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Inhibitory spillover in adolescence: a fMRI study
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Inhibitory spillover in adolescence: a fMRI study
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Running Head: INHIBITORY SPILLOVER IN ADOLESCENCE
Inhibitory Spillover In Adolescence: A fMRI Study
Sarah A. Stoycos
University of Southern California
May 2017
Master of Arts (Psychology)
INHIBITORY SPILLOVER IN ADOLESCENCE
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Table of Contents
Abstract...........................................................................................................................................3
Introduction....................................................................................................................................4
Method ..........................................................................................................................................10
Participants.................................................................................................................................10
Procedures..................................................................................................................................11
Measures ....................................................................................................................................11
Results ...........................................................................................................................................13
Anxious and Depressive Symptoms ..........................................................................................13
Emotional Go/No-Go Task........................................................................................................13
Anxious and Depressive Symptoms and Task Performance .....................................................14
fMRI Data Analysis ...................................................................................................................14
Discussion .....................................................................................................................................18
References.....................................................................................................................................21
Tables and Figures.......................................................................................................................26
INHIBITORY SPILLOVER IN ADOLESCENCE
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Abstract
The current study is the first to report on the neural mechanisms of inhibitory spillover
during adolescence, a developmental period characterized by significant biological and
behavioral changes affecting inhibitory ability, emotional saliency, and behavioral control.
Twenty-four adolescents completed functional neuroimaging while completing the emotional
go/no-go task (Berkman, Burklund, & Lieberman, 2009). Adolescents also completed a self-
report measure of anxious and depressive symptoms. The emotional go/no-go fMRI task
required explicit cognitive/motor inhibition in the presence of irrelevant affective stimuli. It was
designed to assess inhibitory spillover from prefrontal cognitive control regions to ventral
affective regions via the shared neural substrate of the rIFG. Results from the present study
replicated the original inhibitory spillover study results (Berkman et al.,2009), dovetail motor
and cognitive inhibition studies, and extend the current literature by examining inhibitory
spillover and adolescent anxious and depressive symptomatology. Overall, these results support
that the rIFG may be the overlapping region for all domains of inhibition, such that any explicit
inhibition may indirectly dampen other neural regions via the rIFG. This effect is particularly
important for the adolescent brain and can inform behavioral interventions for adolescents who
struggle with aligning their actions to their goals, especially when their attention is competing
with emotional stimuli (e.g. teens with symptoms of anxiety and depression).
Keywords: rIFG, adolescence, inhibition, emotional go/no-go, inhibitory spillover, anxious and
depressive symptomatology
INHIBITORY SPILLOVER IN ADOLESCENCE
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Introduction
Adolescence represents a time of dynamic change and development in social, emotional,
and cognitive domains. Adolescents are known to show attentional bias to socioemotional cues
and may make risky or hasty decisions (Blakemore & Choudhury, 2006; Eaton et al., 2006;
Merikangas et al., 2010). Adolescence is also a time of increased risk for psychopathology, with
evidence that mental health problems are higher during this time than in childhood or adulthood
(Davey, Yücel, & Allen, 2008). Many studies have been conducted on adolescent biological and
socioemotional development. However, the nature and function of brain changes occurring in
adolescence that may underlie the onset of psychological symptoms and compromised decision-
making remain unclear (Pfeiffer & Allen, 2012). Recently, researchers have moved towards
studying the neural correlates of emotions, cognition, and behavior using integrated models
rather than treating these as separate constructs. One way to do this is to investigate the interplay
of executive functioning ability as it relates to self-control (as measured by inhibition), emotion
regulation, and behavior (Schmeichel & Tang, 2015). The “emotional go/no-go” task, a
functional magnetic resonance imaging (fMRI) inhibition task (Berkman, Burklund, &
Lieberman, 2009), requires implicit emotional processing in order to effectively engage in
explicit motor and cognitive inhibition. The task is designed to test an integrated model of
emotions, cognitions, and behavior by examining whether explicit cognitive/motor inhibition
leads to “inhibitory spillover” effects in emotion processing regions of the brain. The current
study is the first to administer this task to a sample of adolescents. Studying how emotional cues
and cognitive processes interact in the adolescent brain can help shed light on the neural
underpinnings of emotion regulation and decision-making during this critical developmental
INHIBITORY SPILLOVER IN ADOLESCENCE
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window. Furthermore, the current study also assessed adolescent symptomatology for anxiety
and depression and examined its effect on inhibitory spillover in the brain.
Adolescence is characterized by significant developmental changes in the brain that have
direct implications for self-control and emotion regulation ability (Schmeichel & Tang, 2015,
Casey, 2015; Casey, Getz, & Galvan, 2008; Ernst, Pine, & Hardin, 2006; Mills, Goddings,
Clasen, Giedd, & Blakemore, 2014; Steinberg, 2010). The dual-systems model of adolescence
hypothesizes that adolescents engage in more risky behavior because the ventral affective neural
networks underlying emotional salience and arousal develop faster than the prefrontal control
networks, resulting in decreased emotion regulation from the interaction of the two networks of
neural regions. As the more frontal and cortical areas implicated in the cognitive control neural
networks mature, risky behavior subsides. (Blakemore & Choudhury, 2006; Steinberg, 2005;
Strang, Chein, & Steinberg, 2013; Pfeiffer & Allen, 2012). Another model, the triadic model of
motivated behaviors (Ernst et al., 2006) purports that immature prefrontal areas result in
ineffective orchestration of approach (ventral striatum) and avoidance (amygdala) systems and
that development of regulatory brain regions lag behind development of affective brain systems,
resulting in an increased vulnerability to affective stimuli and related symptomatology, such as
anxious and depressive symptoms (Davey et al., 2008). Other researchers posit that at the onset
of puberty, white matter continues to consistently mature while gray matter follows a nonlinear
trajectory, leading to gaps in synaptic pruning and myelination in frontal versus subcortical
regions throughout adolescence. This discrepancy may underlie adolescents’ vulnerability to
problems with inhibition and susceptibility to psychopathology (Braet et al., 2009; Blakemore &
Choudhury, 2006; Steinberg, 2005; Hare et al., 2008; Motzkin, Philippi, Wolf, Baskaya, &
Koenigs, 2015).
INHIBITORY SPILLOVER IN ADOLESCENCE
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A common foundation across these theories of adolescent development is that prefrontal,
cognitive control brain networks and subcortical, affective networks show different
developmental trajectories. These differing trajectories have a significant effect on adolescents’
daily lives and behavior. Self-control, or the ability to inhibit a prepotent action, thought, or
feeling, requires the coordination of both neural networks. Self-control is most closely related to
the executive function of inhibition and is often studied using inhibitory control paradigms
(Cohen & Lieberman, 2010; Tabibnia et al., 2011). Prior research with adults points to the role of
the right inferior frontal gyrus (rIFG), pars opercularis in supporting self-control across
affective, cognitive, and motor domains (Tabibnia et al., 2015; Berkman et al., 2009; Stevens,
Kiehl, Pearlson, & Calhoun, 2007; Cohen, Berkman, & Lieberman, 2011). Studies looking at
inhibition separately by domain consistently each implicate the rIFG (Miyake et al., 2000;
Carlson & Wang, 2007). Furthermore, behavioral studies of inhibition show correlations between
motor and cognitive inhibitory control and affective versus cognitive/motor inhibitory control
(Miyake et al., 2000; Carlson & Wang, 2007).
Cognitive control, which can be measured by tasks that require inhibition, is intricately
intertwined with emotion regulation. Considerable evidence suggests that cognitive ability
shapes an individual’s emotional experiences, and vice versa, through the shared biological
substrate of the rIFG (Gray, 2004; Schmeichel & Tang, 2015; Tabibnia et al., 2011; Berkman et
al., 2009; Casey et al., 1997; Cohen & Lieberman, 2009; Hare et al., 2008). Behavioral studies in
adults have found better performance on inhibitory control tasks to be linked with individual
differences in the ability to halt facial affect expression (von Hippel & Gonsalkorale, 2005),
decreased emotional reactivity to recalling of negative events (Tang & Schmeichel, 2014), and
successful emotion regulation on a daily basis (Stawski, Almeida, Lachman, Tun, & Rosnick,
INHIBITORY SPILLOVER IN ADOLESCENCE
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2010). Review articles comparing animal and human studies of inhibition and self-control show
evidence for specific recruitment of the rIFG in both humans and animals (Aron, Robbins, &
Poldrack, 2004; Cohen, et al., 2013) and gray matter structural integrity of the rIFG pars
opercularis was found to be linked with self-control performance on motor and affective
inhibition tasks (Tabibnia et al., 2011).
Neuroimaging studies have identified neural correlates of both emotion regulation and
cognitive control/executive functioning ability that overlap with neural correlates of depression
and anxiety (Kerestes, Davey, Stephanou, Whittle, & Harrison, 2014). Depression and anxiety
are both characterized by emotion dysregulation and selective attention to negative stimuli,
which indicate compromised emotion regulation and executive functioning (i.e. monitoring,
inhibiting, switching). Moreover, mood disorders often first emerge in adolescence (Kerestes et
al., 2014). Several studies looking at the neural correlates of depression and anxiety in
adolescence have found differences in the function of prefrontal, cognitive control regions such
as the anterior cingulate cortex (ACC) and the ventromedial prefrontal cortex (VMPFC) which
encapsulates the rIFG (Kerestes et al., 2014; Davey et al., 2008; Anderson & Teicher, 2008).
Additionally, differences in the functioning of subcortical regions such as the amygdala and
striatum have also been identified in people with depressive and anxious symptoms (Kerestes et
al., 2014; Davey et al., 2008; Anderson & Teicher, 2008). These regions overlap with those
necessary for emotion regulation and self-control. The onset of symptoms in adolescence may be
related to the protracted development of the prefrontal and subcortical neural networks necessary
for inhibitory control (Kerestes et al., 2014; Davey et al., 2008; Anderson & Teicher, 2008).
However, very few studies of adolescents have actually studied different domains of
inhibitory control within one model in order to untangle cognitive and affective processes. The
INHIBITORY SPILLOVER IN ADOLESCENCE
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current study is the first to use the emotional go/no-go task (Berkman, et al., 2009) with a sample
of adolescents. The emotional go/no-go inhibition task was designed to assess inhibitory
spillover from cognitive neural networks to the ventral affective system given the overlapping
evidence that intentional inhibition in one domain (i.e. cognitive/motor) may have spillover
effects to neural regions associated with other domains of inhibition (i.e. affective) via the
common substrate of the rIFG pars opercularis (Berkman et al., 2009; Tabibnia et al., 2011).
Specifically, the emotional go/no-go task was the first inhibition task to use emotional stimuli,
but to only target cognitive inhibition in an effort to disentangle recruitment of the rIFG and
down regulation of the amygdala as an explicit process (as in affective inhibition tasks) from an
incidental process of down regulation (Berkman et al., 2009). In other words, would explicit
cognitive and motor inhibition result in subsequent down regulation of the ventral affective
network in response to emotional facial expressions? In this task, participants viewed
photographs of men and women making emotional facial expressions and were asked to press a
button or withhold a button press in response to the sex of the face. The emotion of the face was
irrelevant to the task. The researchers found support for spillover of motor inhibition to limbic
structures: there was decreased amygdala percent signal change during no-go negatively
valenced trials compared to go negative and all positive trials (Berkman et al., 2009).
Additionally, functional connectivity analyses found the activity of the IFG and amygdala to be
inversely correlated (Berkman et al., 2009).
Adolescents may show less effective communication between prefrontal cognitive
networks and ventral affective networks, making adolescence a unique window to study
inhibitory spillover and to understand the effects of ongoing neural network refinement and its
subsequent effect on behavior. Furthermore, adolescence is a time of heightened vulnerability to
INHIBITORY SPILLOVER IN ADOLESCENCE
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mood disorders, including onset of depressive and anxious symptoms, which may be linked with
the development of the emotional brain and its interrelationship with cognitive control neural
networks. Therefore, this study also examined how depressive and anxious symptoms may be
linked with inhibitory neural network activity during task performance.
We hypothesized that there would be main effects on whole-brain analyses of task
condition (go versus no-go) and valence (positive versus negative emotional faces) compared to
baseline during the emotional go/no-go fMRI task. Specifically, we expected that no-go versus
go trials would recruit more ACC and rIFG, given previous literature suggesting that inhibition
requires effortful cognitive control. We also expected negative versus positive trials would
recruit more amygdala, ACC, striatum, and anterior insula activity, given evidence that
negatively valenced facial expressions are closely related to threat, demanding greater attention
resulting in increased arousal in limbic regions (Ohman, 2005). We expected to find an
interaction of condition and valence, such that no-go, negatively valenced trials (compared to go,
negative trials) would be accompanied by greater prefrontal activation and decreased subcortical
activation. We expected this because of the inhibitory spillover hypothesis (Berkman et al.,
2009) and the evidence supporting the rIFG as a cross-domain mechanism for self control. We
also hypothesized that the higher an adolescent self-reported anxious and depressive symptoms,
the more BOLD signal recruitment would occur in ventral affective regions (amygdala, insula)
and the rIFG during no-go, negatively valenced trials compared to baseline and compared to
positively valenced trials. Extant literature supports that youths and adults with anxiety and
depression exhibit increased activation in the aforementioned regions (Kerestes et al., 2014;
Davey et al., 2008; Anderson & Teicher, 2008). Lastly, we hypothesized that there would be
increased functional connectivity between the rIFG and amygdala during the no-go, negative
INHIBITORY SPILLOVER IN ADOLESCENCE
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trials compared to go, negative trials, given the inhibitory spillover hypothesis and incidental
down-regulation of ventral affective regions during inhibition (Berkman et al., 2009).
Method
Participants
Participants were recruited from an ongoing, multi-wave longitudinal study of the effects
of family and community environments on youth development (Margolin et al., 2010).
Participants and their families were first recruited from the community via advertising and word
of mouth when the youths were 12 to 13 years old. Inclusion criteria included proficiency in
English, and that the family of the youth had been living together for the past three years.
Exclusion criteria included contraindications to MRI scanning (e.g. metal implants, braces, tic
disorder), left-handedness, and daily use of psychoactive medications.
For the current study, 43 families from the larger study were recruited when the youths
were between 15 to 19 years old. Of the 43 families contacted, 24 youths and their families were
eligible and willing to participate in scanning. Of the 24 youths eligible for scanning, only 21
youths completed the anxious and depressive symptoms questionnaires which were collected
during the third visit (after the MRI visit). All 24 youths’ fMRI data were used for whole-brain
task analyses, and whole-brain analyses with anxiety/depression scores as a regressor were
completed on the 21 youths with data. The recruited sample is representative of the racially and
ethnically diverse community in south central Los Angeles. Fifty-four percent of youths (13
youth) identified as Caucasian, 24% (6 youth) identified as multiracial, 12% (3 youth) identified
as African-American, and 9% (2 youth) identified as Asian American. At the time of the MRI
scan, youths were an average of 17.05 years old (range: 15.47 – 18.72, SD = .85). All
INHIBITORY SPILLOVER IN ADOLESCENCE
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participants provided informed assent and parents provided informed consent as approved by the
Institutional Review Board at an institution in southern California.
Procedures
Participants completed behavioral and self-report measures at their third visit of the larger
longitudinal study when youths were an average of 16.70 years old (range: 14.93 – 18.63, SD =
.94 years). MRI scanning was completed between youths second and third visit for data
collection. Between the third visit behavioral data collection and the MRI visit there was an
average of .08 years (about 1 month; range = -.74 – 1.79 years, SD = .58). Most of third visit data
collection happened within about a year of the MRI scan, except for one participant who
completed it 21 months after the scan.
During the MRI visit, participants completed functional, structural, and resting state
neuroimaging as well as additional behavioral self-report measures. The visit took about two
hours to complete and all participants were compensated for their time at each visit.
Measures
Anxious and Depressive Symptoms. Psychological symptoms were assessed using the
Youth Self Report (YSR; Achenbach & Rescorla, 2001), specifically the Anxious/Depressed
subscale. Scores were taken from data collected at the third visit of the larger longitudinal study,
which occurred within an average of .08 years (about 1 month) from the MRI scan (range: -.74 –
1.79 years, SD = .58). Three participants did not participate in this visit and were excluded from
analyses using the YSR measure.
The Youth Self Report (YSR; Achenbach & Rescorla, 2001) is part of the Achenbach
System of Empirically Based Assessments (ASEBA) and is a screening tool used to assess
behavioral and emotional problems in 11 to 18 year olds. The YSR has 112 items and asks
INHIBITORY SPILLOVER IN ADOLESCENCE
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respondents to answer based on their last six months of behaviors and experiences. Raters
indicate their scores using a three-point Likert scale (0 = very true or often true, 1 = somewhat or
sometimes true, 2 = is not true). The YSR has six syndrome scales, of which the
anxious/depressive syndrome scale was used. The anxious/depressive syndrome scale has 13
items to assess anxiety and depression, such as “I cry a lot,” “I am afraid of certain places or
situations,” and “I am nervous or tense.” The YSR has normative data for age and sex of
participants. Therefore, scores used were age- and sex-normed and reported in T-scores.
Emotional Go/No-Go fMRI Task. The fMRI task used was adapted from the Berkman,
Burklund, and Lieberman (2009) emotional go/no-go paradigm. The mixed block- and event-
related fMRI task consisted of two functional runs; each run had 3 blocks, each with 50
emotional facial expressions (positive: happy; negative: fear, anger). Presentation of stimuli was
optimized using the genetic algorithm program OptimizeDesign (Wager & Nichols, 2003). For
half of the blocks, the participants were asked to press a button each time a male face was shown
(“go” condition) and to inhibit a button press each time a female face was shown (“no-go”
condition; see Figure 1). The “go” sex was counterbalanced within participants; however, the
emotional valences of the expressions were not counterbalanced because they were incidental to
the aim of the task. The positively valenced faces were primarily presented to prevent
habituation to the negatively valenced faces. The “go” sex was presented on 80% of the trials
and the “no-go” sex was presented on 20% of the trials which is consistent with prior go/no-go
paradigms designed to activate inhibitory mechanisms (Berkman et al., 2009; Stevens et al.,
2007).
fMRI Data Acquisition. Whole-brain images were acquired using a Siemens 3 Tesla
MAGNETOM TIM Trio Scanner (Siemens Medical Solutions) with a 12-channel phased-array
INHIBITORY SPILLOVER IN ADOLESCENCE
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head coil. High-resolution, T1-weighted anatomical images were acquired (3D Magnetization
Prepared Rapid Acquisition Gradient Echo; T1 = 900 ms; repetition time, 1950 ms; echo time,
2.26 ms; flip angle, 7°), with an isotropic voxel resolution of 1mm. Functional data were
collected using a T2*-weighted echo-planar imaging (EPI) interleaved sequence (40 2.5 mm
transversal slices; repetition time, 2000 ms; echo time, 25 ms; field of view, 192 mm
2
; 3.0 x 3.0
x 2.5 mm voxels).
Results
Anxious and Depressive Symptoms
Raw scores from the YSR were compiled, and then transformed into T-scores that were
normed for age and sex of each participant. The average T-score on the YSR anxious/depressive
syndrome subscale was 53.95 (SD = 5.07; Range: 50 – 70). The clinical cutoff for the
anxious/depressive syndrome subscale is T = 70, therefore our sample had a below-clinical
threshold average of anxious and depressive symptoms. One participant (T = 70) was above the
clinical cutoff. All other participants were in the normal range. The YSR anxious/depressive
syndrome scale scores were used as a continuous measure in analyses because we focused on a
community sample of adolescents without significant psychopathology.
Emotional Go/No-Go fMRI Task
Overall accuracy of button-press and reaction times were compared across conditions. As
expected in an adolescent sample, there was a significant difference in percent accuracy of
button press on no-go (M = 88.82%, SD = .06) versus go (M = 95.3%, SD = .05) trials, t (23) =
4.22, p < .001, such that participants responded less accurately to no-go trials (see Figure 2).
There were no significant differences on percent accuracy of button press by valence. Similarly,
INHIBITORY SPILLOVER IN ADOLESCENCE
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there were no significant differences in reaction times between positive (M = 57.70, SD = .04)
and negative trials (M = 58.85, SD = .04), t(23) = .79, p =.44.
Anxious and Depressive Symptoms and Task Performance
We examined the relationship between behavioral task performance during the fMRI
inhibition task and anxious and depressive symptoms. No significant associations existed
between reaction times during go trials and YSR anxious/depressive syndrome T scores (all p’s >
.05; see Table 1). There were no significant associations between percent accuracy on negatively
or positively valenced inhibition (no-go) trials, positively valenced go trials and YSR
anxious/depressive syndrome T scores (all p’s > .05). There was a marginally significant
negative relationship between percent accuracy on negatively valenced go trials and YSR
anxious/depressive syndrome T scores, r = -.397, p = .075, such that participants with higher
self-reported symptoms of anxiety and depression were less accurate in their button press on go
trials when negatively valenced faces were presented (see Figure 3).
fMRI Data Analysis
Functional data were preprocessed and analyzed according to the general linear model in
FSL (FMRIB, Oxford, UK). The two runs of the task were concatenated, despiked, motion-
corrected, and spatially smoothed using a 8.0-mm full-width half-maximum Gaussian filter. Data
were aligned to the anatomical grid, transformed to a standardized atlas, and masked with an
extents mask to account for motion artifacts and to exclude voxels without valid data at every TR
for every run, helping to control for false activations. The general linear model had four
explanatory variables (go positive, go negative, no-go positive, and no-go negative) to account
for each condition while minimizing multicollinearity in the overall model. Contrasts of interest
were created as linear combinations of the explanatory variables in order to look at overall task
INHIBITORY SPILLOVER IN ADOLESCENCE
15
effects (go vs. rest; no-go vs. rest; positive vs. rest; negative vs. rest; no-go > go; negative >
positive). Fixation trials were modeled implicitly; baseline was modeled by a first-order function,
and motion artifacts were modeled using the six estimated rigid-body motion parameters. The
train of stimulus events was then convolved with a gamma-variate hemodynamic response
function resulting in a normalized time series with a percent signal change from the mean
represented by amplitude and regression coefficients. This produced the beta-coefficients and
associated t- and z-statistics for each voxel and explanatory variable. All activations reported are
cluster thresholded to a minimum z-statistic of 2.3 and p < .05 and reported coordinates are from
the MNI atlas.
Whole-brain analyses were conducted to examine the overall effect of the task. During
scanning of the first participant, a problem with task administration led to a prolonged fMRI
acquisition for the task. Whole-brain analyses were conducted with and without this participant
to look for differences in data: no major differences in blood oxygenation level dependent
(BOLD) signal were found and therefore all 24 participants’ data were used in task analyses. The
present study focused on negatively rather than positively valenced faces because negatively
valenced faces have been associated with greater activation in key neural regions and may also
be more relevant to depression and anxiety symptomatology (Berkman et al., 2009).
Furthermore, there is literature suggesting that down regulation of limbic activity during
inhibitory control tasks primarily happens in the presence of negative stimuli, not positive stimuli
(Lieberman et al., 2007; Tabibnia et al., 2011).
Neural activations during inhibition trials. As hypothesized, no-go trials compared to go
trials activated bilateral insula, anterior cingulate cortex (ACC), and the rIFG (see Table 2). The
only area more active during go trials (as compared to no-go trials) was the precentral gyrus.
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This pattern of activation and deactivation in inhibition-related brain areas is consistent with
prior studies of the emotional go/no-go paradigm (Berkman et al., 2009).
Neural activations during positively/negatively valenced trials. When looking at neural
activation in response to negative faces compared to baseline, inhibition-related networks were
activated. Specifically, the rIFG pars triangularis and pars opercularis, left ACC, and right
superior frontal gyrus (see Table 3). Additionally, limbic structures were activated including the
right insula and bilateral amygdala. When viewing positive faces (compared to baseline),
participants showed activation in the rIFG pars opercularis, right supplementary motor cortex,
bilateral frontal poles, as well as bilateral insula and bilateral amygdala. When directly
contrasting negative trials compared to positive trials, the rIFG pars opercularis and pars
triangularis and right middle temporal gyrus were activated, as hypothesized. Contrary to our
hypotheses, there were no significant differences in activation of the insula, amygdala, or ACC
when directly comparing negative to positive trials. During both negative and positive trials,
there was deactivation in the bilateral precuneus and the superior lateral occipital cortex.
Neural activations during negative and positive inhibition trials. We compared no-go,
negative trials to go, negative trials, to see whether more neural recruitment of inhibitory regions
would be present during inhibition trials. As expected, there was significantly more activation in
cognitive control network regions (right ACC, rIFG pars triangularis and pars opercularis, and
right middle temporal gyrus) during negatively-valenced inhibition trials compared to
negatively-valenced go trials (see Table 4). Additionally, there was limbic activation in the
bilateral anterior insula. Given a priori hypotheses, an ROI of the amygdala was carried out and
parameter estimates were extracted. In support of the inhibitory spillover hypothesis, there was a
significant reduction in amygdala activation when comparing go, negative trials to no-go,
INHIBITORY SPILLOVER IN ADOLESCENCE
17
negative trials (see Figure 4). For the no-go, positive greater than go, positive contrast, the only
significant cluster of deactivation was in the precentral gyrus. Therefore go, positive trials
recruited more precentral gyrus than no-go, positive trials. As hypothesized, the main contrast of
interest for this study was with the negative inhibition trials and results to positive inhibition
trials were not anticipated. Therefore, all further analyses were only carried out with the negative
inhibition contrasts.
Neural activations in negatively-valenced inhibition trials based on YSR scores. Anxious
and depressive syndrome T-scores from the YSR were used as a regressor in whole-brain
analyses using the no-go, negative compared to go, negative contrast. As hypothesized, there was
a positive linear relationship between YSR T-scores and BOLD activation in the ACC (x = 8, y =
-10, z = 28; see Figure 5a), rIFG pars opercularis (x = 50, y = 20, z = 14; see Figure 5b), insula
(x = 40, y = 18, z = -2; see Figure 5c), and middle temporal gyrus (x = 68, y = -24, z = -10).
Additionally, there was a positive association with BOLD activation in the precuneus (x = -16, y
= -52, z = 38) and frontal pole (x = 26, y = 54, z = -6). Therefore, the higher participants’ self-
reported anxious and depressive symptoms, the more BOLD activation recruited in prefrontal,
cognitive control networks and in subcortical, ventral affective regions (i.e. anterior insula).
Functional connectivity during negatively-valenced inhibition trials. In order to further
test the inhibitory spillover hypothesis that the rIFG may have indirect effects on subcortical
regions during inhibition, we conducted a context-dependent psychophysiological interaction
analysis (gPPI; McLaren et al., 2012). A gPPI analysis is used to look at task-specific changes in
the relationship between activity in an identified seed region and other areas of the brain
(O’Reilly, Woolrich, Behrens, Smith, & Johansen-Berg, 2012). Given the prior literature on the
role of the rIFG pars opercularis (Tabibnia et al., 2011) and its role in inhibitory spillover
INHIBITORY SPILLOVER IN ADOLESCENCE
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(Berkman et al., 2009), an anatomically-defined (Harvard-Oxford Cortical Atlas) ROI of the
rIFG pars opercularis was used as the seed region. The no-go, negative trials compared to go,
negative trials were used as the psychological regressors (task-specific). Then the PPI regressors
were created using a linear combination of the psychological regressors and seed region
regressor (McLaren et al., 2012; O’Reilly et al., 2012). Results indicate that the contrast between
the effects of no-go, negative trials and go, negative trials tends to increase the effect of the rIFG
pars opercularis on the amygdala (x = 20 , y = -10 , z = -12; x = -16, y = -8, z = -12 ) and
anterior insula (x = 44, y = 12 , z = -4; x = -28, y = 22, z = 2; see Figure 6). This is further
support for the inhibitory spillover hypothesis, such that there is greater communication between
the rIFG pars opercularis and ventral affective regions (insula, amygdala) during motor
inhibition trials that also required implicit affective inhibition.
Discussion
The present paper is the first to test the inhibitory spillover hypothesis in adolescence. In
this study, when presented with a cognitive and motor inhibition task with irrelevant affective
stimuli, adolescents exhibited the hypothesized recruitment of the rIFG pars opercularis and
prefrontal cognitive control regions (ACC) along with ventral affective areas (insula, amygdala).
Furthermore, in this study, adolescent amygdala percent signal change decreased significantly
during negatively-valenced inhibition trials and greater functional connectivity between the IFG
pars opercularis and amygdala and insula was seen during the no,go negative trials compared to
go, negative trials. These results dovetail with the literature identifying the rIFG as a key region
involved in inhibition and possible down-regulation of ventral affective regions during
inhibition.
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19
Overall, this study converges with prior findings that the rIFG pars opercularis may be
the common neurobiological substrate underlying cognitive control via the executive function of
inhibition. Recent literature has cited the rIFG pars opercularis as part of a “stop” network
involving the rIFG pars opercularis, the presupplementary motor area, and the right subthalamic
nucleus, which are all linked via white matter tracts (Aron et al., 2007). The subthalamic nucleus
can then quickly inhibit motor and affective responses by communicating with the basal ganglia
(Aron et al., 2007; Tabibnia et al., 2011). Furthermore, lesion studies and comparative studies of
humans and monkeys all implicate Brodman Area 44/the rIFG pars opercularis as pivotal to
response inhibition, noting that this area is one of the last areas of the PFC to develop to maturity
(Aron et al., 2004). The prolonged timeline of maturation of the rIFG pars opercularis and its
white matter tract projections therefore have important implications for inhibitory control in
adolescents, whose PFC development significantly lags behind limbic development.
Furthermore, recent studies also link the rIFG pars opercularis to the anterior insula, citing that
both are necessary for intricate attentional and working memory tasks (Tops and Boksem, 2011).
Furthermore, the present study links biological substrates to adolescent behavior by
examining anxious and depressive symptoms and their relation to brain activation during an
emotional inhibitory control task. In the current study, adolescents who self-reported higher
anxious and depressive symptoms also recruited more rIFG pars opercularis and insula BOLD
activation. This result is consistent with prior research indicating that people with internalizing
disorders required increased recruitment of BOLD signal to inhibitory control neural networks in
order to successfully inhibit (Davey et al., 2008). We also found that the higher the adolescents’
self-reported symptoms, the less accurate their button-presses for the inhibition task. These
findings indicate that perhaps adolescents with anxious and depressive symptoms are needing to
INHIBITORY SPILLOVER IN ADOLESCENCE
20
work extra hard and recruit significantly more neural activation when trying to successfully
inhibit, especially in the presence of threatening, albeit task irrelevant, stimuli.
Despite these strengths, this study also had some limitations. A small sample size was
used and because of problems with data collection, not all data collected could be used for every
analysis. The extant literature would benefit from a replication of this study with a larger sample
size. Furthermore, a community sample of adolescents was used and so anxious and depressive
symptomatology was not representative of clinical depression and anxiety. More research is
needed with adolescents who qualify as a clinical sample of anxious and depressive symptoms. It
would also be beneficial to study developmental changes in inhibitory spillover in early
adolescents, late adolescents, and adults, in order to further understand the role of brain
development, network refinement, and the rIFG in inhibitory control.
Overall, the current findings have important implications for using neurobiological
information to inform interventions for adolescents struggling with inhibitory control. Research
emphasizes the need for neurobiologically informed interventions, especially during adolescence
when ongoing neural development is linked with increases in onset of psychopathology and
behavior problems (Berkman, Graham, and Fisher, 2011). It has been postulated that perhaps not
all emotion regulation skills are as explicit as initially thought, especially given the overlapping
neural substrates involved across affective, cognitive, and motor domains (Burklund et al.,
2014). This means that interventions may be able to target one domain of inhibitory control
while also reaping the benefits from incidental downstream effects to other domains of inhibitory
control (Berkman et al., 2009). In conclusion, this study addressed an important gap in the
current literature by extending the emotional go/no-go and inhibitory spillover field to
adolescence and tying inhibitory control to adolescent symptomatology.
INHIBITORY SPILLOVER IN ADOLESCENCE
21
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Tables and Figures
Table 1. Correlation matrix for participant task performance and psychopathology symptoms.
1 2 3 4 5 6 7 8
1. YSR Anx/Dep Scale T-score
2. Go
-
% Accuracy -.397
+
3. Go
+
% Accuracy -.198 .906**
4. NoGo
-
% Accuracy .157 .354 .370
5. NoGo
+
% Accuracy .384 -.026 -.017 .592**
6. Go % Accuracy -.304 .976** .976** .371 -.022
7. NoGo % Accuracy .317 .159 .172 .864** .917** .170
8. Positive trial Reaction Times .274 .147 .312 .173 -.073 .236 .040
9. Negative Trial Reaction
Times
.020 .071 .004 .106 .195 .038 -.069 .759**
* indicates p < .05
** indicates p < .01
+ indicates p < .08
INHIBITORY SPILLOVER IN ADOLESCENCE
27
Table 2. Neural regions with increased BOLD activity during inhibition.
Region x y z Cluster Size z
NoGo > Baseline
rIFG pars triangularis 52 36 2 22 3.35
rIFG pars opercularis 50 16 26 10 3.13
Amygdala 22 -14 -12 57 3.74
-18 -8 -12 16 3.46
Insula 44 12 -4 748 4.76
-38 12 2 72 3.69
Anterior Cingulate Cortex 8 20 36 28 3.38
Supplementary Motor Cortex 8 6 64 413 4.84
Superior Frontal Gyrus -8 26 62 40 3.73
6 64 22 30 3.25
Lateral Occipital Cortex 42 -80 -2 604 5.37
-56 -62 38 142 4.26
Frontal Pole -2 60 12 22 3.35
Go > Baseline
Frontal Pole 6 62 30 1326 5.18
-2 60 -16 25 4.11
Superior Frontal Gyrus -10 24 62 90 3.86
Frontal Orbital Cortex -38 28 -14 61 3.69
24 26 -14 17 3.27
rIFG pars triangularis 50 38 2 57 3.96
40 32 -14 20 3.57
Supplementary Motor Cortex 6 8 68 46 3.59
Insula 44 10 -4 33 3.3
Amygdala 20 -6 -12 24 3.57
-20 -8 -12 22 3.4
Lateral Occipital Cortex -54 -64 30 15 3.28
NoGo > Go
Insula 32 20 4 271 3.73
-30 20 -2 263 4.3
Frontal Pole 32 44 20 206 3.87
Posterior Cingulate Cortex 2 -30 24 27 3.54
Anterior Cingulate Cortex -10 24 32 16 3.42
rIFG pars triangularis 50 40 16 11 3.23
rIFG pars opercularis 52 16 28 9 2.51
Go > NoGo
Precentral Gyrus -36 -24 62 671 4.53
Note. All activations reported are thresholded to a minimum z-statistic of 2.3 and p < .05. MNI
Coordinates reported.
INHIBITORY SPILLOVER IN ADOLESCENCE
28
Table 3. Neural regions showing increased BOLD activity during emotion facial perception.
Region x y z Cluster Size z
Negative > Baseline
Lateral Occipital Cortex 44 -80 -2 674 5.29
-56 -64 36 38 3.88
Insula 44 10 -4 561 4.21
-28 22 2 18 3.31
Frontal Pole 6 62 32 296 4.14
-2 64 14 9 3.37
Supplementary Motor
Cortex 8 8 64 195 4.3
Hippocampus 30 -18 -12 129 3.96
rIFG pars triangularis 52 36 4 70 3.91
rIFG pars opercularis 50 16 22 10 2.71
Superior Frontal Gyrus -10 26 62 46 3.5
Anterior Cingulate Cortex -8 8 46 26 3.61
Amygdala -18 -6 -12 17 3.44
Positive > Baseline
Frontal Pole -6 56 36 947 4.11
Lateral Occipital Cortex 44 -80 2 496 4.98
-54 -64 30 138 3.75
Insula 44 12 -2 179 4.08
-40 18 -14 116 3.76
Supplementary Motor
Cortex 6 8 68 145 4.36
Superior Frontal Gyrus -10 26 62 100 4.16
Anterior Cingulate Cortex -8 8 46 20 3.48
Amygdala 20 -8 -12 19 3.45
-18 -10 -12 19 3.5
rIFG pars triangularis 52 38 2 11 3.41
Frontal Orbital Cortex 26 26 -14 11 3.29
Negative > Positive
rIFG pars triangularis 56 26 -4 128 3.78
rIFG pars opercularis 44 18 28 11 2.91
Middle Temporal Gyrus 56 -50 6 644 4.55
Precentral Gyrus 40 6 34 50 3.73
Lateral Occipital Cortex -26 -86 6 21 3.77
Superior Frontal Gyrus -24 12 64 14 3.49
Fusiform Gyrus -34 -64 -4 10 3.58
Note. All activations reported are thresholded to a minimum z-statistic of 2.3 and p < .05. MNI
coordinates reported.
INHIBITORY SPILLOVER IN ADOLESCENCE
29
Table 4. Neural regions with increased BOLD activity during condition-by-valence interactions.
Region x y z Cluster Size z
NoGo
-
> Go
-
Middle Temporal Gyrus 56 -46 4 462 4.53
Precentral Gyrus 44 8 34 390 4.24
-40 6 28 6 3.22
Paracingulate Gyrus 2 14 50 363 4.21
rIFG pars triangularis 50 40 16 134 3.87
Lateral Occipital Cortex -28 -74 24 132 4.07
30 -78 22 68 3.51
Precuneus 12 -76 40 91 3.74
Insula -28 20 -2 94 3.62
38 20 -8 55 3.42
Supplementary Motor Cortex 10 4 66 121 3.76
Posterior Cingulate Gyrus 4 -32 24 65 4.01
Frontal Pole -38 60 10 34 3.67
Fusiform Gyrus 32 -66 -6 32 3.65
rIFG pars opercularis -40 6 28 6 3.22
Brain Stem 2 -16 -12 16 3.47
Go
-
> NoGo
-
Precentral Gyrus -44 -14 62 175 3.71
NoGo
+
> Go
+
Anterior Cingulate Gyrus -10 24 32 7 3.3
Go
+
> NoGo
+
Precentral Gyrus -36 -24 64 604 4.46
Anterior Cingulate Gyrus 18 44 2 10 3.25
Note. All activations reported are thresholded to a minimum z-statistic of 2.3 and p < .05. MNI
Coordinates reported.
INHIBITORY SPILLOVER IN ADOLESCENCE
30
Figure 1. Emotional go, no-go fMRI task design.
This figure depicts a fMRI run where the “go” condition was a female face, and the “no-go”
condition was a male face. The affect of the face is irrelevant to the instructed task.
INHIBITORY SPILLOVER IN ADOLESCENCE
31
Figure 2. Percent accuracy in button press during fMRI task.
A significant difference between percent accuracy in button press during go trials (M = 95.3%,
SD = .05) and inhibition of button press during no-go trials (M = 88.82%, SD = .06), t
(23) = 4.22, p < .001.
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20 25
no-go % accurracy go % accuracy
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
no-go % accurracy go % accuracy
*
INHIBITORY SPILLOVER IN ADOLESCENCE
32
R = -.375
Figure 3. Negative association between psychopathology symptoms and task performance.
.
There is a marginally significant negative association between YSR anxious/depressive syndrome
subscale T-scores and fMRI task button-press percent accuracy during negatively valenced
trials, r = -.375, p = .075∗.
20
30
40
50
60
70
80
0.4 0.6 0.8 1 1.2 1.4
YSR_Anx/Dep T score
Go Neg % Accuracy
INHIBITORY SPILLOVER IN ADOLESCENCE
33
Figure 4. Amygdala % signal change by condition and valence.
Percent signal change values for anatomically-defined amygdala ROI during no-go
+
, go
+
, no-go
-
, and go
-
. There was a significant difference between left amygdala signal change during no-go
-
trials compared to go
-
trials, t(22) = 2.17, p = .04, which supports the inhibitory spillover
hypothesis. Intentional motor inhibition led to down-regulation of emotion processing
subcortical region.
0
0.05
0.1
0.15
0.2
0.25
0.3
L. Amygdala % Signal Change
Positive Negative
No-go
Go
*
INHIBITORY SPILLOVER IN ADOLESCENCE
34
Figure 5. Neural activations correlated with YSR anxious/depressive T-scores
As hypothesized, there was a positive linear relationship between YSR T-scores and BOLD
activation in the (a) ACC (x = 8, y = -10, z = 28), (b) rIFG pars opercularis (x = 50, y = 20, z =
14), (c) insula (x = 40, y = 18, z = -2). All activations reported are thresholded to a minimum z-
statistic of 2.3 and p < .05. MNI Coordinates reported.
INHIBITORY SPILLOVER IN ADOLESCENCE
35
Figure 6. gPPI connectivity results: neural regions with activity correlated with rIFG pars
opercularis activity during negative inhibition trials.
Greater communication between the rIFG pars opercularis (anatomically defined seed region)
and ventral affective regions (amygdala: x = 20 , y = -10 , z = -12; x = -16, y = -8, z = -12; and
anterior insula: x = 44, y = 12 , z = -4; x = -28, y = 22, z = 2) during motor inhibition trials that
also required implicit affective inhibition. All activations reported are thresholded to a minimum
z-statistic of 2.3 and p < .05. MNI Coordinates reported.
Abstract (if available)
Abstract
The current study is the first to report on the neural mechanisms of inhibitory spillover during adolescence, a developmental period characterized by significant biological and behavioral changes affecting inhibitory ability, emotional saliency, and behavioral control. Twenty-four adolescents completed functional neuroimaging while completing the emotional go/no-go task (Berkman, Burklund, & Lieberman, 2009). Adolescents also completed a self- report measure of anxious and depressive symptoms. The emotional go/no-go fMRI task required explicit cognitive/motor inhibition in the presence of irrelevant affective stimuli. It was designed to assess inhibitory spillover from prefrontal cognitive control regions to ventral affective regions via the shared neural substrate of the rIFG. Results from the present study replicated the original inhibitory spillover study results (Berkman et al.,2009), dovetail motor and cognitive inhibition studies, and extend the current literature by examining inhibitory spillover and adolescent anxious and depressive symptomatology. Overall, these results support that the rIFG may be the overlapping region for all domains of inhibition, such that any explicit inhibition may indirectly dampen other neural regions via the rIFG. This effect is particularly important for the adolescent brain and can inform behavioral interventions for adolescents who struggle with aligning their actions to their goals, especially when their attention is competing with emotional stimuli (e.g. teens with symptoms of anxiety and depression).
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Inhibitory spillover in adolescence: a fMRI study
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