Functional magnetic resonance imaging of autism spectrum disorders

Abnormalities of brain dynamics based on large-scale cortical network modeling in autism spectrum disorder

Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiological activity of ASD remains unclear. To address this, we propose a large-scale cortical network model based on empirical structural connectivity data using the Wendling model, which successfully simulates both pathological and physiological electroencephalography (EEG) signals. Building on this, the EEG functional network is constructed using the phase lag index, effectively characterizing the functional connectivity. Our modeling results indicate that EEG activity and functional network properties undergo significant changes by globally increasing synaptic coupling strength. Specifically, it leads to abnormal neural oscillations clinically reported in ASD, including the decreased dominant frequency, the decreased relative power in the α band and the increased relative power in the δ+θ band, particularly in the frontal lobe. At the same time, the clustering coefficient and global efficiency of the functional network decrease, while the characteristic path length increases, suggesting that the functional network of ASD is inefficient and poorly integrated. Additionally, we find insufficient functional connectivity across multiple brain regions in ASD, along with decreased wavelet coherence in the α band within the frontal lobe and between the frontal and temporal lobes. Considering that most of the synaptic increases in ASD are limited, brain regions are further randomly selected to increase the local synaptic coupling strength. The results show that disturbances in local brain regions can also facilitate the development of ASD. This study reveals the intrinsic link between synapse increase and abnormal brain activity in ASD, and inspires treatments related to synapse pruning.

Hypercomplex Graph Neural Network: Towards Deep Intersection of Multi-Modal Brain Networks

The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the interplay among anatomical, functional, and physiological brain alterations or developments. Graph Neural Networks (GNNs) have recently become popular in analyzing and fusing multi-modal, graph-structured brain networks. However, effectively learning complementary representations from other modalities remains a significant challenge due to the sophisticated and heterogeneous inter-modal dependencies. Furthermore, most existing studies often focus on specific modalities (e.g., only fMRI and DTI), which limits their scalability to other types of brain networks. To overcome these limitations, we propose a HyperComplex Graph Neural Network (HC-GNN) that models multi-modal networks as hypercomplex tensor graphs. In our approach, HC-GNN is conceptualized as a dynamic spatial graph, where the attentively learned inter-modal associations are represented as the adjacency matrix. HC-GNN leverages hypercomplex operations for inter-modal intersections through cross-embedding and cross-aggregation, enriching the deep coupling of multi-modal representations. We conduct a statistical analysis on the saliency maps to associate disease biomarkers. Extensive experiments on three datasets demonstrate the superior classification performance of our method and its strong scalability to various types of modalities. Our work presents a powerful paradigm for the study of multi-modal brain networks.

Revealing the Multivariate Associations Between Autistic Traits and Principal Functional Connectome

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition characterized by a spectrum of behavioral and cognitive traits. As the characteristics of ASD are highly heterogeneous across individuals, a dimensional approach that overcomes the limitation of the categorical approach is preferred to reveal the symptomatology of ASD. Previous neuroimaging studies demonstrated strong links between large-scale brain networks and autism phenotypes. However, the existing studies have primarily focused on univariate association analysis, which limits our understanding of autism connectopathy. Using resting-state functional magnetic resonance imaging data from 309 participants (168 individuals with ASD and 141 typically developing controls) across a discovery dataset and two independent validation datasets, we identified multivariate associations between high-dimensional neuroimaging features and diverse phenotypic measures (20 or 7 measures). We generated low-dimensional representations of functional connectivity (i.e., gradients) and assessed their multivariate associations with autism-related phenotypes of social, behavioral, and cognitive problems using sparse canonical correlation analysis (SCCA). We selected three functional gradients that represented the cortical axes of the sensory-transmodal, motor-visual, and multiple demand-rests of the brain. The SCCA revealed multivariate associations between gradients and phenotypic measures, which were noted as linked dimensions. We identified three linked dimensions: the links between (1) the first gradient and social impairment, (2) the second and internalizing/externalizing problems, and (3) the third and metacognitive problems. Our findings were partially replicated in two independent validation datasets, indicating robustness. Multivariate association analysis linking high-dimensional neuroimaging and phenotypic features may offer promising avenues for establishing a dimensional approach to autism diagnosis.

Magnetoencephalography for the pediatric population, indications, acquisition and interpretation for the clinician

Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.

Alterations of Sensory-related Functional Brain Network Connectivity in Nrsn2 Homozygous Knockout Mice

Neurensin-2 (Nrsn2) is a neuro-specific gene linked to neurodevelopmental disorders and has recently been reported to function as a bidirectional emotional regulator, highlighting its molecular roles in the nervous system. However, the connections between Nrsn2, brain architecture, and functionality remain to be fully elucidated. Our study utilized 11.7 T multimodal magnetic resonance imaging (MRI) to assess the impact of Nrsn2 gene knockout on the brain's microstructure, regional functional activity, and network connectivity during different developmental phases in mice. We observed significant changes in the functional brain network connectivity of Nrsn2 -/- mice without marked differences in brain microstructure or regional activity. These changes were particularly pronounced in sensory-related areas, such as the gustatory and auditory systems, in both juvenile and adult specimens. Previous studies have correlated the enhanced Default Mode Network (DMN) with depression, and Nrsn2 knockout has been associated with stress resilience. Our findings further revealed reduced connectivity in various DMN regions in adult Nrsn2 -/- mice, suggesting a potential link to increased stress tolerance. Moreover, the sensory system's critical role in environmental perception implies that alterations in network connectivity due to Nrsn2 knockout could affect the processing and integration of external inputs, thereby influencing emotional experiences.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-024-00181-x.

Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2-30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

Prefrontal Cortex Responses to Social Video Stimuli in Young Children with and without Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting individuals worldwide and characterized by deficits in social interaction along with the presence of restricted interest and repetitive behaviors. Despite decades of behavioral research, little is known about the brain mechanisms that influence social behaviors among children with ASD. This, in part, is due to limitations of traditional imaging techniques specifically targeting pediatric populations. As a portable and scalable optical brain monitoring technology, functional near infrared spectroscopy (fNIRS) provides a measure of cerebral hemodynamics related to sensory, motor, or cognitive function. Here, we utilized fNIRS to investigate the prefrontal cortex (PFC) activity of young children with ASD and with typical development while they watched social and nonsocial video clips. The PFC activity of ASD children was significantly higher for social stimuli at medial PFC, which is implicated in social cognition/processing. Moreover, this activity was also consistently correlated with clinical measures, and higher activation of the same brain area only during social video viewing was associated with more ASD symptoms. This is the first study to implement a neuroergonomics approach to investigate cognitive load in response to realistic, complex, and dynamic audiovisual social stimuli for young children with and without autism. Our results further confirm that new generation of portable fNIRS neuroimaging can be used for ecologically valid measurements of the brain function of toddlers and preschool children with ASD.

Atypical functional connectivity between the amygdala and visual, salience regions in infants with genetic liability for autism

The amygdala undergoes a period of overgrowth in the first year of life, resulting in enlarged volume by 12 months in infants later diagnosed with ASD. The overgrowth of the amygdala may have functional consequences during infancy. We investigated whether amygdala connectivity differs in 12-month-olds at high likelihood (HL) for ASD (defined by having an older sibling with autism), compared to those at low likelihood (LL). We examined seed-based connectivity of left and right amygdalae, hypothesizing that the HL and LL groups would differ in amygdala connectivity, especially with the visual cortex, based on our prior reports demonstrating that components of visual circuitry develop atypically and are linked to genetic liability for autism. We found that HL infants exhibited weaker connectivity between the right amygdala and the left visual cortex, as well as between the left amygdala and the right anterior cingulate, with evidence that these patterns occur in distinct subgroups of the HL sample. Amygdala connectivity strength with the visual cortex was related to motor and communication abilities among HL infants. Findings indicate that aberrant functional connectivity between the amygdala and visual regions is apparent in infants with genetic liability for ASD and may have implications for early differences in adaptive behaviors.

Abnormal functional connectivity of the reward network is associated with social communication impairments in autism spectrum disorder: A large-scale multi-site resting-state fMRI study

BACKGROUND

The social motivation hypothesis proposes that the social deficits of autism spectrum disorder (ASD) are related to reward system dysfunction. However, functional connectivity (FC) patterns of the reward network in ASD have not been systematically explored yet.

METHODS

The reward network was defined as eight regions of interest (ROIs) per hemisphere, including the nucleus accumbens (NAc), caudate, putamen, anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), amygdala, and insula. We computed both the ROI-wise resting-state FC and seed-based whole-brain FC in 298 ASD participants and 348 typically developing (TD) controls from the Autism Brain Imaging Data Exchange I dataset. Two-sample t-tests were applied to obtain the aberrant FCs. Then, the association between aberrant FCs and clinical symptoms was assessed with Pearson's correlation or Spearman's correlation. In addition, Neurosynth Image Decoder was used to generate word clouds verifying the cognitive functions of the aberrant pathways. Furthermore, a three-way multivariate analysis of variance (MANOVA) was conducted to examine the effects of gender, subtype and age on the atypical FCs.

RESULTS

For the within network analysis, the left ACC showed weaker FCs with both the right amygdala and left NAc in ASD compared with TD, which were negatively correlated with the Autism Diagnostic Observation Schedule (ADOS) total scores and Social Responsiveness Scale (SRS) total scores respectively. For the whole-brain analysis, weaker FC (i.e., FC between the left vmPFC and left calcarine gyrus, and between the right vmPFC and left precuneus) accompanied by stronger FC (i.e., FC between the left caudate and right insula) were exhibited in ASD relative to TD, which were positively associated with the SRS motivation scores. Additionally, we detected the main effect of age on FC between the left vmPFC and left calcarine gyrus, of subtype on FC between the right vmPFC and left precuneus, of age and age-by-gender interaction on FC between the left caudate and right insula.

CONCLUSIONS

Our findings highlight the crucial role of abnormal FC patterns of the reward network in the core social deficits of ASD, which have the potential to reveal new biomarkers for ASD.

A hybrid graph network model for ASD diagnosis based on resting-state EEG signals

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder and early diagnosis is crucial for effective treatment. Stable and effective biomarkers are essential for understanding the underlying causes of the disorder and improving diagnostic accuracy. Electroencephalography (EEG) signals have proven to be reliable biomarkers for diagnosing ASD. Extracting stable connectivity patterns from EEG signals helps ensure robustness in ASD diagnostic systems. In this study, we propose a hybrid graph convolutional network framework called Rest-HGCN, which utilizes resting-state EEG signals to capture differential patterns of brain connectivity between normal children and ASD patients using graph learning strategies. The Rest-HGCN combines brain network analysis techniques and data-driven strategies to extract discriminative graph features from resting-state EEG signals. By automatically extracting differential graph patterns from these signals, the Rest-HGCN achieves reliable ASD diagnosis. To evaluate the performance of Rest-HGCN, we conducted ASD diagnosis experiments using k-fold cross-validation on the public ABC-CT resting EEG dataset. The proposed Rest-HGCN model achieved accuracies of 87.12 % and 85.32 % in single-subject and cross-experiment analyses, respectively. The results suggest that Rest-HGCN can effectively capture discriminant graph patterns from resting EEG signals and achieve robust ASD diagnosis. This may provide an effective and convenient tool for clinical ASD diagnosis.

Modulatory effects of fMRI acquisition time of day, week and year on adolescent functional connectomes across spatial scales: Implications for inference

Metabolic, hormonal, autonomic and physiological rhythms may have a significant impact on cerebral hemodynamics and intrinsic brain synchronization measured with fMRI (the resting-state connectome). The impact of their characteristic time scales (hourly, circadian, seasonal), and consequently scan timing effects, on brain topology in inherently heterogeneous developing connectomes remains elusive. In a cohort of 4102 early adolescents with resting-state fMRI (median age = 120.0 months; 53.1 % females) from the Adolescent Brain Cognitive Development Study, this study investigated associations between scan time-of-day, time-of-week (school day vs weekend) and time-of-year (school year vs summer vacation) and topological properties of resting-state connectomes at multiple spatial scales. On average, participants were scanned around 2 pm, primarily during school days (60.9 %), and during the school year (74.6 %). Scan time-of-day was negatively correlated with multiple whole-brain, network-specific and regional topological properties (with the exception of a positive correlation with modularity), primarily of visual, dorsal attention, salience, frontoparietal control networks, and the basal ganglia. Being scanned during the weekend (vs a school day) was correlated with topological differences in the hippocampus and temporoparietal networks. Being scanned during the summer vacation (vs the school year) was consistently positively associated with multiple topological properties of bilateral visual, and to a lesser extent somatomotor, dorsal attention and temporoparietal networks. Time parameter interactions suggested that being scanned during the weekend and summer vacation enhanced the positive effects of being scanned in the morning. Time-of-day effects were overall small but spatially extensive, and time-of-week and time-of-year effects varied from small to large (Cohen's f ≤ 0.1, Cohen's d<0.82, p < 0.05). Together, these parameters were also positively correlated with temporal fMRI signal variability but only in the left hemisphere. Finally, confounding effects of scan time parameters on relationships between connectome properties and cognitive task performance were assessed using the ABCD neurocognitive battery. Although most relationships were unaffected by scan time parameters, their combined inclusion eliminated associations between properties of visual and somatomotor networks and performance in the Matrix Reasoning and Pattern Comparison Processing Speed tasks. Thus, scan time of day, week and year may impact measurements of adolescent brain's functional circuits, and should be accounted for in studies on their associations with cognitive performance, in order to reduce the probability of incorrect inference.

Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject's EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies.

Altered motor learning and coordination in mouse models of autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with increasing prevalence. Over 1,000 risk genes have now been implicated in ASD, suggesting diverse etiology. However, the diagnostic criteria for the disorder still comprise two major behavioral domains - deficits in social communication and interaction, and the presence of restricted and repetitive patterns of behavior (RRBs). The RRBs associated with ASD include both stereotyped repetitive movements and other motor manifestations including changes in gait, balance, coordination, and motor skill learning. In recent years, the striatum, the primary input center of the basal ganglia, has been implicated in these ASD-associated motor behaviors, due to the striatum's role in action selection, motor learning, and habit formation. Numerous mouse models with mutations in ASD risk genes have been developed and shown to have alterations in ASD-relevant behaviors. One commonly used assay, the accelerating rotarod, allows for assessment of both basic motor coordination and motor skill learning. In this corticostriatal-dependent task, mice walk on a rotating rod that gradually increases in speed. In the extended version of this task, mice engage striatal-dependent learning mechanisms to optimize their motor routine and stay on the rod for longer periods. This review summarizes the findings of studies examining rotarod performance across a range of ASD mouse models, and the resulting implications for the involvement of striatal circuits in ASD-related motor behaviors. While performance in this task is not uniform across mouse models, there is a cohort of models that show increased rotarod performance. A growing number of studies suggest that this increased propensity to learn a fixed motor routine may reflect a common enhancement of corticostriatal drive across a subset of mice with mutations in ASD-risk genes.

Linehan's biosocial model applied to emotion dysregulation in autism: a narrative review of the literature and an illustrative case conceptualization

Emotion dysregulation (ED) is a transdiagnostic difficulty prevalent in autism spectrum condition (ASC). Importantly, recent research has suggested that ED is involved in self-harm and suicidality. Pre-existing models on the etiology of ED in ASC focus mainly on biological factors to ASC features, such as sensory sensitivities, poor flexibility, and sensitivity to change. However, although psychosocial factors seem to play a role in the emergence of ED in ASC as well (e.g., childhood maltreatment and camouflaging), there is a lack of a comprehensive model conceptualizing biosocial factors involved in ED in autistic people. Linehan's biosocial model (1993) is one of the leading etiological models of ED in borderline personality disorder (BPD). It conceptualizes ED as emerging from transactions between a pre-existing emotional vulnerability in the child and an invalidating developmental environment. Beyond its clinical relevance, Linehan's model has gathered empirical evidence supporting its pertinence in BPD and in other psychiatric disorders. Although ASC and BPD are two distinct diagnoses, because they may share ED, Linehan's biosocial model might be useful for understanding the development of ED in ASC. Hence, this article aims to provide an application and extension of Linehan's model to conceptualize ED in ASC. To do so, we conducted a narrative review of the literature on ED and its underlying factors in ASC from a developmental perspective. To investigate the pertinence of the biosocial model applied to ED in autistic people, we were interested on data on (i) ED and its behavioral correlates in ASC, in relation to the biosocial model, (ii) the potential biological and psychosocial correlates of ED in ASC and (iii) the overlapping difficulties in ASC and BPD. Finally, to assess the pertinence of the model, we applied it to the case of an autistic woman presenting with ED and suicidal behaviors. Our review and application to the case of an autistic woman suggest that ED in ASC encompasses factors related to both biological and psychosocial risk factors as conceptualized in the BPD framework, although in both domains ASC-specific factors might be involved.

Examining the Dominant Presence of Brain Grey Matter in Autism During Functional Magnetic Resonance Imaging

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with symptoms appearing from early childhood. Behavioral modifications, special education, and medicines are used to treat ASD; however, the effectiveness of the treatments depends on early diagnosis of the disorder. The primary approach in diagnosing ASD is based on clinical interviews and valid scales. Still, methods based on brain imaging could also be possible diagnostic biomarkers for ASD.

Methods

To identify the amount of information the functional magnetic resonance imaging (fMRI) reveals on ASD, we reviewed 292 task-based fMRI studies on ASD individuals. This study is part of a systematic review with the registration number CRD42017070975.

Results

We observed that face perception, language, attention, and social processing tasks were mainly studied in ASD. In addition, 73 brain regions, nearly 83% of brain grey matter, showed an altered activation between the ASD and normal individuals during these four tasks, either in a lower or a higher activation.

Conclusion

Using imaging methods, such as fMRI, to diagnose and predict ASD is a great objective; research similar to the present study could be the initial step.

Linking functional and structural brain organisation with behaviour in autism: a multimodal EU-AIMS Longitudinal European Autism Project (LEAP) study

Neuroimaging analyses of brain structure and function in autism have typically been conducted in isolation, missing the sensitivity gains of linking data across modalities. Here we focus on the integration of structural and functional organisational properties of brain regions. We aim to identify novel brain-organisation phenotypes of autism. We utilised multimodal MRI (T1-, diffusion-weighted and resting state functional), behavioural and clinical data from the EU AIMS Longitudinal European Autism Project (LEAP) from autistic (n = 206) and non-autistic (n = 196) participants. Of these, 97 had data from 2 timepoints resulting in a total scan number of 466. Grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps were extracted from respective MRI modalities and were then integrated with Linked Independent Component Analysis. Linear mixed-effects models were used to evaluate the relationship between components and group while accounting for covariates and non-independence of participants with longitudinal data. Additional models were run to investigate associations with dimensional measures of behaviour. We identified one component that differed significantly between groups (coefficient = 0.33, padj = 0.02). This was driven (99%) by variance of the right fusiform gyrus connectopic map 2. While there were multiple nominal (uncorrected p < 0.05) associations with behavioural measures, none were significant following multiple comparison correction. Our analysis considered the relative contributions of both structural and functional brain phenotypes simultaneously, finding that functional phenotypes drive associations with autism. These findings expanded on previous unimodal studies by revealing the topographic organisation of functional connectivity patterns specific to autism and warrant further investigation.

ALERT: Atlas-Based Low Estimation Rank Tensor Approach to Detect Autism Spectrum Disorder. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083014 DOI: 10.1109/embc40787.2023.10340610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

In response to a stimulus, distinct areas of the human brain are activated. Also, it is known that the regions interact with one another. This functional connectivity is helpful to diagnose any neurological abnormality, such as autism spectrum disorder (ASD). This work proposes an approach to construct a functional connectivity network from fMRI image data. For obtaining a functional connectivity network, the time series component of fMRI data is used and from it correlation matrix is calculated showing the degree of interaction among the brain regions. To map the different regions of a brain, the brain atlas is considered. This essentially yields a low-rank tensor approximation of the functional connectivity matrix. A 2D convolutional deep neural network model is built to categorize topological similarity in the functional connectivity matrices related to ASD and typically developing control. The proposed approach has been tested with ABIDE dataset of fMRI data for autism spectrum disorder. Several brain atlases have been considered in the experiment. With a majority voting concept on the results from the atlases, the proposed technique reveals an ASD detection accuracy of 84.79%, which is significantly comparable to the state of the art techniques.Clinical Relevance- ASD is one of the least understood neurological disorders that has been recently recognized to have major sociological consequences on an affected individual's life. A symptom-based diagnosis is in practice. However, this requires prolonged behavioural examinations under the supervision of a highly skilled multidisciplinary team. An early and cost-effective detection using an fMRI image is considered an appropriate, comprehensive, and advanced treatment plan.

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.

Social reward network connectivity differs between autistic and neurotypical youth during social interaction

A core feature of autism is difficulties with social interaction. Atypical social motivation is proposed to underlie these difficulties. However, prior work testing this hypothesis has shown mixed support and has been limited in its ability to understand real-world social-interactive processes in autism. We attempted to address these limitations by scanning neurotypical and autistic youth (n = 86) during a text-based reciprocal social interaction that mimics a "live" chat and elicits social reward processes. We focused on task-evoked functional connectivity (FC) of regions responsible for motivational-reward and mentalizing processes within the broader social reward circuitry. We found that task-evoked FC between these regions was significantly modulated by social interaction and receipt of social-interactive reward. Compared to neurotypical peers, autistic youth showed significantly greater task-evoked connectivity of core regions in the mentalizing network (e.g., posterior superior temporal sulcus) and the amygdala, a key node in the reward network. Furthermore, across groups, the connectivity strength between these mentalizing and reward regions was negatively correlated with self-reported social motivation and social reward during the scanner task. Our results highlight an important role of FC within the broader social reward circuitry for social-interactive reward. Specifically, greater context-dependent FC (i.e., differences between social engagement and non-social engagement) may indicate an increased "neural effort" during social reward and relate to differences in social motivation within autistic and neurotypical populations.

Brain-Derived Neurotrophic Factor (BDNF) in Mechanisms of Autistic-like Behavior in BTBR Mice: Crosstalk with the Dopaminergic Brain System

Disturbances in neuroplasticity undoubtedly play an important role in the development of autism spectrum disorders (ASDs). Brain neurotransmitters and brain-derived neurotrophic factor (BDNF) are known as crucial players in cerebral and behavioral plasticity. Such an important neurotransmitter as dopamine (DA) is involved in the behavioral inflexibility of ASD. Additionally, much evidence from human and animal studies implicates BDNF in ASD pathogenesis. Nonetheless, crosstalk between BDNF and the DA system has not been studied in the context of an autistic-like phenotype. For this reason, the aim of our study was to compare the effects of either the acute intracerebroventricular administration of a recombinant BDNF protein or hippocampal adeno-associated-virus-mediated BDNF overexpression on autistic-like behavior and expression of key DA-related and BDNF-related genes in BTBR mice (a widely recognized model of autism). The BDNF administration failed to affect autistic-like behavior but downregulated Comt mRNA in the frontal cortex and hippocampus; however, COMT protein downregulation in the hippocampus and upregulation in the striatum were insignificant. BDNF administration also reduced the receptor TrkB level in the frontal cortex and midbrain and the BDNF/proBDNF ratio in the striatum. In contrast, hippocampal BDNF overexpression significantly diminished stereotypical behavior and anxiety; these alterations were accompanied only by higher hippocampal DA receptor D1 mRNA levels. The results indicate an important role of BDNF in mechanisms underlying anxiety and repetitive behavior in ASDs and implicates BDNF-DA crosstalk in the autistic-like phenotype of BTBR mice.

Neurobiological correlates and attenuated positive social intention attribution during laughter perception associated with degree of autistic traits

Laughter plays an important role in group formation, signaling social belongingness by indicating a positive or negative social intention towards the receiver. In adults without autism, the intention of laughter can be correctly differentiated without further contextual information. In autism spectrum disorder (ASD), however, differences in the perception and interpretation of social cues represent a key characteristic of the disorder. Studies suggest that these differences are associated with hypoactivation and altered connectivity among key nodes of the social perception network. How laughter, as a multimodal nonverbal social cue, is perceived and processed neurobiologically in association with autistic traits has not been assessed previously. We investigated differences in social intention attribution, neurobiological activation, and connectivity during audiovisual laughter perception in association with the degree of autistic traits in adults [N = 31, M_age (SD) = 30.7 (10.0) years, n_female = 14]. An attenuated tendency to attribute positive social intention to laughter was found with increasing autistic traits. Neurobiologically, autistic trait scores were associated with decreased activation in the right inferior frontal cortex during laughter perception and with attenuated connectivity between the bilateral fusiform face area with bilateral inferior and lateral frontal, superior temporal, mid-cingulate and inferior parietal cortices. Results support hypoactivity and hypoconnectivity during social cue processing with increasing ASD symptoms between socioemotional face processing nodes and higher-order multimodal processing regions related to emotion identification and attribution of social intention. Furthermore, results reflect the importance of specifically including signals of positive social intention in future studies in ASD.

Translating neuroimaging changes to neuro-endophenotypes of autistic spectrum disorder: a narrative review

Background

Autism-spectrum disorder is a neurodevelopmental disorder with heterogeneity in etiopathogenesis and clinical presentation. Neuroanatomical and neurophysiological abnormalities may represent neural endophenotypes for autism spectrum disorders which may help identify subgroups of patients seemingly similar in clinical presentation yet different in their pathophysiological underpinnings. Furthermore, a thorough understanding of the pathophysiology of disease can pave the way to effective treatments, prevention, and prognostic predictions. The aim of this review is to identify the predominant neural endophenotypes in autism-spectrum disorder. The evidence was researched at the following electronic databases: Pubmed, PsycINFO, Scopus, Web of Science, and EMBASE.

Results

Enlarged brain, especially frontotemporal cortices have been consistently reported by structural neuroimaging, whereas functional neuroimaging has revealed frontotemporal dysconnectivity.

Conclusions

Regrettably, many of these findings have not been consistent. Therefore, translating these findings into neural endophenotype is by far an attempt in its budding stage. The structural and functional neuroimaging changes may represent neural endophenotypes unique to autism-spectrum disorder. Despite inconsistent results, a clinically meaningful finding may require combined efforts of autism-spectrum-disorder researchers focused on different aspects of basic, genetic, neuroimaging, and clinical research.

The modulation of emotional and social behaviors by oxytocin signaling in limbic network

Neuropeptides can exert volume modulation in neuronal networks, which account for a well-calibrated and fine-tuned regulation that depends on the sensory and behavioral contexts. For example, oxytocin (OT) and oxytocin receptor (OTR) trigger a signaling pattern encompassing intracellular cascades, synaptic plasticity, gene expression, and network regulation, that together function to increase the signal-to-noise ratio for sensory-dependent stress/threat and social responses. Activation of OTRs in emotional circuits within the limbic forebrain is necessary to acquire stress/threat responses. When emotional memories are retrieved, OTR-expressing cells act as gatekeepers of the threat response choice/discrimination. OT signaling has also been implicated in modulating social-exposure elicited responses in the neural circuits within the limbic forebrain. In this review, we describe the cellular and molecular mechanisms that underlie the neuromodulation by OT, and how OT signaling in specific neural circuits and cell populations mediate stress/threat and social behaviors. OT and downstream signaling cascades are heavily implicated in neuropsychiatric disorders characterized by emotional and social dysregulation. Thus, a mechanistic understanding of downstream cellular effects of OT in relevant cell types and neural circuits can help design effective intervention techniques for a variety of neuropsychiatric disorders.

Facial expression recognition is linked to clinical and neurofunctional differences in autism

BACKGROUND

Difficulties in social communication are a defining clinical feature of autism. However, the underlying neurobiological heterogeneity has impeded targeted therapies and requires new approaches to identifying clinically relevant bio-behavioural subgroups. In the largest autism cohort to date, we comprehensively examined difficulties in facial expression recognition, a key process in social communication, as a bio-behavioural stratification biomarker, and validated them against clinical features and neurofunctional responses.

METHODS

Between 255 and 488 participants aged 6-30 years with autism, typical development and/or mild intellectual disability completed the Karolinska Directed Emotional Faces task, the Reading the Mind in the Eyes Task and/or the Films Expression Task. We first examined mean-group differences on each test. Then, we used a novel intersection approach that compares two centroid and connectivity-based clustering methods to derive subgroups based on the combined performance across the three tasks. Measures and subgroups were then related to clinical features and neurofunctional differences measured using fMRI during a fearful face-matching task.

RESULTS

We found significant mean-group differences on each expression recognition test. However, cluster analyses showed that these were driven by a low-performing autistic subgroup (~ 30% of autistic individuals who performed below 2SDs of the neurotypical mean on at least one test), while a larger subgroup (~ 70%) performed within 1SD on at least 2 tests. The low-performing subgroup also had on average significantly more social communication difficulties and lower activation in the amygdala and fusiform gyrus than the high-performing subgroup.

LIMITATIONS

Findings of autism expression recognition subgroups and their characteristics require independent replication. This is currently not possible, as there is no other existing dataset that includes all relevant measures. However, we demonstrated high internal robustness (91.6%) of findings between two clustering methods with fundamentally different assumptions, which is a critical pre-condition for independent replication.

CONCLUSIONS

We identified a subgroup of autistic individuals with expression recognition difficulties and showed that this related to clinical and neurobiological characteristics. If replicated, expression recognition may serve as bio-behavioural stratification biomarker and aid in the development of targeted interventions for a subgroup of autistic individuals.

The neural effects of oxytocin administration in autism spectrum disorders studied by fMRI: A systematic review

PURPOSE

Oxytocin (OXT) is a hypothalamic neuropeptide that is released from the posterior pituitary gland and at specific targets in the central nervous system (CNS). The prosocial effects of OXT acting in the CNS present it as a potential therapeutic agent for the treatment of aspects of autism spectrum disorder (ASD). In this article, we systematically review the functional MRI (fMRI) literature that reports task-state and resting-state fMRI (rsfMRI) studies of the neural effects of single or multiple dose intranasal OXT (IN-OXT) administration in individuals with ASD.

METHOD

We searched four databases for relevant documents (PubMed, Web of Science, Scopus, and Google Scholar) using the keywords "autism spectrum disorder", "Asperger Syndrome", "oxytocin", and "fMRI". Moreover, we made a manual search to assess the quality of our automatic search. The search was confined to English language articles published in the interval February 2013 until March 2021.

RESULTS

The search yielded 12 fMRI studies with OXT intervention, including 288 individuals with ASD (age 8-55 years) enrolled in randomized, double-blind, placebo-controlled, parallel designs, within-subject-crossover experimental OXT trials. Studies reporting activation task and rsfMRI were summarized with region of interest (ROI) or whole-brain voxel wise analysis. The systematic review of the 12 studies supported the proposition that IN-OXT administration alters brain activation in individuals with ASD. The effects of IN-OXT interacted with the type of the task and the overall results did not indicate restoration of normal brain activation in ASD signature regions albeit the lack of statistical evidence.

CONCLUSION

A large body of evidence consistently indicates that OXT alters activation to fMRI in brain networks of individuals with ASD, but with uncertain implications for alleviation of their social deficits.

Sex Differences in Functional Connectivity Between Resting State Brain Networks in Autism Spectrum Disorder

Functional brain connectivity (FBC) has previously been examined in autism spectrum disorder (ASD) between-resting-state networks (RSNs) using a highly sensitive and reproducible hypothesis-free approach. However, results have been inconsistent and sex differences have only recently been taken into consideration using this approach. We estimated main effects of diagnosis and sex and a diagnosis by sex interaction on between-RSNs FBC in 83 ASD (40 females/43 males) and 85 typically developing controls (TC; 43 females/42 males). We found increased connectivity between the default mode (DM) and (a) the executive control networks in ASD (vs. TC); (b) the cerebellum networks in males (vs. females); and (c) female-specific altered connectivity involving visual, language and basal ganglia (BG) networks in ASD-in suggestive compatibility with ASD cognitive and neuroscientific theories.

Dysfunction of Trio GEF1 involves in excitatory/inhibitory imbalance and autism-like behaviors through regulation of interneuron migration

Autism spectrum disorders (ASDs) are a group of highly inheritable neurodevelopmental disorders. Functional mutations in TRIO, especially in the GEF1 domain, are strongly implicated in ASDs, whereas the underlying neurobiological pathogenesis and molecular mechanisms remain to be clarified. Here we characterize the abnormal morphology and behavior of embryonic migratory interneurons (INs) upon Trio deficiency or GEF1 mutation in mice, which are mediated by the Trio GEF1-Rac1 activation and involved in SDF1α/CXCR4 signaling. In addition, the migration deficits are specifically associated with altered neural microcircuit, decreased inhibitory neurotransmission, and autism-like behaviors, which are reminiscent of some features observed in patients with ASDs. Furthermore, restoring the excitatory/inhibitory (E/I) imbalance via activation of GABA signaling rescues autism-like deficits. Our findings demonstrate a critical role of Trio GEF1 mediated signaling in IN migration and E/I balance, which are related to autism-related behavioral phenotypes.

Quantification of perfusion and metabolism in an autism mouse model assessed by diffuse correlation spectroscopy and near-infrared spectroscopy

There is a need for quantitative biomarkers for early diagnosis of autism. Cerebral blood flow and oxidative metabolism parameters may show superior contrasts for improved characterization. Diffuse correlation spectroscopy (DCS) has been shown to be reliable method to obtain cerebral blood flow contrast in animals and humans. Thus, in this study, we evaluated the combination of DCS and fNIRS in an established autism mouse model. Our results indicate that autistic group had significantly (P = .001) lower (~40%) blood flow (1.16 ± 0.26) × 10^-8  cm² /s), and significantly (P = .015) lower (~70%) oxidative metabolism (52.4 ± 16.6 μmol/100 g/min) compared to control group ([1.93 ± 0.74] × 10^-8  cm² /s, 177.2 ± 45.8 μmol/100 g/min, respectively). These results suggest that the combination of DCS and fNIRS can provide hemodynamic and metabolic contrasts for in vivo assessment of autism pathological conditions noninvasively.

Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features

Background

Autism spectrum disorder (ASD) is characterized by a spectrum of social and communication impairments and rigid and stereotyped behaviors that have a neurodevelopmental origin. Although many imaging studies have reported structural and functional alterations in multiple brain regions, clinically useful diagnostic imaging biomarkers for ASD remain unavailable.

Methods

In this study, we applied machine learning (ML) models to regional volumetric and cortical thickness data from the largest structural magnetic resonance imaging (sMRI) dataset available from the Enhancing Neuro Imaging Genetics Through Meta-Analysis (ENIGMA) consortium (1833 subjects with ASD and 1838 without ASD; age range: 1.5-64; average age: 15.6; male/female ratio: 4.2:1).

Results

The highest classification accuracy on a hold-out test set was achieved using a stacked Extra Tree Classifier. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.62 (95% confidence interval [CI]: 0.57, 0.68) and the area under the precision-recall curve was 0.58. Learning curve analysis showed the good fit of the model and suggests that more training examples will not likely benefit model performance.

Conclusions

Our results suggest that sMRI volumetric and cortical thickness data alone may not provide clinically sufficient useful diagnostic biomarkers for ASD. Developing clinically useful imaging classifiers for ASD will benefit from combining other data modalities or feature types, such as functional MRI data and raw images that can leverage other machine learning (ML) techniques such as convolutional neural networks.

Automated ASD detection using hybrid deep lightweight features extracted from EEG signals

BACKGROUND

Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model.

MATERIALS AND METHOD

We propose a hybrid lightweight deep feature extractor to obtain high classification performance. The system was designed and tested with a big EEG dataset that contained signals from autism patients and normal controls. (i) A new signal to image conversion model is presented in this paper. In this work, features are extracted from EEG signal using one-dimensional local binary pattern (1D_LBP) and the generated features are utilized as input of the short time Fourier transform (STFT) to generate spectrogram images. (ii) The deep features of the generated spectrogram images are extracted using a combination of pre-trained MobileNetV2, ShuffleNet, and SqueezeNet models. This method is named hybrid deep lightweight feature generator. (iii) A two-layered ReliefF algorithm is used for feature ranking and feature selection. (iv) The most discriminative features are fed to various shallow classifiers, developed using a 10-fold cross-validation strategy for automated autism detection.

RESULTS

A support vector machine (SVM) classifier reached 96.44% accuracy based on features from the proposed model.

CONCLUSIONS

The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals. The model is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers.

Neural correlates of RDoC-specific cognitive processes in a high-functional autistic patient: a statistically validated case report

The level of functioning of individuals with autism spectrum disorder (ASD) varies widely. To better understand the neurobiological mechanism associated with high-functioning ASD, we studied the rare case of a female patient with an exceptional professional career in the highly competitive academic field of Mathematics. According to the Research Domain Criteria (RDoC) approach, which proposes to describe the basic dimensions of functioning by integrating different levels of information, we conducted four fMRI experiments targeting the (1) social processes domain (Theory of mind (ToM) and face matching), (2) positive valence domain (reward processing), and (3) cognitive domain (N-back). Patient’s data were compared to data of 14 healthy controls (HC). Additionally, we assessed the subjective experience of our case during the experiments. The patient showed increased response times during face matching and achieved a higher total gain in the Reward task, whereas her performance in N-back and ToM was similar to HC. Her brain function differed mainly in the positive valence and cognitive domains. During reward processing, she showed reduced activity in a left-hemispheric frontal network and cortical midline structures but increased connectivity within this network. During the working memory task patients’ brain activity and connectivity in left-hemispheric temporo-frontal regions were elevated. In the ToM task, activity in posterior cingulate cortex and temporo-parietal junction was reduced. We suggest that the high level of functioning in our patient is rather related to the effects in brain connectivity than to local cortical information processing and that subjective report provides a fruitful framework for interpretation.

The longitudinal stability of fMRI activation during reward processing in adolescents and young adults

BACKGROUND

The use of functional neuroimaging has been an extremely fruitful avenue for investigating the neural basis of human reward function. This approach has included identification of potential neurobiological mechanisms of psychiatric disease and examination of environmental, experiential, and biological factors that may contribute to disease risk via effects on the reward system. However, a central and largely unexamined assumption of much of this research is that neural reward function is an individual difference characteristic that is relatively stable and trait-like over time.

METHODS

In two independent samples of adolescents and young adults studied longitudinally (Ns = 145 & 139, 100% female and 100% male, ages 15-21 and 20-22, 2-4 scans and 2 scans respectively), we tested within-person stability of reward-task BOLD activation, with a median of 1 and 2 years between scans. We examined multiple commonly used contrasts of active states and baseline in both the anticipation and feedback phases of a card-guessing reward task. We examined the effects of cortical parcellation resolution, contrast, network (reward regions and resting-state networks), region-size, and activation strength and variability on the stability of reward-related activation.

RESULTS

In both samples, contrasts of an active state relative to a baseline were more stable (ICC: intra-class correlation; e.g., Win>Baseline; mean ICC = 0.13 - 0.33) than contrasts of two active states (e.g., Win>Loss; mean ICC = 0.048 - 0.05). Additionally, activation in reward regions was less stable than in many non-task networks (e.g., dorsal attention), and activation in regions with greater between-subject variability showed higher stability in both samples.

CONCLUSIONS

These results show that some contrasts from functional neuroimaging activation during a card guessing reward task have partially trait-like properties in adolescent and young adult samples over 1-2 years. Notably, results suggest that contrasts intended to map cognitive function and show robust group-level effects (i.e. Win > Loss) may be less effective in studies of individual differences and disease risk. The robustness of group-level activation should be weighed against other factors when selecting regions of interest in individual difference fMRI studies.

Corticospinal Excitability during a Perspective Taking Task as Measured by TMS-Induced Motor Evoked Potentials

Only by understanding the ability to take a third-person perspective can we begin to elucidate the neural processes responsible for one’s inimitable conscious experience. The current study examined differences in hemispheric laterality during a first-person perspective (1PP) and third-person perspective (3PP) taking task, using transcranial magnetic stimulation (TMS). Participants were asked to take either the 1PP or 3PP when identifying the number of spheres in a virtual scene. During this task, single-pulse TMS was delivered to the motor cortex of both the left and right hemispheres of 10 healthy volunteers. Measures of TMS-induced motor-evoked potentials (MEPs) of the contralateral abductor pollicis brevis (APB) were employed as an indicator of lateralized cortical activation. The data suggest that the right hemisphere is more important in discriminating between 1PP and 3PP. These data add a novel method for determining perspective taking and add to the literature supporting the role of the right hemisphere in meta representation.

ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder which is characterized by impaired communication, and limited social interactions. The shortcomings of current clinical approaches which are based exclusively on behavioral observation of symptomology, and poor understanding of the neurological mechanisms underlying ASD necessitates the identification of new biomarkers that can aid in study of brain development, and functioning, and can lead to accurate and early detection of ASD. In this paper, we developed a deep-learning model called ASD-SAENet for classifying patients with ASD from typical control subjects using fMRI data. We designed and implemented a sparse autoencoder (SAE) which results in optimized extraction of features that can be used for classification. These features are then fed into a deep neural network (DNN) which results in superior classification of fMRI brain scans more prone to ASD. Our proposed model is trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error. We evaluated our proposed deep-learning model using publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset collected from 17 different research centers, and include more than 1,035 subjects. Our extensive experimentation demonstrate that ASD-SAENet exhibits comparable accuracy (70.8%), and superior specificity (79.1%) for the whole dataset as compared to other methods. Further, our experiments demonstrate superior results as compared to other state-of-the-art methods on 12 out of the 17 imaging centers exhibiting superior generalizability across different data acquisition sites and protocols. The implemented code is available on GitHub portal of our lab at: https://github.com/pcdslab/ASD-SAENet.

Structural, Functional, and Molecular Imaging of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder associated with both genetic and environmental risks. Neuroimaging approaches have been widely employed to parse the neurophysiological mechanisms underlying ASD, and provide critical insights into the anatomical, functional, and neurochemical changes. We reviewed recent advances in neuroimaging studies that focused on ASD by using magnetic resonance imaging (MRI), positron emission tomography (PET), or single-positron emission tomography (SPECT). Longitudinal structural MRI has delineated an abnormal developmental trajectory of ASD that is associated with cascading neurobiological processes, and functional MRI has pointed to disrupted functional neural networks. Meanwhile, PET and SPECT imaging have revealed that metabolic and neurotransmitter abnormalities may contribute to shaping the aberrant neural circuits of ASD. Future large-scale, multi-center, multimodal investigations are essential to elucidate the neurophysiological underpinnings of ASD, and facilitate the development of novel diagnostic biomarkers and better-targeted therapy.

Training the social brain: Clinical and neural effects of an 8-week real-time functional magnetic resonance imaging neurofeedback Phase IIa Clinical Trial in Autism

LAY ABSTRACT

Neurofeedback is an emerging therapeutic approach in neuropsychiatric disorders. Its potential application in autism spectrum disorder remains to be tested. Here, we demonstrate the feasibility of real-time functional magnetic resonance imaging volitional neurofeedback in targeting social brain regions in autism spectrum disorder. In this clinical trial, autism spectrum disorder patients were enrolled in a program with five training sessions of neurofeedback. Participants were able to control their own brain activity in this social brain region, with positive clinical and neural effects. Larger, controlled, and blinded clinical studies will be required to confirm the benefits.

Emerging behavioral and neuroimaging biomarkers for early and accurate characterization of autism spectrum disorders: a systematic review

The possibility of early treatment and a better outcome is the direct product of early identification and characterization of any pathological condition. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in social communication, restricted, and repetitive patterns of behavior. In recent times, various tools and methods have been developed for the early identification and characterization of ASD features as early as 6 months of age. Thorough and exhaustive research has been done to identify biomarkers in ASD using noninvasive neuroimaging and various molecular methods. By employing advanced assessment tools such as MRI and behavioral assessment methods for accurate characterization of the ASD features and may facilitate pre-emptive interventional and targeted therapy programs. However, the application of advanced quantitative MRI methods is still confined to investigational/laboratory settings, and the clinical implication of these imaging methods in personalized medicine is still in infancy. Longitudinal research studies in neurodevelopmental disorders are the need of the hour for accurate characterization of brain-behavioral changes that could be monitored over a period of time. These findings would be more reliable and consistent with translating into the clinics. This review article aims to focus on the recent advancement of early biomarkers for the characterization of ASD features at a younger age using behavioral and quantitative MRI methods.

Neural processing of facial identity and expression in adults with and without autism: A multi-method approach

The ability to recognize faces and facial expressions is a common human talent. It has, however, been suggested to be impaired in individuals with autism spectrum disorder (ASD). The goal of this study was to compare the processing of facial identity and emotion between individuals with ASD and neurotypicals (NTs). Behavioural and functional magnetic resonance imaging (fMRI) data from 46 young adults (aged 17-23 years, N_ASD = 22, N_NT = 24) was analysed. During fMRI data acquisition, participants discriminated between short clips of a face transitioning from a neutral to an emotional expression. Stimuli included four identities and six emotions. We performed behavioural, univariate, multi-voxel, adaptation and functional connectivity analyses to investigate potential group differences. The ASD-group did not differ from the NT-group on behavioural identity and expression processing tasks. At the neural level, we found no differences in average neural activation, neural activation patterns and neural adaptation to faces in face-related brain regions. In terms of functional connectivity, we found that amygdala seems to be more strongly connected to inferior occipital cortex and V1 in individuals with ASD. Overall, the findings indicate that neural representations of facial identity and expression have a similar quality in individuals with and without ASD, but some regions containing these representations are connected differently in the extended face processing network.

Facial Expression Processing Across the Autism-Psychosis Spectra: A Review of Neural Findings and Associations With Adverse Childhood Events

Autism spectrum disorder (ASD) and primary psychosis are classified as distinct neurodevelopmental disorders, yet they display overlapping epidemiological, environmental, and genetic components as well as endophenotypic similarities. For instance, both disorders are characterized by impairments in facial expression processing, a crucial skill for effective social communication, and both disorders display an increased prevalence of adverse childhood events (ACE). This narrative review provides a brief summary of findings from neuroimaging studies investigating facial expression processing in ASD and primary psychosis with a focus on the commonalities and differences between these disorders. Individuals with ASD and primary psychosis activate the same brain regions as healthy controls during facial expression processing, albeit to a different extent. Overall, both groups display altered activation in the fusiform gyrus and amygdala as well as altered connectivity among the broader face processing network, probably indicating reduced facial expression processing abilities. Furthermore, delayed or reduced N170 responses have been reported in ASD and primary psychosis, but the significance of these findings is questioned, and alternative frequency-tagging electroencephalography (EEG) measures are currently explored to capture facial expression processing impairments more selectively. Face perception is an innate process, but it is also guided by visual learning and social experiences. Extreme environmental factors, such as adverse childhood events, can disrupt normative development and alter facial expression processing. ACE are hypothesized to induce altered neural facial expression processing, in particular a hyperactive amygdala response toward negative expressions. Future studies should account for the comorbidity among ASD, primary psychosis, and ACE when assessing facial expression processing in these clinical groups, as it may explain some of the inconsistencies and confound reported in the field.

Prefrontal Responses to Odors in Individuals With Autism Spectrum Disorders: Functional NIRS Measurement Combined With a Fragrance Pulse Ejection System

Individuals with autism spectrum disorders (ASD) are impaired not only in social competencies but also in sensory perception, particularly olfaction. The olfactory ability of individuals with ASD has been examined in several psychophysical studies, but the results have been highly variable, which might be primarily due to methodological difficulties in the control of odor stimuli (e.g., the problem of lingering scents). In addition, the neural correlates of olfactory specificities in individuals with ASD remain largely unknown. To date, only one study has investigated this issue using functional magnetic resonance imaging (fMRI). The present study utilized a sophisticated method-a pulse ejection system-to present well-controlled odor stimuli to participants with ASD using an ASD-friendly application. With this advantageous system, we examined their odor detection, identification, and evaluation abilities and measured their brain activity evoked by odors using functional near-infrared spectroscopy (fNIRS). As the odor detection threshold (DT) of participants with ASD was highly variable, these participants were divided into two groups according to their DT: an ASD-Low DT group and an ASD-High DT group. Behavioral results showed that the ASD-High DT group had a significantly higher DT than the typically developing (control) group and the ASD-Low DT group, indicating their insensitivity to the tested odors. In addition, while there was no significant difference in the odor identification ability between groups, there was some discrepancy between the groups' evaluations of odor pleasantness. The brain data identified, for the first time, that neural activity in the right dorsolateral prefrontal cortex (DLPFC) was significantly weaker in the ASD-High DT group than in the control group. Moreover, the strength of activity in the right DLPFC was negatively correlated with the DT. These findings suggest that participants with ASD have impairments in the higher-order function of olfactory processing, such as olfactory working memory and/or attention.

Regulation of autism-relevant behaviors by cerebellar-prefrontal cortical circuits

Cerebellar dysfunction has been demonstrated in autism spectrum disorders (ASDs); however, the circuits underlying cerebellar contributions to ASD-relevant behaviors remain unknown. In this study, we demonstrated functional connectivity between the cerebellum and the medial prefrontal cortex (mPFC) in mice; showed that the mPFC mediates cerebellum-regulated social and repetitive/inflexible behaviors; and showed disruptions in connectivity between these regions in multiple mouse models of ASD-linked genes and in individuals with ASD. We delineated a circuit from cerebellar cortical areas Right crus 1 (Rcrus1) and posterior vermis through the cerebellar nuclei and ventromedial thalamus and culminating in the mPFC. Modulation of this circuit induced social deficits and repetitive behaviors, whereas activation of Purkinje cells (PCs) in Rcrus1 and posterior vermis improved social preference impairments and repetitive/inflexible behaviors, respectively, in male PC-Tsc1 mutant mice. These data raise the possibility that these circuits might provide neuromodulatory targets for the treatment of ASD.

The Importance of Anti-correlations in Graph Theory Based Classification of Autism Spectrum Disorder

With the release of the multi-site Autism Brain Imaging Data Exchange, many researchers have applied machine learning methods to distinguish between healthy subjects and autistic individuals by using features extracted from resting state functional MRI data. An important part of applying machine learning to this problem is extracting these features. Specifically, whether to include negative correlations between brain region activities as relevant features and how best to define these features. For the second question, the graph theoretical properties of the brain network may provide a reasonable answer. In this study, we investigated the first issue by comparing three different approaches. These included using the positive correlation matrix (comprising only the positive values of the original correlation matrix), the absolute value of the correlation matrix, or the anticorrelation matrix (comprising only the negative correlation values) as the starting point for extracting relevant features using graph theory. We then trained a multi-layer perceptron in a leave-one-site out manner in which the data from a single site was left out as testing data and the model was trained on the data from the other sites. Our results show that on average, using graph features extracted from the anti-correlation matrix led to the highest accuracy and AUC scores. This suggests that anti-correlations should not simply be discarded as they may include useful information that would aid the classification task. We also show that adding the PCA transformation of the original correlation matrix to the feature space leads to an increase in accuracy.

An Updated Review of Tuberous Sclerosis Complex-Associated Autism Spectrum Disorder

Tuberous sclerosis complex (TSC) is a neurocutaneous disorder caused by mutations of either the TSC1 or TSC2 gene. Various neuropsychiatric features, including autism, are prevalent in TSC. Recently, significant progress has been possible with the prospective calculation of the prevalence of autism in TSC, identification of early clinical and neurophysiological biomarkers to predict autism, and investigation of different therapies to prevent autism in this high-risk population. The author provides a narrative review of recent findings related to biomarkers for diagnosis of autism in TSC, as well as recent studies related to the management of TSC-associated autism. Further sophisticated modeling and analysis are required to understand the role of different models-tuber models, seizures and related neurophysiological factors models, genotype models, and brain connectivity models-to unravel the neurobiological basis of autism in TSC. Early neuropsychologic assessments may be beneficial in this high-risk group. Targeted intervention to improve visual skill, cognition, and fine motor skills with later addition of social skill training can be helpful. Multicenter, prospective studies are ongoing to identify if presymptomatic treatment with vigabatrin in patients with TSC can improve outcomes, including autism. Several studies indicated reasonable safety of everolimus in young children, and its potential application in high-risk infants with TSC, before the closure of the temporal window of permanent changes, maybe undertaken shortly.

Antineuroinflammatory therapy: potential treatment for autism spectrum disorder by inhibiting glial activation and restoring synaptic function

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by deficits in social interactions and perseverative and stereotypical behavior. Growing evidence points toward a critical role for synaptic dysfunction in the onset of ASD, and synaptic function is influenced by glial cells. Considering the evidence that neuroinflammation in ASD is mediated by glial cells, one hypothesis is that reactive glial cells, under inflammatory conditions, contribute to the loss of synaptic functions and trigger ASD. Ongoing pharmacological treatments for ASD, including oxytocin, vitamin D, sulforaphane, and resveratrol, are promising and are shown to lead to improvements in behavioral performance in ASD. More importantly, their pharmacological mechanisms are closely related to anti-inflammation and synaptic protection. We focus this review on the hypothesis that synaptic dysfunction caused by reactive glial cells would lead to ASD, and discuss the potentials of antineuroinflammatory therapy for ASD.

Shank3 mutation in a mouse model of autism leads to changes in the S-nitroso-proteome and affects key proteins involved in vesicle release and synaptic function

Mutation in the SHANK3 human gene leads to different neuropsychiatric diseases including Autism Spectrum Disorder (ASD), intellectual disabilities and Phelan-McDermid syndrome. Shank3 disruption in mice leads to dysfunction of synaptic transmission, behavior, and development. Protein S-nitrosylation, the nitric oxide (NO^•)-mediated posttranslational modification (PTM) of cysteine thiols (SNO), modulates the activity of proteins that regulate key signaling pathways. We tested the hypothesis that Shank3 mutation would generate downstream effects on PTM of critical proteins that lead to modification of synaptic functions. SNO-proteins in two ASD-related brain regions, cortex and striatum of young and adult InsG3680(+/+) mice (a human mutation-based Shank3 mouse model), were identified by an innovative mass spectrometric method, SNOTRAP. We found changes of the SNO-proteome in the mutant compared to WT in both ages. Pathway analysis showed enrichment of processes affected in ASD. SNO-Calcineurin in mutant led to a significant increase of phosphorylated Synapsin1 and CREB, which affect synaptic vesicle mobilization and gene transcription, respectively. A significant increase of 3-nitrotyrosine was found in the cortical regions of the adult mutant, signaling both oxidative and nitrosative stress. Neuronal NO^• Synthase (nNOS) was examined for levels and localization in neurons and no significant difference was found in WT vs. mutant. S-nitrosoglutathione concentrations were higher in mutant mice compared to WT. This is the first study on NO^•-related molecular changes and SNO-signaling in the brain of an ASD mouse model that allows the characterization and identification of key proteins, cellular pathways, and neurobiological mechanisms that might be affected in ASD.

Genetics of structural and functional brain changes in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurological and developmental disorder characterized by social impairment and restricted interactive and communicative behaviors. It may occur as an isolated disorder or in the context of other neurological, psychiatric, developmental, and genetic disorders. Due to rapid developments in genomics and imaging technologies, imaging genetics studies of ASD have evolved in the last few years. Increased risk for ASD diagnosis is found to be related to many specific single-nucleotide polymorphisms, and the study of genetic mechanisms and noninvasive imaging has opened various approaches that can help diagnose ASD at the nascent level. Identifying risk genes related to structural and functional changes in the brain of ASD patients provide a better understanding of the disease's neuropsychiatry and can help identify targets for therapeutic intervention that could be useful for the clinical management of ASD patients.

Diagnosing autism spectrum disorder using brain entropy: A fast entropy method

BACKGROUND AND OBJECTIVE

Previous resting-state fMRI-based diagnostic models for autism spectrum disorder (ASD) were based on traditional linear features. The complexity of the ASD brain remains unexplored.

METHODS

To increase our understanding of the nonlinear neural mechanisms in ASD, entropy (i.e., approximate entropy (ApEn) and sample entropy (SampEn)) method was used to analyze the resting-state fMRI datasets collected from 21 ASD patients and 26 typically developing (TD) individuals. Here, a fast entropy algorithm was proposed through matrix computation. We combined entropy with a support-vector machine and selected "important entropy" as features to diagnose ASD. The classification performance of the fast entropy method was compared to the state-of-the-art functional connectivity (FC) method.

RESULTS

The area under the receiver operating characteristic curve based on FC was 0.62. The areas under the receiver operating characteristic curves based on ApEn and SampEn were 0.79 and 0.89, respectively. The results showed that the proposed fast entropy method was more efficacious than the FC method. In addition, lower entropy was found in the ASD patients. The ApEn of the left postcentral gyrus (r_s = -0.556, p = 0.009) and the SampEn of the right lingual gyrus (r_s = -0.526, p = 0.014) were both significantly negatively related to Autism Diagnostic Observation Schedule total scores in the ASD patients. The proposed algorithm for entropy computation was faster than the traditional entropy method.

CONCLUSIONS

Our study provides a new perspective to better understand the neural mechanisms of ASD. Brain entropy based on a fast algorithm was applied to distinguish ASD patients from TD individuals. ApEn and SampEn could be potential biomarkers in ASD investigations.

A Nexus Model of Restricted Interests in Autism Spectrum Disorder

Restricted interests (RIs) in autism spectrum disorder (ASD) are clinically impairing interests of unusual focus or intensity. They are a subtype of restricted and repetitive behaviors which are one of two diagnostic criteria for the disorder. Despite the near ubiquity of RIs in ASD, the neural basis for their development is not well understood. However, recent cognitive neuroscience findings from nonclinical samples and from individuals with ASD shed light on neural mechanisms that may explain the emergence of RIs. We propose the nexus model of RIs in ASD, a novel conceptualization of this symptom domain that suggests that RIs may reflect a co-opting of brain systems that typically serve to integrate complex attention, memory, semantic, and social communication functions during development. The nexus model of RIs hypothesizes that when social communicative development is compromised, brain functions typically located within the lateral surface of cortex may expand into social processing brain systems and alter cortical representations of various cognitive functions during development. These changes, in turn, promote the development of RIs as an alternative process mediated by these brain networks. The nexus model of RIs makes testable predictions about reciprocal relations between the impaired development of social communication and the emergence of RIs in ASD and suggests novel avenues for treatment development.

Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder

BACKGROUND

People with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have abnormalities in frontal, temporal, parietal and striato-thalamic networks. It is unclear to what extent these abnormalities are distinctive or shared. This comparative meta-analysis aimed to identify the most consistent disorder-differentiating and shared structural and functional abnormalities.

METHODS

Systematic literature search was conducted for whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies of cognitive control comparing people with ASD or ADHD with typically developing controls. Regional gray matter volume (GMV) and fMRI abnormalities during cognitive control were compared in the overall sample and in age-, sex- and IQ-matched subgroups with seed-based d mapping meta-analytic methods.

RESULTS

Eighty-six independent VBM (1533 ADHD and 1295 controls; 1445 ASD and 1477 controls) and 60 fMRI datasets (1001 ADHD and 1004 controls; 335 ASD and 353 controls) were identified. The VBM meta-analyses revealed ADHD-differentiating decreased ventromedial orbitofrontal (z = 2.22, p < 0.0001) but ASD-differentiating increased bilateral temporal and right dorsolateral prefrontal GMV (zs ⩾ 1.64, ps ⩽ 0.002). The fMRI meta-analyses of cognitive control revealed ASD-differentiating medial prefrontal underactivation but overactivation in bilateral ventrolateral prefrontal cortices and precuneus (zs ⩾ 1.04, ps ⩽ 0.003). During motor response inhibition specifically, ADHD relative to ASD showed right inferior fronto-striatal underactivation (zs ⩾ 1.14, ps ⩽ 0.003) but shared right anterior insula underactivation.

CONCLUSIONS

People with ADHD and ASD have mostly distinct structural abnormalities, with enlarged fronto-temporal GMV in ASD and reduced orbitofrontal GMV in ADHD; and mostly distinct functional abnormalities, which were more pronounced in ASD.

Characterizing intrinsic functional connectivity in relation to impaired self-regulation in intellectually able male youth with autism spectrum disorder

While a considerable number of youth with autism spectrum disorder exhibit impaired self-regulation (dysregulation), little is known about the neural correlates of dysregulation in autism spectrum disorder. In a sample of intellectually able boys with autism spectrum disorder (further categorized as those with and without dysregulation) and typically developing boys (aged 7–17 years), we conducted a multivariate connectome-wide association study to examine the intrinsic functional connectivity with resting-state functional magnetic resonance imaging. Dysregulation was defined by the sum of Attention, Aggression, and Anxiety/Depression subscales on the Child Behavior Checklist. We identified that both categorical and dimensional neural correlates of dysregulation in youth with autism spectrum disorder involved atypical connectivity among the components of multiple brain networks, especially between those subserving sensorimotor processing and salience encoding, beyond higher-level cognitive control circuitries. Interaction within the attention network might serve as autism spectrum disorder–specific neural correlates underpinning dysregulation. Our results highlight that the inter-individual variability in dysregulation might contribute to the inconsistency in the neuroimaging literature of autism spectrum disorder. Collectively, the present findings provide evidence to suggest that dysregulation might be considered as both categorical and dimensional moderators to parse heterogeneity of autism spectrum disorder.

Lay Abstract

Impaired self-regulation (i.e., dysregulation in affective, behavioral, and cognitive control), is commonly present in young people with autism spectrum disorder (ASD). However, little is known about what is happening in people’s brains when self-regulation is impaired in young people with ASD. We used a technique called functional MRI (which measures brain activity by detecting changes associated with blood flow) at a resting state (when participants are not asked to do anything) to research this in intellectually able young people with ASD. We found that brains with more connections, especially between regions involved in sensorimotor processing and regions involved in the processes that enable peoples to focus their attention on the most pertinent features from the sensory environment (salience processing), were related to more impaired self-regulation in young people with and without ASD. We also found that impaired self-regulation was related to increased communication within the brain system involved in voluntary orienting attention to a sensory cue (the dorsal attention network) in young people with ASD. These results highlight how different people have different degrees of dysregulation, which has been largely overlooked in the earlier brain imaging reports on ASD. This might contribute to understanding some of the inconsistencies in the existing published literature on this topic.