3D distributed deep learning framework for prediction of human intelligence from brain MRI

Seungwook Han; Yan Zhang; Yihui Ren; Jonathan Posner; Shinjae Yoo; Jiook Cha

doi:10.1117/12.2549758

28 February 2020 3D distributed deep learning framework for prediction of human intelligence from brain MRI

Seungwook Han, Yan Zhang, Yihui Ren, Jonathan Posner, Shinjae Yoo, Jiook Cha

Author Affiliations +

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113171X (2020) https://doi.org/10.1117/12.2549758
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Intelligence is a complex, multi-dimensional concept that encompasses multiple brain circuits. Understanding the underpinnings of the human brain requires not only accurate feature extraction from often noisy non-invasive brain imaging data (e.g., MRI), but also rigorous modeling of the complex relationships among distributed brain systems. In this work, we implement a highly scalable end-to-end computational learning framework – that is, a 3D deep convolutional neural network (CNN) to predict fluid intelligence scores directly from 3D brain MRI without any theory- or rule-based feature engineering. We address and overcome the challenge of processing large data (i.e. 44 GB of MRI) by using distributed deep learning techniques. The dataset originates from the Adolescent Brain Cognitive Development (ABCD) study, with 5832 subjects in the training set, 1251 in the validation set, and 1250 in the test set. The single-task ResNet50-3D model achieved mean squared errors of 0.73637 and 0.74535 respectively on the validation and test sets. The multi-task ResNet50-3D model achieved mean squared errors of 0.74418 and 0.75626 respectively on the validation and test sets. These results demonstrate not only that the prediction of fluid intelligence scores directly from structural and diffusion brain MRI is feasible but also that this scalable computational learning framework could be further developed for data-driven human neurocognitive research.

Citation Download Citation

Seungwook Han, Yan Zhang, Yihui Ren, Jonathan Posner, Shinjae Yoo, and Jiook Cha "3D distributed deep learning framework for prediction of human intelligence from brain MRI", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113171X (28 February 2020); https://doi.org/10.1117/12.2549758

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KEYWORDS

Magnetic resonance imaging

Data modeling

Brain

3D modeling

Neuroimaging

Performance modeling

Neural networks

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