Xin Hou1, Zong Zhang1, Chen Zhao1, Lian Duan1, Yilong Gong1, Zheng Li2,3, Chaozhe Zhu1,3. 1. Beijing Normal University, IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China. 2. Beijing Normal University at Zhuhai, Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Zhuhai, China. 3. Beijing Normal University, Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing, China.
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies. Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS. Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection. Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS. Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.
Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies. Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS. Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection. Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS. Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.
Authors: Hasan Ayaz; Wesley B Baker; Giles Blaney; David A Boas; Heather Bortfeld; Kenneth Brady; Joshua Brake; Sabrina Brigadoi; Erin M Buckley; Stefan A Carp; Robert J Cooper; Kyle R Cowdrick; Joseph P Culver; Ippeita Dan; Hamid Dehghani; Anna Devor; Turgut Durduran; Adam T Eggebrecht; Lauren L Emberson; Qianqian Fang; Sergio Fantini; Maria Angela Franceschini; Jonas B Fischer; Judit Gervain; Joy Hirsch; Keum-Shik Hong; Roarke Horstmeyer; Jana M Kainerstorfer; Tiffany S Ko; Daniel J Licht; Adam Liebert; Robert Luke; Jennifer M Lynch; Jaume Mesquida; Rickson C Mesquita; Noman Naseer; Sergio L Novi; Felipe Orihuela-Espina; Thomas D O'Sullivan; Darcy S Peterka; Antonio Pifferi; Luca Pollonini; Angelo Sassaroli; João Ricardo Sato; Felix Scholkmann; Lorenzo Spinelli; Vivek J Srinivasan; Keith St Lawrence; Ilias Tachtsidis; Yunjie Tong; Alessandro Torricelli; Tara Urner; Heidrun Wabnitz; Martin Wolf; Ursula Wolf; Shiqi Xu; Changhuei Yang; Arjun G Yodh; Meryem A Yücel; Wenjun Zhou Journal: Neurophotonics Date: 2022-08-30 Impact factor: 4.212
Authors: Alexander Ruesch; J Chris McKnight; Andreas Fahlman; Barbara G Shinn-Cunningham; Jana M Kainerstorfer Journal: Front Physiol Date: 2022-01-17 Impact factor: 4.566