Dataset Information

Connectivity cluster analysis for discovering discriminative subnetworks in schizophrenia.

ABSTRACT: In this manuscript, we present connectivity cluster analysis (CoCA), a novel computational framework that takes advantage of structure of the brain networks to magnify reproducible signals and quash noise. Resting state functional Magnetic Resonance Imaging (fMRI) data that is used in estimating functional brain networks is often noisy, leading to reduced power and inconsistent findings across independent studies. There is a need for techniques that can unearth signals in noisy datasets, while addressing redundancy in the functional connections that are used for testing association. CoCA is a data driven approach that addresses the problems of redundancy and noise by first finding groups of region pairs that behave in a cohesive way across the subjects. These cohesive sets of functional connections are further tested for association with the disease. CoCA is applied in the context of patients with schizophrenia, a disorder characterized as a disconnectivity syndrome. Our results suggest that CoCA can find reproducible sets of functional connections that behave cohesively. Applying this technique, we found that the connectivity clusters joining thalamus to parietal, temporal, and visuoparietal regions are highly discriminative of schizophrenia patients as well as reproducible using retest data and replicable in an independent confirmatory sample.

SUBMITTER: Atluri G

PROVIDER: S-EPMC6869283 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Connectivity cluster analysis for discovering discriminative subnetworks in schizophrenia.

Atluri Gowtham G Steinbach Michael M Lim Kelvin O KO Kumar Vipin V MacDonald Angus A

Human brain mapping 20141113 2

In this manuscript, we present connectivity cluster analysis (CoCA), a novel computational framework that takes advantage of structure of the brain networks to magnify reproducible signals and quash noise. Resting state functional Magnetic Resonance Imaging (fMRI) data that is used in estimating functional brain networks is often noisy, leading to reduced power and inconsistent findings across independent studies. There is a need for techniques that can unearth signals in noisy datasets, while a ...[more]

PMID: 25394864

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Project description:BackgroundStudy of biological networks is an essential first step to understand the complex functions they govern in different organisms. The topology of interactions that define how biological networks operate is often determined through high-throughput experiments. Noisy nature of high-throughput experiments, however, can result in multiple alternative network topologies that explain this data equally well. One key step to resolve the differences is to identify the subnetworks which appear significantly more frequently in a biological network data set than expected.MethodWe present a method named SiS (Significant Subnetworks) to find subnetworks with the largest probability to appear in a collection of biological networks. We define these subnetworks as the most probable subnetworks. SiS summarizes the interactions in the given collection of networks in a special template network. It uses the template network to guide the search for most probable subnetworks. It computes the lower and upper bound scores on how good the potential solutions are (i.e., the number of input networks that contain the subnetwork). As the search continues, it tightens the bound dynamically and prunes a massive number of unpromising solutions in that process.Results and conclusionsExperiments on comprehensive data sets depict that the most probable subnetworks found by SiS in a large collection of networks are also very frequent as well. In metabolic network data set, we found that subnetworks in eukaryote are more conserved than those of prokaryote. SiS also scales well to large data sets and subnetworks and runs orders of magnitude faster than an existing method, MULE. Depending on the size of the subnetwork in the same data set, the running time of SiS ranges from a few seconds to minutes; MULE, on the other hand, runs either for hours or does not even finish in days. In human transcription regulatory network data set, SiS finds a large backbone subnetwork that appears frequently regardless of diverse cell types.

Dataset Information

Connectivity cluster analysis for discovering discriminative subnetworks in schizophrenia.

Publications

Connectivity cluster analysis for discovering discriminative subnetworks in schizophrenia.

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