Skip to main content

A functional neuroimaging study of fusiform response to restricted interests in children and adolescents with autism spectrum disorder



While autism spectrum disorder (ASD) is characterized by both social communication deficits and restricted and repetitive patterns of behavior and interest, literature examining possible neural bases of the latter class of symptoms is limited. The fusiform face area (FFA) is a region in the ventral temporal cortex that not only shows preferential responsiveness to faces but also responds to non-face objects of visual expertise. Because restricted interests in ASD are accompanied by high levels of visual expertise, the objective of this study was to determine the extent to which this region responds to images related to restricted interests in individuals with ASD, compared to individuals without ASD who have a strong hobby or interest.


Children and adolescents with and without ASD with hobbies or interests that consumed a pre-determined minimum amount of time were identified, and the intensity, frequency, and degree of interference of these interests were quantified. Each participant underwent functional magnetic resonance imaging (fMRI) while viewing images related to their personal restricted interests (in the ASD group) or strong interest or hobby (in the comparison group). A generalized linear model was used to compare the intensity and spatial extent of fusiform gyrus response between groups, controlling for the appearance of faces in the stimuli.


Images related to interests and expertise elicited response in FFA in both ASD and typically developing individuals, but this response was more robust in ASD.


These findings add neurobiological support to behavioral observations that restricted interests are associated with enhanced visual expertise in ASD, above and beyond what would be expected for simply a strong interest. Further, the results suggest that brain regions associated with social functioning may not be inherently less responsive in ASD, but rather may be recruited by different environmental stimuli. This study contributes to our understanding of the neural basis of restricted interests in ASD and may provide clues toward developing novel interventions.


Autism spectrum disorder (ASD) is associated with deficits in social abilities and interest, often combined with restricted patterns of behavior that include increased attention to and focus on narrow topics of interest [1]. A majority of neuroimaging studies in ASD have focused on abnormalities in the social brain [44], which have been associated with behavioral deficits including atypical eye contact, reduced attention to faces, and poor theory of mind [13, 41, 42]. Functional abnormalities have been identified in several brain structures, including the amygdala, superior temporal sulcus (STS), and a functionally-defined, face-selective area of the fusiform gyrus commonly referred to as the fusiform face area (FFA; [25, 26]). While these studies have explored the role of the social brain in ASD symptoms, the literature examining possible neural bases of increased levels of repetitive behaviors and circumscribed interests is far more limited.

Over the past two decades, a growing body of research has indicated that children with ASD show increased attention to and preference for non-social stimuli at the cost of reduced interest in social stimuli (e.g., faces), which are generally preferred by typically developing children [7, 31, 40]. Klin and colleagues [29, 30] have suggested that individuals with autism find objects, rather than social stimuli, to be most salient. This atypical attentional bias may lead to increased interaction with and preference for the non-social environment at the expense of social development. Infants and young children with ASD show greater preference for viewing objects relative to faces [35, 50], spurring suggestions that atypical attentional biases early in development might initiate a cascade of social deficits observed in ASD across the lifespan [55]. Indeed, topics of circumscribed interest elicit greater positive affect and arousal than do social stimuli in older individuals with ASD [48].

A relatively new line of research suggests that the neural reward system is differentially responsive in individuals with ASD, which may explain the atypical preferences individuals with ASD display for non-social stimuli [9, 10]. For example, whereas social stimuli (i.e., faces) activate neural reward circuitry in typical individuals [32], this is not the case for individuals with ASD [14, 58]. However, these differences do not seem to reflect pervasive deficits in reward system responsivity in ASD. Intact or enhanced responses in reward regions have been shown in response to monetary reward [12, 39, 54], object images as rewards in gambling paradigms [15], and food images under mild fasting conditions [5]. Most recently, individuals with ASD were found to show increased reward system response relative to controls in response to images of their particular areas of interest [4]. It remains unknown to what extent images related to restricted interests may engage other neural regions that are associated with processing social stimuli. Moreover, whether social motivation, expertise, and skill deficits are primarily related to altered reward system tuning versus broader neural differences, such as in visual processing, is a topic of debate.

The fusiform gyrus, and the FFA in particular, has consistently been found to respond atypically in ASD. The FFA is a region in the ventral temporal cortex that shows preferential responsiveness to faces in typical individuals [25, 26] and is thought to respond selectively to static, invariant aspects of human faces, such as identity. This function is in contrast to other regions of the social brain, such as the posterior STS, which respond to dynamic aspects of faces, such as expression [2]. However, the FFA is not exclusively responsive to faces, as it also responds preferentially when observers identify non-face objects from domains for which they have high levels of visual expertise [19]. According to the expertise hypothesis, in typical individuals, faces represent merely one example of a stimulus class for which people have developed significant identification expertise [61]. Thus, though the FFA is an integral part of the social brain, it is also involved in the processing of other, often non-social, stimuli for which individuals have visual expertise. Individuals with ASD show diminished FFA response to face stimuli [8, 53, 56], though there is evidence that their FFA responds more typically when viewing pictures of familiar faces [45], animal faces [67], or when visual scan paths of faces are directly manipulated [43]. Additionally, individuals with milder autism symptoms may show relatively intact FFA response to faces [52]. There is very little information, however, about how the fusiform gyrus responds to non-face objects of perceptual expertise in individuals with ASD. Given the expertise associated with highly restricted or circumscribed interests, it is of interest to determine whether images related to these interests recruit the FFA in ASD.

The lack of cortical face specialization in ASD may be a byproduct of broadly reduced social interest, leading to reduced attention to and regard for faces and thus reduced expertise for faces [11, 20]. Interestingly, a single-subject study examining FFA function in a boy with ASD and a strong interest in Digimon demonstrated that presenting images of Digimon characters reliably engaged fusiform regions where human face images did not [21]. The ability of fictional characters for which children have intense interest, and presumably great expertise, to elicit robust FFA activation has also been demonstrated in typically developing children [22]. Combined, these studies support the notion that, beyond its role as part of the social brain, the FFA is preferentially responsive to topics of intense interest in both typically and atypically developing children. Grelotti and colleagues suggested that differential engagement of FFA may have been the result of differences in the relative amount of time the individual with ASD spent looking at or thinking about people versus Digimon over his lifetime, resulting in differing levels of expertise and cortical specialization [21]. More broadly, their finding suggests that FFA can indeed respond preferentially to highly familiar classes of stimuli in ASD. However, this case study has yet to be replicated in a larger sample of individuals with ASD. In addition, it remains unknown whether objects of restricted interest and expertise that do not consistently include faces (as Digimon characters do) will also engage the FFA in individuals with ASD.

In the present study, we examined response of the fusiform gyrus to individualized stimulus sets reflecting images of participants’ particular areas of interest in a sample of children and adolescents with and without ASD. In this same sample, individuals with ASD had increased reward system response to images of their intense interest [4]. Here, we extended our examination of atypical neural specialization and the neural basis of restricted interests in ASD to the fusiform gyrus. In keeping with the assumption that individuals with ASD often develop high levels of perceptual expertise for objects or scenes related to their restricted interests, we hypothesized that children and adolescents with ASD would show enhanced activation of fusiform regions in response to these images relative to individuals with typical development who had a strong but less intense or all-consuming hobby or interest.



We tested 21 male children and adolescents with a diagnosis of ASD and 23 typically developing (TD) male controls group-matched for age and IQ (Table 1), overlapping with the sample described in previous papers from our lab [4, 5]. All participants were required to have a full-scale IQ score of 70 or above, and individuals in the ASD group met cutoff criteria for autism on both the Autism Diagnostic Observation Schedule (ADOS; [33]) and Autism Diagnostic Interview-Revised [34], administered by research-reliable examiners. Exclusion criteria for both groups included genetic disorder, neurological disorder such as epilepsy, recent history of psychiatric or learning disorders, and MRI contraindications. Children and adolescents with ASD were excluded if they were currently taking psychotropic medications, with the exception of four who were prescribed short-acting stimulant medication, but withheld it for 24 hours prior to scanning to ensure clearance [27]. No participants in the TD group had a first-degree relative with ASD, per parent report, and all were screened for behaviors consistent with an ASD diagnosis using the Social Communication Questionnaire [47]. This study was approved by the Vanderbilt University Institutional Review Board, all parents signed informed consent, and all children and adolescents signed informed assent prior to participating in study procedures.

Table 1 Participant characteristics

Interest assessment

Parents were interviewed about their child’s interests and hobbies during initial phone screening. Across ASD and TD groups, children were eligible for this study if (1) they engaged with their primary interest a minimum of 1 hour per day on average and (2) the content of their interest could be represented in pictures (e.g., maps or Pokemon, but not listening to music because of the difficulty in representing the salient feature(s) visually). During the study, parents of children in both groups were administered the Yale Special Interests Interview [59] regarding the primary interest chosen from the phone screen. The YSII assesses the presence or absence of a circumscribed interest based on the intensity, duration, and degree of specialized knowledge about the topic. The interests presented pictorially to children in the experimental tasks were those confirmed with the YSII to be most salient at the time of their participation in the study. While the YSII indicated that interference as a result of interests was greater in the ASD vs. TD group, t(29) = 6.85, p < 0.001 (ASD: mean = 6.27, SD = 2.46; TD: mean = 1.00; SD = 1.79), children in both groups met inclusion criteria for strong interests on the phone screen. More detailed information about interest topics of each participant is presented in Additonal file 1: Table S1.


The stimuli, fMRI task, imaging acquisition protocol, post-scan memory test, and data processing and analysis steps have previously been described in Cascio et al. [4, 5] but are also detailed below.


For this task, 38 static images from two separate conditions (76 images total) were presented. In the “own interest” condition, image sets were individually customized to depict each child’s particular area of interest (assessment of which is described in detail in [4]) . In the “others’ interest” condition, image sets consisted of a non-overlapping mix of multiple participants’ own interest images, which were the same across all participants with the exception of replacing any pictures that overlapped with the participant’s own interest. Following detailed phone screenings and parent interviews regarding each child’s interest, static images depicting this interest in landscape layout were retrieved, in .jpg format, from internet searches. Retrieved images were then manipulated in Adobe Photoshop, such that all had final dimensions of 800 × 600 pixels per inch. As imaging analyses focused on the neural response in face-processing regions, ratings were conducted to ensure that the proportion of images containing faces did not differ between ASD and TD participants. All images were independently rated regarding whether a face was present in the image (binary: yes or no) by an undergraduate research assistant, who was blind to study hypotheses and diagnostic category of the participant for which each stimulus was generated. The proportion of images containing faces within the own interest condition stimulus set was computed separately for each participant. In addition, a similar procedure was used to rate the colorfulness and visual complexity of images within the own interest condition, with individual images rated on separate 1–5 scales in terms of their colorfulness and complexity. Independent-sample t tests were used to determine whether the proportion of images containing faces, the mean colorfulness, or the visual complexity of images differed between the ASD and TD groups. Analyses of the content of images from individual participants’ own interest stimulus sets (Fig. 1) indicated that the ASD and TD groups did not differ in the percentage of images containing faces, t(35) = 1.577, p = .12 (ASD: mean = 65.85 %; SD = 39.94 %; TD: mean = 81.98 %; SD = 17.36 %). However, because of the significance of this potential confound, the percentage of images containing faces was used as a covariate in fMRI analyses (see below). Own interest images also did not differ between groups with regard to their colorfulness, t(35) = 0.036, p = .97, or their visual complexity, t(35) = 1.340, p = .19. Sample own interest images from a representative subset of ASD and TD participants are depicted in Fig. 1.

Fig. 1
figure 1

Sample stimuli from the “own interest" condition from five participants with ASD and five with TD

fMRI task

Before scanning, participants were acclimated to the scanner environment using a mock scanner. Participants completed a passive viewing, block design task, split across five 4-min runs. At the start of each run, instructions were presented indicating that participants should remain still and attend to each picture displayed because they would be asked about the pictures following the scan (see post-scan memory test below). Runs consisted of three 20-s blocks of each of four conditions (12 blocks total), with blocks presented in a pseudo-randomized order that was fixed across participants. Each block contained five images, each presented for 3500 ms, followed by a 500-ms presentation of a white fixation cross on a black background. In total, across all blocks and runs, participants viewed 75 images from each condition, 38 of which were unique and none of which were presented more than twice. The four conditions were as follows: (1) own interest; (2) others’ interest; (3) primary reward (not included in the current analyses of FFA response to intense interests); and (4) visual baseline. The “visual baseline” blocks consisted of images from the experimental conditions that had been rotated 180° and subjected to a Gaussian blur. Images were presented in randomized order across the five runs using Eprime 2.0 (Psychology Software Tools, Inc., Sharpsburg, PA). They were projected onto a screen behind the scanner bore that participants viewed with a mirror attached to the head coil.

Image acquisition

fMRI data were acquired using a 3.0 Tesla Philips Achieva MRI scanner with an eight-channel SENSE head coil. Whole-brain functional images were acquired using axial oblique slices (tilted 15° anterior higher than posterior relative to the AC-PC line) with an isotropic 2.5-mm3 voxel size (TR = 2 s, TE = 25 ms, flip angle = 90°, acquisition matrix = 96 × 96, no gap). The first two volumes of each functional run were discarded for equilibration. High-resolution anatomical images were acquired in the sagittal plane using a T1-weighted volumetric 3D SPGR sequence (TR = 7.9 ms, TE = 3.7 ms, flip angle = 7°, acquisition matrix: 256 × 256, 1-mm3 isotropic resolution). Foam cushioning between participants’ head and the birdcage coil was used to minimize motion. During the structural, scout, and reference scans, participants watched a preferred video, with the restriction that it could not be related to the topic presented in their own interest condition. During the functional scan, participants were instructed simply to pay attention to each picture. They were informed prior to scanning that they would be tested after the scan regarding how many pictures they remembered.

Post-scan memory test

Immediately following the scanning session, participants were tested with a computer-based task to confirm they had been attending during the passive viewing scanning paradigm. In this task, the 38 previously viewed “own” and “other” images were presented, in randomized order, along with 19 novel images from each condition. Participants were instructed to press “1” on the keyboard if they had seen the image in the scanner and “2” if they had never seen it before. Participants received feedback regarding the accuracy of their response for each trial. For each condition, hit and false alarm rates were calculated and Z-scored to compute d prime. For final analyses, fMRI data were excluded for participants whose d prime was less than 1.32 (a value corresponding to a 75 % correct rate for both old and new images). Using this criterion, imaging data from three children in the TD group were excluded. Groups did not differ on post-scan memory task performance; own interest: t(29) = 0.57, p = .57 (ASD: mean = 6.13; SD = 3.08; TD: mean = 5.50; SD = 3.10), other interest: t(29) = 0.73, p = .47 (ASD: mean = 5.24; SD = 1.91; TD: mean = 4.78; SD = 1.56).

Image preprocessing and quality assurance

Images were analyzed using SPM8 running in Matlab 7.13.0 (R2011b) ( For each run, functional images were realigned to the first volume and resliced. All realigned functional volumes were then warped to the standard Montreal Neurological Institute (MNI) template brain, and normalized functional images were smoothed with a Gaussian kernel of 6 mm FWHM. Realignment parameters used to identify runs with motion criteria exceeding 3-mm translation and/or 3° rotation led to the exclusion of four participants (ASD: n = 2; TD: n = 2) who had fewer than three functional runs meeting inclusion criteria.

Overall, between exclusions made for poor performance on the post-scan memory test and excess motion, two participants with ASD and five TD participants were excluded, yielding a final sample of 19 in the ASD group and 18 in the TD group. Independent samples t tests confirmed that the final groups did not differ in age, t(35) = −.54, p = .59, IQ, t(35) = 1.22, p = .23, or mean number of included runs, t(35) = 0.33, p = .75. Final ASD and TD groups also did not differ in either average translational, t(35) = 0.633, p = .53 (ASD: mean = .0041; SD = .07; TD: mean = −.0096; SD = .06), or rotational, t(35) = 0.068, p = .95 (ASD: mean = .00065; SD = .0015; TD: mean = .00069; SD = .0015), motion for included runs. Characteristics of the final sample are reported in Table 1.

Statistical analysis

Individual-level analyses were performed using the general linear model design matrix, modeled using the canonical hemodynamic response function (HRF). To minimize the contribution of volumes with motion spikes to the overall model, the robust weighted least squares (rWLS; [16]) toolbox was used to inversely weight volumes according to their variance due to noise. Next, each model was estimated with the classical restricted maximum likelihood approach for spatially smoothed images and individual-level contrasts were created by subtracting the BOLD pattern of activation for others’ interest condition from the own interest condition (own-other), across all included runs.

Group-level analyses were completed in two stages. After specifying the general linear model with percent of images containing faces (see above) as a covariate of no interest, one-sample t tests were used to create within-group contrasts between conditions. Next, independent-sample t tests were conducted to compare contrasts between the ASD and TD groups. For all analyses, proportion of images containing faces was used as a covariate of no interest to control for differences in activation that might be confounded by differences in the face content of images.

Regions of interest

In an effort to improve statistical power in the absence of independent data for individual-level functional localization of FFAs, we employed an alternative method for conducting region of interest (ROI) analyses in two stages. First, for preliminary analyses, we created a broad ROI mask for the entire fusiform gyrus using automated anatomical labeling (AAL) regions from the Wake Forest University Pick Atlas [36]. This mask was applied for all group results and a threshold of Z >1.65 (uncorrected p < 0.05) with a cluster size of at least ten voxels was used to identify voxels with a statistically significant BOLD response. Significant clusters were then submitted to correction for family-wise error using AlphaSim ( AlphaSim implements a Monte Carlo simulation procedure in order to determine the probability of a false positive from the frequency count of cluster sizes [18]. Masks for each ROI were then registered to functional image space and used with a corrected p value of .05 and smoothing kernel of 6 mm in 5000 iterations of the simulation procedure.

Second, in a more spatially constrained analysis, we created four 5-mm radius spherical ROI masks corresponding to bilateral posterior and anterior portions of FFA: FFA1 and FFA2, respectively [37, 46, 66]. To test for specificity of group effects to the FFA, which is part of the ventral visual stream, we created bilateral approximately 12-mm radius spheres in an extrastriate region of comparable hierarchy in the dorsal visual stream, area MT/V5. FFA masks were centered on the group-averaged peaks of face-selectivity reported by Pinsk and colleagues [46], transformed to the MNI coordinate system: rFFA1 (34, −65, −16); rFFA2 (36, −53, −19); lFFA1 (−38, −65, −18); lFFA2 (−38, −51, −18). MT/V5 masks were centered on coordinates reported by Watson and colleagues [65] transformed to MNI space: rMT (40, −67, −3), lMT (−42, −79, −3). These masks were created using Marsbar [3] and applied in all group analyses. Statistical effects were probed from within the restricted cortices. To identify clusters with statistically significant BOLD response within the masks, we used a threshold of Z > 1.65 (uncorrected p value of .05) but did not include a cluster size threshold because of the much smaller size of these ROIs relative to the broad fusiform gyrus mask.


Within-group contrasts

In both groups, viewing images of one’s own interest elicited significantly higher BOLD signal relative to viewing images of other children’s interest (own-other) in discrete clusters within the FFA masks. These findings held both within the broad AAL-defined fusiform gyrus mask (Table 2, Fig. 2a, b) as well as within the more precise bilateral FFA1 and FFA2 masks (Table 3, Fig. 2c–f).

Table 2 Within-group contrasts for AAL-defined fusiform gyrus masks
Fig. 2
figure 2

Within-group activations for the own-other contrast within the bilateral AAL-defined fusiform gyrus mask (a, b), as well as posterior FFA1 (c, d) and anterior FFA2 (e, f) masks, defined by coordinates provided in [46], show that both children with ASD and TD activated bilateral fusiform regions when viewing images of their own interests. Between-group activations within bilateral AAL-defined fusiform gyrus mask (g), as well as left posterior FFA1 anterior FFA2 masks (h, i), reveal that children with ASD show more robust recruitment of fusiform regions than do children with TD when viewing images of their own interest. Bilateral FFA1 (in blue) and FFA2 (in red) masks are depicted in Fig. 2 j

Table 3 Within-group contrasts for Pinsk ROIs

Between-group contrasts

When directly comparing the ASD and TD groups, the “own-other” contrast elicited significantly greater response in ASD than TD in both left and right fusiform regions (Table 4, Fig. 2g). In contrast, no clusters within the fusiform gyrus showed greater BOLD response to the own-other contrast in the TD group, relative to the ASD group. In the more stringent analyses using separate ROI masks defined from coordinates Pinsk et al. [46] identified for left and right FFA1 and FFA2, the own-other contrast again elicited greater response in the ASD than the TD group. Specifically, there were clusters within both left and right FFA1 and FFA2 masks that showed significantly greater BOLD signal in ASD than TD (Table 5, Fig. 2h, i). Similar to analyses of the broader fusiform gyrus, there were no clusters in either hemisphere that elicited greater response in the TD relative to the ASD group for own-other contrast within the FFA1 or FFA2 masks. There were no significant group differences in either direction for the control extrastriate region MT/V5.

Table 4 Between-group contrasts for AAL-defined fusiform gyrus mask
Table 5 Between-group contrasts for Pinsk ROIs

Two three-way interactions were conducted to evaluate whether group differences in FFA activation to objects of ones own interest were affected by laterality (right vs. left) or location (anterior vs. posterior) of the FFA region for either cluster size or height threshold. Results of the analyses for cluster size revealed a significant main effect of location, F(1,35) = 17.92, p < 0.001, and a significant laterality by location interaction, F(1,35) = 22.70, p < 0.001, but no interactions including group. Results of the analyses for height threshold revealed a significant main effect of location, F(1,35) = 10.23, p = 0.003, but no other main effects or interactions. Pairwise post hoc tests to explore the nature of significant effects revealed that, across ASD and TD groups, the extent of activation was significantly more robust in FFA1 versus FFA2 in the right hemisphere, t(36) = 6.190, p < 0.001, whereas FFA1 and FFA2 activation clusters in the left hemisphere were highly similar, t(36) = 0.015, p = 0.99.


In the current study, we explored whether the fusiform gyrus, a hub for expert level individuation of faces and objects, was responsive to customized stimulus sets reflecting the time-intensive hobbies and interests of children and adolescents with and without ASD. Our results indicate that images from topics of interest can elicit response from expertise-responsive regions in both ASD and TD individuals, but that this response is more robust in ASD. This finding is consistent with the notion that the intensity of restricted interests in ASD [62] is likely to result in unusually high levels of perceptual expertise. Although we did not ask participants to perform a perceptual discrimination task among exemplars in the categories of their topic of interest, the enhanced recruitment of the FFA in the ASD group provides indirect support for this intuitive idea.

Visual expertise has been associated with increased reward system activation, particularly in the medial orbital frontal cortex (OFC) and cingulate Kirk et al. [28]. We have previously shown that both the amygdala and the OFC are engaged by individuals with ASD when viewing images of their restricted interest, with the OFC being particularly selective for images of one’s own interest versus novel pictures of others’ interest [4]. This finding demonstrated that (1) areas of the brain that subserve social functions are not globally impaired or under-responsive in ASD, and (2) reward processing regions may be especially responsive to circumscribed areas of interest in ASD, which often involving objects and other non-social stimuli. These results converge with the current findings to suggest that both reward and expertise-related visual processing areas show enhanced response to topics of intense interest in ASD. These findings could offer insight into mechanisms by which neural networks involved in social behavior could develop atypical specialization in ASD.

Johnson [23] proposed that, early in development, subcortical limbic structures including the amygdala ensure infants attend preferentially to faces, which initially provides extensive visual input to specific areas in the fusiform gyrus, and ultimately leads to the development of a specialized face-processing region, or the FFA [23, 24]. If limbic structures such as the amygdala are less responsive to human faces in ASD and more responsive to objects or scenes related to restricted interests throughout development, this mechanism could lead to atypical cortical specialization of traditionally face-responsive regions. Thus, as we show here, regions of the brain that are typically responsive to social stimuli (faces) may function better in their other capacities; in this case, the FFA in its role as a hub for perceptual expertise. Investigation of limbic and reward circuitry in infants who will later develop ASD is an important area for further study in order to determine the developmental course by which atypical neural specialization within social brain regions emerges in ASD.

Although some percentage of both groups’ topics of interests in this study included images that contained faces (e.g., several football players engaging in a tackle, cartoon scenes with landscape and entire figures), none of them had faces as a sole or primary focus (as stimuli that optimally recruit the FFA do). Our finding of greater activation to own vs. other images in both ASD and TD provide support, across both typical and atypical development, in favor of the FFA as a broader expertise region, rather than a face-specific region. Although there was not a significant group difference in the number of images that contained faces, there was a trend for that number to be higher in the TD group. We accounted for this by covarying for the percentage of images that contained faces within each participant’s own image set. Importantly, if the lower representation of faces in one group’s stimulus set were to have an effect on FFA activation, it would be expected to result in reduced FFA activation relative to the second group, whose images contained more faces. However, our results are in the opposite direction; we saw enhanced, not diminished, response of FFA in the ASD group, whose image sets contained marginally fewer faces. Future studies directly comparing FFA response to both faces and topics of special interest in a carefully selected population of individuals with and without ASD whose interests do not relate to topics where faces may be present would be helpful for further parsing the question of specialization in the FFA in ASD.

Our findings are also consistent with the idea that diminished responses of regions that are typically interrogated with face stimuli may not reflect generalized pathology in ASD but rather differential tuning [60] toward stimuli that are less conventionally social than isolated faces, such as vehicles or anime scenes. While the literature reporting reduced FFA response to faces in ASD is somewhat mixed, it does seem clear that human faces do not engage this region to the same extent and with the same reliability as in TD individuals [8, 56, 57], particularly in individuals with severe symptoms [52]. It is unknown at what point in development this difference may emerge, but early failure to develop specialized function within the neuroanatomical structures typically subserving social functions may recursively shape the response properties of these structures, ultimately leading to difficulty engaging in the social world for people with ASD [43]. In infants later diagnosed with ASD, decreased visual attention to social scenes is evident by six months of age [6], while in the same developmental window attention to non-social objects is enhanced [17]. This preference for looking at objects is sustained throughout early childhood [48, 49, 51]. Thus, differential visual salience of environmental (versus conventionally social) stimuli may result in atypical focus on these stimuli, leading to differently appropriated visual expertise, and in turn predisposing the FFA to respond preferentially to these stimuli in ASD.

The localization of posterior and middle portions of the FFA is still a relatively new convention in the literature [46, 66]. Little is known about the response property distinctions or similarities in these posterior and anterior portions; however, a recent study with typical subjects showed evidence that FFA1 and FFA2 both respond to non-face objects of expertise but that bilateral FFA2 may be most robust to conditions of competition [38]. In addition, neuroanatomical work has shown that cross-correlations in cortical thickness of within-hemisphere FFAs (rFFA1 × rFFA2 and lFFA1 × lFFA2) are higher in the left hemisphere than the right (McGugin, Van Gulick, Gauthier, submitted). Consistent with this work, our results indicated that the extent of the own-other response for both TD and ASD populations was significantly different for anterior and posterior FFAs in the right hemisphere, with no difference in the response properties of FFAs in the left hemisphere. These data support the hypothesis that the two right FFAs may be more distinct relative to the two left FFAs. In addition, they support the idea that, for objects of expertise, the FFA functions similarly in ASD as in TD. However, caution should be taken when interpreting these results, given that in the current work, posterior and middle portions of the FFA were not localized on an individual subject basis, so it is possible that they reflect parts of a single FFA.

Our findings have important translational implications. Recent research has demonstrated that responsivity of regions of the social brain can be modulated by behavioral intervention targeting social motivation in children with ASD [63]. Specifically, children with ASD who initially showed atypical recruitment of posterior STS during a social perception task exhibited increased compensatory activation in related brain regions, including the amygdala and striatal reward regions, following administration of pivotal response treatment (PRT). These neural changes paralleled behavioral and clinical evidence showing improvement in social functioning in treated children [64]. The fact that both the reward system and FFA can be robustly recruited in ASD given stimuli of appropriate salience provides an additional avenue for intervention. For example, pairing stimuli of particular interest and expertise (which more automatically engage reward and visual expertise regions in ASD that are also robustly recruited by social stimuli in TD) with social input or interactions (e.g., a smile, eye contact, a verbal request directed to another person) may serve to shape these regions toward responding more robustly to social input in ASD as well. Thus, it is possible that PRT and other interventions targeting social motivation may be able to modify and normalize responsiveness of the FFA for faces, harnessing topics of interest and expertise to increase neural specialization for social stimuli and thereby perhaps offering a mechanism by which the brain may enable more effective functioning within the social world. This possibility could be explored directly through intervention studies where neural response in “social” brain regions, including FFA and reward systems, is probed with stimuli reflecting both social and restricted interest categories in order to test the degree to which these regions interact and are shaped both over the course of development and through behavioral pairings during intervention.

Our study utilized a unique design featuring personalized stimuli to assess recruitment of a social brain region during viewing of images relates to individuals’ topics of intense interest. The use of individualized stimulus sets enabled us to explore recruitment of expertise-responsive brain regions to participants’ specific hobbies and interests. However, this design inherently introduced heterogeneity of stimuli across individuals. We addressed this issue in several ways. First, we used a standard size and resolution for each image. Second, analyses indicated that neither visual complexity nor proportion of images containing faces differed between groups. Third, we statistically controlled for individual differences in the degree to which faces were present in an individual’s stimulus set by adding a covariate to the model. Another possible limitation of this study is that we used a passive viewing task and did not use eye tracking or measure dwell time while participants viewed task images; however, participants were asked to view and remember presented images for post-scan testing, an exclusion criterion was used based on post-scan memory test performance, and groups did not differ in their performance on this task. In this study, the FFA region was not localized on an individual subject basis using a canonical face localizer task; however, the appropriateness of using such a localizer task in a clinical population with known face-processing impairments could be questionable. Despite the lack of localizer, both broader analyses of the entire fusiform gyrus and more stringent analyses limited to literature-based coordinates for anterior and posterior FFA regions converged in showing that individuals with ASD recruited fusiform clusters more robustly than individuals with TD when viewing images of their interest. Finally, though our aims focused on analyses of group differences in the FFA and we controlled for nonspecific extrastriate group differences by examining area MT/V5, it is possible that results we observe are reflective of broader differences within the ventral visual stream.


This study used a novel task design to probe whether the fusiform gyrus can be effectively recruited by individuals with ASD in the context of viewing images of their topics of intense interest. Whereas studies using face stimuli have often shown decreased recruitment of the FFA in ASD, our results reveal that this region is recruited to an enhanced degree in ASD for objects of intense interest, which are likely highly familiar and thus associated with perceptual expertise. This finding is consistent with a previously reported case study [20] and with previous work indicating intact FFA response to familiar faces in ASD [45]. Taken together, these findings support the notion that key nodes of the social brain are not globally dysfunctional in ASD, but rather may be differentially tuned toward responding to stimuli of particular familiarity, interest, or expertise, which are typically less conventionally social than stimuli of unfamiliar faces used in most research studies. Future studies could examine FFA response to both face and expertise-related stimuli concurrently and could limit expertise-related stimuli to objects or scenes with no human figures to determine more directly the extent to which increased FFA recruitment for processing non-social stimuli may come at the expense of its recruitment for social stimuli. This line of work may prove informative both for understanding the neural basis of restricted interests in ASD and for developing and refining interventions targeting development of expertise, efficiency, and proficiency with processing social information.


  1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub, 2013.

  2. Bernstein M, Yovel G. Two neural pathways of face processing: a critical evaluation of current models. Neurosci Biobehav Rev. 2015;55:536–46.

    Article  PubMed  Google Scholar 

  3. Brett M, Anton J-L, Valabregue R, Poline J-B. Region of interest analysis using the MarsBar toolbox for SPM 99. Neuroimage. 2002;16(2):S497.

    Google Scholar 

  4. Cascio CJ, Foss-Feig JH, Heacock J, Schauder KB, Loring WA, Rogers BP, Bolton S. Affective neural response to restricted interests in autism spectrum disorders. J Child Psychol Psychiatry. 2014;55(2):162–71. doi:10.1111/jcpp.12147.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Cascio CJ, Foss-Feig JH, Heacock JL, Newsom CR, Cowan RL, Benningfield MM, Cao A. Response of neural reward regions to food cues in autism spectrum disorders. J Neurodev Disord. 2012;4(1):9. doi:10.1186/1866-1955-4-9.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chawarska K, Macari S, Shic F. Decreased spontaneous attention to social scenes in 6-month-old infants later diagnosed with autism spectrum disorders. Biol Psychiatry. 2013;74(3):195–203.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Chevallier C, Kohls G, Troiani V, Brodkin ES, Schultz RT. The social motivation theory of autism. Trends Cogn Sci. 2012;16(4):231–9. doi:10.1016/j.tics.2012.02.007.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Critchley HD, Daly EM, Bullmore ET, Williams SC, Van Amelsvoort T, Robertson DM, Howlin P. The functional neuroanatomy of social behaviour changes in cerebral blood flow when people with autistic disorder process facial expressions. Brain. 2000;123(11):2203–12.

    Article  PubMed  Google Scholar 

  9. Dawson G, Carver L, Meltzoff AN, Panagiotides H, McPartland J, Webb SJ. Neural correlates of face and object recognition in young children with autism spectrum disorder, developmental delay, and typical development. Child Dev. 2002;73(3):700–17.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Dawson G, Webb SJ, McPartland J. Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies. Dev Neuropsychol. 2005;27(3):403–24.

    Article  PubMed  Google Scholar 

  11. Dawson G, Webb SJ, Wijsman E, Schellenberg G, Estes A, Munson J, Faja S. Neurocognitive and electrophysiological evidence of altered face processing in parents of children with autism: Implications for a model of abnormal development of social brain circuitry in autism. Dev Psychopathol. 2005;17:679–97.

    Article  PubMed  Google Scholar 

  12. Delmonte S, Balsters JH, McGrath J, Fitzgerald J, Brennan S, Fagan AJ, Gallagher L. Social and monetary reward processing in autism spectrum disorders. Molecular autism. 2012;3(7):1–13.

    Google Scholar 

  13. Dichter GS. Functional magnetic resonance imaging of autism spectrum disorders. Dialogues Clin Neurosci. 2012;14(3):319.

    PubMed  PubMed Central  Google Scholar 

  14. Dichter GS, Felder JN, Green SR, Rittenberg AM, Sasson NJ, Bodfish JW. Reward circuitry function in autism spectrum disorders. Soc Cogn Affect Neurosci. 2012;7(2):160–72. doi:10.1093/scan/nsq095.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Dichter GS, Richey JA, Rittenberg AM, Sabatino A, Bodfish JW. Reward circuitry function in autism during face anticipation and outcomes. J Autism Dev Disord. 2012;42(2):147–60. doi:10.1007/s10803-011-1221-1.

    Article  PubMed  Google Scholar 

  16. Diedrichsen J, Shadmehr R. Detecting and adjusting for artifacts in fMRI time series data. Neuroimage. 2005;27(3):624–34. doi:10.1016/j.neuroimage.2005.04.039.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Elison JT, Sasson NJ, Turner-Brown LM, Dichter GS, Bodfish JW. Age trends in visual exploration of social and nonsocial information in children with autism. Research in autism spectrum disorders. 2012;6(2):842–51.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Forman SD, Cohen JD, Fitzgerald M, Eddy WF, Mintun MA, Noll DC. Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn Reson Med. 1995;33(5):636–47.

    Article  CAS  PubMed  Google Scholar 

  19. Gauthier I, Skudlarski P, Gore JC, Anderson AW. Expertise for cars and birds recruits brain areas involved in face recognition. Nat Neurosci. 2000;3(2):191–7.

    Article  CAS  PubMed  Google Scholar 

  20. Grelotti DJ, Gauthier I, Schultz RT. Social interest and the development of cortical face specialization: what autism teaches us about face processing. Dev Psychobiol. 2002;40(3):213–25.

    Article  PubMed  Google Scholar 

  21. Grelotti DJ, Klin AJ, Gauthier I, Skudlarski P, Cohen DJ, Gore JC, Schultz RT. fMRI activation of the fusiform gyrus and amygdala to cartoon characters but not to faces in a boy with autism. Neuropsychologia. 2005;43(3):373–85. doi:10.1016/j.neuropsychologia.2004.06.015.

    Article  PubMed  Google Scholar 

  22. James TW, James KH. Expert individuation of objects increases activation in the fusiform face area of children. Neuroimage. 2013;67:182–92. doi:10.1016/j.neuroimage.2012.11.007.

    Article  PubMed  Google Scholar 

  23. Johnson MH. Subcortical face processing. Nat Rev Neurosci. 2005;6(10):766–74.

    Article  CAS  PubMed  Google Scholar 

  24. Johnson MH, Griffin R, Csibra G, Halit H, Farroni T, De Haan M, Richards J. The emergence of the social brain network: Evidence from typical and atypical development. Dev Psychopathol. 2005;17(03):599–619.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kanwisher N, McDermott J, Chun MM. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neurosci. 1997;17(11):4302–11.

    CAS  PubMed  Google Scholar 

  26. Kanwisher N, Yovel G. The fusiform face area: a cortical region specialized for the perception of faces. Philosophical Transactions of the Royal Society B: Biological Sciences. 2006;361(1476):2109–28.

    Article  Google Scholar 

  27. Kimko HC, Cross JT, Abernethy DR. Pharmacokinetics and clinical effectiveness of methylphenidate. Clin Pharmacokinet. 1999;37(6):457–70.

    Article  CAS  PubMed  Google Scholar 

  28. Kirk U, Skov M, Christensen MS, Nygaard N. Brain correlates of aesthetic expertise: a parametric fMRI study. Brain Cogn. 2009;69(2):306–15.

    Article  PubMed  Google Scholar 

  29. Klin A, Jones W, Schultz R, Volkmar F. The enactive mind, or from actions to cognition: lessons from autism. Philos Trans R Soc Lond B Biol Sci. 2003;358(1430):345–60. doi:10.1098/rstb.2002.1202.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Klin A, Jones W, Schultz R, Volkmar F, Cohen D. Defining and quantifying the social phenotype in autism. Am J Psychiatry. 2002;159:895–908.

    Article  PubMed  Google Scholar 

  31. Kuhl PK, Coffey-Corina S, Padden D, Dawson G. Links between social and linguistic processing of speech in preschool children with autism: behavioral and electrophysiological measures. Dev Sci. 2005;8(1):F1–12.

    Article  PubMed  Google Scholar 

  32. Lin RC. Special issue: new concepts in developing brain disorders-autism. Anat Rec (Hoboken). 2011;294(10):1613–4. doi:10.1002/ar.21461.

    Article  Google Scholar 

  33. Lord C, Risi S, Lambrecht L, Cook Jr EH, Leventhal BL, DiLavore PC, Rutter M. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord. 2000;30(3):205–23.

    Article  CAS  PubMed  Google Scholar 

  34. Lord C, Rutter M, Le Couteur A. Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994;24(5):659–85.

    Article  CAS  PubMed  Google Scholar 

  35. Maestro S, Muratori F, Cavallaro MC, Pei F, Stern D, Golse B, Palacio-Espasa F. Attentional skills during the first 6 months of age in autism spectrum disorder. J Am Acad Child Adoesc Psychiatry. 2002;41(10):1239–45. doi:10.1097/01.CHI.0000020277.43550.02.

    Article  Google Scholar 

  36. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage. 2003;19(3):1233–9.

    Article  PubMed  Google Scholar 

  37. McGugin RW, Gatenby JC, Gore JC, Gauthier I. High-resolution imaging of expertise reveals reliable object selectivity in the fusiform face area related to perceptual performance. Proc Natl Acad Sci. 2012;109(42):17063–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. McGugin RW, Newton AT, Gore JC, Gauthier I. Robust expertise effects in right FFA. Neuropsychologia. 2014;63:135–44.

    Article  PubMed  PubMed Central  Google Scholar 

  39. McPartland JC, Crowley MJ, Perszyk DR, Mukerji CE, Naples AJ, Wu J, Mayes LC. Preserved reward outcome processing in ASD as revealed by event-related potentials. J Neurodevelopmental Disord. 2012;4(16):1–9.

    Google Scholar 

  40. Mundy P, Neal R. Neural plasticity, joint attention and autistic developmental pathology. International review of research in mental retardation. 2001;23:139–68.

    Article  Google Scholar 

  41. Pelphrey K, Adolphs R, Morris JP. Neuroanatomical substrates of social cognition dysfunction in autism. Ment Retard Dev Disabil Res Rev. 2004;10(4):259–71. doi:10.1002/mrdd.20040.

    Article  PubMed  Google Scholar 

  42. Pelphrey KA, Shultz S, Hudac CM, Vander Wyk BC. Research review: constraining heterogeneity: the social brain and its development in autism spectrum disorder. J Child Psychol Psychiatry. 2011;52(6):631–44. doi:10.1111/j.1469-7610.2010.02349.x.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Perlman SB, Hudac CM, Pegors T, Minshew NJ, Pelphrey KA. Experimental manipulation of face-evoked activity in the fusiform gyrus of individuals with autism. Soc Neurosci. 2011;6(1):22–30. doi:10.1080/17470911003683185.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Philip RC, Dauvermann MR, Whalley HC, Baynham K, Lawrie SM, Stanfield AC. A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. Neurosci Biobehav Rev. 2012;36(2):901–42.

    Article  PubMed  Google Scholar 

  45. Pierce K, Redcay E. Fusiform function in children with an autism spectrum disorder is a matter of “who”. Biol Psychiatry. 2008;64(7):552–60. doi:10.1016/j.biopsych.2008.05.013.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Pinsk MA, Arcaro M, Weiner KS, Kalkus JF, Inati SJ, Gross CG, Kastner S. Neural representations of faces and body parts in macaque and human cortex: a comparative FMRI study. J Neurophysiol. 2009;101(5):2581–600.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Rutter M, Bailey A, & Lord C. The social communication questionnaire. Los Angeles: Western Psychological Services. 2003

  48. Sasson NJ, Dichter GS, Bodfish JW. Affective responses by adults with autism are reduced to social images but elevated to images related to circumscribed interests. PLoS ONE. 2012;7(8), e42457. doi:10.1371/journal.pone.0042457.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sasson NJ, Elison JT, Turner-Brown LM, Dichter GS, Bodfish JW. Brief report: circumscribed attention in young children with autism. J Autism Dev Disord. 2011;41(2):242–7.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Sasson NJ, & Touchstone EW. Visual attention to competing social and object images by preschool children with autism spectrum disorder. Journal of autism and developmental disorders. 2014;44(3):584-592.

  51. Sasson NJ, Turner-Brown LM, Holtzclaw TN, Lam KS, Bodfish JW. Children with autism demonstrate circumscribed attention during passive viewing of complex social and nonsocial picture arrays. Autism Res. 2008;1(1):31–42.

    Article  PubMed  Google Scholar 

  52. Scherf KS, Elbich D, Minshew N, Behrmann M. Individual differences in symptom severity and behavior predict neural activation during face processing in adolescents with autism. NeuroImage: Clinical. 2015;7:53–67.

    Article  Google Scholar 

  53. Scherf KS, Luna B, Minshew N, Behrmann M. Location, location, location: alterations in the functional topography of face-but not object-or place-related cortex in adolescents with autism. Front Hum Neurosci. 2010;4:26.

    PubMed  PubMed Central  Google Scholar 

  54. Schmitz N, Rubia K, van Amelsvoort T, Daly E, Smith A, Murphy DG. Neural correlates of reward in autism. Br J Psychiatry. 2008;192(1):19–24. doi:10.1192/bjp.bp.107.036921.

    Article  PubMed  Google Scholar 

  55. Schultz RT. Developmental deficits in social perception in autism: the role of the amygdala and fusiform face area. Int J Dev Neurosci. 2005;23(2-3):125–41. doi:10.1016/j.ijdevneu.2004.12.012.

    Article  PubMed  Google Scholar 

  56. Schultz RT, Gauthier I, Klin A, Fulbright RK, Anderson AW, Volkmar F, Gore JC. Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and Asperger syndrome. Arch Gen Psychiatry. 2000;57(4):331–40.

    Article  CAS  PubMed  Google Scholar 

  57. Schultz RT, Grelotti DJ, Klin A, Kleinman J, Van der Gaag C, Marois R, Skudlarski P. The role of the fusiform face area in social cognition: implications for the pathobiology of autism. Philos Trans R Soc Lond B Biol Sci. 2003;358(1430):415–27. doi:10.1098/rstb.2002.1208.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Scott-Van Zeeland AA, Dapretto M, Ghahremani DG, Poldrack RA, Bookheimer SY. Reward processing in autism. Autism Res. 2010;3(2):53–67. doi:10.1002/aur.122.

    PubMed  PubMed Central  Google Scholar 

  59. South M, Klin A, & Ozonoff S. (1999). The Yale Special Interests Interview. Unpublished Work.

  60. South M, Ozonoff S, Suchy Y, Kesner RP, McMahon WM, Lainhart JE. Intact emotion facilitation for nonsocial stimuli in autism: Is amygdala impairment in autism specific for social information? 2008.

    Google Scholar 

  61. Tarr MJ, Gauthier I. FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise. Nat Neurosci. 2000;3(8):764–9.

    Article  CAS  PubMed  Google Scholar 

  62. Turner-Brown LM, Lam KS, Holtzclaw TN, Dichter GS, Bodfish JW. Phenomenology and measurement of circumscribed interests in autism spectrum disorders. Autism. 2011;15(4):437–56. doi:10.1177/1362361310386507.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Ventola P, Friedman HE, Anderson LC, Wolf JM, Oosting D, Foss-Feig J, Pelphrey KA. Improvements in social and adaptive functioning following short-duration PRT program: a clinical replication. J Autism Dev Disord. 2014;44(11):2862–70.

    Article  PubMed  Google Scholar 

  64. Ventola P, Yang DY, Friedman HE, Oosting D, Wolf J, Sukhodolsky DG, & Pelphrey KA. Heterogeneity of neural mechanisms of response to pivotal response treatment. Brain Imaging and Behavior. 2015;9(1):74-88.

  65. Watson JD, Myers R, Frackowiak RS, Hajnal JV, Woods RP, Mazziotta JC, Zeki S. Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cereb Cortex. 1993;3(2):79–94.

    Article  CAS  PubMed  Google Scholar 

  66. Weiner KS, Grill-Spector K. Sparsely-distributed organization of face and limb activations in human ventral temporal cortex. Neuroimage. 2010;52(4):1559–73.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Whyte EM, Behrmann M, Minshew NJ, Garcia NV, Scherf KS. Animal, but not human, faces engage the distributed face network in adolescents with autism. Dev Sci. 2015.

Download references


The authors wish to thank Kimberly Schauder, Jessica Heacock Jones, and Akua Cosby for their assistance with data acquisition and management; Jennifer Milsten and William Yue for coding the stimulus images; Aize Cao and Baxter Rogers for the fMRI QA and processing assistance; and Cassandra Newsom, Whitney Loring, and Courtney Burnette for clinical assessment of participants with ASD.


This work was supported by NIMH grants K01 MH090232, R01 MH102272, and R21 MH101321 to CJC.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Carissa J. Cascio.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JHF and CJC conceptualized and designed this study. JHF, CJC, LM, and RM contributed to the data collection and/or analysis. JHF, RM, IG, PV, and CJC contributed to drafting the manuscript. All authors read and approved the final manuscript.

Additional file

Additional file 1: Table S1.

Interest topics for ASD and TD participants. (DOC 36 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Foss-Feig, J.H., McGugin, R.W., Gauthier, I. et al. A functional neuroimaging study of fusiform response to restricted interests in children and adolescents with autism spectrum disorder. J Neurodevelop Disord 8, 15 (2016).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Autism spectrum disorder
  • Fusiform face area
  • Restricted interests
  • Expertise
  • fMRI
  • Fusiform gyrus