Alterations in Connectome Dynamics in Autism Spectrum Disorder: A Harmonized Mega- and Meta-analysis Study Using the Autism Brain Imaging Data Exchange Dataset

Rightward brain structural asymmetry in young children with autism

To understand the neural mechanism of autism spectrum disorder (ASD) and developmental delay/intellectual disability (DD/ID) that can be associated with ASD, it is important to investigate individuals at an early stage with brain, behavioural and also genetic measures, but such research is still lacking. Here, using the cross-sectional sMRI data of 1030 children under 8 years old, we employed developmental normative models to investigate the atypical development of gray matter volume (GMV) asymmetry in individuals with ASD without DD/ID, ASD with DD/ID and individuals with only DD/ID, and their associations with behavioral and clinical measures and transcription profiles. By extracting the individual deviations of patients from the typical controls with normative models, we found a commonly abnormal pattern of GMV asymmetry across all ASD children: more rightward laterality in the inferior parietal lobe and precentral gyrus, and higher individual variability in the temporal pole. Specifically, ASD with DD/ID children showed a severer and more extensive abnormal pattern in GMV asymmetry deviation values, which was linked with both ASD symptoms and verbal IQ. The abnormal pattern of ASD without DD/ID children showed higher and more extensive individual variability, which was linked with ASD symptoms only. DD/ID children showed no significant differences from healthy population in asymmetry. Lastly, the GMV laterality patterns of all patient groups were significantly associated with both shared and unique gene expression profiles. Our findings provide evidence for rightward GMV asymmetry of some cortical regions in young ASD children (1-7 years) in a large sample (1030 cases), show that these asymmetries are related to ASD symptoms, and identify genes that are significantly associated with these differences.

Novel Genes Associated With Working Memory Are Identified by Combining Connectome, Transcriptome, and Genome

Working memory (WM) plays a crucial role in human cognition. Previous candidate and genome-wide association studies have reported many genetic variations associated with WM. However, little research has examined genetic basis of WM by using transcriptome, even though it reflects gene function more directly than does the genome. Here we propose a new approach to exploring the genetic mechanisms of WM by integrating connectome, transcriptome, and genome data in a high-quality dataset comprising 481 Chinese healthy adults. First, relevance vector regression was used to define WM-related brain regions. Second, genes differentially expressed within these regions were identified using the Allen Human Brain Atlas (AHBA) dataset. Finally, two independent datasets were used to validate these genes' contributions to WM. With this method, we identified 24 novel genes and 20 of them were confirmed in the large-scale datasets of ABCD and UK Biobank. These novel genes were enriched in the cellular component of collagen-containing extracellular matrix and the CCL18 signaling pathway. Our method offers an effective approach to integrating multimodal gene discovery and demonstrates the superiority of expression data. This new method and the newly identified genes deserve more attention in the future.

Altered Temporospatial Variability of Dynamic Amplitude of Low-Frequency Fluctuation in Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with altered brain activity. However, little is known about the integrated temporospatial variation of dynamic spontaneous brain activity in ASD. In the present study, resting-state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically-matched typically developmental controls (TC) children obtained from the Autism Brain Imaging Data Exchange database. Using the sliding-window approach, temporal, spatial, and temporospatial variability of dynamic amplitude of low-frequency fluctuation (tvALFF, svALFF, and tsvALFF) were calculated for each participant. Group-comparisons were further performed at global, network, and brain region levels to quantify differences between ASD and TC groups. The relationship between temporospatial dynamic amplitude of low-frequency fluctuation variation alterations and clinical symptoms of ASD was finally explored by a support vector regression model. Relative to TC, we found enhanced tvALFF in visual network (Vis), somatomotor network (SMT), and salience/ventral attention network (SVA) of ASD, and weakened tvALFF in dorsal attention network (DAN) of ASD. Besides, ASD showed decreased svALFF in Vis, SVA, and limbic network (Limbic), and increased svALFF in DAN and default mode network (DMN). Elevated tsvALFF was found in the Vis, SMT, and DMN of ASD. More importantly, the altered tsvALFF from the DMN can predict the symptom severity of ASD. These findings demonstrate altered temporospatial dynamics of the spontaneous brain activity in ASD and provide novel insights into the neural mechanism underlying ASD.

Age-related unstable transient states and imbalanced activation proportion of brain networks in people with autism spectrum disorder: A resting-state fMRI study using coactivation pattern analyses

The atypical static brain functions related to the executive control network (ECN), default mode network (DMN), and salience network (SN) in people with autism spectrum disorder (ASD) has been widely reported. However, their transient functions in ASD are not clear. We aim to identify transient network states (TNSs) using coactivation pattern (CAP) analysis to characterize the age-related atypical transient functions in ASD. CAP analysis was performed on a resting-state fMRI dataset (78 ASD and 78 healthy control (CON) juveniles, 54 ASD and 54 CON adults). Six TNSs were divided into the DMN-TNSs, ECN-TNSs, and SN-TNSs. The DMN-TNSs were major states with the highest stability and proportion, and the ECN-TNSs and SN-TNSs were minor states. Age-related abnormalities on spatial stability and TNS proportion were found in ASD. The spatial stability of DMN-TNSs was found increasing with age in CON, but was not found in ASD. A lower proportion of DMN-TNSs was found in ASD compared with CON of the same age, and ASD juveniles had a higher proportion of SN-TNSs while ASD adults had a higher proportion of ECN-TNSs. The abnormalities on spatial stability and TNS proportion were related to social deficits. Our results provided new evidence for atypical transient brain functions in people with ASD.

Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia

Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.

Sex differences in the flexibility of dynamic network reconfiguration of autism spectrum disorder based on multilayer network

Dynamic network reconfiguration alterations in the autism spectrum disorder (ASD) brain have been frequently reported. However, since the prevalence of ASD in males is approximately 3.8 times higher than that in females, and previous studies of dynamic network reconfiguration of ASD have predominantly used male samples, it is unclear whether sex differences exist in dynamic network reconfiguration in ASD. This study used resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database, which included balanced samples of 64 males and 64 females with ASD, along with 64 demographically-matched typically developing control (TC) males and 64 TC females. The multilayer network analysis was used to explore the flexibility of dynamic network reconfiguration. The two-way analysis of variance was further performed to examine the sex-related changes in ASD in flexibility of dynamic network reconfiguration. A diagnosis-by-sex interaction effect was identified in the cingulo-opercular network (CON), central executive network (CEN), salience network (SN), and subcortical network (SUB). Compared with TC females, females with ASD showed lower flexibility in CON, CEN, SN, and SUB. The flexibility of CEN and SUB in males with ASD was higher than that in females with ASD. In addition, the flexibility of CON, CEN, SN, and SUB predicted the severity of social communication impairments and stereotyped behaviors and restricted interests only in females with ASD. These findings highlight significant sex differences in the flexibility of dynamic network reconfiguration in ASD and emphasize the importance of further study of sex differences in future ASD research.

Aberrant Flexibility of Dynamic Brain Network in Patients with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a collection of neurodevelopmental disorders whose pathobiology remains elusive. This study aimed to investigate the possible neural mechanisms underlying ASD using a dynamic brain network model and a relatively large-sample, multi-site dataset. Resting-state functional magnetic resonance imaging data were acquired from 208 ASD patients and 227 typical development (TD) controls, who were drawn from the multi-site Autism Brain Imaging Data Exchange (ABIDE) database. Brain network flexibilities were estimated and compared between the ASD and TD groups at both global and local levels, after adjusting for sex, age, head motion, and site effects. The results revealed significantly increased brain network flexibilities (indicating a decreased stability) at the global level, as well as at the local level within the default mode and sensorimotor areas in ASD patients than TD participants. Additionally, significant ASD-related decreases in flexibilities were also observed in several occipital regions at the nodal level. Most of these changes were significantly correlated with the Autism Diagnostic Observation Schedule (ADOS) total score in the entire sample. These results suggested that ASD is characterized by significant changes in temporal stabilities of the functional brain network, which can further strengthen our understanding of the pathobiology of ASD.

Atypical dynamic neural configuration in autism spectrum disorder and its relationship to gene expression profiles

Although it is well recognized that autism spectrum disorder (ASD) is associated with atypical dynamic functional connectivity patterns, the dynamic changes in brain intrinsic activity over each time point and the potential molecular mechanisms associated with atypical dynamic temporal characteristics in ASD remain unclear. Here, we employed the Hidden Markov Model (HMM) to explore the atypical neural configuration at every scanning time point in ASD, based on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange. Subsequently, partial least squares regression and pathway enrichment analysis were employed to explore the potential molecular mechanism associated with atypical neural dynamics in ASD. 8 HMM states were inferred from rs-fMRI data. Compared to typically developing, individuals on the autism spectrum showed atypical state-specific temporal characteristics, including number of states and occurrences, mean life time and transition probability between states. Moreover, these atypical temporal characteristics could predict communication difficulties of ASD, and states assoicated with negative activation in default mode network and frontoparietal network, and positive activation in somatomotor network, ventral attention network, and limbic network, had higher predictive contribution. Furthermore, a total of 321 genes was revealed to be significantly associated with atypical dynamic brain states of ASD, and these genes are mainly enriched in neurodevelopmental pathways. Our study provides new insights into characterizing the atypical neural dynamics from a moment-to-moment perspective, and indicates a linkage between atypical neural configuration and gene expression in ASD.

Atypical dynamic network reconfiguration and genetic mechanisms in patients with major depressive disorder

BACKGROUND

Brain dynamics underlie complex forms of flexible cognition or the ability to shift between different mental modes. However, the precise dynamic reconfiguration based on multi-layer network analysis and the genetic mechanisms of major depressive disorder (MDD) remains unclear.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data were acquired from the REST-meta-MDD consortium, including 555 patients with MDD and 536 healthy controls (HC). A time-varying multi-layer network was constructed, and dynamic modular characteristics were used to investigate the network reconfiguration. Additionally, partial least squares regression analysis was performed using transcriptional data provided by the Allen Human Brain Atlas (AHBA) to identify genes associated with atypical dynamic network reconfiguration in MDD.

RESULTS

In comparison to HC, patients with MDD exhibited lower global and local recruitment coefficients. The local reduction was particularly evident in the salience and subcortical networks. Spatial transcriptome correlation analysis revealed an association between gene expression profiles and atypical dynamic network reconfiguration observed in MDD. Further functional enrichment analyses indicated that positively weighted reconfiguration-related genes were primarily associated with metabolic and biosynthetic pathways. Additionally, negatively enriched genes were predominantly related to programmed cell death-related terms.

CONCLUSIONS

Our findings offer robust evidence of the atypical dynamic reconfiguration in patients with MDD from a novel perspective. These results offer valuable insights for further exploration into the mechanisms underlying MDD.

Cortical gradient perturbation in attention deficit hyperactivity disorder correlates with neurotransmitter-, cell type-specific and chromosome- transcriptomic signatures

AIMS

This study aimed to illuminate the neuropathological landscape of attention deficit hyperactivity disorder (ADHD) by a multiscale macro-micro-molecular perspective from in vivo neuroimaging data.

METHODS

The "ADHD-200 initiative" repository provided multi-site high-quality resting-state functional connectivity (rsfc-) neuroimaging for ADHD children and matched typically developing (TD) cohort. Diffusion mapping embedding model to derive the functional connectome gradient detecting biologically plausible neural pattern was built, and the multivariate partial least square method to uncover the enrichment of neurotransmitomic, cellular and chromosomal gradient-transcriptional signatures of AHBA enrichment and meta-analytic decoding.

RESULTS

Compared to TD, ADHD children presented connectopic cortical gradient perturbations in almost all the cognition-involved brain macroscale networks (all pBH <0.001), but not in the brain global topology. As an intermediate phenotypic variant, such gradient perturbation was spatially enriched into distributions of GABAA/BZ and 5-HT2A receptors (all pBH <0.01) and co-varied with genetic transcriptional expressions (e.g. DYDC2, ATOH7, all pBH <0.01), associated with phenotypic variants in episodic memory and emotional regulations. Enrichment models demonstrated such gradient-transcriptional variants indicated the risk of both cell-specific and chromosome- dysfunctions, especially in enriched expression of oligodendrocyte precursors and endothelial cells (all pperm <0.05) as well enrichment into chromosome 18, 19 and X (pperm <0.05).

CONCLUSIONS

Our findings bridged brain macroscale neuropathological patterns to microscale/cellular biological architectures for ADHD children, demonstrating the neurobiologically pathological mechanism of ADHD into the genetic and molecular variants in GABA and 5-HT systems as well brain-derived enrichment of specific cellular/chromosomal expressions.

Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy

Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.

Brain functional connectivity alterations of Wernicke's area in individuals with autism spectrum conditions in multi-frequency bands: A mega-analysis

Characterized by severe deficits in communication, most individuals with autism spectrum conditions (ASC) experience significant language dysfunctions, thereby impacting their overall quality of life. Wernicke's area, a classical and traditional brain region associated with language processing, plays a substantial role in the manifestation of language impairments. The current study carried out a mega-analysis to attain a comprehensive understanding of the neural mechanisms underpinning ASC, particularly in the context of language processing. The study employed the Autism Brain Image Data Exchange (ABIDE) dataset, which encompasses data from 443 typically developing (TD) individuals and 362 individuals with ASC. The objective was to detect abnormal functional connectivity (FC) between Wernicke's area and other language-related functional regions, and identify frequency-specific altered FC using Wernicke's area as the seed region in ASC. The findings revealed that increased FC in individuals with ASC has frequency-specific characteristics. Further, in the conventional frequency band (0.01-0.08 Hz), individuals with ASC exhibited increased FC between Wernicke's area and the right thalamus compared with TD individuals. In the slow-5 frequency band (0.01-0.027 Hz), increased FC values were observed in the left cerebellum Crus II and the right lenticular nucleus, pallidum. These results provide novel insights into the potential neural mechanisms underlying communication deficits in ASC from the perspective of language impairments.

Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as "presynapse," "behavior," and "modulation of chemical synaptic transmission" in autism spectrum disorder's brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.

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.

Towards a biologically annotated brain connectome

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

A study of genetic heterogeneity in autism spectrum disorders based on plasma proteomic and metabolomic analysis: multiomics study of autism heterogeneity

Genetic heterogeneity poses a challenge to research and clinical translation of autism spectrum disorder (ASD). In this study, we conducted a plasma proteomic and metabolomic study of children with ASD with and without risk genes (de novo mutation) and controls to explore the impact of genetic heterogeneity on the search for biomarkers for ASD. In terms of the proteomic and metabolomic profiles, the groups of children with ASD carrying and those not carrying de novo mutation tended to cluster and overlap, and integrating them yielded differentially expressed proteins and differential metabolites that effectively distinguished ASD from controls. The mechanisms associated with them focus on several common and previously reported mechanisms. Proteomics results highlight the role of complement, inflammation and immunity, and cell adhesion. The main pathways of metabolic perturbations include amino acid, vitamin, glycerophospholipid, tryptophan, and glutamates metabolic pathways and solute carriers-related pathways. Integrating the two omics analyses revealed that L-glutamic acid and malate dehydrogenase may play key roles in the pathogenesis of ASD. These results suggest that children with ASD may have important underlying common mechanisms. They are not only potential therapeutic targets for ASD but also important contributors to the study of biomarkers for the disease.

Candidate biomarkers in psychiatric disorders: state of the field

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

Regional Dynamic Neuroimaging Changes of Adults with Autism Spectrum Disorder

Most neuroimaging studies investigating autism spectrum disorder (ASD) have focused on static brain function, but ignored the dynamic features of spontaneous brain activities in the temporal dimension. Research of dynamic brain regional activities might help to fully investigate the mechanisms of ASD patients. This study aimed to examine potential changes in the dynamic characteristics of regional neural activities in adult ASD patients and to detect whether the changes were associated with Autism Diagnostic Observation Schedule (ADOS) scores. Resting-state functional MRI was obtained on 77 adult ASD patients and 76 healthy controls. The dynamic regional homogeneity (dReHo) and dynamic amplitude of low-frequency fluctuations (dALFF) were compared between the two groups. Correlation analyses were also performed between dReHo and dALFF in areas showing group differences and ADOS scores. In ASD group, significant differences in dReHo were observed in the left middle temporal gyrus (MTG.L). Besides, we found increased dALFF in the left middle occipital gyrus (MOG.L), left superior parietal gyrus (SPG.L), left precuneus (PCUN.L), left inferior temporal gyrus (ITG.L), and right inferior frontal gyrus, orbital part (ORBinf.R). Furthermore, a significant positive correlation was found between dALFF in the PCUN.L and the ADOS_TOTAL scores, ADOS_SOCIAL scores; the dALFF in the ITG.L, SPG.L was positively associated with ADOS_SOCIAL scores. In conclusion, adults with ASD have a wide area of dynamic regional brain function abnormalities. These suggested that dynamic regional indexes might be used as a powerful measure to help us obtain a more comprehensive understanding of neural activity in adult ASD patients.

Translational Neuroscience Approaches to Understanding Autism

While autism spectrum disorder affects nearly 2% of children in the United States, little is known with certainty concerning the etiologies and brain systems involved. This is due, in part, to the substantial heterogeneity in the presentation of the core symptoms of autism as well as the great number of co-occurring conditions that are common in autistic individuals. Understanding the neurobiology of autism is further hampered by the limited availability of postmortem brain tissue to determine the cellular and molecular alterations that take place in the autistic brain. Animal models therefore provide great translational value in helping to define the neural systems that constitute the social brain and mediate repetitive behaviors or interests. If they are based on genetic or environmental factors that contribute to autism, organisms from flies to nonhuman primates may serve as models of the neural structure or function of the autistic brain. Ultimately, successful models can also be employed to test the safety and effectiveness of potential therapeutics. This is an overview of the major animal species that are currently used as models of autism, including an appraisal of the advantages and limitations of each.

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites (U.S., Belgium, and Japan) and different developmental stages (children and adolescents). Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults (area under the curve [AUC] = 0.70) and Japanese adults (AUC = 0.81). The neuromarker demonstrated significant generalization for children (AUC = 0.66) and adolescents (AUC = 0.71; all P < 0.05 , family-wise-error corrected). We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. These FCs largely centered on social brain regions such as the amygdala, hippocampus, dorsomedial and ventromedial prefrontal cortices, and temporal cortices. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

Disrupted network integration and segregation involving the default mode network in autism spectrum disorder

Changes in the brain's default mode network (DMN) in the resting state are closely related to autism spectrum disorder (ASD). Module segmentation can effectively elucidate the neural mechanism of ASD and explore intra- and inter-network connections by means of the participation coefficient (PC). We used that resting-state fMRI data from 269 ASD patients and 340 healthy controls (HCs) in the current study. From the results, ASD subjects showed a significantly higher PC of the DMN than HC subjects. This difference was related to lower intra-module connections within the DMN and higher inter-network connections between the DMN and other networks. When the subjects were split into age groups, the results were verified in the 7-12- and 12-18-year-old age groups but not in the young adult group (18-25 years). When the subjects were divided according to different subtypes of ASD, the results were also observed in the classic autism and pervasive developmental disorder groups, but not in the Asperger disorder group. In conclusions, less developed network segregation in the DMN could be a valid biomarker for ASD. This provides network scientists with new insights into the intermodular connectivity configurations of complex networks from different dimensions in a systematic and comprehensive manner.

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.

Ensemble Model for Diagnostic Classification of Alzheimer's Disease Based on Brain Anatomical Magnetic Resonance Imaging

Alzheimer's is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about the brain's anatomy and enables the identification of diagnostic features. Artificial intelligence (AI) in neuroimaging has the potential to significantly enhance the treatment process for Alzheimer's disease (AD). The objective of this study is two-fold: (1) to compare existing Machine Learning (ML) algorithms for the classification of AD. (2) To propose an effective ensemble-based model for the same and to perform its comparative analysis. In this study, data from the Alzheimer's Diseases Neuroimaging Initiative (ADNI), an online repository, is utilized for experimentation consisting of 2125 neuroimages of Alzheimer's disease (n = 975), mild cognitive impairment (n = 538) and cognitive normal (n = 612). For classification, the framework incorporates a Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN) followed by some variations of Support Vector Machine (SVM), such as SVM (RBF kernel), SVM (Polynomial Kernel), and SVM (Sigmoid kernel), as well as Gradient Boost (GB), Extreme Gradient Boosting (XGB) and Multi-layer Perceptron Neural Network (MLP-NN). Afterwards, an Ensemble Based Generic Kernel is presented where Master-Slave architecture is combined to attain better performance. The proposed model is an ensemble of Extreme Gradient Boosting, Decision Tree and SVM_Polynomial kernel (XGB + DT + SVM). At last, the proposed method is evaluated using cross-validation using statistical techniques along with other ML models. The presented ensemble model (XGB + DT + SVM) outperformed existing state-of-the-art algorithms with an accuracy of 89.77%. The efficiency of all the models was optimized using Grid-based tuning, and the results obtained after such process showed significant improvement. XGB + DT + SVM with optimized parameters outperformed all other models with an efficiency of 95.75%. The implication of the proposed ensemble-based learning approach clearly shows the best results compared to other ML models. This experimental comparative analysis improved understanding of the above-defined methods and enhanced their scope and significance in the early detection of Alzheimer's disease.

Functional Connectome-Based Predictive Modeling in Autism

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

Meta-connectomic analysis maps consistent, reproducible, and transcriptionally relevant functional connectome hubs in the human brain

Human brain connectomes include sets of densely connected hub regions. However, the consistency and reproducibility of functional connectome hubs have not been established to date and the genetic signatures underlying robust hubs remain unknown. Here, we conduct a worldwide harmonized meta-connectomic analysis by pooling resting-state functional MRI data of 5212 healthy young adults across 61 independent cohorts. We identify highly consistent and reproducible connectome hubs in heteromodal and unimodal regions both across cohorts and across individuals, with the greatest effects observed in lateral parietal cortex. These hubs show heterogeneous connectivity profiles and are critical for both intra- and inter-network communications. Using post-mortem transcriptome datasets, we show that as compared to non-hubs, connectome hubs have a spatiotemporally distinctive transcriptomic pattern dominated by genes involved in the neuropeptide signaling pathway, neurodevelopmental processes, and metabolic processes. These results highlight the robustness of macroscopic connectome hubs and their potential cellular and molecular underpinnings, which markedly furthers our understanding of how connectome hubs emerge in development, support complex cognition in health, and are involved in disease.

Disrupted dynamic network reconfiguration of the brain functional networks of individuals with autism spectrum disorder

Human and animal studies on brain functions in subjects with autism spectrum disorder have confirmed the aberrant organization of functional networks. However, little is known about the neural features underlying these impairments.

Using community structure analyses (recruitment and integration), the current study explored the functional network features of individuals with autism spectrum disorder from one database (101 individuals with autism spectrum disorder and 120 healthy controls) and tested the replicability in an independent database (50 individuals with autism spectrum disorder and 74 healthy controls). Additionally, the study divided subjects into different age groups and tested the features in different subgroups.

As for recruitment, subjects with autism spectrum disorder had lower coefficients in the default mode network and basal ganglia network than healthy controls. The integration results showed that subjects with autism spectrum disorder had a lower coefficient than healthy controls in the default mode network -medial frontal network and basal ganglia network -limbic networks. The results for the default mode network were mostly replicated in the independent database, but the results for the basal ganglia network were not. The results for different age groups were also analyzed, and the replicability was tested in different databases.

The lower recruitment in subjects with autism spectrum disorder suggests that they are less efficient at engaging these networks when performing relevant tasks. The lower integration results suggest impaired flexibility in cognitive functions in individuals with autism spectrum disorder. All these findings might explain why subjects with autism spectrum disorder show impaired brain networks and have important therapeutic implications for developing potentially effective interventions.