Bayesian STAI Anxiety Index Predictions Based on Prefrontal Cortex NIRS Data for the Resting State

• Published in: Advances in experimental medicine and biology Vol. 765; pp. 251 - 256
• Main Authors: Sato, Masakaze, Ishikawa, Wakana, Suzuki, Tomohiko, Matsumoto, Takashi, Tsujii, Takeo, Sakatani, Kaoru
• Format: Book Chapter Journal Article
• Language: English
• Published: United States Springer 01.01.2013; Springer New York
• Series: Advances in Experimental Medicine and Biology
• Subjects: Adult; Anxiety - physiopathology; Bayes Theorem; Cerebrovascular Circulation - physiology; Female; Hemoglobins - metabolism; Humans; Immunology; Male; NIRS; Oxyhemoglobins - metabolism; Physiology; Prefrontal cortex; Prefrontal Cortex - physiopathology; Respiratory medicine; Rest - physiology; Spectroscopy, Near-Infrared; STAI anxiety index; Surveys and Questionnaires; Young Adult
• ISBN: 9781493902507; 1493902504; 9781461447719; 1461447712
• ISSN: 0065-2598; 2214-8019
• DOI: 10.1007/978-1-4614-4989-8_35

Several distinctive activity patterns have been observed in the brain at rest. The aim of this study was to determine whether the STAI index can be predicted from changes in the oxy- and deoxy-hemoglobin (Hb) concentrations by using two-channel prefrontal cortex (PFC) NIRS data for the resting state. The study population comprised 19 subjects. Each subject performed four trials, each of which consisted of resting with no task for 3 min. Data were acquired using a portable NIRS device equipped with two channels. The prediction algorithm was derived within a Bayesian machine learning framework. The prediction errors for seven subjects were not greater than 5.0. Because the STAI index varied between 20 and 80, these predictions appeared reasonable. The present method allowed prediction of mental status based on the NIRS data at resting condition obtained in the PFC.