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

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Marco Pagani ◽  
Noemi Barsotti ◽  
Alice Bertero ◽  
Stavros Trakoshis ◽  
Laura Ulysse ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Large Scale ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Spine Density ◽  
Large Scale Network ◽  
Synaptic Pathology ◽  
Transcriptomic Signature

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

scholarly journals A cross-species link between mTOR-related synaptic pathology and functional hyperconnectivity in autism

2020 ◽  
Author(s):  
Marco Pagani ◽  
Alice Bertero ◽  
Stavros Trakoshis ◽  
Laura Ulysse ◽  
Andrea Locarno ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Resting State Fmri ◽  
Spine Density ◽  
Neural Basis ◽  
Multi Scale ◽  
Large Scale Network ◽  
Synaptic Pathology ◽  
Transcriptomic Signature ◽  
Synaptic Pruning

AbstractPostmortem studies have revealed increased density of excitatory synapses in the brains of individuals with autism, with a putative link to aberrant mTOR-dependent synaptic pruning. Autism is also characterized by atypical macroscale functional connectivity as measured with resting-state fMRI (rsfMRI). These observations raise the question of whether excess of synapses cause aberrant functional connectivity in autism. Using rsfMRI, electrophysiology and in silico modelling in Tsc2 haploinsufficient mice, we show that mTOR-dependent increased spine density is associated with autism-like stereotypies and cortico-striatal hyperconnectivity. These deficits are completely rescued by pharmacological inhibition of mTOR. Notably, we further demonstrate that children with idiopathic autism exhibit analogous cortical-striatal hyperconnectivity, and document that this connectivity fingerprint is enriched for autism-dysregulated genes interacting with mTOR or TSC2. Finally, we show that the identified transcriptomic signature is predominantly expressed in a subset of children with autism, thereby defining a segregable autism subtype. Our findings causally link mTOR-related synaptic pathology to large-scale network aberrations, revealing a unifying multi-scale framework that mechanistically reconciles developmental synaptopathy and functional hyperconnectivity in autism.SignificanceAberrant brain functional connectivity is a hallmark of autism, but the neural basis of this phenomenon remains unclear. We show that a mouse line recapitulating mTOR-dependent synaptic pruning deficits observed in postmortem autistic brains exhibits widespread functional hyperconnectivity. Importantly, pharmacological normalization of mTOR signalling completely rescues synaptic, behavioral and functional connectivity deficits. We also show that a similar connectivity fingerprint can be isolated in human fMRI scans of people with autism, where it is linked to over-expression of mTOR-related genes. Our results reveal a unifying multi-scale translational framework that mechanistically links aberrations in synaptic pruning with functional hyperconnectivity in autism.

scholarly journals Reproducible functional connectivity alterations are associated with autism spectrum disorder

2018 ◽  
Author(s):  
Štefan Holiga ◽  
Joerg F. Hipp ◽  
Christopher H. Chatham ◽  
Pilar Garces ◽  
Will Spooren ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Large Scale ◽  
Clinical Symptoms ◽  
Brain Activity ◽  
Network Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Replication Procedure ◽  
Medication Status

AbstractDespite the high clinical burden little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here we address these questions in the most comprehensive, large-scale effort to date comprising evaluation of four large ASD cohorts. We followed a strict exploration and replication procedure to identify core rs-fMRI functional connectivity (degree centrality) alterations associated with ASD as compared to typically developing (TD) controls (ASD: N=841, TD: N=984). We then tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status and clinical symptom severity. We find reproducible patterns of ASD-associated functional hyper- and hypo-connectivity with hypo-connectivity being primarily restricted to sensory-motor regions and hyper-connectivity hubs being predominately located in prefrontal and parietal cortices. We establish shifts in between-network connectivity from outside to within the identified regions as a key driver of these abnormalities. The magnitude of these alterations is linked to core ASD symptoms related to communication and social interaction and is not affected by age, sex or medication status. The identified brain functional alterations provide a reproducible pathophysiological phenotype underlying the diagnosis of ASD reconciling previous divergent findings. The large effect sizes in standardized cohorts and the link to clinical symptoms emphasize the importance of the identified imaging alterations as potential treatment and stratification biomarkers for ASD.

scholarly journals Non-replication of functional connectivity differences in autism spectrum disorder across multiple sites and denoising strategies

2019 ◽  
Author(s):  
Ye He ◽  
Lisa Byrge ◽  
Daniel P Kennedy
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Extensive Literature ◽  
Clinical Biomarkers ◽  
Cohort Differences

AbstractA rapidly growing number of studies on autism spectrum disorder (ASD) have used resting-state fMRI to identify alterations of functional connectivity, with the hope of identifying clinical biomarkers or underlying neural mechanisms. However, results have been largely inconsistent across studies, and there is therefore a pressing need to determine the primary factors influencing replicability. Here, we used resting-state fMRI data from the Autism Brain Imaging Data Exchange to investigate two potential factors: denoising strategy and data site (which differ in terms of sample, data acquisition, etc.). We examined the similarity of both group-average functional connectomes and group-level differences (ASD vs. control) across 33 denoising pipelines and four independently-acquired datasets. The group-average connectomes were highly consistent across pipelines (r = 0.92±0.06) and sites (r = 0.88±0.02). However, the group differences, while still consistent within site across pipelines (r = 0.76±0.12), were highly inconsistent across sites regardless of choice of denoising strategies (r = 0.07±0.04), suggesting lack of replication may be strongly influenced by site and/or cohort differences. Across-site similarity remained low even when considering the data at a large-scale network level or when considering only the most significant edges. We further show through an extensive literature survey that the parameters chosen in the current study (i.e., sample size, age range, preprocessing methods) are quite representative of the published literature. These results highlight the importance of examining replicability in future studies of ASD, and, more generally, call for extra caution when interpreting alterations in functional connectivity across groups of individuals.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

scholarly journals Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

2015 ◽  
Vol 72 (8) ◽  
pp. 767 ◽  
Author(s):  
Leonardo Cerliani ◽  
Maarten Mennes ◽  
Rajat M. Thomas ◽  
Adriana Di Martino ◽  
Marc Thioux ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Resting State Networks

Somatosensory Regions Show Limited Functional Connectivity Differences in Youth with Autism Spectrum Disorder

2018 ◽  
Vol 8 (9) ◽  
pp. 558-566 ◽  
Author(s):  
Brian Cechmanek ◽  
Harriet Johnston ◽  
Sherene Vazhappilly ◽  
Catherine Lebel ◽  
Signe Bray
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

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