Activated fibers: fiber-centered activation detection in task-based FMRI.

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Computational Neuroscience

Background:

• Task-based functional magnetic resonance imaging (fMRI) commonly uses the generalized linear model (GLM) to identify brain regions activated by stimuli.
• A core GLM assumption is that brain responses (BOLD signals) mirror the stimulus timing.
• This assumption is extended to white matter tracts for a novel activation detection approach.

Purpose of the Study:

• To develop and validate a new method for detecting activated brain regions in task-based fMRI.
• To leverage dynamic functional connectivity (DFC) of white matter fibers as a proxy for brain response.
• To compare the sensitivity of the DFC-based GLM method against traditional voxel-based GLM.

Main Methods:

• Utilized dynamic functional connectivity (DFC) curves derived from white matter fibers.
• Applied the generalized linear model (GLM) to DFC curves to detect Activated Fibers (AFs).
• Integrated multimodal data, including task-based fMRI and diffusion tensor imaging (DTI).

Main Results:

• DFC curves of fibers connecting active regions showed positive correlation with the stimulus paradigm.
• The novel GLM approach successfully detected Activated Fibers (AFs).
• Detected AFs encompassed most regions found by voxel-based GLM and identified additional activated areas.

Conclusions:

• The DFC-based GLM method offers a more sensitive approach to detecting brain activation in fMRI studies.
• Traditional voxel-based GLM may be overly conservative, potentially missing some activated brain regions.
• This method enhances the understanding of functional brain networks during cognitive tasks.