Quantifying brain connectivity signatures by means of polyconnectomic scoring.
| Title: | Quantifying brain connectivity signatures by means of polyconnectomic scoring. |
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| Authors: | Libedinsky I; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Helwegen K; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Simón LG; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Gruber M; Institute for Translational Psychiatry, University of Münster, Münster, Germany.; Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Germany.; Repple J; Institute for Translational Psychiatry, University of Münster, Münster, Germany.; Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Germany.; Kircher T; Department of Psychiatry and Psychotherapy, University of Marburg, Germany.; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany.; Dannlowski U; Institute for Translational Psychiatry, University of Münster, Münster, Germany.; van den Heuvel MP; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.; Department of Child and Adolescent Psychiatry and Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. |
| Source: | BioRxiv : the preprint server for biology [bioRxiv] 2023 Sep 27. Date of Electronic Publication: 2023 Sep 27. |
| Publication Type: | Preprint |
| Language: | English |
| Journal Info: | Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE |
| Abstract: | A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements. |
| Competing Interests: | Competing interests The authors declare no competing interests. |
| Comments: | Update in: Biol Psychiatry. 2025 Jun 1;97(11):1045-1058. doi: 10.1016/j.biopsych.2024.10.007.. (PMID: 39424166) |
| Grant Information: | U01 AG024904 United States AG NIA NIH HHS; P01 AG003991 United States AG NIA NIH HHS; P01 AG026276 United States AG NIA NIH HHS; R01 EB009352 United States EB NIBIB NIH HHS; P20 GM103472 United States GM NIGMS NIH HHS; R01 AG043434 United States AG NIA NIH HHS; U01 MH097435 United States MH NIMH NIH HHS; P20 RR021938 United States RR NCRR NIH HHS; UL1 TR000448 United States TR NCATS NIH HHS |
| Entry Date(s): | Date Created: 20231009 Date Completed: 20250911 Latest Revision: 20250911 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC10557693 |
| DOI: | 10.1101/2023.09.26.559327 |
| PMID: | 37808808 |
| Database: | MEDLINE |
Preprint