02867nas a2200241 4500008004100000022001400041245010500055210006900160260001200229300000900241520204300250100002402293700001802317700001702335700001902352700003002371700001902401700002302420700001902443700002302462700002302485856011702508 2023 eng d a1933-069300aData-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.0 aDataefficient restingstate functional magnetic resonance imaging c04/2023 a1-123 a

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.

1 aLuckett, Patrick, H1 aPark, Ki, Yun1 aLee, John, J1 aLenze, Eric, J1 aWetherell, Julie, Loebach1 aEyler, Lisa, T1 aSnyder, Abraham, Z1 aAnces, Beau, M1 aShimony, Joshua, S1 aLeuthardt, Eric, C uhttps://neurotechcenter.org/publications/2023/data-efficient-resting-state-functional-magnetic-resonance-imaging