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

finger tapping functional magnetic resonance imaging (fMRI) functional near-infrared spectroscopy (fNIRS) maximum entropy on the mean (MEM) near infra-red optical tomography (NIROT) personalized optimal montage

Journal

Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065

Informations de publication

Date de publication:
15 10 2021
Historique:
revised: 25 05 2021
received: 25 02 2021
accepted: 11 06 2021
pubmed: 4 8 2021
medline: 22 3 2022
entrez: 3 8 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 34342073
doi: 10.1002/hbm.25566
pmc: PMC8449120
doi:

Types de publication

Evaluation Study Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4823-4843

Subventions

Organisme : CIHR
ID : CIHR MOP 133619
Pays : Canada

Informations de copyright

© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

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Auteurs

Zhengchen Cai (Z)

Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.

Makoto Uji (M)

Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.

Ümit Aydin (Ü)

Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

Giovanni Pellegrino (G)

Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada.

Amanda Spilkin (A)

Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.

Édouard Delaire (É)

Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.

Chifaou Abdallah (C)

Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada.

Jean-Marc Lina (JM)

Département de Génie Electrique, École de Technologie Supérieure, Montréal, Québec, Canada.
Centre De Recherches En Mathématiques, Montréal, Québec, Canada.

Christophe Grova (C)

Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.
Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada.
Centre De Recherches En Mathématiques, Montréal, Québec, Canada.

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