Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (ϿICA)
Z Chen, VD Calhoun�- Journal of Neuroscience Methods, 2016 - Elsevier
Journal of Neuroscience Methods, 2016•Elsevier
Background Conventionally, independent component analysis (ICA) is performed on an
fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse
problem of fMRI, we can reconstruct the brain magnetic susceptibility (Ͽ) functional states.
Upon the reconstructed Ͽ dataspace, we propose an ICA-based brain functional Ͽ mapping
method (ϿICA) to extract task-evoked brain functional map. New methods A complex
division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase�…
fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse
problem of fMRI, we can reconstruct the brain magnetic susceptibility (Ͽ) functional states.
Upon the reconstructed Ͽ dataspace, we propose an ICA-based brain functional Ͽ mapping
method (ϿICA) to extract task-evoked brain functional map. New methods A complex
division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase�…
Background
Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (Ͽ) functional states. Upon the reconstructed Ͽ dataspace, we propose an ICA-based brain functional Ͽ mapping method (ϿICA) to extract task-evoked brain functional map.
New methods
A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain Ͽ response dataset. ϿICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D Ͽ dataspace.
Results
With finger-tapping experiments on a 7T system, the ϿICA-extracted Ͽ-depicted functional map is similar to the SPM-inferred functional Ͽ map by a spatial correlation of 0.67���0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81���0.05. The understanding of the inferiority of ϿICA to AICA for task-evoked functional map is an ongoing research topic.
Comparison with existing methods
For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare ϿICA with AICA for extracting task-correlated timecourses and functional maps.
Conclusion
ϿICA can extract a Ͽ-depicted task-evoked brain functional map from a reconstructed Ͽ dataspace without the knowledge about brain hemodynamic responses. The ϿICA-extracted brain functional Ͽ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA.
Elsevier