Resting state network mapping in individuals using deep learning.

TitleResting state network mapping in individuals using deep learning.
Publication TypeJournal Article
Year of Publication2023
AuthorsLuckett, PH, Lee, JJ, Park, KYun, Raut, RV, Meeker, KL, Gordon, EM, Snyder, AZ, Ances, BM, Leuthardt, EC, Shimony, JS
JournalFront Neurol
Date Published01/2023

INTRODUCTION: Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings.

METHODS: In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in = 2010 healthy participants. After training, maps that represent the probability of a voxel belonging to a particular RSN were generated for each participant, and then used to calculate mean and standard deviation (STD) probability maps, which are made publicly available. Further, we compared our results to previously published resting state and task-based functional mappings.

RESULTS: Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD.

DISCUSSION: The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.

Alternate JournalFront Neurol
PubMed ID36712434
PubMed Central IDPMC9878609
Grant ListP01 AG003991 / AG / NIA NIH HHS / United States
P01 AG026276 / AG / NIA NIH HHS / United States
R01 CA203861 / CA / NCI NIH HHS / United States
R01 AG057680 / AG / NIA NIH HHS / United States
R01 MH118031 / MH / NIMH NIH HHS / United States
R01 NR012657 / NR / NINR NIH HHS / United States
P41 EB018783 / EB / NIBIB NIH HHS / United States
R01 NR012907 / NR / NINR NIH HHS / United States
K23 MH081786 / MH / NIMH NIH HHS / United States

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