Parsing the neural correlates of anxious apprehension and anxious arousal in the grey-matter of ...

15 Pages·2017·1.24 MB·English

by  Peter J. Castagna

Brain Imaging and Behavior
DOI 10.1007/s11682-017-9772-1
ORIGINAL RESEARCH
Parsing the neural correlates of anxious apprehension
and anxious arousal in the grey-matter of healthy youth
Peter J. Castagna1 · Scott Roye1 · Matthew Calamia1,2 · Joshua Owens-French1 ·
Thompson E. Davis III1 · Steven G. Greening1,2  

© Springer Science+Business Media, LLC 2017
Abstract Neuroscientific and psychological research findings from the affective and developmental cognitive
posits that there are two transdiagnostic facets of anxiety: neurosciences.
anxious arousal and anxious apprehension. Though these
two facets of anxiety are distinct, they are often subsumed Keywords Anxiety · Cortical thickness · Grey-matter ·
into one domain (e.g., trait anxiety). The primary goal of Youth · Affective neuroscience · MRI
the current study was to delineate the relationship between
anxious arousal and cortical thickness versus the relation-
ship between anxious apprehension and cortical thickness Introduction
in a sample of typically functioning youth. The secondary
aim was to determine where in the brain cortical thickness Child anxiety disorders are highly prevalent with estimates
significantly correlated with both components of anxiety. ranging from 3 to 32% depending on the disorders included,
Results indicated that the right anterior insula has a stronger sample, methodology, and time period (Cartwright-Hatton
relationship to anxious arousal, whereas the dorsolateral et al. 2006; Merikangas et al. 2010). While current nosol-
prefrontal cortex and left anterior insula were found to cor- ogy identifies ten possible anxiety disorders, neurobiological
relate with both anxious arousal and apprehension. We also evidence suggests that anxiety may not be a categorical sepa-
observed volumetric differences in the amygdala and hip- ration from normalcy (Hyman 2010; Sanislow et al. 2010).
pocampus between anxious arousal and anxious apprehen- Converging neuroscience and psychological research points
sion. Whereas anxious arousal, but not apprehension, pre- to two transdiagnostic facets of anxiety: anxious arousal
dicted left amygdala volume, anxious apprehension, but not (AAr) and anxious apprehension (AAp; Sharp et al. 2015).
arousal, predicted right hippocampal volume. These findings AAr denotes, for example, hypervigilance and hyperarousal
demonstrated that there are both differences and similari- of the sympathetic nervous system. In contrast, AAp refers
ties in the neural regions that contribute to independent fac- the tendency to engage in negative, repetitive thinking (e.g.,
ets of anxiety. Results are discussed in terms of previous worry). Although these two facets of anxiety are distinct,
they are also related and often collapsed into one domain
(e.g., trait anxiety). In the neuroimaging literature, the use of
a single measure of anxiety has begun to provide tremendous
Electronic supplementary material The online version of
insight into the neural basis of anxiety (e.g., Haddad et al.
this article (https://doi.org/10.1007/s11682-017-9772-1) contains
2015; Hare et al. 2008; Lau et al. 2011). However, this col-
supplementary material, which is available to authorized users.
lapsing of AAr and AAp may be obscuring a more complete
* Steven G. Greening explanation of the neural underpinnings of anxiety. A better
[email protected]
understanding of AAr and AAp may help elucidate the fac-
1 Department of Psychology, Louisiana State University, 236 tors influencing anxiety during development (Sharp et al.
Audubon Hall, Baton Rouge, LA 70803, USA 2015). The primary purpose of the current study is to delin-
2 Pennington Biomedical Research Center, Baton Rouge, LA, eate the relationship between AAr and cortical thickness
USA versus the relationship between AAp and cortical thickness
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Brain Imaging and Behavior
in a sample of typically functioning youth. The secondary as a domain-general structure that plays a role in reducing
aim is to determine where in the brain cortical thickness learned fear by encoding a “safety signal.” Finally, a review
significantly correlates with both components of anxiety. conducted by Etkin et al. (2011), posited that vmPFC might
Most closely related to AAr, anxious and fear-related serve a general negative emotion inhibitory function.
reactivity has been linked with a core network of cortical In addition to the cortical regions implicated above in the
and subcortical brain regions including the anterior insula emotion regulation process, the hippocampus also appears to
(aIN) and the amygdala in both adolescents and adults be involved in regulating the amygdala (Alvarez et al. 2008;
(Haddad et al. 2015; Hare et al. 2008; Lau et al. 2011; Milad Milad and Quirk 2002; Ehrlich et al. 2009; Silvers et al.
and Quirk 2012; Sehlmeyer et al. 2009; Silvers et al. 2015, 2015). For example, the hippocampus can affect the amyg-
2016a, b). The aIN plays a crucial role in emotional reactiv- dala response to emotional stimuli when forming episodic
ity, interoceptive awareness (Craig 2009), and the generation memories of the emotional significance and interpretation
of affective responses (Phillips 2003). It is also a target, and of events (Phelps 2004). Moreover, studies on fear extinc-
it is modulated by emotion regulation (Caria et al. 2010; tion and regulation, find that a neural circuit including the
Morris et al. 1998). Similarly, amygdala activity has been hippocampus is implicated in the contextual regulation of
consistently found during fear conditioning (e.g., Haddad fear (Maren et al. 2013; Åhs et al. 2015). Taken together,
et al. 2015), and while encoding emotionally salient stimuli research to date has found that anxiety/fear reactivity typi-
(Canli et al. 2000). Amygdala activity has also been found to cally involves the amygdala and insula, which can be modu-
be perturbed in those with an anxiety disorder (Rauch et al. lated by frontoparietal, cognitive control, networks and by
2003). Together, the aIN and the amygdala have been impli- interconnections with the hippocampus. While much of the
cated both in emotional reactivity and emotional attention existing evidence implicating the above regions is based
(LeDoux 1995; Menon and Uddin 2010), and in anxiety- on studies of neural function, it has been previously dem-
related processes in individuals with anxiety disorders (Etkin onstrated that cortical thickness relates to findings derived
and Wager 2007). Moreover, their activity reflects individual from studies utilizing functional magnetic resonance imag-
differences in both state and trait anxiety (Bishop et al. 2004; ing (Choi et al. 2008; Ilg et al. 2008).
Carlson et al. 2011; Etkin et al. 2004; Greening et al. 2016; To date only two studies have evaluated the relationship
Sehlmeyer et al. 2011; Somerville et al. 2004, 2010; Stein between grey-matter (i.e., cortical thickness) and anxiety in
et al. 2007). youth, and both found a consistent anxiety by age interac-
Juxtaposed to AAp and maladaptive thinking, the healthy tion. Ducharme and colleagues (2014) found that the cortical
cognitive regulation of anxious and fear-related reactivity thickness in portions of the vmPFC is positively correlated
(i.e., emotion regulation) requires recruitment of aspects with self-reported anxiety in youth and young adults. Using
of the prefrontal cortex. For instance, interactions between a region-of-interest analysis, Newman and colleagues (2016)
prefrontal cortex regions such as ventrolateral (vlPFC), found that anxiety was negatively correlated with vmPFC
dorsolateral (dlPFC), and dorsomedial (dmPFC) prefrontal cortical thickness in children and younger youth, though
cortices have been implicated in healthy adults during the these associations diminished with age. Newman et al. also
cognitive control of negative emotion (Wager et al. 2008; found that higher anxiety in children may be characterized
Buhle et al. 2014; Silvers et al. 2015, 2016a). Silvers and by delayed expansion of the ventral PFC and an altered tra-
colleagues (2016a) recently extended adult emotion regu- jectory of cortical thinning. Moreover, they found a positive
lation findings to children and adolescents. They observed association between anxiety and the inferior parietal lobe,
greater dlPFC recruitment during reappraisal (compared to a supramarginal, and superior temporal gyrus.
control condition), and found that vlPFC recruitment medi- Although there is a growing consensus in the literature
ates the relationship between increasing age and diminish- regarding the relationship between cortical thickness and
ing amygdala response. Additionally, they found that ado- global anxiety, research parsing the neural correlates of dif-
lescents had a greater dmPFC response to negative versus ferent facets of anxiety is needed. Moreover, research on cor-
neutral emotional scenes. tical thickness and anxiety in youth has generally relied on
There is a substantial literature that suggests that both parent-reported anxiety; however, research has demonstrated
AAr and AAp may relate to vmPFC thickness. The somatic that youth are more accurate reporters of their anxiety (De
marker hypothesis (Damasio et al. 1991) posits that emo- Los Reyes and Kazdin 2005).
tion-based signals arising from the body are integrated in The current study looks to fill these critical gaps in the lit-
the vmPFC to regulate decision-making in complex and/or erature by delineating differences, as well as similarities, in
uncertain situations. Further, Diekhof et al. (2011) suggested cortical thickness in youth-reported AAr and AAp. Utilizing
that vmPFC acts as a domain-general regulator of negative a publicly available online database, we first determined the
affect in complex, cognitive regulation contexts. Similarly, relationship between cortical thickness and AAr, and corti-
Schiller and Delgado (2010) hypothesize that vmPFC serves cal thickness and AAp in a sample of youth, independently.
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Brain Imaging and Behavior
Next, to address our primary aim we quantify the neural a Siemens TrioTM 3.0 T MRI scanner. The 3D T1-weighted
differences using an interaction analysis. Last, we address images were acquired using a magnetization-prepared rapid
our secondary aim, by quantifying the significant similari- gradient echo (MPRAGE) sequence (TR/TE = 2500/3.5 ms,
ties between AAr and cortical thickness and AAp and corti- inversion time = 1200 ms, FA = 8°, FOV = 256 × 256 mm2,
cal thickness using a conjunction analysis. Given previous voxel size = 1.0 × 1.0 × 1.0 mm3, number of slices = 192) and
research on the effects of age on the correlation between were used for spatial normalization and group-specific tem-
cortical thickness and anxiety (Ducharme et al. 2014; New- plate generation. More details of the MR protocol are avail-
man et al. 2016), the well-established gender differences in able online (http://fcon_1000.projects.nitrc.org/indi/enhanced/
anxiety prevalence (Cartwright-Hatton et al. 2006; Merikan- mri_protocol.html). Further phenotypic information may be
gas et al. 2010), and the effects of handedness on regional accessed via the NKI website (see http://fcon_1000.projects.
asymmetries (Hamilton et al. 2007) our analyses controlled nitrc.org/indi/enhanced).
for the effects of age, gender, and handedness. We tested
the hypothesis that AAr, compared to AAp, would be more Cortical reconstruction and calculation of thickness
strongly correlated with grey-matter in regions associated
with emotional reactivity, including cortical thickness of the Cortical thickness was estimated from the structural magnetic
aIN and amygdala volume. In contrast, we predicted that resonance images using FreeSurfer software (http://surfer.
AAp would be more strongly correlated with regions impli- nmr.mgh.harvard.edu, Dale et al. 1999), a set of automated
cated in cognitive facets of anxiety, including both corti- tools for the reconstruction of brain cortical surface (Fischl
cal thickness in areas implicated in the cognitive control of and Dale 2000). First, we used the T1-weighted images to
emotions such as dlPFC, dmPFC, and vlPFC, and volume segment cerebral white matter and to estimate the grey-white
in the hippocampus. Based on the only two previous stud- matter interface. Then topographical defects in the grey-white
ies regarding cortical thickness and trait-anxiety in youth, estimate were fixed. This grey-white matter estimate was used
along with a growing literature on the role of the vmPFC as as the starting point of a deformable surface algorithm search-
a domain-general regulator of negative affect, we predicted ing for the pial surface. The whole cortex of each individual
that the conjunction analysis would reveal that vmPFC thick- subject was visually inspected for inaccuracies in segmen-
ness was positively correlated with both AAr and AAp. tation and manually corrected if necessary. Local cortical
thickness was measured based on the difference between the
position of equivalent vertices in the pial and grey-white mat-
Materials and methods ter surfaces. The surface of the grey-white matter border was
inflated and differences between subjects in the depth of gyri
Participants and sulci were normalized. Each subject’s reconstructed brain
was morphed and registered to an average spherical surface.
The present study included a total of thirty-five typically In order to obtain cortical thickness difference maps the data
developing youth (8–17 years; Mage = 13, SD = 2.5; 17 were smoothed on the surface using a Gaussian smoothing
females, 18 males) from the NKI Rockland Sample (NKI- kernel with a full-width half maximum of 10 mm. Statistical
RS), which is provided by the Nathan Kline Institute (NKI, thickness difference maps were constructed using t-statistics.
NY, USA) and publicly available at the International Neu- We used a regression approach to focus on the correlation
roimaging Data-sharing Initiative (INDI) online database with anxious arousal (i.e., Physical subscale of MASC) and
(http://fcon_1000.projects.nitrc.org/indi/pro/nki.html; anxious apprehension (i.e., Social Anxiety subscale of Multi-
Nooner et al. 2012). The NKI institutional review board dimensional Anxiety Scale for Children; MASC), controlling
approved all approvals and procedures for collection and for age, sex, and handedness using general linear modeling
sharing of data. Written informed consent was obtained from (www.surfer.nmr.mgh.harvard.edu). Our rationale for using
each participant. For those children who were unable to give these subscales as measures of anxious arousal and anxious
informed consent, written informed consent was obtained apprehension is described in more detail in the following sec-
from their legal guardian. Further details regarding the tion. Only regions that survived a Monte Carlo correction
study image acquisition protocol are available on the INDI (p < .05) are shown.
website.
Anxiety measure
Imagining protocol
Multidimensional anxiety scale for children (MASC)
The following description of the imaging protocol is taken
from NKI source (http://fcon_1000.projects.nitrc.org/indi/ The MASC (March 1998) is a 39-item, 4-point Likert-type
enhanced/mri_protocol.html). All subjects were scanned using scale with robust psychometric properties (March et al.
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1999; Rynn et al. 2006). The MASC is a child self-report and handedness as covariates in all statistical models since
questionnaire for symptoms of anxiety, with high scores age is negatively associated with cortical thickness (Fjell
indicating greater childhood anxiety. It is widely used et al. 2009; Salat et al. 2004; Westlye et al. 2009), girls tend
and generally recommended as a key tool for child anxi- to have elevated levels of anxiety compared to boys (Kes-
ety assessment (Silverman and Ollendick 2005). The three sler et al. 2012), and handedness significantly influences
empirically derived factor index scores are Social Anxiety, regional asymmetries (Hamilton et al. 2007). The correla-
Physical Symptoms, and Harm Avoidance. The MASC has tions between full scale intelligence quotient (WASI) and
shown good internal consistency ratings, with Cronbach’s anxious arousal and anxious apprehension (p = .59; p = .39,
alpha ranging from 0.74 to 0.85 (March et al. 1999). The respectively) were not significant and therefore not included
current study used the Physical Symptoms index as a meas- as a covariate. Thus, all GLMs were controlled for age as a
ure of anxious arousal (e.g., “I get shaky or jittery” and “My continuous variable, while the effects of gender and handed-
heart races or skips a beat”). In contrast, anxious apprehen- ness were controlled for as categorical variables.
sion was assessed using the Social Anxiety index total score, To reduce the probability of Type I errors, all cortical
as items deal with cognitive facets of anxiety (e.g., “I worry thickness analyses were corrected for multiple comparisons
about other people laughing at me” and “I worry about being using cluster-wise inference by means of Z Monte Carlo
called on in class”). Research has not found any age effects simulations as implemented in FreeSurfer (Hayasaka and
on youth’s self-reporting on the MASC (Grills-Taquechel Nichols 2003; Hagler et al. 2006). The Z Monte Carlo simu-
et al. 2008; Wood et al. 2002). Furthermore, research has lations tests the data against an empirical null distribution of
shown that the MASC Social Anxiety index is unable to maximum cluster size across 10,000 permutations, synthe-
significantly distinguish either children with comorbid social sized with a cluster-forming threshold of p < .05 (two-sided),
anxiety disorder and generalized anxiety disorder, or chil- yielding clusters fully corrected for multiple comparisons
dren with social anxiety disorder and comorbid separation across the surfaces. Clusters with a clusterwise corrected p
anxiety disorder (Viana et al. 2008). The Social Anxiety (CPW) < 0.05 were regarded as significant. These analyses
index has been highly correlated (r = .55) with the Penn yielded clusters fully corrected for multiple comparisons
State Worry Questionnaire, as well as the Generalized Anxi- across the surface. The initial cluster-forming threshold
ety Disorder Questionnaire for DSM-IV (r = .68; Boelen and employed in this way was p < .05. We also present surface-
Reijntjes 2009). Conversely, measures of worry typically based maps for each corrected cluster, which use Freesurf-
have lower correlations with anxious arousal scales (e.g., er’s standard -log(p) as the measure, but we have converted
r = .27; Moran et al. 2012). In sum, there is evidence that the the scale to the corresponding p-values for interpretability.
MASC Social Anxiety index is a valid measure of anxious Given the well-established roles of the amygdala and
apprehension. The Harm Avoidance index was not examined hippocampus in global anxiety, we ran a region of interest
as it primarily deals with behavioral facets of anxiety, which analysis with the amygdala, hippocampus volumes, and two
were beyond the scope of the current project. facets of anxiety. Specifically, four multiple linear regres-
sions were conducted to predict (right and left) amygdala or
Statistical analyses hippocampus volumes. All multiple linear regressions were
conducted in the same manner: the first block included age
The first step towards testing our primary hypothesis was to and handedness as covariates, whereas the second block
quantify the relations in cortical thickness between youth included both anxious arousal and anxious apprehension.
on self-reported measures of anxious arousal and anxious Age and handedness were selected as covariates as previ-
apprehension, respectively. This hypothesis was tested ver- ous literature has demonstrated that handedness, but not
tex-wise across the brain surface by fitting general linear gender, affect amygdalar and hippocampal volume (Szabo
models (GLMs) of the effect of MASC scores (i.e., anxious et al. 2001).
arousal and anxious apprehension) on cortical thickness in In sum, we first determined the independent associa-
every vertex across the surface. Thus, we performed sepa- tions between anxious arousal and cortical thickness and
rate analyses for anxious arousal and anxious apprehension anxious apprehension and cortical thickness in youth. We
to determine their independent relationships with cortical next completed an interaction analysis followed by conjunc-
thickness across youth. Our primary hypothesis was that tion analysis to determine the differences and similarities,
there would be quantitative differences in cortical thick- respectively, between anxious arousal and cortical thickness
ness between youth’s self-reported anxious arousal and versus anxious apprehension and cortical thickness. Finally,
anxious apprehension. Our secondary hypothesis was that a region of interest analysis was conducted to examine
there would be quantitative similarities in cortical thick- whether well-established subcortical regions (i.e., amygdala
ness between youth on self-reported measures of anxious and hippocampus) differentially relate to anxious arousal
arousal and anxious apprehension. We included age, gender, and anxious apprehension.
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Results handedness as covariates. All clusters showed a positive
effect, and no negative effects between either AAr or AAp
Demographics and cortical thickness were observed. Overall, AAr was
significantly predictive of cortical thickness in a number of
Youths’ demographic, psychometric, and neurocognitive regions implicated in motor planning, somatosensation and
data are reported in Table 1. All subjects had at the Wechsler arousal, including the supplementary motor area, postcentral
Abbreviated Scale of Intelligence, a Full-Scale Intelligence gyrus and aIN. Both AAr and AAp appeared significantly
Quotient over 80 (see mean and minimum/maximum values predictive of cortical thickness in various frontoparietal
in Table 1). At the time of data acquisition from the NKI- regions involved in attention and cognition control networks
RS database, T1-weighted MRI scans were available for 46 and emotion regulation, including the dlPFC and the inferior
children and adolescents. Four youth were excluded from the parietal lobe.
analysis because their MRI scan did not survive quality con- Given the role age has on the relationship between
trol inspection. Seven additional participants were excluded anxiety and cortical thickness (e.g., Ducharme et al. 2014;
because of missing MASC scores. Thus, the final sample, Newman et al. 2016), we ran follow-up analyses with age
on which the analyses were performed included 35 youth. removed as a covariate and included as a between-subjects
factor (i.e., median age split) to test the prediction that the
Independent general linear models of anxious arousal relationship between AAr and cortical thickness and AAp
and cortical thickness, and anxious apprehension and cortical thickness would not differ between age groups.
and cortical thickness Two Independent GLMs (i.e., AAr and cortical thickness
between age groups; AAp and cortical thickness between
In order to first determine the independent relationship age groups) were conducted in the same manner outlined
between anxious arousal (AAr) and cortical thickness, and above. Results for both AAr and AAp, respectively, indi-
anxious apprehension (AAp) and cortical thickness we ran cated that no clusters survived correction.
two separate, independent GLMs testing the linear effects
between the given component of anxiety and cortical thick- General linear model of the interaction between anxious
ness in a vertex-wise manner. We then generated spatial Z arousal and anxious apprehension
Monte Carlo Simulation (zMCS) corrected p-maps from
the GLMs. The full results for each of these independent Figure 1 shows Monte Carlo Simulation (MCS) corrected
regression analyses can be found in Table 2. We then gener- maps from a GLM testing the interaction in cortical thick-
ated spatial Z Monte Carlo Simulation (zMCS) corrected ness between AAr and AAp in a cluster-wise manner, cor-
p-maps from the GLMs. The full results for each of these rect to p < .05. The MCS, as implemented in Freesurfer,
independent regression analyses can be found in Table 2. We utilizes the covariance matrix of the observed variables
provide information for each significant cluster, after zMCS (computed for given values on the parameters in the model)
correction, from the linear regression with age, gender, and and then data are generated on the observed variables from a
multivariate distribution having this covariance matrix. The
MCS then tests the data against an empirical null distribu-
Table 1 Demographics and psychometric scales
tion of maximum cluster size across 10,000 permutations.
Demographics (n = 35) N (%) or mean ± SD (range) Full results can be found in Table 3, which shows significant
Males 18 (51.4%) zMCS corrected clusters resulting from the interaction in
Age (years) 13.09 ± 2.55 (8–17) cortical thickness with AAr and AAp and age, gender, and
Right handedness 30 (85.7%) handedness as covariates. The interaction analysis examined
Race whether there were regions in which cortical thickness was
 White 25 (68.6%) predicted by AAr significantly more than by AAp, and vice
 Black 7 (20.0%) versa. All clusters showed a positive effect, indicating that
 Asian 4 (11.4%) cortical thickness in the observed regions was significantly
Ethnicity-Non-Hispanic/Spanish 24 (68.6%) related to AAr compared to AAp. A lack of negative effects
Psychometric scales (n = 35) indicated that cortical thickness was not predicted signifi-
MASC-Social anxiety subscale (i.e., 49.17 ± 9.40 (35–75) cantly better by AAp compared to AAr in any regions. Most
anxious apprehension) notably, in the left hemisphere, significant clusters were
MASC-Physical anxiety subscale (i.e., 43.37 ± 8.31 (33–65) observed in the precentral gyrus extending into the postcen-
anxious arousal) tral gyrus (p < .01) and superior frontal gyrus, including sup-
MASC-Total score 46.37 ± 9.97 (26–70) plementary motor area (SMA) (p < .01). Similarly, the right
WASI-FSIQ 108.82 ± 12.05 (83–129)
hemisphere had significant clusters in the precentral gyrus
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Brain Imaging and Behavior
Table 2 The independent relationships between anxious arousal and cortical thickness and anxious apprehension and cortical thickness
Variable/Location L/R X Y Z Cluster size Max effect-size CWP
Anxious arousal
 Positive effects
  Caudal middle Frontal/Dorsomedial prefrontal cortex L − 34.9 16.2 24.2 829.51 4.52 0.03
  Superior-inferior parietal/Supramarginal/Superior-Inferior temporal L − 20.6 − 69.1 34.6 7751.72 3.74 0.01
  Precuneus/Paracentral gyrus L − 7.0 − 51.2 57.6 921.39 3.40 0.01
  Inferior temporal L − 44.9 − 18.8 − 28.1 1166.66 3.24 0.01
  Postcentral/Precentral gyrus L − 48.3 − 25.2 56.6 1411.87 2.85 0.01
  Rostral middle frontal/Dorsal lateral prefrontal cortex L − 20.7 57.7 13.9 2330.22 2.63 0.01
  Anterior insula/Superior temporal L − 37.6 − 8.9 − 13.4 1647.04 2.60 0.01
  Lateral occipital L − 32.9 − 87.0 − 4.3 599.44 2.00 0.04
  Parsorbitalis L − 36.0 44.8 − 10.6 653.88 1.45 0.04
  Superior frontal/Dorsal medial prefrontal cortex R 23.0 − 1.7 47.2 3429.90 4.08 0.01
  Superior parietal/Inferior parietal R 24.3 − 55.8 55.7 2700.31 3.83 0.01
  Inferior temporal R 50.6 − 57.1 − 10.0 902.05 3.62 0.01
  Superior temporal R 59.4 − 7.6 0.2 566.73 3.54 0.05
  Superior frontal/Dorsal lateral prefrontal cortex R 8.1 46.1 36.0 2451.76 3.23 0.01
  Inferior parietal/Supramarginal gyrus R 53.3 − 44.0 18.4 549.17 2.94 0.05
  Parahippocampal/Anterior insula R 21.7 − 43.9 − 9.6 2474.14 2.89 0.01
Anxious apprehension
 Positive effects
  Temporal pole/Anterior insula L − 33.0 14.5 − 37.6 2316.99 3.10 0.01
  Parsopercularis/Ventral lateral prefrontal cortex L − 50.2 17.5 12.4 845.29 2.66 0.01
  Inferior parietal L − 38.2 − 82.9 24.0 507.84 2.09 0.05
  Entorhinal/Temporal pole R 27.1 3.9 − 36.3 1352.60 3.22 0.01
  Rostral middle frontal/Dorsal lateral prefrontal cortex R 47.6 32.1 25.2 606.20 2.51 0.04
  Inferior parietal R 42.2 − 64.0 43.6 843.03 2.13 0.03
  Parsorbitalis/Lateral orbitofrontal/Ventral lateral prefrontal cortex R 43.6 27.9 − 13.4 677.18 1.96 0.04
Max Effect-Size based on the voxel of peak effect size, expressed in Freesurfer’s -log10(p); CWP is the cluster-wise probability, which is the
corrected p-value; Cluster size is m m2
(p < .05) and caudal middle frontal gyrus including an aspect against type one errors within the conjunction analysis, we
of SMA (p < .01). Also in the right hemisphere, we observed utilized corrected p-maps from the two independent GLMs
a large cluster that included the supramarginal gyrus extend- that only include clusters that survived correction (i.e.,
ing into the superior and inferior temporal gyrus, and, criti- AAr and cortical thickness ∩ AAp and cortical thickness).
cally, reaching into the anterior insula (p < .05). Figure 2 shows the results of the conjunction between AAr
and cortical thickness, and AAp and cortical thickness in a
Conjunction between anxious arousal and cortical cluster-wise manner. All clusters showed a positive effect,
thickness, and anxious apprehension and cortical indicating that clusters increase in thickness in youth report-
thickness ing higher levels of AAr and AAp. The full results of the
conjunction are displayed in Table 4. The conjunction anal-
Given the apparent similarities between cortical thickness ysis revealed robust bilateral clusters in the frontoparietal
and each of AAr and AAp, we quantified these similarities regions implicated in emotion regulation. Specifically, we
using a conjunction analysis. This allowed for the quantifica- found significant clusters in bilateral dlPFC, and bilateral
tion of contiguous volumes in which anxiety was associated inferior parietal lobe (IPL). We also observed significant
with cortical thickness. To determine if significant overlap clusters in the left aIN and the right temporal pole. All sig-
exists for areas in which cortical thickness is predicted by nificant clusters showed a positive association with AAr and
both AAr and AAp we performed a conjunction analysis; AAp, indicating that youth with elevated levels of AAr and
this combines the two independent GLMs (i.e., AAr and AAp had increased cortical thickness in the aforementioned
cortical thickness and AAp and cortical thickness). To guard regions.
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Fig. 1 Significant interaction between AAr and cortical thickness flight response. aIN = anterior insula, ITG = inferior temporal gyrus,
versus AAp and cortical thickness. This interaction revealed a sig- PreCG = precentral gyrus, PostCG = postcentral gyrus, SFG = supe-
nificantly stronger, positive, relationship between AAr and cortical rior frontal gyrus, SMA = supplementary motor area, SMG = supra-
thickness compared to the relationship between AAp and cortical marginal gyrus, STG = superior temporal gyrus. LH = left hemi-
thickness. Specifically, higher anxious arousal, but not anxious appre- sphere, RH = right hemisphere. The red-yellow scale represents the
hension, is associated with greater cortical thickness in a number of size of the p-value
regions involved with motoric and autonomic aspects of the fight-or-
Table 3 Interaction between cortical thickness, anxious arousal and anxious apprehension
Variable/Location L/R X Y Z Cluster Size Max effect-size CWP
Positive effects
 Precentral/Postcentral L − 14.0 − 18.4 66.9 1209.06 3.45 0.01
 Superior frontal gyrus/Supplementary motor area L − 21.0 20.6 56.2 1068.98 2.17 0.04
 Supramarginal gyrus/Superior-inferior temporal gyrus/ R 43.7 − 37.9 39.5 6147.22 4.66 0.01
Anterior insula
 Precentral gyrus R 36.8 − 15.2 55.6 700.04 3.08 0.04
 Caudal middle frontal gyrus/Supplementary motor area R 40.6 16.0 41.7 1421.76 2.31 0.04
 Parahippocampal gyrus R 23.9 − 26.3 − 22.0 712.20 2.81 0.01
Max Effect-Size based on the voxel of peak effect size, expressed in Freesurfer’s -log10(p); CWP is the cluster-wise probability, which is the
corrected p-value; Cluster size is m m2
These findings indicate that there is a core set of Region of interest analysis: correlations
regions in which cortical thickness in youth is associated and hierarchical linear regressions with amygdala
with more domain general anxiety (i.e., independent of and hippocampus volume
the facet; related to both AAr and AAp). We found no
evidence for a negative relationship between brain thick- Given the well-established roles of the amygdala and hip-
ness and either AAr and/or AAp. pocampus in global anxiety, we ran a region of interest
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Fig. 2 Significant conjunction between both areas in which AAr STG = anterior insula/supplementary motor cortex, dlPFC = dorsolat-
relates to cortical thickness and areas in which AAp relates to corti- eral prefrontal cortex, IPL/LO = inferior parietal lobe/lateral occipital
cal thickness. This conjunction revealed a significant cortical thick- lobe, IPL/STS = inferior parietal lobe/superior temporal sulcus, MTG/
ness clusters positively related to both AAr and AAp. Specifically, ITG = middle temporal gyrus/inferior temporal gyrus, SPL = superior
higher AAr and AAp demonstrated similar relationships to cortical parietal lobe. LH = left hemisphere, RH = right hemisphere. The red-
thickness in a number of regions involved in emotion regulation. aIN/ yellow scale represents the size of the p-value
Table 4 Conjunction analysis between cortical thickness, anxious arousal and anxious apprehension
Variable/Location L/R X Y Z Cluster size Max effect-size
Positive effects
 Superior temporal gyrus/Anterior insula L − 38.7 − 8.9 − 12.8 969.05 1.95
 Parsopercularis/Dorsolateral prefrontal cortex L − 35.2 9.9 21.7 291.65 2.00
 Inferior parietal lobe/Lateral occipital lobe L − 37.8 − 79.2 26.1 487.11 2.09
 Middle-inferior temporal gyrus R 40.0 6.3 − 31.7 1067.09 2.15
 Superior parietal lobe R 34.0 − 61.1 44.3 191.97 1.72
 Rostral middle frontal gyrus/Dorsal lateral prefron- R 47.2 32.3 21.6 584.59 2.51
tal cortex
 Inferior parietal lobe/Superior temporal sulcus R 45.7 − 55.1 25.9 477.08 2.04
Max Effect-Size based on the voxel of peak effect size, expressed in Freesurfer’s -log10(p); CWP is the cluster-wise probability, which is the
corrected p-value; Cluster size is m m2
analysis with the amygdala and hippocampus and two facets hippocampus volume, while controlling for the collin-
of anxiety. Partial correlations controlling for the effects of earity between AAr and AAp, four separate hierarchical
age and gender were examined. AAp did not significantly linear regressions were conducted. All regressions were
correlate with amygdala volume (p = .25). In contrast, AAr conducted in the same manner: the first block included
demonstrated a significant correlation with the right amyg- age and handedness as covariates; the second block
dala volume (r = .32, p < .05) but not left amygdala volume included both AAr and AAp to predict either right or left
(r = .31, p = .07; see Fig. 3). Conversely, both AAr (r = .39, amygdala and hippocampal volume. Age and handedness
p < .05) and AAp (r = .42, p < .05) significantly correlated were selected as covariates as previous literature has dem-
with right, but not left, hippocampal volume (see Fig. 3). onstrated that handedness, but not gender, affect amyg-
To further explore whether these two facets of anxi- dala and hippocampus volume (Szabo et al. 2001). When
ety are differentially related to both amygdala and predicting left amygdala volume, AAr, but not AAp,
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Brain Imaging and Behavior
Fig. 3 Independent partial correlations between each of AAr and and left amygdala volume. (c) The significant partial correlation
AAp with grey-matter volume in amygdala and hippocampus, con- (r = .39, p < .05) between AAr and right hippocampal volume. (d)
trolling for age and gender. (a) The significant partial correlation The significant partial correlation (r = .42, p < .05) between AAp and
(r = .32, p < .05) between AAr and right amygdala. (b) The trending right hippocampal volume
towards significant partial correlation (r = .31, p < .07) between AAr
was a significant predictor, with higher arousal predict- Discussion
ing greater left amygdala volume (β= 0.50; t(31) = 2.16,
p < .05). When predicting right amygdala volume, no The goal of the current study was to delineate the neural
predictors were significant. In contrast, right hippocam- correlates of two facets of anxiety, anxious arousal and anx-
pal volume was significantly predicted by AAp (β= 0.45; ious apprehension (AAr and AAp, respectively), that are
t(31) = 2.11, p < .05), but not AAr (p > .05). No predictors often collapsed into one domain (e.g., trait anxiety). We
were significant in predicting left hippocampal volume examined the neural similarities and neural differences in
(all p’s > 0.05). Overall, these findings suggest left amyg- cortical thickness associated with each facet of anxiety in
dala and right hippocampal volume can be differentially a sample of typically functioning youth. Our results identi-
predicted by AAr and AAp, respectively, when control- fied distinct regions of cortical grey-matter independently
ling for age, handedness, and the collinearity between related to each of AAr and AAp. Specifically, we observed
AAp and AAr. that the correlation between AAr and cortical thickness
Exploratory correlations were examined between AAr, was significantly greater in the right aIN, right SMA, left
AAp, and the volume of parts of the basal ganglia and supramarginal and left/right precentral gyrus compared
thalamus. The full results are presented in the Supple- to the relationship between AAp and cortical thickness.
mental Materials section (see Supp. Table 1). Notably, Moreover, AAr was found to be related to both right and
whereas AAr, but not AAp, was significantly correlated left aIN, whereas AAp was only related to the left aIN. We
with the left thalamus volume (r = .50, p < .01; r = .26, found no regions in which the correlation between AAp and
p > .05, respectively), AAp, but not AAr, was signifi- cortical thickness was significantly greater than AAr and
cantly correlated with right caudate volume (r = .35, thickness. We also found significant differences in subcorti-
p < .05; r = .29, p > .05, respectively). cal grey volume, specifically, AAr, but not AAp, predicted
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Brain Imaging and Behavior
left amygdala volume whereas AAp, but not AAr, predicted Notably, this included a cluster in the more dorsal and pos-
right hippocampal volume. Additionally, we found that a terior aspects of the frontal lobe, which potentially over-
positive correlation between cortical thickness and each of laps with the supplementary motor area, and a cluster that
AAp and AAr in regions implicated in emotional reactivity, reached into aspects of the supramarginal gyrus. The premo-
such as the aIN, and of the frontoparietal cortices involved tor cortex has been implicated in motivation (Roesch and
in cognitive control and cognitive emotion regulation. Olson 2004), though some have suggested that this may bet-
Overall, these findings provide partial support for our ter reflect enhanced arousal (Ernst and Paulus 2005), which
first hypothesis that AAr, compared to AAp, would be more has some support (Colibazzi et al. 2010). The AAr appears
strongly correlated with cortical thickness of the aIN and to relate to regions implicated in the fear system broadly,
amygdala volume. These results also partially support our including motor regions involved in the motor responses
second hypothesis that AAp would be more strongly corre- associated with a fight-or-flight response. Finally, we found
lated with regions implicated in cognitive facets of anxiety that the supramarginal gyrus had a stronger association with
(i.e., dlPFC, dmPFC, and vlPFC) and hippocampus volume. AAr. This finding mirror that of Newman and colleagues
Our final hypothesis, that the vmPFC would be positively (2016), and extends it by suggesting that this region may not
correlated with both AAr and AAp, was not supported by be domain general (i.e., related to overall trait anxiety), but
our results, though we did observe overlap if several regions domain specific to AAr.
including bilateral frontoparietal regions. Additionally, we found the left, but not right, anterior
insula (aIN) was positively correlated with both AAr and
Interaction analysis AAp. From a network perspective, Menon and Uddin (2010)
posit that the aIN along with the anterior cingulate cortex
Our interaction analysis provided novel findings that AAp and vlPFC are the primary components of the salience net-
and AAr also have domain specific relationships with corti- work that aid in the detection of important environmental
cal thickness. These findings provide some support for the stimuli. During cognitively demanding tasks, the central-
distinction between these two transdiagnostic facets of anxi- executive network (i.e., dlPFC and posterior parietal cor-
ety (Sharp et al. 2015). Somewhat surprisingly, all effects tex) and the salience network typically show increases in
were in the same direction, which indicate that thickness in activation, as vmPFC and posterior cingulate cortex (i.e.,
the observed regions were associated with a more robust the default mode network) showed decreases in activation
positive relationship to AAr compared to AAp. (Beckmann et al. 2005; Fox et al. 2006; Greicius et al. 2003;
Our first observation was that a number of the regions Seeley et al. 2007). Moreover, the salience network has been
we found more significantly related to AAr are those areas found to play a causal role in switching between the central-
that are related to aspects of sensation and interoception. executive network and the default mode network (Sridharan
For example, the right precentral gyrus stretching into the et al. 2008). Thus, our finding that the aIN was related to
paracentral lobule was found to be more specific to AAr in both AAr and AAp may reflect that the salience network
our study. This result highlights the role of somatosensory has a role in anxiety. Consistent with previous findings
cortical regions in AAr. This region is primarily involved in regarding hemispheric lateralization in the aIN, our findings
tactile sensation; however, some have suggested that soma- parallel those of Etkin and Wager’s (2007) meta-analysis
tosensory representations also aid in emotion recognition of emotional processing in individuals with social anxiety
by allowing an individual to link non-tactile perceptual cues and specific phobias. Etkin and Wager concluded that the
to bodily states associated with each emotional category relationship between left insula and anxiety may be domain
(Damasio 1996). Lesion studies have indicated that the inac- general. Conversely, we found evidence for domain specific-
tivation of somatosensory cortex impedes the recognition ity in the right anterior insula such that the correlation was
of emotion from vocal expressions (Adolphs et al. 2002; stronger for AAr and right aIN thickness (when compared
Banissy et al. 2010) and facial (Adolphs et al. 2000; Pitcher to AAp), and provides support for AAr being related to both
et al. 2008). Greater white-matter connectivity between the right and left insula activity, whereas AAp is only associated
amygdala and the paracentral lobule is also positively pre- with activity in the left aIN. Our findings add to a growing
dictive of individual differences in domain general anxiety literature on the lateralization of the insula. A meta-analysis
(i.e., trait anxiety; Greening and Mitchell 2015). Our results of insula laterality found that evidence that perceiving and
suggest that these areas may be more strongly linked to phys- experiencing emotion activated left insula, commensurate
iological sensations related to anxiety as opposed to higher with our finding that AAp was positively related to left, but
order cognitive processes related to anxiety (i.e., AAp). not right, insula thickness (Duerden et al. 2013).
Our interaction analysis revealed areas that are more Considering the well-established roles of the amyg-
commonly associated with cognitive and executive pro- dala and hippocampus in trait anxiety, we ran a region-
cesses were more strongly related to AAr compared to AAp. of-interest (ROI) analysis with these two regions and
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