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

ABSTRACT:

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 health

SUBMITTER: Luckett PH 

PROVIDER: S-EPMC10576012 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature