in adolescence.
Aggression exposure and brain volume 6
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The discussion explores implications of these distinct aggression exposures on
brain volume as well as the discrepancies that emerged between different methodological
approaches.
Introduction: Background and Significance
Early Life Stress
Early life stress (ELS) has been found to be associated with adverse
developmental outcomes such as alcoholism, drug abuse, depression, and suicide
attempts (Felliti, et al., 1998). One pathway hypothesized to explain this relationship is
that ELS may be linked to alterations in brain structure and function (Teicher, Anderson,
Polcari, Anderson, Navalta, & Kim, 2003; Twardosz & Lutzker, 2009). ELS, such as
familial aggression, abuse, or community violence, can contribute to a cascade of events
in the stress response system and the brain, which may lead to later deficits in memory,
emotion regulation, and impulse control (Twardosz & Lutzker, 2009).
Adverse events have been operationalized as different categories of abuse or
aggression, such as childhood verbal abuse, physical abuse, sexual abuse, as well as
intraparental violence. Many of these categories have been studied in association with
brain volume. For example, childhood sexual abuse has been found to be associated with
reduced gray matter in the visual cortex (Tomoda, Navalta, Polcari, Sadato & Teicher,
2009). Similarly, physical abuse (Teicher, Samson, Polcari, & McGreenery, 2006) has
been found to be associated with decreases in hippocampal volume (Perry, 2001)
whereas, verbal/emotional abuse has been found to be associated with increased gray
matter volume in the superior temporal gyrus (Tomoda et al., 2009; van Harmelen, van
Tol, van der Wee, Veltman, Aleman, Psinhoven, van Buchem, Zitman, Penninx &
Aggression exposure and brain volume 7
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Elzinga, 2010). Neglect has also been found to be associated with reduced corpus
callosum volume (Teicher, Dumont, Ito, Vaituzis, Giedd, & Andersen, 2004). Exposure
to harsh corporal punishment and exposure to childhood emotional maltreatment have
been found to be associated with decreased volume in the prefrontal cortical gray matter
(Tomoda et al., 2009; van Haremelen, et al., 2010).
These adverse advents have also been examined in terms of negative
developmental outcomes. Physical abuse and verbal abuse have been found to be
associated with symptoms of depression and anxiety (Teicher, et al., 2004; Teicher,
Samson, Polcari, & McGreenery, 2006). Similarly, family conflict has been found to be
significantly associated with risk for substance use disorders, and externalizing problems
(Skeer, McCormick, Norman, Buka & Gilman, 2009). Therefore, the literature supports
possible long-term associations of ELS and brain morphology, particularly in areas of the
brain linked to affective processing, emotion regulation, and executive functioning.
Violence exposure, as opposed to naturally occurring situations such as disasters
or chronic illness, is particularly important to investigate in regards to development as it
violates the child’s assumption of a safe environment, and is often experienced
chronically (Margolin, 1998). Investigating the different pathways by which different
types of violence exposure may be associated with development may help to identify
specific problematic outcomes (Bell & Jenkins, 1993; Garbarino, Dubrow, Kostelny &
Pardo, 1992, Lynch & Cicchetti, 1998). Although many studies have looked at the
relationship between childhood life stress on volumetric differences in brain structure, to
our knowledge no studies have looked at the direct relationship between community
violence exposure and brain volume in children and adolescents. Similarly, most studies
Aggression exposure and brain volume 8
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focus on children specifically recruited from stressful or abusive households. The current
study builds on this literature by recruiting children from a diverse community sample.
Children’s exposure to violence rarely occurs with solely one type of violence
exposure (Dodge, Bates, & Petit, 1997; Margolin, 1998; Rossman & Rossenberg, 1992).
Consistent overlap exists between exposure to marital violence and child physical abuse
(Appel & Holden 1998; Jouriles & LeCompte 1991; Straus, Gelles, & Steinmets,1980;
Wolfe, Jaffe, Wilson, & Zak, 1985), as well as between exposure to community violence
and intrafamilial violence (Bell & Jenkins, 1993; Garbarino, Dubrow, Kostelny & Pardo,
1992, Lynch & Cicchetti, 1998). Similarly, exposure to multiple types of aggression and
violence has been associated with more serious outcomes, such as school failure, drug
and alcohol problems, and serious psychopathology (Egeland, 1997; Lynch & Cichetti,
1998; Wolfe & McGee, 1994). Therefore, this project examined the neural correlates of
two types of ELS: family aggression and community violence.
Community Violence
Although less studied than exposure to aggression or abuse within the family,
community violence has been shown to be associated with negative developmental
outcomes (Campbell & Schwarz, 1996; Glodich, 1998; Koop & Lundberg, 1992; Pynoos,
1993). Research suggests that both witnessing and being a victim of community violence
may be a risk factor for the development of increased anxiety and depressive symptoms
and aggressive behaviors in children (Kliewer, Leport, Oskin, & Johnson, 1998; Lynch &
Cichetti, 1998; Miller, Wasserman, Neugebauer, Gorman-Smith & Kamboukos, 1999).
However, the extant research is equivocal about the differential effects of direct
Aggression exposure and brain volume 9
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victimization vs. witnessing violence (Fitzpatrick, 1993; Schwab-Stone, Ayers, Kasprow,
Voyce Barone, & Shrivere, 1995; Singer, Anglin, Song, & Lunghofe, 1995).
Community violence is distinct from domestic violence and violence between
family members, as well as child physical, sexual, or emotional abuse, and is defined by
exposure to violence or aggression by a member or members of the community. Margolin
& Gordis (2000) claim that community violence, intraparental violence, and child
physical or sexual abuse are similar in the sense that they pose threats to the child’s
personal safety and violate the idea that the immediate environment is a safe haven. The
emotional security hypothesis (Davies & Cumming, 1994) posits that aggression
exposure affects symptoms through this feeling that the environment is no longer safe.
Psychobiological consequences of community violence exposure in children have
been found to resemble those from exposure to familial aggression or abuse (Margolin &
Gordis, 2000; Lynch, 2003). The psychobiological consequences of community violence
have been assessed in terms of the relationship between stress reactivity and exposure to
community violence. Studies have found both a hypoarousal of the stress response
system, such as lower resting heart rate in a sample of urban school-aged children
(Krenichyn, Saegert, & Evans, 2001), as well as hyperarousal of the stress response
system associated with community violence exposure. For example, Wilson and
colleagues (2002) found that African American children who either witnessed or were
victimized by violence were less likely to show a normal drop in blood pressure at night.
To our knowledge, no neuroimaging studies have investigated community violence
exposure and its concurrent association with brain structure. However, similar
neurocognitive deficiencies in memory, and intelligence, have been found in adults who
Aggression exposure and brain volume 10
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were exposed to community violence as those exposed to familial aggression and abuse
(Bower & Silvers, 1998; Margolin & Gordis, 2000). Therefore, community violence
exposure may have similar associations with brain structure as familial aggression or
abuse. However, familial aggression and community violence also differ in important
ways. For example, family members share genes, such that genetic risk for both brain
structure and for aggressive behaviors may overlap within family environments
(Margolin & Gordis, 2000). Community violence is more tied to external circumstances,
although very highly linked to socioeconomic status and also potentially shared within a
family.
Children living with violence also tend to experience other adverse environmental
factors such as poverty, poor nutrition, overcrowding, and substance abuse (Garmezy &
Matsen, 1994; Smith & Thornberry, 1995, Vig 1996). Low socioeconomic status has also
been found to be associated with differences in brain volume, stress reactivity, executive
functioning, as well as negative developmental outcomes such as substance abuse and
psychopathology (Hanson, Chandra, Wolfe & Pollack, 2011; Lupien, King, Meaney, &
McEwen, 2000: Sarsour, Sheridan, Jutte, Nuru-Jetter, Hinshaw & Boyce, 2010). Lower
parental income was found to be associated with smaller hippocampal volume in a
sample of healthy children (Hanson et al., 2011). Thus, low SES is a contributing factor
to the adversities experienced by children exposed to violence and will need to be
considered as a covariate in any investigation of early life stress and later neurobiological
and developmental outcomes.

Aggression exposure and brain volume 11
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How does ELS shape the brain?
The brain may be affected by early life stress in multiple ways. For example, ELS
increases reactivity to potential stressors in the environment and the resulting alterations
in stress, specifically stress hormones, modify typical processes of brain development
such as neurogenesis, synaptic overproduction, pruning and myelination (Twardosz &
Lutzker, 2009). The biological mechanisms involved in exposure to early life stress or
adverse events have been explored by a number of researchers (Cichetti & Lynch, 1995;
Glaser, 2000; Perry, Polland, Blakley, Baker & Vigilante, 1995; Twardosz & Lutzker,
2010). In several studies, ELS was linked with dysregulations of the stress system, e.g.
the Hypothalamic Pituitary Adrenal (HPA) axis (eg. Bremner & Vermetten, 2001; De
Bellis, Hooper, & Sapia, 2005; Navalta, Tomoda, & Teicher, 2008; Nemeroff, 2004;
Tarullo & Gunnar, 2006; Teicher et al., 2003; Van Voorhees & Scarpa, 2004; Watts-
English, Fortson, Gibler, Hooper, & De Bellis, 2006). The type of dysregulation depends
on numerous aspects of the individual’s experience, and can involve either a dampening
or an overactivation of the stress response system (Twardosz & Lutzker, 2009).
When a child is confronted with a stressor the HPA axis plays a central role in the
child’s response. After recognizing the threat, the hypothalamus releases corticotropin
releasing factor (CRF), which stimulates the pituitary gland to release adrenocorticotropic
hormone (ACTH) and causes the adrenal gland to release glucocorticoid hormones such
as cortisol (Tottenham & Sheridan, 2010; Twardosz & Lutzker, 2009). Cortisol then
sends feedback to the brain to terminate the stress response. Excess glucocorticoid levels,
or dysregulated cortisol patterns, can have deleterious effects on the brain, such as
damage to the hippocampus (Bremner, 2006; Sapolsky, 2003). However, as previously
Aggression exposure and brain volume 12
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mentioned, previous studies have found evidence for both hyper-arousal and hypo-
arousal of the stress response system after exposure to a stressor (Lynch, 2003). These
differences in arousal to stress could lead to differential effects on the brain.
The brain’s malleability during the early years of life is another mechanism by
which exposure to violence may relate to children’s neurobiology. During childhood the
brain is dependent on stimuli from the environment to organize its underlying structure.
Once the environmental stimulus occurs, the architecture of circuits is altered in such a
way that certain patterns of activity are preferred (Knudsen, 2004). For example,
sensitive periods exist such that if a stimulus is present during this time, certain
mechanisms will develop, but if the stimulus is missing the mechanism may not develop.
For example, evidence suggests a strong relationship between the age of exposure to
language and the ultimate language ability achieved by the individual (Newport, 2003).
Similarly, the drive to form attachments with caregivers in early life may be ‘hard-wired’
in the brain and include structures such as the amygdala. However, experiences with
attachment figures directly shapes the organization of the neural “attachment system”
(Siegel, 2001). For example, institutionally reared children, deprived of a consistent
relationship with a caregiver, show atypically large amygdala volumes (Tottenham et al.,
2010).
Additionally, if the child experiences a traumatic event and the neural systems
involved such as fear or stress circuitry are over or under-activated this event may affect
the organization of these systems (Bremner, 2006; Sapolsky, 2003; Tottenham &
Sheridan, 2010). Children who experience maltreatment often become hypervigilant to
threat, leading to overactivation of the amygdala and potentially to molecular changes in
Aggression exposure and brain volume 13
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the structure and function of the amygdala and its concomitant circuitry (Tottenham &
Sheridan, 2010). Therefore, if specific types of stimuli are not available or are
overemphasized during critical periods for organization and development of the brain,
certain brain structures and functions may be affected (Twardosz & Lutzker, 2009).
Although the study of early life stress in humans and later neurobiological
development is in a nascent stage, some volumetric differences in certain structures have
been found. For example, the hippocampus is important for socio-emotional functioning
and appears to be affected by the activity of the HPA axis due to its long period of
development and high density of glucocorticoid receptors (Tottenham & Sheridan, 2010).
Before reviewing the literature on ELS and specific brain regions, brain development
during adolescence will be reviewed to illustrate the unique constellation of brain
structures undergoing development during this period.
The Adolescent Brain.
A neuroscience systems-based developmental model of adolescent behavior
(Ernst, Pine, & Hardin, 2006) suggests that adolescent behavior originates from
developmental changes in three neural systems. The first system supports reward-related
or approach behavior with key brain regions including the ventral striatum, and in
particular the nucleus accumbens, mediated by dopaminergic activity. The second system
is the harm avoidance system including regions such as the amygdala, mediated by
serotonergic activity. The third system supports the regulation of both approach and
avoidance systems involving medial/ventral prefrontal regions. In adolescence it has been
found that the reward system develops first and is more sensitive than the avoidance
systems, while the regulatory system is still undergoing maturation (Ernst et al., 2006).
Aggression exposure and brain volume 14
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Structural neuroimaging findings suggest that the adolescent brain undergoes
considerable synaptogenesis. From the age of 12 a decrease in cortical gray matter can be
observed as well as an increase in cerebral white matter, which occurs throughout
childhood and young adulthood (Caviness, Kennedy, Richelme, Rademacher, & Filipek,
1996; Giedd, Blumenthal, Jeffries, Castellanos, Liu, Zijdenbos, 1999). Reward and
cognitive control systems consist of dopamine-rich frontal and striatal regions and
undergo significant structural changes throughout adolescence (Giedd et al., 1996;
Sowell, Thompson, Holmes, Jernigan, & Toga, 1999). This finding supports the idea of
an immature regulatory system in adolescence as the prefrontal cortex shows one of the
most prolonged developmental trajectories, maturing into the mid twenties (Giedd et al.,
1999; Gogtay et al., 2004). The connections between the subcortical structures and the
prefrontal regions allow for greater cognitive control, suggesting that the protracted
development of the prefrontal cortex is linked with the development of cognitive control
during adolescence.
The following sections review the literature on ELS and specific brain structures
that have been consistently linked to ELS, namely the hippocampus and the amygdala.
Specific Brain Structures Linked with ELS
Hippocampus. The hippocampus is a subcortical structure that has been
associated with memory consolidation (Markowitsch & Pritzel, 1985) and provides a
negative feedback mechanism to modify the stress response. Smaller hippocampi have
been found in adults who have experienced ELS (Bremner, Randall, Vermetten, Staib,
Bronen, Mazure, Capelli, McCarthy, Innis, & Charney, 1997; Heim, Newport, Wagner,
Wilcox, Miller, & Nemeroff, 2002; Keenan, Jacobson, Soleymani, Mayes, Stress &
Aggression exposure and brain volume 15
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Yaldoo, 1996; Lupien, Fiocoo, Wan, Maheu, Lord, Schramek, & Tu, 2005; Newcomer,
Craft, Hershey, Askins, & Bardgett, 1994; Teicher et al. 2006; Wolkowitz, Reus,
Weingartner, Thompson, Breier, Doran, Rubinow, & Pickar, 1990), but not in children
(Carrion et al., 2001; De Bellis, et al., 2002; De Bellis, Hall, Boring, Frustaci & Moritz,
2001; Woon & Hedges, 2008). During times of low stress, glucocorticoids released by
the stress system aid memory formation by enhancing hippocampus excitability
(Diamond, Bennett, Fleshner, & Rose, 1992; Pavlides, Kimura, Magarinos, &
McEwen,1994; Pavlides, Watanabe, & McEwen, 1993).
During the HPA-activated stress response, hippocampal functioning is disrupted.
Prolonged exposure to glucocorticoids from chronic stress results in reduced dendritic
spines and eventually apoptosis of hippocampal neurons (Daimond et al., 1992; Pavlides
et al., 1993). Researchers argue that the neurotoxic effects of cortisol occur gradually and
are thus only measurable in adulthood. One longitudinal study attempted to address this
by imaging children before puberty and 2 to 3 years later to determine if childhood stress
could produce hippocampal volume differences in adolescence. The investigators did not
find differences in the adolescents, although their participants may have been too young
for differences to be measured (De Bellis et al., 2001). Similarly, it has been
hypothesized that certain structures are maximally vulnerable during certain windows of
development, and that not all neural regions have the same developmental trajectory
(Bourgeois, 1997; Giedd et al., 1996; Huttenlocher & Dabholkar, 1997; Giedd et al.,
1996;Tottenham & Sheridan, 2010). For example, Rao et al. (2010) found that the timing
of early childhood stress differentially affected hippocampal development such that only
parental nurturance at age 4 predicted adolescent hippocampal volume, whereas parental
Aggression exposure and brain volume 16
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nurturance at age 8 did not. This finding may explain equivocal findings of differential
hippocampal volumes in studies of children and adolescents. However, multiple studies
of adolescents with depression have found smaller hippocampal volume and early life
stress exposure (Frodl, Reinhold, Koutsouleris, Reiser, & Mesienzahl, 2009; Roa et al.,
2010). Thus, three factors may lead to differential findings in hippocampal volume due to
childhood stress exposure: (1) timing of exposure and (2) timing of measurement, and (3)
individual differences in psychopathology. The current project is well suited to assess
questions of timing due to the fact that aggression data was collected during an earlier
wave of a longitudinal study and neural data was collected during mid-adolescence.
Amygdala. The amygdala is a subcortical limbic system structure that has been
implicated in learning about the salience and emotional significance of stimuli (Davis &
Whalen, 2001). The hippocampal volumetric differences found in adulthood but not in
childhood due to ELS may have to do with neurobiological changes in other structures,
such as the amygdala (Tottehnam & Sheridan, 2010). The differences in volumetric
measurement in the hippocampus and the amygdala may be due to the time course of the
molecular events that occur in the two structures (Tottenham & Sheridan, 2010). The
amygdala is typically the first structure affected by a threat or stressor, causing cells in
the amygdala to initiate the HPA cascade, which produces chemical downstream effects
on the hippocampus. (Honkaniemi, Kainu, Ceccatelli, Rechardt, Hokfelt, & Pelto-
Huikko, 1992). The hippocampus exerts its effects on the system by negatively feeding
back on the HPA axis to inhibit its activity (Herman & Cullinana, 1997). These
differences in the stress reactivity cascade may lead to differential findings of
hippocampus and amygdala volume in childhood versus adulthood.
Aggression exposure and brain volume 17
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The amygdala is involved in fear learning and in learning about the emotional and
biological significance of the environment (Davis & Whalen, 2001, LeDoux, 1993;
Pessoa & Adolphs, 2010; Tottehnham, Hare, & Casey, 2009). Similarly to the
hippocampus, the amygdala has a protracted development extending into late childhood
and undergoes a refinement of activation during childhood exhibited by higher activation
of the amygdala to neutral (vs. emotional) faces in children than adults (Lobaugh,
Gibson, & Taylore, 2006; Thomas, Drevets, Whalen, Eccard, Dahl, Ryan, & Casey,
2010). Lesion studies have found that early damage to the amygdala leads to deficiencies
in fear learning later in life, as well as significant impairment in processing of facial
expression (Adolphs, Tranel, Damasio, & Damasio, 1994), whereas this has not been
found in later occurring insults (Hamann, Ely, Hoffman, Kilts, 2002; Adolphs, Tranel,
Damasio & Damasio, 1994). Similarly, childhood adverse experiences produce long-term
changes in the amygdala structurally due to high levels of circulating glucocorticoids and
endogenously produced cortisol that decreases a child’s threshold for reacting to
emotional events (Tottenham & Sheridan, 2010). Smaller amygdalae have been found in
adults who experienced childhood adverse event exposure (Driessen, Herrmann, Stahl,
Zwaan, Meier, Hill, Osterheider,& Peterson, 2000; Schmahl, Vermetten, Elzinga, &
Bremner, 2003). Larger amygdalae have been found in a population of children affected
by adverse caregiving (Tottenham, Hare, Quinn, McCarry, Nurse, Gilhooly et al., 2009;
Mehta, Golembo, Nosarti, Colvert, Mota, Williams et al., 2009). These results are
consistent with animal research and human studies that have found larger amygdala
volume and activity due to early life stress in children (Tottenham & Sheridan, 2010;
Vyas, Pillai, & Chattarji, 2004; Yang, Hou, Ma, Liu, Zhang, Zhou, Xu & Li, 2007). The
Aggression exposure and brain volume 18
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research suggests that the amygdala undergoes expansive growth and hyperactivity after
a stressor, thus larger volume measured by MRI in childhood. Subsequently, after a
prolonged period this hyperactivity and increase in glucocorticoid response may result in
cellular atrophy and smaller volumes measured by MRI in adulthood. We now transition
from the discussion of specific brain regions to a description of the tools used to measure
the specific volumes in these regions, which introduces Aim 2 of this project.
Aim 2: Approaches Used To Estimate Brain Volume
Manual and automated segmentation approaches. Volumetric differences due
to early life stress can be examined with many different methodological approaches, such
as manual tracing and automated segmentation. For example, several software packages
are available to facilitate analysis of brain structure; The Oxford Centre for Functional
MRI of the Brain (FMRIB) Software Library (FSL) is a program that uses automated
segmentation to give volumetric output for analyses or runs whole-brain analyses based
on already defined atlases. Automated segmentation uses previously determined
probabilistic maps of brain structures to segment and label study-specific brains. Manual
tracing involves hand-tracing regions of interest (ROI) based on the individuals’ brain
structure using predetermined landmarks to find certain structures in the MRI image.
Although automated segmentation has been shown to be comparable to manual tracing in
adult populations (Seixas, Saade, Conci, de Souza, Tovar-Moll, & Bramatti, 2010) it is
less well documented whether automated segmentation works in adolescents given that
they may have differences in head size, shape, or a differing pace of growth of different
structures (Casey, Jones & Hare, 2008).
Aggression exposure and brain volume 19
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Despite the availability of these automated techniques, relatively few studies have
compared automated segmentation and manual tracing of brain structures (Barnes, Foster,
Boyes, Pepple, Moore, Schott, et al., 2008; Jatzko, Rothenhofer, Schmitt, Glaser,
Demirakca, Weber-Fahr et al. 2006; Powell, Magnotta, Johnson, Jammalamadaka,
Pierson & Andreasen, 2008). In a report introducing the subcortical segmentation
capacities of the program FreeSurfer, Fischl et al. (2002) validated the automated results
for every subcortical region. The automated method (FreeSurfer) overlapped with manual
segmentation an average of 90%, and had an average of 15% volume difference. The
authors determined that the automated segmentation was a valid measure of subcortical
volume. This study was conducted on 134 adults and older adults, but adolescents were
not included in the sample.
Few studies have begun to validate automated methods such as FMRIB’s
Integrated Registration and Segmentation Tool (FIRST) of FSL and FreeSurfer in normal
subjects (Morey, Petty, Xu, Hayes, Wagner, Lewis, 2009). In this study FreeSurfer
slightly overestimated the hippocampal volume, but amygdala measurement with
FreeSurfer was more highly correlated to manual tracing than FIRST. To our knowledge,
no studies have validated automated segmentation with manual tracing in adolescents.
The hippocampus and the amygdala are the most well-researched areas in automated
versus manual segmentation. To address the differences in manual versus automated
segmentation in subcortical structures in adolescents as well as validate the automated
findings, we manually traced bilateral amygdalae and hippocampi in our subjects and
used these outputs to validate the automated segmentation in FSL.
Aggression exposure and brain volume 20
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Some studies have compared manual and automated segmentations. However, the
literature generally does not follow a specific rubric to determine if an automated
segmentation is “close enough” to the manual segmentation. Therefore, automated
segmentation analyses in the published literature may not accurately describe the region
of interest. Due to the proliferation of many techniques in the literature, it may be
necessary to understand if different techniques may lead to different estimations of brain
volume and therefore different associations with a variable of interest. The current study
addressed whether different segmentation estimates (manual and automated) would lead
to different associations with family aggression and community violence exposure.
Therefore, all analyses comparing family aggression exposure and community violence
exposure were run with all bilateral manual and automated segmentations.
Total Brain Volume Estimation
In addition to different methods for subcortical segmentation, different methods
for estimating total brain volume exist. Adjusting for total brain volume is a necessary
component in volumetric analysis given that the differences in head size can lead to
systematic differences in brain volume between individuals (Free et al., 1995; Scahill &
Frost 2003; Whitwell et al., 2001). This may be a particularly important covariate in
adolescence given that the brain is still developing during this time. However, many
different techniques exist to measure total brain volume (TBV) or intracranial volume
(ICV). Most commonly, an estimate of ICV is used, but there is a wide range of methods
for calculating ICV. These include tissue compartment addition (grey matter plus white
matter plus cerebral spinal fluid (CSF)) (Courchesne et al., 2000; Lemaitre et al., 2005;
Rudick et al., 1999; Smith et al., 2007); registration based estimation (Smith et al., 2002)
Aggression exposure and brain volume 21
"
estimates generated from points making an ellipsoid (Pfefferbaum et al., 2000); area of
mid-sagittal slice section (Raz et al., 1997); and semi-automatic segmentation of every
tenth slice (Scahill et al., 2003). In addition there are many ways in which head size
correction is applied, including simple division of volumes by the calculation of total
brain volume (Chan et al., 2001b); regression association correction (Scahill et al 2003);
and statistical adjustment with total brain volume calculation as a covariate in the
analysis. Both these differing calculation methods as well as statistical covariation
methods have been shown to introduce bias into studies of volumetric differences in the
brain (Nordenskjold, Malmberg, Larsson, Simmons, Brooks, Lind et al., 2013). Similarly,
although some studies have tried to address which method remains stable over time
(Pengas et al., 2009), and which methods best account for age and gender related
differences in brain size (Barnes, Ridgway, Bartlett, Henley, Lehmann, Hobbs et al.,
2010; Peelle, Cusack, Henson, 2012), a “gold standard” method for ICV or TBV
measurement does not exist. Therefore, the current study used three different calculation
methods and two different statistical analyses to assess TBV, and results for each method
will be presented.
The following aims were proposed:
Specific Aim 1: To explore the association of familial aggression and community
violence with subcortical brain volume.
H1a: Greater exposure to both community and familial aggression will be
associated with a smaller hippocampal volume.
H1b: Greater exposure to both community violence and familial aggression will be
associated with a larger amygdala volume.
Aggression exposure and brain volume 22
"
Specific Aim 2a: To compare an automated approach to structural MRI analysis
(FSL) with manual tracing in an adolescent population.
H2: Automated segmentation processes may be unreliable with adolescent brains.
We hypothesize that manual tracing may improve specificity given that
adolescent brains may be smaller and develop at different rates than the
standardized adult brains used in developing automated segmentation protocols.
Specific Aim 2b: To investigate whether differences in total brain volume
calculation would lead to different associations between brain volume and
the variables of interest, family aggression exposure and community violence
exposure.
H2b. No specific hypotheses were stated, given the diversity in the literature on
the calculation of total brain volume.
Methods

Participants

Participants were recruited from a longitudinal study of family environments and
youth development (USC Family Studies Project; PI: Gayla Margolin) that was
conducted at USC. The youth and their families were recruited through newspaper ads,
fliers, and referrals from other participants when the youth were 12-13 years old. To be
eligible the families must have lived together for the prior 3 years, had to include a child
in middle school (grades 6-8), and had to be able to complete questionnaires in English.
Participants for the current study were recruited from a subset of 43 families who
participated in a videotaped discussion in their second wave of study participation
(average age 15.51). The eligibility criteria for the MRI portion of the study included that
Aggression exposure and brain volume 23
"
youth be right-handed, not have metal in their body that would be contraindicated for
MRI scanning, and not be currently taking any psychoactive medications. Of the 43
families contacted, seven youth were ineligible, five declined to participate, and seven
could not be reached or scheduled. Twenty-four youth participated in the scanning study,
but one youth did not have useable structural data because the scanning quality was poor.
Thus, the final sample consisted of 23 adolescents. One was left-handed, 14 (58%) were
male, and they averaged 17.05 years of age (range 15.47-18.72). The sample was diverse,
reflective of the urban community from which the sample was drawn: 56.5% (13 youth)
identified as Caucasian, 26% (6 youth) identified as multiracial, 9% (1 youth) identified
as African-American, and 9% (1 youth) identified as Asian American. The participants
were from relatively affluent backgrounds on average, although there was a wide range of
parental incomes: (mean income= $110,014; SD= 71738; range = $8,000- $255,000).
Procedure
Family aggression and community violence exposure data was collected during
the first visit of the longitudinal study. Participants were brought into the lab for a 3-4
hour visit, and completed questionnaire measures (described below). The scanning
procedures occurred on a separate day an average of 4.0 years (SD= 5.4 months range =
3.2 - 5 years) after this visit. All participants were scanned for approximately two hours
using a battery that included functional, resting state, and structural scans.
Scanner protocol
Whole brain images were acquired using a Siemens 3 Tesla MAGNETON TIM
Trio Scanner with a 12-channel matrix head coil. We used a T2* weighted Echo Planar
(EPI) sequence (TR = 2 sec, TE = 30 ms, flip angle = 90°) with a voxel resolution of
Aggression exposure and brain volume 24
"
3mm × 3mm × 4.5mm. Thirty-two transverse slices were continuously acquired to cover
the whole brain and brain stem, with breaks between runs. Anatomical images were
acquired using a magnetization prepared rapid acquisition gradient (MPRAGE) sequence
(TI = 900 ms, TR = 1950 ms, TE = 2.26 ms, flip angle = 7°), isotropic voxel resolution of
1mm.
Measures

Family aggression exposure. A family aggression variable was created
combining spousal aggression ratings (from the Domestic Conflict Index (DCI);
Margolin, John, & Foo, 1998) and parent-child conflict ratings (adapted from the Conflict
Tactics Scale—Parent/Child (PCC); Straus, Hamby & Finkehlhor, 1998), which were
assessed at the first time point of the Family Studies longitudinal study when the
participant was 12-13 years old. Both parents and the youth reported on parents’ spousal
aggression. Parents reported their own behavior and their partners’ behavior, and youth
reported each of their parents’ behavior. The spousal aggression questionnaire asked how
many times, over the previous year, 42 different aggressive behaviors had occurred.
These items included physical aggression (e.g., shaking or slapping the spouse) and
emotional aggression (swearing at the spouse).
For the Conflict Tactics Scale—Parent/Child, fathers and children reported on
father-child and mothers and children reported on mother-child aggression. This
questionnaire asked how many times the previous year any of 17 aggressive behaviors
had occurred; these included physical aggression (e.g. shaking or slapping the child) and
emotional aggression (wearing at a child; threatening to kick a child out of the house).
Before computing final scores, a maximum reporter variable was created for
Aggression exposure and brain volume 25
"
mother and father’s behavior for each questionnaire. On both questionnaires the highest
number of incidences that each participant (mother, father or child) reported for that item
was chosen as the endorsement for that item. This strategy helps adjust for underreporting
biases in family conflict studies (Margolin, Vickerman, Oliver, & Gordis, 2010). Non-
aversive items such as “how many times in the past year have you explained why
something your child did was wrong,” were dropped from the questionnaires. To
combine the DCI and the PCC, each item was changed to a 0-3 scale based on the
maximum item endorsement: 0 = the item never occurred in the past year; 1 = once in the
past year; 2 = 2-5 times in the past year; 3= 6 + times in the past year. This was done for
both questionnaires, and separately for mother’s behavior and father’s behavior. Means
across items were then taken for each questionnaire and each parent: Domestic Conflict
Index Mother Behavior (mean= .52, SD = .22, range= .02-.9), Domestic Conflict Index
Father Behavior (mean = .41, SD= .21, range=.0-.9), Conflict Tactics Scale-Parent/Child
Mother Behavior (mean = .72, SD= .52, range = .0-2), Conflict Tactics Scale-
Parent/Child Father Behavior (mean = .46, SD= .5, range = .0-1.7). Subsequently, an
average was taken per subject across mother and father behavior and then averaged
mother and father scores were averaged across questionnaires. The final variable includes
an average of both parents across questionnaires for each subject. This ensures that each
questionnaire and each parent were weighted equally (mean = .52, SD = .28, range = .01-
1.11).
Community violence exposure. Community violence exposure was measured
using a modified version of the Survey of Children’s Exposure to Community Violence,
which measures witnessing, victimization, and hearing about different types of violence
Aggression exposure and brain volume 26
"
in the family’s own neighborhood over the past year. The test-retest reliability of the full
measure has been found to be .81 in a sample of elementary school children living in
violent neighborhoods (Richters & Martinez, 1993). The version of the questionnaire
used in the present study included 20 items asking about victimization and severe
witnessing experiences (e.g., seeing someone being shot), but did not include items
regarding hearing about an incident of community violence or less severe witnessing
experiences (e.g., seeing someone being arrested). For each item, youth were asked to
report on the frequency of each event occurring during the past year using a 4-category
scale ranging from never to 3+ times. Although both youths and parents reported on
youth’s exposure to community violence, since parents tend to underreport youth
community violence exposure relative to their child’s report (Baucom, Borofsky,
Vickerman, & Margolin, 2013), only youth reports were used in the present study. Mean
responses across the 20 items were calculated (mean = .07, SD= .08, range=0 -.25).
Analyses
Preprocessing. For automated procedures the T1 images for each participant was
brain extracted using FSL’s Brain Extraction Tool (BET). Before any manual or
automated segmentation was performed brains were realigned (but not resized: see Allen
et al., 2008 for discussion) along a plane running through the anterior and posterior
commissures (i.e. the AC-PC line). First, the anterior commissure (AC), posterior
commissure (PC), and center of the brain were found manually and entered into a script
for alignment. This script aligned images orthogonally to the bicomissural plane. The
image was rotated and interpolated until the anterior commissure and the posterior
commissure could be visualized on the same axial section. This procedure is commonly
Aggression exposure and brain volume 27
"
used in structural brain analysis and ensures that coronal slices in all subjects are
perpendicular to a uniformly and anatomically defined axis of the brain (Allen et al.
2008). All manual and automatic methods were performed with these AC-PC aligned
images.
Whole Brain Analyses. Whole brain analyses were run on all participants using
FSL’s Voxel Based Morphometry program, (VBM (Douaud et al.,
2007, http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM), an optimized VBM protocol (Good
et al., 2001) carried out with FSL tools (Smith etal., 2004). This technique is “optimized”
in that it incorporates additional spatial processing steps to improve image registration
and segmentation. The program created a study specific gray matter template using all the
T1 images from the participants. After this, General Linear Model (GLM) files were
created, modeling for (1) family aggression exposure, and (2) community violence
exposure while covarying for total brain volume, age, and gender. Lastly, voxel by voxel
comparisons using permutation based non-parametric testing (FSL’s Randomise) were
run, correcting for multiple comparisons across space.
Automated Segmentation.
FSL. FSL’s FIRST was used to automatically segment the T1 images into
anatomical ROI’s, specifically the amygdala and the hippocampus. The program uses
FSL utilities to segment the brain into discrete subcortical structures. After the brain was
segmented, command line functions from FSL’s software package, specifically fslstats,
was used to extract volume data for the amygdala and the hippocampus, based on the
Harvard-Oxford Subcortical Atlas.
Aggression exposure and brain volume 28
"
Manual Segmentation. The following anatomical procedures were used to
manually trace bilateral amygdalae and hippocampi.
Anatomical definition of the hippocampus. As seen in Figure 1 the neuroanatomic
criteria chosen for hippocampal delineation were taken from existing protocols (Narr et
al., 2004). The hippocampi were traced in coronal brain slices from anterior to posterior,
using fslview tools. All three (sagittal, coronal, and axial) planes were viewed
simultaneously to facilitate the accurate identification of neuroanatomic boundaries.
Hippocampal tracing in each hemisphere began at the indentation of the hippocampal
sulcus, or the most medial point of the hippocampus in the coronal plane. The alveus of
the hippocampus was used as the superior boundary and the white matter of the
parahippocampal gyrus as the inferior boundary. The inferior temporal horn of the lateral
ventricle was used as the lateral boundary and the ambient cistern as the medial
boundary. Hippocampal tracing was continued posteriorly until hippocampal gray matter
formed an oval mass medial to the atrium of the lateral ventricles. The hippocampi were
traced three times, twice by the same tracer (intra-rater reliability= 0.8) and once by an
expert tracer (inter-rater reliability= 0.6). Subsequently, a thresholded mask was created
using only the voxels that were chosen in all three tracings. Volume data was extracted,
using FSL utilities, from all masks and entered into SPSS.
Anatomical definition of the amygdala. Separate left and right amygdala masks
were hand-drawn onto each participant’s T1-weighted image in the coronal plane
according to tracing procedures described by Allen et al. (2005) (shown in Figure 2). The
amygdala was demarcated by superior, inferior, medial, and lateral boundaries and traced
in the medial temporal lobe. The anterior boundary of the amygdala was arbitrarily
Aggression exposure and brain volume 29
"
defined in a coronal slice exactly three slices posterior to the slice where the frontal lobe
merges with the temporal lobe. The superior boundary of the amygdala was defined as
the CSF within the temporal horn of the lateral ventricle for more anterior slices, while
the visible gray-white matter boundary served as the superior border in more posterior
slices. CSF defined the dorsomedial boundary, while the lateral boundary was defined as
the border between amygdala gray matter and parahippocampal white matter. In anterior
coronal slices, the inferior boundary was first demarcated by parahippocampal white
matter and extended dorso-medially until the line connected with CSF. As the amygdala
moved above the hippocampal gray matter in posterior slices, the inferior boundary was
traced along the white matter strand of the alveus. Three different raters also traced
bilateral amygdalae (inter-rater reliability ranging from = 0.4-0.6), after which a
thresholded mask was made including all voxels that were chosen in at least 2 out of the
3 tracings. This majority voting procedure for manual tracing has been shown to be
effective in a number of contexts (Aljabar, Heckemann, Hammers, Hajnal, & Reuckert,
2009). Volume data was extracted, using FSL utilities, from all masks and entered into
SPSS.
Total Brain Volume
To address individual differences in brain size, total brain volume was entered
into the analyses as a covariate. As previously discussed, many different measurements of
total brain volume are accepted in the literature. Three different measurements of total
brain volume were used. (1) The SIENAX tool in FSL was used to determine each
participant’s intracranial volume (ICV). Secondly FSL’s FAST (FSL’s automated
segmentation tool) was also used to segment each participant’s brain into white matter,
Aggression exposure and brain volume 30
"
gray matter, and CSF. Therefore, (2) total gray matter (GM) and (3) total brain volume
(TBV: gray matter plus white matter) were calculated using the extracted volumes from
FSL FAST. Ratios were then calculated for each extracted segmentation and total gray
matter volume, as well as total brain matter volume. The statistical analyses described
below were run with (1) ICV as a covariate, (2) ratio of segmented volume and GM as
the dependent variable, and (3) ratio of segmented volume and TBV as the dependent
variable.
Statistical Analyses
Analysis of automated segmentation performance. Comparisons of the
automated and manual segmentations were done to validate the success of the automated
segmentation procedure. The automated segmentation methods were compared to manual
tracing using the following criteria from Morey et al., 2009: (1) percent volume overlap
or Dice’s coefficient, (2) percent volume difference and (3) correlation between
automated measures and manual tracing. Percent volume overlap was calculated by
means of FSL v3.2 (http://www.fmrib.ox.acu.uk/fsl/) library functions flsmaths and
fslstats also used by Morey et al., 2009. Percent overlap is defined as the volume of the
intersection of the two segmentations (S
1
and S
2
), divided by the mean volume of these
same segmentations, multiplied by 100 (Eq. 1). Segmented labels from FIRST were
extracted using fslmaths.
O(S
1,
S
2
) =
!!!∩!!!
!!!!!
!
!!!100 (1)

Percent volume difference was also calculated and is defined as the absolute volume
difference between two measures of the same structure divided by the mean volume of
Aggression exposure and brain volume 31
"
both segmentations and multiplied by 100 (Eq. 2). This calculation, unlike overlap
percentage is insensitive to the spatial shift of the segmentations.
D(S
1,
S
2
) =
!!!!!
(
!!!!!
!
)
!!!100 (2)
Pearson’s correlations were also calculated between manual and automated volumes. A
strong correlation (.8 or above) indicates small volumes for small structures and large
volumes for large structures. The current study assumed that volumes calculated by
manual tracing are closest to the true volumes for a specific structure.

Analysis of segmentations and behavioral measures. Multivariable linear
regressions were run assessing the relationship between (1) family aggression exposure
and (2) community violence exposure on the following subcortical volumes: (1) left
hippocampus, (2) right hippocampus, (3) left amygdala, and (4) right amygdala, each
with two different segmentation methods (automated and manual), and three different
models for ICV estimation. Thus, 48 multivariable linear regressions were run, all
accounting for age, gender, and total brain volume (see section on total brain volume
estimates for further explanation).
Results
Correlations between all study variables were run and are presented in Table 1. A
significant negative correlation was found between the family’s socioeconomic status
(SES) and community violence exposure, but no relationship was found between SES
and family aggression exposure. Thus, only the community violence exposure analyses
adjust for SES.

Aggression exposure and brain volume 32
"
Whole-Brain Analysis
No differences in regional brain volume due to exposure to family aggression or
community violence exposure were found using FSL-VBM.
Automated Segmentation Performance
Table 2 summarizes bilateral hippocampal and amygdala mean volumes obtained
by both manual and automated segmentation methods. Averages of the FSL
segmentations were significantly larger than manual segmentations for hippocampal
volumes, but not for amygdala volumes. Table 3 shows the results of the
similarity/discrepancy analysis between the manual and automated segmentation methods
for all structures. Overlap percentage was better for manual and automated segmentations
of bilateral amygdalae than bilateral hippocampi. Overlap indicates spatial similarity
between segmentations, thus the automated and manual segmentations of bilateral
amygdala account for a similar location in the brain, and less similar location for bilateral
hippocampi. Similarly, less percent difference was found in amygdala segmentations than
hippocampal segmentations. Percent difference accounts for the number of voxels (mm
3
)
that differ between segmentations. For example, right amygdala segmentations differ by
20% of voxels, whereas right hippocampal segmentations differ by 71%. The larger
percent difference in hippocampal volumes may be due to automated segmentations
being significantly larger than manual segmentations. Only right hippocampal
segmentations were significantly correlated and were in the moderate range.
Associations between Brain Volume and Family Aggression Exposure
Automated segmentations. A significant relationship between exposure to
family aggression and brain volume was found in bilateral hippocampi using the
Aggression exposure and brain volume 33
"
automated segmentation estimates. This relationship differed, however, given differences
in modeling for total brain volume. As seen in Table 4 and Figure 3, a significant
negative relationship between left hippocampal volume and family aggression exposure
was found with all three total brain volume estimates. However, a significant negative
relationship between right hippocampal volume and family aggression exposure was
found using only the ratio of total GM and TBV. No other significant relationships were
found between family aggression exposure and brain volume using automated
segmentations.
Manual Segmentations. As shown in Figure 4 and Table 4, a significant positive
relationship was found between right amygdala volume and family aggression exposure
using manual segmentations. This association was found using all three total brain
volume estimates. No other relationships between family aggression exposure and
amygdalae or hippocampal volume were found using the manual segmentations.
Associations between Brain Volume and Community Violence Exposure
As reported in Table 5, no significant relationships between exposure to
community violence and any of the four automated segmentations were found for
analyses using any TBV calculation method. Similarly, no significant relationships
between community violence exposure and brain volume were found in any of the four
manually traced subcortical structures using any TBV calculation method.
Discussion
The current study aimed (1) to investigate the associations of family aggression
and community violence exposure on subcortical brain volume, (2a) to compare manual
versus automated methods of estimating subcortical brain volume, (2b) and to identify
Aggression exposure and brain volume 34
"
differences in total brain volume calculation in the investigation of the association
between family aggression and community violence exposure on brain volume.
Although exposure to violence in childhood, specifically child abuse and neglect,
has been studied in the literature (e.g. Choi et al., 2008; Hanson et al., 2010; Tomoda et
al., 2009; van Haremelen et al., 2010), little is known of how low-to-moderate levels of
violence may be associated with the developing brain. The current study builds on
existing literature showing an association between moderate levels (within a community
sample) of family aggression and community violence and brain volume in subcortical
structures.
Manual Segmentations
Larger right amygdala volume was found to be associated with higher levels of
family aggression exposure using manually traced estimates of amygdala volume. This
result was found regardless of TBV calculation used. No other significant relationships
between bilateral hippocampi or left amygdala and family aggression exposure were
found. No significant relationship between community violence exposure and brain
volume was found for bilateral hippocampi or amygdalae. The positive association
between amygdala volume and family aggression exposure may be explained by the
hypothesis that early exposure to stress or threat can lead to an hyperactivation of the
amygdala in childhood, which first leads to an expansion of the structure in adolescence
and over time may cause cell atrophy and eventual smaller amygdala volume measured
by MRI (Tottenham & Sheridan, 2010). Thus, those participants who had greater
exposure to family aggression may have experienced this exposure chronically, causing
hyperactivation of the amygdala, and thus an expansion of subcortical volume as
Aggression exposure and brain volume 35
"
measured in adolescence. The current study builds on the existing literature documenting
this hypothesis by demonstrating this association in a community sample with relatively
low rates of aggression exposure.
Automated Segmentations
Smaller left hippocampal volume was found to be associated with greater family
aggression exposure using automated segmentation estimates of hippocampal volume
with all TBV estimates. Similarly, right hippocampal volume was found to be associated
with greater family aggression exposure using automated segmentation estimates, but
only using ratio GM and TBV estimates. This finding builds on previous research
indicating an association between early life stress and smaller hippocampal volume
measured in adulthood. Previous studies have questioned whether this association occurs
after atrophy throughout childhood and adolescence, or if this association can be
measured before adulthood. The current study indicates that this association may be
measurable in late adolescence.
Complicating the interpretation of the data, the automated segmentations of the
bilateral hippocampi were almost double the size of the manually traced segmentations.
This may be due to an overestimation of hippocampal volumes by automated procedures
through the use of adult brain atlases. Thus, the association between smaller hippocampal
volume and early family aggression exposure with the automated hippocampi
segmentations, may be due to the hippocampi being adult sized and thus show a similar
association between early life stress and hippocampal volume that is usually found in
adulthood. If the manual segmentations are taken as the gold standard, then it may be
Aggression exposure and brain volume 36
"
inferred that there is no relationship between hippocampal volume and family aggression
exposure, or this relationship does not develop until later in adulthood.
Community Violence Exposure
The current study found no relationship between community violence exposure
and subcortical brain volume. This could be due to (1) difficulty associated with
measuring community violence exposure, (2) timing of community violence versus
family aggression exposure, or (3) differences in genetic factors between family
aggression and community violence exposure.
Firstly, due to the concurrence of community violence exposure with other
difficulties in the environment such as low socio-economic status or lack of access to
resources, community violence is very difficult to measure on its own (Margolin &
Gordis, 2000). The current investigation used youth report of community violence
exposure, given that parents tend to underreport community violence exposure for youth.
In contrast, the family aggression exposure variable uses a maximum reporter approach
to sum across all members of the family. This may be a more valid measurement of
aggression exposure and may be used for community violence exposure in the future.
Similarly, future studies may use more objective measurements of community violence
exposure such as crime reports in each subject’s zip code.
Secondly, community violence exposure may become more salient as children
grow older, whereas family aggression exposure most likely occurs chronically
throughout childhood, including early childhood. Thus, the associations between
community violence exposure and brain volume may show up later in life than family
aggression exposure.
Aggression exposure and brain volume 37
"
Lastly, the associations between family aggression exposure and brain volume
may be due to genetic factors underlying both family aggression and brain morphology.
Since community violence may not be due to genetic factors, there may be no
relationship between community violence and brain volume given the lack of a common
genetic link. Although these three reasons for the lack of relationship between
community violence and brain volume represent limitations or future directions for the
current investigation, the null results for the relationship between community violence
exposure and brain volume found in the current study should not be over-interpreted.
Manual versus Automated Segmentations
The current study compared two methods for estimating subcortical brain volume.
Firstly, FSL, a well-researched and widely utilized program for estimating brain volume
was used, and secondly, manual tracing by multiple experienced raters was completed.
Although, manual tracing is used as the “gold standard” for subcortical volume
estimation, automated programs are widely used and reported in the literature. This study
also used three different measurements of total brain volume that are each used and
published in the literature, to determine if these different methodologies may affect
statistical results.
FSL and manual tracing methods found contradictory results in the current
investigation. Similarly, these results were slightly more discrepant when using different
methods of estimating total brain volume. Specifically, the negative relationship between
right hippocampal volume and family aggression exposure was found when using ratio of
GM and TBV as the dependent variable, but not when using the scaling factor ICV as a
covariate. As stated previously, the scaling factor ICV uses a validated template of
Aggression exposure and brain volume 38
"
normal adult brains to estimate the amount of adjustment needed to fit the current brain
into the template space. However, FSL used adult brains to create this template, which
may make it less sensitive to total brain volume measurement in adolescents. Similarly,
this method is not used as often as the other two methods for total brain volume
calculation in the accepted literature. Therefore, calculating total gray matter or white
matter plus gray matter (TBV) may be a more sensitive measurement of total brain
volume in the current population. It also should be noted that although the TBV
measurements were only slightly discrepant, the automated results and not the manual
results were affected by these different calculations. Manually traced segmentations may
be less affected by TBV variables. These results suggest that (1) manually traced results
may be more accurate in the current investigation and (2) automated subcortical volume
estimates may not be reliable in an adolescent population
In sum, the current investigation found associations between aggression exposure
and volume differences in the adolescent brain. Similarly, it may be concluded that with
an adolescent population in particular automated methods for subcortical brain volume
estimation may be unreliable. However, the current investigation is limited in the
following ways. First, although the study is longitudinal, the specific timing of family
aggression or community violence exposure is unclear. Second, no baseline MRI scans
exist to account for individual differences in brain volume before the event of violence
exposure. Third, although a sample size of 23 is not unusual in the neuroimaging
literature, the sample size may be limited for statistical power. Future investigations
would benefit from longitudinal scanning data, as well as objective measurements of
aggression exposure such as records of violent events in the community or real-time data
Aggression exposure and brain volume 39
"
collection of aggressive events in families. Similarly, investigating alternate automated
segmentation software may be beneficial given the amount of time and effort needed for
manual segmentation methods. Given the limitations of the current investigation,
however, the results support the assertion that aggression exposure within a community
sample can have measurable associations with brain volume, and may be an important
avenue of exploration to determine the lasting effects of aggression exposure in early life.
The results further suggest that the choice of methods in any given structural
analytic investigation can drastically influence the results. Similarly, the choice of total
brain volume estimation can also influence investigations of this kind. The current study
thus represents a caution to both researchers and readers of structural neuroimaging
investigations to be skeptical of the measures used for a specific population and a specific
research question. It also suggests that the field of structural neuroimaging needs to
become more rigorous and systematic in the ways in which methods are chosen and
carried out. Specifically, the current results suggest that more investigations need to be
done to perfect automated segmentations to be able to capture more discrete individual
differences in neurobiology.
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Aggression exposure and brain volume 58
#
Table#1##
!
!
Summary!of!Intercorrelations!of!Age,!Gender,!SES,!Family!Aggression!Exposure,!Community!Violence!Exposure,!and!All!Manual!and!
Automated!Segmentations!Adjusted!by!Total!Brain!Volume!
!
!
# 1! 2! 3! 4! 5! 6! 7! 8! 9! 10! 11! 12! 13!
1.#Age##
---# # # # # # # # # # # # #
2.#Gender## .30# ---# # # # # # # ! # # # !
3.#SES# .07# .17# ---# # ! # # # # # # # #
4.#Family#Aggression#
-.11# .19# .07# ---# # # # # # ! # # #
5.#CV##
-.07# .07# ".48*! .05# ---# # # # # # # # #
6.#Auto#L#Hippo# .01# -.05# .10# ".72**! -.17# ---# ! # # # # # #
7.#Auto#R#Hippo#
-.27# .21# -.04# -.34# .07# .60**! ---# # # ! ! # #
8.#Auto#L#Amyg#
.12# .32# .04# -.10# .21# .25# .39# ---# # # ! ! !
9.#Auto#R#Amyg# -.28# .48*# .12# -.02# .09# .31# .58**! .25# ---# ! ! # #
10.#Manual#L#Hippo# -.15# -.21# .09# .01# -.21# .13# -.06# -.25# -.21# --# ! ! !
11.#Manual#R#Hippo#
.08# -.03# -.05# -.08# .08#
.41
++
#
.50*! -.27# .13# .28# --# ! !
12.#Manual#L#Amyg#
-.30# -.20# .23# .35
+
# -.36
+
# -.06# -.01# .17# .13# .45*! -.02# --# !
13.#Manual#R#Amyg#
-.25# -.25# .26# .42*! -.33# -.09# -.01# -.03# .08# .50*! .14# .75**! --#
Note.!
+
p
!
#++
p!Hippo=Hippocampus.#Amyg#=#Amygdala.!#
#
#
#
Aggression exposure and brain volume 59
#
Table#2#
Means,'Standard'Deviations'and'Comparison'of'Means'between'Manual'and'
Automated'Segmentations.''
# Manual#Segmentation# FSL#Segmentation# T5Test#
Left#Amygdala,#mean#
(S.D)#
1222#(263)#
#
1268#(207)#
#
t#(22)#=#-.82#
Right#Amygdala,#
mean#(S.D)#
1193#(282)#
#
1330#(241)#
#
t(22)=#-2.02
+
#
Left#Hippocampus,#
mean#(S.D)#
1913#(329)#
#
3854#(418)#
#
t(22)=-19.80**#
Right#Hippocampus,#
mean#(S.D)#
1844#(282)# 3890#(425)#
#
t(22)=#-26.92**#
Note.#
+
p
'
<'.06'*p<.05'**p<.01#
Aggression exposure and brain volume 60
#
# #
#
Table#3#
#
Summary'of'Automated'Segmentation'Performance,'Percent'Volume'Overlap,'Percent'Volume'
Difference,'and'Pearson’s'Correlations'between'Automated'and'Manual'Segmentations#
Manual#vs.#
FSL##
Segmentations#
Left#Amygdala#
######
Right#Amygdala#
#
Left#Hippocampus#
#
Right#Hippocampus#
#
# M#(#+SD)## M#(#+SD)# M#(#+SD)# M(#+SD)#
%#Overlap# 73%#(#+13)# 73%#(#+#14)# 64%#(#+#7)# 61%#(#+#6)#
%#Difference# 20%#(+#11)# 22%(#+#18)# 67%##(#+#16)# 71%#(#+#12)#
Pearson#
Correlation#
.31# .20# 0.37# 0.56**#
Note.#*p<.05'**p<.01'
Aggression exposure and brain volume 61
#
Table#4#
Separate'Multivariate'Linear'Regression'Analyses'of'Family'Aggression'Exposure'
with'Manual'and'Automated'Bilateral'Hippocampal'and'Amygdala'Segmentations,'
Adjusting'for'Age,'Gender,'and'Total'Brain'Volume''
#
Segmentation# Structure# ICV#
Factor#
GM#Ratio# TBV#Ratio#
#
# # β# β# β#
Automated## # " " "
# L.#Hippocampus# #.60*" #.74**# #.75**"
# R.#Hippocampus# 5.39# #.48*" #.48*"
# L.#Amygdala# 5.26# 5.17# 5.18#
# R.#Amygdala# 5.03# 5.20# 5.20#
Manual# # " " #
' L.#Hippocampus# 5.01" .02" .04#
' R.#Hippocampus# .05" 5.08" 5.06#
' L.#Amygdala#
.38
+"
.36
+
" .37
+
#
' R.#Amygdala## .46*" .45*" .47*#
Note.'
+
p
'
<'.10'*p'<'.05,'**p'<'.001#.#L=#Left,#R#=#Right.#ICV=#Scaling#Factor#ICV#determined#by#the#FSL#
tool#SIENAX.#GM#Ratio#=#Ratio#of#each#segmentation#to#total#gray#matter#volume.#TBV#Ratio#=#Ratio#
of#each#segmentation#to#total#gray#matter#plus#white#matter.###
#
"
Aggression exposure and brain volume 62
#
#
#

#
#
#
#
"
#
#
"
"
"
Table#5#
Separate'Multivariate'Linear'Regression'Analyses'of'Community'Violence'Exposure'
and'Bilateral'Hippocampal'and'Amygdala'Volume'from'both'Automated'and'Manual'
Segmentations,'Adjusting'for'Age,'Gender,'and'Total'Brain'Volume'
#
Segmentations# Structure# ICV# GM#Ratio# TBV#Ratio#
Automated## # # # #
# L.#Hippocampus# 5.10# 5.10# 5.17#
# R.#Hippocampus# 5.03# .09# .02#
# L.#Amygdala# .24# .25# .19#
# R.#Amygdala# 5.01# .06# .01#
# # # # #
Manual## L.#Hippocampus# 5.19# 5.14# 5.20#
# R.#Hippocampus# .06# .13# .09#
# L.#Amygdala# .12# 5.32# 5.37#
# R.#Amygdala## 5.34# 5.29# 5.33#
Note.'*p'<'.05,'**p'<'.001.#L#=#Left,#R#=#Right.#ICV=#Scaling#Factor#ICV#determined#by#the#FSL#tool#
SIENAX.#GM#Ratio#=#Ratio#of#each#segmentation#to#total#gray#matter#volume.#TBV#Ratio#=#Ratio#of#
each#segmentation#to#total#gray#matter#plus#white#matter#
#
#
#
#
#
#
#
#
"
Aggression exposure and brain volume 63
#
"
Figure#1.#Select#Pictures#of#Coronal#Slices#Depicting#a#Manually#Traced#Left#
Hippocampus#mask.###
'
'
'
Figure#1.#Select#slices#of#a#left#hippocampus#mask#traced#by#an#individual#rater#using#
FSL.##
#
#
#
#
"
"
"
#
#
#
#
#
#
Aggression exposure and brain volume 64
#
Figure#2.#Select#Pictures#of#Coronal#Slices#Depicting#a#Manually#Traced#Right#
Amygdala#Mask###
'
'
Figure#2.#Select#slices#of#a#right#amygdala#mask#traced#by#an#individual#rater#using#
FSL.#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
Aggression exposure and brain volume 65
#
Figure#3.#Results#of#a#Partial#Correlation#Representing#the#Relationship#between#
Family#Aggression#Exposure#and#Bilateral#Hippocampal#Volume#Measured#by#FSL#
Segmentation#
'
'
'
Figure#3.#A#significant#negative#correlation#was#found#between#bilateral#
hippocampal#volume#and#family#aggression#exposure#using#TBV#and#GM#ratio#
estimates#to#account#for#total#brain#volume.#TBV#estimates#are#used#here#for##
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
0#
0.2#
0.4#
0.6#
0.8#
1#
1.2#
1500# 2000# 2500# 3000# 3500# 4000# 4500# 5000#
Family"Aggression"Exposure"(Z#
Score)"
HIppocampal"Volume"(mm3)"
Left#Hippocampus#
Right#Hippocampus#
Aggression exposure and brain volume 66
#
Figure#4.#Results#of#a#Partial#Correlation#Representing#the#Relationship#between#
Family#Aggression#Exposure#and#Right#Amygdala#Volume#Measured#by#Manual#
Segmentation#
#
'
'
Figure#4.#A#significant#positive#correlation#was#found#between#right#amygdala#
volume#and#family#aggression#exposure#using#TBV#and#GM#ratio#estimates#to#
account#for#total#brain#volume.#TBV#estimates#are#used#here#for#simplification.#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#
#

0.0005#
0.001#
0.0015#
0.002#
0.0025#
0.003#
513# 58# 53# 2# 7# 12#
Right"Amygdala"Volume"(mm3)""
Family"Aggression"Exposure"(Z#Score)"
ID#
Aggression exposure and brain volume 67
#
Appendix: Measures
Survey of Children’s Exposure to Community Violence
This questionnaire asks about certain events that you, or someone you know, have
actually seen, heard, or experienced in your neighborhood, community, or school in the
past year.

We are not asking about events that you have seen on television, or heard on the news.
We want you to think about your own neighborhood and community.

In your neighborhood, community, or school...

Have you seen someone force their way into YOUR car, home, or other place in the past
year?

Never ___ Once ___ Twice ___ More than twice ___

Have you been inside your home when someone tried to break in, in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you seen someone force their way into ANOTHER PERSON’S car, home, or other
place in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you been chased by a gang or by dangerous people in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you seen someone being chased by a gang or dangerous people in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you been beaten with a stick, bat, club, or other hard object by someone from your
neighborhood or school (not in your family) in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you seen someone beating another person from your neighborhood or school with a
stick, bat, club, or other hard object in the past year?
Aggression exposure and brain volume 68
#

Never ___ Once ___ Twice ___ More than twice ___

Have you been beaten up by someone from your neighborhood or school (not in your
family) in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you seen someone beating up another person from your neighborhood or school in
the past year?

Never ___ Once ___ Twice ___ More than twice ___

Has someone threatened to stab you in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you seen someone threatening to stab another person in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you been stabbed in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you seen someone being stabbed in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you heard gun shots (not including holidays) in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Has someone threatened to shoot you in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you seen someone threatening to shoot another person in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you been shot in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Aggression exposure and brain volume 69
#
Have you seen someone being shot in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you seen someone being murdered (not on T.V.) in the past year?

Never ___ Once ___ Twice ___ More than twice ___

Have you seen a dead person (not at a funeral) in the past year?

Never ___ Once ___ Twice ___ More than twice ___
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Asset Metadata

Creator Lyden, Hannah M. (author)

Core Title Family aggression exposure and community violence exposure associated with brain volume in late adolescence: a comparison of automated versus manual segmentation

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Publication Date 06/30/2015

Defense Date 05/27/2015

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag automated segmentation,brain volume,community violence,family aggression,manual segmentation,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Saxbe, Darby E. (committee chair), Kaplan, Jonas (committee member), Manis, Franklin R. (committee member), Margolin, Gayla (committee member)

Creator Email hannah.marieadams@gmail.com,lyden@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-583439

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