Region of interest based analysis of functional imaging data

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Biomedical Engineering

Background:

• Standard fMRI analysis often relies on whole-brain normalization, which can obscure region-specific effects.
• Intersubject anatomical variability poses a challenge for accurate localization of brain activity.

Purpose of the Study:

• To present novel fMRI analysis techniques for hypothesis testing at the region of interest (ROI) level.
• To introduce a Matlab toolbox for creating subject-specific ROI masks and performing regional response analysis.

Main Methods:

• Development of an SPM-compatible Matlab toolbox for defining subject-specific ROIs using anatomical markers.
• Application of multivariate time-series analysis techniques to test functional hypotheses on regional brain responses.
• Comparison of the proposed ROI-level analysis with standard whole-brain, voxel, and cluster-level analyses.

Main Results:

• The developed toolbox enables precise, subject-specific ROI definition.
• Region-level functional analysis effectively compensates for intersubject anatomical variability.
• The proposed method demonstrates finer localization and increased sensitivity to task-related effects compared to standard techniques.

Conclusions:

• Subject-specific ROI definition combined with region-level analysis offers a more direct link between hypotheses and statistical testing.
• This approach enhances the sensitivity and accuracy of fMRI studies for investigating localized brain function.
• The presented techniques provide a valuable advancement for neuroimaging research focused on specific brain regions.