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

Author:

Luckett Patrick H.1,Park Ki Yun12,Lee John J.3,Lenze Eric J.4,Wetherell Julie Loebach56,Eyler Lisa T.6,Snyder Abraham Z.37,Ances Beau M.7,Shimony Joshua S.3,Leuthardt Eric C.128910

Affiliation:

1. Department of Neurological Surgery, Division of Neurotechnology, Washington University School of Medicine;

2. Neuroscience, Washington University School of Medicine, St. Louis, Missouri;

3. Mallinckrodt Institute of Radiology, Washington University School of Medicine;

4. Departments of Psychiatry,

5. Mental Health Impact Unit 3, VA San Diego Healthcare System, San Diego;

6. Department of Psychiatry, University of California, San Diego, California;

7. Neurology, and

8. Departments of Biomedical Engineering and

9. Mechanical Engineering and Materials Science, Washington University in St. Louis, Missouri;

10. Center for Innovation in Neuroscience and Technology, Division of Neurotechnology, Washington University School of Medicine;

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 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.

Publisher

Journal of Neurosurgery Publishing Group (JNSPG)

Subject

Genetics,Animal Science and Zoology

Reference45 articles.

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