Developmental brain structural atypicalities in autism: a voxel-based morphometry analysis
Child and Adolescent Psychiatry and Mental Health volume 16, Article number: 7 (2022)
Structural magnetic resonance imaging (sMRI) studies have shown atypicalities in structural brain changes in individuals with autism spectrum disorder (ASD), while a noticeable discrepancy in their results indicates the necessity of conducting further researches.
The current study investigated the atypical structural brain features of autistic individuals who aged 6–30 years old. A total of 52 autistic individuals and 50 age-, gender-, and intelligence quotient (IQ)-matched typically developing (TD) individuals were included in this study, and were assigned into three based cohorts: childhood (6–12 years old), adolescence (13–18 years old), and adulthood (19–30 years old). Analyses of whole-brain volume and voxel-based morphometry (VBM) on the sMRI data were conducted.
No significant difference was found in the volumes of whole-brain, gray matter, and white matter between the autism and TD groups in the three age-based cohorts. For VBM analyses, the volumes of gray matter in the right superior temporal gyrus and right inferior parietal lobule in the autism group (6–12 years old) were smaller than those in the TD group; the gray matter volume in the left inferior parietal lobule in the autism group (13–18 years old) was larger than that in the TD group; the gray matter volume in the right middle occipital gyrus in the autism group (19–30 years old) was larger than that in the TD group, and the gray matter volume in the left posterior cingulate gyrus in the autism group was smaller than that in the TD group.
Autistic individuals showed different atypical regional gray matter volumetric changes in childhood, adolescence, and adulthood compared to their TD peers, indicating that it is essential to consider developmental stages of the brain when exploring brain structural atypicalities in autism.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that appears before the age of 3 years old. Its main clinical manifestations are social communication and communication impairments, restricted and repetitive behaviors, and limited interests . The prevalence of ASD is increasing year-by-year, and it was reported by the Centers for Disease Control and Prevention that the prevalence of ASD was as high as 1/44, seriously impairing the social functions of patients and imposing a heavy burden on their families and society . Previous studies have suggested that the etiology of ASD might be highly related to the interactions between genetic and environmental factors, while its pathological mechanism has still remained elusive [3, 4]. The brain structural developmental atypicalities caused by genetic and environmental factors might be largely involved in the neural mechanisms of ASD, as reported in previous studies [5, 6].
Structural magnetic resonance imaging (sMRI) studies have suggested the accelerated growth of the brain in individuals with ASD shortly after birth, which led to the increase of head circumference and the whole-brain volume [7, 8]. At the age of 2–4 years old, the whole brain volume of individuals with ASD was enlarged by about 10% [9, 10]. The increase in the whole-brain volume of ASD individuals aging 1–2 years old may involve almost all brain regions . Regarding the whole-brain volume study of ASD in childhood, adolescence, and early adulthood, results of the researches were mostly inconsistent [5, 12, 13]. For instance, Aylward Minshew et al.  showed that the whole-brain volume of autistic children aging under 12 years old increased by about 5%, whereas the whole-brain volume of autistic adolescents and adults showed no difference compared with typically developing (TD) children. However, another study reported that the total brain volume was enlarged by 5–7% during adolescence . Moreover, Riddle Cascio et al.  used voxel-based morphometry (VBM) to analyze sMRI data, a method that examines structural changes of the brain at the millimeter range, and found that the total brain and grey matter (GM) volumes were enlarged by approximately 1–2% in ASD, however, the effect reached statistical significance on only the cohort of all subjects, rather than on childhood, adolescence, and adulthood cohorts. Regarding the total brain GM volume in ASD, Freitag Luders et al.  found that the GM volume of adolescents in the ASD group increased compared with the TD group. Mitelman Bralet et al.  demonstrated that the GM volume in autistic adults also increased compared with TD adults who aged 21–34 years old. Some studies also reported the correlation of autistic symptoms with the structural atypicalities in ASD individuals. Mitchell et al. suggested that the reduction in the volume of the dorsolateral prefrontal cortex in ASD individuals was correlated with social and communication scores of Autism Diagnostic Observation Schedule (ADOS) . Hollander et al. reported that the increase in the volume of the right caudate nucleus in ASD adults was positively correlated with the Restricted and Repetitive Behaviors (RRB) score of Autism Diagnostic Interview (ADI) .
The results of the regional specificity of structural brain abnormalities in ASD in childhood, adolescence, and early adulthood were also inconsistent [5, 11, 12]. Previous studies have suggested that the increase of total brain volume after birth in ASD individuals may involve the increase in the volume of almost all brain regions , including the frontal lobe [18, 19], temporal lobe , occipital lobe , cerebellum , amygdala , or the increased volume of some brain regions and decreased volume of other brain regions . A previous research demonstrated that brain overgrowth in early ASD mainly involves cortical thickening . The results of the regional specificity of structural brain abnormalities in autistic children, adolescents, and adults were widely reported, including frontal lobe [24, 25], temporal lobe , parietal lobe , occipital lobe , amygdala , caudate nucleus , hippocampus , thalamus , and cerebellum .
The inconsistencies in the results of sMRI studies of ASD might be correlated with several factors: first, the participants’ range of age who were included in those studies was inconsistent, including childhood, adolescence, and adulthood cohorts . These inconsistencies might cause difficulties in summarizing atypical structural brain areas at different developmental stages. Second, these inconsistencies in brain structure atypicalities in ASD were also related to participants’ gender and intelligence quotient (IQ) . Third, in a number of those studies, the small sample size may also contribute to the inconsistencies in the results.
The present cross-sectional study included sMRI data of autistic individuals and TD individuals who aged 6–30 years old to explore the age-related differences of whole-brain volume and the GM volume between the two groups. Different analyses were conducted on the three age-based cohorts: late childhood (6–12 years old), adolescence (13–18 years old), and adulthood (19–30 years old). We predicted that probably no significant group-level brain structural atypicalities would be found between the two groups in the cohort of all subjects (i.e., 6–30 years old). However, the whole-brain volume and/or regional specificity of structural brain abnormalities in childhood, adolescence, and adulthood cohorts could be identified in the autism group compared with TD group.
A total of 52 high-functioning autistic individuals who aged 6–30 years old and met the Diagnostic and Statistical Manual-IV (DSM-IV) criteria for autism were recruited from the outpatient clinic of the Peking University Sixth Hospital (Beijing, China) between March 2013 and January 2017. The diagnosis was performed by two deputy chief physicians or chief physicians using the DSM-IV criteria. Besides, 50 TD individuals who aged 6–30 years old were enrolled from January 2016 to January 2017. All participants were right-handed and had an IQ greater than 70 measured with either the Chinese-Wechsler Intelligence Scale for Children (C-WISC) or the Wechsler Adult Intelligence Scale-Revised in China (WAIS-RC) [32, 33]. Participants were excluded if they had mental disorders (other than autism), suffered from severe physical diseases, neurological diseases and brain trauma, with a history of consumption of psychotropic drugs, being unable to cooperate in examinations, or had metal implants (e.g., non-removable dentures). All participants were divided into three age-based cohorts as follows: childhood: 6–12 years old (autism: n = 24; TD: n = 19); adolescence: 13–18 years old (autism: n = 18; TD: n = 18); and adulthood: 19–30 years old (autism: n = 10; TD: n = 13). Age, gender, and IQ were matched for autism and TD groups in each age-based group (refer to Table 1 for demographic data). This study was approved by the Ethics Committee of the Sixth Hospital of Peking University. Children and their guardians, as well as adult subjects understood the content and objective of the study and agreed to participate in the study. All children’s guardians and adult subjects signed the written informed consent forms.
MRI data collection
All MRI data were collected by the GE Discovery 750 3.0T magnetic resonance scanner (GE Healthcare, Chicago, IL, USA) in the Peking University Third Hospital using 8-channel phased array head coil. Subjects were placed in supine position and fixed with foam pad during scanning. Besides, three-dimensional (3D) T1 SPGR sequence sagittal scanning was carried out with the following parameters: repetition time (TR) = 4.78 ms, echo time (TE) = 2.02 ms, flip angle = 15°, field of view (FOV) = 24 mm × 24 mm, matrix size = 240 × 240, slice thickness = 1.0 mm, voxel size = 1.0 mm × 1.0 mm × 1.0 mm; 166 slices of images were collected from the whole-brain.
MRI data processing
All MRI data were processed using MATLAB R2009a software. Image preprocessing was conducted based on the VBM8-DARTEL toolbox (http://dbm.neuro.uni-je-na.de/vbm) in SPM8 software (http://www.fil.ion.ucl.ac.uk/spm/software/spm8). Each time, only one age-based group of autism and TD groups was processed. The three age-based cohorts were processed as follows: (a) AC-PC correction for MR images; (b) anatomical segmentation of T1-weighted structural images using segmentation template of ‘New Segment’ to extract the original image and volumes of GM, white matter (WM), and cerebrospinal fluid (CBF); (c) using DARTEL toolbox to average each part of the image, to achieve initial registration template, followed by matching the images and templates according to WM, GM, and CBF volumes to obtain the re-averaged images and to generate the optimal template of DARTEL through six iterations; (d) utilization of nonlinear transformation to match the initial segmentation image to the optimal template of DARTEL; (e) the deformable field obtained by the DARTEL was used to register the images at the Montreal Neurological Institute (MNI) platform and to generate volumetric modulated maps. All image positions and individual voxel sizes (1.5 mm × 1.5 mm × 1.5 mm) were aligned to achieve spatial volume comparability; (f) Gauss smoothing of standardized images was performed (full width at half maximum (FWHM) = 6 mm) to improve the signal-to-noise ratio (SNR) of MR images.
For the statistical analysis of whole-brain volume, single variable covariance analysis was employed, with inclusion of whole-brain volume, whole-WM volume, and whole-GM volume as dependent variables, as well as gender, age, and IQ as covariates.
To perform VBM analysis in SPM8 software, using a generalized linear model, the smoothed gray matter images of autism and TD groups were tested by voxel-based double-sample t-test. Age, gender, IQ, and intracranial volume (WM volume + GM volume + CBF volume) were taken as covariates, and the statistical analysis of whole-brain volume was carried out voxel-by-voxel. The Gaussian random field theory was used for multiple comparisons. Threshold of voxel level was set to P < 0.001, and the cluster number of voxels was considered significant if it was more than 50 .
The comparison of the volumes of whole-brain, whole-GM, and whole-WM between the two groups in distinct developmental cohorts (6–12, 13–18, and 19–30 years old) showed that, there was no significant difference between the autism and TD groups in the three age-based cohorts (all P > 0.05) (Additional file 1: Tables S1–S3).
Regional GM volume in ASD: VBM
6–12 years old: In the older childhood cohort, two regions showed a significantly smaller GM volume in the autism group than that in the TD group: clusters of right superior temporal gyrus and right inferior parietal lobule (P < 0.001, uncorrected, cluster number > 50, voxel size = 3.375). No region was found in the autism group with a greater volume than the TD group (Fig. 1 and Table 2).
13–18 years old: In the adolescent cohort, one region showed a significantly greater GM volume in the autism group than that in the TD group: left inferior parietal lobule (P < 0.001, uncorrected, cluster number > 50, voxel size = 3.375). No region was found in the autism group with a smaller volume than the TD group (Fig. 1 and Table 2).
19–30 years old: In the young adult cohort, one region showed a significantly larger GM volume in the autism group than that in the TD group: right middle occipital gyrus (P < 0.001, uncorrected, cluster number > 50, voxel size = 3.375). One region demonstrated a significantly smaller GM volume in the autism group than that in the TD group: left anterior cingulate gyrus (P < 0.001, uncorrected, cluster number > 50, voxel size = 3.375) (Fig. 1 and Table 2).
The current study included 52 autistic individuals and 50 TD individuals, who were matched for age, gender, and IQ. No significant difference was found in the volumes of whole-brain, whole-GM, and whole-WM between the two groups in distinct developmental stages (6–12, 13–18, and 19–30 years old). However, using VBM analyses, different GM regions showed significant differences in volume between the autism and TD groups in different age-based cohorts, involving parietal lobe, occipital lobe, temporal lobe, and cingulate gyrus.
Whole-brain volume in the autism
The present study compared the whole-brain volume, whole-GM volume, and whole-WM volume of subjects between the autism and TD groups among the three age-based cohorts (6–12, 13–18, and 19–30 years old), and no significant volume-based difference was noted between the two groups in each age-based cohort. The results were consistent with previous studies, which demonstrated that autistic individuals exhibited no significant differences on whole-brain volume, whole-GM volume, and whole-WM volume compared with TD individuals.
Regarding the comparison of whole-brain volume, Jou Minshew et al.  recruited 22 autistic children and 22 TD children who aged 8–12 years old, and they found no significant difference in the whole-brain volume between the two groups. Tepest Jacobi et al.  enrolled 29 autistic adults and 29 TD adults, and also found no significant difference in whole-brain volume between two groups. Riddle Cascio et al.  used sMRI data from a large cohort that included 539 autistic patients and 573 healthy controls, and they found no significant difference in the whole-brain volume between childhood (6–12.6 years old), early adolescence (12.7–16.1 years old), late adolescence (16.2–22 years old), and adult (older than 22 years old) autistic and TD individuals.
Regarding the analysis of whole-brain GM volume, one study recruited 86 autistic individuals and 90 TD individuals who aged 7–29 years old, and the results showed that there was no significant difference in the whole-brain GM volume between the two groups . Another study suggested that the differences in GM volume between autistic and TD adolescents and adults were more reflected in the imbalance of GM volume in local brain regions than in whole-brain GM volume .
Regarding the comparison of whole-brain WM volume, Radua Via et al.  conducted a meta-analysis of the articles that were recorded in the PubMed database from 2002 to 2010 and concentrated on WM volume of autistic individuals, and they found no significant difference in the whole-brain WM volume between ASD and TD groups.
However, few previous studies also showed that the whole-brain volume of ASD group was larger than that of TD group, which was inconsistent with the results of the present study. This inconsistency could be largely related to different IQ ranges and sample size in the two groups. For instance, Freitag Luders et al.  enrolled 15 autistic individuals and 15 TD individuals who aged 14–22 years old, and the results of the IQ test showed significant differences between the two groups, and they also found that the whole-brain volume, whole-brain GM volume, and whole-brain GM volume of the ASD group were all larger than those of the TD group. To better compare the whole-brain volume between autistic and TD individuals, participants with different IQ ranges and larger sample size need to be involved in the future studies.
Atypical regional GM volume in the autism group in the three age-based cohorts: VBM
Right superior temporal gyrus
It was found in the current study that the GM volume in the right superior temporal gyrus of 6–12-year-old autism group was smaller than that of the TD group, while there was no significant difference between the two groups in age-based cohorts of 13–18 and 19–30 years old. Previous studies have shown that the superior temporal gyrus was closely associated with language , visual function , auditory function , and social cognition . Social cognition refers to the processing of facial expression, eye gaze, physical movement, and other information by individuals in social communication. Its main objective is to recognize and understand individuals’ mental status . The posterior part of superior temporal gyrus, which involves advanced cortical integration function, integrates sensory information and limbic system information, is the core cortical area of social brain . A previous research  recruited 21 autistic children and 12 healthy controls who aged 7–11 years old, and it was revealed that the density of GM in superior temporal sulcus of autistic individuals decreased compared with TD individuals, which supported the results of the present study. Kates Mostofsky et al.  analyzed brain sMRI data of five 7-year-old autistic twins and found that the volumes of their superior temporal gyrus were smaller than those of TD children. From twin studies, structural atypicalities of superior temporal gyrus were noted to be correlated with heredity in ASD.
Compared with left superior temporal gyrus, right superior temporal gyrus has more important functional significance for autistic individuals. Previous studies have found that the right superior temporal gyrus in ASD individuals played a dual-role in language and social cognition. Boddaert Belin et al.  found that the activation of left superior temporal gyrus was more obvious than that of the right side in healthy controls when recognizing and understanding speech, while the activation of the right superior temporal gyrus was more obvious in autistic adults. A previous research  found that delayed development of the integrated function of design action and language in autistic children was related to the development of right superior temporal gyrus. The above-mentioned studies may indicate that the right superior temporal gyrus atypicalities are closely related to autistic symptoms, and have important pathological significance for autistic individuals.
In the present study, no significant difference was detected in the GM volume of the right superior temporal gyrus between the autism and TD groups in the cohorts of 13–18 and 19–30 years old. The atypicality of GM volume in the right superior temporal gyrus was considered age-dependent, as shown in numerous previous studies. A prospective study of GM development included 100 autistic individuals and 117 TD individuals who aged 3–34 years old, and they found that the GM volume in temporal lobe decreased with the increase of age in both groups. The developmental trajectory analyses showed that the GM volume in temporal lobe of ASD group was smaller than that of TD group before the age of 14 years old, while there was no significant difference between the two groups after 14 years old . Dickstein Pescosolido et al.  conducted a meta-analysis of the task-based MRI study of ASD (535 autistic children and 604 autistic adults), and found that during social tasks, the right superior temporal gyrus of autistic children was less activated than that of autistic adults. Therefore, it is essential to include longitudinal data to assess the volume of this brain area in ASD individuals at different developmental stages, which is the gold standard of developmental studies.
In addition, a number of scholars have found that changes in cell volume in superior temporal gyrus were associated with ASD , which further explained the pathology of the superior temporal gyrus in ASD. A previous research  found that the superior temporal gyrus of autistic individuals was associated with the increased transcription levels of several immune system-related genes with noticeable variations, which was noted to be associated with the characteristic innate immune response of neurodevelopmental diseases. It is therefore highly advantageous to further identify susceptible genes and pathological mechanism.
Although the results of the present study are mainly consistent with most previous studies, there are still some inconsistencies. For instance, one study enrolled 18 autistic individuals and 19 TD individuals who aged 10–16 years old, and found that the right superior temporal gyrus volume significantly increased in ASD group. The discrepancy could be related to the fact that the mentioned study only compared the right superior temporal gyrus volume without distinguishing WM from GM [41, 50]. Other studies did not find atypical volume of left superior temporal gyrus in autistic individuals, or reported that the left superior temporal gyrus volume decreased in autistic individuals rather than in the right side. The inconsistencies in these results could be related to numerous factors, including methodological differences, differences in participants’ clinical data, etc.
Inferior parietal lobule
The current study found that the GM volume of the right inferior parietal lobule was smaller in the autism group than that in the TD group in children (6–12 years old), and the GM volume of the left inferior parietal lobule was larger in the autism group than that in the TD group in adolescents (13–18 years old). No significant difference was observed in the GM volume of bilateral inferior parietal lobule between the autism group and TD group in young adults (19–30 years old). The inferior parietal lobule is involved in sensory input, especially visual and spatial localization . It is also a part of human mirror nervous system  involving image thinking, imitative action , eye contact , and semantic processing , and was considered as one of the most highly connected hubs in brain . Venkataraman Duncan et al.  found that the left inferior parietal lobule of ASD individuals was involved in the formation of social pathological networks. A meta-analysis of fMRI studies revealed that the anterior inferior parietal lobule of ASD individuals was atypically activated during observation and imitation, and mirror neuron dysfunction existed . Several previous studies have shown that the GM volume in the right inferior parietal lobule of autistic children was smaller than that of the TD children, and the reduction of GM volume was positively correlated with the severity of social disorders . Mengotti D’Agostini et al.  enrolled 20 autistic children and 22 TD children (4–14 years old), and they found that the GM volume in the left parietal lobule was larger in autistic children than that in TD children. The above-mentioned findings all supported the results of the present study. Piven Arndt et al.  enrolled 35 autistic individuals and 36 TD individuals (12–29 years old), and demonstrated that the parietal lobe volume of autistic individuals was enlarged compared with that in TD individuals, which was partly consistent with the results of our study.
The current study found that the left inferior parietal lobule in the autism group was larger in adolescence than that in the TD group, whereas there was no significant difference between autistic adults and TD adults. It is speculated that the GM volume in the left inferior parietal lobule of ASD individuals may gradually decrease from adolescence to adulthood, and similar results have been reported by previous studies. A study on the brain structure of autistic adolescents included 25 autistic individuals and 25 TD individuals (10–18 years old). The results showed that the GM volume in bilateral inferior parietal lobule decreased with the increase of age in ASD group, while it increased with the elevation of age in TD group , which was in agreement with the results of the current study. Christian et al. recruited 28 autistic adults and 28 TD adults (20–55 years old), and found that the left inferior parietal lobule of autistic individuals was thinner than that of the TD individuals and tended to decrease with age in ASD individuals .
The results of the present study revealed that the atypicalities of bilateral inferior parietal lobules in autistic individuals of different age-based cohorts were different. This could be related to the involvement of the lateralization of brain structure, as well as the influences of the age-dependent factors on the development of inferior parietal lobules in ASD individuals. A previous study reported that the left inferior parietal lobule of autistic adolescents was larger than the right lobule . Another study also found an increase in the left cerebral asymmetry in autistic individuals , which supported the results of the current study.
Based on the above-mentioned studies, despite the differences in age and clinical characteristics in those studies examining parietal lobules of autistic individuals or the inconsistency of previous research results, a great number of studies have shown that bilateral inferior parietal lobules of autistic individuals were significantly different in structure and function from those of TD individuals, which fully illustrated the pathological significance of bilateral inferior parietal lobules of ASD individuals. The results of the present study also provided evidence for atypicalities of bilateral inferior parietal lobules of ASD individuals.
Right middle occipital gyrus
The current study revealed that the GM volume in the right middle occipital gyrus in the autism group (19–30 years old) was larger than that in the TD group, and there was no significant difference between the autism and TD groups in age-based cohorts of 6–12 and 13–18 years old. The results of the current study were partly consistent with those of previous studies. One study included 38 autistic individuals and 46 TD individuals who aged 6–17 years old. No significant difference was found in GM volume of middle occipital lobe . Ecker Marquand et al.  enrolled autistic adults and TD adults who aged 20–68 years old and found that the occipital lobe cortex of autistic individuals was thicker than that of TD individuals. The above-mentioned results were consistent with the findings of the present study. Piven Arndt et al.  enrolled 35 autistic individuals and 36 TD individuals (12–29 years old, 18 years old on average). It was found that the occipital lobe volume in autistic group was enlarged compared with the TD group, which partly supported the results of the present study. However, there have been inconsistencies in the results of previous reports and the current study. A meta-analysis of GM atypicalities examining autistic individuals who aged 6–14 years old, and it was found that the GM volumes in the left anterior occipital gyrus and left inferior occipital gyrus of autistic individuals were enlarged  compared with TD individuals, while there was no significant difference in occipital lobe volume between 6 and 18-year-old autism group and control group in the current study. Another study enrolled autistic individuals with an average age of 26 years old and TD individuals, and they found that the GM volume in occipital lobe of ASD group was smaller  compared with TD group. Some studies found that the size of occipital lobe was correlated to IQ and the severity of ASD. Therefore, the reasons for the inconsistencies in those results could be related to a variety of factors, including sample size, age, IQ, severity of symptoms, and lateralization anormaly of occipital lobe. Despite inconsistencies in the results of previous studies, one meta-analysis on GM atypicalities in ASD revealed that most studies showed structural atypicalities in the occipital lobe of autistic individuals .
The occipital lobe is responsible for visual spatial information processing, as well as the processing of body language and emotional regulation . Previous fMRI studies have shown that autistic individuals relied more on occipital primary visual function to encode external information in social and non-social tasks, while TD individuals relied more on language . Autistic individuals atypically activated the occipital gyrus rather than the traditional spindle facial area during facial processing tasks . Besides, a previous fMRI study revealed that there were atypical developmental patterns in the middle occipital gyrus of autistic children and adolescents compared with TD individuals . Besides, the atypical development of the occipital lobe could also be related to the altered gene expression or neurometabolity. Ginsberg Rubin et al.  found atypical gene expression in occipital lobe of autistic adults, including mitochondrial oxidative phosphorylation and down-regulation of protein translation genes. Levitt O’Neill et al.  found atypical neurometabolites in the occipital cortex of autistic individuals who aged 5–16 years old using proton magnetic resonance spectroscopy. Therefore, molecular and imaging studies suggested that occipital lobe and middle occipital gyrus played an important role in the pathogenesis of ASD. The results of the present study provided more reliable evidence for occipital lobe atypicalities in ASD.
Left posterior cingulate gyrus
In the present study, it was revealed that the GM volume in the left posterior cingulate gyrus of 19–30-year-old autism group was smaller than that of TD group, and there was no significant difference in the GM volume in the left posterior cingulate gyrus between 6–12-year-old and 13–18-year-old autism group and TD group. Chandley Crawford et al.  found that pyramidal neurons in cingulate cortex of autistic adults were lower than those of TD adults, and the gene expression was atypical, which supported the results of the current study. Sussman, Leung  studied 72 autistic individuals and 138 TD individuals who aged 4–18 years old, and found that the left cingulate gyrus of autistic individuals gradually thinned with age, while the posterior cingulate gyrus of TD individuals increased with age . This may explain that left posterior cingulate gyrus in this study did not show a significant difference between ASD group and TD group at the ages of 6–12 and 13–18 years old, while the left posterior cingulate gyrus of ASD group was smaller than that of TD group at the age of 19–30 years old. The autopsy study of adult autistic individuals revealed that the cellular structure of posterior cingulate gyrus changed including irregularly distributed neurons, and the boundary between layers IV and V was difficult to distinguish, suggesting that there were atypical patterns of development and migration of neurons in the posterior cingulate gyrus of autistic individuals . Geurts et al.  found that the ASD symptom severity was correlated with left posterior cingulate volume, while ADHD symptom severity was associated with the volume of the right parietal lobe. It was suggested that the GM volume in the left posterior cingulate gyrus has important pathological significance for ASD.
Previous studies have shown atypical levels of neurotransmitters in the left posterior cingulate gyrus of autistic individuals, including the decrease of serotonin 5-HT receptors in the autistic adults’ posterior cingulate cortex, and the important role of 5-HT in synaptogenesis, nerve growth, and neuronal migration . Using proton magnetic resonance spectroscopy, Nakamura et al.  found that 5-HT decreased in the cingulate gyrus of autistic individuals and was associated with their social cognitive deficits. Levitt et al. found atypical neurometabolites in the left cingulate gyrus of autistic individuals .
The cingulate cortex involves various functions, including motor control , cognitive control , conflict monitoring , and social cognition . These functions are partly neuronal functions of cingulate cortex itself and partly functional connections with other brain regions. The cingulate cortex was considered as one of the atypical brain areas that was closely related to the pathology of ASD. Posterior cingulate cortex is a part of the human facial expression processing neural network  and an important area of marginal-cortical network that is responsible for social emotional behavior, and is closely associated with social deficits in ASD. A meta-analysis of fMRI studies on ASD found that the activation of cingulate gyrus in autistic adults was significantly weaker than that in TD adults . These findings fully demonstrated the functional atypicalities of the left posterior cingulate gyrus in ASD, which could be due to the local atypical structure of the left posterior cingulate gyrus or the typical connection of neural network in ASD. Another study found that in non-social tasks, the left cingulate gyrus of autistic adults was weaker than that in autistic children . In TD individuals, the functional connectivity between posterior cingulate gyrus and medial prefrontal cortex increased with age, while it decreased with age in ASD group . It is suggested that the left cingulate gyrus and the posterior cingulate gyrus of autistic individuals had different functional levels at different ages, which could be related to the changes of GM volume in the left posterior cingulate gyrus of autistic individuals at different ages.
There are also some inconsistencies between the results of previous results and findings of the current study. For instance, Cauda et al.  found that the GM volume in the cingulate gyrus of autistic adult patients increased compared with that of TD adults. A meta-analysis did not find a significant difference in the GM volume of cingulate cortex between autistic adults and TD adults . The heterogeneity among the results of previous studies could be related to the differences in sample size, age, disease severity, and research method .
In conclusion, the current study showed the whole-brain volume, whole-brain WM volume, and whole-brain GM volume of autistic individuals who aged 6–12, 13–18, and 19–30 years old showed no significant difference compared to TD individuals. The brain areas with atypical GM volume of autistic individuals in the three age-based cohorts were different, involving the right superior temporal gyrus, the inferior parietal lobule, the right middle occipital gyrus, and the left posterior cingulate gyrus. These brain areas were of great significance for us to further understand the neuropathological mechanism of ASD. However, the results of the present study were related to only autistic individuals who aged 6–30 years old and cannot be extended to autistic individuals in other age ranges. Additionally, except for the left inferior parietal lobule, other atypical brain areas were only obtained at the uncorrected level (P < 0.001). In the future study, it is essential to expand the sample size and include more autistic individuals with different levels of IQ, and to conduct a rigorous statistical analysis to verify our findings.
Availability of data and materials
All the clinical data used to support the findings of this study may be released upon application to the data access manager, who can be contacted at firstname.lastname@example.org.
Autism spectrum disorder
Structural magnetic resonance imaging
Typically developing individuals
Autism diagnostic observation schedule
Restricted and repetitive behaviors
Autism diagnostic interview
Diagnostic and statistical manual-IV
The Chinese-Wechsler Intelligence Scale for Children
The Wechsler Adult Intelligence Scale-Revised in China
Field of view
Statistical parameter software
Montreal Neurological Institute
Full width at half maximum
American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Washington, DC: American Psychiatric Association; 2013.
Maenner MJ, Shaw KA, Bakian AV, Bilder DA, Durkin MS, Esler A, et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveill Summ. 2021;70(11):1.
Mezzelani A, Landini M, Facchiano F, Raggi ME, Villa L, Molteni M, et al. Environment, dysbiosis, immunity and sex-specific susceptibility: a translational hypothesis for regressive autism pathogenesis. Nutr Neurosci. 2015;18(4):145–61.
LaSalle JM, Powell WT, Yasui DH. Epigenetic layers and players underlying neurodevelopment. Trends Neurosci. 2013;36(8):460–70.
Herbert MR, Ziegler DA, Deutsch CK, O’Brien LM, Lange N, Bakardjiev A, et al. Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain. 2003;126(Pt 5):1182–92.
Brambilla P, Hardan A, di Nemi SU, Perez J, Soares JC, Barale F. Brain anatomy and development in autism: review of structural MRI studies. Brain Res Bull. 2003;61(6):557–69.
Piven J, Arndt S, Bailey J, Havercamp S, Andreasen NC, Palmer P. An MRI study of brain size in autism. Am J Psychiatry. 1995;152(8):1145–9.
Courchesne E, Karns CM, Davis HR, Ziccardi R, Carper RA, Tigue ZD, et al. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology. 2001;57(2):245–54.
Courchesne E, Pierce K, Schumann CM, Redcay E, Buckwalter JA, Kennedy DP, et al. Mapping early brain development in autism. Neuron. 2007;56(2):399–413.
Sparks BF, Friedman SD, Shaw DW, Aylward EH, Echelard D, Artru AA, et al. Brain structural abnormalities in young children with autism spectrum disorder. Neurology. 2002;59(2):184–92.
Palmen SJ, Hulshoff Pol HE, Kemner C, Schnack HG, Durston S, Lahuis BE, et al. Increased gray-matter volume in medication-naive high-functioning children with autism spectrum disorder. Psychol Med. 2005;35(4):561–70.
Aylward EH, Minshew NJ, Field K, Sparks BF, Singh N. Effects of age on brain volume and head circumference in autism. Neurology. 2002;59(2):175–83.
Freitag CM, Luders E, Hulst HE, Narr KL, Thompson PM, Toga AW, et al. Total brain volume and corpus callosum size in medication-naive adolescents and young adults with autism spectrum disorder. Biol Psychiatry. 2009;66(4):316–9.
Riddle K, Cascio CJ, Woodward ND. Brain structure in autism: a voxel-based morphometry analysis of the autism brain imaging database exchange (ABIDE). Brain Imaging Behav. 2017;11(2):541–51.
Mitelman SA, Bralet M-C, Haznedar MM, Hollander E, Shihabuddin L, Hazlett EA, et al. Diametrical relationship between gray and white matter volumes in autism spectrum disorder and schizophrenia. Brain Imaging Behav. 2017;11(6):1823–35.
Mitchell SR, Reiss AL, Tatusko DH, Ikuta I, Kazmerski DB, Botti JA, et al. Neuroanatomic alterations and social and communication deficits in monozygotic twins discordant for autism disorder. Am J Psychiatry. 2009;166(8):917–25.
Hollander E, Anagnostou E, Chaplin W, Esposito K, Haznedar MM, Licalzi E, et al. Striatal volume on magnetic resonance imaging and repetitive behaviors in autism. Biol Psychiatry. 2005;58(3):226–32.
Tate DF, Bigler ED, McMahon W, Lainhart J. The relative contributions of brain, cerebrospinal fluid-filled structures and non-neural tissue volumes to occipital-frontal head circumference in subjects with autism. Neuropediatrics. 2007;38(1):18–24.
Courchesne E, Pierce K. Brain overgrowth in autism during a critical time in development: implications for frontal pyramidal neuron and interneuron development and connectivity. Int J Dev Neurosci. 2005;23(2–3):153–70.
Nickl-Jockschat T, Habel U, Maria Michel T, Manning J, Laird AR, Fox PT, et al. Brain structure anomalies in autism spectrum disorder—a meta-analysis of VBM studies using anatomic likelihood estimation. Hum Brain Mapp. 2012;33(6):1470–89.
Hazlett HC, Poe MD, Gerig G, Smith RG, Piven J. Cortical gray and white brain tissue volume in adolescents and adults with autism. Biol Psychiatry. 2006;59(1):1–6.
Mosconi MW, Cody-Hazlett H, Poe MD, Gerig G, Gimpel-Smith R, Piven J. Longitudinal study of amygdala volume and joint attention in 2- to 4-year-old children with autism. Arch Gen Psychiatry. 2009;66(5):509–16.
Duerden EG, Mak-Fan KM, Taylor MJ, Roberts SW. Regional differences in grey and white matter in children and adults with autism spectrum disorders: an activation likelihood estimate (ALE) meta-analysis. Autism Res. 2012;5(1):49–66.
Sussman D, Leung R, Vogan V, Lee W, Trelle S, Lin S, et al. The autism puzzle: diffuse but not pervasive neuroanatomical abnormalities in children with ASD. NeuroImage Clin. 2015;8:170–9.
Grecucci A, Rubicondo D, Siugzdaite R, Surian L, Job R. Uncovering the social deficits in the autistic brain. A source-based morphometric study. Front Neurosci. 2016;10:388.
Boddaert N, Chabane N, Gervais H, Good CD, Bourgeois M, Plumet MH, et al. Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study. Neuroimage. 2004;23(1):364–9.
Mengotti P, D’Agostini S, Terlevic R, De Colle C, Biasizzo E, Londero D, et al. Altered white matter integrity and development in children with autism: a combined voxel-based morphometry and diffusion imaging study. Brain Res Bull. 2011;84(2):189–95.
Pierce K, Müller R-A, Ambrose J, Allen G, Courchesne E. Face processing occurs outside the fusiformface area’in autism: evidence from functional MRI. Brain. 2001;124(10):2059–73.
Rojas DC, Peterson E, Winterrowd E, Reite ML, Rogers SJ, Tregellas JR. Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiatry. 2006;6:56.
Scott JA, Schumann CM, Goodlin-Jones BL, Amaral DG. A comprehensive volumetric analysis of the cerebellum in children and adolescents with autism spectrum disorder. Autism Res. 2009;2(5):246–57.
Yang X, Si T, Gong Q, Qiu L, Jia Z, Zhou M, et al. Brain gray matter alterations and associated demographic profiles in adults with autism spectrum disorder: A meta-analysis of voxel-based morphometry studies. Aust N Z J Psychiatry. 2016;50(8):741–53.
Gong Y. The manual of Wechsler adult intelligence scale revised in China. China: Changsha Hunan Medical University Press; 1992.
Gong Y, Cai T. Manual of Chinese revised Wechsler intelligence scale for children. Changsha: Hunan Atlas Publishing House; 1993.
D’Mello AM, Crocetti D, Mostofsky SH, Stoodley CJ. Cerebellar gray matter and lobular volumes correlate with core autism symptoms. Neuroimage Clin. 2015;7:631–9.
Jou RJ, Minshew NJ, Melhem NM, Keshavan MS, Hardan AY. Brainstem volumetric alterations in children with autism. Psychol Med. 2009;39(8):1347–54.
Tepest R, Jacobi E, Gawronski A, Krug B, Moller-Hartmann W, Lehnhardt FG, et al. Corpus callosum size in adults with high-functioning autism and the relevance of gender. Psychiatry Res. 2010;183(1):38–43.
Lin H-Y, Ni H-C, Lai M-C, Tseng W-YI, Gau SS-F. Regional brain volume differences between males with and without autism spectrum disorder are highly age-dependent. Mol Autism. 2015;6(1):29.
Radua J, Via E, Catani M, Mataix-Cols D. Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls. Psychol Med. 2011;41(7):1539–50.
Makris N, Pandya DN. The extreme capsule in humans and rethinking of the language circuitry. Brain Struct Funct. 2009;213(3):343–58.
Edgar JC, Fisk Iv CL, Berman JI, Chudnovskaya D, Liu S, Pandey J, et al. Auditory encoding abnormalities in children with autism spectrum disorder suggest delayed development of auditory cortex. Mol Autism. 2015;6:69.
Bigler ED, Mortensen S, Neeley ES, Ozonoff S, Krasny L, Johnson M, et al. Superior temporal gyrus, language function, and autism. Dev Neuropsychol. 2007;31(2):217–38.
Allison T, Puce A, McCarthy G. Social perception from visual cues: role of the STS region. Trends Cogn Sci. 2000;4(7):267–78.
Boddaert N, Chabane N, Barthelemy C, Bourgeois M, Poline JB, Brunelle F, et al. Bitemporal lobe dysfonction in infantile autism: positron emission tomography study. J Radiol. 2002;83(12 Pt 1):1829–33.
Kates WR, Mostofsky SH, Zimmerman AW, Mazzocco MM, Landa R, Warsofsky IS, et al. Neuroanatomical and neurocognitive differences in a pair of monozygous twins discordant for strictly defined autism. Ann Neurol. 1998;43(6):782–91.
Boddaert N, Belin P, Chabane N, Poline JB, Barthelemy C, Mouren-Simeoni MC, et al. Perception of complex sounds: abnormal pattern of cortical activation in autism. Am J Psychiatry. 2003;160(11):2057–60.
Hubbard AL, McNealy K, Scott-Van Zeeland AA, Callan DE, Bookheimer SY, Dapretto M. Altered integration of speech and gesture in children with autism spectrum disorders. Brain Behav. 2012;2(5):606–19.
Wallace GL, Eisenberg IW, Robustelli B, Dankner N, Kenworthy L, Giedd JN, et al. Longitudinal cortical development during adolescence and young adulthood in autism spectrum disorder: increased cortical thinning but comparable surface area changes. J Am Acad Child Adolesc Psychiatry. 2015;54(6):464–9.
Dickstein DP, Pescosolido MF, Reidy BL, Galvan T, Kim KL, Seymour KE, et al. Developmental meta-analysis of the functional neural correlates of autism spectrum disorders. J Am Acad Child Adolesc Psychiatry. 2013;52(3):279-89 e16.
Garbett K, Ebert PJ, Mitchell A, Lintas C, Manzi B, Mirnics K, et al. Immune transcriptome alterations in the temporal cortex of subjects with autism. Neurobiol Dis. 2008;30(3):303–11.
Waiter GD, Williams JH, Murray AD, Gilchrist A, Perrett DI, Whiten A. Structural white matter deficits in high-functioning individuals with autistic spectrum disorder: a voxel-based investigation. Neuroimage. 2005;24(2):455–61.
Bremmer F, Schlack A, Duhamel JR, Graf W, Fink GR. Space coding in primate posterior parietal cortex. Neuroimage. 2001;14(1 Pt 2):S46-51.
Yang J, Hofmann J. Action observation and imitation in autism spectrum disorders: an ALE meta-analysis of fMRI studies. Brain Imaging Behav. 2016;10(4):960–9.
Rizzolatti G, Sinigaglia C. The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations. Nat Rev Neurosci. 2010;11(4):264–74.
Calder AJ, Beaver JD, Winston JS, Dolan RJ, Jenkins R, Eger E, et al. Separate coding of different gaze directions in the superior temporal sulcus and inferior parietal lobule. Curr Biol. 2007;17(1):20–5.
Wang J, Conder JA, Blitzer DN, Shinkareva SV. Neural representation of abstract and concrete concepts: a meta-analysis of neuroimaging studies. Hum Brain Mapp. 2010;31(10):1459–68.
Tomasi D, Volkow ND. Association between functional connectivity hubs and brain networks. Cereb Cortex. 2011;21(9):2003–13.
Venkataraman A, Duncan JS, Yang DY, Pelphrey KA. An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism. Neuroimage Clin. 2015;8:356–66.
Cheng Y, Chou KH, Fan YT, Lin CP. ANS: aberrant neurodevelopment of the social cognition network in adolescents with autism spectrum disorders. PLoS ONE. 2011;6(4): e18905.
Piven J, Arndt S, Bailey J, Andreasen N. Regional brain enlargement in autism: a magnetic resonance imaging study. J Am Acad Child Adolesc Psychiatry. 1996;35(4):530–6.
Scheel C, Rotarska-Jagiela A, Schilbach L, Lehnhardt FG, Krug B, Vogeley K, et al. Imaging derived cortical thickness reduction in high-functioning autism: key regions and temporal slope. Neuroimage. 2011;58(2):391–400.
Herbert MR, Harris GJ, Adrien KT, Ziegler DA, Makris N, Kennedy DN, et al. Abnormal asymmetry in language association cortex in autism. Ann Neurol. 2002;52(5):588–96.
Foster NE, Doyle-Thomas KA, Tryfon A, Ouimet T, Anagnostou E, Evans AC, et al. Structural gray matter differences during childhood development in autism spectrum disorder: a multimetric approach. Pediatr Neurol. 2015;53(4):350–9.
Ecker C, Marquand A, Mourão-Miranda J, Johnston P, Daly EM, Brammer MJ, et al. Describing the brain in autism in five dimensions—magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J Neurosci. 2010;30(32):10612–23.
Liu J, Yao L, Zhang W, Xiao Y, Liu L, Gao X, et al. Gray matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping. Eur Child Adolesc Psychiatry. 2017;26(8):933–45.
Ecker C, Suckling J, Deoni SC, Lombardo MV, Bullmore ET, Baron-Cohen S, et al. Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study. Arch Gen Psychiatry. 2012;69(2):195–209.
Libero LE, Stevens CE Jr, Kana RK. Attribution of emotions to body postures: an independent component analysis study of functional connectivity in autism. Hum Brain Mapp. 2014;35(10):5204–18.
Sahyoun CP, Belliveau JW, Soulieres I, Schwartz S, Mody M. Neuroimaging of the functional and structural networks underlying visuospatial vs. linguistic reasoning in high-functioning autism. Neuropsychologia. 2010;48(1):86–95.
Gadgil M, Peterson E, Tregellas J, Hepburn S, Rojas DC. Differences in global and local level information processing in autism: an fMRI investigation. Psychiatry Res. 2013;213(2):115–21.
Ginsberg MR, Rubin RA, Falcone T, Ting AH, Natowicz MR. Brain transcriptional and epigenetic associations with autism. PLoS ONE. 2012;7(9): e44736.
Levitt JG, O’Neill J, Blanton RE, Smalley S, Fadale D, McCracken JT, et al. Proton magnetic resonance spectroscopic imaging of the brain in childhood autism. Biol Psychiatry. 2003;54(12):1355–66.
Chandley MJ, Crawford JD, Szebeni A, Szebeni K, Ordway GA. NTRK2 expression levels are reduced in laser captured pyramidal neurons from the anterior cingulate cortex in males with autism spectrum disorder. Mol Autism. 2015;6:28.
Oblak AL, Rosene DL, Kemper TL, Bauman ML, Blatt GJ. Altered posterior cingulate cortical cyctoarchitecture, but normal density of neurons and interneurons in the posterior cingulate cortex and fusiform gyrus in autism. Autism Res. 2011;4(3):200–11.
Geurts HM, Ridderinkhof KR, Scholte HS. The relationship between grey-matter and ASD and ADHD traits in typical adults. J Autism Dev Disord. 2013;43(7):1630–41.
Oblak A, Gibbs TT, Blatt GJ. Reduced serotonin receptor subtypes in a limbic and a neocortical region in autism. Autism Res. 2013;6(6):571–83.
Nakamura K, Sekine Y, Ouchi Y, Tsujii M, Yoshikawa E, Futatsubashi M, et al. Brain serotonin and dopamine transporter bindings in adults with high-functioning autism. Arch Gen Psychiatry. 2010;67(1):59–68.
Libero LE, Reid MA, White DM, Salibi N, Lahti AC, Kana RK. Biochemistry of the cingulate cortex in autism: an MR spectroscopy study. Autism Res. 2016;9(6):643–57.
Amiez C, Petrides M. Neuroimaging evidence of the anatomo-functional organization of the human cingulate motor areas. Cereb Cortex. 2014;24(3):563–78.
Botvinick MM. Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function. Cogn Affect Behav Neurosci. 2007;7(4):356–66.
Apps MA, Lesage E, Ramnani N. Vicarious reinforcement learning signals when instructing others. J Neurosci. 2015;35(7):2904–13.
Haxby JV, Hoffman EA, Gobbini MI. The distributed human neural system for face perception. Trends Cogn Sci. 2000;4(6):223–33.
Di Martino A, Ross K, Uddin LQ, Sklar AB, Castellanos FX, Milham MP. Functional brain correlates of social and nonsocial processes in autism spectrum disorders: an activation likelihood estimation meta-analysis. Biol Psychiatry. 2009;65(1):63–74.
Doyle-Thomas KA, Lee W, Foster NE, Tryfon A, Ouimet T, Hyde KL, et al. Atypical functional brain connectivity during rest in autism spectrum disorders. Ann Neurol. 2015;77(5):866–76.
Cauda F, Geda E, Sacco K, D’Agata F, Duca S, Geminiani G, et al. Grey matter abnormality in autism spectrum disorder: an activation likelihood estimation meta-analysis study. J Neurol Neurosurg Psychiatry. 2011;82(12):1304–13.
Uddin LQ, Supekar K, Menon V. Reconceptualizing functional brain connectivity in autism from a developmental perspective. Front Hum Neurosci. 2013;7:458.
We would like to thank Peking University Sixth Hospital for supporting this research. We also thank all participants and their families.
This work was supported by National Key R&D Program of China (2017YFC1309900); Beijing Municipal Science and Technology Commission (Z121107001012036, 7164314); and the National Natural Science Foundation of China (81873804 and 81471382).
Ethics approval and consent to participate
This study was conducted under the approval of the Ethics Committee of Peking University Sixth Hospital. This article does not contain any studies with animals performed by any of the authors. The participants and their parents were asked to sign an informed consent prior to their participation in the study.
Consent for publication
All authors agreed the possible publication of this article on Child and Adolescent Psychiatry and Mental Health. The participant has consented to the submission of the article to the journal.
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Whole brain volume comparison between ASD and TD group in Childhood (6-12 years old). Table S2. Whole brain volume comparison between ASD and TD group in Adolescents (13-18 years old). Table S3. Whole brain volume comparison between ASD and TD group in Adulthood (19-30 years old).
About this article
Cite this article
Wang, H., Ma, ZH., Xu, LZ. et al. Developmental brain structural atypicalities in autism: a voxel-based morphometry analysis. Child Adolesc Psychiatry Ment Health 16, 7 (2022). https://doi.org/10.1186/s13034-022-00443-4