Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

• Published in: Journal of visualized experiments no. 166
• Main Authors: Saita, Kazuya, Morishita, Takashi, Arima, Hisatomi, Ogata, Toshiyasu, Inoue, Tooru
• Format: Journal Article
• Language: English
• Published: United States 03.12.2020
• Subjects: Aged; brain; Cerebral Cortex - physiology; computer software; Data Analysis; Female; hemoglobin; Humans; Imaging, Three-Dimensional; Linear Models; magnetism; Male; near-infrared spectroscopy; Neuroimaging; qualitative analysis; Software; Spectroscopy, Near-Infrared; surgery
• ISSN: 1940-087X; 1940-087X
• DOI: 10.3791/61836

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical treatment. Among the many neuroimaging technologies used to measure brain activity, functional near-infrared spectroscopy (fNIRS) enables the evaluation of dynamic cortical activities by measuring the local hemoglobin levels similar to functional magnetic resonance imaging (fMRI). Also, due to lesser physical restriction in fNIRS, multiple variants of sensorimotor tasks can be evaluated. Many laboratories have developed several methods for fNIRS data analysis; however, despite the fact that the general principles are the same, there is no universally standardized method. Here, we present the qualitative and comparative analytic methods of data obtained from a multi-channel fNIRS experiment using a block design. For qualitative analysis, we used a software for NIRS as a mass-univariate approach based on the generalized linear model. The NIRS-SPM analysis shows qualitative results for each session by visualizing the activated area during the task. In addition, the non-invasive three-dimensional digitizer can be used to estimate the fNIRS channel locations relative to the brain. To corroborate the NIRS-SPM findings, the amplitude of the changes in hemoglobin levels induced by the sensorimotor task can be statistically analyzed by comparing the data obtained from two different sessions (before and after intervention) of the same study subject using a multi-channel hierarchical mixed model. Our methods can be used to measure the pre- vs. post-intervention analysis in a variety of neurological disorders such as movement disorders, cerebrovascular diseases, and neuropsychiatric disorders.