A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionApplied Mathematics and Computational Science Program
Statistics Program
Date
2018-01-22Online Publication Date
2018-01-22Print Publication Date
2018-09Permanent link to this record
http://hdl.handle.net/10754/626948
Metadata
Show full item recordAbstract
Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs)—coarser or larger spatial units—rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect activation patterns in the brain and hence produce misleading results. We introduce a multi-resolution spatio-temporal model and a computationally efficient methodology to estimate cognitive control related activation and whole-brain connectivity. The proposed model allows for testing voxel-specific activation while accounting for non-stationary local spatial dependence within anatomically defined ROIs, as well as regional dependence (between-ROIs). The model is used in a motor-task fMRI study to investigate brain activation and connectivity patterns aimed at identifying associations between these patterns and regaining motor functionality following a stroke.Citation
Castruccio S, Ombao H, Genton MG (2018) A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data. Biometrics. Available: http://dx.doi.org/10.1111/biom.12844.Publisher
WileyJournal
BiometricsPubMed ID
29359375arXiv
arXiv:1602.02435Additional Links
http://onlinelibrary.wiley.com/doi/10.1111/biom.12844/fullae974a485f413a2113503eed53cd6c53
10.1111/biom.12844
Scopus Count
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