Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism

AUTS2 disruption causes neuronal differentiation defects in human cerebral organoids through hyperactivation of the WNT/β-catenin pathway

Individuals with the Autism Susceptibility Candidate 2 (AUTS2) gene disruptions exhibit symptoms such as intellectual disability, microcephaly, growth retardation, and distinct skeletal and facial differences. The role of AUTS2 in neurodevelopment has been investigated using animal and embryonic stem cell models. However, the precise molecular mechanisms of how AUTS2 influences neurodevelopment, particularly in humans, are not thoroughly understood. Our study employed a 3D human cerebral organoid culture system, in combination with genetic, genomic, cellular, and molecular approaches, to investigate how AUTS2 impacts neurodevelopment through cellular signaling pathways. We used CRISPR/Cas9 technology to create AUTS2-deficient human embryonic stem cells and then generated cerebral organoids with these cells. Our transcriptomic analyses revealed that the absence of AUTS2 in cerebral organoids reduces the populations of cells committed to the neuronal lineage, resulting in an overabundance of cells with a transcription profile resembling that of choroid plexus (ChP) cells. Intriguingly, we found that AUTS2 negatively regulates the WNT/β-catenin signaling pathway, evidenced by its overactivation in AUTS2-deficient cerebral organoids and in luciferase reporter cells lacking AUTS2. Importantly, treating the AUTS2-deficient cerebral organoids with a WNT inhibitor reversed the overexpression of ChP genes and increased the downregulated neuronal gene expression. This study offers new insights into the role of AUTS2 in neurodevelopment and suggests potential targeted therapies for neurodevelopmental disorders.

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

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

Glymphatic Imaging in Pediatrics

The glymphatic system, which facilitates cerebrospinal fluid (CSF) flow through the brain parenchyma, is important for brain development and waste clearance. Advances in imaging techniques, particularly magnetic resonance imaging, have make it possible to evaluate glymphatic structures and functions in vivo. Recently, several studies have focused on the development and alterations of the glymphatic system in pediatric disorders. This review discusses the development of the glymphatic system, advances of imaging techniques and their applications in pediatric disorders. First, the results of the reviewed studies indicate that the development of the glymphatic system is a long-lasting process that continues into adulthood. Second, there is a need for improved glymphatic imaging techniques that are non-invasive and fast to improve suitability for pediatric applications, as some of existing methods use contrast injection and are susceptible to motion artifacts from long scanning times. Several novel techniques are potentially feasible for pediatric patients and may be used in the future. Third, the glymphatic dysfunction is associated with a large number of pediatric disorders, although only a few have recently been investigated. In conclusion, research on the pediatric glymphatic system remains an emerging field. The preliminary applications of glymphatic imaging techniques have provided unique insight into the pathological mechanism of pediatric diseases, but mainly limited in visualization of enlarged perivascular spaces and morphological measurements on CSF volumes. More in-depth studies on glymphatic functions are required to improve our understanding of the mechanisms underlying brain development and pediatric diseases. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Differences in Intrinsic Gray Matter Connectivity and Their Genomic Underpinnings in Autism Spectrum Disorder

BACKGROUND

Autism is a heterogeneous neurodevelopmental condition accompanied by differences in brain connectivity. Structural connectivity in autism has mainly been investigated within the white matter. However, many genetic variants associated with autism highlight genes related to synaptogenesis and axonal guidance, thus also implicating differences in intrinsic (i.e., gray matter) connections in autism. Intrinsic connections may be assessed in vivo via so-called intrinsic global and local wiring costs.

METHODS

Here, we examined intrinsic global and local wiring costs in the brain of 359 individuals with autism and 279 healthy control participants ages 6 to 30 years from the EU-AIMS LEAP (Longitudinal European Autism Project). FreeSurfer was used to derive surface mesh representations to compute the estimated length of connections required to wire the brain within the gray matter. Vertexwise between-group differences were assessed using a general linear model. A gene expression decoding analysis based on the Allen Human Brain Atlas was performed to link neuroanatomical differences to putative underpinnings.

RESULTS

Group differences in global and local wiring costs were predominantly observed in medial and lateral prefrontal brain regions, in inferior temporal regions, and at the left temporoparietal junction. The resulting neuroanatomical patterns were enriched for genes that had been previously implicated in the etiology of autism at genetic and transcriptomic levels.

CONCLUSIONS

Based on intrinsic gray matter connectivity, the current study investigated the complex neuroanatomy of autism and linked between-group differences to putative genomic and/or molecular mechanisms to parse the heterogeneity of autism and provide targets for future subgrouping approaches.

Whole-brain gray matter maturation trajectories associated with autistic traits from adolescence to early adulthood

A growing number of evidence supports a continued distribution of autistic traits in the general population. However, brain maturation trajectories of autistic traits as well as the influence of sex on these trajectories remain largely unknown. We investigated the association of autistic traits in the general population, with longitudinal gray matter (GM) maturation trajectories during the critical period of adolescence. We assessed 709 community-based adolescents (54.7% women) at age 14 and 22. After testing the effect of sex, we used whole-brain voxel-based morphometry to measure longitudinal GM volumes changes associated with autistic traits measured by the Social Responsiveness Scale (SRS) total and sub-scores. In women, we observed that the SRS was associated with slower GM volume decrease globally and in the left parahippocampus and middle temporal gyrus. The social communication sub-score correlated with slower GM volume decrease in the left parahippocampal, superior temporal gyrus, and pallidum; and the social cognition sub-score correlated with slower GM volume decrease in the left middle temporal gyrus, the right ventromedial prefrontal and orbitofrontal cortex. No longitudinal association was found in men. Autistic traits in young women were found to be associated with specific brain trajectories in regions of the social brain and the reward circuit known to be involved in Autism Spectrum Disorder. These findings support both the hypothesis of an earlier GM maturation associated with autistic traits in adolescence and of protective mechanisms in women. They advocate for further studies on brain trajectories associated with autistic traits in women.

Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference

BACKGROUND

Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies.

METHODS

Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD.

RESULTS

We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network.

CONCLUSIONS

The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.

White and gray matter correlates of theory of mind in autism: a voxel-based morphometry study

Autism spectrum disorder (ASD) is characterized by difficulties in theory of mind (ToM) and social communication. Studying structural and functional correlates of ToM in the brain and how autistic and nonautistic groups differ in terms of these correlates can help with diagnosis and understanding the biological mechanisms of ASD. In this study, we investigated white matter volume (WMV) and gray matter volume (GMV) differences between matching autistic and nonautistic samples, and how these structural features relate to age and ToM skills, indexed by the Reading the Mind in the Eyes (RMIE) measure. The results showed widespread GMV and WMV differences between the two groups in regions crucial for social processes. The autistic group did not express the typically observed negative GMV and positive WMV correlations with age at the same level as the nonautistic group, pointing to abnormalities in developmental structural changes. In addition, we found differences between the two groups in how GMV relates to ToM, particularly in the left frontal regions, and how WMV relates to ToM, mostly in the cingulate and corpus callosum. Finally, GMV in the left insula, a region that is part of the salience network, was found to be crucial in distinguishing ToM performance between the two groups.

The State of the Dopaminergic and Glutamatergic Systems in the Valproic Acid Mouse Model of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a progressive neurodevelopmental disorder mainly characterized by deficits in social communication and stereotyped behaviors and interests. Here, we aimed to investigate the state of several key players in the dopamine and glutamate neurotransmission systems in the valproic acid (VPA) animal model that was administered to E12.5 pregnant females as a single dose (450 mg/kg). We report no alterations in the number of mesencephalic dopamine neurons or in protein levels of tyrosine hydroxylase in either the striatum or the nucleus accumbens. In females prenatally exposed to VPA, levels of dopamine were slightly decreased while the ratio of DOPAC/dopamine was increased in the dorsal striatum, suggesting increased turn-over of dopamine tone. In turn, levels of D1 and D2 dopamine receptor mRNAs were increased in the nucleus accumbens of VPA mice suggesting upregulation of the corresponding receptors. We also report decreased protein levels of striatal parvalbumin and increased levels of p-mTOR in the cerebellum and the motor cortex of VPA mice. mRNA levels of mGluR1, mGluR4, and mGluR5 and the glutamate receptor subunits NR1, NR2A, and NR2B were not altered by VPA, nor were protein levels of NR1, NR2A, and NR2B and those of BDNF and TrkB. These findings are of interest as clinical trials aiming at the dopamine and glutamate systems are being considered.

Neuroanatomical correlates of autism spectrum disorders: A meta-analysis of structural magnetic resonance imaging (MRI) studies

Autism spectrum disorders (ASD) are neurodevelopmental disorders correlated to various neuroanatomical modifications. We aimed to identify neuroanatomical changes assessed in magnetic resonance imaging (MRI) studies of autism spectrum disorder (ASD) through Activation Likelihood Estimate (ALE) meta-analysis. We included 19 peer-reviewed magnetic resonance imaging (MRI) studies that analyzed cortical volume in patients with ASD compared to healthy control subjects (HCs). The between-group analyses comparing subjects with ASD to HCs showed a volumetric reduction of a large cluster in the right brain, including the uncus/amygdala, parahippocampal gyrus, and entorhinal cortex, and putamen. The anomalies are primarily found in the right hemisphere, involved in social cognitive function, particularly impaired in ASD. These results correlate with several clinical aspects of ASD. These volumetric alterations can be considered a major correlate of disease in the context of multifactorial etiology. Further studies on brain lateralization in ASD are needed, considering the clinical phenotype variability of these disorders.

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.

Translational Magnetic Resonance Imaging in Autism Spectrum Disorder From the Mouse Model to Human

Autism spectrum disorder (ASD) is a heterogeneous syndrome characterized by behavioral features such as impaired social communication, repetitive behavior patterns, and a lack of interest in novel objects. A multimodal neuroimaging using magnetic resonance imaging (MRI) in patients with ASD shows highly heterogeneous abnormalities in function and structure in the brain associated with specific behavioral features. To elucidate the mechanism of ASD, several ASD mouse models have been generated, by focusing on some of the ASD risk genes. A specific behavioral feature of an ASD mouse model is caused by an altered gene expression or a modification of a gene product. Using these mouse models, a high field preclinical MRI enables us to non-invasively investigate the neuronal mechanism of the altered brain function associated with the behavior and ASD risk genes. Thus, MRI is a promising translational approach to bridge the gap between mice and humans. This review presents the evidence for multimodal MRI, including functional MRI (fMRI), diffusion tensor imaging (DTI), and volumetric analysis, in ASD mouse models and in patients with ASD and discusses the future directions for the translational study of ASD.

The role of head circumference and cerebral volumes to phenotype male adults with autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) has been repeatedly associated with enlargements of head circumference in children with ASD. However, it is unclear if these enlargements persist into adulthood. This is the first study to investigate head circumference in a large sample of adults with ASD.

METHODS

We apply a fully automated magnetic resonance imaging (MRI) based measurement approach to compute head circumference by combining 3D and 2D image processing. Head circumference was compared between male adults with ASD (n = 120) and healthy male controls (n = 136), from the Autism Brain Imaging Data Exchange (ABIDE) database. To explain which brain alterations drive our results, secondary analyses were performed for 10 additional morphological brain metrics.

RESULTS

ASD subjects showed an increase in head circumference (p = .0018). In addition, ASD patients had increased ventricular surface area (SA) (p = .0013). Intracranial volume, subarachnoidal cerebrospinal fluid (CSF) volume, and gray matter volume explained 50% of head circumference variance. Using a linear support vector machine, we gained an ASD classification accuracy of 73% (sensitivity 92%, specificity 68%) using head circumference and brain-morphological metrics as input features. Head circumference, ventricular SA, ventricular CSF volume, and ventricular asymmetry index contributed to 85% of feature weighting relevant for classification.

CONCLUSION

Our results suggest that head circumference increases in males with ASD persist into adulthood. Results may be driven by morphological alterations of ventricular CSF. The presented approach for an automated head circumference measurement allows for the retrospective investigation of large MRI datasets in neuropsychiatric disorders.

Modeling dopamine dysfunction in autism spectrum disorder: From invertebrates to vertebrates

Autism Spectrum Disorder (ASD) is a highly heterogeneous neurodevelopmental disorder characterized by deficits in social communication and by patterns of restricted interests and/or repetitive behaviors. The Simons Foundation Autism Research Initiative's Human Gene and CNV Modules now list over 1000 genes implicated in ASD and over 2000 copy number variant loci reported in individuals with ASD. Given this ever-growing list of genetic changes associated with ASD, it has become evident that there is likely not a single genetic cause of this disorder nor a single neurobiological basis of this disorder. Instead, it is likely that many different neurobiological perturbations (which may represent subtypes of ASD) can result in the set of behavioral symptoms that we called ASD. One such of possible subtype of ASD may be associated with dopamine dysfunction. Precise regulation of synaptic dopamine (DA) is required for reward processing and behavioral learning, behaviors which are disrupted in ASD. Here we review evidence for DA dysfunction in ASD and in animal models of ASD. Further, we propose that these studies provide a scaffold for scientists and clinicians to consider subcategorizing the ASD diagnosis based on the genetic changes, neurobiological difference, and behavioral features identified in individuals with ASD.

Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset

OBJECTIVE

The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized.

METHODS

We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data.

RESULTS

Our results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism.

CONCLUSION

Multi-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders.

SIGNIFICANCE

ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry.

Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis

Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.

Brain MRI in Autism Spectrum Disorder: Narrative Review and Recent Advances

Autism spectrum disorder (ASD) is neuropsychiatric continuum of disorders characterized by persistent deficits in social communication and restricted repetitive patterns of behavior which impede optimal functioning. Early detection and intervention in ASD children can mitigate the deficits in social interaction and result in a better outcome. Various non-invasive imaging methods and molecular techniques have been developed for the early identification of ASD characteristics. There is no general consensus on specific neuroimaging features of autism; however, quantitative magnetic resonance techniques have provided valuable structural and functional information in understanding the neuropathophysiology of ASD and how the autistic brain changes during childhood, adolescence, and adulthood. In this review of decades of ASD neuroimaging research, we identify the structural, functional, and molecular imaging clues that most accurately point to the diagnosis of ASD vs. typically developing children. These studies highlight the 1) exaggerated synaptic pruning, 2) anomalous gyrification, 3) interhemispheric under- and overconnectivity, and 4) excitatory glutamate and inhibitory GABA imbalance theories of ASD. The application of these various theories to the analysis of a patient with ASD is mitigated often by superimposed comorbid neuropsychological disorders, evolving brain maturation processes, and pharmacologic and behavioral interventions that may affect the structure and function of the brain. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 3.

Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis

PURPOSE

Autism Spectrum Disorder (ASD) is diagnosed through observation or interview assessments, which is time-consuming, subjective, and with questionable validity and reliability. Thus, we aimed to evaluate the role of machine learning (ML) with neuroimaging data to provide a reliable classification of ASD.

METHODS

A systematic search of PubMed, Scopus, and Embase was conducted to identify relevant publications. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the studies' quality. A bivariate random-effects model meta-analysis was employed to evaluate the pooled sensitivity, the pooled specificity, and the diagnostic performance through the hierarchical summary receiver operating characteristic (HSROC) curve of ML with neuroimaging data in classifying ASD. Meta-regression was also performed.

RESULTS

Forty-four studies (5697 ASD and 6013 typically developing individuals [TD] in total) were included in the quantitative analysis. The pooled sensitivity for differentiating ASD from TD individuals was 86.25 95% confidence interval [CI] (81.24, 90.08), while the pooled specificity was 83.31 95% CI (78.12, 87.48) with a combined area under the HSROC (AUC) of 0.889. Higgins I² (> 90%) and Cochran's Q (p < 0.0001) suggest a high degree of heterogeneity. In the bivariate model meta-regression, a higher pooled specificity was observed in studies not using a brain atlas (90.91 95% CI [80.67, 96.00], p = 0.032). In addition, a greater pooled sensitivity was seen in studies recruiting both males and females (89.04 95% CI [83.84, 92.72], p = 0.021), and combining imaging modalities (94.12 95% [85.43, 97.76], p = 0.036).

CONCLUSION

ML with neuroimaging data is an exciting prospect in detecting individuals with ASD but further studies are required to improve its reliability for usage in clinical practice.

Biological implications of genetic variations in autism spectrum disorders from genomics studies

Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition characterized by atypical social interaction and communication together with repetitive behaviors and restricted interests. The prevalence of ASD has been increased these years. Compelling evidence has shown that genetic factors contribute largely to the development of ASD. However, knowledge about its genetic etiology and pathogenesis is limited. Broad applications of genomics studies have revealed the importance of gene mutations at protein-coding regions as well as the interrupted non-coding regions in the development of ASD. In this review, we summarize the current evidence for the known molecular genetic basis and possible pathological mechanisms as well as the risk genes and loci of ASD. Functional studies for the underlying mechanisms are also implicated. The understanding of the genetics and genomics of ASD is important for the genetic diagnosis and intervention for this condition.

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder

Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.

Brain imaging-based machine learning in autism spectrum disorder: methods and applications

Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is comprised of a constellation of behavioral symptoms. Non-invasive brain imaging techniques, such as magnetic resonance imaging (MRI), provide a valuable objective measurement of the brain. Many efforts have been devoted to developing imaging-based diagnostic tools for ASD based on machine learning (ML) technologies. In this survey, we review recent advances that utilize machine learning approaches to classify individuals with and without ASD. First, we provide a brief overview of neuroimaging-based ASD classification studies, including the analysis of publications and general classification pipeline. Next, representative studies are highlighted and discussed in detail regarding different imaging modalities, methods and sample sizes. Finally, we highlight several common challenges and provide recommendations on future directions. In summary, identifying discriminative biomarkers for ASD diagnosis is challenging, and further establishing more comprehensive datasets and dissecting the individual and group heterogeneity will be critical to achieve better ADS diagnosis performance. Machine learning methods will continue to be developed and are poised to help advance the field in this regard.

Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI

BACKGROUND

Autism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.

METHODS

We introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.

LIMITATIONS

While this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.

RESULTS

Our models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity.

CONCLUSION

This study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism.

Brain mRNA Expression Associated with Cortical Volume Alterations in Autism Spectrum Disorder

Numerous studies report abnormal cerebral cortex volume (CCV) in autism spectrum disorder (ASD); however, genes related to CCV abnormalities in ASD remain largely unknown. Here, we identify genes associated with CCV alterations in ASD by performing spatial correlations between the gene expression of 6 donated brains and neuroimaging data from 1,404 ASD patients and 1,499 controls. Based on spatial correlations between gene expression and CCV differences from two independent meta-analyses and between gene expression and individual CCV distributions of 404 patients and 496 controls, we identify 417 genes associated with both CCV differences and individual CCV distributions. These genes are enriched for genetic association signals and genes downregulated in the ASD post-mortem brain. The expression patterns of these genes are correlated with brain activation patterns of language-related neural processes frequently impaired in ASD. These findings highlight a model whereby genetic risk impacts gene expression (downregulated), which leads to CCV alterations in ASD.

EEG Correlates of Cognitive Functions and Neuropsychiatric Disorders: A Review of Oscillatory Activity and Neural Synchrony Abnormalities

Background:

A large body of evidence suggested that disruption of neural rhythms and synchronization of brain oscillations are correlated with a variety of cognitive and perceptual processes. Cognitive deficits are common features of psychiatric disorders that complicate treatment of the motivational, affective and emotional symptoms.

Objective:

Electrophysiological correlates of cognitive functions will contribute to understanding of neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and developing novel targets for the treatment of cognitive impairments.

Methods:

This review includes a description of brain oscillations in Alzheimer’s disease, bipolar disorder, attention-deficit/hyperactivity disorder, major depression, obsessive compulsive disorders, anxiety disorders, schizophrenia and autism.

Results:

The review clearly shows that the reviewed neuropsychiatric diseases are associated with fundamental changes in both spectral power and coherence of EEG oscillations.

Conclusion:

In this article, we examined the nature of brain oscillations, the association of brain rhythms with cognitive functions and the relationship between EEG oscillations and neuropsychiatric diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric disorders.

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.

Consensus Paper: Cerebellum and Social Cognition

The traditional view on the cerebellum is that it controls motor behavior. Although recent work has revealed that the cerebellum supports also nonmotor functions such as cognition and affect, only during the last 5 years it has become evident that the cerebellum also plays an important social role. This role is evident in social cognition based on interpreting goal-directed actions through the movements of individuals (social "mirroring") which is very close to its original role in motor learning, as well as in social understanding of other individuals' mental state, such as their intentions, beliefs, past behaviors, future aspirations, and personality traits (social "mentalizing"). Most of this mentalizing role is supported by the posterior cerebellum (e.g., Crus I and II). The most dominant hypothesis is that the cerebellum assists in learning and understanding social action sequences, and so facilitates social cognition by supporting optimal predictions about imminent or future social interaction and cooperation. This consensus paper brings together experts from different fields to discuss recent efforts in understanding the role of the cerebellum in social cognition, and the understanding of social behaviors and mental states by others, its effect on clinical impairments such as cerebellar ataxia and autism spectrum disorder, and how the cerebellum can become a potential target for noninvasive brain stimulation as a therapeutic intervention. We report on the most recent empirical findings and techniques for understanding and manipulating cerebellar circuits in humans. Cerebellar circuitry appears now as a key structure to elucidate social interactions.

Reliability and comparability of human brain structural covariance networks

Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.

A Novel Method for High-Dimensional Anatomical Mapping of Extra-Axial Cerebrospinal Fluid: Application to the Infant Brain

Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development.

The role of cerebellum in the child neuropsychological functioning

This chapter proposes a review of neuropsychologic and behavior findings in pediatric pathologies of the cerebellum, including cerebellar malformations, pediatric ataxias, cerebellar tumors, and other acquired cerebellar injuries during childhood. The chapter also contains reviews of the cerebellar mutism/posterior fossa syndrome, reported cognitive associations with the development of the cerebellum in typically developing children and subjects born preterm, and the role of the cerebellum in neurodevelopmental disorders such as autism spectrum disorders and developmental dyslexia. Cognitive findings in pediatric cerebellar disorders are considered in the context of known cerebellocerebral connections, internal cellular organization of the cerebellum, the idea of a universal cerebellar transform and computational internal models, and the role of the cerebellum in specific cognitive and motor functions, such as working memory, language, timing, or control of eye movements. The chapter closes with a discussion of the strengths and weaknesses of the cognitive affective syndrome as it has been described in children and some conclusions and perspectives.

Cortical Gray Matter Injury in Encephalopathy of Prematurity: Link to Neurodevelopmental Disorders

Preterm-born infants frequently suffer from an array of neurological damage, collectively termed encephalopathy of prematurity (EoP). They also have an increased risk of presenting with a neurodevelopmental disorder (e.g., autism spectrum disorder; attention deficit hyperactivity disorder) later in life. It is hypothesized that it is the gray matter injury to the cortex, in addition to white matter injury, in EoP that is responsible for the altered behavior and cognition in these individuals. However, although it is established that gray matter injury occurs in infants following preterm birth, the exact nature of these changes is not fully elucidated. Here we will review the current state of knowledge in this field, amalgamating data from both clinical and preclinical studies. This will be placed in the context of normal processes of developmental biology and the known pathophysiology of neurodevelopmental disorders. Novel diagnostic and therapeutic tactics required integration of this information so that in the future we can combine mechanism-based approaches with patient stratification to ensure the most efficacious and cost-effective clinical practice.

Disruption and Compensation of Sulcation-based Covariance Networks in Neonatal Brain Growth after Perinatal Injury

Perinatal brain injuries in preterm neonates are associated with alterations in structural neurodevelopment, leading to impaired cognition, motor coordination, and behavior. However, it remains unknown how such injuries affect postnatal cortical folding and structural covariance networks, which indicate functional parcellation and reciprocal brain connectivity. Studying 229 magnetic resonance scans from 158 preterm neonates (n = 158, mean age = 28.2), we found that severe injuries including intraventricular hemorrhage, periventricular leukomalacia, and ventriculomegaly lead to significantly reduced cortical folding and increased covariance (hyper-covariance) in only the early (<31 weeks) but not middle (31-35 weeks) or late stage (>35 weeks) of the third trimester. The aberrant hyper-covariance may drive acceleration of cortical folding as a compensatory mechanism to "catch-up" with normal development. By 40 weeks, preterm neonates with/without severe brain injuries exhibited no difference in cortical folding and covariance compared with healthy term neonates. However, graph theory-based analysis showed that even after recovery, severely injured brains exhibit a more segregated, less integrated, and overall inefficient network system with reduced integration strength in the dorsal attention, frontoparietal, limbic, and visual network systems. Ultimately, severe perinatal injuries cause network-level deviations that persist until the late stage of the third trimester and may contribute to neurofunctional impairment.

Morphofunctional Alterations of the Hypothalamus and Social Behavior in Autism Spectrum Disorders

An accumulating body of evidence indicates a tight relationship between the endocrine system and abnormal social behavior. Two evolutionarily conserved hypothalamic peptides, oxytocin and arginine-vasopressin, because of their extensively documented function in supporting and regulating affiliative and socio-emotional responses, have attracted great interest for their critical implications for autism spectrum disorders (ASD). A large number of controlled trials demonstrated that exogenous oxytocin or arginine-vasopressin administration can mitigate social behavior impairment in ASD. Furthermore, there exists long-standing evidence of severe socioemotional dysfunctions after hypothalamic lesions in animals and humans. However, despite the major role of the hypothalamus for the synthesis and release of oxytocin and vasopressin, and the evident hypothalamic implication in affiliative behavior in animals and humans, a rather small number of neuroimaging studies showed an association between this region and socioemotional responses in ASD. This review aims to provide a critical synthesis of evidences linking alterations of the hypothalamus with impaired social cognition and behavior in ASD by integrating results of both anatomical and functional studies in individuals with ASD as well as in healthy carriers of oxytocin receptor (OXTR) genetic risk variant for ASD. Current findings, although limited, indicate that morphofunctional anomalies are implicated in the pathophysiology of ASD and call for further investigations aiming to elucidate anatomical and functional properties of hypothalamic nuclei underlying atypical socioemotional behavior in ASD.

Targeting Gamma-Related Pathophysiology in Autism Spectrum Disorder Using Transcranial Electrical Stimulation: Opportunities and Challenges

A range of scalp electroencephalogram (EEG) abnormalities correlates with the core symptoms of autism spectrum disorder (ASD). Among these are alterations of brain oscillations in the gamma-frequency EEG band in adults and children with ASD, whose origin has been linked to dysfunctions of inhibitory interneuron signaling. While therapeutic interventions aimed to modulate gamma oscillations are being tested for neuropsychiatric disorders such as schizophrenia, Alzheimer's disease, and frontotemporal dementia, the prospects for therapeutic gamma modulation in ASD have not been extensively studied. Accordingly, we discuss gamma-related alterations in the setting of ASD pathophysiology, as well as potential interventions that can enhance gamma oscillations in patients with ASD. Ultimately, we argue that transcranial electrical stimulation modalities capable of entraining gamma oscillations, and thereby potentially modulating inhibitory interneuron circuitry, are promising methods to study and mitigate gamma alterations in ASD. Autism Res 2020, 13: 1051-1071. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Brain functions are mediated by various oscillatory waves of neuronal activity, ranging in amplitude and frequency. In certain neuropsychiatric disorders, such as schizophrenia and Alzheimer's disease, reduced high-frequency oscillations in the "gamma" band have been observed, and therapeutic interventions to enhance such activity are being explored. Here, we review and comment on evidence of reduced gamma activity in ASD, arguing that modalities used in other disorders may benefit individuals with ASD as well.

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.

Age-related differences in white matter diffusion measures in autism spectrum condition

BACKGROUND

Autism spectrum condition (ASC) is accompanied by developmental differences in brain anatomy and connectivity. White matter differences in ASC have been widely studied with diffusion imaging but results are heterogeneous and vary across the age range of study participants and varying methodological approaches. To characterize the neurodevelopmental trajectory of white matter maturation, it is necessary to examine a broad age range of individuals on the autism spectrum and typically developing controls, and investigate age × group interactions.

METHODS

Here, we employed a spatially unbiased tract-based spatial statistics (TBSS) approach to examine age-related differences in white matter connectivity in a sample of 41 individuals with ASC, and 41 matched controls between 7-17 years of age.

RESULTS

We found significant age-related differences between the ASC and control group in widespread brain regions. This included age-related differences in the uncinate fasciculus, corticospinal tract, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, anterior thalamic radiation, superior longitudinal fasciculus and forceps major. Measures of fractional anisotropy (FA) were significantly positively associated with age in both groups. However, this relationship was significantly stronger in the ASC group relative to controls. Measures of radial diffusivity (RD) were significantly negatively associated with age in both groups, but this relationship was significantly stronger in the ASC group relative to controls.

LIMITATIONS

The generalisability of our findings is limited by the restriction of the sample to right-handed males with an IQ > 70. Furthermore, a longitudinal design would be required to fully investigate maturational processes across this age group.

CONCLUSIONS

Taken together, our findings suggest that autistic males have an altered trajectory of white matter maturation relative to controls. Future longitudinal analyses are required to further characterize the extent and time course of these differences.

Early brain abnormalities in infants born very preterm predict under-reactive temperament

BACKGROUND

Early temperament may mediate the association between brain abnormalities following preterm birth and neurodevelopmental outcomes.

AIMS

This exploratory study investigated whether brain abnormalities in infants born very preterm predicted temperament.

STUDY DESIGN

Infants born prematurely underwent structural MRI at term. Mother self-reported depression symptoms at the scanning visit, and the Infant Behavior Questionnaire-Revised-Short (IBQ-R-S) about their infant at 3-months corrected age.

SUBJECTS

Infants (n = 214) born at ≤32 weeks gestation (M = 29.29, SD = 2.60). Average post-menstrual age at the MRI scan was 42.72 weeks (SD = 1.30). The majority of the infants were male (n = 115), and Caucasian (n = 145) or African American (n = 58). The average birthweight in grams was 1289.75 (SD = 448.5).

OUTCOME MEASURES

Infant Behavior Questionnaire-Revised-Short (IBQ-R-S) subscales.

RESULTS

Multivariate regression showed white matter abnormalities predicted lower ratings on High Intensity Pleasure and Vocal Reactivity, grey matter abnormalities predicted lower ratings on High Intensity Pleasure and Cuddliness, and cerebellar abnormalities predicted lower ratings on Fear and Sadness IBQ-R-S subscales adjusting for gestational age and sex. The pattern of results was essentially unchanged when maternal depression and socioeconomic status were included in the model.

CONCLUSIONS

Early MRI-diagnosed brain abnormalities in infants born very preterm were associated less vocalization and engagement during cuddling, decreased ability to take pleasure in stimulating activities, and lower emotionality in fear and sadness domains. Although replication is warranted, an under-reactive temperament in infants born preterm may have a neurobiological basis.

Brain imaging findings in children and adolescents with mental disorders: A cross-sectional review

Background

While brain imaging studies of juvenile patients has expanded in recent years to investigate the cerebral neurophysiologic correlates of psychiatric disorders, this research field remains scarce. The aim of the present review was to cluster the main mental disorders according to the differential brain location of the imaging findings recently reported in children and adolescents reports. A second objective was to describe the worldwide distribution and the main directions of the recent magnetic resonance imaging (MRI) and positron tomography (PET) studies in these patients.

Methods

A survey of 423 MRI and PET articles published between 2005 and 2008 was performed. A principal component analysis (PCA), then an activation likelihood estimate (ALE) meta-analysis, were applied on brain regional information retrieved from articles in order to cluster the various disorders with respect to the cerebral structures where alterations were reported. Furthermore, descriptive analysis characterized the literature production.

Results

Two hundred and seventy-four articles involving children and adolescent patients were analyzed. Both the PCA and ALE methods clustered, three groups of diagnosed psychiatric disorders, according to the brain structural and functional locations: one group of affective disorders characterized by abnormalities of the frontal-limbic regions; a group of mental disorders with “cognition deficits” mainly related to cortex abnormalities; and one psychomotor condition associated with abnormalities in the basal ganglia. The descriptive analysis indicates a focus on attention deficit hyperactivity disorders and autism spectrum disorders, a general steady rise in the number of annual reports, and lead of US research.

Conclusion

This cross-sectional review of child and adolescent mental disorders based on neuroimaging findings suggests overlaps of brain locations that allow to cluster the diagnosed disorders into three sets with respectively marked affective, cognitive, and psychomotor phenomenology. Furthermore, the brain imaging research effort was unequally distributed across disorders, and did not reflect their prevalence.

Atypical Brain Structures as a Function of Gray Matter Volume (GMV) and Gray Matter Density (GMD) in Young Adults Relating to Autism Spectrum Traits

Individuals with autistic traits are those who present in the normal population with characteristics of social, communication, personality, and cognitive impairments but do not meet the clinical threshold for autism spectrum disorder (ASD). Most studies have focused on the abnormalities in ASD patients rather than on individuals with autistic traits. In this study, we focused on the behaviors of a large sample (N = 401) of Chinese individuals with different levels of autistic traits, measured using the Autism Spectrum Quotient, and applied voxel-based morphometry (VBM) to determine their association to differences in brain structure. The results mainly showed that the correlation between gray matter volume (GMV) and gray matter density of the brain and the Autism Spectrum Quotient was significant in these regions: the right middle frontal gyrus, which are involved in social processing and social reasoning; the left parahippocampal gyrus, which is involved in socioemotional behaviors and unconscious relational memory encoding; and the right superior parietal lobule, which are involved in cognitive control and the ability to show attention to detail. These findings reveal that people with autistic traits in the normal population have atypical development in GMV and gray matter density, which may affect their social functioning and communication ability.

"Motion or Emotion? Recognition of Emotional Bodily Expressions in Children With Autism Spectrum Disorder With and Without Intellectual Disability"

The recognition of emotional body movement (BM) is impaired in individuals with Autistic Spectrum Disorder ASD, yet it is not clear whether the difficulty is related to the encoding of body motion, emotions, or both. Besides, BM recognition has been traditionally studied using point-light displays stimuli (PLDs) and is still underexplored in individuals with ASD and intellectual disability (ID). In the present study, we investigated the recognition of happy, fearful, and neutral BM in children with ASD with and without ID. In a non-verbal recognition task, participants were asked to recognize pure-body-motion and visible-body-form stimuli (by means of point-light displays-PLDs and full-light displays-FLDs, respectively). We found that the children with ASD were less accurate than TD children in recognizing both the emotional and neutral BM, either when presented as FLDs or PLDs. These results suggest that the difficulty in understanding the observed BM may rely on atypical processing of BM information rather than emotion. Moreover, we found that the accuracy improved with age and IQ only in children with ASD without ID, suggesting that high level of cognitive resources can mediate the acquisition of compensatory mechanisms which develop with age.

A network clustering based feature selection strategy for classifying autism spectrum disorder

BACKGROUND

Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.

METHODS

In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification.

RESULTS

The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network.

CONCLUSION

It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

Maturational Changes in Human Dorsal and Ventral Visual Networks

Developmental neuroimaging studies report the emergence of increasingly diverse cognitive functions as closely entangled with a rise-fall modulation of cortical thickness (CTh), structural cortical and white-matter connectivity, and a time-course for the experience-dependent selective elimination of the overproduced synapses. We examine which of two visual processing networks, the dorsal (DVN; prefrontal, parietal nodes) or ventral (VVN; frontal-temporal, fusiform nodes) matures first, thus leading the neuro-cognitive developmental trajectory. Three age-dependent measures are reported: (i) the CTh at network nodes; (ii) the matrix of intra-network structural connectivity (edges); and (iii) the proficiency in network-related neuropsychological tests. Typically developing children (age ~6 years), adolescents (~11 years), and adults (~21 years) were tested using multiple-acquisition structural T1-weighted magnetic resonance imaging (MRI) and neuropsychology. MRI images reconstructed into a gray/white/pial matter boundary model were used for CTh evaluation. No significant group differences in CTh and in the matrix of edges were found for DVN (except for the left prefrontal), but a significantly thicker cortex in children for VVN with reduced prefrontal ventral-fusiform connectivity and with an abundance of connections in adolescents. The higher performance in children on tests related to DVN corroborates the age-dependent MRI structural connectivity findings. The current findings are consistent with an earlier maturational course of DVN.

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.

Diminished single-stimulus response in vmPFC to favorite people in children diagnosed with Autism Spectrum Disorder

From an early age, individuals with autism spectrum disorder (ASD) spend less time engaged in social interaction compared to typically developing peers (TD). One reason behind this behavior may be that the brains of children diagnosed with ASD do not attribute enough value to potential social exchanges as compared to the brains of typically developing children; thus, potential social exchanges are avoided because other environmental stimuli are more highly valued by default. Neurobiological investigations into the mechanisms underlying value-based decision-making has shown that the ventral medial prefrontal cortex (vmPFC) is critical for encoding the expected outcome value of different actions corresponding to distinct environmental cues. Here, we tested the hypothesis that the responsiveness of the vmPFC in children diagnosed with ASD (compared to TD controls) is diminished for visual cues that represent highly valued social interaction. Using a passive picture viewing task and functional magnetic resonance imaging (fMRI) we measured the response of an a priori defined region of interest in the vmPFC in children diagnosed with ASD and an age-matched TD cohort. We show that the average response of the vmPFC is significantly diminished in the ASD group. Further, we demonstrate that a single-stimulus and less than 30 s of fMRI data are sufficient to differentiate the ASD and TD cohorts. These findings are consistent with the hypothesis that the brains of children with ASD do not encode the value of social exchange in the same manner as TD children. The latter finding suggests the possibility of utilizing single-stimulus fMRI as a potential biologically based diagnostic tool to augment traditional clinical approaches.

The Understanding Capacity and Information Dynamics in the Human Brain

This article proposes a theory of neuronal processes underlying cognition, focusing on the mechanisms of understanding in the human brain. Understanding is a product of mental modeling. The paper argues that mental modeling is a form of information production inside the neuronal system extending the reach of human cognition "beyond the information given" (Bruner, J.S., Beyond the Information Given, 1973). Mental modeling enables forms of learning and prediction (learning with understanding and prediction via explanation) that are unique to humans, allowing robust performance under unfamiliar conditions having no precedents in the past history. The proposed theory centers on the notions of self-organization and emergent properties of collective behavior in the neuronal substrate. The theory motivates new approaches in the design of intelligent artifacts (machine understanding) that are complementary to those underlying the technology of machine learning.

White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models

Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for autism spectrum disorder (ASD). In this study, we examined the structural connectome of children with ASD using edge density imaging (EDI), and then applied machine-learning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8-12 years were included: 14 with ASD and 33 typically developing children. The edge density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging. Tract-based spatial statistics was used for voxel-wise comparison and coregistration of ED maps in addition to conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine-learning models: naïve Bayes, random forest, support vector machines (SVMs), and neural networks. For these models, cross-validation was performed with stratified random sampling ( × 1,000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared with those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%) and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD, and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.

Reduction in grey matter volume and its correlation with clinical symptoms in Chinese boys with low functioning autism spectrum disorder

BACKGROUND

Previous studies have reported changes within grey matter (GM) volume in children with autism spectrum disorder (ASD); however, little is known about GM volume changes and the relation with symptom severity in Chinese boys with low functioning autism spectrum disorder (LFASD).

METHOD

GM volume was analysed using SPM 8 and compared between 16 boys with LFASD as well as 16 typically developing (TD) boys (using REST 1.8). Additionally, the correlation between GM volume and clinical symptoms was analysed, using subscales within the Autism Behaviour Checklist (ABC).

RESULTS

The comparison showed a reduced volume of GM in 11 clusters in the boys with LFASD (i.e., the left inferior frontal gyrus, orbital part; right superior temporal gyrus, superior frontal gyrus, dorsolateral; precuneus and postcentral; bilateral rectus and middle temporal gyrus) and 1 area with increased GM volume (right caudate) compared to the TD group. Additionally, the GM volume of the left inferior frontal gyrus, orbital part was negatively correlated with the Social subscale score of the ABC (r = -0.765, P = 0.002), and the GM volume of the left Rectus was negatively associated with the Language, Body concept and Self-care subscale scores and the total score on the ABC(r = -0.624, P = 0.023; r = -0.657, P = 0.011; r = -0.618, P = 0.025; r = -0.625, P = 0.022). Further, the GM volume of the right Caudate was negatively correlated with the Sensory subscale on the ABC (r = -0.593, P = 0.033).

CONCLUSION

In conclusion, the current study's findings display that the GM volume was widely reduced in Chinese boys with LFASD compared to TD boys and negatively correlated with the clinical symptoms, indicating a possible pathological mechanism of LFASD.

Cerebrospinal fluid and the early brain development of autism

BACKGROUND

There is currently a renaissance of interest in the many functions of cerebrospinal fluid (CSF). Altered flow of CSF, for example, has been shown to impair the clearance of pathogenic inflammatory proteins involved in neurodegenerative diseases, such as amyloid-β. In addition, the role of CSF in the newly discovered lymphatic system of the brain has become a prominently researched area in clinical neuroscience, as CSF serves as a conduit between the central nervous system and immune system.

MAIN BODY

This article will review the importance of CSF in regulating normal brain development and function, from the prenatal period throughout the lifespan, and highlight recent research that CSF abnormalities in autism spectrum disorder (ASD) are present in infancy, are detectable by conventional structural MRI, and could serve as an early indicator of altered neurodevelopment.

CONCLUSION

The identification of early CSF abnormalities in children with ASD, along with emerging knowledge of the underlying pathogenic mechanisms, has the potential to serve as early stratification biomarkers that separate children with ASD into biological subtypes that share a common pathophysiology. Such subtypes could help parse the phenotypic heterogeneity of ASD and map on to targeted, biologically based treatments.

Support Vector Machines, Multidimensional Scaling and Magnetic Resonance Imaging Reveal Structural Brain Abnormalities Associated With the Interaction Between Autism Spectrum Disorder and Sex

Despite substantial efforts, it remains difficult to identify reliable neuroanatomic biomarkers of autism spectrum disorder (ASD) based on magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Studies which use standard statistical methods to approach this task have been hampered by numerous challenges, many of which are innate to the mathematical formulation and assumptions of general linear models (GLM). Although the potential of alternative approaches such as machine learning (ML) to identify robust neuroanatomic correlates of psychiatric disease has long been acknowledged, few studies have attempted to evaluate the abilities of ML to identify structural brain abnormalities associated with ASD. Here we use a sample of 110 ASD patients and 83 typically developing (TD) volunteers (95 females) to assess the suitability of support vector machines (SVMs, a robust type of ML) as an alternative to standard statistical inference for identifying structural brain features which can reliably distinguish ASD patients from TD subjects of either sex, thereby facilitating the study of the interaction between ASD diagnosis and sex. We find that SVMs can perform these tasks with high accuracy and that the neuroanatomic correlates of ASD identified using SVMs overlap substantially with those found using conventional statistical methods. Our results confirm and establish SVMs as powerful ML tools for the study of ASD-related structural brain abnormalities. Additionally, they provide novel insights into the volumetric, morphometric, and connectomic correlates of this epidemiologically significant disorder.

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.

The effect of age on vertex-based measures of the grey-white matter tissue contrast in autism spectrum disorder

Background

Histological evidence suggests that autism spectrum disorder (ASD) is accompanied by a reduced integrity of the grey-white matter boundary. This has also recently been confirmed by a structural neuroimaging study in vivo reporting significantly reduced grey-white matter tissue contrast (GWC) in adult individuals (18–42 years of age) with ASD relative to typically developing (TD) controls. However, it remains unknown whether the neuroanatomical differences in ASD at the grey-white matter boundary are stable across development or are age-dependent.

Methods

Here, we examined differences in the neurodevelopmental trajectories of GWC in a cross-sectional sample of 77 male ASD individuals and 76 typically developing (TD) controls across childhood and early adulthood (from 7 to 25 years).

Results

Using nested model comparisons, we first established that the developmental trajectory of GWC is complex in many regions across the cortex and includes linear and non-linear effects of age. Second, while ASD individuals have significantly reduced GWC overall, these differences are age-dependent and are most prominent during childhood (< 15 years).

Conclusions

Taken together, our findings suggest that differences in GWC in ASD are unlikely to reflect atypical grey matter cytoarchitecture alone, but may also represent other aspects of the cortical architecture such as age-dependent variability in myelin integrity.

Electronic supplementary material

The online version of this article (10.1186/s13229-018-0232-6) contains supplementary material, which is available to authorized users.