fNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls

Abstract

In recent years, major depressive disorder (MDD) has been shown to negatively impact physical recovery in a variety of patients. Functional near-infrared spectroscopy (fNIRS) is a tool that can potentially supplement clinical interviews and mental state examinations to establish a psychiatric diagnosis and monitor treatment progress. Thirty-two subjects, including 16 patients clinically diagnosed with MDD and 16 healthy controls (HCs), participated in the study. Brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) responses were recorded using a 22-channel continuouswave fNIRS device while the subjects performed the emotional sound test. This study evaluated the difference between MDD patients and HCs using a variety of methods. In a comparison of the Pearson correlation coefficients between the HbO/HbR responses of each fNIRS channel and four scores, MDD patients and HCs had significantly different Athens Insomnia Scale (AIS) scores. By quantitative evaluation of the functional association, we found that MDD patients had aberrant functional connectivity compared with HCs. Furthermore, we concluded that compared with HCs, there were marked abnormalities in blood oxygen in the bilateral ventrolateral prefrontal cortex (VLPFC) and bilateral dorsolateral prefrontal cortex (DLPFC). Four statistical-based features extracted from HbO signals and four vector-based features from both HbO and HbR served as inputs to four simple neural networks (multilayer neural network (MNN), feedforward neural network (FNN), cascade forward neural network (CFNN) and recurrent neural network (RNN)). Through an analysis of combinations of different features, the combination of 4 common features (mean, STD, area under the receiver operating characteristic curve (AUC)and slope) yielded the highest classification accuracy of 89.74% for fear emotion. The combination of four novel feature (∆CBV, ∆COE, |L| and K) resulted in a classification accuracy of 99.94% for fear emotion. The top 10 common and novel features were selected by the ReliefF feature selection algorithm, resulting in classification accuracies of 83.52% and 91.99%, respectively. This study identified the AUC and angle K as specific neuromarkers for predicting MDD across specific depression-related regions of the prefrontal cortex (PFC). These findings suggest that the fNIRS measurement of the PFC may serve as a supplementary test in routine clinical practice to further support a diagnosis of MDD.

Joint-Channel-Connectivity-Based Feature Selection and Classification on fNIRS for Stress Detection in Decision-Making

“…Subsequently, we compared our approach with various machine learning methods such as k-nearest neighbor (KNN), LDA, SVM, and random forest (RF). In particular, two feature selection techniques based on mutual information (MI) and ReliefF [41,42] were applied before the four comparison methods, respectively. All methods, including the comparison methods and ours, used the same strategy for parameter tuning and model training.…”

Deep learning in fNIRS: a review

“…Chao et al. 78 used a cascade forward neural network (a network similar to an MLP) to perform and achieved an average classification accuracy of 99.94% between depressed and healthy subjects when a fear stimulus was presented across 32 subjects. Chou et al.…”

“…As can be seen in Table 2, this is a larger sample size than any of the other fNIRS studies presented, which lends to confidence in the generalizability of this neural network to new subjects. Chao et al 78 used a cascade forward neural network (a network similar to an MLP) to perform and achieved an average classification accuracy of 99.94% between depressed and healthy subjects when a fear stimulus was presented across 32 subjects. Chou et al 79 used an MLP network to classify between subjects with first episode schizophrenia and healthy subjects, achieving a classification accuracy of 79.7% with only about 160 s of recorded data from each subject.…”

Estimation of Respiratory Rate from Functional Near-Infrared Spectroscopy (fNIRS): A New Perspective on Respiratory Interference

“…Compared to other neuroimaging techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), fNIRS provides better spatial and temporal resolutions, respectively [ 3 , 4 ]. Thus, a wide range of studies in different cognitive tasks and clinical settings have employed fNIRS, e.g., [ 5 , 6 , 7 , 8 ].…”