Abstract
Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants, respectively. We trained a multilayer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92 %. Predictions for the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96 % of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.
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Data Availability
Both BIL&GIN and MRi-Share data are open to collaborations and partnerships and support local, national and international collaborations from the public or private sector. For BIL&GIN, requests to access the database should be sent through the GIN website https://www.gin.cnrs.fr/en/current-research/axis2/bilgin-en/. For MRi-Share, requests to access the database should be sent to contact@i-share.fr.
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Acknowledgements
This work has been funded by a governmental grant of “Agence Nationale de la Recherche (ANR)” (Ginesilab, ANR-16-LCV2-0006), a University Bordeaux/CEA/CNRS and Fealinx joint laboratory. The i-Share cohort has been funded by a grant ANR-10-COHO-05-01 as part of the “Programme pour les Investissements d’Avenir”). Supplementary funding was received from the “Conseil Régional de Nouvelle-Aquitaine”, reference 4370420. The MRi-Share cohort has been supported by ANR-10-LABX-57 (TRAIL). Computer time for the MRi-Share study was provided by the MCIA (Mésocentre de Calcul Intensif Aquitain) of the Université de Bordeaux and the Université de Pau et des Pays de l’Adour.
Funding
This work has been funded by a grant from “Agence Nationale de la Recherche” (Ginesilab, ANR-16-LCV2-0006), a University Bordeaux/CEA/CNRS and Fealinx joint laboratory. The i-Share cohort has been funded by a grant ANR-10-COHO-05-01 as part of the “Programme pour les Investissements d’Avenir”). Supplementary funding was received from the “Conseil Régional of Nouvelle-Aquitaine”, reference 4370420. The MRi-Share cohort has been supported by ANR-10-LABX-57 (TRAIL).
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PB, MJ designed the study. PB, VV, M-FG, PYH, MJ designed the code and ran the analysis. BM (BIL&GIN and MRi-Share) and CT (iShare) supervised the acquisition of the cohorts. All authors participated in the manuscript writing.
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The BIL&GIN study was approved by the ethics committee of Basse-Normandie (France). The MRi-Share study was approved by the ethics committee of Bordeaux (France).
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Nozais, V., Boutinaud, P., Verrecchia, V. et al. Deep Learning‐based Classification of Resting‐state fMRI Independent‐component Analysis. Neuroinform 19, 619–637 (2021). https://doi.org/10.1007/s12021-021-09514-x
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DOI: https://doi.org/10.1007/s12021-021-09514-x