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.
Highlights
Schizophrenia is a severe mental illness associated with the symptoms such as hallucination and delusion
Using the seed-based region-of-interest correlation analysis, Woodward et al [5] discovered that RSNs were differentially affected in schizophrenic patients, and Sridharan et al [6] demonstrated that the anterior insula played a causal role in functions switching between the central-executive network(CEN) and default mode network(DMN)
Classification results Using 550 features in the feature selection, support vector machine (SVM) clustering results demonstrated that the final correct classification rate was 93.2% (100% of healthy controls and 86.4% of schizophrenic patients) (Figure 2)
Summary
Schizophrenia 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. The seed-based method, which only focuses on a handful of brain regions of interest and doesn’t examine functional connectivity patterns on a whole-brain scale, may not be a effective approach to reveal the pathological mechanism that leads to schizophrenia. Several studies have suggested that rest-based functional analyses can detect more complete and accurate information of functional connectivity [7,8], and be easier to perform resting-state neuroimaging in schizophrenic patients in contrast to task-related imaging. We used wholebrain, resting-state fMRI data for the analyses performed in this study
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.