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

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 diagno...