High Classification Accuracy for Schizophrenia with Rest and Task fMRI Data

Abstract

We present a novel method to extract classification features from functional magnetic resonance imaging (fMRI) data collected at rest or during the performance of a task. By combining a two-level feature identification scheme with kernel principal component analysis (KPCA) and Fisher’s linear discriminant analysis (FLD), we achieve high classification rates in discriminating healthy controls from patients with schizophrenia. Experimental results using leave-one-out cross-validation show that features extracted from the default mode network (DMN) lead to a classification accuracy of over 90% in both data sets. Moreover, using a majority vote method that uses multiple features, we achieve a classification accuracy of 98% in auditory oddball (AOD) task and 93% in rest data. Several components, including DMN, temporal, and medial visual regions, are consistently present in the set of features that yield high classification accuracy. The features we have extracted thus show promise to be used as biomarkers for schizophrenia. Results also suggest that there may be different advantages to using resting fMRI data or task fMRI data.

Recognition of early-onset schizophrenia using deep-learning method

“…Resting-state connectivity presented good potential classification capacity (79.3% for classification accuracy, 87.4% for sensitivity, 82.2% for specificity, p < 0.05 for permuted test). Many other studies used resting-state functional connectivity to differentiate schizophrenia patients from controls (Mikolas et al 2016;Cabral et al 2016;Arbabshirani et al 2013;Shen et al 2010;Du et al 2012;Skåtun et al 2016). Two strategies were used in these studies to overcome over-fitting problem.…”

Feature selection using genetic algorithm for classification of schizophrenia using fMRI data

“…In the last years, researchers have tried to propose methods for classification of patients with severe mental illness. It was done to exam differences between patient and controls groups, based on neuroscientific measures [3]. In this regard, researchers have used event-related potentials (ERP) derived from the electroencephalogram (EEG) for finding abnormalities in schizophrenia patients for many years.…”

Multivariate Classification of Blood Oxygen Level-Dependent fMRI Data with Diagnostic Intention: A Clinical Perspective

“…This constitutes a paradigm shift from comparative univariate to discriminative multivariate analyses of fMRI data. An exhaustive overview of these previous studies by using either task-based or task-free 55, fMRI is given in On-line Tables 1 and 2. An overview of particularly reliable studies with above-average statistical power is presented in Fig 2.…”

“…98,104 Recent further developments in diagnostic MVPA are not solely based on one of these methods. For example, Du et al 55 combined both task-and task-free fMRI in schizophrenia in a small study. Additionally, combinations of fMRI measures with volumetric data, 41,[48][49][50]63,76,[78][79][80][81]86,89 DTI, 46,49,92 as well as genetics 42 and behavioral data, 40,41,50,76 have been used as features in MVPA analyses.…”

“…Recently, a significant number of studies 55, have been based on task-free fMRI acquisitions, so-called resting-state fMRI, which focuses on the functional connectivity of distant brain regions in terms of signal cofluctuations and therefore on the integrity of large-scale brain networks. 98,99 A potential benefit of this method is that typical networks seem to be robustly identifiable in individual subjects.…”