Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis

Abstract People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual (“visual2”), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients— both affective and non-affective—exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged and normalized the visual2 and somatomotor network connections, and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p=2e-10, Hedges’ g=1.05). This “somato-visual” biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, anxiety, or demographics. It had moderate test-retest reliability (ICC=.61) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC=.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work was supported by a K01MH108783 to BPK and a Hendershot pilot grant to BPK through the Psychiatry Department at the University of Rochester. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used ONLY openly available human data that were originally located in the HCP Early Psychosis dataset and OpenNeuro. The HCP data are public (https://nda.nih.gov/edit_collection.html?id=2914). The UCLA data are located on OpenNeuro.org (accession number: ds000030), and so too are the Rutgers patient and control data (ds005073, ds003404, respectively). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Since our last submission, we noticed an error in our code such that--when calculating the cortico-cortical connectivity values for each person and each network--we over-weighted the connectivity weights immediately below the matrix diagonal by a factor of 2. After correcting this issue, our results changed by about 1-2 percent, and none of our results changed qualitatively. We have updated all figures, tables, and text with the new information. Moreover, we have added additional references related to motion confounds in resting-state functional connectivity analyses, and have updated one additional reference in the Discussion so that it refers to functional (rather than structural) connectivity. We have also added a number of small wording changes. Data and code availability The HCP data are public (https://nda.nih.gov/edit_collection.html?id=2914). The UCLA data are located on OpenNeuro.org (accession number: ds000030), and so too are the Rutgers patient and control data (ds005073, ds003404, respectively). Preprocessing was done with fmriprep v.21.0.1 (www.fmriprep.org). Code for estimating RSFC and applying the Cole-Anticevic Brain Network Partition are on GitHub (https://github.com/ColeLab/ActflowToolbox/; https://github.com/ColeLab/ColeAnticevicNetPartition).