Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.

TitleData-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.
Publication TypeJournal Article
Year of Publication2023
AuthorsLuckett, PH, Park, KYun, Lee, JJ, Lenze, EJ, Wetherell, JLoebach, Eyler, LT, Snyder, AZ, Ances, BM, Shimony, JS, Leuthardt, EC
JournalJ Neurosurg
Date Published04/2023

OBJECTIVE: Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data.

METHODS: Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme.

RESULTS: The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations.

CONCLUSIONS: Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

Alternate JournalJ Neurosurg
PubMed ID37060318
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 AG049369 / AG / NIA NIH HHS / United States
R01 MH118031 / MH / NIMH NIH HHS / United States
P41 EB018783 / EB / NIBIB NIH HHS / United States
P50 HD103525 / HD / NICHD NIH HHS / United States
UL1 TR002345 / TR / NCATS NIH HHS / United States
R01 NR012907 / NR / NINR NIH HHS / United States
K23 MH081786 / MH / NIMH NIH HHS / United States

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