A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/626948
MetadataShow full item record
AbstractFunctional 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.
CitationCastruccio 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.
- Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models.
- Authors: Shen Y, Mayhew SD, Kourtzi Z, Tiňo P
- Issue date: 2014 Jan 1
- Resting state networks in empirical and simulated dynamic functional connectivity.
- Authors: Glomb K, Ponce-Alvarez A, Gilson M, Ritter P, Deco G
- Issue date: 2017 Oct 1
- Spatio-Spectral Mixed Effects Model for Functional Magnetic Resonance Imaging Data.
- Authors: Kang H, Ombao H, Linkletter C, Long N, Badre D
- Issue date: 2012
- In Vivo Observations of Rapid Scattered Light Changes Associated with Neurophysiological Activity
- Authors: Rector DM, Yao X, Harper RM, George JS, Frostig RD
- Issue date: 2009
- Spatio-temporal analysis of auditory cortex activation as detected with silent event related fMRI.
- Authors: Christensen WF, Yetkin FZ
- Issue date: 2005 Aug 30