title: Activation detection in functional near-infrared spectroscopy by wavelet coherence.
abstract: Functional near-infrared spectroscopy (fNIRS) detects hemodynamic responses in the cerebral cortex by transcranial spectroscopy. However, measurements recorded by fNIRS not only consist of the desired hemodynamic response but also consist of a number of physiological noises. Because of these noises, accurately detecting the regions that have an activated hemodynamic response while performing a task is a challenge when analyzing functional activity by fNIRS. In order to better detect the activation, we designed a multiscale analysis based on wavelet coherence. In this method, the experimental paradigm was expressed as a binary signal obtained while either performing or not performing a task. We convolved the signal with the canonical hemodynamic response function to predict a possible response. The wavelet coherence was used to investigate the relationship between the response and the data obtained by fNIRS at each channel. Subsequently, the coherence within a region of interest in the time-frequency domain was summed to evaluate the activation level at each channel. Experiments on both simulated and experimental data demonstrated that the method was effective for detecting activated channels hidden in fNIRS data.
authors.affiliation: Chinese Academy of Sciences, Institute of Automation, Brainnetome Center, Beijing 100190, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Zhang
authors.affiliation: University of Electronic Science and Technology of China, School of Life Science and Technology, Key Laboratory for NeuroInformation of Ministry of Education, Chengdu 610054, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yu
authors.affiliation: University of Electronic Science and Technology of China, School of Life Science and Technology, Key Laboratory for NeuroInformation of Ministry of Education, Chengdu 610054, China.
authors.forename: Ruirui
authors.initials: R
authors.lastname: Zhao
authors.affiliation: University of Electronic Science and Technology of China, School of Life Science and Technology, Key Laboratory for NeuroInformation of Ministry of Education, Chengdu 610054, China.
authors.forename: Wenting
authors.initials: W
authors.lastname: Xu
authors.affiliation: Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing 100875, China.
authors.forename: Haijing
authors.initials: H
authors.lastname: Niu
authors.affiliation: Chinese Academy of Sciences, Institute of Automation, Brainnetome Center, Beijing 100190, China.
authors.forename: Yujin
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Chinese Academy of Sciences, Institute of Automation, Brainnetome Center, Beijing 100190, ChinadChinese Academy of Sciences, Institute of Automation, National Laboratory of Pattern Recognition, Beijing 100190, China.
authors.forename: Nianming
authors.initials: N
authors.lastname: Zuo
authors.affiliation: Chinese Academy of Sciences, Institute of Automation, Brainnetome Center, Beijing 100190, ChinabUniversity of Electronic Science and Technology of China, School of Life Science and Technology, Key Laboratory for NeuroInformation of Ministry of Education.
authors.forename: Tianzi
authors.initials: T
authors.lastname: Jiang
mesh_terms: D001921:Brain / Q000098:blood supply / Q000502:physiology; D006439:Hemodynamics / Q000502:physiology; D006801:Humans; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D058067:Wavelet Analysis