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Abstract Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Yet, the difficulty of reliable training on high-dimensional low sample size datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this work, we introduce a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. Results successfully demonstrate that the proposed framework enables learning the dynamics of resting-state fMRI directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction.
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A Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks
We present a novel deep learning framework to automatically compute independently salient networks in the brain that characterize the underlying changes in the brain in association with clinically observed assessments. Unsupervised approaches for high-dimensional neuroimaging data focus on computing low-dimensional brain components for subsequent analysis, while supervised learning approaches aim for predictive performance and yielding a single list of associative feature importance, thus making it hard to interpret at the level of brain subsystems. Our approach integrates the goals of decomposition into lower dimensional subspaces and, identifying salient brain subsystems into a single automated framework. We first train a convolutional neural network on structural brain features to predict clinical assessments, followed by a multi-step decomposition in the saliency space to compute salient brain networks that intrinsically characterize the brain changes associated with the assessment. Through a repeated training procedure on an Alzheimer’s disease (AD) dataset, we show that our method effectively computes AD-related salient brain subsystems directly from high-dimensional neuroimaging data, while maintaining predictive performance. Such approaches are crucial for data-driven biomarker development for brain disorders.
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- Award ID(s):
- 2112455
- PAR ID:
- 10569567
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-6929-8
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- Princeton, NJ, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CISS59072.2024.10480204
