fNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 2211 - 2221
• Main Authors: Chao, Jinlong, Zheng, Shuzhen, Wu, Hongtong, Wang, Dixin, Zhang, Xuan, Peng, Hong, Hu, Bin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; activation patterns; Algorithms; Artificial intelligence; Artificial neural networks; Biological neural networks; Classification; Continuous radiation; Correlation; Correlation coefficient; Correlation coefficients; Depression; Diagnosis; Emotions; Fear; Feature extraction; feature selection; Functional near-infrared spectroscopy (fNIRS); Indexes; Infrared spectra; Infrared spectroscopy; Insomnia; major depressive disorder (MDD); Medical imaging; Mental depression; Multilayers; Near infrared radiation; Neural networks; Oxyhemoglobin; Patients; Prefrontal cortex; Recurrent neural networks; simple neural networks; Sleep disorders; Task analysis
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2021.3115266

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 continuous-wave 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 , <inline-formula> <tex-math notation="LaTeX">\vert {L} \vert </tex-math></inline-formula> 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.