Spatially regularized machine learning for task and resting-state fMRI.

Journal: Journal of neuroscience methods

Published Date: Jan 15, 2016

Abstract

BACKGROUND: Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades.

Authors

Xiaomu Song

Department of Electrical Engineering, School of Engineering, Widener University, Kirkbride Hall, Room 369, One University Place, Chester, PA 19013, United States. Electronic address: xmsong@widener.edu.
Lawrence P Panych

Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States.
Nan-kuei Chen

Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Hock Plaza, Durham, NC 27710, United States.

Keywords

Brain Brain Mapping Computer Simulation Datasets as Topic Fingers Humans Linear Models Magnetic Resonance Imaging Mental Processes Models, Neurological Motor Activity Neural Pathways Neuropsychological Tests Reproducibility of Results Rest Support Vector Machine Visual Perception

External Resources

View on PubMed Access via DOI PubMed (26470627)

Spatially regularized machine learning for task and resting-state fMRI.

Abstract

Authors

Keywords

External Resources

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