Deep Neural Network to Differentiate Brain Activity Between Patients With First-Episode Schizophrenia and Healthy Individuals: A Multi-Channel Near Infrared Spectroscopy Study
Journal
Frontiers in Psychiatry
Journal Volume
12
Date Issued
2021
Author(s)
Abstract
Backgrounds: Reduced brain cortical activity over the frontotemporal regions measured by near infrared spectroscopy (NIRS) has been reported in patients with first-episode schizophrenia (FES). This study aimed to differentiate between patients with FES and healthy controls (HCs) on basis of the frontotemporal activity measured by NIRS with a support vector machine (SVM) and deep neural network (DNN) classifier. In addition, we compared the accuracy of performance of SVM and DNN. Methods: In total, 33 FES patients and 34 HCs were recruited. Their brain cortical activities were measured using NIRS while performing letter and category versions of verbal fluency tests (VFTs). The integral and centroid values of brain cortical activity in the bilateral frontotemporal regions during the VFTs were selected as features in SVM and DNN classifier. Results: Compared to HCs, FES patients displayed reduced brain cortical activity over the bilateral frontotemporal regions during both types of VFTs. Regarding the classifier performance, SVM reached an accuracy of 68.6%, sensitivity of 70.1%, and specificity of 64.6%, while DNN reached an accuracy of 79.7%, sensitivity of 88.8%, and specificity of 74.9% in the classification of FES patients and HCs. Conclusions: Compared to findings of previous structural neuroimaging studies, we found that using DNN to measure the NIRS signals during the VFTs to differentiate between FES patients and HCs could achieve a higher accuracy, indicating that NIRS can be used as a potential marker to classify FES patients from HCs. Future additional independent datasets are needed to confirm the validity of our model. ? Copyright ? 2021 Chou, Yao, Zheng, Liou, Liu, Lane, Yang and Wang.
Subjects
deep learning
deep neural network
fNIRS
machine learning
near infrared spectroscopy
schizophrenia
adult
Article
brain function
clinical article
comorbidity
controlled study
diagnostic accuracy
electroencephalogram
electroencephalography
female
fluency disorder
frontal lobe
human
male
neuroimaging
nuclear magnetic resonance imaging
positron emission tomography
psychosis
sensitivity and specificity
single photon emission computed tomography
support vector machine
temporal lobe
validity
SDGs
Type
journal article