Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns

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.

Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm

In the field of neuroimaging, more studies of abnormalities in brain regions of the autism spectrum disorder (ASD) usually focused on two brain regions connected, and less on abnormalities of higher-order interactions of brain regions. To explore the complex relationships of brain regions, we used the partial entropy decomposition (PED) algorithm to capture higher-order interactions by computing the higher-order dependencies of all three brain regions (triads). We proposed a method for examining the effect of individual brain regions on triads based on the PED and surrogate tests. The key triads were discovered by analyzing the effects. Further, the hypergraph modularity maximization algorithm revealed the higher-order brain structures, of which the link between right thalamus and left thalamus in ASD was more loose compared with the typical control (TC). Redundant key triad (left cerebellum crus 1 and left precuneus and right inferior occipital gyrus) exhibited a discernible attenuation in interaction in ASD, while the synergistic key triad (right cerebellum crus 1 and left postcentral gyrus and left lingual gyrus) indicated a notable decline. The results of classification model further confirmed the potential of the key triads as diagnostic biomarkers.

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

The effects of rTMS on motor recovery after stroke: a systematic review of fMRI studies

Repetitive transcranial magnetic stimulation (rTMS) has been widely used in motor rehabilitation after stroke, and functional magnetic resonance imaging (fMRI) has been used to investigate the neural mechanisms of motor recovery during stroke therapy. However, there is no review on the mechanism of rTMS intervention for motor recovery after stroke based on fMRI explicitly. We aim to reveal and summarize the neural mechanism of the effects of rTMS on motor function after stroke as measured by fMRI. We carefully performed a literature search using PubMed, EMBASE, Web of Science, and Cochrane Library databases from their respective inceptions to November 2022 to identify any relevant randomized controlled trials. Researchers independently screened the literature, extracted data, and qualitatively described the included studies. Eleven studies with a total of 420 poststroke patients were finally included in this systematic review. A total of 338 of those participants received fMRI examinations before and after rTMS intervention. Five studies reported the effects of rTMS on activation of brain regions, and four studies reported results related to brain functional connectivity (FC). Additionally, five studies analyzed the correlation between fMRI and motor evaluation. The neural mechanism of rTMS in improving motor function after stroke may be the activation and FCs of motor-related brain areas, including enhancement of the activation of motor-related brain areas in the affected hemisphere, inhibition of the activation of motor-related brain areas in the unaffected hemisphere, and changing the FCs of intra-hemispheric and inter-hemispheric motor networks.

Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images

This study aims to increase the accuracy of autism spectrum disorder (ASD) diagnosis based on cognitive and behavioral phenotypes through multiple neuroimaging modalities. We apply machine learning (ML) algorithms to classify ASD patients and healthy control (HC) participants using structural magnetic resonance imaging (s-MRI) together with resting state functional MRI (rs-f-MRI and f-MRI) data from the large multisite data repository ABIDE (autism brain imaging data exchange) and identify important brain connectivity features. The 2D f-MRI images were converted into 3D s-MRI images, and datasets were preprocessed using the Montreal Neurological Institute (MNI) atlas. The data were then denoised to remove any confounding factors. We show, by using three fusion strategies such as early fusion, late fusion, and cross fusion, that, in this implementation, hybrid convolutional recurrent neural networks achieve better performance in comparison to either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The proposed model classifies subjects as autistic or not according to how functional and anatomical connectivity metrics provide an overall diagnosis based on the autism diagnostic observation schedule (ADOS) standard. Our hybrid network achieved an accuracy of 96% by fusing s-MRI and f-MRI together, which outperforms the methods used in previous studies.

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.

Potential Cross Talk between Autism Risk Genes and Neurovascular Molecules: A Pilot Study on Impact of Blood Brain Barrier Integrity

Autism Spectrum Disorder (ASD) is a common pediatric neurobiological disorder with up to 80% of genetic etiologies. Systems biology approaches may make it possible to test novel therapeutic strategies targeting molecular pathways to alleviate ASD symptoms. A clinical database of autism subjects was queried for individuals with a copy number variation (CNV) on microarray, Vineland, and Parent Concern Questionnaire scores. Pathway analyses of genes from pathogenic CNVs yielded 659 genes whose protein-protein interactions and mRNA expression mapped 121 genes with maximal antenatal expression in 12 brain regions. A Research Domain Criteria (RDoC)-derived neural circuits map revealed significant differences in anxiety, motor, and activities of daily living skills scores between altered CNV genes and normal microarrays subjects, involving Positive Valence (reward), Cognition (IQ), and Social Processes. Vascular signaling was identified as a biological process that may influence these neural circuits. Neuroinflammation, microglial activation, iNOS and 3-nitrotyrosine increase in the brain of Semaphorin 3F- Neuropilin 2 (Sema 3F-NRP2) KO, an ASD mouse model, agree with previous reports in the brain of ASD individuals. Signs of platelet deposition, activation, release of serotonin, and albumin leakage in ASD-relevant brain regions suggest possible blood brain barrier (BBB) deficits. Disruption of neurovascular signaling and BBB with neuroinflammation may mediate causative pathophysiology in some ASD subgroups. Although preliminary, these data demonstrate the potential for developing novel therapeutic strategies based on clinically derived data, genomics, cognitive neuroscience, and basic neuroscience methods.

Low-Frequency Repetitive Transcranial Magnetic Stimulation Restores Dynamic Functional Connectivity in Subcortical Stroke

Background and Purpose: Strokes consistently result in brain network dysfunction. Previous studies have focused on the resting-state characteristics over the study period, while dynamic recombination remains largely unknown. Thus, we explored differences in dynamics between brain networks in patients who experienced subcortical stroke and the effects of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) on dynamic functional connectivity (dFC). Methods: A total of 41 patients with subcortical stroke were randomly divided into the LF-rTMS (n = 23) and the sham stimulation groups (n = 18). Resting-state functional MRI data were collected before (1 month after stroke) and after (3 months after stroke) treatment; a total of 20 age- and sex-matched healthy controls were also included. An independent component analysis, sliding window approach, and k-means clustering were used to identify different functional networks, estimate dFC matrices, and analyze dFC states before treatment. We further assessed the effect of LF-rTMS on dFCs in patients with subcortical stroke. Results: Compared to healthy controls, patients with stroke spent significantly more time in state I [p = 0.043, effect size (ES) = 0.64] and exhibited shortened stay in state II (p = 0.015, ES = 0.78); the dwell time gradually returned to normal after LF-rTMS treatment (p = 0.015, ES = 0.55). Changes in dwell time before and after LF-rTMS treatment were positively correlated with changes in the Fugl-Meyer Assessment for Upper Extremity (pr = 0.48, p = 0.028). Moreover, patients with stroke had decreased dFCs between the sensorimotor and cognitive control domains, yet connectivity within the cognitive control network increased. These abnormalities were partially improved after LF-rTMS treatment. Conclusion: Abnormal changes were noted in temporal and spatial characteristics of sensorimotor domains and cognitive control domains of patients who experience subcortical stroke; LF-rTMS can promote the partial recovery of dFC. These findings offer new insight into the dynamic neural mechanisms underlying effect of functional recombination and rTMS in subcortical stroke. Registration: http://www.chictr.org.cn/index.aspx, Unique.identifier: ChiCTR1800019452.