Functional MRI connectivity accurately distinguishes cases with psychotic disorders from healthy controls, based on cortical features associated with neurodevelopment

Background Machine learning (ML) can distinguish cases with psychotic disorder from healthy controls based on magnetic resonance imaging (MRI) data, with reported accuracy in the range 60-100%. It is not yet clear which MRI metrics are the most informative for case-control ML. Methods We analysed multi-modal MRI data from two independent case-control studies of patients with psychotic disorders (cases, N = 65, 28; controls, N = 59, 80) and compared ML accuracy across 5 MRI metrics. Cortical thickness, mean diffusivity and fractional anisotropy were estimated at each of 308 cortical regions, as well as functional and structural connectivity between each pair of regions. Functional connectivity data were also used to classify non-psychotic siblings of cases (N=64) and to distinguish cases from controls in a third independent study (cases, N=67; controls, N = 81). Results In both principal studies, the most diagnostic metric was fMRI connectivity: the areas under the receiver operating characteristic curve were 92% and 77%, respectively. The cortical map of diagnostic connectivity features was replicable between studies ( r = 0.31, P < 0.001); correlated with replicable case-control differences in fMRI degree centrality, and with prior cortical maps of aerobic glycolysis and adolescent development of functional connectivity; predicted intermediate probabilities of psychosis in siblings; and replicated in the third case-control study. Conclusions ML most accurately distinguished cases from controls by a replicable pattern of fMRI connectivity features, highlighting abnormal hubness of cortical nodes in an anatomical pattern consistent with the concept of psychosis as a disorder of network development. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This 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 holds 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 is supported by grants from the ERC (grant 677467) and SFI (12/IP/1359). ETB is supported by a NIHR Senior Investigator Award. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. ### Author Declarations All relevant ethical guidelines have been followed and any necessary IRB and/or ethics committee approvals have been obtained. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes Any clinical trials involved have been registered with an ICMJE-approved registry such as ClinicalTrials.gov and the trial ID is included in the manuscript. Not Applicable I have followed all appropriate research reporting guidelines and uploaded the relevant Equator, ICMJE or other checklist(s) as supplementary files, if applicable. Not Applicable The code and pre-processed data used in these analyses will be shared via GitHub and FigShare at the time of publication.