Data for "Functional MRI connectivity accurately distinguishes cases with psychotic disorders from healthy controls, based on cortical features associated with brain network development"

This dataset accompanies the paper "Functional MRI connectivity accurately distinguishes cases with psychotic disorders from healthy controls, based on cortical features associated with brain network development", Morgan* and Young* et al, 2020.
Pre-processed data:
Regional CT, MD and FA values are provided here, as well as DTI networks. CT is given for the Maastricht, Dublin and Cobre datasets, whilst MD, FA and DTI are given for the Maastricht and Dublin datasets. Age, sex and group information is also provided (1=male, 2=female; 1=control, 2=case). Please note that fMRI data and the code to perform machine learning analyses is available on GitHub at: https://github.com/jmyoung36/fMRI_connectivity_accurately_distinguishes_cases.

ML outputs:
Predicted probabilities and regional ML feature weights are provided (see the paper for details).
Notes:
All data was parcellated according to an atlas with 308 regions, created by Dr Rafael Romero-Garcia- see Romero-Garcia et al, NeuroImage 2012 (https://doi.org/10.1016/j.neuroimage.2011.10.086). Please cite that paper, as well as Morgan* and Young* et al 2020, if you use this data for your own research.

The study was supported by
grants from the European Commission (PSYSCAN - Translating neuroimaging
findings from research into clinical practice; ID: 603196) and the NIHR
Cambridge Biomedical Research Centre (Mental Health). The Cobre data was
downloaded from the COllaborative Informatics and Neuroimaging Suite Data
Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was
performed at the Mind Research Network, and funded by a Center of Biomedical
Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr.
Vince Calhoun. SEM was supported by a Henslow
Fellowship at Lucy Cavendish College, University of Cambridge, funded by
the Cambridge Philosophical Society. KJW was funded by an Alan Turing Institute
Research Fellowship under EPSRC Research grant TU/A/000017. MPvdH was supported
by a NWO VIDI and ALW open grant and a MQ fellowship. GD was supported by grants
from the ERC (grant 677467) and SFI (12/IP/1359). ETB was supported by a NIHR
Senior Investigator Award.