Evaluation of a personalized functional near infra‐red optical tomography workflow using maximum entropy on the mean

• Published in: Human brain mapping Vol. 42; no. 15; pp. 4823 - 4843
• Main Authors: Cai, Zhengchen, Uji, Makoto, Aydin, Ümit, Pellegrino, Giovanni, Spilkin, Amanda, Delaire, Édouard, Abdallah, Chifaou, Lina, Jean‐Marc, Grova, Christophe
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.10.2021
• Subjects: Adult; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Brain research; Customization; Data acquisition; Data processing; Deoxygenation; Digitization; Electroencephalography; Entropy; finger tapping; Fluctuations; Functional magnetic resonance imaging; functional magnetic resonance imaging (fMRI); functional near‐infrared spectroscopy (fNIRS); Functional Neuroimaging - standards; Hemodynamics; Hemoglobin; Humans; Image reconstruction; Infrared spectroscopy; Inverse problems; Localization; Magnetic resonance imaging; Magnetic Resonance Imaging - standards; Maximum entropy; maximum entropy on the mean (MEM); Medical imaging; Motor task performance; Near infrared radiation; near infra‐red optical tomography (NIROT); Neuroimaging; Optimization; personalized optimal montage; Spectroscopy, Near-Infrared - standards; Tomography; Tomography, Optical - standards; Workflow; Young Adult; Canada; functional magnetic resonance imaging (fMRI); maximum entropy on the mean (MEM); functional near-infrared spectroscopy (fNIRS); finger tapping; near infra-red optical tomography (NIROT); personalized optimal montage
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25566

In the present study, we proposed and evaluated a workflow of personalized near infra‐red optical tomography (NIROT) using functional near‐infrared spectroscopy (fNIRS) for spatiotemporal imaging of cortical hemodynamic fluctuations. The proposed workflow from fNIRS data acquisition to local 3D reconstruction consists of: (a) the personalized optimal montage maximizing fNIRS channel sensitivity to a predefined targeted brain region; (b) the optimized fNIRS data acquisition involving installation of optodes and digitalization of their positions using a neuronavigation system; and (c) the 3D local reconstruction using maximum entropy on the mean (MEM) to accurately estimate the location and spatial extent of fNIRS hemodynamic fluctuations along the cortical surface. The workflow was evaluated on finger‐tapping fNIRS data acquired from 10 healthy subjects for whom we estimated the reconstructed NIROT spatiotemporal images and compared with functional magnetic resonance imaging (fMRI) results from the same individuals. Using the fMRI activation maps as our reference, we quantitatively compared the performance of two NIROT approaches, the MEM framework and the conventional minimum norm estimation (MNE) method. Quantitative comparisons were performed at both single subject and group‐level. Overall, our results suggested that MEM provided better spatial accuracy than MNE, while both methods offered similar temporal accuracy when reconstructing oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentration changes evoked by finger‐tapping. Our proposed complete workflow was made available in the brainstorm fNIRS processing plugin—NIRSTORM, thus providing the opportunity for other researchers to further apply it to other tasks and on larger populations. This study was to introduce and evaluate a workflow for personalized near infra‐red optical tomography (NIROT). The workflow comprises a full pipeline from functional near‐infrared spectroscopy (fNIRS) data acquisition to local 3D imaging of cortical hemodynamic fluctuations.