Outcome classification of preschool children with autism spectrum disorders using MRI brain measures

Review of structural neuroimaging and genetic findings in autism spectrum disorder - a clinical perspective

Autism spectrum disorders (ASDs) are neurodevelopmental conditions characterized by deficits in social relationships and communication and restrictive, repetitive behaviors and interests. ASDs form a heterogeneous group from a clinical and genetic perspective. Currently, ASDs diagnosis is based on the clinical observation of the individual's behavior. The subjective nature of behavioral diagnoses, in the context of ASDs heterogeneity, contributes to significant variation in the age at ASD diagnosis. Early detection has been proved to be critical in ASDs, as early start of appropriate therapeutic interventions greatly improve the outcome for some children. Structural magnetic resonance imaging (MRI) is widely used in the diagnostic work-up of neurodevelopmental conditions, including ASDs, mostly for brain malformations detection. Recently, the focus of brain imaging shifted towards quantitative MRI parameters, aiming to identify subtle changes that may establish early detection biomarkers. ASDs have a strong genetic component; deletions and duplications of several genomic loci have been strongly associated with ASDs risk. Consequently, a multitude of neuroimaging and genetic findings emerged in ASDs in the recent years. The association of gross or subtle changes in brain morphometry and volumes with different genetic defects has the potential to bring new insights regarding normal development and pathomechanisms of various disorders affecting the brain. Still, the clinical implications of these discoveries and the impact of genetic abnormalities on brain structure and function are unclear. Here we review the literature on brain imaging correlated with the most prevalent genomic imbalances in ASD, and discuss the potential clinical impact.

Self-supervised learning with application for infant cerebellum segmentation and analysis

Accurate tissue segmentation is critical to characterize early cerebellar development in the first two postnatal years. However, challenges in tissue segmentation arising from tightly-folded cortex, low and dynamic tissue contrast, and large inter-site data heterogeneity have limited our understanding of early cerebellar development. In this paper, we propose an accurate self-supervised learning framework for infant cerebellum segmentation. We validate its accuracy using 358 subjects from three datasets. Our results suggest the first six months exhibit the most rapid and dynamic changes, with gray matter (GM) playing a dominant role in cerebellar growth over white matter (WM). We also find both GM and WM volumes are larger in males than females, and GM and WM volumes are larger in autistic males than neurotypical males. Application of our method to a larger population will fuel more cerebellar studies, ultimately advancing our comprehension of its structure and function in neurotypical and disordered development.

Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data

Multimodal imaging studies targeting preschoolers and low-functioning autism spectrum disorder (ASD) patients are scarce. We applied machine learning classifiers to parameters from T1-weighted MRI and DTI data of 58 children with ASD (age 3-6 years) and 48 typically developing controls (TDC). Classification performance reached an accuracy, sensitivity, and specificity of 88.8%, 93.0%, and 83.8%, respectively. The most prominent features were the cortical thickness of the right inferior occipital gyrus, mean diffusivity of the middle cerebellar peduncle, and nodal efficiency of the left posterior cingulate gyrus. Machine learning-based analysis of MRI data was useful in distinguishing low-functioning ASD preschoolers from TDCs. Combination of T1 and DTI improved classification accuracy about 10%, and large-scale multi-modal MRI studies are warranted for external validation.

Automatic detection of autism spectrum disorder (ASD) in children using structural magnetic resonance imaging with machine vision system

Background

Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system whose main manifestations are defects in social interactions, communication, repetitive behaviors, and limited interests. Over the years, the use of magnetic resonance imaging (MRI) to help identify patterns that are common in people with autism has increased for classification purposes. This study propose a method for classifying ASD patients versus controls using structural MRI information. In order to increase the accuracy of this method, the volume and surface features of the structural images are used simultaneously.

Results

The accuracy of diagnosis respectively was 86.29%, 71.15%, 86.53%, and 88.46% with SVM, RF, KNN, and ANN classifiers. The highest accuracy of diagnosis was obtained using ANN.

Conclusions

Since clinical evaluations for the diagnosis of autism are extremely time-consuming and depend on the expertise of a specialist, the importance of intelligent diagnosis of this disorder becomes clear. The aim of this study was to design an intelligent system to diagnose autism spectrum disorder.

A narrative review of MRI changes correlated to signs and symptoms of autism

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that occurs during early childhood. The change from being normal across several contexts to displaying the behavioral phenotype of ASD occurs in infants and toddlers with autism. Findings provided by magnetic resonance imaging (MRI)-based research owing to the developmental phase, including potential pathways underlying the pathogenesis of the condition and the potential for signs and symptomatic risk prediction. The present study focuses on the characteristic features of magnetic resonance imaging autistic brain, how these changes are correlated to autism signs and symptoms and the implications of MRI as a potential tool for the early diagnosis of ASD. PRISMA style was used to conduct this review. Research articles related to the key concepts of this review, which is looking at MRI brain changes in autistic patients, were revised and incorporated with what is known with the pathophysiology of brain regions in relation to signs and symptoms of autism. Studies on brain MRI of autism were revied for major brain features and regions such as brain volume, cortex and hippocampus. This review reveals that brain changes seen in MRI are highly correlated with the signs and symptoms of autism. There are numerous distinct features noted in an autistic brain using MRI. Based on these findings, various developmental brain paths and autistic behavior culminate in a typical diagnosis, and it is possible that addressing these trajectories would improve the accuracy in which children are detected and provide the necessary treatment.

Deficiency of nde1 in zebrafish induces brain inflammatory responses and autism-like behavior

The cytoskeletal protein NDE1 plays an important role in chromosome segregation, neural precursor differentiation, and neuronal migration. Clinical studies have shown that NDE1 deficiency is associated with several neuropsychiatric disorders including autism. Here, we generated nde1 homologous deficiency zebrafish (nde1^−/−) to elucidate the cellular molecular mechanisms behind it. nde1^−/− exhibit increased neurological apoptotic responses at early infancy, enlarged ventricles, and shrank valvula cerebelli in adult brain tissue. Behavioral analysis revealed that nde1^−/− displayed autism-like behavior traits such as increased locomotor activity and repetitive stereotype behaviors and impaired social and kin recognition behaviors. Furthermore, nde1 mRNA injection rescued apoptosis in early development, and minocycline treatment rescued impaired social behavior and overactive motor activity by inhibiting inflammatory cytokines. In this study, we revealed that nde1 homozygous deletion leads to abnormal neurological development with autism-related behavioral phenotypes and that inflammatory responses in the brain are an important molecular basis behind it.

•

nde1^−/− zebrafish display autism-like behavior features

•

nde1 deficiency results in immune responses in the brain

•

Minocycline treatment inhibits immune responses in the adult nde1^−/− brain

•

Minocycline rescued the impaired social behavior and locomotor activity

Cerebellar and Striatal Implications in Autism Spectrum Disorders: From Clinical Observations to Animal Models

Autism spectrum disorders (ASD) are complex conditions that stem from a combination of genetic, epigenetic and environmental influences during early pre- and postnatal childhood. The review focuses on the cerebellum and the striatum, two structures involved in motor, sensory, cognitive and social functions altered in ASD. We summarize clinical and fundamental studies highlighting the importance of these two structures in ASD. We further discuss the relation between cellular and molecular alterations with the observed behavior at the social, cognitive, motor and gait levels. Functional correlates regarding neuronal activity are also detailed wherever possible, and sexual dimorphism is explored pointing to the need to apprehend ASD in both sexes, as findings can be dramatically different at both quantitative and qualitative levels. The review focuses also on a set of three recent papers from our laboratory where we explored motor and gait function in various genetic and environmental ASD animal models. We report that motor and gait behaviors can constitute an early and quantitative window to the disease, as they often correlate with the severity of social impairments and loss of cerebellar Purkinje cells. The review ends with suggestions as to the main obstacles that need to be surpassed before an appropriate management of the disease can be proposed.

Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder

Empirical evidence suggests that children with autism spectrum disorder (ASD) show abnormal behavior during delay eyeblink conditioning. They show a higher conditioned response learning rate and earlier peak latency of the conditioned response signal. The neuronal mechanisms underlying this autistic behavioral phenotype are still unclear. Here, we use a physiologically constrained spiking neuron model of the cerebellar-cortical system to investigate which features are critical to explaining atypical learning in ASD. Significantly, the computer simulations run with the model suggest that the higher conditioned responses learning rate mainly depends on the reduced number of Purkinje cells. In contrast, the earlier peak latency mainly depends on the hyper-connections of the cerebellum with sensory and motor cortex. Notably, the model has been validated by reproducing the behavioral data collected from studies with real children. Overall, this article is a starting point to understanding the link between the behavioral and neurobiological basis in ASD learning. At the end of the paper, we discuss how this knowledge could be critical for devising new treatments.

Different Eye Tracking Patterns in Autism Spectrum Disorder in Toddler and Preschool Children

BACKGROUND

Children with autism spectrum disorder (ASD) have been observed to be associated with fixation abnormality as measured eye tracking, but the dynamics behind fixation patterns across age remain unclear.

MATERIALS AND METHODS

In this study, we investigated gaze patterns between toddlers and preschoolers with and without ASD while they viewed video clips and still images (i.e., mouth-moving face, biological motion, mouthing face vs. moving object, still face picture vs. objects, and moving toys).

RESULTS

We found that the fixation time percentage of children with ASD showed significant decrease compared with that of TD children in almost all areas of interest (AOI) except for moving toy (helicopter). We also observed a diagnostic group (ASD vs. TD) and chronological age (Toddlers vs. preschooler) interaction for the eye AOI during the mouth-moving video clip. Support vector machine analysis showed that the classifier could discriminate ASD from TD in toddlers with an accuracy of 80% and could discriminate ASD from TD in preschoolers with an accuracy of 71%.

CONCLUSION

Our results suggest that toddlers and preschoolers may be associated with both common and distinct fixation patterns. A combination of eye tracking and machine learning methods has the potential to shed light on the development of new early screening/diagnosis methods for ASD.

Unique Morphometric Features of the Cerebellum and Cerebellocerebral Structural Correlation Between Autism Spectrum Disorder and Schizophrenia

Background

Although cerebellar morphological involvement has been increasingly recognized in autism spectrum disorder (ASD) and schizophrenia (SZ), the extent to which there are morphological differences between them has not been definitively quantified. Furthermore, although previous studies have demonstrated increased anatomical cerebellocerebral correlations in both conditions, differences between their associations have not been well characterized.

Methods

We compared cerebellar volume between males with ASD (n = 31), males with SZ (n = 28), and typically developing males (n = 49). A total of 31 cerebellar subregions were investigated with the cerebellum segmented into their constituent lobules, in gray matter (GM) and white matter (WM) separately. Additionally, structural correlations with the contralateral cerebrum were analyzed for each cerebellar lobule.

Results

We found significantly larger WM volume in the bilateral lobules VI and Crus I in the ASD group than in other groups. While WM or GM volumes of these right lobules had positive associations with ASD symptoms, there was a negative association between GM volume of the right Crus I and SZ symptoms. We further observed, in the ASD group specifically, significant correlations between WM of the right lobule VI and WM of the left frontal pole (r = 0.67) and between GM of the right lobule VI and the left caudate (r = 0.60).

Conclusions

Our findings support evidence that cerebellar morphology is involved in ASD and SZ with different mechanisms. Furthermore, this study showed that these biological differences require consideration when determining diagnostic criteria and treatment for these disorders.

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.

A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder

Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with >80% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.

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.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Autism spectrum disorder risk prediction: A systematic review of behavioral and neural investigations

A reliable diagnosis of autism spectrum disorder (ASD) is difficult to make until after toddlerhood. Detection in an earlier age enables early intervention, which is typically more effective. Recent studies of the development of brain and behavior in infants and toddlers have provided important insights in the diagnosis of autism. This extensive review focuses on published studies of predicting the diagnosis of autism during infancy and toddlerhood younger than 3 years using behavioral and neuroimaging approaches. After screening a total of 782 papers, 17 neuroimaging and 43 behavioral studies were reviewed. The features for prediction consist of behavioral measures using screening tools, observational and experimental methods, brain volumetric measures, and neural functional activation and connectivity patterns. The classification approaches include logistic regression, linear discriminant function, decision trees, support vector machine, and deep learning based methods. Prediction performance has large variance across different studies. For behavioral studies, the sensitivity varies from 20% to 100%, and specificity ranges from 48% to 100%. The accuracy rates range from 61% to 94% in neuroimaging studies. Possible factors contributing to this inconsistency may be partially due to the heterogeneity of ASD, different targeted populations (i.e., high-risk group for ASD and general population), age when the features were collected, and validation procedures. The translation to clinical practice requires extensive further research including external validation with large sample size and optimized feature selection. The use of multi-modal features, e.g., combination of neuroimaging and behavior, is worth further investigation to improve the prediction accuracy.

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.

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.

Regionally specific TSC1 and TSC2 gene expression in tuberous sclerosis complex

Tuberous sclerosis complex (TSC), a heritable neurodevelopmental disorder, is caused by mutations in the TSC1 or TSC2 genes. To date, there has been little work to elucidate regional TSC1 and TSC2 gene expression within the human brain, how it changes with age, and how it may influence disease. Using a publicly available microarray dataset, we found that TSC1 and TSC2 gene expression was highest within the adult neo-cerebellum and that this pattern of increased cerebellar expression was maintained throughout postnatal development. During mid-gestational fetal development, however, TSC1 and TSC2 expression was highest in the cortical plate. Using a bioinformatics approach to explore protein and genetic interactions, we confirmed extensive connections between TSC1/TSC2 and the other genes that comprise the mammalian target of rapamycin (mTOR) pathway, and show that the mTOR pathway genes with the highest connectivity are also selectively expressed within the cerebellum. Finally, compared to age-matched controls, we found increased cerebellar volumes in pediatric TSC patients without current exposure to antiepileptic drugs. Considered together, these findings suggest that the cerebellum may play a central role in TSC pathogenesis and may contribute to the cognitive impairment, including the high incidence of autism spectrum disorder, observed in the TSC population.

Reduced structural complexity of the right cerebellar cortex in male children with autism spectrum disorder

The cerebellum contains 80% of all neurons in the human brain and contributes prominently to implicit learning and predictive processing across motor, sensory, and cognitive domains. As morphological features of the cerebellum in atypically developing individuals remain unexplored in-vivo, this is the first study to use high-resolution 3D fractal analysis to estimate fractal dimension (FD), a measure of structural complexity of an object, of the left and right cerebellar cortex (automatically segmented from Magnetic Resonance Images using FreeSurfer), in male children with Autism Spectrum Disorders (ASD) (N = 20; mean age: 8.8 years old, range: 7.13-10.27) and sex, age, verbal-IQ, and cerebellar volume-matched typically developing (TD) boys (N = 18; mean age: 8.9 years old, range: 6.47-10.52). We focus on an age range within the 'middle and late childhood' period of brain development, between 6 and 12 years. A Mann-Whitney U test revealed a significant reduction in the FD of the right cerebellar cortex in ASD relative to TD boys (P = 0.0063, Bonferroni-corrected), indicating flatter and less regular surface protrusions in ASD relative to TD males. Consistent with the prediction that the cerebellum participates in implicit learning, those ASD boys with a higher (vs. lower) PIQ>VIQ difference showed higher, more normative complexity values, closer to TD children, providing new insight on our understanding of the neurological basis of differences in verbal and performance cognitive abilities that often characterize individuals with ASD.

The journey to autism: Insights from neuroimaging studies of infants and toddlers

By definition, autism spectrum disorder (ASD) is a neurodevelopmental disorder that emerges during early childhood. It is during this time that infants and toddlers transition from appearing typical across multiple domains to exhibiting the behavioral phenotype of ASD. Neuroimaging studies focused on this period of development have provided crucial knowledge pertaining to this process, including possible mechanisms underlying pathogenesis of the disorder and offering the possibility of prodromal or presymptomatic prediction of risk. In this paper, we review findings from structural and functional brain imaging studies of ASD focused on the first years of life and discuss implications for next steps in research and clinical applications.

Cerebellar Volume in Autism: Literature Meta-analysis and Analysis of the Autism Brain Imaging Data Exchange Cohort

BACKGROUND

The neuroanatomical bases of autism spectrum disorder remain largely unknown. Among the most widely discussed candidate endophenotypes, differences in cerebellar volume have been often reported as statistically significant.

METHODS

We aimed at objectifying this possible alteration by performing a systematic meta-analysis of the literature and an analysis of the ABIDE (Autism Brain Imaging Data Exchange) cohort. Our meta-analysis sought to determine a combined effect size of autism spectrum disorder diagnosis on different measures of the cerebellar anatomy as well as the effect of possible factors of variability across studies. We then analyzed the cerebellar volume of 328 patients and 353 control subjects from the ABIDE project.

RESULTS

The meta-analysis of the literature suggested a weak but significant association between autism spectrum disorder diagnosis and increased cerebellar volume (p = .049, uncorrected), but the analysis of ABIDE did not show any relationship. The studies meta-analyzed were generally underpowered; however, the number of statistically significant findings was larger than expected.

CONCLUSIONS

Although we could not provide a conclusive explanation for this excess of significant findings, our analyses would suggest publication bias as a possible reason. Finally, age, sex, and IQ were important sources of cerebellar volume variability, although independent of autism diagnosis.

Increased Left Inferior Temporal Gyrus Was Found in Both Low Function Autism and High Function Autism

Previous neuroimaging studies of autism spectrum disorder (ASD) have focused on subjects with IQ > 70 or ASD without considering IQ levels. It remains unclear whether differences in brain anatomy in this population are associated with variations in clinical phenotype. In this study, 19 children with low functioning autism (LFA) and 19 children with high functioning autism (HFA) were compared with 27 healthy controls (HC). We found increased gray matter volume (GMV) in the left inferior temporal gyrus in subjects with both HFA and LFA and increased GMV of left middle temporal gyrus BA21 was found only in the LFA group. A significant negative correlation was found between the left inferior temporal gyrus (LITG) and the score of repetitive behavior in the HFA group.

Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction and communication, as well as repetitive and restrictive behaviours. The etiological and phenotypic complexity of ASD has so far hindered the development of clinically useful biomarkers for the condition. Neuroimaging studies have been valuable in establishing a biological basis for ASD. Increasingly, neuroimaging has been combined with 'machine learning'-based pattern classification methods to make individual diagnostic predictions. Moving forward, the hope is that these techniques may not only facilitate the diagnostic process but may also aid in fractionating the ASD phenotype into more biologically homogeneous sub-groups, with defined pathophysiology, predictable outcomes and/or responses to targeted treatments and/or interventions. This review chapter will first introduce 'machine learning' and pattern recognition methods in general, with a focus on their application to diagnostic classification. It will highlight why such approaches to biomarker discovery may have advantages over more conventional analytical methods. Magnetic resonance imaging (MRI) findings of atypical brain structure, function and connectivity in ASD will be briefly reviewed before we describe how pattern recognition has been applied to generate predictive models for ASD. Last, we will discuss some limitations and pitfalls of pattern recognition analyses in ASD and consider how the field can advance beyond the prediction of binary outcomes.

Tracing the temporal stability of autism spectrum diagnosis and severity as measured by the Autism Diagnostic Observation Schedule: A systematic review and meta-analysis

Background

Exploring ways to improve the trajectory and symptoms of autism spectrum disorder is prevalent in research, but less is known about the natural prognosis of autism spectrum disorder and course of symptoms. The objective of this study was to examine the temporal stability of autism spectrum disorder and autism diagnosis, and the longitudinal trajectories of autism core symptom severity. We furthermore sought to identify possible predictors for change.

Methods

We searched PubMed, PsycInfo, EMBASE, Web of Science, Cochrane Library up to October 2015 for prospective cohort studies addressing the autism spectrum disorder/autism diagnostic stability, and prospective studies of intervention effects. We included people of all ages with autism spectrum disorder/autism or at risk of having autism spectrum disorder, who were diagnosed and followed up for at least 12 months using the Autism Diagnostic Observation Schedule (ADOS). Both continuous ADOS scores and dichotomous diagnostic categories were pooled in random-effects meta-analysis and meta-regression.

Results

Of 1443 abstracts screened, 44 were eligible of which 40 studies contained appropriate data for meta-analysis. A total of 5771 participants from 7 months of age to 16.5 years were included. Our analyses showed no change in ADOS scores across time as measured by Calibrated Severity Scores (mean difference [MD] = 0.05, 95% CI -0.26 to 0.36). We observed a minor but statistically significant change in ADOS total raw scores (MD = -1.51, 95% CI -2.70 to -0.32). There was no improvement in restricted and repetitive behaviours (standardised MD [SMD] = -0.04, 95% CI -0.19 to 0.11), but a minor improvement in social affect over time (SMD = -0.31, 95% CI -0.50 to -0.12). No changes were observed for meeting the autism spectrum disorder criteria over time (risk difference [RD] = -0.01, 95% CI -0.03 to 0.01), but a significant change for meeting autism criteria over time (RD = -0.18, 95% CI -0.29 to -0.07). On average, there was a high heterogeneity between studies (I² range: 65.3% to 93.1%).

Discussion

While 18% of participants shifted from autism to autism spectrum disorder diagnosis, the overall autism spectrum disorder prevalence was unchanged.

Overall autism core symptoms were remarkably stable over time across childhood indicating that intervention studies should focus on other areas, such as quality of life and adaptive functioning. However, due to high heterogeneity between studies and a number of limitations in the studies, the results need to be interpreted with caution.

Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder

Children with neurodevelopmental disorders benefit most from early interventions and treatments. The development and validation of brain-based biomarkers to aid in objective diagnosis can facilitate this important clinical aim. The objective of this review is to provide an overview of current progress in the use of neuroimaging to identify brain-based biomarkers for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), two prevalent neurodevelopmental disorders. We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting limitations of the data currently available. The most successful machine learning methods that have been developed and applied to date are discussed. Overall, there is increasing evidence that specific features (for example, functional connectivity, gray matter volume) of brain regions comprising the salience and default mode networks can be used to discriminate ASD from typical development. Brain regions contributing to successful discrimination of ADHD from typical development appear to be more widespread, however there is initial evidence that features derived from frontal and cerebellar regions are most informative for classification. The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental disorders. At present, however, the field has yet to identify reliable and reproducible biomarkers for these disorders, and must address issues related to clinical heterogeneity, methodological standardization and cross-site validation before further progress can be achieved.

Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies

Searching for effective biomarkers is one of the most challenging tasks in the research field of Autism Spectrum Disorder (ASD). Magnetic resonance imaging (MRI) provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation, connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and large-scale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder mainly showed atypical social interaction, communication, and restricted, repetitive patterns of behavior, interests and activities. Now clinic diagnosis of ASD is mostly based on psychological evaluation, clinical observation and medical history. All these behavioral indexes could not avoid defects such as subjectivity and reporter-dependency. Therefore researchers devoted themselves to seek relatively stable biomarkers of ASD as supplementary diagnostic evidence. The goal of present study is to generate relatively stable predictive model based on anatomical brain features by using machine learning technique. Forty-six ASD children and thirty-nine development delay children aged from 18 to 37 months were evolved in. As a result, the predictive model generated by regional average cortical thickness of regions with top 20 highest importance of random forest classifier showed best diagnostic performance. And random forest was proved to be the optimal approach for neuroimaging data mining in small size set and thickness-based classification outperformed volume-based classification and surface area-based classification in ASD. The brain regions selected by the models might attract attention and the idea of considering biomarkers as a supplementary evidence of ASD diagnosis worth exploring. Autism Res 2017, 0: 000-000. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 620-630. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey

Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. Multiple MRI modalities, such as different types of the sMRI and DTI, have been employed to investigate facets of ASD in order to better understand this complex syndrome. This paper reviews recent applications of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI), to study autism spectrum disorder (ASD). Main reported findings are sometimes contradictory due to different age ranges, hardware protocols, population types, numbers of participants, and image analysis parameters. The primary anatomical structures, such as amygdalae, cerebrum, and cerebellum, associated with clinical-pathological correlates of ASD are highlighted through successive life stages, from infancy to adulthood. This survey demonstrates the absence of consistent pathology in the brains of autistic children and lack of research investigations in patients under 2 years of age in the literature. The known publications also emphasize advances in data acquisition and analysis, as well as significance of multimodal approaches that combine resting-state, task-evoked, and sMRI measures. Initial results obtained with the sMRI and DTI show good promise toward the early and non-invasive ASD diagnostics.

Localized Misfolding Within Broca's Area as a Distinctive Feature of Autistic Disorder

BACKGROUND

Recent neuroimaging studies suggest that autism spectrum disorder results from abnormalities in the cortical folding pattern. Usual morphometric measurements have failed to provide reliable neuroanatomic markers. Here, we propose that sulcal pits, which are the deepest points in each fold, are suitable candidates to uncover this atypical cortical folding.

METHODS

Sulcal pits were extracted from a magnetic resonance imaging database of 102 children (1.5-10 years old) distributed in three groups: children with autistic disorder (n = 59), typically developing children (n = 22), and children with pervasive developmental disorder not otherwise specified (n = 21). The geometrical properties of sulcal pits were compared between these three groups.

RESULTS

Fold-level analyses revealed a reduced pit depth in the left ascending ramus of the Sylvian fissure in children with autistic disorder only. The depth of this central fold of Broca's area was correlated with the social communication impairments that are characteristic of the pathology.

CONCLUSIONS

Our findings support an atypical gyrogenesis of this specific fold in autistic disorder that could be used for differential diagnosis. Sulcal pits constitute valuable markers of the cortical folding dynamics and could help for the early detection of atypical brain maturation.

Cerebellar Development and Autism Spectrum Disorder in Tuberous Sclerosis Complex

Approximately 50% of patients with the genetic disease tuberous sclerosis complex present with autism spectrum disorder. Although a number of studies have investigated the link between autism and tuberous sclerosis complex, the etiology of autism spectrum disorder in these patients remains unclear. Abnormal cerebellar function during critical phases of development could disrupt functional processes in the brain, leading to development of autistic features. Accordingly, the authors review the potential role of cerebellar dysfunction in the pathogenesis of autism spectrum disorder in tuberous sclerosis complex. The authors also introduce conditional knockout mouse models of Tsc1 and Tsc2 that link cerebellar circuitry to the development of autistic-like features. Taken together, these preclinical and clinical investigations indicate the cerebellum has a profound regulatory role during development of social communication and repetitive behaviors.

Head circumference and brain size in autism spectrum disorder: A systematic review and meta-analysis

Macrocephaly and brain overgrowth have been associated with autism spectrum disorder. We performed a systematic review and meta-analysis to provide an overall estimate of effect size and statistical significance for both head circumference and total brain volume in autism. Our literature search strategy identified 261 and 391 records, respectively; 27 studies defining percentages of macrocephalic patients and 44 structural brain imaging studies providing total brain volumes for patients and controls were included in our meta-analyses. Head circumference was significantly larger in autistic compared to control individuals, with 822/5225 (15.7%) autistic individuals displaying macrocephaly. Structural brain imaging studies measuring brain volume estimated effect size. The effect size is higher in low functioning autistics compared to high functioning and ASD individuals. Brain overgrowth was recorded in 142/1558 (9.1%) autistic patients. Finally, we found a significant interaction between age and total brain volume, resulting in larger head circumference and brain size during early childhood. Our results provide conclusive effect sizes and prevalence rates for macrocephaly and brain overgrowth in autism, confirm the variation of abnormal brain growth with age, and support the inclusion of this endophenotype in multi-biomarker diagnostic panels for clinical use.

Genes, circuits, and precision therapies for autism and related neurodevelopmental disorders

Research in the genetics of neurodevelopmental disorders such as autism suggests that several hundred genes are likely risk factors for these disorders. This heterogeneity presents a challenge and an opportunity at the same time. Although the exact identity of many of the genes remains to be discovered, genes identified to date encode proteins that play roles in certain conserved pathways: protein synthesis, transcriptional and epigenetic regulation, and synaptic signaling. The next generation of research in neurodevelopmental disorders must address the neural circuitry underlying the behavioral symptoms and comorbidities, the cell types playing critical roles in these circuits, and common intercellular signaling pathways that link diverse genes. Results from clinical trials have been mixed so far. Only when we can leverage the heterogeneity of neurodevelopmental disorders into precision medicine will the mechanism-based therapeutics for these disorders start to unlock success.

Cerebro-cerebellar circuits in autism spectrum disorder

The cerebellum is one of the most consistent sites of abnormality in autism spectrum disorder (ASD) and cerebellar damage is associated with an increased risk of ASD symptoms, suggesting that cerebellar dysfunction may play a crucial role in the etiology of ASD. The cerebellum forms multiple closed-loop circuits with cerebral cortical regions that underpin movement, language, and social processing. Through these circuits, cerebellar dysfunction could impact the core ASD symptoms of social and communication deficits and repetitive and stereotyped behaviors. The emerging topography of sensorimotor, cognitive, and affective subregions in the cerebellum provides a new framework for interpreting the significance of regional cerebellar findings in ASD and their relationship to broader cerebro-cerebellar circuits. Further, recent research supports the idea that the integrity of cerebro-cerebellar loops might be important for early cortical development; disruptions in specific cerebro-cerebellar loops in ASD might impede the specialization of cortical regions involved in motor control, language, and social interaction, leading to impairments in these domains. Consistent with this concept, structural, and functional differences in sensorimotor regions of the cerebellum and sensorimotor cerebro-cerebellar circuits are associated with deficits in motor control and increased repetitive and stereotyped behaviors in ASD. Further, communication and social impairments are associated with atypical activation and structure in cerebro-cerebellar loops underpinning language and social cognition. Finally, there is converging evidence from structural, functional, and connectivity neuroimaging studies that cerebellar right Crus I/II abnormalities are related to more severe ASD impairments in all domains. We propose that cerebellar abnormalities may disrupt optimization of both structure and function in specific cerebro-cerebellar circuits in ASD.

Gray Matter Alterations in Young Children with Autism Spectrum Disorders: Comparing Morphometry at the Voxel and Regional Level

BACKGROUND AND PURPOSE

Sophisticated algorithms to infer disease diagnosis, pathology progression and patient outcome are increasingly being developed to analyze brain MRI data. They have been successfully implemented in a variety of diseases and are currently investigated in the field of neuropsychiatric disorders, including autism spectrum disorder (ASD). We aim to test the ability to predict ASD from subtle morphological changes in structural magnetic resonance imaging (sMRI).

METHODS

The analysis of sMRI of a cohort of male ASD children and controls matched for age and nonverbal intelligence quotient (NVIQ) has been carried out with two widely used preprocessing software packages (SPM and Freesurfer) to extract brain morphometric information at different spatial scales. Then, support vector machines have been implemented to classify the brain features and to localize which brain regions contribute most to the ASD-control separation.

RESULTS

The features extracted from the gray matter subregions provide the best classification performance, reaching an area under the receiver operating characteristic curve (AUC) of 74%. This value is enhanced to 80% when considering only subjects with NVIQ over 70.

CONCLUSIONS

Despite the subtle impact of ASD on brain morphology and a limited cohort size, results from sMRI-based classifiers suggest a consistent network of altered brain regions.

Up-Regulation of Oligodendrocyte Lineage Markers in the Cerebellum of Autistic Patients: Evidence from Network Analysis of Gene Expression

Autism is a neurodevelopmental disorder manifested by impaired social interaction, deficits in communication skills, restricted interests, and repetitive behaviors. In neurodevelopmental, neurodegenerative, and psychiatric disorders, glial cells undergo morphological, biochemical, and functional rearrangements, which are critical for neuronal development, neurotransmission, and synaptic connectivity. Cerebellar function is not limited to motor coordination but also contributes to cognition and may be affected in autism. Oligodendrocytes and specifically oligodendroglial precursors are highly susceptible to oxidative stress and excitotoxic insult. In the present study, we searched for evidence for developmental oligodendropathy in the context of autism by performing a network analysis of gene expression of cerebellar tissue. We created an in silico network model (OLIGO) showing the landscape of interactions between oligodendrocyte markers and demonstrated that more than 50 % (16 out of 30) of the genes within this model displayed significant changes of expression (corrected p value <0.05) in the cerebellum of autistic patients. In particular, we found up-regulation of OLIG2-, MBP-, OLIG1-, and MAG-specific oligodendrocyte markers. We postulate that aberrant expression of oligodendrocyte-specific genes, potentially related to changes in oligodendrogenesis, may contribute to abnormal cerebellar development, impaired myelination, and anomalous synaptic connectivity in autism spectrum disorders (ASD).

Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates

Neuroimaging techniques, such as fMRI, structural MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) have uncovered evidence for widespread functional and anatomical brain abnormalities in autism spectrum disorder (ASD) suggesting it to be a system-wide neural systems disorder. Nevertheless, most previous studies have focused on examining one index of neuropathology through a single neuroimaging modality, and seldom using multiple modalities to examine the same cohort of individuals. The current study aims to bring together multiple brain imaging modalities (structural MRI, DTI, and 1H-MRS) to investigate the neural architecture in the same set of individuals (19 high-functioning adults with ASD and 18 typically developing (TD) peers). Morphometry analysis revealed increased cortical thickness in ASD participants, relative to typical controls, across the left cingulate, left pars opercularis of the inferior frontal gyrus, left inferior temporal cortex, and right precuneus, and reduced cortical thickness in right cuneus and right precentral gyrus. ASD adults also had reduced fractional anisotropy (FA) and increased radial diffusivity (RD) for two clusters on the forceps minor of the corpus callosum, revealed by DTI analyses. 1H-MRS results showed a reduction in the N-acetylaspartate/Creatine ratio in dorsal anterior cingulate cortex (dACC) in ASD participants. A decision tree classification analysis across the three modalities resulted in classification accuracy of 91.9% with FA, RD, and cortical thickness as key predictors. Examining the same cohort of adults with ASD and their TD peers, this study found alterations in cortical thickness, white matter (WM) connectivity, and neurochemical concentration in ASD. These findings underscore the potential for multimodal imaging to better inform on the neural characteristics most relevant to the disorder.

Cerebellar gray matter and lobular volumes correlate with core autism symptoms

Neuroanatomical differences in the cerebellum are among the most consistent findings in autism spectrum disorder (ASD), but little is known about the relationship between cerebellar dysfunction and core ASD symptoms. The newly-emerging existence of cerebellar sensorimotor and cognitive subregions provides a new framework for interpreting the functional significance of cerebellar findings in ASD. Here we use two complementary analyses — whole-brain voxel-based morphometry (VBM) and the SUIT cerebellar atlas — to investigate cerebellar regional gray matter (GM) and volumetric lobular measurements in 35 children with ASD and 35 typically-developing (TD) children (mean age 10.4 ± 1.6 years; range 8–13 years). To examine the relationships between cerebellar structure and core ASD symptoms, correlations were calculated between scores on the Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview (ADI) and the VBM and volumetric data. Both VBM and the SUIT analyses revealed reduced GM in ASD children in cerebellar lobule VII (Crus I/II). The degree of regional and lobular gray matter reductions in different cerebellar subregions correlated with the severity of symptoms in social interaction, communication, and repetitive behaviors. Structural differences and behavioral correlations converged on right cerebellar Crus I/II, a region which shows structural and functional connectivity with fronto-parietal and default mode networks. These results emphasize the importance of the location within the cerebellum to the potential functional impact of structural differences in ASD, and suggest that GM differences in cerebellar right Crus I/II are associated with the core ASD profile.

•

The cerebellum is one of the most consistent sites of abnormality in autism.

•

We investigated cerebellar structure in autism using two independent methods.

•

Cerebellar gray matter was reduced in several regions in children with autism.

•

The degree of cerebellar gray matter reduction predicted core autism symptom severity.

•

Structural differences and behavioral correlations converged on cerebellar Crus I/II.

Cerebro-cerebellar circuits in autism spectrum disorder

The cerebellum is one of the most consistent sites of abnormality in autism spectrum disorder (ASD) and cerebellar damage is associated with an increased risk of ASD symptoms, suggesting that cerebellar dysfunction may play a crucial role in the etiology of ASD. The cerebellum forms multiple closed-loop circuits with cerebral cortical regions that underpin movement, language, and social processing. Through these circuits, cerebellar dysfunction could impact the core ASD symptoms of social and communication deficits and repetitive and stereotyped behaviors. The emerging topography of sensorimotor, cognitive, and affective subregions in the cerebellum provides a new framework for interpreting the significance of regional cerebellar findings in ASD and their relationship to broader cerebro-cerebellar circuits. Further, recent research supports the idea that the integrity of cerebro-cerebellar loops might be important for early cortical development; disruptions in specific cerebro-cerebellar loops in ASD might impede the specialization of cortical regions involved in motor control, language, and social interaction, leading to impairments in these domains. Consistent with this concept, structural, and functional differences in sensorimotor regions of the cerebellum and sensorimotor cerebro-cerebellar circuits are associated with deficits in motor control and increased repetitive and stereotyped behaviors in ASD. Further, communication and social impairments are associated with atypical activation and structure in cerebro-cerebellar loops underpinning language and social cognition. Finally, there is converging evidence from structural, functional, and connectivity neuroimaging studies that cerebellar right Crus I/II abnormalities are related to more severe ASD impairments in all domains. We propose that cerebellar abnormalities may disrupt optimization of both structure and function in specific cerebro-cerebellar circuits in ASD.

Diagnosis of autism spectrum disorders using regional and interregional morphological features

This article describes a novel approach to identify autism spectrum disorder (ASD) utilizing regional and interregional morphological patterns extracted from structural magnetic resonance images. Two types of features are extracted to characterize the morphological patterns: (1) Regional features, which includes the cortical thickness, volumes of cortical gray matter, and cortical-associated white matter regions, and several subcortical structures extracted from different regions-of-interest (ROIs); (2) Interregional features, which convey the morphological change pattern between pairs of ROIs. We demonstrate that the integration of regional and interregional features via multi-kernel learning technique can significantly improve the classification performance of ASD, compared with using either regional or interregional features alone. Specifically, the proposed framework achieves an accuracy of 96.27% and an area of 0.9952 under the receiver operating characteristic curve, indicating excellent diagnostic power and generalizability. The best performance is achieved when both feature types are weighted approximately equal, indicating complementary between these two feature types. Regions that contributed the most to classification are in line with those reported in the previous studies, particularly the subcortical structures that are highly associated with human emotional modulation and memory formation. The autistic brains demonstrate a significant rightward asymmetry pattern particularly in the auditory language areas. These findings are in agreement with the fact that ASD is a behavioral- and language-related neurodevelopmental disorder. By concurrent consideration of both regional and interregional features, the current work presents an effective means for better characterization of neurobiological underpinnings of ASD that facilitates its identification from typically developing children.

In vivo detection of reduced Purkinje cell fibers with diffusion MRI tractography in children with autistic spectrum disorders

Postmortem neuropathology studies report reduced number and size of Purkinje cells (PC) in a majority of cerebellar specimens from persons diagnosed with autism spectrum disorders (ASD). We used diffusion weighted MRI tractography to investigate whether structural changes associated with reduced number and size of PC, could be detected in vivo by measuring streamlines connecting the posterior-lateral region of the cerebellar cortex to the dentate nucleus using an independent component analysis with a ball and stick model. Seed regions were identified in the cerebellar cortex, and streamlines were identified to two sorting regions, the dorsal dentate nucleus (DDN) and the ventral dentate nucleus (VDN), and probability of connection and measures of directional coherence for these streamlines were calculated. Tractography was performed in 14 typically developing children (TD) and 15 children with diagnoses of ASD. Decreased numbers of streamlines were found in the children with ASD in the pathway connecting cerebellar cortex to the right VDN (p-value = 0.015). Reduced fractional anisotropy (FA) values were observed in pathways connecting the cerebellar cortex to the right DDN (p-value = 0.008), the right VDN (p-value = 0.010) and left VDN (p-value = 0.020) in children with ASD compared to the TD group. In an analysis of single subjects, reduced FA in the pathway connecting cerebellar cortex to the right VDN was found in 73% of the children in the ASD group using a threshold of 3 standard errors of the TD group. The detection of diffusion changes in cerebellum may provide an in vivo biomarker of Purkinje cell pathology in children with ASD.

Altered functional connectivity of the language network in ASD: role of classical language areas and cerebellum

The development of language, social interaction and communicative skills is remarkably different in the child with autism spectrum disorder (ASD). Atypical brain connectivity has frequently been reported in this patient population. However, the neural correlates underlying their disrupted language development and functioning are still poorly understood. Using resting state fMRI, we investigated the functional connectivity properties of the language network in a group of ASD patients with clear comorbid language impairment (ASD-LI; N = 19) and compared them to the language related connectivity properties of 23 age-matched typically developing children. A verb generation task was used to determine language components commonly active in both groups. Eight joint language components were identified and subsequently used as seeds in a resting state analysis. Interestingly, both the interregional and the seed-based whole brain connectivity analysis showed preserved connectivity between the classical intrahemispheric language centers, Wernicke's and Broca's areas. In contrast however, a marked loss of functional connectivity was found between the right cerebellar region and the supratentorial regulatory language areas. Also, the connectivity between the interhemispheric Broca regions and modulatory control dorsolateral prefrontal region was found to be decreased. This disruption of normal modulatory control and automation function by the cerebellum may underlie the abnormal language function in children with ASD-LI.

Personality and self-insight in individuals with autism spectrum disorder

Autism spectrum disorder (ASD) involves widespread difficulties in social interaction, communication, and behavioral flexibility. Consequently, individuals with ASD are believed to exhibit a number of unique personality tendencies, including a lack of insight into those tendencies. However, surprisingly little research has examined these issues. Study 1 compared self-reports of Big Five personality traits in adults with ASD (n = 37) to those of typically developing (TD) adults (n = 42). Study 2 examined whether any observed personality differences replicated in children/adolescents with ASD (n = 50) and TD controls (n = 50) according to self- and parent reports of personality. Study 2 also assessed level of self-insight in individuals with ASD relative to TD individuals by examining the degree to which self-reports converged with parent reports in terms of self-other agreement and self-enhancement (vs. self-diminishment) biases. Individuals with ASD were more Neurotic and less Extraverted, Agreeable, Conscientious, and Open to Experience. These personality differences replicated for (a) children, adolescents, and adults; (b) self- and parent reports; and (c) males and females. However, personality traits were far from perfect predictors of ASD vs. TD group membership, did not predict within-group variability in ASD symptom severity, and had differential links to maladjustment in the ASD and TD groups, suggesting that ASD represents more than just an extreme standing on trait dimensions. Finally, individuals with ASD had a tendency to self-enhance and TD individuals, to self-diminish, but both groups showed comparable self-other agreement. Thus, individuals with ASD exhibit distinct personalities relative to TD individuals but may have a similar level of insight into them.

Motor difficulties in autism spectrum disorder: linking symptom severity and postural stability

Postural stability is a fundamental aspect of motor ability that allows individuals to sustain and maintain the desired physical position of one's body. The present study examined postural stability in average-IQ adolescents and adults with Autism Spectrum Disorder (ASD). Twenty-six individuals with ASD and 26 age-and-IQ-matched individuals with typical development stood on one leg or two legs with eyes opened or closed on a Wii balance board. Results indicated significant group differences in postural stability during one-legged standing, but there were no significant group differences during two-legged standing. This suggests that static balance during more complex standing postures is impaired in average-IQ individuals with ASD. Further, current ASD symptoms were related to postural stability during two-legged standing in individuals with ASD. Future directions and clinical implications are discussed.

Early growth patterns in children with autism

BACKGROUND

Case-control studies have found increased head growth during the first year of life in children with autism spectrum disorder. Length and weight have not been as extensively studied, and there are few studies of population-based samples.

METHODS

The study was conducted in a sample of 106,082 children from the population-based Norwegian Mother and Child Cohort. The children were born in 1999-2009; by the end of follow-up on 31 December 2012, the age range was 3.6 through 13.1 years (mean 7.4 years). Measures were obtained prospectively until age 12 months for head circumference and 36 months for length and weight. We compared growth trajectories in autism spectrum disorder cases and noncases using Reed first-order models.

RESULTS

Subjects included 376 children (310 boys and 66 girls) with specialist-confirmed autism spectrum disorder. In boys with autism spectrum disorder, mean head growth was similar to that of other boys, but variability was greater, and 8.7% had macrocephaly (head circumference > 97th cohort percentile) by 12 months of age. Autism spectrum disorder boys also had slightly increased body growth, with mean length 1.1 cm above and mean weight 300 g above the cohort mean for boys at age 12 months. Throughout the first year, the head circumference of girls with autism spectrum disorder was reduced-by 0.3 cm at birth and 0.5 cm at 12 months. Their mean length was similar to that of other girls, but their mean weight was 150-350 g below at all ages from birth to 3 years. The reductions in mean head circumference and weight in girls with autism spectrum disorder appear to be driven by those with intellectual disability, genetic disorders, and epilepsy.

DISCUSSION

Growth trajectories in children with autism spectrum disorder diverge from those of other children and the differences are sex specific. Previous findings of increased mean head growth were not replicated.