Identify schizophrenia using resting-state functional connectivity: an exploratory research and analysis

Abstract

BackgroundSchizophrenia is a severe mental illness associated with the symptoms such as hallucination and delusion. The objective of this study was to investigate the abnormal resting-state functional connectivity patterns of schizophrenic patients which could identify furthest patients from healthy controls.MethodsThe whole-brain resting-state fMRI was performed on patients diagnosed with schizophrenia (n = 22) and on age- and gender-matched, healthy control subjects (n = 22). To differentiate schizophrenic individuals from healthy controls, the multivariate classification analysis was employed. The weighted brain regions were got by reconstruction arithmetic to extract highly discriminative functional connectivity information.ResultsThe results showed that 93.2% (p < 0.001) of the subjects were correctly classified via the leave-one-out cross-validation method. And most of the altered functional connections identified located within the visual cortical-, default-mode-, and sensorimotor network. Furthermore, in reconstruction arithmetic, the fusiform gyrus exhibited the greatest amount of weight.ConclusionsThis study demonstrates that schizophrenic patients may be successfully differentiated from healthy subjects by using whole-brain resting-state fMRI, and the fusiform gyrus may play an important functional role in the physiological symptoms manifested by schizophrenic patients. The brain region of great weight may be the problematic region of information exchange in schizophrenia. Thus, our result may provide insights into the identification of potentially effective biomarkers for the clinical diagnosis of schizophrenia.

Structure and dynamics of functional networks in child-onset schizophrenia

“…Indeed, our classification of patients with child-onset schizophrenia achieved a sensitivity of 90% and specificity of 74%. This finding supports the notion that analysis based on network measures and data mining methods may present a possible strategy for automatic diagnostics for neurological disorders Previous studies on restingstate fMRI achieved similar levels of specificity and accuracy, not only for schizophrenia in adults (Tang et al, 2012;Venkataraman et al, 2012;Shen et al, 2010), but also for other neurological disorders (Welsh et al, 2013;Zeng et al, 2013;Tang et al, 2013).…”

“…In adult schizophrenic patients previous studies already illustrated the usefulness of non-invasive diagnosis based on the analysis of restingstate fMRI data. Such analysis of brain network properties achieved values for correct classification of up to 94% with 75% accuracy (Tang et al, 2012;Venkataraman et al, 2012;Shen et al, 2010). However, similar analysis of fMRI data from patients with childonset schizophrenia is currently lacking.…”

Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM

“…Experiments on the classification of Schizophrenia patients based http://dx.doi.org/10.1016/j.neunet.2015.04.002 0893-6080/© 2015 Elsevier Ltd. All rights reserved. on fMRI data have been reported with small datasets, achieving good classification accuracies, e.g., Tang et al showed a 93.2% (Tang, Wang, Cao, & Tan, 2012) with 44 matched subjects and Yu et al (2013) experiment with healthy patient siblings achieves 62% accuracy.…”

A texture-based method for classification of schizophrenia using fMRI data

“…Preprocessing including realignment and normalization, was performed in the statistical parametric mapping software (SPM8) [14]. Many researchers [3,[17][18][19][20] applied smoothing on the fMRI data, in addition to realignment and normalization preprocessing techniques. In this paper, we focus on the different between a voxel and its neighbors for classification process.…”