J Adam Noah1, Xian Zhang1, Swethasri Dravida2, Courtney DiCocco3, Tatsuya Suzuki4,5, Richard N Aslin6,7, Ilias Tachtsidis8, Joy Hirsch1,8,9,10. 1. Yale School of Medicine, Department of Psychiatry, Brain Function Laboratory, New Haven, Connecticut, United States. 2. Yale School of Medicine, Interdepartmental Neuroscience Program New Haven, Connecticut, United States. 3. Yale School of Medicine, Brain Function Laboratory, New Haven, Connecticut, United States. 4. Meiji University, Graduate School of Science and Technology, Electrical Engineering Program, Kawasaki, Japan. 5. Meiji University, School of Science and Technology, Department of Electronics and Bioinformatics, Kawasaki, Japan. 6. Haskins Laboratories, New Haven, Connecticut, United States. 7. Yale University, Department of Psychology, New Haven, Connecticut, United States. 8. University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom. 9. Yale School of Medicine, Department of Neuroscience, New Haven, Connecticut, United States. 10. Yale School of Medicine, Department of Comparative Medicine, New Haven, Connecticut, United States.
Abstract
Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.
Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.
Authors: Androu Abdalmalak; Sergio L Novi; Karnig Kazazian; Loretta Norton; Tatiana Benaglia; Marat Slessarev; Derek B Debicki; Keith St Lawrence; Rickson C Mesquita; Adrian M Owen Journal: Front Neurosci Date: 2022-03-08 Impact factor: 4.677