A practical model-based segmentation approach for improved activation detection in single-subject functional magnetic resonance imaging studies.

ABSTRACT: Functional magnetic resonance imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and single-subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single-subject and low-signal fMRI by developing a computationally feasible and methodologically sound model-based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish voxels and

SUBMITTER: Chen WC 

PROVIDER: S-EPMC10543117 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature