An assessment of the autism neuroimaging literature for the prospects of re-executability

Background: The degree of reproducibility of the neuroimaging literature in psychiatric application areas has been called into question and the issues that relate to this reproducibility are extremely complex. Some of these complexities have to do with the underlying biology of the disorders that we study and others arise due to the technology we apply to the analysis of the data we collect. Ultimately, the observations we make get communicated to the rest of the community through publications in the scientific literature. Methods: We sought to perform a 're-executability survey' to evaluate the recent neuroimaging literature with an eye toward seeing if the technical aspects of our publication practices are helping or hindering the overall quest for a more reproducible understanding of brain development and aging. The topic areas examined include availability of the data, the precision of the imaging method description and the reporting of the statistical analytic approach, and the availability of the complete results. We applied the survey to 50 publications in the autism neuroimaging literature that were published between September 16, 2017 to October 1, 2018. Results: The results of the survey indicate that for the literature examined, data that is not already part of a public repository is rarely available, software tools are usually named but versions and operating system are not, it is expected that reasonably skilled analysts could approximately perform the analyses described, and the complete results of the studies are rarely available.  Conclusions: We have identified that there is ample room for improvement in research publication practices. We hope exposing these issues in the retrospective literature can provide guidance and motivation for improving this aspect of our reporting practices in the future.

An assessment of the autism neuroimaging literature for the prospects of re-executability

Background: The degree of reproducibility of the neuroimaging literature in psychiatric application areas has been called into question and the issues that relate to this reproducibility are extremely complex. Some of these complexities have to do with the underlying biology of the disorders that we study and others arise due to the technology we apply to the analysis of the data we collect. Ultimately, the observations we make get communicated to the rest of the community through publications in the scientific literature. Methods: We sought to perform a 're-executability survey' to evaluate the recent neuroimaging literature with an eye toward seeing if our publication practices are helping or hindering the overall quest for a more reproducible understanding of brain development and aging. The topic areas examined include availability of the data, the precision of the imaging method description and the reporting of the statistical analytic approach, and the availability of the complete results. We applied the survey to 50 publications in the autism neuroimaging literature that were published between September 16, 2017 to October 1, 2018. Results: The results of the survey indicate that for the literature examined, data that is not already part of a public repository is rarely available, software tools are usually named but versions and operating system are not, it is expected that reasonably skilled analysts could approximately perform the analyses described, and the complete results of the studies are rarely available.  Conclusions: We have identified that there is ample room for improvement in research publication practices. We hope exposing these issues in the retrospective literature can provide guidance and motivation for improving this aspect of our reporting practices in the future.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Genetic and environmental influences on corticostriatal circuits in twins with autism

BACKGROUND

Corticostriatal circuits (CSC) have been implicated in the presentation of some restricted and repetitive behaviours (RRBs) in children with autism-spectrum disorder (ASD), and preliminary evidence suggests that disruptions in these pathways may be associated with differences in genetic and environmental influences on brain development. The objective of this investigation was to examine the impact of genetic and environmental factors on CSC regions in twins with and without ASD and to evaluate their relationship with the severity of RRBs.

METHODS

We obtained T1-weighted MRIs from same-sex monozygotic and dizygotic twin pairs, aged 6–15 years. Good-quality data were available from 48 ASD pairs (n = 96 twins; 30 pairs concordant for ASD, 15 monozygotic and 15 dizygotic; 18 pairs discordant for ASD, 4 monozygotic and 14 dizygotic) and 34 typically developing control pairs (n = 68 twins; 20 monozygotic and 14 dizygotic pairs). We generated structural measures of the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), caudate, putamen, pallidum and thalamus using FreeSurfer. Twin pair comparisons included intraclass correlation analyses and ACE modelling (a2 = additive genetics; c2 = common or shared environment; e2 = unique or nonshared environment). We also assessed correlations with RRB severity.

RESULTS

Structural variation in CSC regions was predominantly genetically mediated in typically developing twins (a2 = 0.56 to 0.87), except for ACC white matter volume (a2 = 0.42, 95% confidence interval [CI] 0.08 to 0.77). We also observed similar magnitudes of genetic influence in twins with ASD (a2 = 0.65 to 0.97), but the cortical thickness of the ACC (c2 = 0.44, 95% CI 0.22 to 0.66) and OFC (c2 = 0.60, 95% CI 0.25 to 0.95) was primarily associated with environmental factors in only twins with ASD. Twin pair differences in OFC grey matter volume were also correlated with RRB severity and were predominantly environmentally mediated.

LIMITATIONS

We obtained MRIs on 2 scanners, and analytical approaches could not identify specific genetic and environmental factors.

CONCLUSION

Genetic factors primarily contribute to structural variation in subcortical CSC regions, regardless of ASD, but environmental factors may exert a greater influence on the development of grey matter thickness in the OFC and ACC in children with ASD. The increased vulnerability of OFC grey matter to environmental influences may also mediate some heterogeneity in RRB severity in children with ASD.

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

Background

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

Methods

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

Results

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

Conclusion

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

Brain MRI analysis using a deep learning based evolutionary approach

Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear understanding of why they perform so well, or what they have learned. In this paper, a three-dimensional convolutional neural network (3D-CNN) is employed to classify brain MRI scans into two predefined groups. In addition, a genetic algorithm based brain masking (GABM) method is proposed as a visualization technique that provides new insights into the function of the 3D-CNN. The proposed GABM method consists of two main steps. In the first step, a set of brain MRI scans is used to train the 3D-CNN. In the second step, a genetic algorithm (GA) is applied to discover knowledgeable brain regions in the MRI scans. The knowledgeable regions are those areas of the brain which the 3D-CNN has mostly used to extract important and discriminative features from them. For applying GA on the brain MRI scans, a new chromosome encoding approach is proposed. The proposed framework has been evaluated using ADNI (including 140 subjects for Alzheimer's disease classification) and ABIDE (including 1000 subjects for Autism classification) brain MRI datasets. Experimental results show a 5-fold classification accuracy of 0.85 for the ADNI dataset and 0.70 for the ABIDE dataset. The proposed GABM method has extracted 6 to 65 knowledgeable brain regions in ADNI dataset (and 15 to 75 knowledgeable brain regions in ABIDE dataset). These regions are interpreted as the segments of the brain which are mostly used by the 3D-CNN to extract features for brain disease classification. Experimental results show that besides the model interpretability, the proposed GABM method has increased final performance of the classification model in some cases with respect to model parameters.

Age-variant and age-invariant features of functional brain organization in middle-aged and older autistic adults

Background

The majority of research effort into autism has been dedicated to understanding mechanisms during early development. As a consequence, research on the broader life course of an autism spectrum condition (ASC) has largely been neglected and almost nothing is known about ASC beyond middle age. Differences in brain connectivity that arise during early development may be maintained across the lifespan and may play protective or detrimental roles in older age.

Method

This study explored age-related differences in functional connectivity across middle and older age in clinically diagnosed autistic adults (n = 44, 30-73 years) and in an age-matched typical comparison group (n = 45).

Results

The results indicated parallel age-related associations in ASC and typical aging for the local efficiency and connection strength of the default mode network and for the segregation of the frontoparietal control network. In contrast, group differences in visual network connectivity are compatible with a safeguarding interpretation of less age-related decline in brain function in ASC. This divergence was mirrored in different associations between visual network connectivity and reaction time variability in the ASC and comparison group.

Limitations

The study is cross-sectional and may be affected by cohort effects. As all participants received their autism diagnosis in adulthood, this might hinder generalizability.

Conclusion

These results highlight the complexity of aging in ASC with both parallel and divergent trajectories across different aspects of functional network organization.

Aging with Autism Departs Greatly from Typical Aging

Autism has been largely portrayed as a psychiatric and childhood disorder. However, autism is a lifelong neurological condition that evolves over time through highly heterogeneous trajectories. These trends have not been studied in relation to normative aging trajectories, so we know very little about aging with autism. One aspect that seems to develop differently is the sense of movement, inclusive of sensory kinesthetic-reafference emerging from continuously sensed self-generated motions. These include involuntary micro-motions eluding observation, yet routinely obtainable in fMRI studies to rid images of motor artifacts. Open-access repositories offer thousands of imaging records, covering 5-65 years of age for both neurotypical and autistic individuals to ascertain the trajectories of involuntary motions. Here we introduce new computational techniques that automatically stratify different age groups in autism according to probability distance in different representational spaces. Further, we show that autistic cross-sectional population trajectories in probability space fundamentally differ from those of neurotypical controls and that after 40 years of age, there is an inflection point in autism, signaling a monotonically increasing difference away from age-matched normative involuntary motion signatures. Our work offers new age-appropriate stochastic analyses amenable to redefine basic research and provide dynamic diagnoses as the person's nervous systems age.

Diagnostic Accuracy of Indian Scale for Assessment of Autism in Indian Children Aged 2-5 Years

OBJECTIVE

To determine the diagnostic accuracy of Indian Scale for Assessment of Autism (ISAA) in children aged between 2-5 years. Design: Setting:.

STUDY DESIGN

Study of diagnostic accuracy.

PARTICIPANTS

A consecutive sample of 500 children with suspected Autism (delay or regression of developmental milestones, delay or regression in speech, age-inappropriate understanding, behaviour, play and/or social interaction) was recruited.

SETTING

Tertiary level hospital, (November 2015 - November 2017).

PROCEDURE

Each child underwent an expert comprehensive assessment of Autism (reference tool) that included history, observation, examination, diagnostic criteria for Autism Spectrum Disorder (ASD) of the Diagnostic and Statistical Manual of Mental Disorders', 5th edition, Childhood Autism Rating Scale-2 (CARS2), developmental status and adaptive function. This was followed by the administration of ISAA (test tool) in Hindi language. Parameters of diagnostic accuracy and Receiver Operating Characteristic curves were computed.

MAIN OUTCOME MEASURES

ASD based on (i) expert assessment, (ii) CARS-2, and (iii) ISAA.

RESULTS

In children aged 2-3 years, sensitivity of ISAA was 100% (95% CI 98.2% -100%), specificity 28.9% (95% CI 17.7% to 43.4%), positive likelihood ratio 1.4 and negative likelihood ratio 0. In 3-5 year olds, sensitivity was 99.6% (95% CI 97.6% to 99.6%), specificity 33.3% (95% CI 15.1% to 58.3%), positive likelihood ration 1.5 and negative likelihood ratio 0.01. The degrees of autism based on the existing cut off values were inaccurate.

CONCLUSIONS

ISAA has sub-optimal performance in diagnosing and assessing severity in 2-5 year old children.

Atypical Local and Distal Patterns of Occipito-frontal Functional Connectivity are Related to Symptom Severity in Autism

Autism spectrum disorders (ASDs) are increasingly prevalent neurodevelopmental disorders characterized by sociocommunicative impairments. Growing consensus indicates that neurobehavioral abnormalities require explanation in terms of interconnected networks. Despite theoretical speculations about increased local and reduced distal connectivity, links between local and distal functional connectivity have not been systematically investigated in ASDs. Specifically, it remains open whether hypothesized local overconnectivity may reflect isolated versus overly integrative processing. Resting state functional MRI data from 57 children and adolescents with ASDs and 51 typically developing (TD) participants were included. In regional homogeneity (ReHo) analyses, pericalcarine visual cortex was found be locally overconnected (ASD > TD). Using this region as seed in whole-brain analyses, we observed overconnectivity in distal regions, specifically middle frontal gyri, for an ASD subgroup identified through k-means clustering. While in this subgroup local occipital to distal frontal overconnectivity was associated with greater symptom severity, a second subgroup showed the opposite pattern of connectivity and symptom severity correlations. Our findings suggest that increased local connectivity in ASDs is region-specific and may be partially associated with more integrative long-distance connectivity. Results also highlight the need to test for subtypes, as differential patterns of brain-behavior links were observed in two distinct subgroups of our ASD cohort.

Restricted, Repetitive, and Stereotypical Patterns of Behavior in Autism-an fMRI Perspective

The main objective of this paper is to determine whether resting-state fMRI can identify functional connectivity differences between individuals with autism who experience severe issues with restricted, repetitive, and stereotypical behaviors, those who experience only mild issues, and controls. We use resting-state fMRI data from the ABIDE-I preprocessed repository, with participants grouped according to their ADI-R Restricted, Repetitive, and Stereotyped Patterns of Behavior Subscore. Three processing methods are used for analysis. A time-correlation approach establishes a basic baseline, and we introduce a method based on sliding time windows, with means across time adjusted to consider the fraction of time the correlation measure is above/below average. We complement these with a band-limited coherence approach. For completeness, preprocessing schemes with and without global signal regression are considered. Our results are in line with recent ones which find both over- and under-connectivities in the autistic brain. We find that there are indeed significant differences in connectivity between various regions that differentiate between ASD subjects with severe stereotypical/restrictive behavior issues, those with only mild issues, and controls. Interestingly, for some regions, the "signature" of subjects in the milder of the ASD groups appears to be distinct (i.e., over- or under-connected) relative to both the more severe ASD group and the controls.

Atypical functional connectome hierarchy in autism

One paradox of autism is the co-occurrence of deficits in sensory and higher-order socio-cognitive processing. Here, we examined whether these phenotypical patterns may relate to an overarching system-level imbalance-specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Combining connectome gradient and stepwise connectivity analysis based on task-free functional magnetic resonance imaging (fMRI), we demonstrated atypical connectivity transitions between sensory and higher-order default mode regions in a large cohort of individuals with autism relative to typically-developing controls. Further analyses indicated that reduced differentiation related to perturbed stepwise connectivity from sensory towards transmodal areas, as well as atypical long-range rich-club connectivity. Supervised pattern learning revealed that hierarchical features predicted deficits in social cognition and low-level behavioral symptoms, but not communication-related symptoms. Our findings provide new evidence for imbalances in network hierarchy in autism, which offers a parsimonious reference frame to consolidate its diverse features.

Evaluation of Differences in Temporal Synchrony Between Brain Regions in Individuals With Autism and Typical Development

IMPORTANCE

Despite reports of widespread but heterogeneous atypicality of functional connectivity in individuals with autism, little is known regarding the temporal dynamics of functional brain connections and how they relate to autistic traits.

OBJECTIVE

To investigate differences in temporal synchrony between brain regions in individuals with autism and those with typical development.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study, conducted at the University of Utah, included 90 adolescent and adult male participants. A larger sample from the multisite Autism Brain Imaging Data Exchange (ABIDE) was also used as a replication sample. The study includes data acquired between December 2016 and April 2018. Aggregate data included in the replication sample were released to the public in August 2012 (ABIDE I) and June 2016 (ABIDE II). Data analysis were conducted between January 2018 and April 2018.

EXPOSURES

Male individuals diagnosed as having autism (n = 52) and typically developing male individuals (n = 38).

MAIN OUTCOMES AND MEASURES

Long duration (30 minutes/individual) of multiband, multiecho functional magnetic resonance imaging was acquired to estimate functional connectivity between brain regions. Sustained connectivity, a measure of functional connectivity duration, as well as lagged temporal dynamics related to functional connectivity, were compared between groups for 361 gray matter regions of interest and a 17-network parcellation. Lagged findings were replicated in the larger ABIDE sample (n = 1402). Sustained connectivity findings were also associated with behavioral and cognitive variables.

RESULTS

In 52 males with autism (mean [SD] age, 27.73 [8.66] years) and 38 control males with typical development (mean [SD] age, 27.09 [7.49] years), increases in both sustained and functional connectivity at several lags were found in individuals with autism compared with the control group. Group differences in functional connectivity were replicated in the larger ABIDE data set at a 6-second lag. Measures of symptom severity in individuals with autism were positively associated with sustained connectivity values. In the control group, sustained connectivity was negatively associated with cognitive processing. A replication sample (n = 1402) composed of 579 individuals with autism (80 female and 499 male; mean [SD] age, 15.08 [6.89] years) and 823 in the control group (211 female and 612 male; mean [SD] age, 15.06 [6.79] years) from the ABIDE data set was also analyzed.

CONCLUSIONS AND RELEVANCE

Whereas the magnitude of functional connectivity in autism is variable across brain regions, participant samples, and development, prolonged temporal synchrony of functional connections is reproducibly observed in autism, suggesting a potential mechanism for core symptoms.

A Mixed-Effects Model for Detecting Disrupted Connectivities in Heterogeneous Data

The human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson's disease, Alzheimer's disease, and autism. functional magnetic resonance imaging has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose a special type of mixed-effects model together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities for developing a neural network in whole brain studies. Results are illustrated with a large data set known as autism brain imaging data exchange which includes 361 subjects from eight medical centers.

Gyrification changes are related to cognitive strengths in autism

Background

Behavioral, cognitive and functional particularities in autism differ according to autism subgroups and might be associated with domain-specific cognitive strengths. It is unknown whether structural changes support this specialization. We investigated the link between cortical folding, its maturation and cognitive strengths in autism subgroups presenting verbal or visuo-spatial peaks of abilities.

Methods

We measured gyrification, a structural index related to function, in 55 autistic participants with (AS-SOD, N = 27) or without (AS-NoSOD, N = 28) a speech onset delay (SOD) with similar symptom severity but respectively perceptual and verbal cognitive strengths, and 37 typical adolescents and young adults matched for intelligence and age. We calculated the local Gyrification Index (lGI) throughout an occipito-temporal region of interest and independently modeled age and peak of ability effects for each group.

Results

Unique gyrification features in both autistic groups were detected in localized clusters. When comparing the three groups, gyrification was found lower in AS-SOD in a fusiform visual area, whereas it was higher in AS-NoSOD in a temporal language-related region. These particular areas presented age-related gyrification differences reflecting contrasting local maturation pathways in AS. As expected, peaks of ability were found in a verbal subtest for the AS-NoSOD group and in the Block Design IQ subtest for the AS-SOD group.

Conclusions

Irrespective of their direction, regional gyrification differences in visual and language processing areas respectively reflect AS-SOD perceptual and AS-NoSOD language-oriented peaks. Unique regional maturation trajectories in the autistic brain may underline specific cognitive strengths, which are key variables for understanding heterogeneity in autism.

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Subgrouping the autism spectrum (AS) partly accounts for its heterogeneity.

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AS individuals with a speech onset delay (SOD) show perceptual cognitive strengths.

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AS individuals without a SOD show language-related cognitive strengths.

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AS subgroups show unique gyrification patterns in areas related to their strengths.

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Cortical structural maturation may be related to domain-specific strengths in AS.

Therapeutic use of carbohydrate-restricted diets in an autistic child; a case report of clinical and 18FDG PET findings

The ketogenic diet (KD) is a high-fat, adequate-protein, and low-carbohydrate diet that has been used successfully in the treatment of refractory epilepsies for almost 100 years. There has been accumulating evidence to show that the KD may provide a therapeutic benefit in autism spectrum disorders, albeit by a yet-unknown mechanism. We report a case of a 6-year-old patient with high-functioning autism and subclinical epileptic discharges who responded poorly to several behavioural and psychopharmacological treatments. The patient was subsequently placed on the KD due to significant glucose hypometabolism in the brain as revealed by an 18FDG PET. As soon as one month after starting the KD, the patient's behavior and intellect improved (in regard to hyperactivity, attention span, abnormal reactions to visual and auditory stimuli, usage of objects, adaptability to changes, communication skills, fear, anxiety, and emotional reactions); these improvements continued until the end of the observation period at 16 months on the KD. The 18FDG PET, measured at 12 months on the KD, revealed that 18F-FDG uptake decreased markedly and diffusely in the whole cerebral cortex with a relatively low reduction in basal ganglia in comparison to the pre-KD assessment. It warrants further investigation if the 18FDG PET imaging could serve as a biomarker in identifying individuals with autism who might benefit from the KD due to underlying abnormalities related to glucose hypometabolism.

Aberrant hemodynamic responses in autism: Implications for resting state fMRI functional connectivity studies

Functional MRI (fMRI) is modeled as a convolution of the hemodynamic response function (HRF) and an unmeasured latent neural signal. However, HRF itself is variable across brain regions and subjects. This variability is induced by both neural and non-neural factors. Aberrations in underlying neurochemical mechanisms, which control HRF shape, have been reported in autism spectrum disorders (ASD). Therefore, we hypothesized that this will lead to voxel-specific, yet systematic differences in HRF shape between ASD and healthy controls. As a corollary, we also hypothesized that such alterations will lead to differences in estimated functional connectivity in fMRI space compared to latent neural space. To test these hypotheses, we performed blind deconvolution of resting-state fMRI time series acquired from large number of ASD and control subjects obtained from the Autism Brain Imaging Data Exchange (ABIDE) database (N = 1102). Many brain regions previously implicated in autism showed systematic differences in HRF shape in ASD. Specifically, we found that precuneus had aberrations in all HRF parameters. Consequently, we obtained precuneus-seed-based functional connectivity differences between ASD and controls using fMRI as well as using latent neural signals. We found that non-deconvolved fMRI data failed to detect group differences in connectivity between precuneus and certain brain regions that were instead observed in deconvolved data. Our results are relevant for the understanding of hemodynamic and neurochemical aberrations in ASD, as well as have methodological implications for resting-state functional connectivity studies in Autism, and more generally in disorders that are accompanied by neurochemical alterations that may impact HRF shape.

A prospective study of fetal head growth, autistic traits and autism spectrum disorder

Altered trajectories of brain growth are often reported in Autism Spectrum Disorder (ASD), particularly during the first year of life. However, less is known about prenatal head growth trajectories, and no study has examined the relation with postnatal autistic symptom severity. The current study prospectively examined the association between fetal head growth and the spectrum of autistic symptom severity in two large population-based cohorts, including a sample of individuals with clinically diagnosed ASD. This study included 3,820 children from two longitudinal prenatal cohorts in The Netherlands and Australia, comprising 60 individuals with a confirmed diagnosis of ASD. Latent growth curve models were used to examine the relationship between fetal head circumference measured at three different time points and autistic traits measured in postnatal life using either the Social Responsiveness Scale or the Autism-Spectrum Quotient. While lower initial prenatal HC was weakly associated with increasing autistic traits in the Dutch cohort, this relationship was not observed in the Australian cohort, nor when the two cohorts were analysed together. No differences in prenatal head growth were found between individuals with ASD and controls. This large population-based study identified no consistent association across two cohorts between prenatal head growth and postnatal autistic traits. Our mixed findings suggest that further research in this area is needed. Autism Res 2018, 11: 602-612. © 2018 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc.

LAY SUMMARY

It is not known whether different patterns of postnatal brain growth in Autism Spectrum Disorder (ASD) also occurs prenatally. We examined fetal head growth and autistic symptoms in two large groups from The Netherlands and Australia. Lower initial prenatal head circumference was associated with autistic traits in the Dutch, but not the Australian, group. No differences in head growth were found in individuals with ASD and controls when the data was combined. Our mixed findings suggest that more research in this area is needed.

Optically derived metabolic and hemodynamic parameters predict hippocampal neurogenesis in the BTBR mouse model of autism

In this study, we made use of dual-wavelength laser speckle imaging (DW-LSI) to assess cerebral blood flow (CBF) in the BTBR-genetic mouse model of autism spectrum disorder, as well as control (C57Bl/6J) mice. Since the deficits in social behavior demonstrated by BTBR mice are attributed to changes in neural tissue structure and function, we postulated that these changes can be detected optically using DW-LSI. BTBR mice demonstrated reductions in both CBF and cerebral oxygen metabolism (CMRO₂ ), as suggested by studies using conventional neuroimaging technologies to reflect impaired neuronal activation and cognitive function. To validate the monitoring of CBF by DW-LSI, measurements with laser Doppler flowmetry (LDF) were also performed which confirmed the lowered CBF in the autistic-like group. Furthermore, we found in vivo cortical CBF measurements to predict the rate of hippocampal neurogenesis, measured ex vivo by the number of neurons expressing doublecortin or the cellular proliferation marker Ki-67 in the dentate gyrus, with a strong positive correlation between CBF and neurogenesis markers (Pearson, r = 0.78; 0.9, respectively). These novel findings identifying cortical CBF as a predictive parameter of hippocampal neurogenesis highlight the power and flexibility of the DW-LSI and LDF setups for studying neurogenesis trends under normal and pathological conditions.

Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data

Ultra-high dimensional variable selection has become increasingly important in analysis of neuroimaging data. For example, in the Autism Brain Imaging Data Exchange (ABIDE) study, neuroscientists are interested in identifying important biomarkers for early detection of the autism spectrum disorder (ASD) using high resolution brain images that include hundreds of thousands voxels. However, most existing methods are not feasible for solving this problem due to their extensive computational costs. In this work, we propose a novel multiresolution variable selection procedure under a Bayesian probit regression framework. It recursively uses posterior samples for coarser-scale variable selection to guide the posterior inference on finer-scale variable selection, leading to very efficient Markov chain Monte Carlo (MCMC) algorithms. The proposed algorithms are computationally feasible for ultra-high dimensional data. Also, our model incorporates two levels of structural information into variable selection using Ising priors: the spatial dependence between voxels and the functional connectivity between anatomical brain regions. Applied to the resting state functional magnetic resonance imaging (R-fMRI) data in the ABIDE study, our methods identify voxel-level imaging biomarkers highly predictive of the ASD, which are biologically meaningful and interpretable. Extensive simulations also show that our methods achieve better performance in variable selection compared to existing methods.

Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism

BACKGROUND AND OBJECTIVE

The autism spectrum disorder (ASD) diagnosis requires a long and elaborate procedure. Due to the lack of a biomarker, the procedure is subjective and is restricted to evaluating behavior. Several attempts to use functional MRI as an assisting tool (as classifier) have been reported, but they barely reach an accuracy of 80%, and have not usually been replicated or validated with independent datasets. Those attempts have used functional connectivity and structural measurements. There is, nevertheless, evidence that not the topology of networks, but their temporal dynamics is a key feature in ASD. We therefore propose a novel MRI-based ASD biomarker by analyzing temporal brain dynamics in resting-state fMRI.

METHODS

We investigate resting-state fMRI data from 2 independent datasets of adolescents: our in-house data (12 ADS, 12 controls), and the Leuven dataset (12 ASD, 18 controls, from Leuven university). Using independent component analysis we obtain relevant socio-executive resting-state networks (RSNs) and their associated time series. Upon these time series we extract wavelet coherence maps. Using these maps, we calculate our dynamics metric: time of in-phase coherence. This novel metric is then used to train classifiers for autism diagnosis. Leave-one-out cross validation is applied for performance evaluation. To assess inter-site robustness, we also train our classifiers on the in-house data, and test them on the Leuven dataset.

RESULTS

We distinguished ASD from non-ASD adolescents at 86.7% accuracy (91.7% sensitivity, 83.3% specificity). In the second experiment, using Leuven dataset, we also obtained the classification performance at 86.7% (83.3% sensitivity, and 88.9% specificity). Finally we classified the Leuven dataset, with classifiers trained with our in-house data, resulting in 80% accuracy (100% sensitivity, 66.7% specificity).

CONCLUSIONS

This study shows that change in the coherence of temporal neurodynamics is a biomarker of ASD, and wavelet coherence-based classifiers lead to robust and replicable results and could be used as an objective diagnostic tool for ASD.

Topological Properties of the Structural Brain Network in Autism via ϵ-Neighbor Method

OBJECTIVE

Topological characteristics of the brain can be analyzed using structural brain networks constructed by diffusion tensor imaging (DTI). When a brain network is constructed by the existing parcellation method, the structure of the network changes depending on the scale of parcellation and arbitrary thresholding. To overcome these issues, we propose to construct brain networks using the improved $\varepsilon $-neighbor construction, which is a parcellation free network construction technique.

METHODS

We acquired DTI from 14 control subjects and 15 subjects with autism. We examined the differences in topological properties of the brain networks constructed using the proposed method and the existing parcellation between the two groups.

RESULTS

As the number of nodes increased, the connectedness of the network decreased in the parcellation method. However, for brain networks constructed using the proposed method, connectedness remained at a high level even with an increase in the number of nodes. We found significant differences in several topological properties of brain networks constructed using the proposed method, whereas topological properties were not significantly different for the parcellation method.

CONCLUSION

The brain networks constructed using the proposed method are considered as more realistic than a parcellation method with respect to the stability of connectedness. We found that subjects with autism showed the abnormal characteristics in the brain networks. These results demonstrate that the proposed method may provide new insights to analysis in the structural brain network.

SIGNIFICANCE

We proposed the novel brain network construction method to overcome the shortcoming in the existing parcellation method.

Cerebellar involvement in autism and ADHD

The cerebellum has long been known for its importance in motor learning and coordination. However, increasing evidence supports a role for the cerebellum in cognition and emotion. Consistent with a role in cognitive functions, the cerebellum has emerged as one of the key brain regions affected in nonmotor disorders, including autism spectrum disorder and attention deficit-hyperactivity disorder. Here, we discuss behavioral, postmortem, genetic, and neuroimaging studies in humans in order to understand the cerebellar contributions to the pathogenesis of both disorders. We also review relevant animal model findings.

Face processing in a case of high functioning autism with developmental prosopagnosia

The ability to "read" the information about facial identity, expressed emotions, and intentions is crucial for non‑verbal social interaction. Neuroimaging and clinical studies consequently link face perception with fusiform gyrus (FG) and occipital face area (OFA) activity. Here we investigated face processing in an adult, patient PK, diagnosed with both high functioning autism spectrum disorder (ASD) and developmental prosopagnosia (DP). Both disorders have a significant impact on face perception and recognition, thus creating a unique neurodevelopmental condition. We used eye‑tracking and functional magnetic resonance imaging (fMRI) method. Eye‑tracking and fMRI results of PK were compared to results of control subjects. Patient PK showed atypical gaze‑fixation strategy during face perception and typical patterns of brain activations in the FG and OFA. However, a significant difference between PK and control subjects was found in the left anterior superior temporal sulcus/middle temporal gyrus (aSTS/MTG). In PK the left aSTS/MTG was hypo‑activated in comparison to the control subjects. Additionally, functional connectivity analysis revealed decreased inter‑hemispheric connectivity between right and left aSTS/MTG in 'ASD and DP' patient during face recognition performance as compared to the control subjects. The lack of activity in the left aSTS/MTG observed in the case of the clinical subject, combined with the behavioral, eye‑tracking, and neuropsychological results, suggests that impairment of the cognitive mechanism of face recognition involves higher level of processing. It seems to be related to insufficient access to semantic knowledge about the person when prompted by face stimuli.

Individuals with autism have higher 8-Iso-PGF2α levels than controls, but no correlation with quantitative assay of Paraoxonase 1 serum levels

Autism spectrum disorder (ASD) represents a very large set of neurodevelopmental issues with diverse clinical outcomes. Various hypotheses have been put forth for the etiology of autism spectrum disorder, including issues pertaining to oxidative stress. In this study, we conducted measurements of serum 8-Iso-Prostaglanding F2 α (8-iso-PGF2α, which is the results of non-enzimatically mediated polyunsaturated fatty acid oxidation) in a population of individuals with autism and a control group of age and sex matched controls. A quantitative assay of Paraoxonase 1 (PON1) was conducted. Data regarding comorbidities, structural MRI scans, medication, intelligence quotient (IQ) and Childhood Autism Rating Scale scores (CARS) were also included in our study. Our results show that patients diagnosed with autism have higher levels of 8-iso-PGF2α than their neurotypical counterparts. Levels of this particular metabolite, however, do not correlate with quantitative serum levels of Paraoxonase 1, which has been shown to be altered in individuals with autism. Neither 8-iso-PGF2α nor quantitative levels of PON1 provide any meaningful correlation with clinical or neuroimaging data in this study group. Future research should focus on providing data regarding PON 1 phenotype, in addition to standard quantitative measurements, in relation to 8-iso-PGF2α as well as other clinical and structural brain findings.