Transfer Learning to Decode Brain States Reflecting the Relationship Between Cognitive Tasks

Qu, Youzhi; Jian, Xinyao; Che, Wenxin; Du, Penghui; Fu, Kai Ming G.; Liu, Quanying

doi:10.1007/978-981-19-8222-4_10

Cited by 3 publications

(1 citation statement)

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“…Additionally, the mapping of cognitive tasks to brain regions illustrates the overlap among different tasks, which reflects the similarity between cognitive tasks. Tasks that perform similar functions often show overlapping activation in brain regions [55,56]. To further explore the applicability of the HMTFS method in neuroscience, we used the difficulty of language tasks as a test case.…”

Section: Discussionmentioning

confidence: 99%

Hypergraph-Based Multitask Feature Selection with Temporally Constrained Group Sparsity Learning on fMRI

Qu,

Fu,

Wang

et al. 2024

Mathematics

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Localizing the brain regions affected by tasks is crucial to understanding the mechanisms of brain function. However, traditional statistical analysis does not accurately identify the brain regions of interest due to factors such as sample size, task design, and statistical effects. Here, we propose a hypergraph-based multitask feature selection framework, referred to as HMTFS, which we apply to a functional magnetic resonance imaging (fMRI) dataset to extract task-related brain regions. HMTFS is characterized by its ability to construct a hypergraph through correlations between subjects, treating each subject as a node to preserve high-order information of time-varying signals. Additionally, it manages feature selection across different time windows in fMRI data as multiple tasks, facilitating time-constrained group sparse learning with a smoothness constraint. We utilize a large fMRI dataset from the Human Connectome Project (HCP) to validate the performance of HMTFS in feature selection. Experimental results demonstrate that brain regions selected by HMTFS can provide higher accuracy for downstream classification tasks compared to other competing feature selection methods and align with findings from previous neuroscience studies.

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Section: Discussionmentioning

confidence: 99%