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We study how human brains activate to process input information and execute necessary cognitive tasks. Understanding the process is crucial in improving our diagnostic and treatment of different neurological disorders. Given functional MRI images recorded when human subjects execute tasks with different levels of information uncertainty, we need to identify the similarity and difference between brain activities at different regions of interest (ROIs), and thus gain insights into the underlying mechanism. To achieve this goal, we propose a new ROI- reweight 3D convolutional neural network (CNN). Our CNN not only learns to classify the task-evoked fMRIs with a high accuracy, but also locates crucial ROIs based on a reweight layer. Our findings reveal several brain regions to be crucial in differentiating brain activity patterns facing tasks of different uncertainty levels.
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Constraining functional coactivation with a cluster-based structural connectivity network
Abstract In this article, we propose a two-step pipeline to explore task-dependent functional coactivations of brain clusters with constraints from the structural connectivity network. In the first step, the pipeline employs a nonparametric Bayesian clustering method that can estimate the optimal number of clusters, cluster assignments of brain regions of interest (ROIs), and the strength of within- and between-cluster connections without any prior knowledge. In the second step, a factor analysis model is applied to functional data with factors defined as the obtained structural clusters and the factor structure informed by the structural network. The coactivations of ROIs and their clusters can be studied by correlations between factors, which can largely differ by ongoing cognitive task. We provide a simulation study to validate that the pipeline can recover the underlying structural and functional network. We also apply the proposed pipeline to empirical data to explore the structural network of ROIs obtained by the Gordon parcellation and study their functional coactivations across eight cognitive tasks and a resting-state condition.
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- Award ID(s):
- 1847603
- PAR ID:
- 10421960
- Date Published:
- Journal Name:
- Network Neuroscience
- Volume:
- 6
- Issue:
- 4
- ISSN:
- 2472-1751
- Page Range / eLocation ID:
- 1032 to 1065
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1162/netn_a_00242
