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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1904.05449 |
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| _version_ | 1866910674966282240 |
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| author | Dai, Mengyu Zhang, Zhengwu Srivastava, Anuj |
| author_facet | Dai, Mengyu Zhang, Zhengwu Srivastava, Anuj |
| contents | Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time-series data, over a set of brain regions, as a trajectory on the space of covariance matrices, or symmetric-positive definite matrices (SPDMs). We use a recently developed metric on the space of SPDMs for quantifying differences across FC observations, and for clustering and classification of FC trajectories. To facilitate large scale and high-dimensional data analysis, we propose a novel, metric-based dimensionality reduction technique to reduce data from large SPDMs to small SPDMs. We illustrate this comprehensive framework using data from the Human Connectome Project (HCP) database for multiple subjects and tasks, with task classification rates that match or outperform state-of-the-art techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1904_05449 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices Dai, Mengyu Zhang, Zhengwu Srivastava, Anuj Computer Vision and Pattern Recognition Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time-series data, over a set of brain regions, as a trajectory on the space of covariance matrices, or symmetric-positive definite matrices (SPDMs). We use a recently developed metric on the space of SPDMs for quantifying differences across FC observations, and for clustering and classification of FC trajectories. To facilitate large scale and high-dimensional data analysis, we propose a novel, metric-based dimensionality reduction technique to reduce data from large SPDMs to small SPDMs. We illustrate this comprehensive framework using data from the Human Connectome Project (HCP) database for multiple subjects and tasks, with task classification rates that match or outperform state-of-the-art techniques. |
| title | Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1904.05449 |