Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders

Ilan Libedinsky; Koen Helwegen; Jackson Boonstra; Laura Guerrero Simón; Marius Gruber; Jonathan Repple; Tilo Kircher; Udo Dannlowski; Martijn P van den Heuvel

doi:10.1016/j.biopsych.2024.10.007

Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders

Ilan Libedinsky, Koen Helwegen, Jackson Boonstra, Laura Guerrero Simón, Marius Gruber, Jonathan Repple, Tilo Kircher, Udo Dannlowski, Martijn P van den Heuvel

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

BACKGROUND: Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.

METHODS: The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.

RESULTS: PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [pFDR] = [1.8 × 10-16, 4.5 × 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 × 10-5), lower cognitive performance (pFDR < 5.3 × 10-5), and lower general well-being (pFDR < 9.7 × 10-4).

CONCLUSIONS: Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.

Original language	English
Number of pages	14
Journal	Biological psychiatry
DOIs	https://doi.org/10.1016/j.biopsych.2024.10.007
Publication status	E-pub ahead of print - 16 Oct 2024

Bibliographical note

Funding

Analyses were carried out on the Genetic Cluster Computer hosted by the Dutch National computing and Networking Services SURFsara and financed by the Netherlands Organization for Scientific Research (NWO: 480-05-003), the VU University (Amsterdam, The Netherlands) and the Dutch Brain Foundation. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California. The Healthy Brain Network is supported by philanthropic contributions from the following individuals, foundations and organizations: Margaret Bilotti; Brooklyn Nets; Agapi and Bruce Burkard; James Chang; Phyllis Green and Randolph Cowen; Grieve Family Fund; Susan Miller and Byron Grote; Sarah and Geoff Gund; George Hall; Jonathan M. Harris Family Foundation; Joseph P. Healey; The Hearst Foundations; Eve and Ross Jaffe; Howard & Irene Levine Family Foundation; Rachael and Marshall Levine; George and Nitzia Logothetis; Christine and Richard Mack; Julie Minskoff; Valerie Mnuchin; Morgan Stanley Foundation; Amy and John Phelan; Roberts Family Foundation; Jim and Linda Robinson Foundation, Inc.; Linda and Richard Schaps; Zibby Schwarzman; Abigail Pogrebin and David Shapiro; Stavros Niarchos Foundation; Preethi Krishna and Ram Sundaram; Amy and John Weinberg; Donors to the 2013 Child Advocacy Award Dinner Auction; Donors to the 2012 Brant Art Auction. This study was funded by a ERC Consolidator Grant (101001062 CONNECT) from the European Research Council (ERC) to M.P.v.d.H. COBRE data were 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 the Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to V. Calhoun. Data collection and sharing for this project was funded by the Frontotemporal Lobar Degeneration Neuroimaging Initiative (National Institutes of Health Grant R01 AG032306). The study is coordinated through the University of California, San Francisco, Memory and Aging Center. NIFD data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data collection and sharing for SRPBS was provided by the DecNef Department at the Advanced Telecommunication Research Institute International, Kyoto, Japan. This work was supported by the Japan Agency for Medical Research and Development (AMED) under Grant Numbers JP17dm0107044, JP20dm0307004, JP20dm0307008, JP20dm0307002, and JP20dm0307009. The FOR 2107 cohort was funded by the German Research Foundation (DFG grants FOR2107 KI588/14-1, and KI588/14-2, and KI588/20-1, KI588/22-1 to Tilo Kircher, Marburg, Germany; DA1151/5-1, DA1151/5-2, DA1151/9-1, DA1151/10-1, DA1151/11-1 to Udo Dannlowski; SFB-TRR393 to Tilo Kircher and Udo Dannlowski). and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of M\u00FCnster (grant Dan3/022/22 to Udo Dannlowski). Biosamples and corresponding data were sampled, processed and stored in the Marburg Biobank CBBMR. We acknowledge the contribution of data made available through SchizConnect (http://schizconnect.org; funded by NIMH [grant 1U01 MH097435]), NIMH Data Archive ( https://nda.nih.gov ), OpenNeuro ( http://openneuro.org ), and XNAT Central ( http://central.xnat.org ).

Funders	Funder number
Dutch National computing and Networking Services SURFsara
National Institute of Biomedical Imaging and Bioengineering
Hearst Foundations
Vrije Universiteit Amsterdam
Roberts Family Foundation
Dutch Brain Foundation
John Phelan
Alzheimer's Disease Neuroimaging Initiative
European Research Council
DOD ADNI
National Institute on Aging
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	480-05-003
Center of Biomedical Research Excellence	5P20RR021938/P20GM103472
National Institutes of Health	R01 AG032306, U01 AG024904
U.S. Department of Defense	W81XWH-12-2-0012
Japan Agency for Medical Research and Development	JP17dm0107044, JP20dm0307004, JP20dm0307002, JP20dm0307009, JP20dm0307008
Deutsche Forschungsgemeinschaft	DA1151/9-1, DA1151/5-2, DA1151/10-1, DA1151/5-1, DA1151/11-1, FOR2107 KI588/14-1, KI588/20-1, SFB-TRR393, KI588/22-1, KI588/14-2
National Institute of Mental Health	1U01 MH097435

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Libedinsky, I., Helwegen, K., Boonstra, J., Simón, L. G., Gruber, M., Repple, J., Kircher, T., Dannlowski, U., & van den Heuvel, M. P. (2024). Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders. Biological psychiatry. Advance online publication. https://doi.org/10.1016/j.biopsych.2024.10.007

@article{f77550163c11413387f070c5926ba2b5,

title = "Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders",

abstract = "BACKGROUND: Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.METHODS: The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.RESULTS: PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [pFDR] = [1.8 × 10-16, 4.5 × 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 × 10-5), lower cognitive performance (pFDR < 5.3 × 10-5), and lower general well-being (pFDR < 9.7 × 10-4).CONCLUSIONS: Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.",

author = "Ilan Libedinsky and Koen Helwegen and Jackson Boonstra and Sim{\'o}n, {Laura Guerrero} and Marius Gruber and Jonathan Repple and Tilo Kircher and Udo Dannlowski and {van den Heuvel}, {Martijn P}",

year = "2024",

month = oct,

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doi = "10.1016/j.biopsych.2024.10.007",

language = "English",

journal = "Biological psychiatry",

issn = "0006-3223",

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T1 - Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders

AU - Libedinsky, Ilan

AU - Helwegen, Koen

AU - Boonstra, Jackson

AU - Simón, Laura Guerrero

AU - Gruber, Marius

AU - Repple, Jonathan

AU - Kircher, Tilo

AU - Dannlowski, Udo

AU - van den Heuvel, Martijn P

PY - 2024/10/16

Y1 - 2024/10/16

N2 - BACKGROUND: Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.METHODS: The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.RESULTS: PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [pFDR] = [1.8 × 10-16, 4.5 × 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 × 10-5), lower cognitive performance (pFDR < 5.3 × 10-5), and lower general well-being (pFDR < 9.7 × 10-4).CONCLUSIONS: Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.

AB - BACKGROUND: Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.METHODS: The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.RESULTS: PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [pFDR] = [1.8 × 10-16, 4.5 × 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 × 10-5), lower cognitive performance (pFDR < 5.3 × 10-5), and lower general well-being (pFDR < 9.7 × 10-4).CONCLUSIONS: Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.

U2 - 10.1016/j.biopsych.2024.10.007

DO - 10.1016/j.biopsych.2024.10.007

M3 - Article

C2 - 39424166

SN - 0006-3223

JO - Biological psychiatry

JF - Biological psychiatry

ER -

Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders

Abstract

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