Journal of Autism and Developmental Disorders

, Volume 47, Issue 12, pp 3682–3691 | Cite as

Amygdala Volume Differences in Autism Spectrum Disorder Are Related to Anxiety

  • John D. Herrington
  • Brenna B. Maddox
  • Connor M. Kerns
  • Keiran Rump
  • Julie A. Worley
  • Jennifer C. Bush
  • Alana J. McVey
  • Robert T. Schultz
  • Judith S. Miller
S.I. : Anxiety in Autism Spectrum Disorders


Recent studies suggest that longstanding findings of abnormal amygdala morphology in ASD may be related to symptoms of anxiety. To test this hypothesis, fifty-three children with ASD (mean age = 11.9) underwent structural MRI and were divided into subgroups to compare those with at least one anxiety disorder diagnosis (n = 29) to those without (n = 24) and to a typically developing control group (TDC; n = 37). Groups were matched on age and intellectual level. The ASD and anxiety group showed decreased right amygdala volume (controlled for total brain volume) relative to ASD without anxiety (p = .04) and TDCs (p = .068). Results suggest that youth with ASD and co-occurring anxiety have a distinct neurodevelopmental trajectory.


Amygdala Anxiety disorders Brain morphometry Comorbidity 


Because of the role of amygdala in the perception, encoding, and retrieval of emotional information (Whalen and Phelps 2009), it has been widely implicated in autism spectrum disorder (ASD)—perhaps more than any other region. However, despite widely documented differences in amygdala function in ASD, there is surprisingly little consensus regarding putative differences in amygdala anatomical structure. The literature on amygdala volume differences in ASD has been mixed, with findings of increased (Howard et al. 2000; Mosconi et al. 2009; Munson et al. 2006), decreased (Aylward et al. 1999; Nacewicz et al. 2006; Pierce et al. 2001) and comparable sizes (Haznedar et al. 2000). Some of the most compelling recent evidence indicates that amygdala volume follows a developmental trajectory whereby exuberant growth in childhood is followed by deceleration—and null volume differences—by adolescence (Schumann et al. 2004, 2010; Schumann and Nordahl 2011). A key implication of this work is that the binary question of whether ASD is associated with amygdala volume differences is likely to be overly simplistic.

Although seldom addressed by the ASD amygdala literature, amygdala structure is likely to vary along multiple clinical dimensions that cannot be entirely reduced to social abilities. Anxiety represents one of these dimensions. More than 40% of the ASD population experiences anxiety disorders that can be as challenging as, if not more than, the core symptoms of ASD themselves (Kerns et al. 2014; Simonoff et al. 2008; Sukhodolsky et al. 2008; van Steensel et al. 2011). Furthermore, entirely independent from ASD research, an extensive affective neuroscience literature strongly implicates abnormal amygdala function and structure in anxiety disorders.

There have been several studies on amygdala volume in both child and adult anxiety disorder populations, with the preponderance of evidence appearing to point to decreased volume (Blackmon et al. 2011; Fisler et al. 2013b; Hayano et al. 2009; Massana et al. 2003; Milham et al. 2005; Mueller et al. 2013). Excitotoxicity represents one possible mechanism for this decrease—where increased activity leads to degraded cellular architecture (for discussion see Milham et al. 2005). For example, Blackmon et al. (2011) hypothesized anxiety-driven increases in activity may diminish gray matter integrity in corticoid structures such as amygdala (while other meso- and isocortical structures may actually yield increased gray matter). This hypothesis is corroborated by non-human animal research; for example, in rodents, smaller basolateral amygdala has been associated with increased fear conditioning (Yang et al. 2008).

However, it should be noted that there are also studies associating anxiety with increased, rather than decreased, amygdala volume (De Bellis et al. 2000; Qin et al. 2014; van der Plas et al. 2010). The putative mechanisms of such an increase are rather different—namely increased arborization and diminished pruning (i.e., Qin et al. 2014). Although more data in this area are clearly needed, it is possible that there are, in fact, multiple, distinct neurodevelopmental pathways leading to anxiety disorders—which makes transdiagnostic research on the neurobiology of anxiety all the more pressing.

Overall, differences in amygdala function and structure are thought to be part of the etiology of anxiety disorders. It is therefore remarkable that there are no studies of amygdala volume in ASD that formally consider the co-occurrence of anxiety disorders (though see Juranek et al. 2006, for an examination of the relationship between amygdala volume and the anxious/depressed subdomain of the Child Behavior Checklist).

The absence of amygdala studies of anxiety in ASD is even more remarkable when considering the compelling evidence on this topic from non-human primate research. More than a century of preclinical lesion studies have implicated amygdala in normative social contact, with lesions to amygdala precipitating breakdowns in a variety of social behaviors (Aggleton and Passingham 1981; Brothers 1990; Brown and Schafer 1888; Kling and Brothers 1992; Kluver and Bucy 1939). However, as has recently been pointed out (Amaral et al. 2003), the majority of these studies had significant methodological limitations related to peer versus maternal rearing (most have relied on the former) and the precision of the lesioning procedures applied (which generally affect tissue outside of amygdala nuclei, as well as axonal projection pathways to and from other brain areas).

Research by Baumann, Amaral, and Schumann and others have accounted for these limitations and significantly altered how we understand the relationship between amygdala, social behavior, and anxiety (Amaral 2002; Amaral et al. 2003; Bauman et al. 2004a, b; Kazama et al. 2012; Prather et al. 2001). In a series of studies, they reported that, when provided something approximating a typical social and familial environment (maternal rearing and a semi-naturalistic field cage), monkeys with neonatal amygdala lesions developed “a complete repertoire of species-typical social signals” (Amaral et al. 2003, p. 298). However, neonates with amygdala lesions showed a significant increase in fear responses, particularly during social interaction (Prather et al. 2001).

These results open up important, largely unanswered questions about the relationship between amygdala, social deficits, and anxiety in ASD. One of the hypotheses proposed by the authors was that amygdala plays a critical role in the regulation of emotions during social contact, and the absence of amygdala in lesioned monkeys results not in social skill deficits per se, but in the inability to effectively regulate emotions and apply social skills when needed (Amaral et al. 2003). This hypothesis remains in need of further empirical support. Nevertheless, this series of primate studies suggest that amygdala differences in ASD have as much or more to do with the experience of anxiety than the development of social skills per se.

The present study tested the hypothesis that the co-occurrence of anxiety disorders in ASD (henceforth called “ASD + Anxiety”) can be distinguished from ASD without clinical anxiety (henceforth called “ASD Alone”) based on amygdala morphology (i.e., global and regional volume differences). Here, we rely primarily on a categorical approach—reporting data on a sample that has been characterized in depth for the presence or absence of formal anxiety disorder diagnoses via gold standard diagnostic interviewing. We also use a dimensional approach (an anxiety questionnaire) to examine the relation between anxiety symptoms and amygdala volume in ASD. Based on the amygdala/anxiety disorder literature (reviewed above), our primary prediction is that individuals with ASD + Anxiety show decreased amygdala volume relative to individuals with ASD Alone, and to age-matched typically developing controls (TDCs).



A total of 63 youth with ASD (19 female, mean age = 11.8) and 37 TDCs (15 female, mean age = 11.6) participated as part of a larger study on the co-occurrence of anxiety in ASD (conducted under the Institutional Review Board approval of the Children’s Hospital of Philadelphia). Based on diagnostic testing (discussed below), the ASD group was further subdivided into participants who had one or more co-occurring anxiety disorder(s) (ASD + Anxiety; n = 29, mean age = 11.9), and those who did not (ASD Alone; n = 24, mean age = 12.3). Ten participants with Anxiety Disorder Not Otherwise Specified were excluded from analysis (see more about this group below). Figure 1 displays the counts of specific anxiety disorder diagnoses within the sample (Separation Anxiety, Social Anxiety, Generalized Anxiety, and Specific Phobia; note that many participants had multiple anxiety disorder diagnoses, and that sample sizes within each specific diagnosis were too limited to examine amygdala differences between anxiety disorders). The three groups were matched on age (F(2,87) = 0.44, p = .65, all simple effect ps ≥ 0.62) and intellectual ability, as indexed by the General Conceptual Ability scale of the Differential Abilities Scale—Second Edition (DAS-II; Elliot 2007), F(2,84) = 1.56, p = .22 (all simple effect p’s ≥ 0.25; DAS-II data were unavailable for 1 participant in the ASD + Anxiety group and 2 participants in the TDC group). The ASD + Anxiety and ASD Alone groups were matched on overall ASD symptoms, as measured by the Social Communication Questionnaire (SCQ; Chandler et al. 2007), t(51) = 0.34, p = .73, and Autism Diagnostic Observation Schedule (Lord et al. 2002) Composite Score (using ADOS-2 algorithms; Lord et al. 2012), t(42) = 1.5, p = .14. According to parent report, all participants were free of neurological or seizure disorders. In the ASD + Anxiety group, 6 participants were taking SSRIs, 7 were taking stimulants, 2 were taking non-stimulant medication for attentional issues, and 1 was taking an atypical antipsychotic (Abilify). In the ASD Alone group, 8 participants were taking SSRIs, 5 were taking stimulants, and 5 were taking non-stimulant medication for attentional issues. No other psychoactive medications were reported. See Table 1 for sample characteristics.

Fig. 1

Anxiety disorder diagnoses in the ASD + Anxiety sample. Venn diagram illustrating diagnoses represented by the ASD + Anxiety sample (total n = 29). The following anxiety disorder diagnoses were included in the sample: generalized anxiety disorder (labeled GAD), specific phobia, social anxiety disorder (labeled Social Anxiety), and separation anxiety disorder (labeled Separation)

Table 1

Participant characteristics


N (F/M)



ADOS comp

SCQ total*

SCARED total*

Right amygdala volume

Left amygdala volume

Total brain volume

ASD + Anx

29 (7/22)

11.9/2.7 (7.6–17.5)

103/18 (66–142)

5.9/2.2 (1–10)

21.2/5.2 (10–31)

26.9/9.1 (14–41)

971/274 (370–1521)

1027/242 (696–1573)

1,269,832/103,544 (1,102,880–1,487,027)

ASD alone

24 (10/14)

12.3/3.2 (7.6–17.3)

104/19 (79–136)

7.0/2.5 (2–10)

21.8/5.7 (12–34)

10.6/6.2 (2–26)

1104/259 (718–1757)

1055/266 (750–2066)

1,232,086/124,692 (970,219–1,423,785)


37 (15/22)

11.6/2.8 (6.9–17.7)

110/17 80–155


1.4/1.3 (0–4)

5.9/6.3 (0–22)

1041/210 (550–1501)

1034/173 (557–1367)

1,198,117/154,819 (968,896–1,553,983)

Except for the N column (sample size, broken down by Female/Male), all data are presented as M/SD. IQ is captured by GCA (General Conceptual Ability), measured via the Differential Abilities Scale Second Edition (Elliot 2007), a standardized score (mean/SD = 100/15). ASD + Anx: the ASD + Anxiety Group. ADOS: Autism Diagnostic Observation Schedule (administered to the ASD group only), with the scores converted to the more recent ADOS-2 algorithm (Lord et al. 2012); the Comparison scores are presented since participants ranged across Modules 2–4. Although a small number of ASD participants had severity scores between 1 and 3, they nevertheless met criteria for an ASD diagnosis via expert clinical assessment and consensus. SCARED: Screen for Child Anxiety Related Emotional Disorders, parent report (Birmaher et al. 1997). Amygdala and total brain volumes are reported in mm3

The psychodiagnostic battery consisted of assessment and interview measures for ASD and pediatric anxiety. ASD diagnoses were driven by the ADOS-2 and either the Autism Diagnostic Inventory—Revised (ADI-R; Le Couteur et al. 2003) or parent interview guided by the SCQ. Anxiety disorders were assessed using the Anxiety Disorder Interview Schedule for DSM-IV (ADIS-IV; Silverman et al. 2001) with the Autism Spectrum Addendum (ASA; see Herrington et al. 2017; Kerns et al. 2014, 2015) for examples by the investigative team of the use of the ADIS-IV/ASA among individuals with ASD). For all participants, the ADIS-IV was administered to a primary caregiver. The child version of the ADIS-IV was also given to 29 participants; for these participants, diagnosis was established via clinical consensus between child and parent interviews. Both the ADOS and ADIS-IV were administered by research-reliable clinical psychologists and clinical psychology trainees. All diagnoses were established based on these instruments and the consensus of the clinical team. In addition to these measures, the parents of participants completed the Screen for Child Anxiety Related Emotional Disorders (SCARED; Birmaher et al. 1997). The SCARED is comprised of four anxiety subscales representing panic/somatic symptoms, generalized anxiety, separation anxiety, and social anxiety. The total score represents a sum of the four subscale scores. This instrument has a total score cutoff of ≥25 for clinically significant anxiety; with an average score of 27, the ASD + Anxiety group was above this cutoff, but the ASD Alone group, was below (average score of 11; see Table 1). Other than the ADIS-IV and ADOS, no other psychodiagnostic assessment instruments were administered.

The diagnosis of anxiety disorders in ASD poses significant challenges, as many of the symptoms of ASD resemble those of anxiety, and are therefore difficult to categorize (Kerns and Kendall 2012; Wood and Gadow 2010). In clinical practice, many children with ASD and co-occurring anxiety receive the diagnosis of Unspecified Anxiety Disorder (or Anxiety Disorder Not Otherwise Specified, prior to DSM-5) for symptoms such as distress and worry about changes in routine or unusual phobias, rather than a specific anxiety disorder diagnosis. Ten of the 63 ASD participants in our sample presented with these symptoms, but did not meet full criteria for a DSM-IV defined specific anxiety disorder. Although this population is of significant theoretical interest, we excluded them from the present study in order to better compare participants with ASD alone to participants with ASD and anxiety disorders that are experienced by the general population.

MRI Data Collection and Analysis

Participants completed a scanning protocol involving structural, functional, and diffusion-weighted imaging. Only the structural MRI (sMRI) data are considered here; these data consisted of a 5.2 min Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) sequence (TR = 1900 ms, TE = 2.54 ms, TI = 900 ms, flip angle = 9°, 0.8 × 0.8 × 0.9 mm voxels).

Amygdala volumes were identified using the FMRIB Integrated Registration and Segmentation Tool (FIRST; Patenaude et al. 2011), part of the FSL (Smith et al. 2004) software package. The past 10 years have witnessed remarkable advances in the use of automated image segmentation; FIRST has been repeatedly vetted for validity and reliability (Morey et al. 2009, 2010), and is now widely used in psychiatry research (for recent examples see Fein et al. 2013; Fein and Fein 2013; Fisler et al. 2013a; Gerritsen et al. 2012; Herz et al. 2015). Automated segmentation procedures have several advantages over manual segmentation; in particular, they diminish certain types of bias (especially those related to individual differences in manual tracing), are scalable to large datasets, and are much more conducive to independent replication and verification.

Prior to amygdala segmentation, non-brain areas were identified and removed using Advanced Normalization Tools (ANTs; Avants et al. 2014), and bias corrected using the N4 algorithm (Tustison et al. 2010). The resulting images were then submitted to FIRST. All amygdala segmentations were visually inspected for all participants. For those participants with segmentation errors, sMRI data were inspected for errors in brain extraction, image artifact, and excessive head motion. In those instances where clear errors in brain segmentation were identified, these were corrected either by modifying ANTs registration parameters or running the brain through FSL’s Brain Extraction Tool (Smith 2002). Two participants (1 ASD + Anxiety, 1 TDC) had inaccurate amygdala segmentations that could not be remedied with modified brain extraction, due either to excessive head motion, image artifact, or both; these subjects were removed from analysis (and are not included in any reported sample size counts).

Amygdala segmentations were examined in two different ways: volume measurement and vertex analyses. For volume measurement, the three groups (ASD + Anxiety, ASD Alone, and TDC) were compared to one another via one-way ANOVA and post hoc t-tests. In order to separate differences in amygdala volume from overall differences in brain size and development, amygdala volume differences between groups were examined after scaling (dividing) by total brain volume. In addition to these categorical analyses, the relationship between amygdala volume and anxiety was examined dimensionally via Pearson correlations with the SCARED total and subscale scores.

To visualize within-amygdala volumetric differences between the ASD + Anxiety and ASD Alone groups, amygdala segmentations were also submitted to vertex analysis, which allows characterization of regional volume variations at a structure’s surface. FIRST’s implementation of vertex analysis involves the creation of a surface mesh model for each participant’s amygdalae. The Euclidean distance between vertices can then be used as a dependent variable in general linear model-based analysis to examine group differences (i.e., per-vertex t-tests). Family-wise error for tests on individual vertices was maintained at p < .05 via the application of cluster size thresholds derived from permutation testing (via FSL’s program randomize; Winkler et al. 2014).


Group Differences in Brain Volume

In order to examine the impact of scaling for whole brain volume, a post hoc ANOVA was carried out to examine whether the groups significantly differed in brain volume. There was no statistically significant difference in brain volumes between the groups, F(2,87) = 2.40, p = .10, though the ASD + Anxiety group showed increased volume compared to the other groups, albeit below the level of statistical significance (p = .08). The ASD + Anxiety and ASD Alone groups did not differ from one another in brain volume (p = .56), nor did the ASD Alone and TDC groups (p = .59).

Group Differences in Amygdala Volume

Volumes for left and right amygdala were submitted to separate one-way ANOVAs examining the effect of group (ASD + Anxiety, ASD Alone, and TDC). This analysis showed a significant effect of group for right amygdala, F(2,87) = 3.81, p = .03. Simple effects tests (Tukey’s Range Test) indicated that this overall effect was primarily driven by decreased right amygdala volume in the ASD + Anxiety group relative to the ASD Alone group (p = .04; see Fig. 2 left panel). The ASD + Anxiety group showed marginally decreased right amygdala volume relative to TDC (p = .068). There was no difference in right amygdala volume for the ASD Alone group relative to TDC (p = .86). The overall group ANOVA for left amygdala was not significant, F(2,87) = 1.12, p = .33.

Fig. 2

Amygdala volume differences in ASD with and without anxiety. Left panel z-transformed right amygdala volumes (scaled for total brain volume) for each group. Right panel rendering of vertex analysis comparing right amygdala volume between the ASD + Anxiety and ASD Alone groups. Portions of amygdala colored in red were significantly decreased in volume in the ASD + Anxiety group at p < .05 (corrected for multiple comparisons using permutation testing)

Individual Differences in Amygdala Volume

Post hoc regression analyses were carried out to examine which anxiety symptom dimensions (measured via the parent-report SCARED) best accounted for right amygdala volume differences across all individuals with ASD (by virtue of how they were selected, the TDC group was at floor on all symptom measures, and was therefore excluded from individual difference analysis). The SCARED total score was related to right amygdala volume, r = −.21, p = .09, though this did not reach p < .05 (see Fig. 3 scatterplot). Of the four subscales, the only one to reach statistical significance was the panic/somatic subscale, where increased symptoms predicted decreased right amygdala volume, r = −.30, p = .02. None of the other three subscales: generalized anxiety, separation anxiety, nor social anxiety were significantly correlated with right amygdala volume (rs = −0.20, −0.17, and −0.03, and ps = 0.11, 0.19, and 0.80, respectively). The present findings are consistent with fMRI data from a separate sample of individuals with ASD, where amygdala activation was significantly correlated with the panic/somatic subdomain of the SCARED (though, interestingly, the most robust correlation in that study was within left amygdala; <BLINDED REFERENCE>).

Fig. 3

Individual differences in right amygdala volume and anxiety symptoms among individuals with ASD. Both the ASD + Anxiety and ASD Alone ground are included in the plot

Vertex Analyses

Post hoc vertex analyses were carried out on right amygdala to localize areas of significant difference between the ASD + Anxiety and ASD Alone groups. These analyses localized decreased amygdala volume in ASD + Anxiety to an area corresponding primarily to the lateral nucleus, but extending dorsally into basolateral and central nuclei as well (see Fig. 2 right panel).


The present study provides the first evidence that anxiety disorder diagnoses moderate, and perhaps mediate, the relationship between amygdala volume and ASD. In youth, the presence of anxiety in the context of ASD was associated with decreased amygdala volume, as predicted. Conversely, findings of increased amygdala volume in ASD may hold only for those who do not have an anxiety disorder. These results represent an important clarification of decades of research on amygdala volume in ASD.

The present data relate to an ongoing debate regarding the nature of the relationship between ASD and anxiety (Kerns and Kendall 2012; Mazefsky et al. 2013; Mazefsky and Herrington 2014; Wood and Gadow 2010). Much of this debate concerns the extent to which anxiety in ASD represents the co-occurrence of two disorders versus a distinct disorder or specific ASD phenotype. It may be tempting to use these data to suggest that individuals with ASD and co-occurring anxiety have a distinct neurobiological etiology relative to individuals with ASD alone. If this were true, it would have major implications for how we understand the complex comorbidity of anxiety in ASD. It would suggest that clinical anxiety in ASD is more than just a manifestation of ASD itself, but is, in fact, a distinct disorder that is either superimposed on ASD or a unique manifestation of ASD.

However, these data do not, by themselves, speak conclusively to a distinct etiology for anxiety in the context of ASD. There are other possible interpretations of these results that need to be evaluated in future research. For example, although speculative, it is possible that ASD groups with and without anxiety share a similar neurodevelopmental profile of amygdala morphology but that, at some stage of development, the two groups diverge. It is important to note that this divergence may be as much environmental as biological (e.g., the result of adverse experiences associated with the symptoms of ASD). Interestingly, the age of our sample (12 years) corresponds to the point at which Schumann et al. reported seeing comparable amygdala sizes between ASD and TDC samples (Schumann et al. 2004). Our findings are consistent with this, when looking at the ASD group as a whole (irrespective of anxiety level).

Because the majority of amygdala morphometry studies in ASD focus on adults, there are presently few data to speak to this possibility. Studies from neurobiology hold tremendous promise for enriching our understanding of the overlap between anxiety in ASD—but this promise is most likely to be fulfilled in the context of large-scale studies tracking the developmental history of amygdala morphology, anxiety, and ASD symptoms. There is an especially pressing need for longitudinal studies on early childhood development, as some manifestations of anxiety in ASD emerge very early (for example, see Zwaigenbaum et al. 2005).

A thorough examination of amygdala structure findings in anxiety-alone samples (without ASD) further complicates the premise that the present data point unequivocally to a single mechanism of anxiety shared by those with and without anxiety. As discussed above, the anxiety-alone literature points to several examples of both decreased and increased amygdala volume. Upon close inspection, there would appear to be no reason why anxiety disorders themselves might not include multiple putative neurodevelopmental trajectories—some, perhaps, leading to decreased volume, with others leading to an increase. There seems little a priori reason not to presume that this diversity of amygdala-mediated deficit profiles would not apply to individuals with ASD as well. The present findings underscore the need to consider changes in amygdala structure alongside variations in the development of both ASD and anxiety symptoms.

Specific findings from this dataset also warrant more detailed follow-up in future studies. For example, it is presently unclear why the amygdala findings were identified in the right but not left hemisphere. This is especially intriguing given very recent evidence that functional MRI activity in left amygdala correlates anxiety symptoms during face perception (Herrington et al. 2016). Although brain structure and function need not have a reciprocal relationship, the lateralization discrepancy between these two studies is noteworthy and in need of further study and theoretical support. The few existing theories on the hemispheric specialization of amygdala for aspects of emotion (for example, see Baas et al. 2004) may shed some light on this, were they to be formally tested in ASD populations.

It is also unclear what specific portions of right amygdala drove the group differences. Vertex analyses showed that an extended portion of the dorsal and anterior surface of amygdala were decreased in ASD + Anxiety. However, even on a state-of-the-art 3-Tesla MRI machine, the limited spatial resolution afforded by structural MRI hinders the identification of morphological differences in amygdala subregions. This is a significant problem for the field, as amygdala includes subnuclei that are functionally heterogeneous (Swanson and Petrovich 1998). Nevertheless, emerging theories about amygdala subregions may speak directly to the present findings—in particular, recent functional work by Bickart et al. suggesting that distinct portions of amygdala support approach versus avoidance tendencies (Bickart et al. 2014, 2012, 2011). Methodological advances in MRI technology and image analysis will be critical to identifying which portions of amygdala are affected in ASD with and without co-occurring anxiety (i.e., Entis et al. 2012; Tyszka and Pauli 2016).

In future research, it will be important to clarify the relationship between anxiety and total brain volume. The ASD + Anxiety and ASD Alone groups in the present study were well-matched on brain volume, which suggests that amygdala volume differences between these two groups cannot be attributed to differences in overall brain size. However, the ASD + Anxiety group showed marginally increased brain volume relative to controls (whereas ASD Alone showed no significant difference). This result is intriguing, given the long history of research on increased brain size in ASD (Carper et al. 2002; Courchesne et al. 2001; Hazlett et al. 2005; Sacco et al. 2015; Sparks et al. 2002; Stanfield et al. 2008). The result warrants replication and extension, ideally in the context of research examining broad patterns of brain development and their relationship to anxiety in ASD.

It will also be important to follow up on the relationship between specific anxiety subdomains and neuroanatomy. When examining anxiety symptoms dimensionally (i.e., domains on the parent-report SCARED), the strongest relationship we observed was for the panic/somatic subscale. This is consistent with recent fMRI evidence (Herrington et al. 2016), as well as volumetric studies of panic disorder (Hayano et al. 2009; Massana et al. 2003). Given the relationship between physiological symptoms of panic and difficulties in arousal regulation, this result raises the hypothesis that core symptoms of ASD and frequently co-occurring arousal regulation difficulties have a shared diathesis (see Mazefsky et al. 2013). The identification of specific symptom domains associated with amygdala morphology clearly requires much larger samples than are typical of existing structural MRI studies of ASD. Future research would also benefit from the formal assessment of other psychiatric disorders (such as attention deficit/hyperactivity disorder).

The present findings should also be complemented by designs that focus less on categorical diagnoses and more on transdiagnostic symptom dimensions. The present use of a rigorously characterized sample has several advantages—in particular, it limits diagnostic overlap and likely maximizes observed effect sizes (by identifying participants with frank anxiety disorder symptoms). However, the identification of unique dimensions related to amygdala volume will necessitate larger samples and more sensitive symptom measures that do not rely on categorical diagnoses. The present findings suggest that this approach has particular promise in identifying the precise manifestations of amygdala differences—and in advancing our understanding of the relationship between anxiety and the core symptoms of ASD.



We are very grateful to the many families who participated in this research. The design and conduct of the study, collection, management, and analysis were supported by grants from the Pennsylvania Department of Health (SAP # 4100042728 to R. Schultz), the National Institute of Child Health and Development (P30 HD026979, to M. Yudkoff), and National Institute of Mental Health (RC1MH08879 and R01 MH073084-01 to R. Schultz). The data collection, management, and analysis for the manuscript were also supported by funds from Shire Pharmaceuticals. Additionally, R. Schultz reported receiving lecture fees and research funds from Pfizer. Portions of this manuscript were presented at the 14th Annual International Meeting for Autism Research (Salt Lake City, 2015).


The design and conduct of the study, collection, management, and analysis were supported by grants from the Pennsylvania Department of Health (SAP # 4100042728 to R. Schultz), the National Institute of Child Health and Development (P30 HD026979, to M. Yudkoff), and National Institute of Mental Health (RC1MH08879 and R01 MH073084-01 to R. Schultz). The data collection, management, and analysis for the manuscript were also supported by funds from Shire Pharmaceuticals.

Author Contributions

JDH, RTS, and JSM conceived of the study and participated in study design. JDH, BBM, CMK, JAW, JCB, AJM, and JSM participated in data collection. JDH and JSM participated in data analysis. All authors participated in manuscript preparation.

Compliance with Ethical Standards

Conflict of interest

J. Herrington, J. Miller, and R. Schultz reported having received lecture fees and/or research funds from Shire Pharmaceuticals. Additionally, R. Schultz reported receiving research funding from Pfizer. Coauthors Maddox, Kerns, Rump, Worley, Bush, and McVey reported no potential conflicts of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from the parent or legal guardian of all individual participants included in the study, in accordance with the guidelines of the Children’s Hospital of Philadelphia Institutional Review Board.


  1. Aggleton, J. P., & Passingham, R. E. (1981). Syndrome produced by lesions of the amygdala in monkeys (Macaca mulatta). Journal of Comparative and Physiological Psychology, 95(6), 961–977.CrossRefPubMedGoogle Scholar
  2. Amaral, D. G. (2002). The primate amygdala and the neurobiology of social behavior: implications for understanding social anxiety. Biological Psychiatry, 51(1), 11–17.CrossRefPubMedGoogle Scholar
  3. Amaral, D. G., Bauman, M. D., & Schumann, C. M. (2003). The amygdala and autism: implications from non-human primate studies. Genes, Brain, and Behavior, 2(5), 295–302.CrossRefPubMedGoogle Scholar
  4. Avants, B. B., Tustison, N. J., Stauffer, M., Song, G., Wu, B., & Gee, J. C. (2014). The Insight ToolKit image registration framework. Frontiers in Neuroinformatics. doi: 10.3389/fninf.2014.00044.PubMedPubMedCentralGoogle Scholar
  5. Aylward, E. H., Minshew, N. J., Goldstein, G., Honeycutt, N. A., Augustine, A. M., Yates, K. O., … Pearlson, G. D. (1999). MRI volumes of amygdala and hippocampus in non-mentally retarded autistic adolescents and adults. Neurology, 53(9), 2145–2150.CrossRefPubMedGoogle Scholar
  6. Baas, D., Aleman, A., & Kahn, R. (2004). Lateralization of amygdala activation: a systematic review of functional neuroimaging studies. Brain Research Reviews, 45(2), 96–103.CrossRefPubMedGoogle Scholar
  7. Bauman, M. D., Lavenex, P., Mason, W. A., Capitanio, J. P., & Amaral, D. G. (2004a). The development of mother-infant interactions after neonatal amygdala lesions in rhesus monkeys. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 24(3), 711–721. doi: 10.1523/JNEUROSCI.3263-03.2004.CrossRefGoogle Scholar
  8. Bauman, M. D., Lavenex, P., Mason, W. A., Capitanio, J. P., & Amaral, D. G. (2004b). The development of social behavior following neonatal amygdala lesions in rhesus monkeys. Journal of Cognitive Neuroscience, 16(8), 1388–1411. doi: 10.1162/0898929042304741.CrossRefPubMedGoogle Scholar
  9. Bickart, K. C., Dickerson, B. C., & Barrett, L. F. (2014). The amygdala as a hub in brain networks that support social life. Neuropsychologia, 63, 235–248. doi: 10.1016/j.neuropsychologia.2014.08.013.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Bickart, K. C., Hollenbeck, M. C., Barrett, L. F., & Dickerson, B. C. (2012). Intrinsic amygdala-cortical functional connectivity predicts social network size in humans. The Journal of Neuroscience, 32(42), 14729–14741. doi: 10.1523/JNEUROSCI.1599-12.2012.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Bickart, K. C., Wright, C. I., Dautoff, R. J., Dickerson, B. C., & Barrett, L. F. (2011). Amygdala volume and social network size in humans. Nature Neuroscience, 14(2), 163–164. doi: 10.1038/nn.2724.
  12. Birmaher, B., Khetarpal, S., Brent, D., Cully, M., Balach, L., Kaufman, J., & Neer, S. M. (1997). The Screen for Child Anxiety Related Emotional Disorders (SCARED): Scale construction and psychometric characteristics. Journal of the American Academy of Child and Adolescent Psychiatry, 36(4), 545–553. doi: 10.1097/00004583-199704000-00018.CrossRefPubMedGoogle Scholar
  13. Blackmon, K., Barr, W. B., Carlson, C., Devinsky, O., Dubois, J., Pogash, D., … Thesen, T. (2011). Structural evidence for involvement of a left amygdala-orbitofrontal network in subclinical anxiety. Psychiatry Research, 194(3), 296–303. doi: 10.1016/j.pscychresns.2011.05.007.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Brothers, L. (1990). The social brain: A project for integrating primate behaviour and neurophysiology in a new domain. Concepts in Neuroscience, 1, 27–51.Google Scholar
  15. Brown, S., & Schafer, E. A. (1888). An investigation into the functions of the occipital and temporal lobes of the monkey’s brain. Philosophical Transactions of the Royal Society of London. B, 179, 303–327.CrossRefGoogle Scholar
  16. Carper, R. A., Moses, P., Tigue, Z. D., & Courchesne, E. (2002). Cerebral lobes in autism: Early hyperplasia and abnormal age effects. NeuroImage, 16(4), 1038–1051.CrossRefPubMedGoogle Scholar
  17. Chandler, S., Charman, T., Baird, G., Simonoff, E., Loucas, T., Meldrum, D., … Pickles, A. (2007). Validation of the social communication questionnaire in a population cohort of children with autism spectrum disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 46(10), 1324–1332. doi: 10.1097/chi.0b013e31812f7d8d.CrossRefGoogle Scholar
  18. Courchesne, E., Karns, C. M., Davis, H. R., Ziccardi, R., Carper, R. A., Tigue, Z. D., … Courchesne, R. Y. (2001). Unusual brain growth patterns in early life in patients with autistic disorder: An MRI study. Neurology, 57(2), 245–254.CrossRefPubMedGoogle Scholar
  19. De Bellis, M. D., Casey, B. J., Dahl, R. E., Birmaher, B., Williamson, D. E., Thomas, K. M., … Ryan, N. D. (2000). A pilot study of amygdala volumes in pediatric generalized anxiety disorder. Biological Psychiatry, 48(1), 51–57.CrossRefPubMedGoogle Scholar
  20. Elliot, C. (2007). The differential abilities scale, Second Edition (2nd ed.). San Antonio, TX: Harcourt Assessments, Inc.Google Scholar
  21. Entis, J. J., Doerga, P., Barrett, L. F., & Dickerson, B. C. (2012). A reliable protocol for the manual segmentation of the human amygdala and its subregions using ultra-high resolution MRI. NeuroImage, 60(2), 1226–1235. doi: 10.1016/j.neuroimage.2011.12.073.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Fein, G., & Fein, D. (2013). Subcortical volumes are reduced in short-term and long-term abstinent alcoholics but not those with a comorbid stimulant disorder. NeuroImage Clinical, 3, 47–53. doi: 10.1016/j.nicl.2013.06.018.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Fein, G., Greenstein, D., Cardenas, V. A., Cuzen, N. L., Fouche, J.-P., Ferrett, H., … Stein, D. J. (2013). Cortical and subcortical volumes in adolescents with alcohol dependence but without substance or psychiatric comorbidities. Psychiatry Research, 214(1), 1–8. doi: 10.1016/j.pscychresns.2013.06.001.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Fisler, M. S., Federspiel, A., Horn, H., Dierks, T., Schmitt, W., Wiest, R., … Soravia, L. M. (2013a). Spider phobia is associated with decreased left amygdala volume: a cross-sectional study. BMC Psychiatry, 13, 70. doi: 10.1186/1471-244X-13-70.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Fisler, M. S., Federspiel, A., Horn, H., Dierks, T., Schmitt, W., Wiest, R., … Soravia, L. M. (2013b). Spider phobia is associated with decreased left amygdala volume: a cross-sectional study. BMC Psychiatry, 13(1), 70. doi: 10.1186/1471-244X-13-70.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Gerritsen, L., Rijpkema, M., van Oostrom, I., Buitelaar, J., Franke, B., Fernández, G., & Tendolkar, I. (2012). Amygdala to hippocampal volume ratio is associated with negative memory bias in healthy subjects. Psychological Medicine, 42(2), 335–343. doi: 10.1017/S003329171100122X.CrossRefPubMedGoogle Scholar
  27. Hayano, F., Nakamura, M., Asami, T., Uehara, K., Yoshida, T., Roppongi, T., … Hirayasu, Y. (2009). Smaller amygdala is associated with anxiety in patients with panic disorder. Psychiatry and Clinical Neurosciences, 63(3), 266–276. doi: 10.1111/j.1440-1819.2009.01960.x.CrossRefPubMedGoogle Scholar
  28. Hazlett, H. C., Poe, M., Gerig, G., Smith, R. G., Provenzale, J., Ross, A., … Piven, J. (2005). Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years. Archives of General Psychiatry, 62(12), 1366–1376.CrossRefPubMedGoogle Scholar
  29. Haznedar, M. M., Buchsbaum, M. S., Wei, T. C., Hof, P. R., Cartwright, C., Bienstock, C. A., & Hollander, E. (2000). Limbic circuitry in patients with autism spectrum disorders studied with positron emission tomography and magnetic resonance imaging. The American Journal of Psychiatry, 157(12), 1994–2001. doi: 10.1176/appi.ajp.157.12.1994.CrossRefPubMedGoogle Scholar
  30. Herrington, J. D., Miller, J. S., Pandey, J., & Schultz, R. T. (2016). Anxiety and social deficits have distinct relationships with amygdala function in autism spectrum disorder. Social Cognitive and Affective Neuroscience, 11(6), 907–914. doi: 10.1093/scan/nsw015.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Herrington, J. D., Maddox, B. B., McVey, A. J., Franklin, M. E., Yerys, B. E., Miller, J. S., & Schultz, R. T. (2017). Negative valence in autism spectrum disorder: The relationship between amygdala activity, selective attention, and co-occurring anxiety. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. doi: 10.1016/j.bpsc.2017.03.009.
  32. Herz, D. M., Haagensen, B. N., Christensen, M. S., Madsen, K. H., Rowe, J. B., Løkkegaard, A., & Siebner, H. R. (2015). Abnormal dopaminergic modulation of striato-cortical networks underlies levodopa-induced dyskinesias in humans. Brain: A Journal of Neurology, 138(Pt 6), 1658–1666. doi: 10.1093/brain/awv096.CrossRefGoogle Scholar
  33. Howard, M. A., Cowell, P. E., Boucher, J., Broks, P., Mayes, A., Farrant, A., & Roberts, N. (2000). Convergent neuroanatomical and behavioural evidence of an amygdala hypothesis of autism. Neuroreport, 11(13), 2931–2935.CrossRefPubMedGoogle Scholar
  34. Juranek, J., Filipek, P. A., Berenji, G. R., Modahl, C., Osann, K., & Spence, M. A. (2006). Association between amygdala volume and anxiety level: magnetic resonance imaging (MRI) study in autistic children. Journal of Child Neurology, 21(12), 1051–1058.CrossRefPubMedGoogle Scholar
  35. Kazama, A. M., Heuer, E., Davis, M., & Bachevalier, J. (2012). Effects of neonatal amygdala lesions on fear learning, conditioned inhibition, and extinction in adult macaques. Behavioral Neuroscience, 126(3), 392–403. doi: 10.1037/a0028241.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Kerns, C. M., & Kendall, P. C. (2012). The presentation and classification of anxiety in autism spectrum disorder. Clinical Psychology: Science and Practice, 19(4), 323–347. doi: 10.1111/cpsp.12009.Google Scholar
  37. Kerns, C. M., Kendall, P. C., Berry, L., Souders, M. C., Franklin, M. E., Schultz, R. T., … Herrington, J. (2014). Traditional and atypical presentations of anxiety in youth with autism spectrum disorder. Journal of Autism and Developmental Disorders, 44(11), 2851–2861. doi: 10.1007/s10803-014-2141-7.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Kerns, C. M., Maddox, B. B., Kendall, P. C., Rump, K., Berry, L., Schultz, R. T., … Miller, J. (2015). Brief measures of anxiety in non-treatment-seeking youth with autism spectrum disorder. Autism, 19(8), 969–979. doi: 10.1177/1362361314558465.CrossRefPubMedGoogle Scholar
  39. Kling, A. S., & Brothers, L. A. (1992). The amygdala and social behavior. In The amygdala: Neurobiological aspects of emotion, memory, and mental dysfunction (pp. 353–377). New York, NY: Wiley-Liss.Google Scholar
  40. Kluver, H., & Bucy, P. (1939). Preliminary analysis of functions of the temporal lobes in monkeys. Archives of Neurology and Psychiatry, 42(6), 979–1000.CrossRefGoogle Scholar
  41. Le Couteur, A., Lord, C., & Rutter, M. (2003). The autism diagnostic interview-revised (ADI-R). Los Angeles, CA: Western Psychological Services.Google Scholar
  42. Lord, C., Rutter, M., DiLavore, P. C., & Risi, S. (2002). Autism diagnostic observation schedule. Los Angeles, CA: Western Psychological Services.Google Scholar
  43. Lord, C., Rutter, M., DiLavore, P. C., Risi, S., Gotham, K., & Bishop, S. L. (2012). Autism diagnostic observation schedule (2nd ed.). Los Angeles, CA: Western Psychological Services.Google Scholar
  44. Massana, G., Serra-Grabulosa, J. M., Salgado-Pineda, P., Gastó, C., Junqué, C., Massana, J., … Salamero, M. (2003). Amygdalar atrophy in panic disorder patients detected by volumetric magnetic resonance imaging. NeuroImage, 19(1), 80–90.CrossRefPubMedGoogle Scholar
  45. Mazefsky, C. A., Herrington, J., Siegel, M., Scarpa, A., Maddox, B. B., Scahill, L., & White, S. W. (2013). The role of emotion regulation in autism spectrum disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 52(7), 679–688. doi: 10.1016/j.jaac.2013.05.006.CrossRefGoogle Scholar
  46. Mazefsky, C. A., & Herrington, J. D. (2014). Autism and anxiety: etiologic factors and transdiagnostic processes. In T. E. Davis III, S. W. White & T. H. Ollendick (Eds.), Handbook of Autism and Anxiety (pp. 91–106). New York, NY : Springer.Google Scholar
  47. Milham, M. P., Nugent, A. C., Drevets, W. C., Dickstein, D. P., Leibenluft, E., Ernst, M., … Pine, D. S. (2005). Selective reduction in amygdala volume in pediatric anxiety disorders: A voxel-based morphometry investigation. Biological Psychiatry, 57(9), 961–966. doi: 10.1016/j.biopsych.2005.01.038.CrossRefPubMedGoogle Scholar
  48. Morey, R. A., Petty, C. M., Xu, Y., Pannu Hayes, J., Wagner, H. R., Lewis, D. V., … McCarthy, G. (2009). A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes. NeuroImage, 45(3), 855–866. doi: 10.1016/j.neuroimage.2008.12.033.CrossRefPubMedGoogle Scholar
  49. Morey, R. A., Selgrade, E. S., Wagner, H. R., Huettel, S. A., Wang, L., & McCarthy, G. (2010). Scan-rescan reliability of subcortical brain volumes derived from automated segmentation. Human Brain Mapping. doi: 10.1002/hbm.20973.PubMedPubMedCentralGoogle Scholar
  50. Mosconi, M. W., Cody-Hazlett, H., Poe, M. D., Gerig, G., Gimpel-Smith, R., & Piven, J. (2009). Longitudinal study of amygdala volume and joint attention in 2- to 4-year-old children with autism. Archives of General Psychiatry, 66(5), 509–516. doi: 10.1001/archgenpsychiatry.2009.19.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Mueller, S. C., Aouidad, A., Gorodetsky, E., Goldman, D., Pine, D. S., & Ernst, M. (2013). Gray matter volume in adolescent anxiety: an impact of the brain-derived neurotrophic factor Val(66)Met polymorphism? Journal of the American Academy of Child and Adolescent Psychiatry, 52(2), 184–195. doi: 10.1016/j.jaac.2012.11.016.CrossRefPubMedPubMedCentralGoogle Scholar
  52. Munson, J., Dawson, G., Abbott, R., Faja, S., Webb, S. J., Friedman, S. D., … Dager, S. R. (2006). Amygdalar volume and behavioral development in autism. Archives of General Psychiatry, 63(6), 686–693. doi: 10.1001/archpsyc.63.6.686.CrossRefPubMedGoogle Scholar
  53. Nacewicz, B. M., Dalton, K. M., Johnstone, T., Long, M. T., McAuliff, E. M., Oakes, T. R., … Davidson, R. J. (2006). Amygdala volume and nonverbal social impairment in adolescent and adult males with autism. Archives of General Psychiatry, 63(12), 1417–1428.CrossRefPubMedPubMedCentralGoogle Scholar
  54. Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 56(3), 907–922. doi: 10.1016/j.neuroimage.2011.02.046.CrossRefPubMedPubMedCentralGoogle Scholar
  55. Pierce, K., Müller, R. A., Ambrose, J., Allen, G., & Courchesne, E. (2001). Face processing occurs outside the fusiform “face area” in autism: evidence from functional MRI. Brain: A Journal of Neurology, 124(10), 2059–2073.CrossRefGoogle Scholar
  56. Prather, M. D., Lavenex, P., Mauldin-Jourdain, M. L., Mason, W. A., Capitanio, J. P., Mendoza, S. P., & Amaral, D. G. (2001). Increased social fear and decreased fear of objects in monkeys with neonatal amygdala lesions. Neuroscience, 106(4), 653–658.CrossRefPubMedGoogle Scholar
  57. Qin, S., Young, C. B., Duan, X., Chen, T., Supekar, K., & Menon, V. (2014). Amygdala subregional structure and intrinsic functional connectivity predicts individual differences in anxiety during early childhood. Biological Psychiatry, 75(11), 892–900. doi: 10.1016/j.biopsych.2013.10.006.CrossRefPubMedGoogle Scholar
  58. Sacco, R., Gabriele, S., & Persico, A. M. (2015). Head circumference and brain size in autism spectrum disorder: A systematic review and meta-analysis. Psychiatry Research: Neuroimaging, 234(2), 239–251. doi: 10.1016/j.pscychresns.2015.08.016.CrossRefPubMedGoogle Scholar
  59. Schumann, C. M., Bloss, C. S., Barnes, C. C., Wideman, G. M., Carper, R. A., Akshoomoff, N., … Courchesne, E. (2010). Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 30(12), 4419–4427. doi: 10.1523/JNEUROSCI.5714-09.2010.CrossRefGoogle Scholar
  60. Schumann, C. M., Hamstra, J., Goodlin-Jones, B. L., Lotspeich, L. J., Kwon, H., Buonocore, M. H., … Amaral, D. G. (2004). The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 24(28), 6392–6401. doi: 10.1523/JNEUROSCI.1297-04.2004.CrossRefGoogle Scholar
  61. Schumann, C. M., & Nordahl, C. W. (2011). Bridging the gap between MRI and postmortem research in autism. Brain Research, 1380, 175–186. doi: 10.1016/j.brainres.2010.09.061.CrossRefPubMedGoogle Scholar
  62. Silverman, W. K., Saavedra, L. M., & Pina, A. A. (2001). Test-retest reliability of anxiety symptoms and diagnoses with the Anxiety Disorders Interview Schedule for DSM-IV: Child and parent versions. Journal of the American Academy of Child and Adolescent Psychiatry, 40(8), 937–944.CrossRefPubMedGoogle Scholar
  63. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child and Adolescent Psychiatry, 47(8), 921–929. doi: 10.1097/CHI.0b013e318179964f.CrossRefPubMedGoogle Scholar
  64. Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155. doi: 10.1002/hbm.10062.CrossRefPubMedGoogle Scholar
  65. Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208–S219.CrossRefPubMedGoogle Scholar
  66. Sparks, B. F., Friedman, S. D., Shaw, D. W., Aylward, E. H., Echelard, D., Artru, A. A., … Dager, S. R. (2002). Brain structural abnormalities in young children with autism spectrum disorder. Neurology, 59(2), 184–192.CrossRefPubMedGoogle Scholar
  67. Stanfield, A. C., McIntosh, A. M., Spencer, M. D., Philip, R., Gaur, S., & Lawrie, S. M. (2008). Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies. European Psychiatry, 23(4), 289–299. doi: 10.1016/j.eurpsy.2007.05.006.CrossRefPubMedGoogle Scholar
  68. Sukhodolsky, D. G., Scahill, L., Gadow, K. D., Arnold, L. E., Aman, M. G., McDougle, C. J., … Vitiello, B. (2008). Parent-rated anxiety symptoms in children with pervasive developmental disorders: frequency and association with core autism symptoms and cognitive functioning. Journal of Abnormal Child Psychology, 36(1), 117–128.CrossRefPubMedGoogle Scholar
  69. Swanson, L. W., & Petrovich, G. D. (1998). What is the amygdala? Trends in Neurosciences, 21(8), 323–331.CrossRefPubMedGoogle Scholar
  70. Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320. doi: 10.1109/TMI.2010.2046908.CrossRefPubMedPubMedCentralGoogle Scholar
  71. Tyszka, J. M., & Pauli, W. M. (2016). In vivo delineation of subdivisions of the human amygdaloid complex in a high-resolution group template: In Vivo Amygdala Subdivisions. Human Brain Mapping. doi: 10.1002/hbm.23289.PubMedPubMedCentralGoogle Scholar
  72. van der Plas, E. A. A., Boes, A. D., Wemmie, J. A., Tranel, D., & Nopoulos, P. (2010). Amygdala volume correlates positively with fearfulness in normal healthy girls. Social Cognitive and Affective Neuroscience, 5(4), 424–431. doi: 10.1093/scan/nsq009.CrossRefPubMedPubMedCentralGoogle Scholar
  73. van Steensel, F. J. A., Bögels, S. M., & Perrin, S. (2011). Anxiety disorders in children and adolescents with autistic spectrum disorders: a meta-analysis. Clinical Child and Family Psychology Review, 14(3), 302–317. doi: 10.1007/s10567-011-0097-0.CrossRefPubMedPubMedCentralGoogle Scholar
  74. Whalen, P. J., & Phelps, E. A. (Eds.). (2009). The human amygdala. New York: Guilford Press.Google Scholar
  75. Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381–397. doi: 10.1016/j.neuroimage.2014.01.060.CrossRefGoogle Scholar
  76. Wood, J. J., & Gadow, K. D. (2010). Exploring the nature and function of anxiety in youth with autism spectrum disorders. Clinical Psychology: Science and Practice, 17(4), 281–292. doi: 10.1111/j.1468-2850.2010.01220.x.Google Scholar
  77. Yang, R. J., Mozhui, K., Karlsson, R.-M., Cameron, H. A., Williams, R. W., & Holmes, A. (2008). Variation in mouse basolateral amygdala volume is associated with differences in stress reactivity and fear learning. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 33(11), 2595–2604. doi: 10.1038/sj.npp.1301665.CrossRefGoogle Scholar
  78. Zwaigenbaum, L., Bryson, S., Rogers, T., Roberts, W., Brian, J., & Szatmari, P. (2005). Behavioral manifestations of autism in the first year of life. International Journal of Developmental Neuroscience, 23(2–3), 143–152. doi: 10.1016/j.ijdevneu.2004.05.001.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • John D. Herrington
    • 1
    • 2
  • Brenna B. Maddox
    • 1
  • Connor M. Kerns
    • 4
    • 5
  • Keiran Rump
    • 2
  • Julie A. Worley
    • 6
  • Jennifer C. Bush
    • 7
  • Alana J. McVey
    • 8
  • Robert T. Schultz
    • 1
    • 2
    • 3
  • Judith S. Miller
    • 1
    • 2
  1. 1.Center for Autism ResearchThe Children’s Hospital of PhiladelphiaPhiladelphiaUSA
  2. 2.Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.Department of Pediatrics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  4. 4.AJ Drexel Autism Institute & Community Health & Prevention, School of Public HealthDrexel UniversityPhiladelphiaUSA
  5. 5.Center for Health InnovationAdelphi UniversityGarden CityUSA
  6. 6.SPIN IncPhiladelphiaUSA
  7. 7.Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
  8. 8.Department of PsychologyMarquette UniversityMilwaukeeUSA

Personalised recommendations