An MRI study of increased cortical thickness in autism

Biomarkers of preschool children with autism spectrum disorder: quantitative analysis of whole-brain tissue component volumes, intelligence scores, ADOS-CSS, and ages of first-word production and walking onset

BACKGROUND

Preschooling is a critical time for intervention in children with autism spectrum disorder (ASD); thus, we analyzed brain tissue component volumes (BTCVs) and clinical indicators in preschool children with ASD to identify new biomarkers for early screening.

METHODS

Eighty preschool children (3-6 years) with ASD were retrospectively included. The whole-brain myelin content (MyC), white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and non-WM/GM/MyC/CSF brain component volumes were obtained using synthetic magnetic resonance imaging (SyMRI). Clinical data, such as intelligence scores, autism diagnostic observation schedule-calibrated severity scores, age at first production of single words (AFSW), age at first production of phrases (AFP), and age at walking onset (AWO), were also collected. The correlation between the BTCV and clinical data was evaluated, and the effect of BTCVs on clinical data was assessed by a regression model.

RESULTS

WM and GM volumes were positively correlated with intelligence scores (both P < 0.001), but WM and GM did not affect intelligence scores (P = 0.116, P = 0.290). AWO was positively correlated with AFSW and AFP (both P < 0.001). The multivariate linear regression analysis revealed that MyC, AFSW, AFP, and AWO were significantly different (P = 0.005, P < 0.001, P < 0.001).

CONCLUSIONS

This study revealed positive correlations between WM and GM volumes and intelligence scores. Whole-brain MyC affected AFSW, AFP, and AWO in preschool children with ASD. Noninvasive quantification of BTCVs via SyMRI revealed a new visualizable and quantifiable biomarker (abnormal MyC) for early ASD screening in preschool children.

Brain-Charting Autism and Attention-Deficit/Hyperactivity Disorder Reveals Distinct and Overlapping Neurobiology

BACKGROUND

Autism and attention-deficit/hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology that is still poorly understood. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together and sex differences are often overlooked. Population modeling, often referred to as normative modeling, provides a unified framework for studying age-specific and sex-specific divergences in brain development.

METHODS

Here, we used population modeling and a large, multisite neuroimaging dataset (N = 4255 after quality control) to characterize cortical anatomy associated with autism and ADHD, benchmarked against models of average brain development based on a sample of more than 75,000 individuals. We also examined sex and age differences and relationship with autistic traits and explored the co-occurrence of autism and ADHD.

RESULTS

We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume that was localized to the superior temporal cortex, whereas individuals with ADHD showed more global increases in cortical thickness but lower cortical volume and surface area across much of the cortex. The co-occurring autism+ADHD group showed a unique pattern of widespread increases in cortical thickness and certain decreases in surface area. We also found that sex modulated the neuroanatomy of autism but not ADHD, and there was an age-by-diagnosis interaction for ADHD only.

CONCLUSIONS

These results indicate distinct cortical differences in autism and ADHD that are differentially affected by age and sex as well as potentially unique patterns related to their co-occurrence.

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network

BACKGROUND

Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities.

OBJECTIVE

This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals.

METHODS

In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction.

RESULTS

The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection.

CONCLUSION

The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis.

Ventral temporal and posteromedial sulcal morphology in autism spectrum disorder

Two parallel research tracks link the morphology of small and shallow indentations, or sulci, of the cerebral cortex with functional features of the cortex and human cognition, respectively. The first track identified a relationship between the mid-fusiform sulcus (MFS) in ventral temporal cortex (VTC) and cognition in individuals with Autism Spectrum Disorder (ASD). The second track identified a new sulcus, the inframarginal sulcus (IFRMS), that serves as a tripartite landmark within the posteromedial cortex (PMC). As VTC and PMC are structurally and functionally different in ASD, here, we integrated these two tracks and tested if there are morphological differences in VTC and PMC sulci in a sample of young (5-17 years old) male participants (50 participants with ASD and 50 neurotypical controls). Our approach replicates and extends recent findings in four ways. First, regarding replication, the standard deviation (STD) of MFS cortical thickness (CT) was increased in ASD. Second, MFS length was shorter in ASD. Third, the CT STD effect extended to other VTC and to PMC sulci. Fourth, additional morphological features of VTC sulci (depth, surface area, gray matter volume) and PMC sulci (mean CT) were decreased in ASD, including putative tertiary sulci, which emerge last in gestation and continue to develop after birth. To our knowledge, this study is the most extensive comparison of the sulcal landscape (including putative tertiary sulci) in multiple cortical expanses between individuals with ASD and NTs based on manually defined sulci at the level of individual hemispheres, providing novel targets for future studies of neurodevelopmental disorders more broadly.

Brain structural changes in blindness: a systematic review and an anatomical likelihood estimation (ALE) meta-analysis

In recent decades, numerous structural brain imaging studies investigated purported morphometric changes in early (EB) and late onset blindness (LB). The results of these studies have not yielded very consistent results, neither with respect to the type, nor to the anatomical locations of the brain morphometric alterations. To better characterize the effects of blindness on brain morphometry, we performed a systematic review and an Anatomical-Likelihood-Estimation (ALE) coordinate-based-meta-analysis of 65 eligible studies on brain structural changes in EB and LB, including 890 EB, 466 LB and 1257 sighted controls. Results revealed atrophic changes throughout the whole extent of the retino-geniculo-striate system in both EB and LB, whereas changes in areas beyond the occipital lobe occurred in EB only. We discuss the nature of some of the contradictory findings with respect to the used brain imaging methodologies and characteristics of the blind populations such as the onset, duration and cause of blindness. Future studies should aim for much larger sample sizes, eventually by merging data from different brain imaging centers using the same imaging sequences, opt for multimodal structural brain imaging, and go beyond a purely structural approach by combining functional with structural connectivity network analyses.

Review of Progress in Diagnostic Studies of Autism Spectrum Disorder Using Neuroimaging

This review article summarizes the recent advances in the diagnostic studies of autism spectrum disorders (ASDs) considering some of the most influential research articles from the last two decades. ASD is a heterogeneous neurodevelopmental disorder characterized by abnormalities in social interaction, communication, and behavioral patterns as well as some unique strengths and differences. The current diagnosis systems are based on autism diagnostic observation schedule (ADOS) or autism diagnostic interview-revised (ADI-R), but biological markers are also important for an effective diagnosis of ASDs. The amalgamation of neuroimaging techniques, such as structural and functional magnetic resonance imaging (sMRI and fMRI), with machine-learning and deep-learning approaches helps throw new light on typical biological markers of ASDs at the early stage of life. To assess the performance of a deep neural network, we develop a light-weighted CNN model for ASD classification. The overall accuracy, precision, and F1-score of the proposed model are 99.92%, 99.93% and 99.92%, respectively. All the neuroimaging studies we have reviewed can be divided into 3 categories, viz. thickness, volume and functional connectivity-based studies. We conclude with a discussion of the major findings of considered studies and promising directions for future research in this field.

A convergent structure-function substrate of cognitive imbalances in autism

BACKGROUND

Autism spectrum disorder (ASD) is a common neurodevelopmental diagnosis showing substantial phenotypic heterogeneity. A leading example can be found in verbal and nonverbal cognitive skills, which vary from elevated to impaired compared with neurotypical individuals. Moreover, deficits in verbal profiles often coexist with normal or superior performance in the nonverbal domain.

METHODS

To study brain substrates underlying cognitive imbalance in ASD, we capitalized categorical and dimensional IQ profiling as well as multimodal neuroimaging.

RESULTS

IQ analyses revealed a marked verbal to nonverbal IQ imbalance in ASD across 2 datasets (Dataset-1: 155 ASD, 151 controls; Dataset-2: 270 ASD, 490 controls). Neuroimaging analysis in Dataset-1 revealed a structure-function substrate of cognitive imbalance, characterized by atypical cortical thickening and altered functional integration of language networks alongside sensory and higher cognitive areas.

CONCLUSION

Although verbal and nonverbal intelligence have been considered as specifiers unrelated to autism diagnosis, our results indicate that intelligence disparities are accentuated in ASD and reflected by a consistent structure-function substrate affecting multiple brain networks. Our findings motivate the incorporation of cognitive imbalances in future autism research, which may help to parse the phenotypic heterogeneity and inform intervention-oriented subtyping in ASD.

NRXN1 depletion in the medial prefrontal cortex induces anxiety-like behaviors and abnormal social phenotypes along with impaired neurite outgrowth in rat

BACKGROUND

Neurodevelopmental disorders (NDDs) are a group of disorders induced by abnormal brain developmental processes. The prefrontal cortex (PFC) plays an essential role in executive function, and its role in NDDs has been reported. NDDs are associated with high-risk gene mutations and share partially overlapping genetic abnormalities.

METHODS

Neurexins (NRXNs) are related to autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). NRXN1, an essential susceptibility gene for NDDs, has been reported to be associated with NDDs. However, little is known about its key role in NDDs.

RESULTS

NRXN1 downregulation in the medial PFC induced anxiety-like behaviors and abnormal social phenotypes with impaired neurite outgrowth in Sh-NRXN1 in prefrontal neurons. Moreover, tandem mass tag (TMT)-based proteomic analysis of rat brain samples showed that NRXN1 downregulation led to significant proteome alterations, including pathways related to the extracellular matrix, cell membrane, and morphologic change. Furthermore, full-automatic immunoblotting analysis verified the differently expressed proteins related to cell morphology and membrane structure.

CONCLUSIONS

Our results confirmed the association of NRXN1 with abnormal behaviors in NDDs and provided richer insights into specific prefrontal knockdown in adolescence, potentially expanding the NRXN1 interactome and contributing to human health.

Gray matter asymmetry atypical patterns in subgrouping minors with autism based on core symptoms

Abnormal gray matter (GM) asymmetry has been verified in autism spectrum disorder (ASD), which is characterized by high heterogeneity. ASD is distinguished by three core symptom domains. Previous neuroimaging studies have offered support for divergent neural substrates of different core symptom domains in ASD. However, no previous study has explored GM asymmetry alterations underlying different core symptom domains. This study sought to clarify atypical GM asymmetry patterns underlying three core symptom domains in ASD with a large sample of 230 minors with ASD (ages 7-18 years) and 274 matched TD controls from the Autism Brain Imaging Data Exchange I (ABIDE I) repository. To this end, the scores of the revised autism diagnostic interview (ADI-R) subscales were normalized for grouping ASD into three core-symptom-defined subgroups: social interaction (SI), verbal communication (VA), and restricted repetitive behaviors (RRB). We investigated core-symptom-related GM asymmetry alterations in ASD resulting from advanced voxel-based morphometry (VBM) by general linear models. We also examined the relationship between GM asymmetry and age and between GM asymmetry and symptom severity assessed by the Autism Diagnostic Observation Schedule (ADOS). We found unique GM asymmetry alterations underlying three core-symptom-defined subgroups in ASD: more rightward asymmetry in the thalamus for SI, less rightward asymmetry in the superior temporal gyrus, anterior cingulate and caudate for VA, and less rightward asymmetry in the middle and inferior frontal gyrus for RRB. Furthermore, the asymmetry indexes in the thalamus were negatively associated with ADOS_SOCIAL scores in the general ASD group. We also showed significant correlations between GM asymmetry and age in ASD and TD individuals. Our results support the theory that each core symptom domain of ASD may have independent etiological and neurobiological underpinnings, which is essential for the interpretation of heterogeneity and the future diagnosis and treatment of ASD.

Structural brain abnormalities and their association with language impairment in school-aged children with Autism Spectrum Disorder

Language impairment is comorbid in most children with Autism Spectrum Disorder (ASD) but its neural basis is poorly understood. Using structural magnetic resonance imaging (MRI), the present study provides the whole-brain comparison of both volume- and surface-based characteristics between groups of children with and without ASD and investigates the relationships between these characteristics in language-related areas and the language abilities of children with ASD measured with standardized tools. A total of 36 school-aged children participated in the study: 18 children with ASD and 18 age- and sex-matched typically developing controls. The results revealed that multiple regions differed between groups of children in gray matter volume, gray matter thickness, gyrification, and cortical complexity (fractal dimension). White matter volume and sulcus depth did not differ between groups of children in any region. Importantly, gray matter thickness and gyrification of language-related areas were related to language functioning in children with ASD. Thus, the results of the present study shed some light on the structural brain abnormalities associated with language impairment in ASD.

Molecular Abnormalities in BTBR Mice and Their Relevance to Schizophrenia and Autism Spectrum Disorders: An Overview of Transcriptomic and Proteomic Studies

Animal models of psychopathologies are of exceptional interest for neurobiologists because these models allow us to clarify molecular mechanisms underlying the pathologies. One such model is the inbred BTBR strain of mice, which is characterized by behavioral, neuroanatomical, and physiological hallmarks of schizophrenia (SCZ) and autism spectrum disorders (ASDs). Despite the active use of BTBR mice as a model object, the understanding of the molecular features of this strain that cause the observed behavioral phenotype remains insufficient. Here, we analyzed recently published data from independent transcriptomic and proteomic studies on hippocampal and corticostriatal samples from BTBR mice to search for the most consistent aberrations in gene or protein expression. Next, we compared reproducible molecular signatures of BTBR mice with data on postmortem samples from ASD and SCZ patients. Taken together, these data helped us to elucidate brain-region-specific molecular abnormalities in BTBR mice as well as their relevance to the anomalies seen in ASDs or SCZ in humans.

Evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging

Background

Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system. Since the core cause of many of the symptoms of autism spectrum disorder is due to changes in the structure of the brain, the importance of examining the structural abnormalities of the brain in these disorder becomes apparent. The aim of this study is evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging (sMRI). sMRI images of 26 autistic and 26 Healthy control subjects in the range of 5–10 years are selected from the ABIDE database. For a better assessment of structural abnormalities, the surface and volume features are extracted together from this images. Then, the extracted features from both groups were compared with the sample t test and the features with significant differences between the two groups were identified.

Results

The results of volume-based features indicate an increase in total brain volume and white matter and a change in white and gray matter volume in brain regions of Hammers atlas in the autism group. In addition, the results of surface-based features indicate an increase in mean and standard deviation of cerebral cortex thickness and changes in cerebral cortex thickness, sulcus depth, surface complexity and gyrification index in the brain regions of the Desikan–Killany cortical atlas.

Conclusions

Identifying structurally abnormal areas of the brain and examining their relationship to the clinical features of Autism Spectrum Disorder can pave the way for the correct and early detection of this disorder using structural magnetic resonance imaging. It is also possible to design treatment for autistic people based on the abnormal areas of the brain, and to see the effectiveness of the treatment using imaging.

Mendelian randomization analyses support causal relationships between brain imaging-derived phenotypes and risk of psychiatric disorders

Observational studies have reported the correlations between brain imaging-derived phenotypes (IDPs) and psychiatric disorders; however, whether the relationships are causal is uncertain. We conducted bidirectional two-sample Mendelian randomization (MR) analyses to explore the causalities between 587 reliable IDPs (N = 33,224 individuals) and 10 psychiatric disorders (N = 9,725 to 161,405). We identified nine IDPs for which there was evidence of a causal influence on risk of schizophrenia, anorexia nervosa and bipolar disorder. For example, 1 s.d. increase in the orientation dispersion index of the forceps major was associated with 32% lower odds of schizophrenia risk. Reverse MR indicated that only genetically predicted schizophrenia was positively associated with two IDPs, the cortical surface area and the volume of the right pars orbitalis. We established the BrainMR database ( http://www.bigc.online/BrainMR/ ) to share our results. Our findings provide potential strategies for the prediction and intervention for psychiatric disorder risk at the brain-imaging level.

Dog Therapy in Supporting the Rehabilitation Process of Children with Autism

Introduction: Autism is not a specific condition. It is, however, a comprehensive disorder of psychomotor and social development. A number of factors, both environmental (family-related) and genetic, are believed to be the cause of autism. The percentage of children affected by autism has been increasing over the past 20 years. It is assumed that statistically, approximately 20 children in every 10,000 will become affected by this condition. Autism is 4 times more common in boys than in girls. The disorder is characterised by impaired mental growth, and, consequently, social and motor development. Research objective: The aim of the article is to present the role of dog therapy in supporting the process of therapeutic rehabilitation among children with autism. In particular, the following aspects were taken into account: breeds of canines used in dog therapy, mechanisms of influence concerning dog therapy on the child's body, as well as the forms and results obtained. Material and methods: The work is a narrative review. It was written on the basis of the document analysis method with the use of quantitative and qualitative techniques, as well as database searches for Polish and foreign scientific literature on the subject, i.e. Web of Science, PubMed and Google Scholar. In the article, the research results are presented in relation to the efficiency of applying dog therapy in the treatment of autistic children between 2002 and 2017, with emphasis on foreign literature. Results: The most commonly used forms of dog therapy used are: Animal Assisted Activity (AAA), Animal Assisted Therapy (AAT) and Animal Assisted Education (AAE). Conclusions: The use of dogs in the process of therapeutic rehabilitation has positive influence both on the autistic child and his/her family environment. It helps cope better with many difficulties and motivates to take up more activities. Dog therapy affects all spheres of personal development, i.e. mental, motor and socio-emotional.

Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset

Machine Learning (ML) techniques have been widely used in Neuroimaging studies of Autism Spectrum Disorders (ASD) both to identify possible brain alterations related to this condition and to evaluate the predictive power of brain imaging modalities. The collection and public sharing of large imaging samples has favored an even greater diffusion of the use of ML-based analyses. However, multi-center data collections may suffer the batch effect, which, especially in case of Magnetic Resonance Imaging (MRI) studies, should be curated to avoid confounding effects for ML classifiers and masking biases. This is particularly important in the study of barely separable populations according to MRI data, such as subjects with ASD compared to controls with typical development (TD). Here, we show how the implementation of a harmo- nization protocol on brain structural features unlocks the case-control ML separation capability in the analysis of a multi-center MRI dataset. This effect is demonstrated on the ABIDE data collection, involving subjects encompassing a wide age range. After data harmonization, the overall ASD vs. TD discrimination capability by a Random Forest (RF) classifier improves from a very low performance (AUC = 0.58 ± 0.04) to a still low, but reasonably significant AUC = 0.67 ± 0.03. The performances of the RF classifier have been evaluated also in the age-specific subgroups of children, adolescents and adults, obtaining AUC = 0.62 ± 0.02, AUC = 0.65 ± 0.03 and AUC = 0.69 ± 0.06, respectively. Specific and consistent patterns of anatomical differences related to the ASD condition have been identified for the three different age subgroups.

Cortical thickness systematically varies with curvature and depth in healthy human brains

Cortical thickness varies throughout the cortex in a systematic way. However, it is challenging to investigate the patterns of cortical thickness due to the intricate geometry of the cortex. The cortex has a folded nature both in radial and tangential directions which forms not only gyri and sulci but also tangential folds and intersections. In this article, cortical curvature and depth are used to characterize the spatial distribution of the cortical thickness with much higher resolution than conventional regional atlases. To do this, a computational pipeline was developed that is capable of calculating a variety of quantitative measures such as surface area, cortical thickness, curvature (mean curvature, Gaussian curvature, shape index, intrinsic curvature index, and folding index), and sulcal depth. By analyzing 501 neurotypical adult human subjects from the ABIDE-I dataset, we show that cortex has a very organized structure and cortical thickness is strongly correlated with local shape. Our results indicate that cortical thickness consistently increases along the gyral-sulcal spectrum from concave to convex shape, encompassing the saddle shape along the way. Additionally, tangential folds influence cortical thickness in a similar way as gyral and sulcal folds; outer folds are consistently thicker than inner.

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.

Shank2/3 double knockout-based screening of cortical subregions links the retrosplenial area to the loss of social memory in autism spectrum disorders

Members of the Shank protein family are master scaffolds of the postsynaptic architecture and mutations within the SHANK genes are causally associated with autism spectrum disorders (ASDs). We generated a Shank2-Shank3 double knockout mouse that is showing severe autism related core symptoms, as well as a broad spectrum of comorbidities. We exploited this animal model to identify cortical brain areas linked to specific autistic traits by locally deleting Shank2 and Shank3 simultaneously. Our screening of 10 cortical subregions revealed that a Shank2/3 deletion within the retrosplenial area severely impairs social memory, a core symptom of ASD. Notably, DREADD-mediated neuronal activation could rescue the social impairment triggered by Shank2/3 depletion. Data indicate that the retrosplenial area has to be added to the list of defined brain regions that contribute to the spectrum of behavioural alterations seen in ASDs.

Preliminary comparative study of cortical thickness in HIV-infected patients with and without working memory deficit

Purpose

Changes in cerebral cortical regions occur in HIV-infected patients, even in those with mild neurocognitive disorders. Working memory / attention is one of the most affected cognitive domain in these patients, worsening their quality of life. Our objective was to assess whether cortical thickness differs between HIV-infected patients with and without working memory deficit.

Methods

Forty-one adult HIV-infected patients with and without working memory deficit were imaged on a 1.5 T scanner. Working memory deficit was classified by composite Z scores for performance on the Digits and Letter-Number Sequencing subtests of the Wechsler Adult Intelligence Scale (third edition; WAIS-III). Cortical thickness was determined using FreeSurfer software. Differences in mean cortical thickness between groups, corrected for multiple comparisons using Monte-Carlo simulation, were examined using the query design estimate contrast tool of the FreeSurfer software.

Results

Greater cortical thickness in left pars opercularis of the inferior frontal gyrus, and rostral and caudal portions of the left middle frontal gyrus (cluster 1; p = .004), and left superior frontal gyrus (cluster 2; p = .004) was observed in HIV-infected patients with working memory deficit compared with those without such deficit. Negative correlations were found between WAIS-III–based Z scores and cortical thickness in the two clusters (cluster 1: ρ = –0.59; cluster 2: ρ = –0.47).

Conclusion

HIV-infected patients with working memory deficit have regions of greater thickness in the left frontal cortices compared with those without such deficit, which may reflect increased synaptic contacts and/or an inflammatory response related to the damage caused by HIV infection.

A translational exploration of the effects of WNT2 variants on altered cortical structures in autism spectrum disorder

BACKGROUND

Evidence suggests that cortical anatomy may be aytpical in autism spectrum disorder. The wingless-type MMTV integration site family, member 2 (WNT2), a candidate gene for autism spectrum disorder, may regulate cortical development. However, it is unclear whether WNT2 variants are associated with altered cortical thickness in autism spectrum disorder.

METHODS

In a sample of 118 people with autism spectrum disorder and 122 typically developing controls, we investigated cortical thickness using FreeSurfer software. We then examined the main effects of the WNT2 variants and the interactions of group × SNP and age × SNP for each hemisphere and brain region that was altered in people with autism spectrum disorder.

RESULTS

Compared to neurotypical controls, people with autism spectrum disorder showed reduced mean cortical thickness in both hemispheres and 9 cortical regions after false discovery rate correction, including the right cingulate gyrus, the orbital gyrus, the insula, the inferior frontal gyrus (orbital part and triangular part), the lateral occipitotemporal gyrus, the posterior transverse collateral sulcus, the lateral sulcus and the superior temporal sulcus. In the full sample, 2 SNPs of WNT2 (rs6950765 and rs2896218) showed age × SNP interactions for the mean cortical thickness of both hemispheres, the middle-posterior cingulate cortex and the superior temporal cortex.

LIMITATIONS

We examined the genetic effect for each hemisphere and the 9 regions that were altered in autism spectrum disorder. The age effect we found in this cross-sectional study needs to be examined in longitudinal studies.

CONCLUSION

Based on neuroimaging and genetic data, our findings suggest that WNT2 variants might be associated with altered cortical thickness in autism spectrum disorder. Whether and how these WNT2 variants might involve cortical thinning requires further investigation.

TRIAL REGISTRATION

ClinicalTrials.gov no. NCT01582256.

PROTOCOL REGISTRATION

National Institutes of Health no. NCT00494754.

Contributions of Sex Chromosomes and Gonadal Hormones to the Male Bias in a Maternal Antibody-Induced Model of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a group of neurodevelopmental conditions that is four times more commonly diagnosed in males than females. While susceptibility genes located in the sex chromosomes have been identified in ASD, it is unclear whether they are sufficient to explain the male bias or whether gonadal hormones also play a key role. We evaluated the sex chromosomal and hormonal influences on the male bias in a murine model of ASD, in which mice are exposed in utero to a maternal antibody reactive to contactin-associated protein-like 2 (Caspr2), which was originally cloned from a mother of a child with ASD (termed C6 mice henceforth). In this model, only male mice are affected. We used the four-core-genotypes (FCG) model in which the Sry gene is deleted from the Y chromosome (Y⁻) and inserted into autosome 3 (TgSry). Thus, by combining the C6 and FCG models, we were able to differentiate the contributions of sex chromosomes and gonadal hormones to the development of fetal brain and adult behavioral phenotypes. We show that the presence of the Y chromosome, or lack of two X chromosomes, irrespective of gonadal sex, increased the susceptibility to C6-induced phenotypes including the abnormal growth of the developing fetal cerebral cortex, as well as a behavioral pattern of decreased open-field exploration in adult mice. Our results indicate that sex chromosomes are the main determinant of the male bias in the maternal C6-induced model of ASD. The less dominant hormonal effect may be due to modulation by sex chromosome genes of factors involved in gonadal hormone pathways in the brain.

Longitudinal Evaluation of Cerebral Growth Across Childhood in Boys and Girls With Autism Spectrum Disorder

BACKGROUND

Cerebral overgrowth is frequently reported in children but not in adults with autism spectrum disorder (ASD). This suggests that early cerebral overgrowth is followed by normalization of cerebral volumes. However, this notion is predicated on cross-sectional research that is vulnerable to sampling bias. For example, autistic individuals with disproportionate megalencephaly, a subgroup with higher rates of intellectual disability and larger cerebral volumes, may be underrepresented in studies of adolescents and adults. Furthermore, extant studies have cohorts that are predominately male, thus limiting knowledge of cerebral growth in females with ASD.

METHODS

Growth of total cerebral volume, gray matter (GM) volume, and white matter volume as well as proportion of GM to total cerebral volume were examined in a longitudinal sample comprising 273 boys (199 with ASD) scanned at up to four time points (mean ages = 38, 50, 64, and 137 months, respectively) and 156 girls (95 with ASD) scanned at up to three time points (mean ages = 39, 53, and 65 months, respectively) using mixed-effects modeling.

RESULTS

In boys with ASD, cerebral overgrowth in the ASD with disproportionate megalencephaly subgroup was predominately driven by increases in GM and persisted throughout childhood without evidence of volumetric regression or normalization. In girls with ASD, cerebral volumes were similar to those in typically developing girls, but growth trajectories of GM and white matter were slower throughout early childhood. The proportion of GM to total cerebral volume declined with age at a slower rate in autistic boys and girls relative to typically developing control subjects.

CONCLUSIONS

Longitudinal evidence does not support the notion that early brain overgrowth is followed by volumetric regression, at least from early to late childhood.

Relationship between local gyrification index and age, intelligence quotient, symptom severity with Autism Spectrum Disorder: A large-scale MRI study

Gyrification is one of the most important characteristics in the cerebral cortex and the local gyrification index (LGI) was used to quantify the regional changes in gyrification. The aim of this study was to evaluate LGI alterations in autism spectrum disorder (ASD) individuals in comparison with typically developing (TD) controls and the association of the LGI with age, intelligence quotient (IQ), and symptom severity in a large multicenter dataset. Structural MRI datasets selected from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) repository (606 ASD individuals and 765 age-matched TD controls) were used to calculate LGI values. The correlation between the LGI and age, IQ, and other clinical measurements were assessed. No differences in LGI were found between ASD individuals and TD controls after FDR multiple comparison correction, however, LGI decreased with age in both ASD and TD groups. In the TD group, a significant positive correlation was found between the LGI and full IQ (FIQ) in the parahippocampal gyrus, parsopercularis of left hemisphere and entorhinal cortex, parahippocampal, superior temporal gyrus of right hemisphere, but was not observed in the ASD group. Furthermore, a positive correlation between the LGI and Autism Diagnostic Interview-Revised (ADI-R) Repetitive and Restrictive Behaviors (RRB) score was found in the left inferior parietal lobule, lateral occipital cortex, superior frontal gyrus and right superior frontal gyrus, inferior temporal gyrus. In summary, these results demonstrate that the ASD is a truly heterogeneous neurodevelopmental disorder. Future investigations are required that group ASD patients into more homogeneous subtypes.

Behavioral Alterations and Decreased Number of Parvalbumin-Positive Interneurons in Wistar Rats after Maternal Immune Activation by Lipopolysaccharide: Sex Matters

Maternal immune activation (MIA) during pregnancy represents an important environmental factor in the etiology of schizophrenia and autism spectrum disorders (ASD). Our goal was to investigate the impacts of MIA on the brain and behavior of adolescent and adult offspring, as a rat model of these neurodevelopmental disorders. We injected bacterial lipopolysaccharide (LPS, 1 mg/kg) to pregnant Wistar dams from gestational day 7, every other day, up to delivery. Behavior of the offspring was examined in a comprehensive battery of tasks at postnatal days P45 and P90. Several brain parameters were analyzed at P28. The results showed that prenatal immune activation caused social and communication impairments in the adult offspring of both sexes; males were affected already in adolescence. MIA also caused prepulse inhibition deficit in females and increased the startle reaction in males. Anxiety and hypolocomotion were apparent in LPS-affected males and females. In the 28-day-old LPS offspring, we found enlargement of the brain and decreased numbers of parvalbumin-positive interneurons in the frontal cortex in both sexes. To conclude, our data indicate that sex of the offspring plays a crucial role in the development of the MIA-induced behavioral alterations, whereas changes in the brain apparent in young animals are sex-independent.

Die Kortexdicke bei Autismus-Spektrum-Störung wird moduliert durch eine komorbide Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung

Zusammenfassung. Theoretischer Hintergrund: Autismus-Spektrum-Störung (ASS) ist eine neuronale Entwicklungsstörung und tritt häufig gemeinsam mit der Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS) auf. Bisher wurde jedoch nur selten untersucht, wie sich Personen mit ASS von Personen mit ASS und komorbider ADHS, sowie von einer gesunden Kontrollgruppe (KG) auf neuroanatomischer Ebene unterscheiden. Fragestellung: In der vorliegenden Studie wurde an 101 Jugendlichen untersucht, ob die Kortexdicke bei ASS von komorbid auftretenden ADHS-Symptomen moduliert wird. Methode: Für jeden Proband_innen berechneten wir auf Basis struktureller T1-gewichteter Magnetresonanztomographie Scans die Kortexdicke an jedem Punkt der Gehirnoberfläche. Ergebnisse: Es zeigten sich signifikante Unterschiede zwischen autistischen Proband_innen mit und ohne ADHS im posterioren Cingulum, der Lingualwindung sowie dem Precuneus der linken Hemisphäre. Diskussion und Schlussfolgerung: Die Ergebnisse implizieren, dass die kortikale Dicke bei ASS durch das gleichzeitige Vorliegen einer ADHS moduliert wird. Diese Erkenntnisse könnten in zukünftigen Studien zur Untersuchung neuroanatomischer Ursachen von ASS und der Unterteilung von Proband_innen in homogenere Subgruppen von Nutzen sein und so der zukünftigen Entwicklung individualisierter Therapien dienen.

Autism Spectrum Disorder and Childhood Apraxia of Speech: Early Language-Related Hallmarks across Structural MRI Study

Autism Spectrum Disorder (ASD) and Childhood Apraxia of Speech (CAS) are developmental disorders with distinct diagnostic criteria and different epidemiology. However, a common genetic background as well as overlapping clinical features between ASD and CAS have been recently reported. To date, brain structural language-related abnormalities have been detected in both the conditions, but no study directly compared young children with ASD, CAS and typical development (TD). In the current work, we aim: (i) to test the hypothesis that ASD and CAS display neurostructural differences in comparison with TD through morphometric Magnetic Resonance Imaging (MRI)-based measures (ASD vs. TD and CAS vs. TD); (ii) to investigate early possible disease-specific brain structural patterns in the two clinical groups (ASD vs. CAS); (iii) to evaluate predictive power of machine-learning (ML) techniques in differentiating the three samples (ASD, CAS, TD). We retrospectively analyzed the T1-weighted brain MRI scans of 68 children (age range: 34-74 months) grouped into three cohorts: (1) 26 children with ASD (mean age ± standard deviation: 56 ± 11 months); (2) 24 children with CAS (57 ± 10 months); (3) 18 children with TD (55 ± 13 months). Furthermore, a ML analysis based on a linear-kernel Support Vector Machine (SVM) was performed. All but one brain structures displayed significant higher volumes in both ASD and CAS children than TD peers. Specifically, ASD alterations involved fronto-temporal regions together with basal ganglia and cerebellum, while CAS alterations are more focused and shifted to frontal regions, suggesting a possible speech-related anomalies distribution. Caudate, superior temporal and hippocampus volumes directly distinguished the two conditions in terms of greater values in ASD compared to CAS. The ML analysis identified significant differences in brain features between ASD and TD children, whereas only some trends in the ML classification capability were detected in CAS as compared to TD peers. Similarly, the MRI structural underpinnings of two clinical groups were not significantly different when evaluated with linear-kernel SVM. Our results may represent the first step towards understanding shared and specific neural substrate in ASD and CAS conditions, which subsequently may contribute to early differential diagnosis and tailoring specific early intervention.

Fractionating autism based on neuroanatomical normative modeling

Autism is a complex neurodevelopmental condition with substantial phenotypic, biological, and etiologic heterogeneity. It remains a challenge to identify biomarkers to stratify autism into replicable cognitive or biological subtypes. Here, we aim to introduce a novel methodological framework for parsing neuroanatomical subtypes within a large cohort of individuals with autism. We used cortical thickness (CT) in a large and well-characterized sample of 316 participants with autism (88 female, age mean: 17.2 ± 5.7) and 206 with neurotypical development (79 female, age mean: 17.5 ± 6.1) aged 6-31 years across six sites from the EU-AIMS multi-center Longitudinal European Autism Project. Five biologically based putative subtypes were derived using normative modeling of CT and spectral clustering. Three of these clusters showed relatively widespread decreased CT and two showed relatively increased CT. These subtypes showed morphometric differences from one another, providing a potential explanation for inconsistent case-control findings in autism, and loaded differentially and more strongly onto symptoms and polygenic risk, indicating a dilution of clinical effects across heterogeneous cohorts. Our results provide an important step towards parsing the heterogeneous neurobiology of autism.

Genetic and environmental influences on structural brain measures in twins with autism spectrum disorder

Atypical growth patterns of the brain have been previously reported in autism spectrum disorder (ASD) but these alterations are heterogeneous across individuals, which may be associated with the variable effects of genetic and environmental influences on brain development. Monozygotic (MZ) and dizygotic (DZ) twin pairs with and without ASD (aged 6-15 years) were recruited to participate in this study. T1-weighted MRIs (n = 164) were processed with FreeSurfer to evaluate structural brain measures. Intra-class correlations were examined within twin pairs and compared across diagnostic groups. ACE modeling was also completed. Structural brain measures, including cerebral and cerebellar gray matter (GM) and white matter (WM) volume, surface area, and cortical thickness, were primarily influenced by genetic factors in TD twins; however, mean curvature appeared to be primarily influenced by environmental factors. Similarly, genetic factors accounted for the majority of variation in brain size in twins with ASD, potentially to a larger extent regarding curvature and subcortical GM; however, there were also more environmental contributions in twins with ASD on some structural brain measures, such that cortical thickness and cerebellar WM volume were primarily influenced by environmental factors. These findings indicate potential neurobiological outcomes of the genetic and environmental risk factors that have been previously associated with ASD and, although preliminary, may help account for some of the previously outlined neurobiological heterogeneity across affected individuals. This is especially relevant regarding the role of genetic and environmental factors in the development of ASD, in which certain brain structures may be more sensitive to specific influences.

Neurocognitive patterns dissociating semantic processing from executive control are linked to more detailed off-task mental time travel

Features of ongoing experience are common across individuals and cultures. However, certain people express specific patterns of thought to a greater extent than others. Contemporary psychological theory assumes that individual differences in thought patterns occur because different types of experience depend on the expression of different neurocognitive processes. Consequently, individual variation in the underlying neurocognitive architecture is hypothesised to determine the ease with which certain thought patterns are generated or maintained. Our study (N = 178) tested this hypothesis using multivariate pattern analysis to infer shared variance among measures of cognitive function and neural organisation and examined whether these latent variables explained reports of the patterns of on-going thoughts people experienced in the lab. We found that relatively better performance on tasks relying primarily on semantic knowledge, rather than executive control, was linked to a neural functional organisation associated, via meta-analysis, with task labels related to semantic associations (sentence processing, reading and verbal semantics). Variability of this functional mode predicted significant individual variation in the types of thoughts that individuals experienced in the laboratory: neurocognitive patterns linked to better performance at tasks that required guidance from semantic representation, rather than those dependent on executive control, were associated with patterns of thought characterised by greater subjective detail and a focus on time periods other than the here and now. These relationships were consistent across different days and did not vary with level of task demands, indicating they are relatively stable features of an individual’s cognitive profile. Together these data confirm that individual variation in aspects of ongoing experience can be inferred from hidden neurocognitive architecture and demonstrate that performance trade-offs between executive control and long-term semantic knowledge are linked to a person’s tendency to imagine situations that transcend the here and now.

Genetics of structural and functional brain changes in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurological and developmental disorder characterized by social impairment and restricted interactive and communicative behaviors. It may occur as an isolated disorder or in the context of other neurological, psychiatric, developmental, and genetic disorders. Due to rapid developments in genomics and imaging technologies, imaging genetics studies of ASD have evolved in the last few years. Increased risk for ASD diagnosis is found to be related to many specific single-nucleotide polymorphisms, and the study of genetic mechanisms and noninvasive imaging has opened various approaches that can help diagnose ASD at the nascent level. Identifying risk genes related to structural and functional changes in the brain of ASD patients provide a better understanding of the disease's neuropsychiatry and can help identify targets for therapeutic intervention that could be useful for the clinical management of ASD patients.

Atypical age-related changes in cortical thickness in autism spectrum disorder

Recent longitudinal neuroimaging and neurophysiological studies have shown that tracking relative age-related changes in neural signals, rather than a static snapshot of a neural measure, could offer higher sensitivity for discriminating typically developing (TD) individuals from those with autism spectrum disorder (ASD). It is not clear, however, which aspects of age-related changes (trajectories) would be optimal for identifying atypical brain development in ASD. Using a large cross-sectional data set (Autism Brain Imaging Data Exchange [ABIDE] repository; releases I and II), we aimed to explore age-related changes in cortical thickness (CT) in TD and ASD populations (age range 6–30 years old). Cortical thickness was estimated from T1-weighted MRI images at three scales of spatial coarseness (three parcellations with different numbers of regions of interest). For each parcellation, three polynomial models of age-related changes in CT were tested. Specifically, to characterize alterations in CT trajectories, we compared the linear slope, curvature, and aberrancy of CT trajectories across experimental groups, which was estimated using linear, quadratic, and cubic polynomial models, respectively. Also, we explored associations between age-related changes with ASD symptomatology quantified as the Autism Diagnostic Observation Schedule (ADOS) scores. While no overall group differences in cortical thickness were observed across the entire age range, ASD and TD populations were different in terms of age-related changes, which were located primarily in frontal and tempo-parietal areas. These atypical age-related changes were also associated with ADOS scores in the ASD group and used to predict ASD from TD development. These results indicate that the curvature is the most reliable feature for localizing brain areas developmentally atypical in ASD with a more pronounced effect with symptomatology and is the most sensitive in predicting ASD development.

A Comprehensive Framework for Differentiating Autism Spectrum Disorder From Neurotypicals by Fusing Structural MRI and Resting State Functional MRI

Autism spectrum disorder is a neurodevelopmental disorder characterized by impaired social abilities and communication difficulties. The golden standard for autism diagnosis in research rely on behavioral features, for example, the autism diagnosis observation schedule, the Autism Diagnostic Interview-Revised. In this study we introduce a computer-aided diagnosis system that uses features from structural MRI (sMRI) and resting state functional MRI (fMRI) to help predict an autism diagnosis by clinicians. The proposed system is capable of parcellating brain regions to show which areas are most likely affected by autism related abnormalities and thus help in targeting potential therapeutic interventions. When tested on 18 data sets (n = 1060) from the ABIDE consortium, our system was able to achieve high accuracy (sMRI 0.75-1.00; fMRI 0.79-1.00), sensitivity (sMRI 0.73-1.00; fMRI 0.78-1.00), and specificity (sMRI 0.78-1.00; fMRI 0.79-1.00). The proposed system could be considered an important step toward helping physicians interpret results of neuroimaging studies and personalize treatment options. To the best of our knowledge, this work is the first to combine features from structural and functional MRI, use them for personalized diagnosis and achieve high accuracies on a relatively large population.

Evidence against the "normalization" prediction of the early brain overgrowth hypothesis of autism

BACKGROUND

The frequently cited Early Overgrowth Hypothesis of autism spectrum disorder (ASD) postulates that there is overgrowth of the brain in the first 2 years of life, which is followed by a period of arrested growth leading to normalized brain volume in late childhood and beyond. While there is consistent evidence for early brain overgrowth, there is mixed evidence for normalization of brain volume by middle childhood. The outcome of this debate is important to understanding the etiology and neurodevelopmental trajectories of ASD.

METHODS

Brain volume was examined in two very large single-site samples of children, adolescents, and adults. The primary sample comprised 456 6-25-year-olds (ASD n = 240, typically developing controls (TDC) n = 216), including a large number of females (n = 102) and spanning a wide IQ range (47-158). The replication sample included 175 males. High-resolution T1-weighted anatomical MRI images were examined for group differences in total brain, cerebellar, ventricular, gray, and white matter volumes.

RESULTS

The ASD group had significantly larger total brain, cerebellar, gray matter, white matter, and lateral ventricular volumes in both samples, indicating that brain volume remains enlarged through young adulthood, rather than normalizing. There were no significant age or sex interactions with diagnosis in these measures. However, a significant diagnosis-by-IQ interaction was detected in the larger sample, such that increased brain volume was related to higher IQ in the TDCs, but not in the ASD group. Regions-of-significance analysis indicated that total brain volume was larger in ASD than TDC for individuals with IQ less than 115, providing a potential explanation for prior inconsistent brain size results. No relationships were found between brain volume and measures of autism symptom severity within the ASD group.

LIMITATIONS

Our cross-sectional sample may not reflect individual changes over time in brain volume and cannot quantify potential changes in volume prior to age 6.

CONCLUSIONS

These findings challenge the "normalization" prediction of the brain overgrowth hypothesis by demonstrating that brain enlargement persists across childhood into early adulthood. The findings raise questions about the clinical implications of brain enlargement, since we find that it neither confers cognitive benefits nor predicts increased symptom severity in ASD.

Dysregulation of synaptic pruning as a possible link between intestinal microbiota dysbiosis and neuropsychiatric disorders

The prenatal and early postnatal stages represent a critical time window for human brain development. Interestingly, this window partly overlaps with the maturation of the intestinal flora (microbiota) that play a critical role in the bidirectional communication between the central and the enteric nervous systems (microbiota-gut-brain axis). The microbial composition has important influences on general health and the development of several organ systems, such as the gastrointestinal tract, the immune system, and also the brain. Clinical studies have shown that microbiota alterations are associated with a wide range of neuropsychiatric disorders including autism spectrum disorder, attention deficit hyperactivity disorder, schizophrenia, and bipolar disorder. In this review, we dissect the link between these neuropsychiatric disorders and the intestinal microbiota by focusing on their effect on synaptic pruning, a vital process in the maturation and establishing efficient functioning of the brain. We discuss in detail how synaptic pruning is dysregulated differently in the aforementioned neuropsychiatric disorders and how it can be influenced by dysbiosis and/or changes in the intestinal microbiota composition. We also review that the improvement in the intestinal microbiota composition by a change in diet, probiotics, prebiotics, or fecal microbiota transplantation may play a role in improving neuropsychiatric functioning, which can be at least partly explained via the optimization of synaptic pruning and neuronal connections. Altogether, the demonstration of the microbiota's influence on brain function via microglial-induced synaptic pruning addresses the possibility that the manipulation of microbiota-immune crosstalk represents a promising strategy for treating neuropsychiatric disorders.

Genetic and Environmental Influences on Lobar Brain Structures in Twins With Autism

This investigation examined whether the variation of cerebral structure is associated with genetic or environmental factors in children with autism spectrum disorder (ASD) compared with typically developing (TD) controls. T1-weighted magnetic resonance imaging scans were obtained from twin pairs (aged 6-15 years) in which at least one twin was diagnosed with ASD or both were TD. Good quality data were available from 30 ASD, 18 discordant, and 34 TD pairs (n = 164). Structural measures (volume, cortical thickness, and surface area) were generated with FreeSurfer, and ACE modeling was completed. Lobar structures were primarily genetically mediated in TD twins (a2 = 0.60-0.89), except thickness of the temporal (a2 = 0.33 [0.04, 0.63]) and occipital lobes (c2 = 0.61 [0.45, 0.77]). Lobar structures were also predominantly genetically mediated in twins with ASD (a2 = 0.70-1.00); however, thickness of the frontal (c2 = 0.81 [0.71, 0.92]), temporal (c2 = 0.77 [0.60, 0.93]), and parietal lobes (c2 = 0.87 [0.77, 0.97]), and frontal gray matter (GM) volume (c2 = 0.79 [0.63, 0.95]), were associated with environmental factors. Conversely, occipital thickness (a2 = 0.93 [0.75, 1.11]) did not exhibit the environmental contributions that were found in controls. Differences in GM volume were associated with social communication impairments for the frontal (r = 0.52 [0.18, 0.75]), temporal (r = 0.61 [0.30, 0.80]), and parietal lobes (r = 0.53 [0.19, 0.76]). To our knowledge, this is the first investigation to suggest that environmental factors influence GM to a larger extent in children with ASD, especially in the frontal lobe.

Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder

Significant heterogeneity across aetiologies, neurobiology and clinical phenotypes have been observed in individuals with autism spectrum disorder (ASD). Neuroimaging-based neuroanatomical studies of ASD have often reported inconsistent findings which may, in part, be attributable to an insufficient understanding of the relationship between factors influencing clinical heterogeneity and their relationship to brain anatomy. To this end, we performed a large-scale examination of cortical morphometry in ASD, with a specific focus on the impact of three potential sources of heterogeneity: sex, age and full-scale intelligence (FIQ). To examine these potentially subtle relationships, we amassed a large multi-site dataset that was carefully quality controlled (yielding a final sample of 1327 from the initial dataset of 3145 magnetic resonance images; 491 individuals with ASD). Using a meta-analytic technique to account for inter-site differences, we identified greater cortical thickness in individuals with ASD relative to controls, in regions previously implicated in ASD, including the superior temporal gyrus and inferior frontal sulcus. Greater cortical thickness was observed in sex specific regions; further, cortical thickness differences were observed to be greater in younger individuals and in those with lower FIQ, and to be related to overall clinical severity. This work serves as an important step towards parsing factors that influence neuroanatomical heterogeneity in ASD and is a potential step towards establishing individual-specific biomarkers.

The Neurodevelopment of Autism from Infancy Through Toddlerhood

Autism spectrum disorder (ASD) emerges during early childhood and is marked by a relatively narrow window in which infants transition from exhibiting normative behavioral profiles to displaying the defining features of the ASD phenotype in toddlerhood. Prospective brain imaging studies in infants at high familial risk for autism have revealed important insights into the neurobiology and developmental unfolding of ASD. In this article, we review neuroimaging studies of brain development in ASD from birth through toddlerhood, relate these findings to candidate neurobiological mechanisms, and discuss implications for future research and translation to clinical practice.

Individual variation in patterns of task focused, and detailed, thought are uniquely associated within the architecture of the medial temporal lobe

Understanding the neural processes that support different patterns of ongoing thought is an important goal of contemporary cognitive neuroscience. Early accounts assumed the default mode network (DMN) was especially important for conscious attention to task-irrelevant/personally relevant materials. However, simple task-negative accounts of the DMN are incompatible with more recent evidence that neural patterns within the system can be related to ongoing processing during active task states. To better characterise the contribution of the DMN to ongoing thought, we conducted a cross-sectional analysis of the relationship between the structural organisation of the brain, as indexed by cortical thickness, and patterns of experience, identified using experience sampling in the cognitive laboratory. In a sample of 181 healthy individuals (mean age 20 years, 117 females) we identified an association between cortical thickness in the anterior parahippocampus and patterns of task focused thought, as well as an adjacent posterior region in which cortical thickness was associated with experiences with higher levels of subjective detail. Both regions fell within regions of medial temporal lobe associated with the DMN, yet varied in their functional connectivity: the time series of signals in the 'on-task' region were more correlated with systems important for external task-relevant processing (as determined by meta-analysis) including the dorsal and ventral attention, and fronto-parietal networks. In contrast, connectivity within the region linked to subjective 'detail' was more correlated with the medial core of the DMN (posterior cingulate and the medial pre-frontal cortex) and regions of primary visual cortex. These results provide cross-sectional evidence that confirms a role of the DMN in how detailed experiences are and so provide further evidence that the role of this system in experience is not simply task-irrelevant. Our results also highlight processes within the medial temporal lobe, and their interactions with other regions of cortex, as important in determining multiple aspects of how human cognition unfolds.

Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders

The neurobiology of heterogeneous neurodevelopmental disorders such as autism spectrum disorders (ASD) are still unclear. Despite extensive efforts, most findings are difficult to reproduce due to high levels of individual variance in phenotypic expression. To quantify individual differences in brain morphometry in ASD, we implemented a novel subject-level, distance-based method on subject-specific attributes. In a large multi-cohort sample, each subject with ASD (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.58) was strictly matched to a control participant (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.70). Intrapair Euclidean distance of MRI brain morphometry and symptom severity measures (Social Responsiveness Scale) were entered into a regularised machine learning pipeline for feature selection, with rigorous out-of-sample validation and permutation testing. Subject-specific structural morphometry features significantly predicted individual variation in ASD symptom severity (19 cortical thickness features, p = 0.01, n = 5000 permutations; 10 surface area features, p = 0.006, n = 5000 permutations). Findings remained robust across subjects and were replicated in validation samples. Identified cortical regions implicate key hubs of the salience and default mode networks as neuroanatomical features of social impairment in ASD. Present results highlight the importance of subject-level markers in ASD, and offer an important step forward in understanding the neurobiology of heterogeneous disorders.

Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants

Brain development and aging are dynamic processes that unfold over years on multiple levels in both healthy and disordered individuals. Recent studies have revealed a disparity between the chronological brain age and the ‘data-driven’ brain age using functional MRI (fMRI) and diffusion MRI (dMRI). Particularly, predicting the ‘brain age’ from connectomic data might help identify relevant connectional biomarkers of neurological disorders that emerge early or late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional connectomic data, here we unprecedentedly propose to predict the morphological age of the brain by solely using morphological brain networks (derived from T1-weighted images) in both healthy and disordered populations. Besides, although T1-weighted MRI was widely used for brain age prediction, it was leveraged from an image-based analysis perspective not from a connectomic perspective. Our method includes the following steps: (i) building multi-view morphological brain networks (M-MBN), (ii) feature extraction and selection, (iii) training a machine-learning regression model to predict age from M-MBN data, and (iv) utilizing our model to identify connectional brain features related to age in both autistic and healthy populations. We demonstrate that our method significantly outperforms existing approaches and discovered brain connectional morphological features that fingerprint the age of brain cortical morphology in both autistic and healthy individuals. In particular, we discovered that the connectional cortical thickness best predicts the morphological age of the autistic brain.

Patient-Derived Stem Cells, Another in vitro Model, or the Missing Link Toward Novel Therapies for Autism Spectrum Disorders?

Autism Spectrum Disorders (ASDs) is a multigenic and multifactorial neurodevelopmental group of disorders diagnosed in early childhood, leading to deficits in social interaction, verbal and non-verbal communication and characterized by restricted and repetitive behaviors and interests. To date, genetic, descriptive and mechanistic aspects of the ASDs have been investigated using mouse models and post-mortem brain tissue. More recently, the technology to generate stem cells from patients' samples has brought a new avenue for modeling ASD through 2D and 3D neuronal models that are derived from a patient's own cells, with the goal of building new therapeutic strategies for treating ASDs. This review analyses how studies performed on mouse models and human samples can complement each other, advancing our current knowledge into the pathophysiology of the ASDs. Regardless of the genetic and phenotypic heterogeneities of ASDs, convergent information regarding the molecular and cellular mechanisms involved in these disorders can be extracted from these models. Thus, considering the complexities of these disorders, patient-derived models have immense potential to elucidate molecular deregulations that contributed to the different autistic phenotypes. Through these direct investigations with the human in vitro models, they offer the potential for opening new therapeutic avenues that can be translated into the clinic.

Local Cortical Gyrification is Increased in Children With Autism Spectrum Disorders, but Decreases Rapidly in Adolescents

Extensive MRI evidence indicates early brain overgrowth in autism spectrum disorders (ASDs). Local gyrification may reflect the distribution and timing of aberrant cortical expansion in ASDs. We examined MRI data from (Study 1) 64 individuals with ASD and 64 typically developing (TD) controls (7-19 years), and from (Study 2) an independent sample from the Autism Brain Imaging Data Exchange (n = 31/group). Local Gyrification Index (lGI), cortical thickness (CT), and surface area (SA) were measured. In Study 1, differences in lGI (ASD > TD) were found in left parietal and temporal and right frontal and temporal regions. lGI decreased bilaterally with age, but more steeply in ASD in left precentral, right lateral occipital, and middle frontal clusters. CT differed between groups in right perisylvian cortex (TD > ASD), but no differences were found for SA. Partial correlations between lGI and CT were generally negative, but associations were weaker in ASD in several clusters. Study 2 results were consistent, though less extensive. Altered gyrification may reflect unique information about the trajectory of cortical development in ASDs. While early overgrowth tends to be undetectable in later childhood in ASDs, findings may indicate that a trace of this developmental abnormality could remain in a disorder-specific pattern of gyrification.

Dissecting the Heterogeneous Cortical Anatomy of Autism Spectrum Disorder Using Normative Models

BACKGROUND

The neuroanatomical basis of autism spectrum disorder (ASD) has remained elusive, mostly owing to high biological and clinical heterogeneity among diagnosed individuals. Despite considerable effort toward understanding ASD using neuroimaging biomarkers, heterogeneity remains a barrier, partly because studies mostly employ case-control approaches, which assume that the clinical group is homogeneous.

METHODS

Here, we used an innovative normative modeling approach to parse biological heterogeneity in ASD. We aimed to dissect the neuroanatomy of ASD by mapping the deviations from a typical pattern of neuroanatomical development at the level of the individual and to show the necessity to look beyond the case-control paradigm to understand the neurobiology of ASD. We first estimated a vertexwise normative model of cortical thickness development using Gaussian process regression, then mapped the deviation of each participant from the typical pattern. For this, we employed a heterogeneous cross-sectional sample of 206 typically developing individuals (127 males) and 321 individuals with ASD (232 males) (6-31 years of age).

RESULTS

We found few case-control differences, but the ASD cohort showed highly individualized patterns of deviations in cortical thickness that were widespread across the brain. These deviations correlated with severity of repetitive behaviors and social communicative symptoms, although only repetitive behaviors survived corrections for multiple testing.

CONCLUSIONS

Our results 1) reinforce the notion that individuals with ASD show distinct, highly individualized trajectories of brain development and 2) show that by focusing on common effects (i.e., the "average ASD participant"), the case-control approach disguises considerable interindividual variation crucial for precision medicine.

Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism

Differences in cortical morphology-in particular, cortical volume, thickness and surface area-have been reported in individuals with autism. However, it is unclear what aspects of genetic and transcriptomic variation are associated with these differences. Here we investigate the genetic correlates of global cortical thickness differences (ΔCT) in children with autism. We used Partial Least Squares Regression (PLSR) on structural MRI data from 548 children (166 with autism, 295 neurotypical children and 87 children with ADHD) and cortical gene expression data from the Allen Institute for Brain Science to identify genetic correlates of ΔCT in autism. We identify that these genes are enriched for synaptic transmission pathways and explain significant variation in ΔCT. These genes are also significantly enriched for genes dysregulated in the autism post-mortem cortex (Odd Ratio (OR) = 1.11, P_corrected  10^-14), driven entirely by downregulated genes (OR = 1.87, P_corrected  10^-15). We validated the enrichment for downregulated genes in two independent data sets: Validation 1 (OR = 1.44, P_corrected = 0.004) and Validation 2 (OR = 1.30; P_corrected = 0.001). We conclude that transcriptionally downregulated genes implicated in autism are robustly associated with global changes in cortical thickness variability in children with autism.

A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data

Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans.

Social Responsiveness Scale (SRS) in Relation to Longitudinal Cortical Thickness Changes in Autism Spectrum Disorder

The relationship between brain development and clinical heterogeneity in autism (ASD) is unknown. This study examines the Social Responsiveness Scale (SRS) in relation to the longitudinal development of cortical thickness. Participants (N = 91 ASD, N = 56 TDC; 3-39 years at first scan) were scanned up to three times over a 7-year period. Mixed-effects models examined cortical thickness in relation to SRS score. ASD participants with higher SRS scores showed regionally increased age-related cortical thinning. Regional thickness differences and reduced age-related cortical thinning were found in predominantly right lateralized regions in ASD with decreasing SRS scores over time. Our findings emphasize the importance of examining clinical phenotypes in brain-based studies of ASD.

Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis

Brain disorders, such as Autism Spectrum Disorder (ASD), alter brain functional (from fMRI) and structural (from diffusion MRI) connectivities at multiple levels and in varying degrees. While unraveling such alterations have been the focus of a large number of studies, morphological brain connectivity has been out of the research scope. In particular, shape-to-shape relationships across brain regions of interest (ROIs) were rarely investigated. As such, the use of networks based on morphological brain data in neurological disorder diagnosis, while leveraging the advent of machine learning, could complement our knowledge on brain wiring alterations in unprecedented ways. In this paper, we use conventional T1-weighted MRI to define morphological brain networks (MBNs), each quantifying shape relationship between different cortical regions for a specific cortical attribute at both low-order and high-order levels. While typical brain connectomes investigate the relationship between two ROIs, we propose high-order MBN which better captures brain complex interactions by modeling the morphological relationship between pairs of ROIs. For ASD identification, we present a connectomic manifold learning framework, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectional features, to perform dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against both supervised and unsupervised state-of-the-art methods, while depicting the most discriminative high- and low-order relationships between morphological regions in the left and right hemispheres.