In pursuit of neurophenotypes: The consequences of having autism and a big brain

Sex differences in trajectories of cortical development in autistic children from 2-13 years of age

Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2-13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.

Correlation analysis of maternal condition during pregnancy with head circumference and autism spectrum disorder: A propensity score-matched study

To determine whether health status during pregnancy is associated with autism spectrum disorder (ASD) and abnormal head circumference (HC) in the offspring. This study included 41 Han children with ASD who visited the Children's Health Clinic of the Second Hospital of Shandong University between March 2018 and February 2019, and 264 Han children with typical development (TD) who visited the clinic during the same period. Physical measurements were performed on the children. The questionnaire obtained information on maternal risk factors that may be related to the increased risk of ASD and folic acid (FA) supplementation. We designed an observational case-control study using propensity score matching and multivariate logistic regression analysis. The incidence of macrocephaly in the ASD group was 22.0%, significantly higher than that in the TD group (1.8%). The incidence of microcephaly in the ASD group was 17.1% (n = 7), significantly higher than that in the TD group (1.8%). The differences between the comparisons were statistically significant. Maternal FA supplementation during pregnancy was significantly associated with ASD (P < .05), with an odds ratio (95% confidence interval of 3.69 (1.76, 7.76)). Also was associated with macrocephaly (P < .05), odds ratio (95% confidence interval) were 8.13 (1.63, 40.61) and 4.16 (1.18, 14.60), respectively. The incidence of abnormal HC was higher in the ASD group than that in the TD group. Maternal FA supplementation during pregnancy may be negatively associated with the occurrence of ASD and abnormal HC in the offspring. Further examination of the role of maternal health status in the etiology of ASD is recommended.

Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology

Background

Autism and attention deficit hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together, and sex differences are often overlooked. Normative modelling provides a unified framework for studying age-specific and sex-specific divergences in neurodivergent brain development.

Methods

Here we use normative modelling and a large, multi-site neuroimaging dataset to characterise cortical anatomy associated with autism and ADHD, benchmarked against models of typical brain development based on a sample of over 75,000 individuals. We also examined sex and age differences, relationship with autistic traits, and explored the co-occurrence of autism and ADHD (autism+ADHD).

Results

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

Conclusions

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

Fetal macrocephaly: Pathophysiology, prenatal diagnosis and management

Macrocephaly means a large head and is defined as a head circumference (HC) above the 98th percentile or greater than +2SD above the mean for gestational age. Macrocephaly can be primary and due to increased brain tissue (megalocephaly), which in most cases is familial and benign or secondary. The latter may be due to various causes, including but not limited to communicating or non-communicating hydrocephalus, cerebral edema, focal and pericerebral increased fluid collections, thickened calvarium and brain tumors. Megalocephaly can be syndromic or non-syndromic. In the former, gyral and structural CNS anomalies are common. It is important to exercise caution when considering a diagnosis of megalocephaly due to limitations in the accuracy of HC measurement, lack of nomograms for specific populations, inconsistencies between prenatal and postnatal HC growth curves and progression over time. The degree of macrocephaly is important, with mild macrocephaly ≤2.5SD carrying a good prognosis, especially when one of the parents has macrocephaly and normal development. Cases in which the patient history and/or physical exam are positive or when parental HC are normal are more worrisome and warrant a neurosonogram, fetal MRI and genetic testing to better delineate the underlying etiology and provide appropriate counseling.

A Fusion-Based Machine Learning Approach for Autism Detection in Young Children Using Magnetoencephalography Signals

In this study, we aimed to find biomarkers of autism in young children. We recorded magnetoencephalography (MEG) in thirty children (4-7 years) with autism and thirty age, gender-matched controls while they were watching cartoons. We focused on characterizing neural oscillations by amplitude (power spectral density, PSD) and phase (preferred phase angle, PPA). Machine learning based classifier showed a higher classification accuracy (88%) for PPA features than PSD features (82%). Further, by a novel fusion method combining PSD and PPA features, we achieved an average classification accuracy of 94% and 98% for feature-level and score-level fusion, respectively. These findings reveal discriminatory patterns of neural oscillations of autism in young children and provide novel insight into autism pathophysiology.

Habituation of auditory responses in young autistic and neurotypical children

Prior studies suggest that habituation of sensory responses is reduced in autism and that diminished habituation could be related to atypical autistic sensory experiences, for example, by causing brain responses to aversive stimuli to remain strong over time instead of being suppressed. While many prior studies exploring habituation in autism have repeatedly presented identical stimuli, other studies suggest group differences can still be observed in habituation to intermittent stimuli. The present study explored habituation of electrophysiological responses to auditory complex tones of varying intensities (50-80 dB SPL), presented passively in an interleaved manner, in a well-characterized sample of 127 autistic (MDQ  = 65.41, SD = 20.54) and 79 typically developing (MDQ  = 106.02, SD = 11.50) children between 2 and 5 years old. Habituation was quantified as changes in the amplitudes of single-trial responses to tones of each intensity over the course of the experiment. Habituation of the auditory N2 response was substantially reduced in autistic participants as compared to typically developing controls, although diagnostic groups did not clearly differ in habituation of the P1 response. Interestingly, the P1 habituated less to loud 80 dB sounds than softer sounds, whereas the N2 habituated less to soft 50 dB sounds than louder sounds. No associations were found between electrophysiological habituation and cognitive ability or participants' caregiver-reported sound tolerance (Sensory Profile Hyperacusis Index). The results present study results extend prior research suggesting habituation of certain sensory responses is reduced in autism; however, they also suggest that habituation differences observed using this study's paradigm might not be a primary driver of autistic participants' real-world sound intolerance.

APPlications of amyloid-β precursor protein metabolites in macrocephaly and autism spectrum disorder

Metabolites of the Amyloid-β precursor protein (APP) proteolysis may underlie brain overgrowth in Autism Spectrum Disorder (ASD). We have found elevated APP metabolites (total APP, secreted (s) APPα, and α-secretase adamalysins in the plasma and brain tissue of children with ASD). In this review, we highlight several lines of evidence supporting APP metabolites' potential contribution to macrocephaly in ASD. First, APP appears early in corticogenesis, placing APP in a prime position to accelerate growth in neurons and glia. APP metabolites are upregulated in neuroinflammation, another potential contributor to excessive brain growth in ASD. APP metabolites appear to directly affect translational signaling pathways, which have been linked to single gene forms of syndromic ASD (Fragile X Syndrome, PTEN, Tuberous Sclerosis Complex). Finally, APP metabolites, and microRNA, which regulates APP expression, may contribute to ASD brain overgrowth, particularly increased white matter, through ERK receptor activation on the PI3K/Akt/mTOR/Rho GTPase pathway, favoring myelination.

Why do we need sex-balanced studies of autism?

Males are diagnosed with autism much more frequently than females, and most research study samples reflect this male predominance. The result is that autistic females are understudied. There is a critical need to increase our understanding of autistic females, both biologically and clinically. The only way to do this is to recruit sex-balanced cohorts in studies so that similarities and differences between males and females can be evaluated in all autism research studies. The purpose of this commentary is to (1) provide historical context about how females came to be under-represented in all research, not just in the field of autism and (2) learn from other areas of health and medicine about the potentially dire consequences of not studying both sexes, and (3) draw attention to the need to recruit sex-balanced cohorts in autism research, particularly in neuroimaging studies.

Distinct patterns of GABAergic interneuron pathology in autism are associated with intellectual impairment and stereotypic behaviors

LAY ABSTRACT

Autism spectrum disorder is a neurodevelopmental condition characterized by deficits in sociability and communication and the presence of repetitive behaviors. How specific pathological alterations of the brain contribute to the clinical profile of autism spectrum disorder remains unknown. We previously found that a specific type of inhibitory interneuron is reduced in number in the autism spectrum disorder prefrontal cortex. Here, we assessed the relationship between interneuron reduction and autism spectrum disorder symptom severity. We collected clinical records from autism spectrum disorder (n = 20) and assessed the relationship between the severity of symptoms and interneuron number. We found that the reduced number of inhibitory interneurons that we previously reported is linked to specific symptoms of autism spectrum disorder, particularly stereotypic movements and intellectual impairments.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Autism spectrum disorder and brain volume link through a set of mTOR-related genes

BACKGROUND

Larger than average head and brain sizes are often observed in individuals with autism spectrum disorders (ASDs). ASD and brain volume are both highly heritable, with multiple genetic variants contributing. However, it is unclear whether ASD and brain volume share any genetic mechanisms. Genes from the mammalian target of rapamycin (mTOR) pathway influence brain volume, and variants are found in rare genetic syndromes that include ASD features. Here we investigated whether variants in mTOR-related genes are also associated with ASD and if they constitute a genetic link between large brains and ASD.

METHODS

We extended our analyses between large heads (macrocephaly) and rare de novo mTOR-related variants in an intellectual disability cohort (N = 2,258). Subsequently using Fisher's exact tests we investigated the co-occurrence of mTOR-related de novo variants and ASD in the de-novo-db database (N = 23,098). We next selected common genetic variants within a set of 96 mTOR-related genes in genome-wide genetic association data of ASD (N = 46,350) to test gene-set association using MAGMA. Lastly, we tested genetic correlation between genome-wide genetic association data of ASD (N = 46,350) and intracranial volume (N = 25,974) globally using linkage disequilibrium score regression as well as mTOR specific by restricting the genetic correlation to the mTOR-related genes using GNOVA.

RESULTS

Our results show that both macrocephaly and ASD occur above chance level in individuals carrying rare de novo variants in mTOR-related genes. We found a significant mTOR gene-set association with ASD (p = .0029) and an mTOR-stratified positive genetic correlation between ASD and intracranial volume (p = .027), despite the absence of a significant genome-wide correlation (p = .81).

CONCLUSIONS

This work indicates that both rare and common variants in mTOR-related genes are associated with brain volume and ASD and genetically correlate them in the expected direction. We demonstrate that genes involved in mTOR signalling are potential mediators of the relationship between having a large brain and having ASD.

Functional connectivity based machine learning approach for autism detection in young children using MEG signals

Objective.Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.Approach.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique.Main results.To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.Significance.Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.

Mechanism of KMT5B haploinsufficiency in neurodevelopment in humans and mice

Pathogenic variants in KMT5B, a lysine methyltransferase, are associated with global developmental delay, macrocephaly, autism, and congenital anomalies (OMIM# 617788). Given the relatively recent discovery of this disorder, it has not been fully characterized. Deep phenotyping of the largest (n = 43) patient cohort to date identified that hypotonia and congenital heart defects are prominent features that were previously not associated with this syndrome. Both missense variants and putative loss-of-function variants resulted in slow growth in patient-derived cell lines. KMT5B homozygous knockout mice were smaller in size than their wild-type littermates but did not have significantly smaller brains, suggesting relative macrocephaly, also noted as a prominent clinical feature. RNA sequencing of patient lymphoblasts and Kmt5b haploinsufficient mouse brains identified differentially expressed pathways associated with nervous system development and function including axon guidance signaling. Overall, we identified additional pathogenic variants and clinical features in KMT5B-related neurodevelopmental disorder and provide insights into the molecular mechanisms of the disorder using multiple model systems.

Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion

Diffusion tensor imaging (DTI) has been used as an outcome measure in clinical trials for several psychiatric disorders but has rarely been explored in autism clinical trials. This is despite a large body of research suggesting altered white matter structure in autistic individuals. The current study is a secondary analysis of changes in white matter connectivity from a double-blind placebo-control trial of a single intravenous cord blood infusion in 2-7-year-old autistic children (1). Both clinical assessments and DTI were collected at baseline and 6 months after infusion. This study used two measures of white matter connectivity: change in node-to-node connectivity as measured through DTI streamlines and a novel measure of feedback network connectivity, Ollivier-Ricci curvature (ORC). ORC is a network measure which considers both local and global connectivity to assess the robustness of any given pathway. Using both the streamline and ORC analyses, we found reorganization of white matter pathways in predominantly frontal and temporal brain networks in autistic children who received umbilical cord blood treatment versus those who received a placebo. By looking at changes in network robustness, this study examined not only the direct, physical changes in connectivity, but changes with respect to the whole brain network. Together, these results suggest the use of DTI and ORC should be further explored as a potential biomarker in future autism clinical trials. These results, however, should not be interpreted as evidence for the efficacy of cord blood for improving clinical outcomes in autism. This paper presents a secondary analysis using data from a clinical trial that was prospectively registered with ClinicalTrials.gov(NCT02847182).

Re-Examining Labels in Neurocognitive Research: Evidence from Bilingualism and Autism as Spectrum-Trait Cases

Despite the fact that the urge to investigate bilingualism and neurodevelopmental disorders as continuous indices rather than categorical ones has been well-voiced among researchers with respect to research methodological approaches, in the recent literature, when it comes to examining language, cognitive skills and neurodivergent characteristics, it is still the case that the most prevalent view is the categorisation of adults or children into groups. In other words, there is a categorisation of individuals, e.g., monolingual vs. bilingual children or children with typical and atypical/non-typical/non-neurotypical development. We believe that this labelling is responsible for the conflicting results that we often come across in studies. The aim of this review is to bring to the surface the importance of individual differences through the study of relevant articles conducted in bilingual children and children with autism, who are ideal for this study. We concur with researchers who already do so, and we further suggest moving away from labels and instead shift towards the view that not everything is either white or black. We provide suggestions as to how this shift could be implemented in research, while mostly aiming at starting a discourse rather than offering a definite path.

Sex-dependent structure of socioemotional salience, executive control, and default mode networks in preschool-aged children with autism

The structure of large-scale intrinsic connectivity networks is atypical in adolescents diagnosed with autism spectrum disorder (ASD or autism). However, the degree to which alterations occur in younger children, and whether these differences vary by sex, is unknown. We utilized structural magnetic resonance imaging (MRI) data from a sex- and age- matched sample of 122 autistic and 122 typically developing (TD) children (2-4 years old) to investigate differences in underlying network structure in preschool-aged autistic children within three large scale intrinsic connectivity networks implicated in ASD: the Socioemotional Salience, Executive Control, and Default Mode Networks. Utilizing structural covariance MRI (scMRI), we report network-level differences in autistic versus TD children, and further report preliminary findings of sex-dependent differences within network topology.

[Early prognostic of ASD: A challenge]

Autism Spectrum Disorders (ASD) are born in the womb generated by intrauterine genetic or environmental insult. ASD diagnostic is made at the age of 3-5 years in Europe and in the US. Relying on this, we have tested the hypothesis of identifying already at birth babies who might be diagnosed later with ASD, thereby facilitating an early use of psychoeducative techniques to attenuate the severity of the symptoms. Here, we discuss the various approaches that have been used to enable an early diagnosis. We have ourselves used an approach based on a "without a priori" machine learning analysis of all maternity biological and ultrasound data available in French maternities (around 116) in utero and after birth. This program made it possible to identify at birth almost all (96%) of babies who will be later neurotypical and around half of those who will be diagnosed with ASD. Some of the parameters allowing this identification were largely unexpected with no known links with ASD. This approach will enable an early identification of babies at risk, but also might be used to diagnose ASD later on, and perhaps could help to get a better understanding of the heterogeneity of ASD.

Gray matter volume alteration is associated with insistence on sameness and cognitive flexibility in autistic youth

Restricted and repetitive behaviors (RRBs) are hallmark characteristics of autism spectrum disorder (ASD). Previous studies suggest that insistence on sameness (IS) characterized as higher-order RRBs may be a promising subgrouping variable for ASD. Cognitive inflexibility may underpin IS behaviors. However, the neuroanatomical correlates of IS and associated cognitive functions remain unclear. We analyzed data from 140 autistic youth and 124 typically developing (TD) youth (mean age = 15.8 years). Autistic youth were stratified by median-split based on three current IS items in the autism diagnostic interview-revised into two groups (high, HIS, n = 70, and low, LIS, n = 70). Differences in cognitive flexibility were assessed by the Cambridge neuropsychological test automated battery (CANTAB). T1-weighted brain structural images were analyzed using voxel-based morphometry (VBM) to identify differences in gray matter (GM) volume among the three groups. GM volume of regions showing group differences was then correlated with cognitive flexibility. The HIS group showed decreased GM volumes in the left supramarginal gyrus compared to the LIS group and increased GM volumes in the vermis VIII and left cerebellar lobule VIII compared to TD individuals. We did not find significant correlations between regional GM volumes and extra-dimensional shift errors. IS may be a unique RRB component and a potentially valuable stratifier of ASD. However, the neurocognitive underpinnings require further clarification. LAY SUMMARY: The present study found parietal, temporal and cerebellar gray matter volume alterations in autistic youth with greater insistence on sameness. The findings suggest that insistence on sameness may be a useful feature to parse the heterogeneity of the autism spectrum yet further research investigating the underlying neurocognitive mechanism is warranted.

Of differing methods, disputed estimates and discordant interpretations: the meta-analytical multiverse of brain volume and IQ associations

Brain size and IQ are positively correlated. However, multiple meta-analyses have led to considerable differences in summary effect estimations, thus failing to provide a plausible effect estimate. Here we aim at resolving this issue by providing the largest meta-analysis and systematic review so far of the brain volume and IQ association (86 studies; 454 effect sizes from k = 194 independent samples; N = 26 000+) in three cognitive ability domains (full-scale, verbal, performance IQ). By means of competing meta-analytical approaches as well as combinatorial and specification curve analyses, we show that most reasonable estimates for the brain size and IQ link yield r-values in the mid-0.20s, with the most extreme specifications yielding rs of 0.10 and 0.37. Summary effects appeared to be somewhat inflated due to selective reporting, and cross-temporally decreasing effect sizes indicated a confounding decline effect, with three quarters of the summary effect estimations according to any reasonable specification not exceeding r = 0.26, thus contrasting effect sizes were observed in some prior related, but individual, meta-analytical specifications. Brain size and IQ associations yielded r = 0.24, with the strongest effects observed for more g-loaded tests and in healthy samples that generalize across participant sex and age bands.

The Synaptic Gene Study: Design and Methodology to Identify Neurocognitive Markers in Phelan-McDermid Syndrome and NRXN1 Deletions

Synaptic gene conditions, i.e., “synaptopathies,” involve disruption to genes expressed at the synapse and account for between 0.5 and 2% of autism cases. They provide a unique entry point to understanding the molecular and biological mechanisms underpinning autism-related phenotypes. Phelan-McDermid Syndrome (PMS, also known as 22q13 deletion syndrome) and NRXN1 deletions (NRXN1ds) are two synaptopathies associated with autism and related neurodevelopmental disorders (NDDs). PMS often incorporates disruption to the SHANK3 gene, implicated in excitatory postsynaptic scaffolding, whereas the NRXN1 gene encodes neurexin-1, a presynaptic cell adhesion protein; both are implicated in trans-synaptic signaling in the brain. Around 70% of individuals with PMS and 43–70% of those with NRXN1ds receive a diagnosis of autism, suggesting that alterations in synaptic development may play a crucial role in explaining the aetiology of autism. However, a substantial amount of heterogeneity exists between conditions. Most individuals with PMS have moderate to profound intellectual disability (ID), while those with NRXN1ds have no ID to severe ID. Speech abnormalities are common to both, although appear more severe in PMS. Very little is currently known about the neurocognitive underpinnings of phenotypic presentations in PMS and NRXN1ds. The Synaptic Gene (SynaG) study adopts a gene-first approach and comprehensively assesses these two syndromic forms of autism. The study compliments preclinical efforts within AIMS-2-TRIALS focused on SHANK3 and NRXN1. The aims of the study are to (1) establish the frequency of autism diagnosis and features in individuals with PMS and NRXN1ds, (2) to compare the clinical profile of PMS, NRXN1ds, and individuals with ‘idiopathic’ autism (iASD), (3) to identify mechanistic biomarkers that may account for autistic features and/or heterogeneity in clinical profiles, and (4) investigate the impact of second or multiple genetic hits on heterogeneity in clinical profiles. In the current paper we describe our methodology for phenotyping the sample and our planned comparisons, with information on the necessary adaptations made during the global COVID-19 pandemic. We also describe the demographics of the data collected thus far, including 25 PMS, 36 NRXN1ds, 33 iASD, and 52 NTD participants, and present an interim analysis of autistic features and adaptive functioning.

The Autism Phenome Project: Toward Identifying Clinically Meaningful Subgroups of Autism

One of the most universally accepted facts about autism is that it is heterogenous. Individuals diagnosed with autism spectrum disorder have a wide range of behavioral presentations and a variety of co-occurring medical and mental health conditions. The identification of more homogenous subgroups is likely to lead to a better understanding of etiologies as well as more targeted interventions and treatments. In 2006, we initiated the UC Davis MIND Institute Autism Phenome Project (APP) with the overarching goal of identifying clinically meaningful subtypes of autism. This ongoing longitudinal multidisciplinary study now includes over 400 children and involves comprehensive medical, behavioral, and neuroimaging assessments from early childhood through adolescence (2-19 years of age). We have employed several strategies to identify sub-populations within autistic individuals: subgrouping by neural, biological, behavioral or clinical characteristics as well as by developmental trajectories. In this Mini Review, we summarize findings to date from the APP cohort and describe progress made toward identifying meaningful subgroups of autism.

Brain exosomes as minuscule information hub for Autism Spectrum Disorder

INTRODUCTION

Autism spectrum disorder (ASD) is a neurodevelopmental disorder initiating in the first three years of life. Early initiation of management therapies can significantly improve the health and quality of life of ASD subjects. Thus, indicating the need for suitable biomarkers for the early identification of ASD. Various biological domains were investigated in the quest for reliable biomarkers. However, most biomarkers are in the preliminary stage, and clinical validation is yet to be defined. Exosome based research gained momentum in various Central Nervous System disorders for biomarker identification. However, the utility and prospect of exosomes in ASD is still underexplored.

AREAS COVERED

In the present review, we summarized the biomarker discovery current status and the future of brain-specific exosomes in understanding pathophysiology and its potential as a biomarker. The studies reviewed herein were identified via systematic search (dated: June 2021) of PubMed using variations related to autism (ASD OR autism OR Autism spectrum disorder) AND exosomes AND/OR biomarkers.

EXPERT OPINION

As exosomess are highly relevant in brain disorders like ASD, direct access to brain tissue for molecular assessment is ethically impossible. Thus investigating the brain-derived exosomes would undoubtedly answer many unsolved aspects of the pathogenesis and provide reliable biomarkers.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Are Vermal Lobules VI-VII Smaller in Autism Spectrum Disorder?

Cerebellar volume, in particular vermal lobule areas VI-VII, have been extensively researched in individuals with autism spectrum disorder (ASD), although findings are often unclear. The aim of the present study is to consolidate all existing cerebellar and age data of individuals with ASD, and compare this data to typically developing (TD) controls. Raw data, or the means and standard deviations of cerebellar volume and age, were obtained from 17 studies (N_Cerebellum: 421 ASD and 370 TD participants; N_VI-VII: 506 ASD and 290 TD participants). Total cerebellar volume, or VI-VII area, was plotted against age and lines of fit of ASD and TD data were compared. Mean differences in cerebellar volume and VI-VII area between participants with ASD and TD participants were then compared via ANCOVA analyses. Findings revealed multiple differences in VI-VII area between participants with ASD and TD participants below 18 years of age. Additionally, cerebellar volume was greater in males with ASD than TD males between 2 and 4 years. In the present study, cerebellar volume and VI-VII area show different rates of change across age for those with autism compared with those without. These morphological differences provide a neurobiological justification to investigate related behavioural correlates.

Autism BrainNet: A Collaboration Between Medical Examiners, Pathologists, Researchers, and Families to Advance the Understanding and Treatment of Autism Spectrum Disorder

CONTEXT.—

Autism spectrum disorder is a neurodevelopmental condition that affects over 1% of the population worldwide. Developing effective preventions and treatments for autism will depend on understanding the neuropathology of the disorder. While evidence from magnetic resonance imaging indicates altered development of the autistic brain, it lacks the resolution needed to identify the cellular and molecular underpinnings of the disorder. Postmortem studies of human brain tissue currently represent the only viable option to pursuing these critical studies. Historically, the availability of autism brain tissue has been extremely limited.

OBJECTIVE.—

To overcome this limitation, Autism BrainNet, funded by the Simons Foundation, was formed as a network of brain collection sites that work in a coordinated fashion to develop a library of human postmortem brain tissues for distribution to researchers worldwide. Autism BrainNet has collection sites (or Nodes) in California, Texas, and Massachusetts; affiliated, international Nodes are located in Oxford, England and Montreal, Quebec, Canada.

DATA SOURCES.—

Pubmed, Autism BrainNet.

CONCLUSIONS.—

Because the death of autistic individuals is often because of an accident, drowning, suicide, or sudden unexpected death in epilepsy, they often are seen in a medical examiner's or coroner's office. Yet, autism is rarely considered when evaluating the cause of death. Advances in our understanding of chronic traumatic encephalopathy have occurred because medical examiners and neuropathologists questioned whether a pathologic change might exist in individuals who played contact sports and later developed severe behavioral problems. This article highlights the potential for equally significant breakthroughs in autism arising from the proactive efforts of medical examiners, pathologists, and coroners in partnership with Autism BrainNet.

Effects of age on loudness-dependent auditory ERPs in young autistic and typically-developing children

Limited research has investigated the development of auditory ERPs in young children, and particularly how stimulus intensity may affect these auditory ERPs. Previous research has also yielded inconsistent findings regarding differences in the development of auditory ERPs in autism and typical development. Furthermore, stimulus intensity may be of particular interest in autism insofar as autistic people may have atypical experiences of sound intensity (e.g., hyperacusis). Therefore, the present study examined associations between age and ERPs evoked by tones of differing intensities (50, 60, 70, and 80 dB SPL) in a large sample of young children (2-5 years) with and without an autism diagnosis. Correlations between age and P1 latencies were examined, while cluster-based permutation testing was used to examine associations between age and neural response amplitudes, as well as group differences in amplitude, over all electrode sites in the longer time window of 1-350 ms. Older autistic participants had faster P1 latencies, but these effects only attained significance over the right hemisphere in response to soft 50 dB sounds. Autistic participants had slower P1 responses to 80 dB sounds over the right hemisphere. Over the scalp regions associated with the later N2 response, more negative response amplitudes (that is, larger N2 responses) were observed in typically-developing than autistic participants. Furthermore, continuous associations between response amplitudes and age suggested that older typically-developing participants exhibited stronger N2 responses to all intensities, though this effect may have at least in part reflected the absence of small positive voltage deflections in the N2 latency window. Age was associated with amplitudes of responses to 50 dB through 70 dB sounds in autism, but in contrast to Typical Development (TD), little evidence of relationships between age and amplitudes in the N2 latency window was found in autism in the 80 dB condition. Although caution should be exercised in interpretation due to the cross-sectional nature of this study, these findings suggest that developmental changes in auditory responses may differ across diagnostic groups in a manner that depends on perceived loudness and/or stimulus intensity.

Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD

To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.

Brief Report: Role of Parent-Reported Executive Functioning and Anxiety in Insistence on Sameness in Individuals with Germline PTEN Mutations

This study aimed to characterize the relationship between insistence on sameness (IS), executive functioning (EF) and anxiety among individuals with PTEN mutations and individuals with macrocephalic ASD. The sample included 38 individuals with PTEN mutation and ASD diagnosis (PTEN-ASD; M_age = 8.93 years, SD_age = 4.75), 23 with PTEN mutation without ASD (PTEN-no ASD; M_age = 8.94 years; SD_age = 4.85) and 25 with ASD and macrocephaly but with no PTEN mutation (Macro-ASD; M_age = 11.99 years; SD_age = 5.15). The final model accounted for 45.7% of variance in IS, with Set-Shifting EF subdomain as a unique independent predictor (t = 4.12, p < 0.001). This investigation provides the first preliminary evidence for the EF-anxiety-IS interrelationship in individuals with PTEN mutations and with macrocephalic ASD.

Diagnostic Approach to Macrocephaly in Children

Macrocephaly affects up to 5% of the pediatric population and is defined as an abnormally large head with an occipitofrontal circumference (OFC) >2 standard deviations (SD) above the mean for a given age and sex. Taking into account that about 2-3% of the healthy population has an OFC between 2 and 3 SD, macrocephaly is considered as "clinically relevant" when OFC is above 3 SD. This implies the urgent need for a diagnostic workflow to use in the clinical setting to dissect the several causes of increased OFC, from the benign form of familial macrocephaly and the Benign enlargement of subarachnoid spaces (BESS) to many pathological conditions, including genetic disorders. Moreover, macrocephaly should be differentiated by megalencephaly (MEG), which refers exclusively to brain overgrowth, exceeding twice the SD (3SD-"clinically relevant" megalencephaly). While macrocephaly can be isolated and benign or may be the first indication of an underlying congenital, genetic, or acquired disorder, megalencephaly is most likely due to a genetic cause. Apart from the head size evaluation, a detailed family and personal history, neuroimaging, and a careful clinical evaluation are crucial to reach the correct diagnosis. In this review, we seek to underline the clinical aspects of macrocephaly and megalencephaly, emphasizing the main differential diagnosis with a major focus on common genetic disorders. We thus provide a clinico-radiological algorithm to guide pediatricians in the assessment of children with macrocephaly.

Cortical morphometry and neural dysfunction in Huntington's disease: a review

Numerous neuroimaging techniques have been used to identify biomarkers of disease progression in Huntington's disease (HD). To date, the earliest and most sensitive of these is caudate volume; however, it is becoming increasingly evident that numerous changes to cortical structures, and their interconnected networks, occur throughout the course of the disease. The mechanisms by which atrophy spreads from the caudate to these cortical regions remains unknown. In this review, the neuroimaging literature specific to T1-weighted and diffusion-weighted magnetic resonance imaging is summarized and new strategies for the investigation of cortical morphometry and the network spread of degeneration in HD are proposed. This new avenue of research may enable further characterization of disease pathology and could add to a suite of biomarker/s of disease progression for patient stratification that will help guide future clinical trials.

Autism classified by magnetic resonance imaging: A pilot study of a potential diagnostic tool

OBJECTIVES

Individual anatomical biomarkers have limited power for the classification of autism. The present study introduces a multivariate classification approach using structural magnetic resonance imaging data from individuals with and without autism.

METHODS

The classifier utilizes z-normalization, parameter weighting, and interindividual comparison on brain segmentation data, for estimation of an individual summed total index (TI). The TI indicates whether the gross morphological pattern of each individual's brain is in the direction of cases or controls.

RESULTS

Morphometric analysis found significant differences within subcortical gray matter structures and limbic areas. There was no significant difference in total brain volume. A case-control pilot-study of TIs in normally intelligent individuals with autism (24) and without (21) yielded a maximal accuracy of 78.9% following cross-validation. It showed a high accuracy compared with machine learning methods when tested on the same dataset. The TI correlated well with the autism quotient (R = 0.51) across groups.

CONCLUSION

These results are on par with studies on autism using machine learning. The main contributions are its transparency and simplicity. The possibility of including additional neuroimaging data further increases the potential of the classifier as a diagnostic aid for neuropsychiatric disorders, as well as a research tool for neuroscientific investigations.

A Longitudinal Study of Local Gyrification Index in Young Boys With Autism Spectrum Disorder

Local gyrification index (LGI), a metric quantifying cortical folding, was evaluated in 105 boys with autism spectrum disorder (ASD) and 49 typically developing (TD) boys at 3 and 5 years-of-age. At 3 years-of-age, boys with ASD had reduced gyrification in the fusiform gyrus compared with TD boys. A longitudinal evaluation from 3 to 5 years revealed that while TD boys had stable/decreasing LGI, boys with ASD had increasing LGI in right inferior temporal gyrus, right inferior frontal gyrus, right inferior parietal lobule, and stable LGI in left lingual gyrus. LGI was also examined in a previously defined neurophenotype of boys with ASD and disproportionate megalencephaly. At 3 years-of-age, this subgroup exhibited increased LGI in right dorsomedial prefrontal cortex, cingulate cortex, and paracentral cortex, and left cingulate cortex and superior frontal gyrus relative to TD boys and increased LGI in right paracentral lobule and parahippocampal gyrus, and left precentral gyrus compared with boys with ASD and normal brain size. In summary, this study identified alterations in the pattern and development of LGI during early childhood in ASD. Distinct patterns of alterations in subgroups of boys with ASD suggests that multiple neurophenotypes exist and boys with ASD and disproportionate megalencephaly should be evaluated separately.

Deconstructing the repetitive behaviour phenotype in autism spectrum disorder through a large population-based analysis

OBJECTIVE

Restricted and repetitive pattern of behaviours and interests (RRB) are a cardinal feature of autism spectrum disorder (ASD), but there remains uncertainty about how these diverse behaviours vary according to individual characteristics. This study provided the largest exploration to date of the relationship between Repetitive Motor Behaviours, Rigidity/Insistence on Sameness and Circumscribed Interests with other individual characteristics in newly diagnosed individuals with ASD.

METHOD

Participants (N = 3,647; 17.7% females; Mage = 6.6 years [SD = 4.7]) were part of the Western Australian (WA) Register for ASD, an independent, prospective collection of demographic and diagnostic data of newly diagnosed cases of ASD in WA. Diagnosticians rated each of the DSM-IV-TR criteria on a 4-point Likert severity scale, and here we focused on the Repetitive Motor Behaviours, Insistence on Sameness and Circumscribed Interests symptoms.

RESULTS

The associations between RRB domains, indexed by Kendall's Tau, were weak, ranging from non-significant for both Circumscribed Interests and Repetitive Motor Behaviours to significant (.20) for Insistence on Sameness and Repetitive Motor Behaviours. Older age at diagnosis was significantly associated with lower Circumscribed Interests and significantly associated with higher Insistence on Sameness and Repetitive Motor Behaviours. Male sex was significantly associated with higher Repetitive Motor Behaviours but not Insistence on Sameness or Circumscribed Interests.

CONCLUSIONS

The pattern of associations identified in this study provides suggestive evidence for the distinctiveness of Repetitive Motor Behaviours, Insistence on Sameness and Circumscribed Interests, highlighting the potential utility of RRB domains for stratifying the larger ASD population into smaller, more phenotypically homogeneous subgroups that can help to facilitate efforts to understand diverse ASD aetiology and inform design of future interventions.

Understanding Hippocampal Development in Young Children With Autism Spectrum Disorder

OBJECTIVE

We examined growth trajectories of hippocampal volume (HV) in early childhood in a longitudinal cohort of male and female participants with autism spectrum disorder (ASD) and typically developing (TD) individuals, and investigated HV in those with large brains. Relations between factors potentially associated with hippocampal size and growth were investigated.

METHOD

Participants received 1 to 3 structural magnetic resonance imaging scans between ages 25 and 80 months (unique participants: ASD, n =200; TD, n =110; total longitudinal scans, n = 593). HV growth during this period was examined using mixed-effects linear models. Associations between early HV and growth rates, and IQ and adaptive functioning, were evaluated.

RESULTS

After accounting for cerebral hemisphere volume, male participants exhibited larger left and right HV than female participants. Hippocampal growth rates did not differ by sex. In children with larger hemisphere volumes, male and female participants with ASD had relatively larger HV than TD participants of similar hemisphere volume. This effect was present in a broader group than only those with disproportionate megalencephaly (male participants with large cerebral volumes relative to body size). Right hippocampi were larger than left hippocampi in both groups and sexes. Right versus left volume differences were greater for ASD. After adjusting for hemisphere volume, male participants with ASD showed a significant positive association between right hippocampal growth and adaptive behavior.

CONCLUSION

HV was relatively greater in ASD in analyses adjusting for hemisphere volume, whereas only subtle differences were observed in HV and growth between participants with ASD and TD participants in unadjusted analyses, suggesting that ASD involves atypical coupling between HV and brain size.

Abnormal Brain Development in Huntington' Disease Is Recapitulated in the zQ175 Knock-In Mouse Model

Emerging cellular and molecular studies are providing compelling evidence that altered brain development contributes to the pathogenesis of Huntington's disease (HD). There has been lacking longitudinal system-level data obtained from in vivo HD models supporting this hypothesis. Our human MRI study in children and adolescents with HD indicates that striatal development differs between the HD and control groups, with initial hypertrophy and more rapid volume decline in HD group. In this study, we aimed to determine whether brain development recapitulates the human HD during the postnatal period. Longitudinal structural MRI scans were conducted in the heterozygous zQ175 HD mice and their littermate controls. We found that male zQ175 HD mice recapitulated the region-specific abnormal volume development in the striatum and globus pallidus, with early hypertrophy and then rapidly decline in the regional volume. In contrast, female zQ175 HD mice did not show significant difference in brain volume development with their littermate controls. This is the first longitudinal study of brain volume development at the system level in HD mice. Our results suggest that altered brain development may contribute to the HD pathogenesis. The potential effect of gene therapies targeting on neurodevelopmental event is worth to consider for HD therapeutic intervention.

Neuroimaging Markers of Risk and Pathways to Resilience in Autism Spectrum Disorder

Autism spectrum disorder is a complex, heterogeneous neurodevelopmental condition of largely unknown etiology. This heterogeneity of symptom presentation, combined with high rates of comorbidity with other developmental disorders and a lack of reliable biomarkers, makes diagnosing and evaluating life outcomes for individuals with autism spectrum disorder a challenge. We review the growing literature on neuroimaging-based biomarkers of risk for the development of autism and explore evidence for resilience in some autistic individuals. The current literature suggests that neuroimaging during early infancy, in combination with prebirth and early genetic studies, is a promising tool for identifying biomarkers of risk, while studies of gene expression and DNA methylation have provided some key insights into mechanisms of resilience. With genetics and the environment contributing to both risk for the development of autism spectrum disorder and conditions for resilience, additional studies are needed to understand how risk and resilience interact mechanistically, whereby factors of risk may engender conditions for adaptation. Future studies should prioritize longitudinal designs in global cohorts, with the involvement of the autism community as partners in research to help identify domains of functioning that hold value and importance to the community.

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.

Head circumference trends in autism between 0 and 100 months

LAY ABSTRACT

Summaries of studies that have measured head size in those with autism, known as meta-analyses, currently exist. However, this approach does not adequately explain extreme cases (such as those with extremely small, or extremely large, head size). Because of this, we obtained all available published data measuring head size (12 studies). The data from each study were then combined to make a larger dataset. We found that females with autism aged 12-17 months had, on average, smaller head sizes. Otherwise, average head size was not atypical in autism. However, we found that males with autism were more likely to have extreme head sizes at birth and between 60 and 100 months, a small head between 6 and 11 months, and a large head between 12 and 17 months. Females with autism were more likely to have extreme head sizes between 36 and 59 months and were less likely at birth. Our approach was able to measure the influence of age and biological sex on head size in autism, as well as the frequency of extreme cases of head size in autism. These results add to what we already know about head size in autism.

Cognitive function and its relationship with brain structure in myotonic dystrophy type 1

Studies have shown relationships between white matter abnormalities and cognitive dysfunction in myotonic dystrophy type 1 (DM1), but comprehensive analysis of potential structure-function relationships are lacking. Fifty adult-onset DM1 individuals (33 female) and 68 unaffected adults (45 female) completed the Wechsler Adult Intelligence Scale-IV (WAIS-IV) to determine the levels and patterns of intellectual functioning. Neuroimages were acquired with a 3T scanner and were processed with BrainsTools. Regional brain volumes (regions of interest, ROIs) were adjusted for inter-scanner variation and intracranial volume. Linear regression models were conducted to assess if group by ROI interaction terms significantly predicted WAIS-IV composite scores. Models were adjusted for age and sex. The DM1 group had lower Perceptual Reasoning Index (PRI), Working Memory Index (WMI), and Processing Speed Index (PSI) scores than the unaffected group (PRI t₍₁₁₃₎  = -3.28, p = 0.0014; WMI t₍₁₁₄₎  = -3.49, p = 0.0007; PSI t₍₁₁₄₎  = -2.98, p = 0.0035). The group by hippocampus interaction term was significant for both PRI and PSI (PRI (t₍₁₁₁₎  = -2.82, p = 0.0057; PSI (t₍₁₁₂₎  = -2.87, p = 0.0049)). There was an inverse association between hippocampal volume and both PRI and PSI in the DM1 group (the higher the volume, the lower the intelligence quotient scores), but no such association was observed in the unaffected group. Enlarged hippocampal volume may underlie some aspects of cognitive dysfunction in adult-onset DM1, suggesting that increased volume of the hippocampus may be pathological.

Emerging biomarkers in autism spectrum disorder: a systematic review

Autism spectrum disorder (ASD) affects approximately 2% of children in the United States (US) yet its etiology is unclear and effective treatments are lacking. Therapeutic interventions are most effective if started early in life, yet diagnosis often remains delayed, partly because the diagnosis of ASD is based on identifying abnormal behaviors that may not emerge until the disorder is well established. Biomarkers that identify children at risk during the pre-symptomatic period, assist with early diagnosis, confirm behavioral observations, stratify patients into subgroups, and predict therapeutic response would be a great advance. Here we underwent a systematic review of the literature on ASD to identify promising biomarkers and rated the biomarkers in regards to a Level of Evidence and Grade of Recommendation using the Oxford Centre for Evidence-Based Medicine scale. Biomarkers identified by our review included physiological biomarkers that identify neuroimmune and metabolic abnormalities, neurological biomarkers including abnormalities in brain structure, function and neurophysiology, subtle behavioral biomarkers including atypical development of visual attention, genetic biomarkers and gastrointestinal biomarkers. Biomarkers of ASD may be found prior to birth and after diagnosis and some may predict response to specific treatments. Many promising biomarkers have been developed for ASD. However, many biomarkers are preliminary and need to be validated and their role in the diagnosis and treatment of ASD needs to be defined. It is likely that biomarkers will need to be combined to be effective to identify ASD early and guide treatment.

Intergenerational Metabolic Syndrome and Neuronal Network Hyperexcitability in Autism

We review evidence that suggests a role for excessive consumption of energy-dense foods, particularly fructose, and consequent obesity and insulin resistance (metabolic syndrome) in the recent increase in prevalence of autism spectrum disorders (ASD). Maternal insulin resistance, obesity, and diabetes may predispose offspring to ASD by mechanisms involving chronic activation of anabolic cellular pathways and a lack of metabolic switching to ketosis resulting in a deficit in GABAergic signaling and neuronal network hyperexcitability. Metabolic reprogramming by epigenetic DNA and chromatin modifications may contribute to alterations in gene expression that result in ASD. These mechanistic insights suggest that interventions that improve metabolic health such as intermittent fasting and exercise may ameliorate developmental neuronal network abnormalities and consequent behavioral manifestations in ASD.

Paternal age contribution to brain white matter aberrations in autism spectrum disorder

AIM

Although advanced parental age holds an increased risk for autism spectrum disorder (ASD), its role as a potential risk factor for an atypical white matter development underlying the pathophysiology of ASD has not yet been investigated. The current study was aimed to detect white matter disparities in ASD, and further investigate the relationship of paternal and maternal age at birth with such disparities.

METHODS

Thirty-nine adult males with high-functioning ASD and 37 typically developing (TD) males were analyzed in the study. The FMRIB Software Library and tract-based spatial statistics were utilized to process and analyze the diffusion tensor imaging data.

RESULTS

Subjects with ASD exhibited significantly higher mean diffusivity (MD) and radial diffusivity (RD) in white matter fibers, including the association (inferior fronto-occipital fasciculus, right inferior longitudinal fasciculus, superior longitudinal fasciculi, uncinate fasciculus, and cingulum), commissural (forceps minor), and projection tracts (anterior thalamic radiation and right corticospinal tract) compared to TD subjects (Padjusted  < 0.05). No differences were seen in either fractional anisotropy or axial diffusivity. Linear regression analyses assessing the relationship between parental ages and the white matter aberrations revealed a positive correlation between paternal age (PA), but not maternal age, and both MD and RD in the affected fibers (Padjusted  < 0.05). Multiple regression showed that only PA was a predictor of both MD and RD.

CONCLUSION

Our findings suggest that PA contributes to the white matter disparities seen in individuals with ASD compared to TD subjects.

Big data approaches to decomposing heterogeneity across the autism spectrum

Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size) and 'deep' (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.

Differential Altered Auditory Event-Related Potential Responses in Young Boys on the Autism Spectrum With and Without Disproportionate Megalencephaly

Autism spectrum disorder (ASD), characterized by impairments in social communication and repetitive behaviors, often includes altered responses to sensory inputs as part of its phenotype. The neurobiological basis for altered sensory processing is not well understood. The UC Davis Medical Investigation of Neurodevelopmental Disorders Institute Autism Phenome Project is a longitudinal, multidisciplinary study of young children with ASD and age-matched typically developing (TD) controls. Previous analyses of the magnetic resonance imaging data from this cohort have shown that ∼15% of boys with ASD have disproportionate megalencephaly (DM) or brain size to height ratio, that is 1.5 standard deviations above the TD mean. Here, we investigated electrophysiological responses to auditory stimuli of increasing intensity (50-80 dB) in young toddlers (27-48 months old). Analyses included data from 36 age-matched boys, of which 24 were diagnosed with ASD (12 with and 12 without DM; ASD-DM and ASD-N) and 12 TD controls. We found that the two ASD subgroups differed in their electrophysiological response patterns to sounds of increasing intensity. At early latencies (55-115 ms), ASD-N does not show a loudness-dependent response like TD and ASD-DM, but tends to group intensities by soft vs. loud sounds, suggesting differences in sensory sensitivity in this group. At later latencies (145-195 ms), only the ASD-DM group shows significantly higher amplitudes for loud sounds. Because no similar effects were found in ASD-N and TD groups, this may be related to their altered neuroanatomy. These results contribute to the effort to delineate ASD subgroups and further characterize physiological responses associated with observable phenotypes. Autism Res 2019, 12: 1236-1250. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Approximately 15% of boys with ASD have much bigger brains when compared to individuals with typical development. By recording brain waves (electroencephalography) we compared how autistic children, with or without big brains, react to sounds compared to typically developing controls. We found that brain responses in the big-brained group are different from the two other groups, suggesting that they represent a specific autism subgroup.

Abnormal brain development in child and adolescent carriers of mutant huntingtin

OBJECTIVE

The huntingtin gene is critical for the formation and differentiation of the CNS, which raises questions about the neurodevelopmental effect of CAG expansion mutations within this gene (mHTT) that cause Huntington disease (HD). We sought to test the hypothesis that child and adolescent carriers of mHTT exhibit different brain growth compared to peers without the mutation by conducting structural MRI in youth who are at risk for HD. We also explored whether the length of CAG expansion affects brain development.

METHODS

Children and adolescents (age 6-18) with a parent or grandparent diagnosed with HD underwent MRI and blinded genetic testing to confirm the presence or absence of mHTT. Seventy-five individuals were gene-expanded (GE) and 97 individuals were gene-nonexpanded (GNE). The GE group was estimated to be on average 35 years from clinical onset. Following an accelerated longitudinal design, age-related changes in brain regions were estimated.

RESULTS

Age-related striatal volume changes differed significantly between the GE and GNE groups, with initial hypertrophy and more rapid volume decline in GE. This pattern was exaggerated with CAG expansion length for CAG > 50. A similar age-dependent group difference was observed for the globus pallidus, but not in other major regions.

CONCLUSION

Our results suggest that pathogenesis of HD begins with abnormal brain development. An understanding of potential neurodevelopmental features associated with mHTT may be needed for optimized implementation of preventative gene silencing therapies, such that normal aspects of neurodevelopment are preserved as neurodegeneration is forestalled.

Enhanced Glutamatergic Currents at Birth in Shank3 KO Mice

Autism spectrum disorders (ASD) are neurodevelopmental disorders induced by genetic and environmental factors. In our recent studies, we showed that the GABA developmental shifts during delivery and the second postnatal week are abolished in two rodent models of ASD. Maternal treatment around birth with bumetanide restored the GABA developmental sequence and attenuated the autism pathogenesis in offspring. Clinical trials conducted in parallel confirmed the usefulness of bumetanide treatment to attenuate the symptoms in children with ASD. Collectively, these observations suggest that an alteration of the GABA developmental sequence is a hallmark of ASD. Here, we investigated whether similar alterations occur in the Shank3 mouse model of ASD. We report that in CA3 pyramidal neurons, the driving force and inhibitory action of GABA are not different in naïve and Shank3-mutant age-matched animals at birth and during the second postnatal week. In contrast, the frequency of spontaneous excitatory postsynaptic currents is already enhanced at birth and persists through postnatal day 15. Therefore, in CA3 pyramidal neurons of Shank3-mutant mice, glutamatergic but not GABAergic activity is affected at early developmental stages, hence reflecting the heterogeneity of mechanisms underlying the pathogenesis of ASD.