Neural Mechanisms of Social Interaction Perception: Observing Interpersonal Synchrony Modulates Action Observation Network Activation and Is Spared in Autism.

How the temporal dynamics of social interactions are perceived arguably plays an important role in how one engages in social interactions and how difficulties in establishing smooth social interactions may occur. One aspect of temporal dynamics in social interactions is the mutual coordination of individuals' behaviors during social interaction, otherwise known as behavioral interpersonal synchrony (IPS). Behavioral IPS has been studied increasingly in various contexts, such as a feature of the social interaction difficulties inherent to autism. To fully understand the temporal dynamics of social interactions, or reductions thereof in autism, the neural basis of IPS perception needs to be established. Thus, the current study's aim was twofold: to establish the basic neuro-perceptual processing of IPS in social interactions for typical observers and to test whether it might differ for autistic individuals. In a task-based fMRI paradigm, participants viewed short, silent video vignettes of humans during social interactions featuring a variation of behavioral IPS. The results show that observing behavioral IPS modulates the Action Observation Network (AON). Interestingly, autistic participants showed similar neural activation patterns as non-autistic participants which were modulated by the behavioral IPS they observed in the videos, suggesting that the perception of temporal dynamics of social interactions is spared and may not underly reduced behavioral IPS often observed in autism. Nevertheless, a general difference in processing social interactions was found in autistic observers, characterized by decreased neural activation in the right middle frontal gyrus, angular gyrus, and superior temporal areas. These findings demonstrate that although the autistic and non-autistic groups indeed differed in the neural processing of social interaction perception, the temporal dynamics of these social interactions were not the reason for these differences in social interaction perception in autism. Hence, spared recruitment of the AON for processing temporal dynamics of social interactions in autism does not account for the widely reported attenuation of IPS in autism and for the widely reported and presently observed differences in social interaction perception in autism.

3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data.

Predictive modeling approaches are enabling progress toward robust and reproducible brain-based markers of neuropsychiatric conditions by leveraging the power of multivariate analyses of large datasets. While deep learning (DL) offers another promising avenue to further advance progress, there are challenges related to implementation in 3D (best for MRI) and interpretability. Here, we address these challenges and describe an interpretable predictive pipeline for inferring Autism diagnosis using 3D DL applied to minimally processed structural MRI scans. We trained 3D DL models to predict Autism diagnosis using the openly available ABIDE I and II datasets (n = 1329, split into training, validation, and test sets). Importantly, we did not perform transformation to template space, to reduce bias and maximize sensitivity to structural alterations associated with Autism. Our models attained predictive accuracies equivalent to those of previous machine learning (ML) studies, while side-stepping the time- and resource-demanding requirement to first normalize data to a template. Our interpretation step, which identified brain regions that contributed most to accurate inference, revealed regional Autism-related alterations that were highly consistent with the literature, encompassing a left-lateralized network of regions supporting language processing. We have openly shared our code and models to enable further progress towards remaining challenges, such as the clinical heterogeneity of Autism and site effects, and to enable the extension of our method to other neuropsychiatric conditions.

A 3D approach to understanding heterogeneity in early developing autisms.

BACKGROUND: Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. METHODS: Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68 months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work. RESULTS: Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms. LIMITATIONS: Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures. CONCLUSIONS: This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.

Superior temporal sulcus folding, functional network connectivity, and autistic-like traits in a non-clinical population.

BACKGROUND: Autistic-like traits (ALT) are prevalent across the general population and might be linked to some facets of a broader autism spectrum disorder (ASD) phenotype. Recent studies suggest an association of these traits with both genetic and brain structural markers in non-autistic individuals, showing similar spatial location of findings observed in ASD and thus suggesting a potential neurobiological continuum. METHODS: In this study, we first tested an association of ALTs (assessed with the AQ questionnaire) with cortical complexity, a cortical surface marker of early neurodevelopment, and then the association with disrupted functional connectivity. We analysed structural T1-weighted and resting-state functional MRI scans in 250 psychiatrically healthy individuals without a history of early developmental disorders, in a first step using the CAT12 toolbox for cortical complexity analysis and in a second step we used regional cortical complexity findings to apply the CONN toolbox for seed-based functional connectivity analysis. RESULTS: Our findings show a significant negative correlation of both AQ total and AQ attention switching subscores with left superior temporal sulcus (STS) cortical folding complexity, with the former being significantly correlated with STS to left lateral occipital cortex connectivity, while the latter showed significant positive correlation of STS to left inferior/middle frontal gyrus connectivity (n = 233; all p < 0.05, FWE cluster-level corrected). Additional analyses also revealed a significant correlation of AQ attention to detail subscores with STS to left lateral occipital cortex connectivity. LIMITATIONS: Phenotyping might affect association results (e.g. choice of inventories); in addition, our study was limited to subclinical expressions of autistic-like traits. CONCLUSIONS: Our findings provide further evidence for biological correlates of ALT even in the absence of clinical ASD, while establishing a link between structural variation of early developmental origin and functional connectivity.

Contracted functional connectivity profiles in autism.

OBJECTIVE: Autism spectrum disorder (ASD) is a neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long- relative to short-range functional connections. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes. METHODS: We studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean ± SD age = 20.8 ± 8.1 years) and 108 neurotypical controls (NT, 19.2 ± 7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types. RESULTS: Compared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores. LIMITATIONS: Despite rigorous harmonization across the three different acquisition sites, heterogeneity in autism poses a potential limitation to the generalizability of our results. Additionally, we focussed male participants, warranting future studies in more balanced cohorts. CONCLUSIONS: Our study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.

Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults.

Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.

Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography.

BACKGROUND: Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits. METHODS: We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models. RESULTS: We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits. LIMITATIONS: Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism. CONCLUSIONS: This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

Differences in regional brain structure in toddlers with autism are related to future language outcomes.

Language and social symptoms improve with age in some autistic toddlers, but not in others, and such outcome differences are not clearly predictable from clinical scores alone. Here we aim to identify early-age brain alterations in autism that are prognostic of future language ability. Leveraging 372 longitudinal structural MRI scans from 166 autistic toddlers and 109 typical toddlers and controlling for brain size, we find that, compared to typical toddlers, autistic toddlers show differentially larger or thicker temporal and fusiform regions; smaller or thinner inferior frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most differences are replicated in an independent cohort of 75 toddlers. These brain alterations improve accuracy for predicting language outcome at 6-month follow-up beyond intake clinical and demographic variables. Temporal, fusiform, and inferior frontal alterations are related to autism symptom severity and cognitive impairments at early intake ages. Among autistic toddlers, brain alterations in social, language and face processing areas enhance the prediction of the child's future language ability.

Neurometabolite differences in Autism as assessed with Magnetic Resonance Spectroscopy: A systematic review and meta-analysis.

1H-Magnetic Resonance Spectroscopy (MRS) is a non-invasive technique that can be used to quantify the concentrations of metabolites in the brain in vivo. MRS findings in the context of autism are inconsistent and conflicting. We performed a systematic review and meta-analysis of MRS studies measuring glutamate and gamma-aminobutyric acid (GABA), as well as brain metabolites involved in energy metabolism (glutamine, creatine), neural and glial integrity (e.g. n-acetyl aspartate (NAA), choline, myo-inositol) and oxidative stress (glutathione) in autism cohorts. Data were extracted and grouped by metabolite, brain region and several other factors before calculation of standardised effect sizes. Overall, we find significantly lower concentrations of GABA and NAA in autism, indicative of disruptions to the balance between excitation/inhibition within brain circuits, as well as neural integrity. Further analysis found these alterations are most pronounced in autistic children and in limbic brain regions relevant to autism phenotypes. Additionally, we show how study outcome varies due to demographic and methodological factors , emphasising the importance of conforming with standardised consensus study designs and transparent reporting.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

Differential neural correlates underlying visuospatial versus semantic reasoning in autistic children.

While fronto-posterior underconnectivity has often been reported in autism, it was shown that different contexts may modulate between-group differences in functional connectivity. Here, we assessed how different task paradigms modulate functional connectivity differences in a young autistic sample relative to typically developing children. Twenty-three autistic and 23 typically developing children aged 6 to 15 years underwent functional magnetic resonance imaging (fMRI) scanning while completing a reasoning task with visuospatial versus semantic content. We observed distinct connectivity patterns in autistic versus typical children as a function of task type (visuospatial vs. semantic) and problem complexity (visual matching vs. reasoning), despite similar performance. For semantic reasoning problems, there was no significant between-group differences in connectivity. However, during visuospatial reasoning problems, we observed occipital-occipital, occipital-temporal, and occipital-frontal over-connectivity in autistic children relative to typical children. Also, increasing the complexity of visuospatial problems resulted in increased functional connectivity between occipital, posterior (temporal), and anterior (frontal) brain regions in autistic participants, more so than in typical children. Our results add to several studies now demonstrating that the connectivity alterations in autistic relative to neurotypical individuals are much more complex than previously thought and depend on both task type and task complexity and their respective underlying cognitive processes.

Mapping the differential impact of spontaneous and conversational laughter on brain and mind: an fMRI study in autism.

Spontaneous and conversational laughter are important socio-emotional communicative signals. Neuroimaging findings suggest that non-autistic people engage in mentalizing to understand the meaning behind conversational laughter. Autistic people may thus face specific challenges in processing conversational laughter, due to their mentalizing difficulties. Using fMRI, we explored neural differences during implicit processing of these two types of laughter. Autistic and non-autistic adults passively listened to funny words, followed by spontaneous laughter, conversational laughter, or noise-vocoded vocalizations. Behaviourally, words plus spontaneous laughter were rated as funnier than words plus conversational laughter, and the groups did not differ. However, neuroimaging results showed that non-autistic adults exhibited greater medial prefrontal cortex activation while listening to words plus conversational laughter, than words plus genuine laughter, while autistic adults showed no difference in medial prefrontal cortex activity between these two laughter types. Our findings suggest a crucial role for the medial prefrontal cortex in understanding socio-emotionally ambiguous laughter via mentalizing. Our study also highlights the possibility that autistic people may face challenges in understanding the essence of the laughter we frequently encounter in everyday life, especially in processing conversational laughter that carries complex meaning and social ambiguity, potentially leading to social vulnerability. Therefore, we advocate for clearer communication with autistic people.

Atypical functional connectivity between the amygdala and visual, salience regions in infants with genetic liability for autism.

The amygdala undergoes a period of overgrowth in the first year of life, resulting in enlarged volume by 12 months in infants later diagnosed with ASD. The overgrowth of the amygdala may have functional consequences during infancy. We investigated whether amygdala connectivity differs in 12-month-olds at high likelihood (HL) for ASD (defined by having an older sibling with autism), compared to those at low likelihood (LL). We examined seed-based connectivity of left and right amygdalae, hypothesizing that the HL and LL groups would differ in amygdala connectivity, especially with the visual cortex, based on our prior reports demonstrating that components of visual circuitry develop atypically and are linked to genetic liability for autism. We found that HL infants exhibited weaker connectivity between the right amygdala and the left visual cortex, as well as between the left amygdala and the right anterior cingulate, with evidence that these patterns occur in distinct subgroups of the HL sample. Amygdala connectivity strength with the visual cortex was related to motor and communication abilities among HL infants. Findings indicate that aberrant functional connectivity between the amygdala and visual regions is apparent in infants with genetic liability for ASD and may have implications for early differences in adaptive behaviors.

Autism-associated brain differences can be observed in utero using MRI.

Developmental changes that occur before birth are thought to be associated with the development of autism spectrum disorders. Identifying anatomical predictors of early brain development may contribute to our understanding of the neurobiology of autism spectrum disorders and allow for earlier and more effective identification and treatment of autism spectrum disorders. In this study, we used retrospective clinical brain magnetic resonance imaging data from fetuses who were diagnosed with autism spectrum disorders later in life (prospective autism spectrum disorders) in order to identify the earliest magnetic resonance imaging-based regional volumetric biomarkers. Our results showed that magnetic resonance imaging-based autism spectrum disorder biomarkers can be found as early as in the fetal period and suggested that the increased volume of the insular cortex may be the most promising magnetic resonance imaging-based fetal biomarker for the future emergence of autism spectrum disorders, along with some additional, potentially useful changes in regional volumes and hemispheric asymmetries.

Dynamic functional connectivity in the right temporoparietal junction captures variations in male autistic trait expression.

Autistic individuals can experience difficulties with attention reorienting and Theory of Mind (ToM), which are closely associated with anterior and posterior subdivisions of the right temporoparietal junction. While the link between these processes remains unclear, it is likely subserved by a dynamic crosstalk between these two subdivisions. We, therefore, examined the dynamic functional connectivity (dFC) between the anterior and posterior temporoparietal junction, as a biological marker of attention and ToM, to test its contribution to the manifestation of autistic trait expression in Autism Spectrum Condition (ASC). Two studies were conducted, exploratory (14 ASC, 15 TD) and replication (29 ASC, 29 TD), using resting-state fMRI data and the Social Responsiveness Scale (SRS) from the Autism Brain Imaging Data Exchange repository. Dynamic Independent Component Analysis was performed in both datasets using the CONN toolbox. An additional sliding-window analysis was performed in the replication study to explore different connectivity states (from highly negatively to highly positively correlated). Dynamic FC was reduced in ASC compared to TD adults in both the exploratory and replication datasets and was associated with increased SRS scores (especially in ASC). Regression analyses revealed that decreased SRS autistic expression was predicted by engagement of highly negatively correlated states, while engagement of highly positively correlated states predicted increased expression. These findings provided consistent evidence that the difficulties observed in ASC are associated with altered patterns of dFC between brain regions subserving attention reorienting and ToM processes and may serve as a biomarker of autistic trait expression.

Lack of effects of eight-week left dorsolateral prefrontal theta burst stimulation on white matter macro/microstructure and connection in autism.

Whether brain stimulation could modulate brain structure in autism remains unknown. This study explored the impact of continuous theta burst stimulation (cTBS) over the left dorsolateral prefrontal cortex (DLPFC) on white matter macro/microstructure in intellectually able children and emerging adults with autism. Sixty autistic participants were randomized (30 active) and received active or sham cTBS for eight weeks twice per week, 16 total sessions using a double-blind (participant-, rater-, analyst-blinded) design. All participants received high-angular resolution diffusion MR imaging at baseline and week 8. Twenty-eight participants in the active group and twenty-seven in the sham group with good imaging quality entered the final analysis. With longitudinal fixel-based analysis and network-based statistics, we found no significant difference between the active and sham groups in changes of white matter macro/microstructure and connections following cTBS. In addition, we found no association between baseline white matter macro/microstructure and autistic symptom changes from baseline to week 8 in the active group. In conclusion, we did not find a significant impact of left DLPFC cTBS on white matter macro/microstructure and connections in children and emerging adults with autism. These findings need to be interpreted in the context that the current intellectually able cohort in a single university hospital site limits the generalizability. Future studies are required to investigate if higher stimulation intensities and/or doses, other personal factors, or rTMS parameters might confer significant brain structural changes visible on MRI in ASD.

Decoding Autism: Uncovering patterns in brain connectivity through sparsity analysis with rs-fMRI data.

BACKGROUND: In the realm of neuro-disorders, precise diagnosis and treatment rely heavily on objective imaging-based biomarker identification. This study employs a sparsity approach on resting-state fMRI to discern relevant brain region connectivity for predicting Autism. NEW METHOD: The proposed methodology involves four key steps: (1) Utilizing three probabilistic brain atlases to extract functionally homogeneous brain regions from fMRI data. (2) Employing a hybrid approach of Graphical Lasso and Akaike Information Criteria to optimize sparse inverse covariance matrices for representing the brain functional connectivity. (3) Employing statistical techniques to scrutinize functional brain structures in Autism and Control subjects. (4) Implementing both autoencoder-based feature extraction and entire feature-based approach coupled with AI-based learning classifiers to predict Autism. RESULTS: The ensemble classifier with the extracted feature set achieves a classification accuracy of 84.7% ± 0.3% using the MSDL atlas. Meanwhile, the 1D-CNN model, employing all features, exhibits superior classification accuracy of 88.6% ± 1.7% with the Smith 2009 (rsn70) atlas. COMPARISON WITH EXISTING METHOD (S): The proposed methodology outperforms the conventional correlation-based functional connectivity approach with a notably high prediction accuracy of more than 88%, whereas considering all direct and noisy indirect region-based functional connectivity, the traditional methods bound the prediction accuracy within 70% to 79%. CONCLUSIONS: This study underscores the potential of sparsity-based FC analysis using rs-fMRI data as a prognostic biomarker for detecting Autism.

Whole-brain structural connectome asymmetry in autism.

Autism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated. We studied whole-brain structural connectivity-based brain asymmetry in individuals with autism using diffusion magnetic resonance imaging obtained from the Autism Brain Imaging Data Exchange initiative. By leveraging dimensionality reduction techniques, we constructed low-dimensional representations of structural connectivity and calculated their asymmetry index. Comparing the asymmetry index between individuals with autism and neurotypical controls, we found atypical structural connectome asymmetry in the sensory and default-mode regions, particularly showing weaker asymmetry towards the right hemisphere in autism. Network communication provided topological underpinnings by demonstrating that the inferior temporal cortex and limbic and frontoparietal regions showed reduced global network communication efficiency and decreased send-receive network navigation in the inferior temporal and lateral visual cortices in individuals with autism. Finally, supervised machine learning revealed that structural connectome asymmetry could be used as a measure for predicting communication-related autistic symptoms and nonverbal intelligence. Our findings provide insights into macroscale structural connectome alterations in autism and their topological underpinnings.

Fetal brain growth and infant autistic traits.

BACKGROUND: Structural differences exist in the brains of autistic individuals. To date only a few studies have explored the relationship between fetal brain growth and later infant autistic traits, and some have used fetal head circumference (HC) as a proxy for brain development. These findings have been inconsistent. Here we investigate whether fetal subregional brain measurements correlate with autistic traits in toddlers. METHODS: A total of 219 singleton pregnancies (104 males and 115 females) were recruited at the Rosie Hospital, Cambridge, UK. 2D ultrasound was performed at 12-, 20- and between 26 and 30 weeks of pregnancy, measuring head circumference (HC), ventricular atrium (VA) and transcerebellar diameter (TCD). A total of 179 infants were followed up at 18-20 months of age and completed the quantitative checklist for autism in toddlers (Q-CHAT) to measure autistic traits. RESULTS: Q-CHAT scores at 18-20 months of age were positively associated with TCD size at 20 weeks and with HC at 28 weeks, in univariate analyses, and in multiple regression models which controlled for sex, maternal age and birth weight. LIMITATIONS: Due to the nature and location of the study, ascertainment bias could also have contributed to the recruitment of volunteer mothers with a higher than typical range of autistic traits and/or with a significant interest in the neurodevelopment of their children. CONCLUSION: Prenatal brain growth is associated with toddler autistic traits and this can be ascertained via ultrasound starting at 20 weeks gestation.