Atypical diffusion tensor hemispheric asymmetry in autism

The correlation between brain structure characteristics and emotion regulation ability in children at high risk of autism spectrum disorder

As indicated by longitudinal observation, autism has difficulty controlling emotions to a certain extent in early childhood, and most children's emotional and behavioral problems are further aggravated with the growth of age. This study aimed at exploring the correlation between white matter and white matter fiber bundle connectivity characteristics and their emotional regulation ability in children with autism using machine learning methods, which can lay an empirical basis for early clinical intervention of autism. Fifty-five high risk of autism spectrum disorder (HR-ASD) children and 52 typical development (TD) children were selected to complete the skull 3D-T1 structure and diffusion tensor imaging (DTI). The emotional regulation ability of the two groups was compared using the still-face paradigm (SFP). The classification and regression models of white matter characteristics and white matter fiber bundle connections of emotion regulation ability in the HR-ASD group were built based on the machine learning method. The volume of the right amygdala (R2 = 0.245) and the volume of the right hippocampus (R2 = 0.197) affected constructive emotion regulation strategies. FA (R2 = 0.32) and MD (R2 = 0.34) had the predictive effect on self-stimulating behaviour. White matter fiber bundle connection predicted constructive regulation strategies (positive edging R2 = 0.333, negative edging R2 = 0.334) and mother-seeking behaviors (positive edging R2 = 0.667, negative edging R2 = 0.363). The emotional regulation ability of HR-ASD children is significantly correlated with the connections of multiple white matter fiber bundles, which is a potential neuro-biomarker of emotional regulation ability.

Exploring cerebral laterality of writing and the relationship to handedness: a functional transcranial Doppler ultrasound investigation

Cerebral lateralization of oral language has been investigated in a plethora of studies and it is well established that the left hemisphere is dominant for production tasks in the majority of individuals. However, few studies have focused on written language and even fewer have sampled left-handers. Writing comprises language and motor components, both of which contribute to cerebral activation, yet previous research has not disentangled. The aim of this study was to disentangle the language and motor components of writing lateralization. This was achieved through the comparison of cerebral activation during (i) written word generation and (ii) letter copying, as assessed by functional Transcranial Doppler (fTCD) ultrasound. We further assessed cerebral laterality of oral language. The sample was balanced for handedness. We preregistered the hypotheses that (i) cerebral lateralization of the linguistic component of writing would be weaker in left-handers compared to right-handers and (ii) oral language and the linguistic component of written language would not be correlated in terms of cerebral lateralization. No compelling evidence for either of our hypotheses was found. Findings highlight the complexity of the processes subserving written and oral language as well as the methodological challenges to isolate the linguistic component of writing.

Contributions to auditory system conduction velocity: insights with multi-modal neuroimaging and machine learning in children with ASD and XYY syndrome

Introduction

The M50 electrophysiological auditory evoked response time can be measured at the superior temporal gyrus with magnetoencephalography (MEG) and its latency is related to the conduction velocity of auditory input passing from ear to auditory cortex. In children with autism spectrum disorder (ASD) and certain genetic disorders such as XYY syndrome, the auditory M50 latency has been observed to be elongated (slowed).

Methods

The goal of this study is to use neuroimaging (diffusion MR and GABA MRS) measures to predict auditory conduction velocity in typically developing (TD) children and children with autism ASD and XYY syndrome.

Results

Non-linear TD support vector regression modeling methods accounted for considerably more M50 latency variance than linear models, likely due to the non-linear dependence on neuroimaging factors such as GABA MRS. While SVR models accounted for ~80% of the M50 latency variance in TD and the genetically homogenous XYY syndrome, a similar approach only accounted for ~20% of the M50 latency variance in ASD, implicating the insufficiency of diffusion MR, GABA MRS, and age factors alone. Biologically based stratification of ASD was performed by assessing the conformance of the ASD population to the TD SVR model and identifying a sub-population of children with unexpectedly long M50 latency.

Discussion

Multimodal integration of neuroimaging data can help build a mechanistic understanding of brain connectivity. The unexplained M50 latency variance in ASD motivates future hypothesis generation and testing of other contributing biological factors.

Hemispheric lateralization of white matter microstructure in children and its potential role in sensory processing dysfunction

Diffusion tensor imaging (DTI) studies have demonstrated white matter microstructural differences between the left and right hemispheres of the brain. However, the basis of these hemispheric asymmetries is not yet understood in terms of the biophysical properties of white matter microstructure, especially in children. There are reports of altered hemispheric white matter lateralization in ASD; however, this has not been studied in other related neurodevelopmental disorders such as sensory processing disorder (SPD). Firstly, we postulate that biophysical compartment modeling of diffusion MRI (dMRI), such as Neurite Orientation Dispersion and Density Imaging (NODDI), can elucidate the hemispheric microstructural asymmetries observed from DTI in children with neurodevelopmental concerns. Secondly, we hypothesize that sensory over-responsivity (SOR), a common type of SPD, will show altered hemispheric lateralization relative to children without SOR. Eighty-seven children (29 females, 58 males), ages 8-12 years, presenting at a community-based neurodevelopmental clinic were enrolled, 48 with SOR and 39 without. Participants were evaluated using the Sensory Processing 3 Dimensions (SP3D). Whole brain 3 T multi-shell multiband dMRI (b = 0, 1,000, 2,500 s/mm2) was performed. Tract Based Spatial Statistics were used to extract DTI and NODDI metrics from 20 bilateral tracts of the Johns Hopkins University White-Matter Tractography Atlas and the lateralization Index (LI) was calculated for each left-right tract pair. With DTI metrics, 12 of 20 tracts were left lateralized for fractional anisotropy and 17/20 tracts were right lateralized for axial diffusivity. These hemispheric asymmetries could be explained by NODDI metrics, including neurite density index (18/20 tracts left lateralized), orientation dispersion index (15/20 tracts left lateralized) and free water fraction (16/20 tracts lateralized). Children with SOR served as a test case of the utility of studying LI in neurodevelopmental disorders. Our data demonstrated increased lateralization in several tracts for both DTI and NODDI metrics in children with SOR, which were distinct for males versus females, when compared to children without SOR. Biophysical properties from NODDI can explain the hemispheric lateralization of white matter microstructure in children. As a patient-specific ratio, the lateralization index can eliminate scanner-related and inter-individual sources of variability and thus potentially serve as a clinically useful imaging biomarker for neurodevelopmental disorders.

Association of adverse childhood experience-related increase in neurite density with sensory over-responsivity in autism spectrum disorder: A neurite orientation dispersion and density imaging study

Sensory over-responsivity (SOR) causes social and daily distress in individuals with autism spectrum disorder (ASD). Compared to typically developed (TD) individuals, ASD individuals are at higher risk of adverse childhood experiences (ACEs), which induce abnormal neuronal development. However, whether or how ACEs are associated with abnormal neural development and SOR in ASD remains to be determined. Forty-five individuals with ASD and 43 TD individuals underwent T1-weighted and neurite orientation dispersion and density imaging; the axonal and dendritic densities were defined as the neurite density index (NDI). Voxel-based analyses were performed to explore the brain regions associated with SOR. The relationships between severity of ACEs and SOR, and NDI in the brain regions were examined. ASD individuals showed a significantly positive association between SOR severity and NDI in the right superior temporal gyrus (STG), which was not found in TD individuals. Severity of ACEs correlated significantly with that of SOR and NDI in the right STG in ASD; ASD individuals having severe SOR showed significantly higher NDI in the right STG than those with mild SOR and TD individuals. In individuals with ASD, NDI in the right STG, but not ACEs, could predict the severity of SOR, which was not shown in TD subjects. Our findings suggest that severe ACEs are involved in excessive neurite density in the right STG in ASD. ACE-associated excessive neurite density in the right STG is critical for SOR in ASD, which may be a therapeutic target in the future.

Atypical structural connectivity of language networks in autism spectrum disorder: A meta-analysis of diffusion tensor imaging studies

Patients with autism spectrum disorder (ASD) often show pervasive and complex language impairments that are closely associated with aberrant structural connectivity of language networks. However, the characteristics of white matter connectivity in ASD have remained inconclusive in previous diffusion tensor imaging (DTI) studies. The current meta‐analysis aimed to comprehensively elucidate the abnormality in language‐related white matter connectivity in individuals with ASD. We searched PubMed, Web of Science, Scopus, and Medline databases to identify relevant studies. The standardized mean difference was calculated to measure the pooled difference in DTI metrics in each tract between the ASD and typically developing (TD) groups. The moderating effects of age, sex, language ability, and symptom severity were investigated using subgroup and meta‐regression analysis. Thirty‐three DTI studies involving 831 individuals with ASD and 836 TD controls were included in the meta‐analysis. ASD subjects showed significantly lower fractional anisotropy or higher mean diffusivity across language‐associated tracts than TD controls. These abnormalities tended to be more prominent in the left language networks than in the right. In addition, children with ASD exhibit more pronounced and pervasive disturbances in white matter connectivity than adults. These results support the under‐connectivity hypothesis and demonstrate the widespread abnormal microstructure of language‐related tracts in patients with ASD. Otherwise, white matter abnormalities in the autistic brain could vary depending on the developmental stage and hemisphere.

Functional Brain Networks in Preschool Children With Autism Spectrum Disorders

OBJECTIVE

The present study aims to investigate the functional brain network characteristics of preschool children with autism spectrum disorder (ASD) through functional connectivity (FC) calculations using resting-state functional MRI (rs-fMRI) and graph theory analysis to better understand the pathogenesis of ASD and provide imaging evidence for the early assessment of this condition.

METHODS

A prospective study of preschool children including 32 with ASD (ASD group) and 22 healthy controls (HC)group was conducted in which all subjects underwent rs-fMRI scans, and then the differences in FC between the two groups was calculated, followed by graph-theoretic analysis to obtain the FC properties of the network.

RESULTS

In the calculation of FC, compared with the children in the HC group, significant increases or decreases in subnetwork connectivity was found in the ASD group. There were 25 groups of subnetworks with enhanced FC, of which the medial prefrontal and posterior cingulate gyrus and angular gyrus were all important components of the default mode network (DMN). There were 11 groups of subnetworks with weakened FC, including the hippocampus, parahippocampal gyrus, superior frontal gyrus, inferior temporal gyrus, precuneus, amygdala, and perirhinal cortex, with the hippocampus and parahippocampal gyrus predominating. In the network properties determined by graph theory, the clustering coefficient and local efficiency of the functional network was increased in the ASD group; specifically, compared with those in the HC group, nodes in the left subinsular frontal gyrus and the right middle temporal gyrus had increased efficiency, and nodes in the left perisylvian cortex, the left lingual gyrus, and the right hippocampus had decreased efficiency.

CONCLUSION

Alterations in functional brain networks are evident in preschool children with ASD and can be detected with sleep rs-fMRI, which is important for understanding the pathogenesis of ASD and assessing this condition early.

Individual prediction of hemispheric similarity of functional connectivity during normal aging

In the aging process of normal people, the functional activity pattern of brain is in constant change, and the change of brain runs through the whole life cycle, which plays a crucial role in the track of individual development. In recent years, some studies had been carried out on the brain functional activity pattern during individual aging process from different perspectives, which provided an opportunity for the problem we want to study. In this study, we used the resting-state functional magnetic resonance imaging (rs-fMRI) data from Cambridge Center for Aging and Neuroscience (Cam-CAN) database with large sample and long lifespan, and computed the functional connectivity (FC) values for each individual. Based on these values, the hemispheric similarity of functional connectivity (HSFC) obtained by Pearson correlation was used as the starting point of this study. We evaluated the ability of individual recognition of HSFC in the process of aging, as well as the variation trend with aging process. The results showed that HSFC could be used to identify individuals effectively, and it could reflect the change rule in the process of aging. In addition, we observed a series of results at the sub-module level and find that the recognition rate in the sub-module was different from each other, as well as the trend with age. Finally, as a validation, we repeated the main results by human brainnetome atlas (BNA) template and without global signal regression, found that had a good robustness. This also provides a new clue to hemispherical change patterns during normal aging.

Atypical Brain Asymmetry in Autism-A Candidate for Clinically Meaningful Stratification

BACKGROUND

Autism spectrum disorder ("autism") is a highly heterogeneous neurodevelopmental condition with few effective treatments for core and associated features. To make progress we need to both identify and validate neural markers that help to parse heterogeneity to tailor therapies to specific neurobiological profiles. Atypical hemispheric lateralization is a stable feature across studies in autism, but its potential as a neural stratification marker has not been widely examined.

METHODS

In order to dissect heterogeneity in lateralization in autism, we used the large EU-AIMS (European Autism Interventions-A Multicentre Study for Developing New Medications) Longitudinal European Autism Project dataset comprising 352 individuals with autism and 233 neurotypical control subjects as well as a replication dataset from ABIDE (Autism Brain Imaging Data Exchange) (513 individuals with autism, 691 neurotypical subjects) using a promising approach that moves beyond mean group comparisons. We derived gray matter voxelwise laterality values for each subject and modeled individual deviations from the normative pattern of brain laterality across age using normative modeling.

RESULTS

Individuals with autism had highly individualized patterns of both extreme right- and leftward deviations, particularly in language, motor, and visuospatial regions, associated with symptom severity. Language delay explained most variance in extreme rightward patterns, whereas core autism symptom severity explained most variance in extreme leftward patterns. Follow-up analyses showed that a stepwise pattern emerged, with individuals with autism with language delay showing more pronounced rightward deviations than individuals with autism without language delay.

CONCLUSIONS

Our analyses corroborate the need for novel (dimensional) approaches to delineate the heterogeneous neuroanatomy in autism and indicate that atypical lateralization may constitute a neurophenotype for clinically meaningful stratification in autism.

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.

Brain and Language Associations in Autism Spectrum Disorder: A Scoping Review

Examining brain and behaviour associations for language in autism spectrum disorder (ASD) may bring us closer to identifying neural profiles that are unique to a subgroup of individuals with ASD identified as language impaired (e.g. ASD LI+). We conducted a scoping review to examine brain regions that are associated with language performance in ASD. Further, we examined methodological differences across studies in how language ability was characterized and what neuroimaging methods were used to explore brain regions. Seventeen studies met inclusion criteria. Brain regions specific to ASD LI+ groups were found, however inconsistencies in brain and language associations were evident across study findings. Participant age, age-appropriate language scores, and neuroimaging methods likely contributed to differences in associations found.

A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is characterised by many of features including problem in social interactions, different ways of learning, some children showing a keen interest in specific subjects, inclination to routines, challenges in typical communication, and particular ways of processing sensory information. Early intervention and suitable supports for these children may make a significant contribution to their development. However, considerable difficulties have been encountered in the screening and diagnosis of ASD. The literature has indicated that certain retinal features are significantly associated with ASD. In this study, we investigated the use of machine learning approaches on retinal images to further enhance the classification accuracy.

METHODS

Forty-six ASD participants were recruited from three special needs schools and 24 normal control were recruited from the community. Among them, 23 age-gender matched ASD and normal control participant-pairs were constructed for the primary analysis. All retinal images were captured using a nonmydriatic fundus camera. Automatic retinal image analysis (ARIA) methodology applying machine-learning technology was used to optimise the information of the retina to develop a classification model for ASD. The model's validity was then assessed using a 10-fold cross-validation approach to assess its validity.

FINDINGS

The sensitivity and specificity were 95.7% (95% CI 76.0%, 99.8%) and 91.3% (95% CI 70.5%, 98.5%) respectively. The area under the ROC curve was 0.974 (95% CI 0.934, 1.000); however, it was noted that the specificity for female participants might not be as high as that for male participants.

INTERPRETATION

Because ARIA is a fully automatic cloud-based algorithm and relies only on retinal images, it can be used as a risk assessment tool for ASD screening. Further diagnosis and confirmation can then be made by professionals, and potential treatment may be provided at a relatively early stage.

Big data approaches to develop a comprehensive and accurate tool aimed at improving autism spectrum disorder diagnosis and subtype stratification

Purpose

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.

Design/methodology/approach

Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.

Findings

By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.

Originality/value

This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.

Increased hippocampal shape asymmetry and volumetric ventricular asymmetry in autism spectrum disorder

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Found increased subcortical asymmetry associated with autism.

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Utilized a new measure of shape asymmetry for analysis of structural differences.

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Observed significantly increased shape asymmetry of the hippocampus.

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Observed significantly increased volumetric asymmetry in the lateral ventricles.

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Focalized abnormalities may result in detectable shape (but not volume) differences.

Autism spectrum disorder (ASD) is a prevalent and fast-growing pervasive neurodevelopmental disorder worldwide. Despite the increasing prevalence of ASD and the breadth of research conducted on the disorder, a conclusive etiology has yet to be established and controversy still exists surrounding the anatomical abnormalities in ASD. In particular, structural asymmetries have seldom been investigated in ASD, especially in subcortical regions. Additionally, the majority of studies for identifying structural biomarkers associated with ASD have focused on small sample sizes. Therefore, the present study utilizes a large-scale, multi-site database to investigate asymmetries in the amygdala, hippocampus, and lateral ventricles, given the potential involvement of these regions in ASD. Contrary to prior work, we are not only computing volumetric asymmetries, but also shape asymmetries, using a new measure of asymmetry based on spectral shape descriptors. This measure represents the magnitude of the asymmetry and therefore captures both directional and undirectional asymmetry. The asymmetry analysis is conducted on 437 individuals with ASD and 511 healthy controls using T1-weighted MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) database. Results reveal significant asymmetries in the hippocampus and the ventricles, but not in the amygdala, in individuals with ASD. We observe a significant increase in shape asymmetry in the hippocampus, as well as increased volumetric asymmetry in the lateral ventricles in individuals with ASD. Asymmetries in these regions have not previously been reported, likely due to the different characterization of neuroanatomical asymmetry and smaller sample sizes used in previous studies. Given that these results were demonstrated in a large cohort, such asymmetries may be worthy of consideration in the development of neurodiagnostic classification tools for ASD.

Biomarkers for Autism Spectrum Disorders (ASD): A Meta-analysis

OBJECTIVE

To compare the reported accuracy and sensitivity of the various modalities used to diagnose autism spectrum disorders (ASD) in efforts to help focus further biomarker research on the most promising methods for early diagnosis.

METHODS

The Medline scientific literature database was searched to identify publications assessing potential clinical ASD biomarkers. Reports were categorized by the modality used to assess the putative markers, including protein, genetic, metabolic, or objective imaging methods. The reported sensitivity, specificity, area under the curve, and overall agreement were summarized and analyzed to determine weighted averages for each diagnostic modality. Heterogeneity was measured using the I² test.

RESULTS

Of the 71 papers included in this analysis, each belonging to one of five modalities, protein-based followed by metabolite-based markers provided the highest diagnostic accuracy, each with a pooled overall agreement of 83.3% and respective weighted area under the curve (AUC) of 89.5% and 88.3%. Sensitivity provided by protein markers was highest (85.5%), while metabolic (85.9%) and protein markers (84.7%) had the highest specificity. Other modalities showed degrees of sensitivity, specificity, and overall agreements in the range of 73%-80%.

CONCLUSIONS

Each modality provided for diagnostic accuracy and specificity similar or slightly higher than those reported for the gold-standard Autism Diagnostic Observation Schedule (ADOS) instrument. Further studies are required to identify the most predictive markers within each modality and to evaluate biological pathways or clustering with possible etiological relevance. Analyses will also be necessary to determine the potential of these novel biomarkers in diagnosing pediatric patients, thereby enabling early intervention.

Altered structural brain connectivity involving the dorsal and ventral language pathways in 16p11.2 deletion syndrome

Copy number variants at the chromosomal locus 16p11.2 contribute to neurodevelopmental disorders such as autism spectrum disorders, epilepsy, schizophrenia, and language and articulation disorders. Here, we provide detailed findings on the disrupted structural brain connectivity in 16p11.2 deletion syndrome (patients: N = 21, age range: 8-16 years; typically developing (TD) controls: 18, 9-16 years) using structural and diffusion MRI. We performed global short-, middle-, long-range, and interhemispheric connectivity analysis in the whole brain using gyral topology-based cortical parcellation. Using region of interest analysis, we studied bilateral dorsal (3 segments of arcuate fasciculus (AF)) and ventral (inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF)) language pathways. Our results showed significantly increased axial (AD) and radial (RD) diffusivities in bilateral anterior AF, decreased volume for left long AF, increased mean diffusivity (MD) and RD for right long AF, and increased AD for bilateral UF in the 16p11.2 deletion group in the absence of significant abnormalities in the whole-brain gyral and interhemispheric connectivity. The selective involvement of the language networks may aid in understanding effects of altered white matter connectivity on neurodevelopmental outcomes in 16p11.2 deletion.

Altered lateralization of dorsal language tracts in 6-week-old infants at risk for autism

Altered structural connectivity has been identified as a possible biomarker of autism spectrum disorder (ASD) risk in the developing brain. Core features of ASD include impaired social communication and early language delay. Thus, examining white matter tracts associated with language may lend further insight into early signs of ASD risk and the mechanisms that underlie language impairments associated with the disorder. Evidence of altered structural connectivity has previously been detected in 6-month-old infants at high familial risk for developing ASD. However, as language processing begins in utero, differences in structural connectivity between language regions may be present in the early infant brain shortly after birth. Here we investigated key white matter pathways of the dorsal language network in 6-week-old infants at high (HR) and low (LR) risk for ASD to identify atypicalities in structural connectivity that may predict altered developmental trajectories prior to overt language delays and the onset of ASD symptomatology. Compared to HR infants, LR infants showed higher fractional anisotropy (FA) in the left superior longitudinal fasciculus (SLF); in contrast, in the right SLF, HR infants showed higher FA than LR infants. Additionally, HR infants showed more rightward lateralization of the SLF. Across both groups, measures of FA and lateralization of these pathways at 6 weeks of age were related to later language development at 18 months of age as well as ASD symptomatology at 36 months of age. These findings indicate that early differences in the structure of language pathways may provide an early predictor of future language development and ASD risk.

Neuroimaging in Psychiatry and Neurodevelopment: why the emperor has no clothes

Neuroimaging has been a dominant force in guiding research into psychiatric and neurodevelopmental disorders for decades, yet researchers have been unable to formulate sensitive or specific imaging tests for these conditions. The search for neuroimaging biomarkers has been constrained by limited reproducibility of imaging techniques, limited tools for evaluating neurochemistry, heterogeneity of patient populations not defined by brain-based phenotypes, limited exploration of temporal components of brain function, and relatively few studies evaluating developmental and longitudinal trajectories of brain function. Opportunities for development of clinically impactful imaging metrics include longer duration functional imaging data sets, new engineering approaches to mitigate suboptimal spatiotemporal resolution, improvements in image post-processing and analysis strategies, big data approaches combined with data sharing of multisite imaging samples, and new techniques that allow dynamical exploration of brain function across multiple timescales. Despite narrow clinical impact of neuroimaging methods, there is reason for optimism that imaging will contribute to diagnosis, prognosis, and treatment monitoring for psychiatric and neurodevelopmental disorders in the near future.

Structural neuroimaging as clinical predictor: A review of machine learning applications

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.

Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders

Recent years have witnessed the proliferation of neuroimaging studies of neurodevelopmental disorders (NDDs), particularly of children with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Tourette's syndrome (TS). Neuroimaging offers immense potential in understanding the biology of these disorders, and how it relates to clinical symptoms. Neuroimaging techniques, in the long run, may help identify neurobiological markers to assist clinical diagnosis and treatment. However, methodological challenges have affected the progress of clinical neuroimaging. This paper reviews the methodological challenges involved in imaging children with NDDs. Specific topics include correcting for head motion, normalization using pediatric brain templates, accounting for psychotropic medication use, delineating complex developmental trajectories, and overcoming smaller sample sizes. The potential of neuroimaging-based biomarkers and the utility of implementing neuroimaging in a clinical setting are also discussed. Data-sharing approaches, technological advances, and an increase in the number of longitudinal, prospective studies are recommended as future directions. Significant advances have been made already, and future decades will continue to see innovative progress in neuroimaging research endeavors of NDDs.

Enhanced Topological Network Efficiency in Preschool Autism Spectrum Disorder: A Diffusion Tensor Imaging Study

Background: The functional mechanism behind autism spectrum disorder (ASD) is not clear, but it is related to a brain connectivity disorder. Previous studies have found that functional brain connectivity of ASD is linked to both increased connections and weakened connections, and the inconsistencies in functional brain connectivity may be related to age. The functional connectivity in adolescents and adults with ASD is generally less than in age-matched controls; functional connectivity in younger children with the disorder appears to be higher. As the basis of the functional network, the structural network is less studied. This study intends to further study the pathogenesis of ASD by analyzing the white matter network of ASD preschool children. Materials and Methods: In this study, Diffusion Tensor Imaging (DTI) was used to scan preschool children (aged 2-6 years, 39 children with ASD, 19 children as controls), and graph theory was used for analysis. Result: Enhanced topological network efficiency was found in the preschool children with ASD. A higher nodal efficiency was found in the left precuneus, thalamus, and bilateral superior parietal cortex, and the nodal efficiency of the left precuneus was positively associated with the severity of ASD. Conclusion: Our research shows the white matter network efficiency of preschoolers with ASD. It supports the theory of excessive early brain growth in ASD, and it shows left brain lateralization. It opens the way for new research perspectives of children with ASD.

Whole brain white matter connectivity analysis using machine learning: An application to autism

In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.

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.

Preliminary validation of the Portuguese Edinburgh Handedness Inventory in an adult sample

The Edinburgh Handedness Inventory (EHI) is persistently the most used inventory to evaluate handedness, being neuropsychological investigation and clinical practice. Despite this, there is no information on how this instrument functions in a Portuguese population. The objective of this study was therefore to examine the sociodemographic influences on handedness and establish psychometric properties of the EHI in a Portuguese sample. The sample consisted of 342 adults (157 men and 185 women), assessed with a battery of neuropsychological tests. The mean EHI Laterality Quotient was 63.52 (SD = 38.00). A much high percentage of ambiguous-handedness compared to left-handedness was detected. An inconsistency was found between the preference for formal education activities (writing-drawing-using scissors) and the remaining EHI activities. From sociodemographic variables, only age, area, and regions of residence showed significant influence on EHI scores. The reliability and temporal reliability of EHI were adequate. Confirmatory factor analysis indicated a one-factor model (χ²/df = 2.141; TLI = 0.972; CFI = 0.979; RMSEA = 0.058). The inconsistency between formal education and nonformal activities could be an indicator of social pressure. The present data give support for the notion that handedness measured by EHI is potentially sensitive to sociodemographic and cultural influences.

Cerebellar contributions to biological motion perception in autism and typical development

Growing evidence suggests that posterior cerebellar lobe contributes to social perception in healthy adults. However, they know little about how this process varies across age and with development. Using cross-sectional fMRI data, they examined cerebellar response to biological (BIO) versus scrambled (SCRAM) motion within typically developing (TD) and autism spectrum disorder (ASD) samples (age 4-30 years old), characterizing cerebellar response and BIO > SCRAM-selective effective connectivity, as well as associations with age and social ability. TD individuals recruited regions throughout cerebellar posterior lobe during BIO > SCRAM, especially bilateral lobule VI, and demonstrated connectivity with right posterior superior temporal sulcus (RpSTS) in left VI, Crus I/II, and VIIIb. ASD individuals showed BIO > SCRAM activity in left VI and left Crus I/II, and bilateral connectivity with RpSTS in Crus I/II and VIIIb/IX. No between-group differences emerged in well-matched subsamples. Among TD individuals, older age predicted greater BIO > SCRAM response in left VIIb and left VIIIa/b, but reduced connectivity between RpSTS and widespread regions of the right cerebellum. In ASD, older age predicted greater response in left Crus I and bilateral Crus II, but decreased effective connectivity with RpSTS in bilateral Crus I/II. In ASD, increased BIO > SCRAM signal in left VI/Crus I and right Crus II, VIIb, and dentate predicted lower social symptomaticity; increased effective connectivity with RpSTS in right Crus I/II and bilateral VI and I-V predicted greater symptomaticity. These data suggest that posterior cerebellum contributes to the neurodevelopment of social perception in both basic and clinical populations. Hum Brain Mapp 38:1914-1932, 2017. © 2017 Wiley Periodicals, Inc.

Multivariate characterization of white matter heterogeneity in autism spectrum disorder

The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders.

Reduced Hemispheric Asymmetry of White Matter Microstructure in Autism Spectrum Disorder

OBJECTIVE

Many past studies have suggested atypical functional and anatomical hemispheric asymmetries in autism spectrum disorder (ASD). However, almost all of these have examined only language-related asymmetries. Here, we conduct a comprehensive investigation of microstructural asymmetries across a large number of fiber tracts in ASD.

METHOD

We used diffusion tensor imaging for a comprehensive investigation of anatomical white matter asymmetries across the entire white matter skeleton, using tract-based spatial statistics in 41 children and adolescents with ASD and a matched group of 44 typically developing (TD) participants.

RESULTS

We found significant asymmetries in the TD group, being rightward for fractional anisotropy and leftward for mean diffusivity (with concordant asymmetries for radial and axial diffusivity). These asymmetries were significantly reduced in the group with ASD: in whole brain analysis for fractional anisotropy, and in a region where several major association and projection tracts travel in close proximity within occipital white matter for mean diffusivity, axial diffusivity, and radial diffusivity. No correlations between global white matter asymmetry and age or socio-communicative abilities were detected.

CONCLUSION

Our findings in TD children and adolescents can be interpreted as reflecting different processing modes (more integrative in the right and more specialized in the left hemisphere). These asymmetries and the "division of labor" between hemispheres implied by them appear to be diminished in autism spectrum disorder.

Characterization of autism spectrum disorder with spontaneous hemodynamic activity

Functional near-infrared spectroscopy (fNIRS) was used to investigate spontaneous hemodynamic activity in the temporal cortex for typically developing (TD) children and children with autism spectrum disorder (ASD). Forty-seven children participated in the experiments including twenty-five with ASD. Compared with TD children, children with ASD showed weaker bilateral resting-state functional connectivity (RSFC), but much stronger fluctuation magnitude in terms of oxy-hemoglobin (HbO₂) and deoxy-hemoglobin (Hb). Differentiating between ASD and TD based on a support vector machine (SVM) model including bilateral RSFC and the fluctuation power of HbO₂ and Hb as variables could achieve high accurate classification with sensitivity of 81.6% and specificity of 94.6%. This study demonstrates optical brain imaging has the potential for screening children with risk of ASD.

White Matter Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder found to have widespread alterations in the function and synchrony of brain regions. These differences may underlie alterations in microstructural organization, such as in white matter pathways. To investigate the diffusion of major white matter tracts, the current study examined multiple indices of white matter diffusion in 42 children and adults with ASD and 44 typically developing (TD) age- and IQ-matched peers using diffusion tensor imaging. Diffusivity measures were compared between groups for the following tracts: bilateral cingulum bundle, corpus callosum, inferior longitudinal fasciculus, superior longitudinal fasciculus, and uncinate fasciculus. Results indicate a significant reduction in fractional anisotropy (FA) for the left superior longitudinal fasciculus (LSLF) in ASD children and adults compared with TD peers. A significant increase in radial diffusivity for ASD participants was also found in the same cluster along the LSLF. In addition, a significant positive correlation emerged for all subjects between FA for the LSLF and age, with FA increasing with age. These findings point to a significant alteration in long-distance white matter connectivity in children and adults with ASD, potentially underscoring the relationship between alterations in white matter diffusion and the ASD phenotype. These results also suggest that the white matter alterations in autism may be subtle and related to the developmental trajectory.

The Broad Autism (Endo)Phenotype: Neurostructural and Neurofunctional Correlates in Parents of Individuals with Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a set of neurodevelopmental disorders with an early-onset and a strong genetic component in their pathogenesis. According to genetic and epidemiological data, ASD relatives present personality traits similar to, but not as severe as the defining features of ASD, which have been indicated as the "Broader Autism Phenotype" (BAP). BAP features seem to be more prevalent in first-degree relatives of individuals with ASD than in the general population. Characterizing brain profiles of relatives of autistic probands may help to understand ASD endophenotype. The aim of this review was to provide an up-to-date overview of research findings on the neurostructural and neurofunctional substrates in parents of individuals with ASD (pASD). The primary hypothesis was that, like for the behavioral profile, the pASD express an intermediate neurobiological pattern between ASD individuals and healthy controls. The 13 reviewed studies evaluated structural magnetic resonance imaging (MRI) brain volumes, chemical signals using magnetic resonance spectroscopy (MRS), task-related functional activation by functional magnetic resonance imaging (fMRI), electroencephalography (EEG), or magnetoencephalography (MEG) in pASD.The studies showed that pASD are generally different from healthy controls at a structural and functional level despite often not behaviorally impaired. More atypicalities in neural patterns of pASD seem to be associated with higher scores at BAP assessment. Some of the observed atypicalities are the same of the ASD probands. In addition, the pattern of neural correlates in pASD resembles that of adult individuals with ASD, or it is specific, possibly due to a compensatory mechanism. Future studies should ideally include a group of pASD and HC with their ASD and non-ASD probands respectively. They should subgrouping the pASD according to the BAP scores, considering gender as a possible confounding factor, and correlating these scores to underlying brain structure and function. These types of studies may help to understand the genetic mechanisms involved in the various clinical dimension of ASD.

The neuroanatomy of autism spectrum disorder: An overview of structural neuroimaging findings and their translatability to the clinical setting

Autism spectrum disorder is a complex neurodevelopmental disorder, which is accompanied by differences in brain anatomy, functioning and brain connectivity. Due to its neurodevelopmental character, and the large phenotypic heterogeneity among individuals on the autism spectrum, the neurobiology of autism spectrum disorder is inherently difficult to describe. Nevertheless, significant progress has been made in characterizing the neuroanatomical underpinnings of autism spectrum disorder across the human life span, and in identifying the molecular pathways that may be affected in autism spectrum disorder. Moreover, novel methodological frameworks for analyzing neuroimaging data are emerging that make it possible to characterize the neuroanatomy of autism spectrum disorder on the case level, and to stratify individuals based on their individual phenotypic make up. While these approaches are increasingly more often employed in the research setting, their applicability in the clinical setting remains a vision for the future. The aim of the current review is to (1) provide a general overview of recent structural neuroimaging findings examining the neuroanatomy of autism spectrum disorder across the human life span, and in males and females with the condition, (2) highlight potential neuroimaging (bio)markers that may in the future be used for the stratification of autism spectrum disorder individuals into biologically homogeneous subgroups and (3) inform treatment and intervention strategies.

Frontal networks in adults with autism spectrum disorder

It has been postulated that autism spectrum disorder is underpinned by an 'atypical connectivity' involving higher-order association brain regions. To test this hypothesis in a large cohort of adults with autism spectrum disorder we compared the white matter networks of 61 adult males with autism spectrum disorder and 61 neurotypical controls, using two complementary approaches to diffusion tensor magnetic resonance imaging. First, we applied tract-based spatial statistics, a 'whole brain' non-hypothesis driven method, to identify differences in white matter networks in adults with autism spectrum disorder. Following this we used a tract-specific analysis, based on tractography, to carry out a more detailed analysis of individual tracts identified by tract-based spatial statistics. Finally, within the autism spectrum disorder group, we studied the relationship between diffusion measures and autistic symptom severity. Tract-based spatial statistics revealed that autism spectrum disorder was associated with significantly reduced fractional anisotropy in regions that included frontal lobe pathways. Tractography analysis of these specific pathways showed increased mean and perpendicular diffusivity, and reduced number of streamlines in the anterior and long segments of the arcuate fasciculus, cingulum and uncinate--predominantly in the left hemisphere. Abnormalities were also evident in the anterior portions of the corpus callosum connecting left and right frontal lobes. The degree of microstructural alteration of the arcuate and uncinate fasciculi was associated with severity of symptoms in language and social reciprocity in childhood. Our results indicated that autism spectrum disorder is a developmental condition associated with abnormal connectivity of the frontal lobes. Furthermore our findings showed that male adults with autism spectrum disorder have regional differences in brain anatomy, which correlate with specific aspects of autistic symptoms. Overall these results suggest that autism spectrum disorder is a condition linked to aberrant developmental trajectories of the frontal networks that persist in adult life.

Investigating the Microstructural Correlation of White Matter in Autism Spectrum Disorder

White matter microstructure forms a complex and dynamical system that is critical for efficient and synchronized brain function. Neuroimaging findings in children with autism spectrum disorder (ASD) suggest this condition is associated with altered white matter microstructure, which may lead to atypical macroscale brain connectivity. In this study, we used diffusion tensor imaging measures to examine the extent that white matter tracts are interrelated within ASD and typical development. We assessed the strength of inter-regional white matter correlations between typically developing and ASD diagnosed individuals. Using hierarchical clustering analysis, clustering patterns of the pairwise white matter correlations were constructed and revealed to be different between the two groups. Additionally, we explored the use of graph theory analysis to examine the characteristics of the patterns formed by inter-regional white matter correlations and compared these properties between ASD and typical development. We demonstrate that the ASD sample has significantly less coherence in white matter microstructure across the brain compared to that in the typical development sample. The ASD group also presented altered topological characteristics, which may implicate less efficient brain networking in ASD. These findings highlight the potential of graph theory based network characteristics to describe the underlying networks as measured by diffusion magnetic resonance imaging and furthermore indicates that ASD may be associated with altered brain network characteristics. Our findings are consistent with those of a growing number of studies and hypotheses that have suggested disrupted brain connectivity in ASD.

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.

Diffusion tensor imaging study in Duchenne muscular dystrophy

BACKGROUND

Duchenne muscular dystrophy (DMD) is a progressive muscle disorder associated with an intellectual deficit which is non-progressive. The aim of this study was to investigate brain microstructural changes in DMD and to explore the relationship between such changes and cognitive impairment.

METHODS

All participants (12 DMD patients, 14 age-matched healthy boys), intelligence quotients (IQs) [both full (FIQ) and verbal (VIQ)] were evaluated using the Wechsler intelligence scale for children China revised (WISC-CR) edition, and brain gray matter (GM) and white matter (WM) changes were mapped using diffusion tensor imaging (DTI) with fractional anisotropy (FA). The differences between groups were analyzed using the t-test and the association of cognition with neuroimaging parameters was evaluated using Pearson's correlation coefficient.

RESULTS

Compared to the normal controls, the DMD group had lower FIQ (82.0±15.39 vs. 120.21±16.06) and significantly lower splenium of corpus callosum (CC) FA values (P<0.05). Splenium of CC FA was positively correlated with VIQ (r=0.588, P=0.044).

CONCLUSIONS

There were microstructural changes of splenium of CC in DMD patients, which was associated with cognitive impairment.

Multimodal Diffusion-MRI and MEG Assessment of Auditory and Language System Development in Autism Spectrum Disorder

BACKGROUND

Auditory processing and language impairments are prominent in children with autism spectrum disorder (ASD). The present study integrated diffusion MR measures of white-matter microstructure and magnetoencephalography (MEG) measures of cortical dynamics to investigate associations between brain structure and function within auditory and language systems in ASD. Based on previous findings, abnormal structure-function relationships in auditory and language systems in ASD were hypothesized.

METHODS

Evaluable neuroimaging data was obtained from 44 typically developing (TD) children (mean age 10.4 ± 2.4 years) and 95 children with ASD (mean age 10.2 ± 2.6 years). Diffusion MR tractography was used to delineate and quantitatively assess the auditory radiation and arcuate fasciculus segments of the auditory and language systems. MEG was used to measure (1) superior temporal gyrus auditory evoked M100 latency in response to pure-tone stimuli as an indicator of auditory system conduction velocity, and (2) auditory vowel-contrast mismatch field (MMF) latency as a passive probe of early linguistic processes.

RESULTS

Atypical development of white matter and cortical function, along with atypical lateralization, were present in ASD. In both auditory and language systems, white matter integrity and cortical electrophysiology were found to be coupled in typically developing children, with white matter microstructural features contributing significantly to electrophysiological response latencies. However, in ASD, we observed uncoupled structure-function relationships in both auditory and language systems. Regression analyses in ASD indicated that factors other than white-matter microstructure additionally contribute to the latency of neural evoked responses and ultimately behavior. RESULTS also indicated that whereas delayed M100 is a marker for ASD severity, MMF delay is more associated with language impairment.

CONCLUSION

Present findings suggest atypical development of primary auditory as well as auditory language systems in ASD. Findings demonstrate the need for additional multimodal studies to better characterize the different structural features (white matter, gray matter, neurochemical concentration) that contribute to brain activity, both in typical development and in ASD. Finally, the neural latency measures were found to be of clinical significance, with M100 associated with overall ASD severity, and with MMF latency associated with language performance.

Relationship between M100 Auditory Evoked Response and Auditory Radiation Microstructure in 16p11.2 Deletion and Duplication Carriers

BACKGROUND AND PURPOSE

Deletion and duplication of chromosome 16p11.2 (BP4-BP5) have been associated with developmental disorders such as autism spectrum disorders, and deletion subjects exhibit a large (20-ms) delay of the auditory evoked cortical response as measured by magnetoencephalography (M100 latency). The purpose of this study was to use a multimodal approach to test whether changes in white matter microstructure are associated with delayed M100 latency.

MATERIALS AND METHODS

Thirty pediatric deletion carriers, 9 duplication carriers, and 39 control children were studied with both magnetoencephalography and diffusion MR imaging. The M100 latency and auditory system DTI measures were compared between groups and tested for correlation.

RESULTS

In controls, white matter diffusivity significantly correlated with the speed of the M100 response. However, the relationship between structure and function appeared uncoupled in 16p11.2 copy number variation carriers. The alterations to auditory system white matter microstructure in the 16p11.2 deletion only partially accounted for the 20-ms M100 delay. Although both duplication and deletion groups exhibit abnormal white matter microstructure, only the deletion group has delayed M100 latency.

CONCLUSIONS

These results indicate that gene dosage impacts factors other than white matter microstructure, which modulate conduction velocity.

Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities

In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7% with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.

Behavioral, perceptual, and neural alterations in sensory and multisensory function in autism spectrum disorder

Although sensory processing challenges have been noted since the first clinical descriptions of autism, it has taken until the release of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in 2013 for sensory problems to be included as part of the core symptoms of autism spectrum disorder (ASD) in the diagnostic profile. Because sensory information forms the building blocks for higher-order social and cognitive functions, we argue that sensory processing is not only an additional piece of the puzzle, but rather a critical cornerstone for characterizing and understanding ASD. In this review we discuss what is currently known about sensory processing in ASD, how sensory function fits within contemporary models of ASD, and what is understood about the differences in the underlying neural processing of sensory and social communication observed between individuals with and without ASD. In addition to highlighting the sensory features associated with ASD, we also emphasize the importance of multisensory processing in building perceptual and cognitive representations, and how deficits in multisensory integration may also be a core characteristic of ASD.

High angular resolution diffusion imaging in a child with autism spectrum disorder and comparison with his unaffected identical twin

In recent years, the use of brain diffusion MRI has led to the hypothesis that children with autism spectrum disorder (ASD) show abnormally connected brains. We used the model of disease-discordant identical twins to test the hypothesis that higher-order diffusion MRI protocols are able to detect abnormal connectivity in a single subject. We studied the structural connectivity of the brain of a child with ASD, and of that of his unaffected identical twin, using high angular resolution diffusion imaging (HARDI) probabilistic tractography. Cortical regions were automatically parcellated from high-resolution structural images, and HARDI-based connection matrices were produced for statistical comparison. Differences in diffusion indexes between subjects were tested by Wilcoxon signed rank test. Tracts were defined as discordant when they showed a between-subject difference of 10 percent or more. Around 11 percent of the discordant intra-hemispheric tracts showed lower fractional anisotropy (FA) values in the ASD twin, while only 1 percent showed higher values. This difference was significant. Our findings in a disease-discordant identical twin pair confirm previous literature consistently reporting lower FA values in children with ASD.

Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders

Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us.

Neuroanatomy of Individual Differences in Language in Adult Males with Autism

One potential source of heterogeneity within autism spectrum conditions (ASC) is language development and ability. In 80 high-functioning male adults with ASC, we tested if variations in developmental and current structural language are associated with current neuroanatomy. Groups with and without language delay differed behaviorally in early social reciprocity, current language, but not current autistic features. Language delay was associated with larger total gray matter (GM) volume, smaller relative volume at bilateral insula, ventral basal ganglia, and right superior, middle, and polar temporal structures, and larger relative volume at pons and medulla oblongata in adulthood. Despite this heterogeneity, those with and without language delay showed significant commonality in morphometric features when contrasted with matched neurotypical individuals (n = 57). In ASC, better current language was associated with increased GM volume in bilateral temporal pole, superior temporal regions, dorsolateral fronto-parietal and cerebellar structures, and increased white matter volume in distributed frontal and insular regions. Furthermore, current language-neuroanatomy correlation patterns were similar across subgroups with or without language delay. High-functioning adult males with ASC show neuroanatomical variations associated with both developmental and current language characteristics. This underscores the importance of including both developmental and current language as specifiers for ASC, to help clarify heterogeneity.

Keeping time in the brain: Autism spectrum disorder and audiovisual temporal processing

A growing area of interest and relevance in the study of autism spectrum disorder (ASD) focuses on the relationship between multisensory temporal function and the behavioral, perceptual, and cognitive impairments observed in ASD. Atypical sensory processing is becoming increasingly recognized as a core component of autism, with evidence of atypical processing across a number of sensory modalities. These deviations from typical processing underscore the value of interpreting ASD within a multisensory framework. Furthermore, converging evidence illustrates that these differences in audiovisual processing may be specifically related to temporal processing. This review seeks to bridge the connection between temporal processing and audiovisual perception, and to elaborate on emerging data showing differences in audiovisual temporal function in autism. We also discuss the consequence of such changes, the specific impact on the processing of different classes of audiovisual stimuli (e.g. speech vs. nonspeech, etc.), and the presumptive brain processes and networks underlying audiovisual temporal integration. Finally, possible downstream behavioral implications, and possible remediation strategies are outlined. Autism Res 2016, 9: 720-738. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Lateralization of Brain Networks and Clinical Severity in Toddlers with Autism Spectrum Disorder: A HARDI Diffusion MRI Study

Recent diffusion tensor imaging studies in adolescents and children with Autism Spectrum Disorder (ASD) have reported a loss or an inversion of the typical left-right lateralization in fronto-temporal regions crucial for sociocommunicative skills. No studies explored atypical lateralization in toddlers and its correlation with clinical severity of ASD. We recruited a cohort of 20 subjects aged 36 months or younger receiving a first clinical diagnosis of ASD (15 males; age range 20-36 months). Patients underwent diffusion MRI (High-Angular-Resolution Diffusion Imaging protocol). Data from cortical parcellation were combined with tractography to obtain a connection matrix and diffusion indexes (DI ) including mean fractional anisotropy (DFA ), number of tracts (DNUM ), and total tract length (DTTL ). A laterality index was generated for each measure, and then correlated with the Autism Diagnostic Observation Schedule-Generic (ADOS-G) total score. Laterality indexes of DFA were significantly correlated with ADOS-G total scores only in two intrafrontal connected areas (correlation was positive in one case and negative in the other). Laterality indexes of DTTL and DNUM showed significant negative correlations (P < 0.05) in six connected areas, mainly fronto-temporal. This study provides first evidence of a significant correlation between brain lateralization of diffusion indexes and clinical severity in toddlers with a first diagnosis of ASD. Significant correlations mainly involved regions within the fronto-temporal circuits, known to be crucial for sociocommunicative skills. It is of interest that all correlations but one were negative, suggesting an inversion of the typical left-right asymmetry in subjects with most severe clinical impairment.

Overdiagnosis in the era of neuropsychiatric imaging

New guidelines proposed by the National Institute of Mental Health are intended to transform the management of patients with psychiatric disorders. It is anticipated that neuroimaging and other biomarkers will play a more prominent role in diagnosis and prognosis, especially in the prodromal phase of illness. Earlier treatment of psychiatric disorders has the potential to improve outcomes significantly. However, diagnosis in the absence of symptoms can lead to overdiagnosis. Overdiagnosis is a problem in many fields of medicine but could pose additional problems in psychiatry because of the stigmatization that often accompanies a diagnosis of mental illness. This review discusses the magnetic resonance imaging methods that hold the most promise for evaluating neuropsychiatric disorders, the likelihood that they could lead to overdiagnosis, and opportunities to minimize the impact of overdiagnosis in psychiatric disorders.

Abnormal auditory and language pathways in children with 16p11.2 deletion

Copy number variations at chromosome 16p11.2 contribute to neurodevelopmental disorders, including autism spectrum disorder (ASD). This study seeks to improve our understanding of the biological basis of behavioral phenotypes common in ASD, in particular the prominent and prevalent disruption of spoken language seen in children with the 16p11.2 BP4–BP5 deletion. We examined the auditory and language white matter pathways with diffusion MRI in a cohort of 36 pediatric deletion carriers and 45 age-matched controls. Diffusion MR tractography of the auditory radiations and the arcuate fasciculus was performed to generate tract specific measures of white matter microstructure. In both tracts, deletion carriers exhibited significantly higher diffusivity than that of controls. Cross-sectional diffusion parameters in these tracts changed with age with no group difference in the rate of maturation. Within deletion carriers, the left-hemisphere arcuate fasciculus mean and radial diffusivities were significantly negatively correlated with clinical language ability, but not non-verbal cognitive ability. Diffusion metrics in the right-hemisphere arcuate fasciculus were not predictive of language ability. These results provide insight into the link between the 16p11.2 deletion, abnormal auditory and language pathway structures, and the specific behavioral deficits that may contribute to neurodevelopmental disorders such as ASD.

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We examined auditory and language white matter tracts in children with the 16p11.2 BP4–BP5 deletion.

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Diffusivity was enhanced in auditory radiation and arcuate fasciculus.

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Arcuate fasciculus microstructure was correlated with language ability in deletion carriers.

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There are correlations in the brain structure and behavioral phenotype in the 16p11.2 deletion carriers.

A decade from discovery to therapy: Lingo-1, the dark horse in neurological and psychiatric disorders

Leucine-rich repeat and immunoglobulin domain-containing protein (Lingo-1) is a potent negative regulator of neuron and oligodendrocyte survival, neurite extension, axon regeneration, oligodendrocyte differentiation, axonal myelination and functional recovery; all processes highly implicated in numerous brain-related functions. Although playing a major role in developmental brain functions, the potential application of Lingo-1 as a therapeutic target for the treatment of neurological disorders has so far been under-estimated. A number of preclinical studies have shown that various methods of antagonizing Lingo-1 results in neuronal and oligodendroglial survival, axonal growth and remyelination; however to date literature has only detailed applications of Lingo-1 targeted therapeutics with a focus primarily on myelination disorders such as multiple sclerosis and spinal cord injury; omitting important information regarding Lingo-1 signaling co-factors. Here, we provide for the first time a complete and thorough review of the implications of Lingo-1 signaling in a wide range of neurological and psychiatric disorders, and critically examine its potential as a novel therapeutic target for these disorders.

EEG biofeedback for autism spectrum disorder: a commentary on Kouijzer et al. (2013)

Research conducted by Kouijzer et al. (Appl Psychophysiol Biofeedback 38(1):17-28, 2013) compared the effects of skin conductance biofeedback and EEG-biofeedback on patients with autistic spectrum disorders to determine their relative efficacy. While they found a difference between treatment and control groups, there was no significant difference on many variables between the two treatment groups. From this, the increase in symptom alleviation from autistic spectrum disorder was attributed to non-specific factors surrounding the study. We now offer alternative explanations for their findings and propose different options for future studies. We hypothesize that the location and type of neurofeedback used adversely impacted the findings. We speculate that had they used a form of EEG-biofeedback that can combat deficiencies in connectivity and also trained the areas of the brain most affected by autism, there may have then been a significant difference between the effectiveness of EEG-biofeedback versus skin conductance biofeedback.

Altered Cortical Thickness and Tract Integrity of the Mirror Neuron System and Associated Social Communication in Autism Spectrum Disorder

Previous studies using neural activity recording and neuroimaging techniques have reported functional deficits in the mirror neuron system (MNS) for individuals with autism spectrum disorder (ASD). However, a few studies focusing on gray and white matter structures of the MNS have yielded inconsistent results. The current study recruited adolescents and young adults with ASD (aged 15-26 years) and age-matched typically developing (TD) controls (aged 14-25 years). The cortical thickness (CT) and microstructural integrity of the tracts connecting the regions forming the classical MNS were investigated. High-resolution T1-weighted imaging and diffusion spectrum imaging were performed to quantify the CT and tract integrity, respectively. The structural covariance of the CT of the MNS regions revealed a weaker coordination of the MNS network in ASD. A strong correlation was found between the integrity of the right frontoparietal tracts and the social communication subscores measured by the Chinese version of the Social Communication Questionnaire. The results showed that there were no significant mean differences in the CTs and tract integrity between the ASD and TD groups, but revealed a moderate or even reverse age effect on the frontal MNS structures in ASD. In conclusion, aberrant structural coordination may be an underlying factor affecting the function of the MNS in ASD patients. The association between the right frontoparietal tracts and social communication performance implies a neural correlate of communication processing in the autistic brain. This study provides evidence of abnormal MNS structures and their influence on social communication in individuals with ASD.

Prospective MEG biomarkers in ASD: pre-clinical evidence and clinical promise of electrophysiological signatures

Autism spectrum disorders (ASD) are characterized by social impairments and restricted/stereotyped behaviors and currently affect an estimated 1 in 68 children aged 8 years old. While there has been substantial recent focus on ASD in research, both the biological pathology and, perhaps consequently, a fully effective treatment have yet to be realized. What has remained throughout is the hypothesis that ASD has neurobiological underpinnings and the observation that both the phenotypic expression and likely the underlying etiology is highly heterogeneous. Given the neurodevelopmental basis of ASD, a biologically based marker (biomarker) could prove useful not only for diagnostic and prognostic purposes, but also for stratification and response indices for pharmaceutical development. In this review, we examine the current state of the field for MEG-related biomarkers in ASD. We describe several potential biomarkers (middle latency delays [M50/M100], mismatch negativity latency, gamma-band oscillatory activity), and investigate their relation to symptomology, core domains of dysfunction (e.g., language impairment), and putative biological underpinnings.