Local functional overconnectivity in posterior brain regions is associated with symptom severity in autism spectrum disorders

Acquisition Time for Resting-State HbO/Hb Coupling Measured by Functional Near-Infrared Spectroscopy in Assessing Autism

Functional near-infrared spectroscopy was used to record spontaneous hemodynamic fluctuations form the bilateral temporal lobes in 25 children with autism spectrum disorder (ASD) and 22 typically developing (TD) children. The coupling between oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) was calculated by Pearson correlation coefficient, showing significant difference between ASD and TD, thus the coupling could be a characteristic feature for ASD. To evaluate the discrimination ability of the feature obtained in different acquisition times, the receiver operating characteristic curve (ROC) was constructed and the area under curve (AUC) was calculated. The results showed AUC > 0.8 when the time duration was longer than 1.5 min, but longer than 4 min, AUC value (~0.87) hardly varied, implying the maximal discrimination ability reached. This study demonstrated the coupling could be one of characteristic features for ASD even acquired in a short measurement time.

Heterogeneity and convergence across seven neuroimaging modalities: a review of the autism spectrum disorder literature

Background

A growing body of literature classifies autism spectrum disorder (ASD) as a heterogeneous, complex neurodevelopmental disorder that often is identified prior to three years of age. We aim to provide a narrative review of key structural and functional properties that differentiate the neuroimaging profile of autistic youth from their typically developing (TD) peers across different neuroimaging modalities.

Methods

Relevant studies were identified by searching for key terms in PubMed, with the most recent search conducted on September 1, 2023. Original research papers were included if they applied at least one of seven neuroimaging modalities (structural MRI, functional MRI, DTI, MRS, fNIRS, MEG, EEG) to compare autistic children or those with a family history of ASD to TD youth or those without ASD family history; included only participants <18 years; and were published from 2013 to 2023.

Results

In total, 172 papers were considered for qualitative synthesis. When comparing ASD to TD groups, structural MRI-based papers (n = 26) indicated larger subcortical gray matter volume in ASD groups. DTI-based papers (n = 14) reported higher mean and radial diffusivity in ASD participants. Functional MRI-based papers (n = 41) reported a substantial number of between-network functional connectivity findings in both directions. MRS-based papers (n = 19) demonstrated higher metabolite markers of excitatory neurotransmission and lower inhibitory markers in ASD groups. fNIRS-based papers (n = 20) reported lower oxygenated hemoglobin signals in ASD. Converging findings in MEG- (n = 20) and EEG-based (n = 32) papers indicated lower event-related potential and field amplitudes in ASD groups. Findings in the anterior cingulate cortex, insula, prefrontal cortex, amygdala, thalamus, cerebellum, corpus callosum, and default mode network appeared numerous times across modalities and provided opportunities for multimodal qualitative analysis.

Conclusions

Comparing across neuroimaging modalities, we found significant differences between the ASD and TD neuroimaging profile in addition to substantial heterogeneity. Inconsistent results are frequently seen within imaging modalities, comparable study populations and research designs. Still, converging patterns across imaging modalities support various existing theories on ASD.

A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and indivinformation of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

Predicting individual autistic symptoms for patients with autism spectrum disorder using interregional morphological connectivity

Intelligent predictive models for autistic symptoms based on neuroimaging datasets were beneficial for the precise intervention of patients with ASD. The goals of this study were twofold: investigating predictive models for autistic symptoms and discovering the brain connectivity patterns for ASD-related behaviors. To achieve these goals, we obtained a cohort of patients with ASD from the ABIDE project. The autistic symptoms were measured using the Autism Diagnostic Observation Schedule (ADOS). The anatomical MRI datasets were preprocessed using the Freesurfer package, resulting in regional morphological features. For each individual, the interregional morphological network was constructed using a novel feature distance-based method. The predictive models for autistic symptoms were built using the support vector regression (SVR) algorithm with feature selection method. The predicted autistic symptoms (i.e., ADOS social score, ADOS behavior) were significantly correlated to the original measures. The most predictive features for ADOS social scores were located in the bilateral fusiform. The most predictive features for ADOS behavior scores were located in the temporal pole and the lingual gyrus. In summary, the autistic symptoms could be predicted using the interregional morphological connectivity and machine learning. The interregional morphological connectivity could be a potential biomarker for autistic symptoms.

Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach

BACKGROUND

Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level.

METHODS

Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks.

RESULTS

We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms.

CONCLUSIONS

Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.

Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis

Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.

Central Executive and Default Mode Networks: An Appraisal of Executive Function and Social Skill Brain-Behavior Correlates in Youth with Autism Spectrum Disorder

Atypical connectivity patterns have been observed for individuals with autism spectrum disorders (ASD), particularly across the triple-network model. The current study investigated brain-behavior relationships in the context of social skills and executive function profiles for ASD youth. We calculated connectivity measures from diffusion tensor imaging using Bayesian estimation and probabilistic tractography. We replicated prior structural equation modeling of behavioral measures with total default mode network (DMN) connectivity to include comparisons with central executive network (CEN) connectivity and CEN-DMN connectivity. Increased within-CEN connectivity was related to metacognitive strengths. Our findings indicate behavior regulation difficulties in youth with ASD may be attributable to impaired connectivity between the CEN and DMN and social skill difficulties may be exacerbated by impaired within-DMN connectivity.

Local gradient analysis of human brain function using the Vogt-Bailey Index

In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Bajada et al. (NeuroImage 221:117140, 2020) as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a scaled cut-set weight that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of small-scale similarity in brain activity: the greater the similarity, the 'heavier' the edges and the more difficult it is to disconnect the network, hence the higher the value of the minimum ratio cut. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

BACKGROUND

The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).

MATERIAL AND METHODS

We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.

RESULTS

The AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.

CONCLUSIONS

Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

Endogenous noise of neocortical neurons correlates with atypical sensory response variability in the Fmr1-/y mouse model of autism

Excessive neural variability of sensory responses is a hallmark of atypical sensory processing in autistic individuals with cascading effects on other core autism symptoms but unknown neurobiological substrate. Here, by recording neocortical single neuron activity in a well-established mouse model of Fragile X syndrome and autism, we characterized atypical sensory processing and probed the role of endogenous noise sources in exaggerated response variability in males. The analysis of sensory stimulus evoked activity and spontaneous dynamics, as well as neuronal features, reveals a complex cellular and network phenotype. Neocortical sensory information processing is more variable and temporally imprecise. Increased trial-by-trial and inter-neuronal response variability is strongly related to key endogenous noise features, and may give rise to behavioural sensory responsiveness variability in autism. We provide a novel preclinical framework for understanding the sources of endogenous noise and its contribution to core autism symptoms, and for testing the functional consequences for mechanism-based manipulation of noise.

Toward an understanding of the role of the exposome on fragile X phenotypes

Fragile X syndrome (FXS) is the leading known monogenetic cause of autism with an estimated 21-50% of FXS individuals meeting autism diagnostic criteria. A critical gap in medical care for persons with autism is an understanding of how environmental exposures and gene-environment interactions affect disease outcomes. Our research indicates more severe neurological and metabolic outcomes (seizures, autism, increased body weight) in mouse and human models of autism spectrum disorders (ASD) as a function of diet. Thus, early-life exposure to chemicals in the diet could cause or exacerbate disease outcomes. Herein, we review the effects of potential dietary toxins, i.e., soy phytoestrogens, glyphosate, and polychlorinated biphenyls (PCB) in FXS and other autism models. The rationale is that potentially toxic chemicals in the diet, particularly infant formula, could contribute to the development and/or severity of ASD and that further study in this area has potential to improve ASD outcomes through dietary modification.

Altered resting-state functional connectivity of the brain in children with autism spectrum disorder

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders. Brain mapping has shown that functional brain connections are altered in autism. This study investigated the pattern of brain connection changes in autistic people compared to healthy people. This study aimed to analyze functional abnormalities within the brain due to ASD, using resting-state functional magnetic resonance imaging (fMRI). Resting-state functional magnetic resonance images of 26 individuals with ASD and 26 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The DPARSF (data processing assistant for resting-state fMRI) toolbox was used for resting-state functional image processing, and features related to functional connections were extracted from these images. Then, the extracted features from both groups were compared using an Independent Two-Sample T Test, and the features with significant differences between the two groups were identified. Compared with healthy controls, individuals with ASD showed hyper-connectivity in the frontal lobe, anterior cingulum, parahippocampal, left precuneus, angular, caudate, superior temporal, and left pallidum, as well as hypo-connectivity in the precentral, left superior frontal, left middle orbitofrontal, right amygdala, and left posterior cingulum. Our findings show that abnormal functional connectivity exists in patients with ASD. This study makes an important advancement in our understanding of the abnormal neurocircuits causing autism.

A convergent structure-function substrate of cognitive imbalances in autism

BACKGROUND

Autism spectrum disorder (ASD) is a common neurodevelopmental diagnosis showing substantial phenotypic heterogeneity. A leading example can be found in verbal and nonverbal cognitive skills, which vary from elevated to impaired compared with neurotypical individuals. Moreover, deficits in verbal profiles often coexist with normal or superior performance in the nonverbal domain.

METHODS

To study brain substrates underlying cognitive imbalance in ASD, we capitalized categorical and dimensional IQ profiling as well as multimodal neuroimaging.

RESULTS

IQ analyses revealed a marked verbal to nonverbal IQ imbalance in ASD across 2 datasets (Dataset-1: 155 ASD, 151 controls; Dataset-2: 270 ASD, 490 controls). Neuroimaging analysis in Dataset-1 revealed a structure-function substrate of cognitive imbalance, characterized by atypical cortical thickening and altered functional integration of language networks alongside sensory and higher cognitive areas.

CONCLUSION

Although verbal and nonverbal intelligence have been considered as specifiers unrelated to autism diagnosis, our results indicate that intelligence disparities are accentuated in ASD and reflected by a consistent structure-function substrate affecting multiple brain networks. Our findings motivate the incorporation of cognitive imbalances in future autism research, which may help to parse the phenotypic heterogeneity and inform intervention-oriented subtyping in ASD.

Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis

Background

Neurobiological models to explain the vulnerability of autism spectrum disorders (ASDs) are scarce, and previous resting-state functional magnetic resonance imaging (rs-fMRI) studies mostly examined static functional connectivity (FC). Given that FC constantly evolves, it is critical to probe FC dynamic differences in ASD patients.

Methods

We characterized recurring phase-locking (PL) states during rest in 45 ASD patients and 47 age- and sex-matched healthy controls (HCs) using Leading Eigenvector Dynamics Analysis (LEiDA) and probed the organization of PL states across different fine grain sizes.

Results

Our results identified five different groups of discrete resting-state functional networks, which can be defined as recurrent PL state overtimes. Specifically, ASD patients showed an increased probability of three PL states, consisting of the visual network (VIS), frontoparietal control network (FPN), default mode network (DMN), and ventral attention network (VAN). Correspondingly, ASD patients also showed a decreased probability of two PL states, consisting of the subcortical network (SUB), somatomotor network (SMN), FPN, and VAN.

Conclusion

Our findings suggested that the temporal reorganization of brain discrete networks was closely linked to sensory to cognitive systems of the brain. Our study provides new insights into the dynamics of brain networks and contributes to a deeper understanding of the neurological mechanisms of ASD.

Modulation effects of the intact motor skills on the relationship between social skills and motion perceptions in children with autism spectrum disorder: A pilot study

BACKGROUND

An individual with autism spectrum disorder (ASD) has social skill, motor skill, and motion perception deficits. However, the relationship among them was not clarified. Therefore, this study aimed to evaluate the effects of motor skills on social skills and motion perception.

METHODS

Five typically developed children and fourteen children with ASD participated in our study. The N200 component, a brain activity indicating motion perception, was induced in mid-temporal (MT/V5) brain area by watching a random dot kinematograph, and was recorded using a scalp electroencephalogram. Furthermore, the social responsiveness scale (SRS) indicating the social skill deficit, the developmental coordination disorder questionnaire (DCDQ) estimating the developmental coordination disorder (DCD), and the movement assessment battery for children second edition (MABC-2) indicating motor skills were recorded in the children with ASD. A hierarchical multiple regression analysis was conducted to examine the modulation effects of motor skills on the relationship between social skills and motion perception. The dependent variable was the N200 latency, and the independent variables were SRS, MABC-2, and combined MABC-2 and SRS.

RESULTS

The N200 latency was more delayed in children with ASD relative that in typically developed children. Intact balance ability modulated the relationship between social skills and N200 latency in children with ASD. Within the high balance ability, when the social skills worsened, the N200 latency was shortened.

CONCLUSIONS

This is the first report that intact motor skills could modulate the relationship between social skills and motion perception.

Morphometric analysis of Corpus Callosum in autistic and typically developing Indian children

Corpus callosum (CC) is the largest commissural white matter bundle in the brain, responsible for the integration of information between hemispheres. Reduction in the size of the CC structure has been predominantly reported in children with autism spectrum disorder (ASD) compared to typically developing children (TD). However, most of these studies are based on high-functioning individuals with ASD but not on an inclusive sample of individuals with ASD with varying abilities. Our current study aimed to examine the CC morphometry between children with ASD and TD in the Indian population. We also compared CC morphometry in autistic children with autism severity, verbal IQ (VIQ) and full-scale IQ (FSIQ). T1-weighted structural images were acquired using a 3T MRI scanner to examine the CC measures in 62 ASD and 17 TD children. The length and height of the CC and the width of genu were decreased in children with ASD compared to TD. There was no significant difference in CC measures based on autism severity, VIQ or FSIQ among children with ASD. To our knowledge, this is the first neuroimaging study to include a significant number (n = 56) of low-functioning ASD children. Our findings suggest the atypical interhemispheric connectivity of CC in ASD.

Brain functional activity-based classification of autism spectrum disorder using an attention-based graph neural network combined with gene expression

Autism spectrum disorder (ASD) is a complex brain neurodevelopmental disorder related to brain activity and genetics. Most of the ASD diagnostic models perform feature selection at the group level without considering individualized information. Evidence has shown the unique topology of the individual brain has a fundamental impact on brain diseases. Thus, a data-constructing method fusing individual topological information and a corresponding classification model is crucial in ASD diagnosis and biomarker discovery. In this work, we trained an attention-based graph neural network (GNN) to perform the ASD diagnosis with the fusion of graph data. The results achieved an accuracy of 79.78%. Moreover, we found the model paid high attention to brain regions mainly involved in the social-brain circuit, default-mode network, and sensory perception network. Furthermore, by analyzing the covariation between functional magnetic resonance imaging data and gene expression, current studies detected several ASD-related genes (i.e. MUTYH, AADAT, and MAP2), and further revealed their links to image biomarkers. Our work demonstrated that the ASD diagnostic framework based on graph data and attention-based GNN could be an effective tool for ASD diagnosis. The identified functional features with high attention values may serve as imaging biomarkers for ASD.

Gene×environment interactions in autism spectrum disorders

There is credible evidence that environmental factors influence individual risk and/or severity of autism spectrum disorders (hereafter referred to as autism). While it is likely that environmental chemicals contribute to the etiology of autism via multiple mechanisms, identifying specific environmental factors that confer risk for autism and understanding how they contribute to the etiology of autism has been challenging, in part because the influence of environmental chemicals likely varies depending on the genetic substrate of the exposed individual. Current research efforts are focused on elucidating the mechanisms by which environmental chemicals interact with autism genetic susceptibilities to adversely impact neurodevelopment. The goal is to not only generate insights regarding the pathophysiology of autism, but also inform the development of screening platforms to identify specific environmental factors and gene×environment (G×E) interactions that modify autism risk. Data from such studies are needed to support development of intervention strategies for mitigating the burden of this neurodevelopmental condition on individuals, their families and society. In this review, we discuss environmental chemicals identified as putative autism risk factors and proposed mechanisms by which G×E interactions influence autism risk and/or severity using polychlorinated biphenyls (PCBs) as an example.

Increased functional activity, bottom-up and intrinsic effective connectivity in autism

Sensory perceptual alterations such as sensory sensitivities in autism have been proposed to be caused by differences in sensory observation (Likelihood) or in forming models of the environment (Prior), which result in an increase in bottom-up information flow relative to top-down control. To investigate this conjecture, we had autistic individuals (AS) and neurotypicals (NT) perform a decision-under-uncertainty paradigm while undergoing functional magnetic resonance imaging (fMRI). There were no group differences in task performance and in Prior and Likelihood representations in brain activity. However, there were significant group differences in overall task activity, with the AS group showing significantly greater activation in the bilateral precuneus, mid-occipital gyrus, cuneus, superior frontal gyrus (SFG) and left putamen relative to the NT group. Further, when pooling the data across both groups, we found that those with higher AQ scores showed greater activity in the left cuneus and precuneus. Effective connectivity analysis using dynamic causal modelling (DCM) revealed that group differences in BOLD signals were underpinned by increased activity within sensory regions and a net increase in bottom-up connectivity from the occipital region to the precuneus and the left SFG. These findings support the hypothesis of increased bottom-up information flow in autism during sensory learning tasks.

Structural filtering of functional data offered discriminative features for autism spectrum disorder

This study attempted to answer the question, "Can filtering the functional data through the frequency bands of the structural graph provide data with valuable features which are not valuable in unfiltered data"?. The valuable features discriminate between autism spectrum disorder (ASD) and typically control (TC) groups. The resting-state fMRI data was passed through the structural graph's low, middle, and high-frequency band (LFB, MFB, and HFB) filters to answer the posed question. The structural graph was computed using the diffusion tensor imaging data. Then, the global metrics of functional graphs and metrics of functional triadic interactions were computed for filtered and unfiltered rfMRI data. Compared to TCs, ASDs had significantly higher clustering coefficients in the MFB, higher efficiencies and strengths in the MFB and HFB, and lower small-world propensity in the HFB. These results show over-connectivity, more global integration, and decreased local specialization in ASDs compared to TCs. Triadic analysis showed that the numbers of unbalanced triads were significantly lower for ASDs in the MFB. This finding may indicate the reason for restricted and repetitive behavior in ASDs. Also, in the MFB and HFB, the numbers of balanced triads and the energies of triadic interactions were significantly higher and lower for ASDs, respectively. These findings may reflect the disruption of the optimum balance between functional integration and specialization. There was no significant difference between ASDs and TCs when using the unfiltered data. All of these results demonstrated that significant differences between ASDs and TCs existed in the MFB and HFB of the structural graph when analyzing the global metrics of the functional graph and triadic interaction metrics. Also, these results demonstrated that frequency bands of the structural graph could offer significant findings which were not found in the unfiltered data. In conclusion, the results demonstrated the promising perspective of using structural graph frequency bands for attaining discriminative features and new knowledge, especially in the case of ASD.

Autism spectrum disorders pathogenesis: Toward a comprehensive model based on neuroanatomic and neurodevelopment considerations

Autism spectrum disorder (ASD) involves alterations in neural connectivity affecting cortical network organization and excitation to inhibition ratio. It is characterized by an early increase in brain volume mediated by abnormal cortical overgrowth patterns and by increases in size, spine density, and neuron population in the amygdala and surrounding nuclei. Neuronal expansion is followed by a rapid decline from adolescence to middle age. Since no known neurobiological mechanism in human postnatal life is capable of generating large excesses of frontocortical neurons, this likely occurs due to a dysregulation of layer formation and layer-specific neuronal migration during key early stages of prenatal cerebral cortex development. This leads to the dysregulation of post-natal synaptic pruning and results in a huge variety of forms and degrees of signal-over-noise discrimination losses, accounting for ASD clinical heterogeneities, including autonomic nervous system abnormalities and comorbidities. We postulate that sudden changes in environmental conditions linked to serotonin/kynurenine supply to the developing fetus, throughout the critical GW7 - GW20 (Gestational Week) developmental window, are likely to promote ASD pathogenesis during fetal brain development. This appears to be driven by discrete alterations in differentiation and patterning mechanisms arising from in utero RNA editing, favoring vulnerability outcomes over plasticity outcomes. This paper attempts to provide a comprehensive model of the pathogenesis and progression of ASD neurodevelopmental disorders.

Brain Structural Covariance Network in Asperger Syndrome Differs From Those in Autism Spectrum Disorder and Healthy Controls

Introduction

Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD).

Method

We compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age=14-50, IQ=92-132), 16 AS (age=7-58, IQ=93-133) and 28 HC (age=9-39, IQ=95-144).

Result

The one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is altered in ASD.

Conclusion

This changed structural covariance could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.

Topological analysis of brain dynamics in autism based on graph and persistent homology

Autism spectrum disorder (ASD) is a heterogeneous disorder with a rapidly growing prevalence. In recent years, the dynamic functional connectivity (DFC) technique has been used to reveal the transient connectivity behavior of ASDs' brains by clustering connectivity matrices in different states. However, the states of DFC have not been yet studied from a topological point of view. In this paper, this study was performed using global metrics of the graph and persistent homology (PH) and resting-state functional magnetic resonance imaging (fMRI) data. The PH has been recently developed in topological data analysis and deals with persistent structures of data. The structural connectivity (SC) and static FC (SFC) were also studied to know which one of the SC, SFC, and DFC could provide more discriminative topological features when comparing ASDs with typical controls (TCs). Significant discriminative features were only found in states of DFC. Moreover, the best classification performance was offered by persistent homology-based metrics and in two out of four states. In these two states, some networks of ASDs compared to TCs were more segregated and isolated (showing the disruption of network integration in ASDs). The results of this study demonstrated that topological analysis of DFC states could offer discriminative features which were not discriminative in SFC and SC. Also, PH metrics can provide a promising perspective for studying ASD and finding candidate biomarkers.

Cognitive theories of autism based on the interactions between brain functional networks

Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of functional interactions. The main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5-14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism.

Functional Brain Network Classification Based on Deep Graph Hashing Learning

Brain network classification using resting-state functional magnetic resonance imaging (rs-fMRI) is an effective analytical method for diagnosing brain diseases. In recent years, brain network classification methods based on deep learning have attracted increasing attention. However, these methods only consider the spatial topological characteristics of the brain network but ignore its proximity relationships in semantic space. To overcome this problem, we propose a novel brain network classification method based on deep graph hashing learning named BNC-DGHL. Specifically, we first extract the deep features of the brain network and then learn a graph hash function based on clinical phenotype labels and the similarity of diagnostic labels. Secondly, we use the learned graph hash function to convert deep features into hash codes, which can maintain the original semantic spatial relationships. Finally, we calculate the distance between hash codes to obtain the predicted category of the brain network. Experimental results on ABIDE I, ABIDE II, and ADHD-200 datasets demonstrate that our method achieves better classification performance of brain diseases compared with some state-of-the-art methods, and the abnormal functional connectivities between brain regions identified may serve as biomarkers associated with related brain diseases.

Acquisition time for functional near-infrared spectroscopy resting-state functional connectivity in assessing autism

SIGNIFICANCE

Resting state functional connectivity (RSFC) can be used to assess autism spectrum disorder (ASD). Measuring RSFC usually takes 5 to 10 min, during which children with ASD may have difficulty keeping their heads motionless. Therefore, a short acquisition time for RSFC would make clinical implementation more feasible.

AIM

To find a suitable acquisition time necessary for measuring RSFC with functional near-infrared spectroscopy (fNIRS) for the differentiation between children with ASD and typically developing (TD) children.

APPROACH

We used fNIRS to record the spontaneous hemodynamic fluctuations from the bilateral temporal lobes of 25 children with ASD and 22 TD children. The recorded signals were truncated into several segments with different time windows, and then the homotopic RSFC was computed for each of these segments and compared between the two groups.

RESULTS

We observed even in a very short time duration of 0.5 min, the RSFC had already existed a significant difference between the two groups, and 2.0 min might be the minimal time required for measuring RSFC for accurate differentiation between the two groups.

CONCLUSIONS

The fNIRS-RSFC acquired even in a short time, e.g., 2.0 min, might be a reliable feature for the differentiation between children with ASD and TD children.

Mapping brain functional and structural abnormities in autism spectrum disorder: moving toward precision treatment

Autism spectrum disorder (ASD) is a formidable challenge for psychiatry and neuroscience because of its high prevalence, lifelong nature, complexity, and substantial heterogeneity. A major goal of neuroimaging studies of ASD is to understand the neurobiological underpinnings of this disorder from multi-dimensional and multi-level perspectives, by investigating how brain anatomy, function, and connectivity are altered in ASD, and how they vary across the population. However, ongoing debate exists within those studies, and neuroimaging findings in ASD are often contradictory. Over the past decade, we have dedicated to delineate a comprehensive and consistent mapping of the abnormal structure and function of the autistic brain, and this review synthesizes the findings across our studies reaching a consensus that the "social brain" are the most affected regions in the autistic brain at different levels and modalities. We suggest that the social brain network can serve as a plausible biomarker and potential target for effective intervention in individuals with ASD.

Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

Disrupted functional connectivity of the primary auditory cortex in autism

Autism Spectrum Disorder (ASD) has been found to influence hearing and sensory integration, while brain functional connectivity in ASD has been repeatedly shown to be atypical. However, functional connectivity of the auditory cortex in ASD has not been well studied. In the current study, we used resting-state functional magnetic resonance imaging data, provided by the Autism Brain Imaging Data Exchange (ABIDE), to examine functional connectivity of the primary auditory cortex in ASD. The study subjects included 68 individuals with ASD and 77 individuals without ASD. In the primary dataset, the ASD group showed lesser functional connectivity between the auditory cortex and four regions: the medial occipital cortex, primary motor cortex, insular cortex, and Wernicke's area. In the replication dataset (44 individuals with ASD and 39 individuals without ASD), reduced connectivity to the medial occipital cortex and primary motor cortex was replicated among these four regions, which have previously been shown to be influenced by ASD. Thus, the reduced functional connectivity to these indicated regions may partly explain deficient sensory integration associated with ASD.

Oxytocin and serotonin in the modulation of neural function: Neurobiological underpinnings of autism-related behavior

Autism spectrum disorders (ASD) is a group of generalized neurodevelopmental disorders. Its main clinical features are social communication disorder and repetitive stereotyped behavioral interest. The abnormal structure and function of brain network is the basis of social dysfunction and stereotyped performance in patients with autism spectrum disorder. The number of patients diagnosed with ASD has increased year by year, but there is a lack of effective intervention and treatment. Oxytocin has been revealed to effectively improve social cognitive function and significantly improve the social information processing ability, empathy ability and social communication ability of ASD patients. The change of serotonin level also been reported affecting the development of brain and causes ASD-like behavioral abnormalities, such as anxiety, depression like behavior, stereotyped behavior. Present review will focus on the research progress of serotonin and oxytocin in the pathogenesis, brain circuit changes and treatment of autism. Revealing the regulatory effect and neural mechanism of serotonin and oxytocin on patients with ASD is not only conducive to a deeper comprehension of the pathogenesis of ASD, but also has vital clinical significance.

Predicting the Symptom Severity in Autism Spectrum Disorder Based on EEG Metrics

Autism spectrum disorder (ASD) is associated with the impaired integrating and segregating of related information that is expanded within the large-scale brain network. The varying ASD symptom severities have been explored, relying on their behaviors and related brain activity, but how to effectively predict ASD symptom severity needs further exploration. In this study, we aim to investigate whether the ASD symptom severity could be predicted with electroencephalography (EEG) metrics. Based on a publicly available dataset, the EEG brain networks were constructed, and four types of EEG metrics were calculated. Then, we statistically compared the brain network differences among ASD children with varying severities, i.e., high/low autism diagnostic observation schedule (ADOS) scores, as well as with the typically developing (TD) children. Thereafter, the EEG metrics were utilized to validate whether they could facilitate the prediction of the ASD symptom severity. The results demonstrated that both high- and low-scoring ASD children showed the decreased long-range frontal-occipital connectivity, increased anterior frontal connectivity and altered network properties. Furthermore, we found that the four types of EEG metrics are significantly correlated with the ADOS scores, and their combination can serve as the features to effectively predict the ASD symptom severity. The current findings will expand our knowledge of network dysfunction in ASD children and provide new EEG metrics for predicting the symptom severity.

Identification of Young High-Functioning Autism Individuals Based on Functional Connectome Using Graph Isomorphism Network: A Pilot Study

Accumulated studies have determined the changes in functional connectivity in autism spectrum disorder (ASD) and spurred the application of machine learning for classifying ASD. Graph Neural Network provides a new method for network analysis in brain disorders to identify the underlying network features associated with functional deficits. Here, we proposed an improved model of Graph Isomorphism Network (GIN) that implements the Weisfeiler-Lehman (WL) graph isomorphism test to learn the graph features while taking into account the importance of each node in the classification to improve the interpretability of the algorithm. We applied the proposed method on multisite datasets of resting-state functional connectome from Autism Brain Imaging Data Exchange (ABIDE) after stringent quality control. The proposed method outperformed other commonly used classification methods on five different evaluation metrics. We also identified salient ROIs in visual and frontoparietal control networks, which could provide potential neuroimaging biomarkers for ASD identification.

The Neurology and Psychopathology of Pica

PURPOSE OF REVIEW

Pica is defined by the American Psychiatric Association's Diagnostic and Statistical Manual, 5th edition (DSM 5) as the ongoing ingestion of materials with no nutritive or food value. More specifically such ingestions must be unremitting for at least 1 month and occur at a developmentally inconsistent age for such behavior. This article reviews the association of pica with pregnancy, micronutrient deficiencies, psychiatric disorders, dementia, and developmental disorders with emphasis on autism spectrum disorders (ASD).

RECENT FINDINGS

Some variants of non-nutritive consumption are prevalent behavioral norms in non-western cultures, so not all picas should be considered pathological. However, the strong association of pica with iron deficiency anemia (IDA) lends credence to the hypothesis that dopamine transmission may be disrupted in this disorder. Picas associated with ASD are resistant to medications but can be treated with applied behavioral analysis therapy (ABA). Etiological hypotheses for pica are explored with a focus on neurobiological, neuroimaging, and psychiatric correlations. Pharmacological management and behavior modification strategies are also discussed. The possibility that pica is a form of addiction analogous to food cravings is introduced and suggested as an area for further research pursuits.

Surgery of autism: Is it possible?

Autism spectrum disorder (ASD) is a developmental disability of the brain that can be associated to severe conductual alterations, such as self or heteroaggression and obsessive and compulsive behavior. Many of these patients do not improve with any pharmacological or behavioral therapy and represent a major social problem. We describe the outcome of patients with ASD, treated with radiofrequency brain lesions combined with Gamma Knife radiosurgery for therapy-resistant aggressiveness, obsessive thoughts, and compulsions. The ASD adapted YBOCS, PCQ and EAE scales assessed the therapeutic effect on symptoms. All patients had a significant reduction of their symptoms (YBOCS:34 and 22 PCQ 42 and 35, EAE 11 and 5.5, respectively), although all needed more than one treatment to maintain this improvement. The treatments resulted very safe for the patients and their neurological status has not change. We conclude that in these patients after surgery, there is a marked improvement in behavior, quality of life and relationship with the environment, with no evidence of secondary damage. Changes in connectivity might mediate the clinical improvement, although it is necessary to confirm these results with further studies.

Translational Magnetic Resonance Imaging in Autism Spectrum Disorder From the Mouse Model to Human

Autism spectrum disorder (ASD) is a heterogeneous syndrome characterized by behavioral features such as impaired social communication, repetitive behavior patterns, and a lack of interest in novel objects. A multimodal neuroimaging using magnetic resonance imaging (MRI) in patients with ASD shows highly heterogeneous abnormalities in function and structure in the brain associated with specific behavioral features. To elucidate the mechanism of ASD, several ASD mouse models have been generated, by focusing on some of the ASD risk genes. A specific behavioral feature of an ASD mouse model is caused by an altered gene expression or a modification of a gene product. Using these mouse models, a high field preclinical MRI enables us to non-invasively investigate the neuronal mechanism of the altered brain function associated with the behavior and ASD risk genes. Thus, MRI is a promising translational approach to bridge the gap between mice and humans. This review presents the evidence for multimodal MRI, including functional MRI (fMRI), diffusion tensor imaging (DTI), and volumetric analysis, in ASD mouse models and in patients with ASD and discusses the future directions for the translational study of ASD.

The Cerebellar Involvement in Autism Spectrum Disorders: From the Social Brain to Mouse Models

Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders that include a variety of forms and clinical phenotypes. This heterogeneity complicates the clinical and experimental approaches to ASD etiology and pathophysiology. To date, a unifying theory of these diseases is still missing. Nevertheless, the intense work of researchers and clinicians in the last decades has identified some ASD hallmarks and the primary brain areas involved. Not surprisingly, the areas that are part of the so-called “social brain”, and those strictly connected to them, were found to be crucial, such as the prefrontal cortex, amygdala, hippocampus, limbic system, and dopaminergic pathways. With the recent acknowledgment of the cerebellar contribution to cognitive functions and the social brain, its involvement in ASD has become unmistakable, though its extent is still to be elucidated. In most cases, significant advances were made possible by recent technological developments in structural/functional assessment of the human brain and by using mouse models of ASD. Mouse models are an invaluable tool to get insights into the molecular and cellular counterparts of the disease, acting on the specific genetic background generating ASD-like phenotype. Given the multifaceted nature of ASD and related studies, it is often difficult to navigate the literature and limit the huge content to specific questions. This review fulfills the need for an organized, clear, and state-of-the-art perspective on cerebellar involvement in ASD, from its connections to the social brain areas (which are the primary sites of ASD impairments) to the use of monogenic mouse models.

Flattened Structural Network Changes and Association of Hyperconnectivity With Symptom Severity in 2-7-Year-Old Children With Autism

Understanding the brain differences present at the earliest possible diagnostic age for autism spectrum disorder (ASD) is crucial for delineating the underlying neuropathology of the disorder. However, knowledge of brain structural network changes in the early important developmental period between 2 and 7 years of age is limited in children with ASD. In this study, we aimed to fill the knowledge gap by characterizing age-related brain structural network changes in ASD from 2 to 7 years of age, and identify sensitive network-based imaging biomarkers that are significantly correlated with the symptom severity. Diffusion MRI was acquired in 30 children with ASD and 21 typically developmental (TD) children. With diffusion MRI and quantified clinical assessment, we conducted network-based analysis and correlation between graph-theory-based measurements and symptom severity. Significant age-by-group interaction was found in global network measures and nodal efficiencies during the developmental period of 2-7 years old. Compared with significant age-related growth of the structural network in TD, relatively flattened maturational trends were observed in ASD. Hyper-connectivity in the structural network with higher global efficiency, global network strength, and nodal efficiency were observed in children with ASD. Network edge strength in ASD also demonstrated hyper-connectivity in widespread anatomical connections, including those in default-mode, frontoparietal, and sensorimotor networks. Importantly, identified higher nodal efficiencies and higher network edge strengths were significantly correlated with symptom severity in ASD. Collectively, structural networks in ASD during this early developmental period of 2-7 years of age are characterized by hyper-connectivity and slower maturation, with aberrant hyper-connectivity significantly correlated with symptom severity. These aberrant network measures may serve as imaging biomarkers for ASD from 2 to 7 years of age.

Astrocytic Gap Junctions Contribute to Aberrant Neuronal Synchronization in a Mouse Model of MeCP2 Duplication Syndrome

Abnormal synchronous neuronal activity has been widely detected by brain imaging of autistic patients, but its underlying neural mechanism remains unclear. Compared with wild-type mice, our in vivo two-photon imaging showed that transgenic (Tg1) mice over-expressing human autism risk gene MeCP2 exhibited higher neuronal synchrony in the young but lower synchrony in the adult stage. Whole-cell recording of neuronal pairs in brain slices revealed that higher neuronal synchrony in young postnatal Tg1 mice was attributed mainly to more prevalent giant slow inward currents (SICs). Both in vivo and slice imaging further demonstrated more dynamic activity and higher synchrony in astrocytes from young Tg1 mice. Blocking astrocytic gap junctions markedly decreased the generation of SICs and overall cell synchrony in the Tg1 brain. Furthermore, the expression level of Cx43 protein and the coupling efficiency of astrocyte gap junctions remained unchanged in Tg1 mice. Thus, astrocytic gap junctions facilitate but do not act as a direct trigger for the abnormal neuronal synchrony in young Tg1 mice, revealing the potential role of the astrocyte network in the pathogenesis of MeCP2 duplication syndrome.

Intrinsic Structural Connectivity of the Default Mode Network and Behavioral Correlates of Executive Function and Social Skills in Youth with Autism Spectrum Disorders

Brain connectivity of individuals with autism spectrum disorders (ASD) is heterogenous, as are the behavioral manifestations. The current study investigated brain-behavior relationships in the context of social skills and executive function profiles with data from the Autism Brain Imaging Database Exchange II. We calculated connectivity measures from diffusion tensor imaging using Bayesian estimation and probabilistic tractography. Subsequently, we performed structural equation modeling by regressing three latent factors, yielded from an exploratory factor analysis, onto total default mode network (DMN) connectivity. Both social regulation processing and self-directed cognitive processing factors moderately, negatively correlated with total DMN connectivity. Our findings indicate social regulation processing difficulties in youth with ASD may be attributable to impaired connectivity between the anterior and posterior DMN.

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.

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.

Sex and Genotype Modulate the Dendritic Effects of Developmental Exposure to a Human-Relevant Polychlorinated Biphenyls Mixture in the Juvenile Mouse

While many neurodevelopmental disorders (NDDs) are thought to result from interactions between environmental and genetic risk factors, the identification of specific gene-environment interactions that influence NDD risk remains a critical data gap. We tested the hypothesis that polychlorinated biphenyls (PCBs) interact with human mutations that alter the fidelity of neuronal Ca²⁺ signaling to confer NDD risk. To test this, we used three transgenic mouse lines that expressed human mutations known to alter Ca²⁺ signals in neurons: (1) gain-of-function mutation in ryanodine receptor-1 (T4826I-RYR1); (2) CGG-repeat expansion in the 5′ non-coding portion of the fragile X mental retardation gene 1 (FMR1); and (3) a double mutant (DM) that expressed both mutations. Transgenic and wildtype (WT) mice were exposed throughout gestation and lactation to the MARBLES PCB mix at 0.1, 1, or 6 mg/kg in the maternal diet. The MARBLES mix simulates the relative proportions of the twelve most abundant PCB congeners found in serum from pregnant women at increased risk for having a child with an NDD. Using Golgi staining, the effect of developmental PCB exposure on dendritic arborization of pyramidal neurons in the CA1 hippocampus and somatosensory cortex of male and female WT mice was compared to pyramidal neurons from transgenic mice. A multilevel linear mixed-effects model identified a main effect of dose driven by increased dendritic arborization of cortical neurons in the 1 mg/kg PCB dose group. Subsequent analyses with genotypes indicated that the MARBLES PCB mixture had no effect on the dendritic arborization of hippocampal neurons in WT mice of either sex, but significantly increased dendritic arborization of cortical neurons of WT males in the 6 mg/kg PCB dose group. Transgene expression increased sensitivity to the impact of developmental PCB exposure on dendritic arborization in a sex-, and brain region-dependent manner. In conclusion, developmental exposure to PCBs present in the gestational environment of at-risk humans interfered with normal dendritic morphogenesis in the developing mouse brain in a sex-, genotype- and brain region-dependent manner. Overall, these observations provide proof-of-principle evidence that PCBs interact with heritable mutations to modulate a neurodevelopmental outcome of relevance to NDDs.

Temporal Lobe Epilepsy and Psychiatric Comorbidity

Most focal seizures originate in the temporal lobe and are commonly divided into mesial and lateral temporal epilepsy, depending upon the neuronal circuitry involved. The hallmark features of the mesial temporal epilepsy are aura, unconsciousness, and automatisms. Symptoms often overlap with the lateral temporal epilepsy. However, the latter present a less evident psychomotor arrest, frequent clones and dystonic postures, and common focal to bilateral tonic–clonic seizures. Sclerosis of the hippocampus is the most frequent cause of temporal lobe epilepsy (TLE). TLE is among all epilepsies the most frequently associated with psychiatric comorbidity. Anxiety, depression, and interictal dysphoria are recurrent psychiatric disorders in pediatric patients with TLE. In addition, these alterations are often combined with cognitive, learning, and behavioral impairment. These comorbidities occur more frequently in TLE with hippocampal sclerosis and with pharmacoresistance. According to the bidirectional hypothesis, the close relationship between TLE and psychiatric features should lead to considering common pathophysiology underlying these disorders. Psychiatric comorbidities considerably reduce the quality of life of these children and their families. Thus, early detection and appropriate management and therapeutic strategies could improve the prognosis of these patients. The aim of this review is to analyze TLE correlation with psychiatric disorders and its underlying conditions.

Classification of ASD based on fMRI data with deep learning

Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects the social abilities of patients. Studies have shown that a small number of abnormal functional connections (FCs) exist in the cerebral hemisphere of ASD patients. The identification of these abnormal FCs provides a biological ground for the diagnosis of ASD. In this paper, we propose a combined deep feature selection (DFS) and graph convolutional network method to classify ASD. Firstly, in the DFS process, a sparse one-to-one layer is added between the input and the first hidden layer of a multilayer perceptron, thus each functional connection (FC) feature can be weighted and a subset of FC features can be selected accordingly. Then based on the selected FCs and the phenotypic information of subjects, a graph convolutional network is constructed to classify ASD and typically developed controls. Finally, we test our proposed method on the ABIDE database and compare it with some other methods in the literature. Experimental results indicate that the DFS can effectively select critical FC features for classification according to the weights of input FC features. With DFS, the performance of GCN classifier can be improved dramatically. The proposed method achieves state-of-the-art performance with an accuracy of 79.5% and an area under the receiver operating characteristic curve (AUC) of 0.85 on the preprocessed ABIDE dataset; it is superior to the other methods. Further studies on the top-ranked thirty FCs obtained by DFS show that these FCs are widespread over the cerebral hemisphere, and the ASD group appears a significantly higher number of weak connections compared to the typically developed group.

Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model

The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.

Relationship between Short-Range and Homotopic Long-Range Resting State Functional Connectivity in Temporal Lobes in Autism Spectrum Disorder

To investigate the relationship between short-range and homotopic long-range resting state functional connectivity (RSFC) in children with autism spectrum disorder (ASD) and typically developing (TD) children, we analyzed functional near-infrared spectroscopy (fNIRS) RSFC in 25 children with ASD and 22 age-matched TD children. The resting state fNIRS signals, including spontaneous fluctuations in the oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb) concentrations, were recorded from the bilateral temporal lobes. We found that (1) there was no difference in the short-range RSFC between the left and right hemisphere in either ASD or TD group; (2) both the short-range and homotopic long-range RSFC were weaker in the ASD than TD group; and (3) the short-range RSFC was stronger than the homotopic long-range RSFC in the ASD group, whereas no such difference was observed in the TD group. These observations might be helpful for a better understanding of the underlying cortical mechanism in ASD.

The Directionality of Fronto-Posterior Brain Connectivity Is Associated with the Degree of Individual Autistic Traits

Altered patterns of brain connectivity have been found in autism spectrum disorder (ASD) and associated with specific symptoms and behavioral features. Growing evidence suggests that the autistic peculiarities are not confined to the clinical population but extend along a continuum between healthy and maladaptive conditions. The aim of this study was to investigate whether a differentiated connectivity pattern could also be tracked along the continuum of autistic traits in a non-clinical population. A Granger causality analysis conducted on a resting-state EEG recording showed that connectivity along the posterior-frontal gradient is sensitive to the magnitude of individual autistic traits and mostly conveyed through fast oscillatory activity. Specifically, participants with higher autistic traits were characterized by a prevalence of ascending connections starting from posterior regions ramping the cortical hierarchy. These findings point to the presence of a tendency within the neural mapping of individuals with higher autistic features in conveying proportionally more bottom-up information. This pattern of findings mimics those found in clinical forms of autism, supporting the idea of a neurobiological continuum between autistic traits and ASD.