Autism and abnormal development of brain connectivity

Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model

Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model (HMM) analysis for resting-state functional MRI (fMRI) from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange (ABIDE). Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls (HCs). Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states (including fractional occupancies and lifetimes) were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network (DMN), sensory processing networks [including visual network, auditory network, and sensory and motor network (SMN)]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD.

GABAergic System Dysfunction in Autism Spectrum Disorders

Autism spectrum disorder (ASD) refers to a series of neurodevelopmental diseases characterized by two hallmark symptoms, social communication deficits and repetitive behaviors. Gamma-aminobutyric acid (GABA) is one of the most important inhibitory neurotransmitters in the central nervous system (CNS). GABAergic inhibitory neurotransmission is critical for the regulation of brain rhythm and spontaneous neuronal activities during neurodevelopment. Genetic evidence has identified some variations of genes associated with the GABA system, indicating an abnormal excitatory/inhibitory (E/I) neurotransmission ratio implicated in the pathogenesis of ASD. However, the specific molecular mechanism by which GABA and GABAergic synaptic transmission affect ASD remains unclear. Transgenic technology enables translating genetic variations into rodent models to further investigate the structural and functional synaptic dysregulation related to ASD. In this review, we summarized evidence from human neuroimaging, postmortem, and genetic and pharmacological studies, and put emphasis on the GABAergic synaptic dysregulation and consequent E/I imbalance. We attempt to illuminate the pathophysiological role of structural and functional synaptic dysregulation in ASD and provide insights for future investigation.

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

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

Atypically high influence of subcortical activity on primary sensory regions in autism

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The age-dependent decrease of subcortico-cortical connectivity is attenuated in ASD.

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Primary sensory regions remain less segregated from subcortical activity in ASD.

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This could underlie an excessive amount of sensory input relayed to the cortex.

Background

Hypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from the persistent overconnectivity between primary sensory regions and deep brain nuclei in both children and adults with ASD – as reported by several previous studies – which could reflect a decreased or arrested maturation of brain connectivity. However, it has not yet been investigated whether this overconnectivity can be modelled as an excessive directional influence of subcortical brain activity on primary sensory cortical regions in ASD, with respect to age-matched typically developing (TD) individuals.

Methods

To this aim, we used dynamic causal modelling to estimate (1) the directional influence of subcortical activity on cortical processing and (2) the functional segregation of primary sensory cortical regions from subcortical activity in 166 participants with ASD and 193 TD participants from the Autism Brain Imaging Data Exchange (ABIDE). We then specifically tested the hypothesis that the age-related changes of these indicators of brain connectivity would differ between the two groups.

Results

We found that in TD participants age was significantly associated with decreased influence of subcortical activity on cortical processing, paralleled by an increased functional segregation of cortical sensory processing from subcortical activity. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD.

Conclusion

This atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory input relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.

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

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

Functional Brain Networks in Preschool Children With Autism Spectrum Disorders

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

iPSC toolbox for understanding and repairing disrupted brain circuits in autism

Over the past decade, tremendous progress has been made in defining autism spectrum disorder (ASD) as a disorder of brain connectivity. Indeed, whole-brain imaging studies revealed altered connectivity in the brains of individuals with ASD, and genetic studies identified rare ASD-associated mutations in genes that regulate synaptic development and function. However, it remains unclear how specific mutations alter the development of neuronal connections in different brain regions and whether altered connections can be restored therapeutically. The main challenge is the lack of preclinical models that recapitulate important aspects of human development for studying connectivity. Through recent technological innovations, it is now possible to generate patient- or mutation-specific human neurons or organoids from induced pluripotent stem cells (iPSCs) and to study altered connectivity in vitro or in vivo upon xenotransplantation into an intact rodent brain. Here, we discuss how deficits in neurodevelopmental processes may lead to abnormal brain connectivity and how iPSC-based models can be used to identify abnormal connections and to gain insights into underlying cellular and molecular mechanisms to develop novel therapeutics.

Right Anterior Theta Hypersynchrony as a Quantitative Measure Associated with Autistic Traits and K-Cl Cotransporter KCC2 Polymorphism

Our aim was to use theta coherence as a quantitative trait to investigate the relation of the polymorphisms in NKCC1 (rs3087889) and KCC2 (rs9074) channel protein genes to autistic traits (AQ) in neurotypicals. Coherence values for candidate connection regions were calculated from eyes-closed resting EEGs in two independent groups. Hypersynchrony within the right anterior region was related to AQ in both groups (p < 0.05), and variability in this hypersynchrony was related to the rs9074 polymorphism in the total group (p < 0.05). In conclusion, theta hypersynchrony within the right anterior region during eyes-closed rest can be considered a quantitative measure for autistic traits. Replicating our findings in two independent populations with different backgrounds strengthens the validity of the current study.

A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys

The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.

Assessment of behavioral, morphological and electrophysiological changes in prenatal and postnatal valproate induced rat models of autism spectrum disorder

Autism spectrum disorders (ASD) are neurodevelopmental disorders, that are characterized by core symptoms, such as alterations of social communication and restrictive or repetitive behavior. The etiology and pathophysiology of disease is still unknown, however, there is a strong interaction between genetic and environmental factors. An intriguing point in autism research is identification the vulnerable time periods of brain development that lack compensatory homeostatic corrections. Valproic acid (VPA) is an antiepileptic drug with a pronounced teratogenic effect associated with a high risk of ASD, and its administration to rats during the gestation is used for autism modeling. It has been hypothesized that valproate induced damage and functional alterations of autism target structures may occur and evolve during early postnatal life. Here, we used prenatal and postnatal administrations of VPA to investigate the main behavioral features which are associated with autism spectrum disorders core symptoms were tested in early juvenile and adult rats. Neuroanatomical lesion of autism target structures and electrophysiological studies in specific neural circuits. Our results showed that prenatal and early postnatal administration of valproate led to the behavioral alterations that were similar to ASD. Postnatally treated group showed tendency to normalize in adulthood. We found pronounced structural changes in the brain target regions of prenatally VPA-treated groups, and an absence of abnormalities in postnatally VPA-treated groups, which confirmed the different severity of VPA across different stages of brain development. The results of this study clearly show time dependent effect of VPA on neurodevelopment, which might be explained by temporal differences of brain regions' development process. Presumably, postnatal administration of valproate leads to the dysfunction of synaptic networks that is recovered during the lifespan, due to the brain plasticity and compensatory ability of circuit refinement. Therefore, investigations of compensatory homeostatic mechanisms activated after VPA administration and directed to eliminate the defects in postnatal brain, may elucidate strategies to improve the course of disease.

Cortical interneurons in autism

The mechanistic underpinnings of autism remain a subject of debate and controversy. Why do individuals with autism share an overlapping set of atypical behaviors and symptoms, despite having different genetic and environmental risk factors? A major challenge in developing new therapies for autism has been the inability to identify convergent neural phenotypes that could explain the common set of symptoms that result in the diagnosis. Although no striking macroscopic neuropathological changes have been identified in autism, there is growing evidence that inhibitory interneurons (INs) play an important role in its neural basis. In this Review, we evaluate and interpret this evidence, focusing on recent findings showing reduced density and activity of the parvalbumin class of INs. We discuss the need for additional studies that investigate how genes and the environment interact to change the developmental trajectory of INs, permanently altering their numbers, connectivity and circuit engagement.

Effects of Creative Movement Therapies on Social Communication, Behavioral-Affective, Sensorimotor, Cognitive, and Functional Participation Skills of Individuals With Autism Spectrum Disorder: A Systematic Review

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder affecting multiple developmental domains including social communication, behavioral-affective, sensorimotor, and cognitive systems. There is growing evidence for the use of holistic, whole-body, Creative Movement Therapies (CMT) such as music, dance, yoga, theater, and martial arts in addressing the multisystem impairments in ASD. We conducted a comprehensive quantitative and qualitative review of the evidence to date on the effects of CMT on multiple systems in individuals with ASD. The strongest evidence, both in terms of quantity and quality, exists for music and martial arts-based interventions followed by yoga and theater, with very limited research on dance-based approaches. Our review of 72 studies (N = 1,939 participants) across participants with ASD ranging from 3 to 65 years of age suggests that at present there is consistent evidence from high quality studies for small-to-large sized improvements in social communication skills following music and martial arts therapies and medium-to-large improvements in motor and cognitive skills following yoga and martial arts training, with insufficient evidence to date for gains in affective, sensory, and functional participation domains following CMT. Although promising, our review serves as a call for more rigorous high-quality research to assess the multisystem effects of CMT in ASD. Based on the existing literature, we discuss implications of our findings for autism researchers and also provide evidence-based guidelines for clinicians to incorporate CMT approaches in their plan of care for individuals with ASD.

Mechanisms governing activity-dependent synaptic pruning in the developing mammalian CNS

Almost 60 years have passed since the initial discovery by Hubel and Wiesel that changes in neuronal activity can elicit developmental rewiring of the central nervous system (CNS). Over this period, we have gained a more comprehensive picture of how both spontaneous neural activity and sensory experience-induced changes in neuronal activity guide CNS circuit development. Here we review activity-dependent synaptic pruning in the mammalian CNS, which we define as the removal of a subset of synapses, while others are maintained, in response to changes in neural activity in the developing nervous system. We discuss the mounting evidence that immune and cell-death molecules are important mechanistic links by which changes in neural activity guide the pruning of specific synapses, emphasizing the role of glial cells in this process. Finally, we discuss how these developmental pruning programmes may go awry in neurodevelopmental disorders of the human CNS, focusing on autism spectrum disorder and schizophrenia. Together, our aim is to give an overview of how the field of activity-dependent pruning research has evolved, led to exciting new questions and guided the identification of new, therapeutically relevant mechanisms that result in aberrant circuit development in neurodevelopmental disorders.

mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity

Postmortem studies have revealed increased density of excitatory synapses in the brains of individuals with autism spectrum disorder (ASD), with a putative link to aberrant mTOR-dependent synaptic pruning. ASD is also characterized by atypical macroscale functional connectivity as measured with resting-state fMRI (rsfMRI). These observations raise the question of whether excess of synapses causes aberrant functional connectivity in ASD. Using rsfMRI, electrophysiology and in silico modelling in Tsc2 haploinsufficient mice, we show that mTOR-dependent increased spine density is associated with ASD -like stereotypies and cortico-striatal hyperconnectivity. These deficits are completely rescued by pharmacological inhibition of mTOR. Notably, we further demonstrate that children with idiopathic ASD exhibit analogous cortical-striatal hyperconnectivity, and document that this connectivity fingerprint is enriched for ASD-dysregulated genes interacting with mTOR or Tsc2. Finally, we show that the identified transcriptomic signature is predominantly expressed in a subset of children with autism, thereby defining a segregable autism subtype. Our findings causally link mTOR-related synaptic pathology to large-scale network aberrations, revealing a unifying multi-scale framework that mechanistically reconciles developmental synaptopathy and functional hyperconnectivity in autism.

Intact predictive motor sequence learning in autism spectrum disorder

Atypical motor learning has been suggested to underpin the development of motoric challenges (e.g., handwriting difficulties) in autism. Bayesian accounts of autistic cognition propose a mechanistic explanation for differences in the learning process in autism. Specifically, that autistic individuals overweight incoming, at the expense of prior, information and are thus less likely to (a) build stable expectations of upcoming events and (b) react to statistically surprising events. Although Bayesian accounts have been suggested to explain differences in learning across a range of domains, to date, such accounts have not been extended to motor learning. 28 autistic and 35 non-autistic controls (IQ > 70) completed a computerised task in which they learned sequences of actions. On occasional "surprising" trials, an expected action had to be replaced with an unexpected action. Sequence learning was indexed as the reaction time difference between blocks which featured a predictable sequence and those that did not. Surprise-related slowing was indexed as the reaction time difference between surprising and unsurprising trials. No differences in sequence-learning or surprise-related slowing were observed between the groups. Bayesian statistics provided anecdotal to moderate evidence to support the conclusion that sequence learning and surprise-related slowing were comparable between the two groups. We conclude that individuals with autism do not show atypicalities in response to surprising events in the context of motor sequence-learning. These data demand careful consideration of the way in which Bayesian accounts of autism can (and cannot) be extended to the domain of motor learning.

Experience-dependent weakening of callosal synaptic connections in the absence of postsynaptic FMRP

Reduced structural and functional interhemispheric connectivity correlates with the severity of Autism Spectrum Disorder (ASD) behaviors in humans. Little is known of how ASD-risk genes regulate callosal connectivity. Here, we show that Fmr1, whose loss-of-function leads to Fragile X Syndrome (FXS), cell autonomously promotes maturation of callosal excitatory synapses between somatosensory barrel cortices in mice. Postnatal, cell-autonomous deletion of Fmr1 in postsynaptic Layer (L) 2/3 or L5 neurons results in a selective weakening of AMPA receptor- (R), but not NMDA receptor-, mediated callosal synaptic function, indicative of immature synapses. Sensory deprivation by contralateral whisker trimming normalizes callosal input strength, suggesting that experience-driven activity of postsynaptic Fmr1 KO L2/3 neurons weakens callosal synapses. In contrast to callosal inputs, synapses originating from local L4 and L2/3 circuits are normal, revealing an input-specific role for postsynaptic Fmr1 in regulation of synaptic connectivity within local and callosal neocortical circuits. These results suggest direct cell autonomous and postnatal roles for FMRP in development of specific cortical circuits and suggest a synaptic basis for long-range functional underconnectivity observed in FXS patients.

Cytokines profile in neonatal and adult wild-type mice post-injection of U. S. pediatric vaccination schedule

Introduction

A recent study from our laboratory demonstrated a number of neurobehavioral abnormalities in mice colony injected with a mouse-weight equivalent dose of all vaccines that are administered to infants in their first 18 months of life according to the U. S. pediatric vaccination schedule.

Cytokines have been studied extensively as blood immune and inflammatory biomarkers, and their association with neurodevelopmental disorders. Given the importance of cytokines in early neurodevelopment, we aimed to investigate the potential post-administration effects of the U. S. pediatric vaccines on circulatory cytokines in a mouse model.

In the current study, cytokines have been assayed at early and late time points in mice vaccinated early in postnatal life and compared with placebo controls.

Materials and methods

Newborn mouse pups were divided into three groups: i) vaccine (V1), ii) vaccine ​× ​3 (V3) and iii) placebo control. V1 group was injected with mouse weight-equivalent of the current U. S. pediatric vaccine schedule. V3 group was injected with same vaccines but at triple the dose and the placebo control was injected with saline. Pups were also divided according to the sampling age into two main groups: acute- and chronic-phase group. Blood samples were collected at postnatal day (PND) 23, two days following vaccine schedule for the acute-phase group or at 67 weeks post-vaccination for the chronic-phase groups. Fifteen cytokines were analyzed: GM-CSF, IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12p70, IL-13, IL-17A, MCP-1, TNF-α, and VEGF-A. Wilcoxon Rank Sum test or unpaired Student's t-test was performed where applicable.

Results

IL-5 levels in plasma were significantly elevated in the V1 and V3 group compared with the control only in the acute-phase group. The elevation of IL-5 levels in the two vaccine groups were significant irrespective of whether the sexes were combined or analyzed separately. Other cytokines (VEGF-A, TNF-α, IL-10, MCP-1, GM-CSF, IL-6, and IL-13) were also impacted, although to a lesser extent and in a sex-dependent manner. In the acute-phase group, females showed a significant increase in IL-10 and MCP-1 levels and a decrease in VEGF-A levels in both V1 and V3 group compared to controls. In the acute-phase, a significant increase in MCP-1 levels in V3 group and CM-CSF levels in V1 and V3 group and decrease in TNF-α levels in V1 group were observed in treated males as compared with controls. In chronic-phase females, levels of VEGF-A in V1 and V3 group, TNF-α in V3 group, and IL-13 in V1 group were significantly decreased in contrast with controls. In chronic-phase males, TNF-α levels were significantly increased in V1 group and IL-6 levels decreased in V3 group in comparison to controls. The changes in levels of most tested cytokines were altered between the early and the late postnatal assays.

Conclusions

IL-5 levels significantly increased in the acute-phase of the treatment in the plasma of both sexes that were subjected to V1 and V3 injections. These increases had diminished by the second test assayed at week 67. These results suggest that a profound, albeit transient, effect on cytokine levels may be induced by the whole vaccine administration supporting our recently published observations regarding the behavioral abnormalities in the same mice. These observations support the view that the administration of whole pediatric vaccines in a neonatal period may impact at least short-term CNS functions in mice.

Altered directed connectivity during processing of predictive stimuli in psychiatric patient populations

OBJECTIVES

The study investigated the role of top-down versus bottom-up connectivity, during the processing of predictive information, in three different psychiatric disorders.

METHODS

Electroencephalography (EEG) was recorded during the performance of a task, which evaluates the ability to use predictive information in order to facilitate predictable versus random target detection. We evaluated EEG event-related directed connectivity, in patients with schizophrenia (SZ), major depressive disorder (MDD), and autism spectrum disorder (ASD), compared with healthy age-matched controls. Directed connectivity was evaluated using phase transfer entropy.

RESULTS

We showed that top-down frontal-parietal connectivity was weaker in SZ (theta and beta bands) and ASD (alpha band) compared to control subjects, during the processing of stimuli consisting of the predictive sequence. In SZ patients, top-down connectivity was also attenuated, during the processing of predictive targets in the beta frequency band. In contrast, compared with controls, MDD patients displayed an increased top-down flow of information, during the processing of predicted targets (alpha band).

CONCLUSIONS

The findings suggest that top-down frontal-parietal connectivity is altered differentially across three major psychiatric disorders, specifically during the processing of predictive stimuli.

SIGNIFICANCE

Altered top-down connectivity may contribute to the specific prediction deficits observed in each of the patient populations.

Neuroplastin in Neuropsychiatric Diseases

Molecular mechanisms underlying neuropsychiatric and neurodegenerative diseases are insufficiently elucidated. A detailed understanding of these mechanisms may help to further improve medical intervention. Recently, intellectual abilities, creativity, and amnesia have been associated with neuroplastin, a cell recognition glycoprotein of the immunoglobulin superfamily that participates in synapse formation and function and calcium signaling. Data from animal models suggest a role for neuroplastin in pathways affected in neuropsychiatric and neurodegenerative diseases. Neuroplastin loss or disruption of molecular pathways related to neuronal processes has been linked to various neurological diseases, including dementia, schizophrenia, and Alzheimer's disease. Here, we review the molecular features of the cell recognition molecule neuroplastin, and its binding partners, which are related to neurological processes and involved in learning and memory. The emerging functions of neuroplastin may have implications for the treatment of diseases, particularly those of the nervous system.

Atypicalities in the developmental trajectory of cortico-striatal functional connectivity in autism spectrum disorder

LAY ABSTRACT

Autism spectrum disorder has long been conceptualized as a disorder of "atypical development of functional brain connectivity (which refers to correlations in activity levels of distant brain regions)." However, most of the research has focused on the connectivity between cortical regions, and much remains unknown about the developmental changes of functional connectivity between subcortical and cortical areas in autism spectrum disorder. We used the technique of resting-state functional magnetic resonance imaging to explore the developmental characteristics of intrinsic functional connectivity (functional brain connectivity when people are asked not to do anything) between subcortical and cortical regions in individuals with and without autism spectrum disorder aged 6-30 years. We focused on one important subcortical structure called striatum, which has roles in motor, cognitive, and affective processes. We found that cortico-striatal intrinsic functional connectivities showed opposite developmental trajectories in autism spectrum disorder and typically developing individuals, with connectivity increasing with age in autism spectrum disorder and decreasing or constant in typically developing individuals. We also found significant negative behavioral correlations between those atypical cortico-striatal intrinsic functional connectivities and autistic symptoms, such as social-communication deficits, and restricted/repetitive behaviors and interests. Taken together, this work highlights that the atypical development of cortico-subcortical functional connectivity might be largely involved in the neuropathological mechanisms of autism spectrum disorder.

The Connectivity Fingerprint of the Fusiform Gyrus Captures the Risk of Developing Autism in Infants with Tuberous Sclerosis Complex

Tuberous sclerosis complex (TSC) is a rare genetic disorder characterized by benign tumors throughout the body; it is generally diagnosed early in life and has a high prevalence of autism spectrum disorder (ASD), making it uniquely valuable in studying the early development of autism, before neuropsychiatric symptoms become apparent. One well-documented deficit in ASD is an impairment in face processing. In this work, we assessed whether anatomical connectivity patterns of the fusiform gyrus, a central structure in face processing, capture the risk of developing autism early in life. We longitudinally imaged TSC patients at 1, 2, and 3 years of age with diffusion compartment imaging. We evaluated whether the anatomical connectivity fingerprint of the fusiform gyrus was associated with the risk of developing autism measured by the Autism Observation Scale for Infants (AOSI). Our findings suggest that the fusiform gyrus connectivity captures the risk of developing autism as early as 1 year of age and provides evidence that abnormal fusiform gyrus connectivity increases with age. Moreover, the identified connections that best capture the risk of developing autism involved the fusiform gyrus and limbic and paralimbic regions that were consistent with the ASD phenotype, involving an increased number of left-lateralized structures with increasing age.

A Neuro-Phenomenological Perspective on the Autism Phenotype

In the current paper, we present a view of autism spectrum disorder (ASD) which avoids the typical relational issues, instead drawing on philosophy, in particular Husserlian phenomenology. We begin by following the recent etiological perspectives that suggest a natural predisposition of a part of individuals with ASD towards hypersensitivity and the reduced influence of cognitive priors (i.e., event schemas). Following this perspective, these two characteristics should be considered as a sort of phenomenological a priori that, importantly, could predispose people with ASD towards a spiritual experience, not intended in its religious meaning, but as an attribute of consciousness that consists of being aware of and attentive to what is occurring in the present moment. Potential clinical implications are discussed.

Subtyping Autism Spectrum Disorder via Joint Modeling of Clinical and Connectomic Profiles

BACKGROUND

Autism Spectrum Disorder (ASD) is a highly heterogeneous developmental disorder with diverse clinical manifestations. Neuroimaging studies have explored functional connectivity (FC) of ASD through resting-state functional MRI studies, however the findings have remained inconsistent, thus reflecting the possibility of multiple subtypes. Identification of the relationship between clinical symptoms and FC measures may help clarify the inconsistencies in earlier findings and advance our understanding of ASD subtypes.

METHODS

Canonical correlation analysis was performed on two-hundred and ten ASD subjects from the Autism Brain Imaging Data Exchange to identify significant linear combinations of resting-state connectomic and clinical profiles of ASD. Then, hierarchical clustering defined ASD subtypes based on distinct brain-behavior relationships. Finally, a support vector machine classifier was used to verify that subtypes were comprised of subjects with distinct clinical and connectivity features.

RESULTS

Three ASD subtypes were identified. Subtype 1 exhibited increased intra-network FC, increased IQ scores and restricted and repetitive behaviors. Subtype 2 was characterized by decreased whole-brain FC and more severe ADI-R and SRS symptoms. Subtype 3 demonstrated mixed FC, low IQ scores, as well as social motivation and verbal deficits. To verify subtype assignment, a multi-class support vector machine using connectomic and clinical profiles yielded an average accuracy of 71.3% and 65.2% respectively for subtype classification, which is significantly higher than chance (33.3%).

CONCLUSION

The present study demonstrates that combining connectomic and behavioral measures is a powerful approach for disease subtyping and suggests that there are ASD subtypes with distinct connectomic and clinical profiles.

Genomic selection signatures in autism spectrum disorder identifies cognitive genomic tradeoff and its relevance in paradoxical phenotypes of deficits versus potentialities

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by paradoxical phenotypes of deficits as well as gain in brain function. To address this a genomic tradeoff hypothesis was tested and followed up with the biological interaction and evolutionary significance of positively selected ASD risk genes. SFARI database was used to retrieve the ASD risk genes while for population datasets 1000 genome data was used. Common risk SNPs were subjected to machine learning as well as independent tests for selection, followed by Bayesian analysis to identify the cumulative effect of selection on risk SNPs. Functional implication of these positively selected risk SNPs was assessed and subjected to ontology analysis, pertaining to their interaction and enrichment of biological and cellular functions. This was followed by comparative analysis with the ancient genomes to identify their evolutionary patterns. Our results identified significant positive selection signals in 18 ASD risk SNPs. Functional and ontology analysis indicate the role of biological and cellular processes associated with various brain functions. The core of the biological interaction network constitutes genes for cognition and learning while genes in the periphery of the network had direct or indirect impact on brain function. Ancient genome analysis identified de novo and conserved evolutionary selection clusters. The de-novo evolutionary cluster represented genes involved in cognitive function. Relative enrichment of the ASD risk SNPs from the respective evolutionary cluster or biological interaction networks may help in addressing the phenotypic diversity in ASD. This cognitive genomic tradeoff signatures impacting the biological networks can explain the paradoxical phenotypes in ASD.

Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity

Autism spectrum disorder (ASD) is a developmental disorder characterized by defects in social interaction. The past functional connectivity studies using resting-state fMRI have found both patterns of hypo-connectivity and hyper-connectivity in ASD and proposed the age as an important factor on functional connectivity disorders. However, this influence is not clearly characterized yet. Previous studies have often examined the functional connectivity disorders in particular brain regions in an age group or a mixture of age groups. The present study compares whole-brain within-connectivity and between-connectivity between ASD individuals and typically developing (TD) controls in three age groups including children (< 11 years), adolescents (11-18 years), and adults (> 18 years), each comprising 21 ASD individuals and 21 TD controls. The age groups were matched for age, Full IQ, and gender. Independent component analysis and dual regression were used to investigate within-connectivity. The full and partial correlations between ICs were used to investigate between-connectivity. Examination of the within-connectivity showed hyper-connectivity, especially in cerebellum and brainstem in ASD children but both hyper/hypo connectivity in adolescents and ASD adults. In ASD children, difference in the between-connectivity among default mode network (DMN), salience-executive network and fronto-parietal network were observed. There was also a negative correlation between DMN and temporal network. Full correlation comparison between ASD adolescents and TD individuals showed significant differences between cerebellum and DMN. Our results supported just the hyper-connectivity in childhood, but both hypo and hyper-connectivity after childhood and hypothesized that abnormal resting connections in ASD exist in the regions of the brain known to be involved in social cognition.

Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning

Creativity is part of human nature and is commonly understood as a phenomenon whereby something original and worthwhile is formed. Owing to this ability, humans can produce innovative information that often facilitates growth in our society. Creativity also contributes to esthetic and artistic productions, such as music and art. However, the mechanism by which creativity emerges in the brain remains debatable. Recently, a growing body of evidence has suggested that statistical learning contributes to creativity. Statistical learning is an innate and implicit function of the human brain and is considered essential for brain development. Through statistical learning, humans can produce and comprehend structured information, such as music. It is thought that creativity is linked to acquired knowledge, but so-called "eureka" moments often occur unexpectedly under subconscious conditions, without the intention to use the acquired knowledge. Given that a creative moment is intrinsically implicit, we postulate that some types of creativity can be linked to implicit statistical knowledge in the brain. This article reviews neural and computational studies on how creativity emerges within the framework of statistical learning in the brain (i.e., statistical creativity). Here, we propose a hierarchical model of statistical learning: statistically chunking into a unit (hereafter and shallow statistical learning) and combining several units (hereafter and deep statistical learning). We suggest that deep statistical learning contributes dominantly to statistical creativity in music. Furthermore, the temporal dynamics of perceptual uncertainty can be another potential causal factor in statistical creativity. Considering that statistical learning is fundamental to brain development, we also discuss how typical versus atypical brain development modulates hierarchical statistical learning and statistical creativity. We believe that this review will shed light on the key roles of statistical learning in musical creativity and facilitate further investigation of how creativity emerges in the brain.

Meta-analytic evidence of differential prefrontal and early sensory cortex activity during non-social sensory perception in autism

To date, neuroimaging research has had a limited focus on non-social features of autism. As a result, neurobiological explanations for atypical sensory perception in autism are lacking. To address this, we quantitively condensed findings from the non-social autism fMRI literature in line with the current best practices for neuroimaging meta-analyses. Using activation likelihood estimation (ALE), we conducted a series of robust meta-analyses across 83 experiments from 52 fMRI studies investigating differences between autistic (n = 891) and typical (n = 967) participants. We found that typical controls, compared to autistic people, show greater activity in the prefrontal cortex (BA9, BA10) during perception tasks. More refined analyses revealed that, when compared to typical controls, autistic people show greater recruitment of the extrastriate V2 cortex (BA18) during visual processing. Taken together, these findings contribute to our understanding of current theories of autistic perception, and highlight some of the challenges of cognitive neuroscience research in autism.

Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

The pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

Biallelic loss-of-function variants in NEMF cause central nervous system impairment and axonal polyneuropathy

We aimed to detect the causative gene in five unrelated families with recessive inheritance pattern neurological disorders involving the central nervous system, and the potential function of the NEMF gene in the central nervous system. Exome sequencing (ES) was applied to all families and linkage analysis was performed on family 1. A minigene assay was used to validate the splicing effect of the relevant discovered variants. Immunofluorescence (IF) experiment was performed to investigate the role of the causative gene in neuron development. The large consanguineous family confirms the phenotype-causative relationship with homozygous frameshift variant (NM_004713.6:c.2618del) as revealed by ES. Linkage analysis of the family showed a significant single-point LOD of 4.5 locus. Through collaboration in GeneMatcher, four additional unrelated families' likely pathogenic NEMF variants for a spectrum of central neurological disorders, two homozygous splice-site variants (NM_004713.6:c.574+1G>T and NM_004713.6:c.807-2A>C) and a homozygous frameshift variant (NM_004713.6: c.1234_1235insC) were subsequently identified and segregated with all affected individuals. We further revealed that knockdown (KD) of Nemf leads to impairment of axonal outgrowth and synapse development in cultured mouse primary cortical neurons. Our study demonstrates that disease-causing biallelic NEMF variants result in central nervous system impairment and other variable features. NEMF is an important player in mammalian neuron development.

Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).

Visual Noise Effect on Contour Integration and Gaze Allocation in Autism Spectrum Disorder

Contradictory results have been obtained in the studies that compare contour integration abilities in Autism Spectrum Disorders (ASDs) and typically developing individuals. The present study aimed to explore the limiting factors of contour integration ability in ASD and verify the role of the external visual noise by a combination of psychophysical and eye-tracking approaches. To this aim, 24 children and adolescents with ASD and 32 age-matched participants with typical development had to detect the presence of contour embedded among similar Gabor elements in a Yes/No procedure. The results obtained showed that the responses in the group with ASD were not only less accurate but also were significantly slower compared to the control group at all noise levels. The detection performance depended on the group differences in addition to the effect of the intellectual functioning of the participants from both groups. The comparison of the agreement and accuracy of the responses in the double-pass experiment showed that the results of the participants with ASD are more affected by the increase of the external noise. It turned out that the internal noise depends on the level of the added external noise: the difference between the two groups was non-significant at the low external noise and significant at the high external noise. In accordance with the psychophysical results, the eye-tracking data indicated a larger gaze allocation area in the group with autism. These findings may imply higher positional uncertainty in ASD due to the inability to maintain the information of the contour location from previous presentations and interference from noise elements in the contour vicinity. Psychophysical and eye-tracking data suggest lower efficiency in using stimulus information in the ASD group that could be caused by fixation instability and noisy and unstable perceptual template that affects noise filtering.

Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.

Autism Spectrum Disorder from the Womb to Adulthood: Suggestions for a Paradigm Shift

The wide spectrum of unique needs and strengths of Autism Spectrum Disorders (ASD) is a challenge for the worldwide healthcare system. With the plethora of information from research, a common thread is required to conceptualize an exhaustive pathogenetic paradigm. The epidemiological and clinical findings in ASD cannot be explained by the traditional linear genetic model, hence the need to move towards a more fluid conception, integrating genetics, environment, and epigenetics as a whole. The embryo-fetal period and the first two years of life (the so-called 'First 1000 Days') are the crucial time window for neurodevelopment. In particular, the interplay and the vicious loop between immune activation, gut dysbiosis, and mitochondrial impairment/oxidative stress significantly affects neurodevelopment during pregnancy and undermines the health of ASD people throughout life. Consequently, the most effective intervention in ASD is expected by primary prevention aimed at pregnancy and at early control of the main effector molecular pathways. We will reason here on a comprehensive and exhaustive pathogenetic paradigm in ASD, viewed not just as a theoretical issue, but as a tool to provide suggestions for effective preventive strategies and personalized, dynamic (from womb to adulthood), systemic, and interdisciplinary healthcare approach.

Autistic traits and individual brain differences: functional network efficiency reflects attentional and social impairments, structural nodal efficiencies index systemising and theory-of-mind skills

BACKGROUND

Autism is characterised not only by impaired social cognitive 'empathising' but also by superior rule-based 'systemising'. These cognitive domains intertwine within the categorical diagnosis of autism, yet behavioural genetics suggest largely independent heritability, and separable brain mechanisms. We sought to determine whether quantitative behavioural measures of autistic traits are dimensionally associated with structural and functional brain network integrity, and whether brain bases of autistic traits vary independently across individuals.

METHODS

Thirty right-handed neurotypical adults (12 females) were administered psychometric (Social Responsiveness Scale, Autism Spectrum Quotient and Systemising Quotient) and behavioural (Attention Network Test and theory-of-mind reaction time) measures of autistic traits, and structurally (diffusion tensor imaging) and functionally (500 s of 2 Hz eyes-closed resting fMRI) derived graph-theoretic measures of efficiency of information integration were computed throughout the brain and within subregions.

RESULTS

Social impairment was positively associated with functional efficiency (r = .47, p = .006), globally and within temporo-parietal and prefrontal cortices. Delayed orienting of attention likewise was associated with greater functional efficiency (r = - .46, p = .0133). Systemising was positively associated with global structural efficiency (r = .38, p = 0.018), driven specifically by temporal pole; theory-of-mind reaction time was related to structural efficiency (r = - .40, p = 0.0153) within right supramarginal gyrus.

LIMITATIONS

Interpretation of these relationships is complicated by the many senses of the term 'connectivity', including functional, structural and computational; by the approximation inherent in group functional anatomical parcellations when confronted with individual variation in functional anatomy; and by the validity, sensitivity and specificity of the several survey and experimental behavioural measures applied as correlates of brain structure and function.

CONCLUSIONS

Functional connectivities highlight distributed networks associated with domain-general properties such as attentional orienting and social cognition broadly, associating more impaired behaviour with more efficient brain networks that may reflect heightened feedforward information flow subserving autistic strengths and deficits alike. Structural connectivity results highlight specific anatomical nodes of convergence, reflecting cognitive and neuroanatomical independence of systemising and theory-of-mind. In addition, this work shows that individual differences in theory-of-mind related to brain structure can be measured behaviourally, and offers neuroanatomical evidence to pin down the slippery construct of 'systemising' as the capacity to construct invariant contextual associations.

Chromatin Remodeler CHD8 in Autism and Brain Development

Chromodomain Helicase DNA-binding 8 (CHD8) is a high confidence risk factor for autism spectrum disorders (ASDs) and the genetic cause of a distinct neurodevelopmental syndrome with the core symptoms of autism, macrocephaly, and facial dysmorphism. The role of CHD8 is well-characterized at the structural, biochemical, and transcriptional level. By contrast, much less is understood regarding how mutations in CHD8 underpin altered brain function and mental disease. Studies on various model organisms have been proven critical to tackle this challenge. Here, we scrutinize recent advances in this field with a focus on phenotypes in transgenic animal models and highlight key findings on neurodevelopment, neuronal connectivity, neurotransmission, synaptic and homeostatic plasticity, and habituation. Against this backdrop, we further discuss how to improve future animal studies, both in terms of technical issues and with respect to the sex-specific effects of Chd8 mutations for neuronal and higher-systems level function. We also consider outstanding questions in the field including 'humanized' mice models, therapeutic interventions, and how the use of pluripotent stem cell-derived cerebral organoids might help to address differences in neurodevelopment trajectories between model organisms and humans.

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

Background:

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

Objective:

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

Methods:

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

Results:

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

Conclusion:

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

Transcranial direct current stimulation modulates brain functional connectivity in autism

Autism spectrum disorder (ASD) is characterized by deficits in social interactions, impairments in language and communication, and highly restricted behavioral interests. Transcranial direct current stimulation (tDCS) is a widely used form of noninvasive stimulation and may have therapeutic potential for ASD. So far, despite the widespread use of this technique in the neuroscience field, its effects on network-level neural activity and the underlying mechanisms of any effects are still unclear. In the present study, we used electroencephalography (EEG) to investigate tDCS induced brain network changes in children with ASD before and after active and sham stimulation. We recorded 5 min of resting state EEG before and after a single session of tDCS (of approximately 20 min) over dorsolateral prefrontal cortex (DLPFC). Two network-based methods were applied to investigate tDCS modulation on brain networks: 1) temporal network dynamics were analyzed by comparing "flexibility" changes before vs after stimulation, and 2) frequency specific network changes were identified using non-negative matrix factorization (NMF). We found 1) an increase in network flexibility following tDCS (rapid network configuration of dynamic network communities), 2) specific increase in interhemispheric connectivity within the alpha frequency band following tDCS. Together, these results demonstrate that tDCS could help modify both local and global brain network dynamics, and highlight stimulation-induced differences in the manifestation of network reconfiguration. Meanwhile, frequency-specific subnetworks, as a way to index local and global information processing, highlight the core modulatory effects of tDCS on the modular architecture of the functional connectivity patterns within higher frequency bands.

Changes in Electroencephalogram Coherence in Adolescents With Autism Spectrum Disorder After a Social Skills Intervention

Autism spectrum disorder (ASD) is a developmental condition that affects social communication and behavior. There is consensus that neurological differences are present in ASD. Further, theories emphasize the mixture of hypo- and hyper-connectivity as a neuropathologies in ASD [O'Reilly, Lewis, & Elsabbagh, 2017]; however, there is a paucity of studies specifically testing neurological underpinnings as predictors of success on social skills interventions. This study examined functional neural connectivity (electroencephalogram [EEG], coherence) of adolescents with ASD before and after the Program for the Education and Enrichment of Relational Skills (PEERS®) intervention, using a randomized controlled trial of two groups: an Experimental ASD (EXP) Group and a Waitlist Control ASD (WL) Group. The study had two purposes. First, the study aimed to determine whether changes in EEG coherence differed for adolescents that received PEERS® versus those that did not receive PEERS®. Results revealed a significant increase in connectivity in the occipital left to temporal left pair for the EXP group after intervention. Second, the study aimed to determine if changes in EEG coherence related to changes in behavior, friendships, and social skills measured by questionnaires. At post-intervention, results indicated: (a) positive change in frontal right to parietal right coherence was linked to an increase in social skills scores; and (b) positive changes in occipital right to temporal right coherence and occipital left to parietal left coherence were linked to an increase in the total number of get-togethers. Results of this study support utilizing neurobehavioral domains as indicators of treatment outcome. Lay Summary: This study examined how well various areas of the brain communicate in adolescents with ASD before and after a social skills intervention. Results revealed increased connectivity in the adolescents that received the intervention. Secondly, the study aimed to determine if changes in connectivity of brain areas related to changes in behavior, friendships, and social skills. Results indicated that changes in connectivity were also linked to increased social skills. Autism Res 2021, 14: 787-803. © 2021 International Society for Autism Research and Wiley Periodicals LLC.

Genetic Transsynaptic Techniques for Mapping Neural Circuits in Drosophila

A neural circuit is composed of a population of neurons that are interconnected by synapses and carry out a specific function when activated. It is the structural framework for all brain functions. Its impairments often cause diseases in the nervous system. To understand computations and functions in a brain circuit, it is of crucial importance to identify how neurons in this circuit are connected. Genetic transsynaptic techniques provide opportunities to efficiently answer this question. These techniques label synapses or across synapses to unbiasedly label synaptic partners. They allow for mapping neural circuits with high reproducibility and throughput, as well as provide genetic access to synaptically connected neurons that enables visualization and manipulation of these neurons simultaneously. This review focuses on three recently developed Drosophila genetic transsynaptic tools for detecting chemical synapses, highlights their advantages and potential pitfalls, and discusses the future development needs of these techniques.

Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms

BACKGROUND

Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, we asked whether electroencephalography measures of neural connectivity at 3 months of age predict autism symptoms at 18 months.

METHODS

Spontaneous electroencephalography data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12 Hz). Support vector regression analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Toddler Module of the Autism Diagnostic Observation Schedule, Second Edition.

RESULTS

Autism Diagnostic Observation Schedule scores predicted by support vector regression algorithms trained on 3-month electroencephalography data correlated highly with Autism Diagnostic Observation Schedule scores measured at 18 months (r = .76, p = .02, root-mean-square error = 2.38). Specifically, lower frontal connectivity and higher right temporoparietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The support vector regression model did not predict cognitive abilities at 18 months (r = .15, p = .36), suggesting specificity of these brain patterns to ASD.

CONCLUSIONS

Using a data-driven, unbiased analytic approach, neural connectivity across frontal and temporoparietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.

Role of mTOR-regulated autophagy in spine pruning defects and memory impairments induced by binge-like ethanol treatment in adolescent mice

Adolescence is a brain maturation developmental period during which remodeling and changes in synaptic plasticity and neural connectivity take place in some brain regions. Different mechanism participates in adolescent brain maturation, including autophagy that plays a role in synaptic development and plasticity. Alcohol is a neurotoxic compound and its abuse in adolescence induces neuroinflammation, synaptic and myelin alterations, neural damage and behavioral impairments. Changes in synaptic plasticity and its regulation by mTOR have also been suggested to play a role in the behavioral dysfunction of binge ethanol drinking in adolescence. Therefore, by considering the critical role of mTOR in both autophagy and synaptic plasticity in the developing brain, the present study aims to evaluate whether binge ethanol treatment in adolescence would induce dysfunctions in synaptic plasticity and cognitive functions and if mTOR inhibition with rapamycin is capable of restoring both effects. Using C57BL/6 adolescent female and male mice (PND30) treated with ethanol (3 g/kg) on two consecutive days at 48-hour intervals over 2 weeks, we show that binge ethanol treatment alters the density and morphology of dendritic spines, effects that are associated with learning and memory impairments and changes in the levels of both transcription factor CREB phosphorylation and miRNAs. Rapamycin administration (3 mg/kg) prior to ethanol administration restores ethanol-induced changes in both plasticity and behavior dysfunctions in adolescent mice. These results support the critical role of mTOR/autophagy dysfunctions in the dendritic spines alterations and cognitive alterations induced by binge alcohol in adolescence.

Altered Temporal Structure of Neural Phase Synchrony in Patients With Autism Spectrum Disorder

Functional connectivity, quantified by phase synchrony, between brain regions is known to be aberrant in patients with autism spectrum disorder (ASD). Here, we evaluated the long-range temporal correlations of time-varying phase synchrony (TV-PS) of electrocortical oscillations in patients with ASD as well as typically developing people using detrended fluctuation analysis (DFA) after validating the scale-invariance of the TV-PS time series. By comparing the DFA exponents between the two groups, we found that those of the TV-PS time series of high-gamma oscillations were significantly attenuated in patients with ASD. Furthermore, the regions involved in aberrant TV-PS time series were mainly within the social ability and cognition-related cortical networks. These results support the notion that abnormal social functions observed in patients with ASD may be caused by the highly volatile phase synchrony states of electrocortical oscillations.

Alpha connectivity and inhibitory control in adults with autism spectrum disorder

BACKGROUND

Individuals with autism spectrum disorder (ASD) often report difficulties with inhibition in everyday life. During inhibition tasks, adults with ASD show reduced activation of and connectivity between brain areas implicated in inhibition, suggesting impairments in inhibitory control at the neural level. Our study further investigated these differences by using magnetoencephalography (MEG) to examine the frequency band(s) in which functional connectivity underlying response inhibition occurs, as brain functions are frequency specific, and whether connectivity in certain frequency bands differs between adults with and without ASD.

METHODS

We analysed MEG data from 40 adults with ASD (27 males; 26.94 ± 6.08 years old) and 39 control adults (27 males; 27.29 ± 5.94 years old) who performed a Go/No-go task. The task involved two blocks with different proportions of No-go trials: Inhibition (25% No-go) and Vigilance (75% No-go). We compared whole-brain connectivity in the two groups during correct No-go trials in the Inhibition vs. Vigilance blocks between 0 and 400 ms.

RESULTS

Despite comparable performance on the Go/No-go task, adults with ASD showed reduced connectivity compared to controls in the alpha band (8-14 Hz) in a network with a main hub in the right inferior frontal gyrus. Decreased connectivity in this network predicted more self-reported difficulties on a measure of inhibition in everyday life.

LIMITATIONS

Measures of everyday inhibitory control were not available for all participants, so this relationship between reduced network connectivity and inhibitory control abilities may not be necessarily representative of all adults with ASD or the larger ASD population. Further research with independent samples of adults with ASD, including those with a wider range of cognitive abilities, would be valuable.

CONCLUSIONS

Our findings demonstrate reduced functional brain connectivity during response inhibition in adults with ASD. As alpha-band synchrony has been linked to top-down control mechanisms, we propose that the lack of alpha synchrony observed in our ASD group may reflect difficulties in suppressing task-irrelevant information, interfering with inhibition in real-life situations.

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.

Super-variants identification for brain connectivity

Identifying genetic biomarkers for brain connectivity helps us understand genetic effects on brain function. The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super‐variant for genetic association detection. Similar to but different from the classic concept of gene, a super‐variant is a combination of alleles in multiple loci but contributing loci can be anywhere in the genome. We hypothesize that the super‐variants are easier to detect and more reliable to reproduce in their associations with brain connectivity. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super‐variants. Specifically, we investigate a discovery set with 16,421 subjects and a verification set with 2,882 subjects, where they are formed according to release date, and the verification set is used to validate the genetic associations from the discovery phase. We identified 12 replicable super‐variants on Chromosomes 1, 3, 7, 8, 9, 10, 12, 15, 16, 18, and 19. These verified super‐variants contain single nucleotide polymorphisms that locate in 14 genes which have been reported to have association with brain structure and function, and/or neurodevelopmental and neurodegenerative disorders in the literature. We also identified novel loci in genes RSPO2 and TMEM74 which may be upregulated in brain issues. These findings demonstrate the validity of the super‐variants and its capability of unifying existing results as well as discovering novel and replicable results.

Using Zebrafish to Model Autism Spectrum Disorder: A Comparison of ASD Risk Genes Between Zebrafish and Their Mammalian Counterparts

Autism spectrum disorders (ASDs) are a highly variable and complex set of neurological disorders that alter neurodevelopment and cognitive function, which usually presents with social and learning impairments accompanied with other comorbid symptoms like hypersensitivity or hyposensitivity, or repetitive behaviors. Autism can be caused by genetic and/or environmental factors and unraveling the etiology of ASD has proven challenging, especially given that different genetic mutations can cause both similar and different phenotypes that all fall within the autism spectrum. Furthermore, the list of ASD risk genes is ever increasing making it difficult to synthesize a common theme. The use of rodent models to enhance ASD research is invaluable and is beginning to unravel the underlying molecular mechanisms of this disease. Recently, zebrafish have been recognized as a useful model of neurodevelopmental disorders with regards to genetics, pharmacology and behavior and one of the main foundations supporting autism research (SFARI) recently identified 12 ASD risk genes with validated zebrafish mutant models. Here, we describe what is known about those 12 ASD risk genes in human, mice and zebrafish to better facilitate this research. We also describe several non-genetic models including pharmacological and gnotobiotic models that are used in zebrafish to study ASD.

Reduced multisensory facilitation exists at different periods of development in autism

Atypical sensory processing is now recognised as a key component of an autism diagnosis. The integration of multiple sensory inputs (multisensory integration (MSI)) is thought to be idiosyncratic in autistic individuals and may have cascading effects on the development of higher-level skills such as social communication. Multisensory facilitation was assessed using a target detection paradigm in 45 autistic and 111 neurotypical individuals, matched on age and IQ. Target stimuli were: auditory (A; 3500 Hz tone), visual (V; white disk 'flash') or audiovisual (AV; simultaneous tone and flash), and were presented on a dark background in a randomized order with varying stimulus onset delays. Reaction time (RT) was recorded via button press. In order to assess possible developmental effects, participants were divided into younger (age 14 or younger) and older (age 15 and older) groups. Redundancy gain (RG) was significantly greater in neurotypical, compared to autistic individuals. No significant effect of age or interaction was found. Race model analysis was used to compute a bound value that represented the facilitation effect provided by MSI. Our results revealed that MSI facilitation occurred (violation of the race model) in neurotypical individuals, with more efficient MSI in older participants. In both the younger and older autistic groups, we found reduced MSI facilitation (no or limited violation of the race model). Autistic participants showed reduced multisensory facilitation compared to neurotypical participants in a simple target detection task, void of social context. This remained consistent across age. Our results support evidence that autistic individuals may not integrate low-level, non-social information in a typical fashion, adding to the growing discussion around the influential effect that basic perceptual atypicalities may have on the development of higher-level, core aspects of autism.

Small Semantic Networks in Individuals with Autism Spectrum Disorder Without Intellectual Impairment: A Verbal Fluency Approach

Individuals with Autism Spectrum Disorder (ASD) experience a variety of symptoms sometimes including atypicalities in language use. The study explored differences in semantic network organisation of adults with ASD without intellectual impairment. We assessed clusters and switches in verbal fluency tasks ('animals', 'human feature', 'verbs', 'r-words') via curve fitting in combination with corpus-driven analysis of semantic relatedness and evaluated socio-emotional and motor action related content. Compared to participants without ASD (n = 39), participants with ASD (n = 32) tended to produce smaller clusters, longer switches, and fewer words in semantic conditions (no p values survived Bonferroni-correction), whereas relatedness and content were similar. In ASD, semantic networks underlying cluster formation appeared comparably small without affecting strength of associations or content.