The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.
Bibliographical noteFunding Information:
National Institute of Health (NIH) grants: R01EB020407, R01EB006841, R01MH094524, P20GM103472 and P30GM122734; National Science Foundation (NSF) grant no. 1539067.
This project was supported by National Institute of Biomedical Imaging and Bioengineering grants R01EB020407, R01EB006841, National Institute of General Medical Sciences grants P20GM103472 and P30GM122734, National Institute of Mental Health grant R01MH094524, and National Science Foundation grant number 1539067. We acknowledge the developers of the package in Python, the , , , , in , and the image viewer at University of Texas Health, San Antonio for data analysis and visualization. Seaborn effsize fifer ppcor sjstats R papaya
© 2020 John Wiley & Sons, Ltd.
- brain laterality
- independent component analysis
- voxel-based morphometry
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, U.S. Gov't, Non-P.H.S.