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Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders.

ABSTRACT: We employed a multi-scale clustering methodology known as "data cloud geometry" to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both "fine" and "coarse" scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of 82.8% and specificity of 82.8%. Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.

SUBMITTER: Wang H

PROVIDER: S-EPMC3466274 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders.

Wang Hui H Chen Chen C Fushing Hsieh H

PloS one 20121008 10

We employed a multi-scale clustering methodology known as "data cloud geometry" to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both "fine" and "coarse" s ...[more]

PMID: 23056205

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Project description:Autism spectrum disorders (ASDs) are a group of heterogeneous neurodevelopmental disorders that are highly heritable and are associated with impaired dynamic functional connectivity (DFC). However, the molecular mechanisms behind DFC alterations remain largely unknown. Eighty-eight patients with ASDs and 87 demographically matched typical controls (TCs) from the Autism Brain Imaging Data Exchange II database were included in this study. A seed-based sliding window approach was then performed to investigate the DFC changes in each of the 29 seeds in 10 classic resting-state functional networks and the whole brain. Subsequently, the relationships between DFC alterations in patients with ASDs and their symptom severity were assessed. Finally, transcription-neuroimaging association analyses were conducted to explore the molecular mechanisms of DFC disruptions in patients with ASDs. Compared with TCs, patients with ASDs showed significantly increased DFC between the right dorsolateral prefrontal cortex (DLPFC) and left fusiform/lingual gyrus, between the DLPFC and the superior temporal gyrus, between the right frontal eye field (FEF) and left middle frontal gyrus, between the FEF and the right angular gyrus, and between the left intraparietal sulcus and the right middle temporal gyrus. Moreover, significant relationships between DFC alterations and symptom severity were observed. Furthermore, the genes associated with DFC changes in ASDs were identified by performing gene-wise across-sample spatial correlation analysis between gene expression extracted from six donors' brain of the Allen Human Brain Atlas and case-control DFC difference. In enrichment analysis, these genes were enriched for processes associated with synaptic signaling and voltage-gated ion channels and calcium pathways; also, these genes were highly expressed in autistic disorder, chronic alcoholic intoxication and several disorders related to depression. These results not only demonstrated higher DFC in patients with ASDs but also provided novel insight into the molecular mechanisms underlying these alterations.

Project description:IntroductionThere has been a growing effort to characterize the time-varying functional connectivity of resting state (RS) fMRI brain networks (RSNs). Although voxel-wise connectivity studies have examined different sliding window lengths, nonsequential volume-wise approaches have been less common.MethodsInspired by earlier co-activation pattern (CAP) studies, we applied hierarchical clustering (HC) to classify the image volumes of the RS-fMRI data on 28 adolescents with autism spectrum disorder (ASD) and their 27 typically developing (TD) controls. We compared the distribution of the ASD and TD groups' volumes in CAPs as well as their voxel-wise means. For simplification purposes, we conducted a group independent component analysis to extract 14 major RSNs. The RSNs' average z-scores enabled us to meaningfully regroup the RSNs and estimate the percentage of voxels within each RSN for which there was a significant group difference. These results were jointly interpreted to find global group-specific patterns.ResultsWe found similar brain state proportions in 58 CAPs (clustering interval from 2 to 30). However, in many CAPs, the voxel-wise means differed significantly within a matrix of 14 RSNs. The rest-activated default mode-positive and default mode-negative brain state properties vary considerably in both groups over time. This division was seen clearly when the volumes were partitioned into two CAPs and then further examined along the HC dendrogram of the diversifying brain CAPs. The ASD group network activations followed a more heterogeneous distribution and some networks maintained higher baselines; throughout the brain deactivation state, the ASD participants had reduced deactivation in 12/14 networks. During default mode-negative CAPs, the ASD group showed simultaneous visual network and either dorsal attention or default mode network overactivation.ConclusionNonsequential volume gathering into CAPs and the comparison of voxel-wise signal changes provide a complementary perspective to connectivity and an alternative to sliding window analysis.

Dataset Information

Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders.

Publications

Extracting multiscale pattern information of fMRI based functional brain connectivity with application on classification of autism spectrum disorders.

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