Resting state network mapping in individuals using deep learning - Luckett, Patrick H.; Lee, John J.; Park, Ki Yun; Raut, Ryan V.; Meeker, Karin L.; Gordon, Evan M.; Snyder, Abraham Z.; Ances, Beau M.; Leuthardt, Eric C.; Shimony, Joshua S. (Frontiers in Neurology, 2023)
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. 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 n=2010 healthy participants. 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. 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. Our results show core regions within each network that exhibit high average probability and low STD. Further, we compared our results to previously published resting state and task-based functional mappings. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.