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Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients

Abstract

Substantial clinical heterogeneity and comorbidity inherent amongst mental disorders limit the identification of neuroimaging biomarkers that can reliably track clinical symptoms. Strategies that enable generation of meaningful and replicable neurobiological markers at the individual level will push the field of neuropsychiatry forward in developing efficacious personalized treatment. The current study included 142 adult patients with a primary diagnosis of schizophrenia (SCZ), bipolar (BP), or attention deficit/hyperactivity disorder (ADHD), and 67 patient ratings across four behavioral measures. Using functional connectivity derived from a personalized fMRI approach, we identified several candidate imaging markers related to dimensional phenotypes across disorders, assessed the internal and external generalizability of these markers, and compared the probability of replicating findings across datasets using individual and group-averaged defined functional regions. We identified subject-specific connections related to three different clinical domains (attention deficit, appetite-energy, psychosis-positive) in a discovery dataset. Importantly, these connectivity biomarkers were robust and were reproduced in an independent validation dataset. For markers related to neurovegetative symptoms (attention deficit, appetite-energy symptoms), the brain connections involved showed similar connectivity patterns across the different diagnoses. However, psychosis-positive symptoms were associated with connections of varying strength across disorders. Finally, we found that markers for symptom domains were replicable for individually-specified connections, but not for group template-derived connections. Our personalized strategies allowed us to identify meaningful and generalizable imaging markers for symptom domains in patients who exhibit high levels of heterogeneity. These biomarkers may shed new light on the connectivity underpinnings of psychiatric symptoms and lead to personalized interventions.

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Fig. 1: Workflow for identifying robust imaging markers of clinical symptom domains.
Fig. 2: Identification and validation of symptom-related imaging makers.
Fig. 3: Replicable markers related to clinical symptom domains.
Fig. 4: Marker for psychosis-positive symptoms showed different connectivity strengths across diagnoses.
Fig. 5: Imaging markers derived from group-level functional regions did not generalize to new data.

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Funding

This work was supported by Changping Laboratory and the Ministry of Science and Technology of China (2021B-01-01), National Natural Science Foundation of China grants Nos. 81790652, 81790650, and NIH grants P50MH106435, 1R01DC017991, 5K01MH111802. LD is supported by a Canadian Institutes of Health Research postdoctoral fellowship, FRN: MFE-171291. These data were obtained from the OpenfMRI database ds000030, funded by Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants).

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ML and HL conceived the study; ML and LD performed the analyses with support from YH and HL; ML, LD, DW, MW, CSH and HL wrote the manuscript. All authors commented on the manuscript.

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Correspondence to Meiyun Wang or Hesheng Liu.

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Li, M., Dahmani, L., Hubbard, C.S. et al. Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients. Neuropsychopharmacol. (2022). https://doi.org/10.1038/s41386-022-01500-4

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