Differences in Cortical Structure and Functional MRI Connectivity in High Functioning Autism

Using adaptive behavior scores to convey level of functioning in children with autism spectrum disorder: Evidence from the Study to Explore Early Development

LAY ABSTRACT

Autistic people are often described as "low-" or "high-functioning" based on their scores on cognitive tests. These terms are common in publications and in everyday communication. However, recent research and feedback from the autistic community suggests that relying on cognitive ability alone to describe functioning may miss meaningful differences in the abilities of autistic children and adults and in the kinds of support they may need. Additional methods are needed to describe "functioning" in autistic children. We examined whether scores from a test measuring adaptive behaviors would provide information on the functional abilities of children with autism that is different from cognitive ability and autism symptom severity. Adaptive behaviors include age-appropriate skills that allow people to function in their everyday lives and social interactions. We found that a large amount of the variation in adaptive behavior scores was not explained by cognitive development, autism symptom severity, and behavioral and emotional problems. In addition, there was a wide range of adaptive ability levels in children with autism in our study, including in those with low, average, or high cognitive scores. Our results suggest that adaptive behavior scores could provide useful information about the strengths and support needs of autistic children above and beyond measures of cognitive ability and autism symptom severity. Adaptive behavior scores provide important information on the needs of autistic people.

Dendritic spines and their role in the pathogenesis of neurodevelopmental and neurological disorders

Since Cajal introduced dendritic spines in the 19th century, they have attained considerable attention, especially in neuropsychiatric and neurologic disorders. Multiple roles of dendritic spine malfunction and pathology in the progression of various diseases have been reported. Thus, it is inevitable to consider these structures as new therapeutic targets for treating neuropsychiatric and neurologic disorders such as autism spectrum disorders, schizophrenia, dementia, Down syndrome, etc. Therefore, we attempted to prepare a narrative review of the literature regarding the role of dendritic spines in the pathogenesis of aforementioned diseases and to shed new light on their pathophysiology.

Functional brain network alterations in the co-occurrence of autism spectrum disorder and attention deficit hyperactivity disorder

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are two highly prevalent and commonly co-occurring neurodevelopmental disorders. The neural mechanisms underpinning the comorbidity of ASD and ADHD (ASD + ADHD) remain unclear. We focused on the topological organization and functional connectivity of brain networks in ASD + ADHD patients versus ASD patients without ADHD (ASD-only). Resting-state functional magnetic resonance imaging (rs-fMRI) data from 114 ASD and 161 typically developing (TD) individuals were obtained from the Autism Brain Imaging Data Exchange II. The ASD patients comprised 40 ASD + ADHD and 74 ASD-only individuals. We constructed functional brain networks for each group and performed graph-theory and network-based statistic (NBS) analyses. Group differences between ASD + ADHD and ASD-only were analyzed at three levels: nodal, global, and connectivity. At the nodal level, ASD + ADHD exhibited topological disorganization in the temporal and occipital regions, compared with ASD-only. At the global level, ASD + ADHD and ASD-only displayed no significant differences. At the connectivity level, the NBS analysis revealed that ASD + ADHD showed enhanced functional connectivity between the prefrontal and frontoparietal regions, as well as between the orbitofrontal and occipital regions, compared with ASD-only. The hippocampus was the shared region in aberrant functional connectivity patterns in ASD + ADHD and ASD-only compared with TD. These findings suggests that ASD + ADHD displays altered topology and functional connectivity in the brain regions that undertake social cognition, language processing, and sensory processing.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

BACKGROUND

The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).

MATERIAL AND METHODS

We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.

RESULTS

The AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.

CONCLUSIONS

Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

Cortical thickness abnormalities in autism spectrum disorder

The pathological mechanism of autism spectrum disorder (ASD) remains unclear. Nowadays, surface-based morphometry (SBM) based on structural magnetic resonance imaging (sMRI) techniques have reported cortical thickness (CT) variations in ASD. However, the findings were inconsistent and heterogeneous. This current meta-analysis conducted a whole-brain vertex-wise coordinate-based meta-analysis (CBMA) on CT studies to explore the most noticeable and robust CT changes in ASD individuals by applying the seed-based d mapping (SDM) program. A total of 26 investigations comprised 27 datasets were included, containing 1,635 subjects with ASD and 1470 HC, along with 94 coordinates. Individuals with ASD exhibited significantly altered CT in several regions compared to HC, including four clusters with thicker CT in the right superior temporal gyrus (STG.R), the left middle temporal gyrus (MTG.L), the left anterior cingulate/paracingulate gyri, the right superior frontal gyrus (SFG.R, medial orbital parts), as well as three clusters with cortical thinning including the left parahippocampal gyrus (PHG.L), the right precentral gyrus (PCG.R) and the left middle frontal gyrus (MFG.L). Adults with ASD only demonstrated CT thinning in the right parahippocampal gyrus (PHG.R), revealed by subgroup meta-analyses. Meta-regression analyses found that CT in STG.R was positively correlated with age. Meanwhile, CT in MFG.L and PHG.L had negative correlations with the age of ASD individuals. These results suggested a complicated and atypical cortical development trajectory in ASD, and would provide a deeper understanding of the neural mechanism underlying the cortical morphology in ASD.

A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework

Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.

Mechanical hierarchy in the formation and modulation of cortical folding patterns

The important mechanical parameters and their hierarchy in the growth and folding of the human brain have not been thoroughly understood. In this study, we developed a multiscale mechanical model to investigate how the interplay between initial geometrical undulations, differential tangential growth in the cortical plate, and axonal connectivity form and regulate the folding patterns of the human brain in a hierarchical order. To do so, different growth scenarios with bilayer spherical models that features initial undulations on the cortex and uniform or heterogeneous distribution of axonal fibers in the white matter were developed, statistically analyzed, and validated by the imaging observations. The results showed that the differential tangential growth is the inducer of cortical folding, and in a hierarchal order, high-amplitude initial undulations on the surface and axonal fibers in the substrate regulate the folding patterns and determine the location of gyri and sulci. The locations with dense axonal fibers after folding settle in gyri rather than sulci. The statistical results also indicated that there is a strong correlation between the location of positive (outward) and negative (inward) initial undulations and the locations of gyri and sulci after folding, respectively. In addition, the locations of 3-hinge gyral folds are strongly correlated with the initial positive undulations and locations of dense axonal fibers. As another finding, it was revealed that there is a correlation between the density of axonal fibers and local gyrification index, which has been observed in imaging studies but not yet fundamentally explained. This study is the first step in understanding the linkage between abnormal gyrification (surface morphology) and disruption in connectivity that has been observed in some brain disorders such as Autism Spectrum Disorder. Moreover, the findings of the study directly contribute to the concept of the regularity and variability of folding patterns in individual human brains.

Inhibition functions can be improved in children with autism spectrum disorders: An eye-tracking study

Cognitive remediation therapy interventions could improve cognitive functioning in subjects with autism. To investigate the benefit of a short cognitive training rehabilitation in children with autism spectrum disorder (ASD) on pursuit and fixation performances. We recruited two groups (G1 and G2) of 30 children with ASD, sex-, IQ- and age-matched (mean 11.6 ± 0.5 years), and pursuit and fixation eye movements were recorded twice at T1 and T2. Between T1 and T2, a 10-min cognitive training was performed by the G1 group only, whereas the G2 group had a 10-min of rest. For all children with ASD enrolled in the study, there was a positive correlation between restricted and repetitive behaviour scores of both Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) and the number of saccades recorded during the fixation task at T1. At T1, oculomotor performances were similar for both groups of ASD children (G1 and G2). At T2, we observed a significant reduction in the number of saccades made during both pursuit and fixation tasks. Our findings underlined the importance to promote cognitive training rehabilitation for children with ASD, leading to a better performance in inhibitory and attention functioning responsible for pursuit and fixation eye movement's performance.

ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1-2 years of age, but ASD diagnoses are not typically made until ages 2-5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3-6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted. Then we evaluated the effectiveness of multiple machine learning classifiers for classifying ASD likelihood. Our findings support our hypothesis that infant ECG signals contain important information about ASD familial likelihood. Amongthe various machine learning algorithms tested, KNN performed best according to sensitivity (0.70 ± 0.117), F1-score (0.689 ± 0.124), precision (0.717 ± 0.128), accuracy (0.70 ± 0.117, p-value = 0.02), and ROC (0.686 ± 0.122, p-value = 0.06). These results suggest that ECG signals contain relevant information about the likelihood of an infant developing ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy.

Morphological Features of Language Regions in Individuals with Tuberous Sclerosis Complex

A significant number of individuals with tuberous sclerosis complex (TSC) exhibit language difficulties. Here, we examined the language-related brain morphometry in 59 participants (7 participants with TSC and comorbid autism spectrum disorder (ASD) (TSC + ASD), 13 with TSC but no ASD (TSC-ASD), 10 with ASD-only (ASD), and 29 typically developing (TD) controls). A hemispheric asymmetry was noted in surface area and gray matter volume of several cortical language areas in TD, ASD, and TSC-ASD groups, but not in TSC + ASD group. TSC + ASD group demonstrated increased cortical thickness and curvature values in multiple language regions for both hemispheres, compared to other groups. After controlling for tuber load in the TSC groups, within-group differences stayed the same but the differences between TSC-ASD and TSC + ASD were no longer statistically significant. These preliminary findings suggest that comorbid ASD in TSC as well as tuber load in TSC is associated with changes in the morphometry of language regions. Future studies with larger sample sizes will be needed to confirm these findings.

Abnormal Brain Structure Is Associated with Social and Communication Deficits in Children with Autism Spectrum Disorder: A Voxel-Based Morphometry Analysis

Structural magnetic resonance imaging (sMRI) studies have shown abnormalities in the brain structure of ASD patients, but the relationship between structural changes and social communication problems is still unclear. This study aims to explore the structural mechanisms of clinical dysfunction in the brain of ASD children through voxel-based morphometry (VBM). After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 98 children aged 8-12 years old with ASD were matched with 105 children aged 8-12 years old with typical development (TD). Firstly, this study compared the differences in gray matter volume (GMV) between the two groups. Then, this study evaluated the relationship between GMV and the subtotal score of communications and social interaction on the Autism Diagnostic Observation Schedule (ADOS) in ASD children. Research has found that abnormal brain structures in ASD include the midbrain, pontine, bilateral hippocampus, left parahippocampal gyrus, left superior temporal gyrus, left temporal pole, left middle temporal gyrus and left superior occipital gyrus. In addition, in ASD children, the subtotal score of communications and social interaction on the ADOS were only significantly positively correlated with GMV in the left hippocampus, left superior temporal gyrus and left middle temporal gyrus. In summary, the gray matter structure of ASD children is abnormal, and different clinical dysfunction in ASD children is related to structural abnormalities in specific regions.

The relationship between alexithymia, interoception, and neural functional connectivity during facial expression processing in autism spectrum disorder

Neural processing differences of emotional facial expressions, while common in autism spectrum disorder (ASD), may be related to co-occurring alexithymia and interoceptive processing differences rather than autism per se. Here, we investigate relationships between alexithymia, interoceptive awareness of emotions, and functional connectivity during observation of facial expressions in youth (aged 8-17) with ASD (n = 28) compared to typically developing peers (TD; n = 37). Behaviorally, we found no significant differences between ASD and TD groups in interoceptive awareness of emotions, though alexithymia severity was significantly higher in the ASD group. In the ASD group, increased alexithymia was significantly correlated with lower interoceptive sensation felt during emotion. Using psycho-physiological interaction (PPI) analysis, the ASD group showed higher functional connectivity between the left ventral anterior insula and the left lateral prefrontal cortex than the TD group when viewing facial expressions. Further, alexithymia was associated with reduced left anterior insula-right precuneus connectivity and reduced right dorsal anterior insula-left ventral anterior insula connectivity when viewing facial expressions. In the ASD group, the degree of interoceptive sensation felt during emotion was positively correlated with left ventral anterior insula-right IFG connectivity when viewing facial expressions. However, across all participants, neither alexithymia nor interoceptive awareness of emotions predicted connectivity between emotion-related brain regions when viewing emotional facial expressions. To summarize, we found that in ASD compared to TD: 1) there is stronger connectivity between the insula and lateral prefrontal cortex; and 2) differences in interhemispheric and within left hemisphere connectivity between the insula and other emotion-related brain regions are related to individual differences in interoceptive processing and alexithymia. These results highlight complex relationships between alexithymia, interoception, and brain processing in ASD.

Relationship between Overweight/Obesity and Social Communication in Autism Spectrum Disorder Children: Mediating Effect of Gray Matter Volume

With advances in medical diagnostic technology, the healthy development of children with autism spectrum disorder (ASD) is receiving more and more attention. In this article, the mediating effect of brain gray matter volume (GMV) between overweight/obesity and social communication (SC) was investigated through the analysis of the relationship between overweight/obesity and SC in autism spectrum disorder children. In total, 101 children with ASD aged 3-12 years were recruited from three special educational centers (Yangzhou, China). Overweight/obesity in children with ASD was indicated by their body mass index (BMI); the Social Responsiveness Scale, Second Edition (SRS-2) was used to assess their social interaction ability, and structural Magnetic Resonance Imaging (sMRI) was used to measure GMV. A mediation model was constructed using the Process plug-in to analyze the mediating effect of GMV between overweight/obesity and SC in children with ASD. The results revealed that: overweight/obesity positively correlated with SRS-2 total points (p = 0.01); gray matter volume in the left dorsolateral superior frontal gyrus (Frontal_Sup_L GMV) negatively correlated with SRS-2 total points (p = 0.001); and overweight/obesity negatively correlated with Frontal_Sup_L GMV (p = 0.001). The Frontal_Sup_L GMV played a partial mediating role in the relationship between overweight/obesity and SC, accounting for 36.6% of total effect values. These findings indicate the significant positive correlation between overweight/obesity and SC; GMV in the left dorsolateral superior frontal gyrus plays a mediating role in the relationship between overweight/obesity and SC. The study may provide new evidence toward comprehensively revealing the overweight/obesity and SC relationship.

Amplitude of low frequency fluctuations during resting state fMRI in autistic children

Resting state fMRI (rs-fMRI) provides an excellent platform for examining the amplitude of low frequency fluctuations (ALFF) and fractional amplitude of low frequency fluctuations (fALFF), which are key indices of brain functioning. However, ALFF and fALFF have been used only sporadically to study autism. rs-fMRI data from 69 children (40 autistic, mean age = 8.47 ± 2.20 years; age range: 5.2 to 13.2; and 29 non-autistic, mean age = 9.02 ± 1.97 years; age range 5.9 to 12.9) were obtained from the Autism Brain Imaging Data Exchange (ABIDE II). ALFF and fALFF were measured using CONN connectivity toolbox and SPM12, at whole-brain & network-levels. A two-sampled t-test and a 2 Group (autistic, non-autistic) × 7 Networks ANOVA were conducted to test group differences in ALFF and fALFF. The whole-brain analysis identified significantly reduced ALFF values for autistic participants in left parietal opercular cortex, precuneus, and right insula. At the network level, there was a significant effect of diagnostic group and brain network on ALFF values, and only significant effect of network, not group, on fALFF values. Regression analyses indicated a significant effect of age on ALFF values of certain networks in autistic participants. Such intrinsically different network-level responses in autistic participants may have implications for task-level recruitment and synchronization of brain areas, which may in turn impact optimal cognitive functioning. Moreover, differences in low frequency fluctuations of key networks, such as the DMN and SN, may underlie alterations in brain responses in autism that are frequently reported in the literature.

Abnormal gray matter structure in children and adolescents with high-functioning autism spectrum disorder

The present study aimed to explore the brain structural mechanisms underlying clinical dysfunction in patients with ASD using magnetic resonance imaging (MRI). After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 111 children and adolescents with high-functioning ASD and 151 typically developing (TD) subjects matched were included in this study. We first compared the differences in gray matter volume (GMV) and gray matter density (GMD) between the two groups. Then, the relationships between GMV/GMD and clinical assessments in ASD patients were evaluated. We found that compared with the TD group, the ASD group showed increased GMV/GMD in the precuneus, thalamus, hippocampus and cingulate gyrus. Moreover, in the ASD group, social interaction was negatively correlated with GMD in the bilateral superior temporal gyrus and limbic system; communication was positively correlated with GMD in the right middle temporal gyrus, inferior frontal gyrus, and anterior cingulate gyrus; and repetitive behavior was positively correlated with GMD in the cerebellum and negatively correlated with GMV in the prefrontal cortex. In conclusion, the gray matter structure in children and adolescents with ASD was abnormal, and different clinical dysfunctions in ASD patients were related to structural abnormalities in specific regions.

Translating neuroimaging changes to neuro-endophenotypes of autistic spectrum disorder: a narrative review

Background

Autism-spectrum disorder is a neurodevelopmental disorder with heterogeneity in etiopathogenesis and clinical presentation. Neuroanatomical and neurophysiological abnormalities may represent neural endophenotypes for autism spectrum disorders which may help identify subgroups of patients seemingly similar in clinical presentation yet different in their pathophysiological underpinnings. Furthermore, a thorough understanding of the pathophysiology of disease can pave the way to effective treatments, prevention, and prognostic predictions. The aim of this review is to identify the predominant neural endophenotypes in autism-spectrum disorder. The evidence was researched at the following electronic databases: Pubmed, PsycINFO, Scopus, Web of Science, and EMBASE.

Results

Enlarged brain, especially frontotemporal cortices have been consistently reported by structural neuroimaging, whereas functional neuroimaging has revealed frontotemporal dysconnectivity.

Conclusions

Regrettably, many of these findings have not been consistent. Therefore, translating these findings into neural endophenotype is by far an attempt in its budding stage. The structural and functional neuroimaging changes may represent neural endophenotypes unique to autism-spectrum disorder. Despite inconsistent results, a clinically meaningful finding may require combined efforts of autism-spectrum-disorder researchers focused on different aspects of basic, genetic, neuroimaging, and clinical research.

Social Functioning and Autistic Behaviors in Youth Following Acquired Brain Injury

Children and adolescents who survive the pediatric intensive care unit (PICU) with an acquired brain injury (ABI) often demonstrate a variety of physical, cognitive, emotional/behavioral, and social sequelae termed post-intensive care syndrome (PICS). Social communication and interaction challenges have also been observed clinically, and there is growing literature documenting these occurrences in youth following ABI. The extent of these social changes varies among patients, and a subset of patients go on to exhibit social and behavioral profiles closely resembling those of autistic youth. We reviewed empirical research regarding social functioning in youth following ABI, as well as the overlap between individuals with ABI and autistic youth, published from January 2009 to August 2022 on PubMed and Scopus databases. Clinical case examples from a well-established post-PICU follow-up program are also provided to exemplify the complexity of this phenomenon.

Frontotemporal neurofibrillary tangles and cerebrovascular lesions are associated with autism spectrum behaviors in late-life dementia

BACKGROUND AND OBJECTIVES

The pathologic substrates or neuroanatomic regions responsible for similarities in behavioral features seen in autism spectrum disorder and late-life dementia remain unknown. The present study examined the neuropathologic features of late-life dementia in research volunteers with and without antemortem behaviors characteristic of autism spectrum disorders.

METHODS

Antemortem cross-sectional assessment of autistic spectrum behaviors proximal to death in persons with diagnosis of mild cognitive impairment or dementia was completed using the Gilliam Autism Rating Scale, 2nd edition (GARS-2), followed by postmortem quantitative and semiquantitative neuropathologic assessment. All individuals who completed the GARS-2 prior to autopsy were included (n = 56) and we note that no participants had known diagnosis of autism spectrum disorder. The GARS-2 was used as an antemortem screening tool to stratify participants into two groups: "Autism Possible/Very Likely" or "Autism Unlikely." Data were analyzed using nonparametric statistics comparing location and scale to evaluate between-group differences in pathologic features.

RESULTS

Neurofibrillary tangles (NFT; p = 0.028) density and tau burden (p = 0.032) in the frontal region, the NFT density (p = 0.048) and neuritic plaque burden (p = 0.042), and the tau burden (p = 0.032) of the temporal region, were significantly different in scale between groups. For measures with significant group differences, the medians of the Autism Possible/Very Likely group were roughly equal to the 75th percentile of the Autism Unlikely group (i.e., the distributions were shifted to the right).

DISCUSSION

This study links behaviors characteristic of autism to increased pathologic tau burden in the frontal and temporal lobes in persons with late-life dementia. Additional studies are needed to determine causal factors and treatment options for behaviors characteristic of autism behaviors in late-life dementias.

Regional homogeneity of adolescents with high-functioning autism spectrum disorder and its association with symptom severity

BACKGROUND AND PURPOSE

Previous studies have revealed abnormal regional homogeneity (ReHo) in individuals with autism spectrum disorder (ASD); however, there is little consistency across the findings within these studies, partly due to small sample size and great heterogeneity among participants between studies. Additionally, few studies have explored the association between ReHo aberrance and clinical symptoms in individuals with ASD.

METHODS

Forty-eight adolescents with high-functioning ASD and 63 group-matched typically developing (TD) controls received functional magnetic resonance imaging at rest. Group-level analysis was performed to detect differences in ReHo between ASD and TD. Evaluation of symptom severity in individuals with ASD was based on the Autism Behavior Checklist (ABC). Voxel-wise correlation analysis was undergone to examine the correlations between the symptom severity and ReHo map in individuals with ASD within brain areas with ReHo abnormalities.

RESULTS

Compared with the TD controls, individuals with ASD exhibited increased ReHo in the bilateral anterior cingulate cortex, left caudate, right posterior cerebellum (cerebellar tonsil), and bilateral brainstem and decreased ReHo in the left precentral gyrus, left inferior parietal lobule, bilateral postcentral gyrus, and right anterior cerebellum (culmen). The correlation analysis indicated that the ReHo value in the brainstem was negatively associated with the ABC total scores and the scores of Relating factor, respectively.

CONCLUSIONS

Our findings indicated that widespread ReHo abnormalities occurred in ASD, shedding light on the underlying neurobiology of pathogenesis and symptomatology of ASD.

A meta-analytic investigation of grey matter differences in anorexia nervosa and autism spectrum disorder

Recent research reports Anorexia Nervosa (AN) to be highly dependent upon neurobiological function. Some behaviours, particularly concerning food selectivity are found in populations with both Autism Spectrum Disorder (ASD) and AN, and there is a proportionally elevated number of anorexic patients exhibiting symptoms of ASD. We performed a systematic review of structural MRI literature with the aim of identifying common structural neural correlates common to both AN and ASD. Across 46 ASD publications, a meta‐analysis of volumetric differences between ASD and healthy controls revealed no consistently affected brain regions. Meta‐analysis of 23 AN publications revealed increased volume within the orbitofrontal cortex and medial temporal lobe, and adult‐only AN literature revealed differences within the genu of the anterior cingulate cortex. The changes are consistent with alterations in flexible reward‐related learning and episodic memory reported in neuropsychological studies. There was no structural overlap between ASD and AN. Findings suggest no consistent neuroanatomical abnormality associated with ASD, and evidence is lacking to suggest that reported behavioural similarities between those with AN and ASD are due to neuroanatomical structural similarities.

Findings related to neuroanatomical structure in AN/ASD demonstrate overlap and require revisiting.

Meta‐analytic findings show structural increase/decrease versus healthy controls (LPFC/MTL/OFC) in AN, but no clusters found in ASD.

The neuroanatomy associated with ASD is inconsistent, but findings in AN reflect condition‐related impairment in executive function and sociocognitive behaviours.

Lateralized Changes in Language Associated Auditory and Somatosensory Cortices in Autism

Lateralized specialization of the two cerebral hemispheres is a fundamental structural hallmark of the human brain and underlies many cognitive functions and behavioral abilities. In typical developing individuals the influence of handedness on performance of various sensory modalities and the cortical processing has been well recognized. Increasing evidence suggests that several neurodevelopmental and psychiatric disorders such as bipolar disorder, schizophrenia, and autism spectrum disorders (ASD) are associated with abnormal patterns of cerebral lateralization. Individuals with ASD exhibit abnormal structural and functional lateralization of circuits subserving motor, auditory, somatosensory, visual face processing, and language-related functions. Furthermore, a high prevalence of atypical handedness has been reported in ASD individuals. While the hemispheric dominance is also related to functions other than handedness, there is a clear relationship between handedness and language-related cortical dominance. This minireview summarizes these recent findings on asymmetry in somatosensory and auditory cortical structures associated with language processing in ASD. I will also discuss the importance of cortical dominance and interhemispheric disruption of balance between excitatory and inhibitory synapses as pathophysiological mechanisms in ASD.

Age-related Brain Morphological Alteration of Medication-naive Boys With High Functioning Autism

RATIONALE AND OBJECTIVE

To investigate age-related brain morphological changes of boys with high functioning autism (HFA).

MATERIALS AND METHODS

Forty-six medication-naive boys with HFA and 48 age-matched typically developing boys (4-12 years old) were included in this study. Structural brain images were processed with FreeSurfer to calculate the brain morphometric features including regional volume, surface area, average cortical thickness, and Gaussian curvature. General linear model was used to identify significant effects of diagnosis and age-by-diagnosis interaction. Correlations between age and the brain morphometric variables of significant clusters were explored.

RESULTS

Primarily, most of the regions with statistically significant intergroup differences were located in the temporal lobe gyri. Importantly, the volume of bilateral superior temporal gyrus (STG) and the average cortical thickness of the right STG demonstrated significantly age-related intergroup differences. Further age-stratified analysis also revealed morphological alterations of STG among subgroups of preschool and school-aged children with or without HFA.

CONCLUSION

The findings demonstrated abnormal age-related volume and cortical thickness atrophy of the STG in HFA children, which reflect brain development trajectories of ASD may initiate to diverge from early overgrowth in childhood period. The anatomical localization of specific brain regions would help us better understand the neurobiology alterations of HFA patients and indicate the effect of age should be carefully delineated and examined in future studies about HFA.

Cortical Thickness and Clinical Findings in Prescholar Children With Autism Spectrum Disorder

The term autism spectrum disorder (ASD) includes a wide variability of clinical presentation, and this clinical heterogeneity seems to reflect a still unclear multifactorial etiopathogenesis, encompassing different genetic risk factors and susceptibility to environmental factors. Several studies and many theories recognize as mechanisms of autism a disruption of brain development and maturation time course, suggesting the existence of common neurobiological substrates, such as defective synaptic structure and aberrant brain connectivity. Magnetic resonance imaging (MRI) plays an important role in both assessment of region-specific structural changes and quantification of specific alterations in gray or white matter, which could lead to the identification of an MRI biomarker. In this study, we performed measurement of cortical thickness in a selected well-known group of preschool ASD subjects with the aim of finding correlation between cortical metrics and clinical scores to understand the underlying mechanism of symptoms and to support early clinical diagnosis. Our results confirm that recent brain MRI techniques combined with clinical data can provide some useful information in defining the cerebral regions involved in ASD although large sample studies with homogeneous analytical and multisite approaches are needed.

The Role of Structure MRI in Diagnosing Autism

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

Resting state electroencephalography microstates in autism spectrum disorder: A mini-review

Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks.

The Links Between Fibromyalgia, Hypermobility and Neurodivergence

Fibromyalgia and joint hypermobility are common coexisting conditions among younger females that are often accompanied with additional features of autonomic dysfunction. Mental health is frequently impacted with these conditions and an association with neurodivergence has been recently established. Neurodivergence is also prevalent among close relatives. Reasons for this association are poorly understood, although genetics, adverse early life experiences and autoimmunity all contribute. Pharmacological responses may differ in neurodivergence, while psychological support requires adaptation for individual sensitivities. Screening patients with fibromyalgia and hypermobility for neurodivergent traits and vice versa may facilitate diagnosis and management.

OUP accepted manuscript

Autism spectrum disorder (ASD) and anxiety disorders (ANX) are common neurodevelopmental conditions with several overlapping symptoms. Notably, many children and adolescents with ASD also have an ANX diagnosis, suggesting shared pathological mechanisms. Here, we leveraged structural imaging and phenotypic data from 112 youth (33 ASD, 37 ANX, 42 typically developing controls) to assess shared and distinct cortical thickness patterns of the disorders. ANX was associated with widespread increases in cortical thickness, while ASD related to a mixed pattern of subtle increases and decreases across the cortical mantle. Despite the qualitative difference in the case–control contrasts, the statistical maps from the ANX-vs-controls and ASD-vs-controls analyses were significantly correlated when correcting for spatial autocorrelation. Dimensional analysis, regressing trait anxiety and social responsiveness against cortical thickness measures, partially recapitulated diagnosis-based findings. Collectively, our findings provide evidence for a common axis of neurodevelopmental disturbances as well as distinct effects of ASD and ANX on cortical thickness.

A bibliometrics analysis and visualization of autism spectrum disorder

BACKGROUND

The prevalence of autism spectrum disorder (ASD) increased rapidly in the last 20 years. Although related research has developed rapidly, little is known about its etiology, diagnostic marker, or drug treatment, which forces researchers to review and summarize its development process and look for the future development direction.

METHODS

We used bibliometrics to analyze papers of ASD in the Web of Science from 1998 to 2021, to draw the network of authors, institutions, countries, and keywords in the ASD field, and visualize the results.

RESULTS

A total of 40,597 papers were included with a continually increasing trend. It turns out that the research on ASD is mainly concentrated in universities. The United States has the largest number of ASD studies, followed by England and Canada. The quality of papers related to ASD is generally high, which shows that ASD research has become a hot spot of scientific research. The keywords of ASD etiology and diagnostic markers can be classified into at least 7 aspects. The detection of keywords shows that ASD research is mostly based on its subtypes, takes children as the study population, focuses on neurodevelopmental imaging or genetics, and pays attention to individual differences. And ASD research has changed greatly under the impact of Corona Virus Disease 2019 in the past 2 years.

CONCLUSION

We consider the future development direction should be based on the improvement of case identification, accurate clinical phenotype, large-scale cohort study, the discovery of ASD etiology and diagnostic markers, drug randomized controlled trials, and telehealth.

Cingulo-opercular and Cingulo-parietal Brain Networks Functional Connectivity in Pre-adolescents: Multiplicative Effects of Race, Ethnicity, and Parental Education

Introduction

A growing body of research has shown a diminished association between socioeconomic status (SES) indicators and a wide range of neuroimaging indicators for racial and ethnic minorities compared to majority groups. However, less is known about these effects for resting-state functional connectivity between various brain networks.

Purpose

This study investigated racial and ethnic variation in the correlation between parental education and resting-state functional connectivity between the cingulo-opercular (CO) and cingulo-parietal (CP) networks in children.

Methods

This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) study; we analyzed the resting-state functional Magnetic Resonance Imaging (rsfMRI) data of 8,464 American pre-adolescents between the ages of 9 and 10. The main outcome measured was resting-state functional connectivity between the CO and CP networks calculated using rsfMRI. The independent variable was parental education, which was treated as a nominal variable. Age, sex, and family marital status were the study covariates. Race and ethnicity were the moderators. Mixed-effects regression models were used for data analysis, with and without interaction terms between parental education and race and ethnicity.

Results

Higher parental education was associated with higher resting-state functional connectivity between the CO and CP networks. Race and ethnicity both showed statistically significant interactions with parental education on children's resting-state functional connectivity between CO and CP networks, suggesting that the correlation between parental education and the resting-state functional connectivity was significantly weaker for Black and Hispanic pre-adolescents compared to White and non-Hispanic pre-adolescents.

Conclusions

In line with the Minorities' Diminished Returns theory, the association between parental education and pre-adolescents resting-state functional connectivity between CO and CP networks may be weaker in Black and Hispanic children than in White and non-Hispanic children. The weaker link between parental education and brain functional connectivity for Blacks and Hispanics than for Whites and non-Hispanics may reflect racism, racialization, and social stratification that collectively minimize the returns of SES indicators, such as parental education for non-Whites, who become others in the US.

Restoration of FMRP expression in adult V1 neurons rescues visual deficits in a mouse model of fragile X syndrome

Many people affected by fragile X syndrome (FXS) and autism spectrum disorders have sensory processing deficits, such as hypersensitivity to auditory, tactile, and visual stimuli. Like FXS in humans, loss of Fmr1 in rodents also cause sensory, behavioral, and cognitive deficits. However, the neural mechanisms underlying sensory impairment, especially vision impairment, remain unclear. It remains elusive whether the visual processing deficits originate from corrupted inputs, impaired perception in the primary sensory cortex, or altered integration in the higher cortex, and there is no effective treatment. In this study, we used a genetic knockout mouse model (Fmr1^KO), in vivo imaging, and behavioral measurements to show that the loss of Fmr1 impaired signal processing in the primary visual cortex (V1). Specifically, Fmr1^KO mice showed enhanced responses to low-intensity stimuli but normal responses to high-intensity stimuli. This abnormality was accompanied by enhancements in local network connectivity in V1 microcircuits and increased dendritic complexity of V1 neurons. These effects were ameliorated by the acute application of GABA_A receptor activators, which enhanced the activity of inhibitory neurons, or by reintroducing Fmr1 gene expression in knockout V1 neurons in both juvenile and young-adult mice. Overall, V1 plays an important role in the visual abnormalities of Fmr1^KO mice and it could be possible to rescue the sensory disturbances in developed FXS and autism patients.

Connectivity alterations in autism reflect functional idiosyncrasy

Autism spectrum disorder (ASD) is commonly understood as an alteration of brain networks, yet case-control analyses against typically-developing controls (TD) have yielded inconsistent results. Here, we devised a novel approach to profile the inter-individual variability in functional network organization and tested whether such idiosyncrasy contributes to connectivity alterations in ASD. Studying a multi-centric dataset with 157 ASD and 172 TD, we obtained robust evidence for increased idiosyncrasy in ASD relative to TD in default mode, somatomotor and attention networks, but also reduced idiosyncrasy in lateral temporal cortices. Idiosyncrasy increased with age and significantly correlated with symptom severity in ASD. Furthermore, while patterns of functional idiosyncrasy were not correlated with ASD-related cortical thickness alterations, they co-localized with the expression patterns of ASD risk genes. Notably, we could demonstrate that patterns of atypical idiosyncrasy in ASD closely overlapped with connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between inter-individual heterogeneity in autism and functional signatures. Our findings provide novel biomarkers to study atypical brain development and may consolidate prior research findings on the variable nature of connectome level anomalies in autism.

Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

The pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

Cortical Gyrification Morphology in Individuals with ASD and ADHD across the Lifespan: A Systematic Review and Meta-Analysis

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are common neurodevelopmental disorders (NDDs) that may impact brain maturation. A number of studies have examined cortical gyrification morphology in both NDDs. Here we review and when possible pool their results to better understand the shared and potentially disorder-specific gyrification features. We searched MEDLINE, PsycINFO, and EMBASE databases, and 24 and 10 studies met the criteria to be included in the systematic review and meta-analysis portions, respectively. Meta-analysis of local Gyrification Index (lGI) findings across ASD studies was conducted with SDM software adapted for surface-based morphometry studies. Meta-regressions were used to explore effects of age, sex, and sample size on gyrification differences. There were no significant differences in gyrification across groups. Qualitative synthesis of remaining ASD studies highlighted heterogeneity in findings. Large-scale ADHD studies reported no differences in gyrification between cases and controls suggesting that, similar to ASD, there is currently no evidence of differences in gyrification morphology compared with controls. Larger, longitudinal studies are needed to further clarify the effects of age, sex, and IQ on cortical gyrification in these NDDs.

Clinical features of behavioral symptoms in patients with semantic dementia: Does semantic dementia cause autistic traits?

Objective

To investigate the behavioral characteristics of semantic dementia (SD) using an instrument originally developed for patients with autism spectrum disorder.

Methods

The behavioral symptoms of 20 patients with SD and 20 patients with Alzheimer’s disease (AD) in both the preclinical state and the dementia state were evaluated using the Pervasive Developmental Disorders Autism Society Japan Rating Scale (PARS).

Results

The SD group showed high prevalence in four behaviors related to stereotypy and social impairment: eating very few food items, selfishness, difficulty in recognizing others’ feeling and thoughts, and interpreting language literally. Scores on the PARS short version, which is sensitive for diagnosis of autism spectrum disorder, were significantly higher in the dementia state than in the preclinical state in both the SD (11.5 ± 6.0 and 1.7 ± 2.5, respectively; t (19) = 6.7, p < 0.001) and AD (6.9 ± 4.6 and 1.7 ± 2.0, respectively; t (19) = 5.1, p < 0.001) groups. PARS short version scores after dementia onset increased in both the SD and AD groups, although the increase was significantly larger in the SD group (F = 5.6, p = 0.023). Additionally, a significantly higher rate of patients exceeded the cutoff score for autism diagnosis in the dementia state in the SD group (75%) than in the AD group (40%; χ² = 5.0, p = 0.025). PARS scores in the dementia state were significantly correlated with illness duration (r = 0.46, p = 0.04) and Mini-Mental State Examination scores (r = −0.75, p < 0.001) in the SD group only.

Conclusions

Although SD and autism spectrum disorder are etiologically distinct diseases, patients with semantic dementia behave like those with autism spectrum disorder. Our findings suggest the symptomatic similarity of the two disorders.

Abnormal Behavior and Cortical Connectivity Deficits in Mice Lacking Usp9x

Genetic association studies have identified many factors associated with neurodevelopmental disorders such as autism spectrum disorder (ASD). However, the way these genes shape neuroanatomical structure and connectivity is poorly understood. Recent research has focused on proteins that act as points of convergence for multiple factors, as these may provide greater insight into understanding the biology of neurodevelopmental disorders. USP9X, a deubiquitylating enzyme that regulates the stability of many ASD-related proteins, is one such point of convergence. Loss of function variants in human USP9X lead to brain malformations, which manifest as a neurodevelopmental syndrome that frequently includes ASD, but the underlying structural and connectomic abnormalities giving rise to patient symptoms is unknown. Here, we analyzed forebrain-specific Usp9x knockout mice (Usp9x-/y) to address this knowledge gap. Usp9x-/y mice displayed abnormal communication and social interaction behaviors. Moreover, the absence of Usp9x culminated in reductions to the size of multiple brain regions. Diffusion tensor magnetic resonance imaging revealed deficits in all three major forebrain commissures, as well as long-range hypoconnectivity between cortical and subcortical regions. These data identify USP9X as a key regulator of brain formation and function, and provide insights into the neurodevelopmental syndrome arising as a consequence of USP9X mutations in patients.

Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities

The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.

Repeated Sessions of Transcranial Direct Current Stimulation on Adolescents With Autism Spectrum Disorder: Study Protocol for a Randomized, Double-Blind, and Sham-Controlled Clinical Trial

Background: Social-emotional difficulties are a core symptom of autism spectrum disorder (ASD). Accordingly, individuals with ASD have problems with social cognition such as recognizing emotions from other peoples' faces. Various results from functional magnetic resonance imaging and electroencephalography studies as well as eye-tracking data reveal a neurophysiological basis of these deficits by linking them to abnormal brain activity. Thus, an intervention targeting the neural origin of ASD impairments seems warranted. A safe method able to influence neural activity is transcranial direct current stimulation (tDCS). This non-invasive brain stimulation method has already demonstrated promising results in several neuropsychiatric disorders in adults and children. The aim of this project is to investigate the effects of tDCS on ASD symptoms and their neural correlates in children and adolescents with ASD. Method: This study is designed as a double-blind, randomized, and sham-controlled trial with a target sample size of 20 male participants (aged 12-17 years) diagnosed with ASD. Before randomization, the participants will be stratified into comorbid depression, comorbid ADHS/conduct disorder, or no-comorbidity groups. The intervention phase comprises 10 sessions of anodal or sham tDCS applied over the left prefrontal cortex within 2 consecutive weeks. To engage the targeted brain regions, participants will perform a social cognition training during the stimulation. TDCS-induced effects on ASD symptoms and involved neural circuits will be investigated through psychological, neurophysiological, imaging, and behavioral data at pre- and post-measurements. Tolerability will be evaluated using a standardized questionnaire. Follow-up assessments 1 and 6 months after the intervention will examine long-lasting effects. Discussion: The results of this study will provide insights into the changeability of social impairments in ASD by investigating social and emotional abilities on different modalities following repeated sessions of anodal tDCS with an intra-simulation training. Furthermore, this trial will elucidate the tolerability and the potential of tDCS as a new treatment approach for ASD in adolescents. Clinical Trial Registration: The study is ongoing and has been registered in the German Registry of Clinical Trials (DRKS00017505) on 02/07/2019.

Understanding the genetic contribution of the human leukocyte antigen system to common major psychiatric disorders in a world pandemic context

The human leukocyte antigen (HLA) is a complex genetic system that encodes proteins which predominantly regulate immune/inflammatory processes. It can be involved in a variety of immuno-inflammatory disorders ranging from infections to autoimmunity and cancers. The HLA system is also suggested to be involved in neurodevelopment and neuroplasticity, especially through microglia regulation and synaptic pruning. Consequently, this highly polymorphic gene region has recently emerged as a major player in the etiology of several major psychiatric disorders, such as schizophrenia, autism spectrum disorder and bipolar disorder and with less evidence for major depressive disorders and attention deficit hyperactivity disorder. We thus review here the role of HLA genes in particular subgroups of psychiatric disorders and foresee their potential implication in future research. In particular, given the prominent role that the HLA system plays in the regulation of viral infection, this review is particularly timely in the context of the Covid-19 pandemic.

A voxel-wise assessment of growth differences in infants developing autism spectrum disorder

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Pediatric neuroimaging study of Autism Spectrum Disorder.

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Longitudinal Tensor Based Morphometry of the presymptomatic period of ASD.

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Differences in voxelwise growth trajectories of children with ASD.

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Regions with differences have been implicated in the core symptoms of ASD.

Autism Spectrum Disorder (ASD) is a phenotypically and etiologically heterogeneous developmental disorder typically diagnosed around 4 years of age. The development of biomarkers to help in earlier, presymptomatic diagnosis could facilitate earlier identification and therefore earlier intervention and may lead to better outcomes, as well as providing information to help better understand the underlying mechanisms of ASD. In this study, magnetic resonance imaging (MRI) scans of infants at high familial risk, from the Infant Brain Imaging Study (IBIS), at 6, 12 and 24 months of age were included in a morphological analysis, fitting a mixed-effects model to Tensor Based Morphometry (TBM) results to obtain voxel-wise growth trajectories. Subjects were grouped by familial risk and clinical diagnosis at 2 years of age. Several regions, including the posterior cingulate gyrus, the cingulum, the fusiform gyrus, and the precentral gyrus, showed a significant effect for the interaction of group and age associated with ASD, either as an increased or a decreased growth rate of the cerebrum. In general, our results showed increased growth rate within white matter with decreased growth rate found mostly in grey matter. Overall, the regions showing increased growth rate were larger and more numerous than those with decreased growth rate. These results detail, at the voxel level, differences in brain growth trajectories in ASD during the first years of life, previously reported in terms of overall brain volume and surface area.

An fMRI Feature Selection Method Based on a Minimum Spanning Tree for Identifying Patients with Autism

Autism spectrum disorder (ASD) is a neurodevelopmental disorder originating in infancy and childhood that may cause language barriers and social difficulties. However, in the diagnosis of ASD, the current machine learning methods still face many challenges in determining the location of biomarkers. Here, we proposed a novel feature selection method based on the minimum spanning tree (MST) to seek neuromarkers for ASD. First, we constructed an undirected graph with nodes of candidate features. At the same time, a weight calculation method considering both feature redundancy and discriminant ability was introduced. Second, we utilized the Prim algorithm to construct the MST from the initial graph structure. Third, the sum of the edge weights of all connected nodes was sorted for each node in the MST. Then, N features corresponding to the nodes with the first N smallest sum were selected as classification features. Finally, the support vector machine (SVM) algorithm was used to evaluate the discriminant performance of the aforementioned feature selection method. Comparative experiments results show that our proposed method has improved the ASD classification performance, i.e., the accuracy, sensitivity, and specificity were 86.7%, 87.5%, and 85.7%, respectively.

The pathoconnectivity network analysis of the insular cortex: A morphometric fingerprinting

Brain disorders tend to impact on many different regions in a typical way: alterations do not spread randomly; rather, they seem to follow specific patterns of propagation that show a strong overlap between different pathologies. The insular cortex is one of the brain areas more involved in this phenomenon, as it seems to be altered by a wide range of brain diseases. On these grounds we thoroughly investigated the impact of brain disorders on the insular cortices analyzing the patterns of their structural co-alteration. We therefore investigated, applying a network analysis approach to meta-analytic data, 1) what pattern of gray matter alteration is associated with each of the insular cortex parcels; 2) whether or not this pattern correlates and overlaps with its functional meta-analytic connectivity; and, 3) the behavioral profile related to each insular co-alteration pattern. All the analyses were repeated considering two solutions: one with two clusters and another with three. Our study confirmed that the insular cortex is one of the most altered cerebral regions among the cortical areas, and exhibits a dense network of co-alteration including a prevalence of cortical rather than sub-cortical brain regions. Regions of the frontal lobe are the most involved, while occipital lobe is the less affected. Furthermore, the co-alteration and co-activation patterns greatly overlap each other. These findings provide significant evidence that alterations caused by brain disorders are likely to be distributed according to the logic of network architecture, in which brain hubs lie at the center of networks composed of co-altered areas. For the first time, we shed light on existing differences between insula sub-regions even in the pathoconnectivity domain.

Cortical signatures in behaviorally clustered autistic traits subgroups: a population-based study

Extensive heterogeneity in autism spectrum disorder (ASD) has hindered the characterization of consistent biomarkers, which has led to widespread negative results. Isolating homogenized subtypes could provide insight into underlying biological mechanisms and an overall better understanding of ASD. A total of 1093 participants from the population-based "Healthy Brain Network" cohort (Child Mind Institute in the New York City area, USA) were selected based on score availability in behaviors relevant to ASD, aged 6-18 and IQ >= 70. All participants underwent an unsupervised clustering analysis on behavioral dimensions to reveal subgroups with ASD traits, identified by the presence of social deficits. Analysis revealed three socially impaired ASD traits subgroups: (1) high in emotionally dysfunctional traits, (2) high in ADHD-like traits, and (3) high in anxiety and depressive symptoms. 527 subjects had good quality structural MRI T1 data. Site effects on cortical features were adjusted using the ComBat method. Neuroimaging analyses compared cortical thickness, gyrification, and surface area, and were controlled for age, gender, and IQ, and corrected for multiple comparisons. Structural neuroimaging analyses contrasting one combined heterogeneous ASD traits group against controls did not yield any significant differences. Unique cortical signatures, however, were observed within each of the three individual ASD traits subgroups versus controls. These observations provide evidence of ASD traits subtypes, and confirm the necessity of applying dimensional approaches to extract meaningful differences, thus reducing heterogeneity and paving the way to better understanding ASD traits.

The putative etiology and prevention of autism

Since the initial psychological report by Leo Kanner in 1943, relatively little formal biochemical/neurological research on the cause of autism, other than peripheral searches for genomic mutations, had been carried until the end of the 20th century. As a result of studies on twin sets and the conclusion that autism was largely a hereditary defect, numerous investigations have sought various genetic faults in particular. However, such studies were able to reveal a plausible etiology for this malady in only a small percentage of instances. Key bio-molecular characteristics of this syndrome have been uncovered when the potential roles of the glia were studied in depth. Findings related to biochemical deficiencies appearing early in the newborn, such as depressed IGF-1 (insulin-like growth factor #1) in neurogenesis/myelination, are becoming emphasized in many laboratories. Progress leading to timely diagnoses and subsequent prevention of central nervous system dysconnectivity now seems plausible. The tendency for an infant to develop autism may currently be determinable and preventable before irreversible psychosocial disturbances become established. These discussions about glial function will be inter-spersed with comments about their apparent relevance to autism. The concluding portion of this presentation will be a detailed review and summation of this diagnosis and prevention proposition.

Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder

BACKGROUND

People with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have abnormalities in frontal, temporal, parietal and striato-thalamic networks. It is unclear to what extent these abnormalities are distinctive or shared. This comparative meta-analysis aimed to identify the most consistent disorder-differentiating and shared structural and functional abnormalities.

METHODS

Systematic literature search was conducted for whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies of cognitive control comparing people with ASD or ADHD with typically developing controls. Regional gray matter volume (GMV) and fMRI abnormalities during cognitive control were compared in the overall sample and in age-, sex- and IQ-matched subgroups with seed-based d mapping meta-analytic methods.

RESULTS

Eighty-six independent VBM (1533 ADHD and 1295 controls; 1445 ASD and 1477 controls) and 60 fMRI datasets (1001 ADHD and 1004 controls; 335 ASD and 353 controls) were identified. The VBM meta-analyses revealed ADHD-differentiating decreased ventromedial orbitofrontal (z = 2.22, p < 0.0001) but ASD-differentiating increased bilateral temporal and right dorsolateral prefrontal GMV (zs ⩾ 1.64, ps ⩽ 0.002). The fMRI meta-analyses of cognitive control revealed ASD-differentiating medial prefrontal underactivation but overactivation in bilateral ventrolateral prefrontal cortices and precuneus (zs ⩾ 1.04, ps ⩽ 0.003). During motor response inhibition specifically, ADHD relative to ASD showed right inferior fronto-striatal underactivation (zs ⩾ 1.14, ps ⩽ 0.003) but shared right anterior insula underactivation.

CONCLUSIONS

People with ADHD and ASD have mostly distinct structural abnormalities, with enlarged fronto-temporal GMV in ASD and reduced orbitofrontal GMV in ADHD; and mostly distinct functional abnormalities, which were more pronounced in ASD.

A Review of Default Mode Network Connectivity and Its Association With Social Cognition in Adolescents With Autism Spectrum Disorder and Early-Onset Psychosis

Recent studies have demonstrated substantial phenotypic overlap, notably social impairment, between autism spectrum disorder (ASD) and schizophrenia. However, the neural mechanisms underlying the pathogenesis of social impairments across these distinct neuropsychiatric disorders has not yet been fully examined. Most neuroimaging studies to date have focused on adults with these disorders, with little known about the neural underpinnings of social impairments in younger populations. Here, we present a narrative review of the literature available through April 2020 on imaging studies of adolescents with either ASD or early-onset psychosis (EOP), to better understand the shared and unique neural mechanisms of social difficulties across diagnosis from a developmental framework. We specifically focus on functional connectivity studies of the default mode network (DMN), as the most extensively studied brain network relevant to social cognition across both groups. Our review included 29 studies of DMN connectivity in adolescents with ASD (Mean age range = 11.2-21.6 years), and 14 studies in adolescents with EOP (Mean age range = 14.2-24.3 years). Of these, 15 of 29 studies in ASD adolescents found predominant underconnectivity when examining DMN connectivity. In contrast, findings were mixed in adolescents with EOP, with five of 14 studies reporting DMN underconnectivity, and an additional six of 14 studies reporting both under- and over-connectivity of the DMN. Specifically, intra-DMN networks were more frequently underconnected in ASD, but overconnected in EOP. On the other hand, inter-DMN connectivity patterns were mixed (both under- and over-connected) for each group, especially DMN connectivity with frontal, sensorimotor, and temporoparietal regions in ASD, and with frontal, temporal, subcortical, and cerebellar regions in EOP. Finally, disrupted DMN connectivity appeared to be associated with social impairments in both groups, less so with other features distinct to each condition, such as repetitive behaviors/restricted interests in ASD and hallucinations/delusions in EOP. Further studies on demographically well-matched groups of adolescents with each of these conditions are needed to systematically explore additional contributing factors in DMN connectivity patterns such as clinical heterogeneity, pubertal development, and medication effects that would better inform treatment targets and facilitate prediction of outcomes in the context of these developmental neuropsychiatric conditions.

Basal ganglia volume and shape in anorexia nervosa

Background

Reward-centred models have proposed that anomalies in the basal ganglia circuitry that underlies reward learning and habit formation perpetuate anorexia nervosa (AN). The present study aimed to investigate the volume and shape of key basal ganglia regions, including the bilateral caudate, putamen, nucleus accumbens (NAcc), and globus pallidus in AN.

Methods

The present study combined data from two existing studies resulting in a sample size of 46 women with AN and 56 age-matched healthy comparison (HC) women. Group differences in volume and shape of the regions of interest were examined. Within the AN group, the impact of eating disorder characteristics on volume and shape of the basal ganglia regions were also explored.

Results

The shape analyses revealed inward deformations in the left caudate, right NAcc, and bilateral ventral and internus globus pallidus, and outward deformations in the right middle and posterior globus pallidus in the AN group.

Conclusions

The present findings appear to fit with the theoretical models suggesting that there are alterations in the basal ganglia regions associated with habit formation and reward processing in AN. Further investigation of structural and functional connectivity of these regions in AN as well as their role in recovery would be of interest.

Autism spectrum disorder

Autism is a frequent, precocious behavioral constellation of social and communicative atypicalities associated with apparently restricted interests and repetitive behavior and paired with an uneven ability profile. Its definition has constantly broadened in the past 75 years, introducing phenotypes increasingly distant from its initial description, heterogeneous in intelligence and speech level, and associated conditions. When it is unassociated with other conditions, its origin is mostly genetic, transmissible, and favored by frequent polymorphisms with small effects present in the general population. Identified de novo rare mutations with large deleterious effects produce phenotypes only loosely related to nonsyndromic autism. Autism is associated with brain reorganization at multiple levels, and with a variant of typical information processing, i.e., the way humans perceive, memorize, manipulate, and attribute emotional value to available information. Its phenotype evolves over the span of life, with an overall reduction of autistic signs, but it still requires some level of support. There is no treatment for this condition; however, it is compatible with high levels of integration into society.

Network curvature as a hallmark of brain structural connectivity

Although brain functionality is often remarkably robust to lesions and other insults, it may be fragile when these take place in specific locations. Previous attempts to quantify robustness and fragility sought to understand how the functional connectivity of brain networks is affected by structural changes, using either model-based predictions or empirical studies of the effects of lesions. We advance a geometric viewpoint relying on a notion of network curvature, the so-called Ollivier-Ricci curvature. This approach has been proposed to assess financial market robustness and to differentiate biological networks of cancer cells from healthy ones. Here, we apply curvature-based measures to brain structural networks to identify robust and fragile brain regions in healthy subjects. We show that curvature can also be used to track changes in brain connectivity related to age and autism spectrum disorder (ASD), and we obtain results that are in agreement with previous MRI studies.

The brain can often continue to function despite lesions in many areas, but damage to particular locations may have serious effects. Here, the authors use the concept of Ollivier-Ricci curvature to investigate the robustness of brain networks.