Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial

Ariana Anderson,Mark S Cohen

doi:10.3389/fnhum.2013.00520

Abstract

Functional network connectivity (FNC) is a method of analyzing the temporal relationship of anatomical brain components, comparing the synchronicity between patient groups or conditions. We use functional-connectivity measures between independent components to classify between Schizophrenia patients and healthy controls during resting-state. Connectivity is measured using a variety of graph-theoretic connectivity measures such as graph density, average path length, and small-worldness. The Schizophrenia patients showed significantly less clustering (transitivity) among components than healthy controls (p < 0.05, corrected) with networks less likely to be connected, and also showed lower small-world connectivity than healthy controls. Using only these connectivity measures, an SVM classifier (without parameter tuning) could discriminate between Schizophrenia patients and healthy controls with 65% accuracy, compared to 51% chance. This implies that the global functional connectivity between resting-state networks is altered in Schizophrenia, with networks more likely to be disconnected and behave dissimilarly for diseased patients. We present this research finding as a tutorial using the publicly available COBRE dataset of 146 Schizophrenia patients and healthy controls, provided as part of the 1000 Functional Connectomes Project. We demonstrate preprocessing, using independent component analysis (ICA) to nominate networks, computing graph-theoretic connectivity measures, and finally using these connectivity measures to either classify between patient groups or assess between-group differences using formal hypothesis testing. All necessary code is provided for both running command-line FSL preprocessing, and for computing all statistical measures and SVM classification within R. Collectively, this work presents not just findings of diminished FNC among resting-state networks in Schizophrenia, but also a practical connectivity tutorial.

Highlights

Functional Magnetic Resonance Imaging is a fourdimensional medical imaging modality that captures changes in blood oxygenation over time, an indirect measure of neuronal activation
The Functional Magnetic Resonance Imaging (fMRI) Blood Oxygen Level Dependent (BOLD) signal is an indirect reflection of neuronal activity captured during an fMRI scan, and analysis is performed under the assumption that neuronal activity coincides with increased blood flow
We did not use any of the demographic information which would likely have improved accuracy, both by controlling for functional brain changes and by controlling for sampling variation. In this sample males were more likely to be Schizophrenia patients than females, so knowing this information would have permitted classification based upon this information, which is parallel to the actual functional connectivity analysis

Summary

Introduction

Functional Magnetic Resonance Imaging (fMRI) is a fourdimensional medical imaging modality that captures changes in blood oxygenation over time, an indirect measure of neuronal activation. An increasing focus is the classification of either mental disorders or states based on the fMRI signal variations within and among brain networks. One method of accomplishing this is through measurements of functional network connectivity (FNC), which infers differences in temporal brain connectivity that may depend on a disease or mental state (Biswal et al, 1995; van de Ven et al, 2004). OVERVIEW OF fMRI Function magnetic resonance imaging is a modality that measures brain activity over time. The fMRI Blood Oxygen Level Dependent (BOLD) signal is an indirect reflection of neuronal activity captured during an fMRI scan, and analysis is performed under the assumption that neuronal activity coincides with increased blood flow. Areas with increased neuronal activity give off a greater MRI signal, which indicates potential neural activity

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