Neuroanatomical Morphometric Characterization of Sex Differences
This repository accompanies below paper:
Sepehrband, F., Lynch, K.M., Cabeen, R.P., González-Zacarías, C., Zhao, L., D’Arcy, M., Kesselman, C., Herting, M.M., Dinov, I.D., Toga, A.W., Clark, K.A., 2018
Neuroanatomical Morphometric Characterization of Sex Differences in Youth Using Multivariate Statistical Learning
NeuroImage, 172:217–227. https://doi.org/10.1016/j.neuroimage.2018.01.065.
NeuroAnat_SexDiff/codecontains source codes
NeuroAnat_SexDiff/democontains .html files of the same codes (compiled and static presentation)
Source codes include:
- Explatory analysis
- Linear regression
- Ordinary least square implementation
- Robust linear modeling with Huber's loss function using least trimmed squares estimator
- Non-parameteric correlation analysis
- Spearman's rank correlation
- Multivariate logistic regression
- Statistical learning
- Support vector machine
- Inferential analysis
- Mapping between-group differences
- Plotting statistical summeries
Raw data and preparation
- Strucutral MRI of the PNC study were used for this study.
- Morphological features of the strucutral images were derived using FreeSurfer toolkit.
- FreeSurfer analysis was performed using LONI pipeline on high performance computing of USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck school of Medicine of USC. The outputs were QC'ed and used in this study.
- Data is available upon request and approval. See LICENSE for more information.
Laboratory of Neuro Imaging,
Mark and Mary Stevens Neuroimaging and Informatics Institute,
Keck School of Medicine,
University of Southern California, Los Angeles, CA, USA