A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Towards collaborative data science in mental health research: The ECNP neuroimaging network accessible data repository
Tekijät: Khuntia, Adyasha; Buciuman, Madalina-Octavia; Fanning, John; Stolicyn, Aleks; Vetter, Clara; Armio, Reetta-Liina; From, Tiina; Goffi, Federica; Hahn, Lisa; Kaufmann, Tobias; Laurikainen, Heikki; Maggioni, Eleonora; Martinez-Zalacain, Ignacio; Ruef, Anne; Dong, Mark Sen; Schwarz, Emanuel; Squarcina, Letizia; Andreassen, Ole; Bellani, Marcella; Brambilla, Paolo; Haren, Neeltje van; Hietala, Jarmo; Lawrie, Stephen M.; Soriano-Mas, Carles; Whalley, Heather; Taquet, Maxime; Meisenzahl, Eva; Falkai, Peter; Wiegand, Ariane; Koutsouleris, Nikolaos
Kustantaja: Elsevier BV
Julkaisuvuosi: 2025
Journal: Neuroscience Applied
Tietokannassa oleva lehden nimi: Neuroscience Applied
Artikkelin numero: 105407
Vuosikerta: 4
eISSN: 2772-4085
DOI: https://doi.org/10.1016/j.nsa.2024.105407
Verkko-osoite: https://doi.org/10.1016/j.nsa.2024.105407
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/484701012
The current biologically uninformed psychiatric taxonomy complicates optimal diagnosis and treatment. Neuroimaging-based machine learning methods hold promise for tackling these issues, but large-scale, representative cohorts are required for building robust and generalizable models. The European College of Neuropsychopharmacology Neuroimaging Network Accessible Data Repository (ECNP-NNADR) addresses this need by collating multi-site, multi-modal, multi-diagnosis datasets that enable collaborative research. The newly established ECNP-NNADR includes 4829 participants across 21 cohorts and 11 distinct psychiatric diagnoses, available via the Virtual Pooling and Analysis of Research data (ViPAR) software. The repository includes demographic and clinical information, including diagnosis and questionnaires evaluating psychiatric symptomatology, as well as multi-atlas grey matter volume regions of interest (ROI). To illustrate the opportunities offered by the repository, two proof-of-concept analyses were performed: (1) multivariate classification of 498 patients with schizophrenia (SZ) and 498 matched healthy control (HC) individuals, and (2) normative age prediction using 1170 HC individuals with subsequent application of this model to study abnormal brain maturational processes in patients with SZ. In the SZ classification task, we observed varying balanced accuracies, reaching a maximum of 71.13% across sites and atlases. The normative-age model demonstrated a mean absolute error (MAE) of 6.95 years [coefficient of determination (R2) = 0.77, P < .001] across sites and atlases. The model demonstrated robust generalization on a separate HC left-out sample achieving a MAE of 7.16 years [R2 = 0.74,P < .001]. When applied to the SZ group, the model exhibited a MAE of 7.79 years [R2 = 0.79, P < .001], with patients displaying accelerated brain-aging with a brain age gap (BrainAGE) of 4.49 (8.90) years. Conclusively, this novel multi-site, multi-modal, transdiagnostic data repository offers unique opportunities for systematically tackling existing challenges around the generalizability and validity of imaging-based machine learning applications for psychiatry.
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Julkaisussa olevat rahoitustiedot:
NK is supported through grants from NIH (U01MH124639-01; ProNET), the Wellcome Trust, the German Innovation Fund (CARE project), the German Federal Ministry of Education and Research (COMMITMENT and BEST projects), as well as ERA PerMed (IMPLEMENT project). AK is funded through the COMMITMENT project. CS-M and the Barcelona site were supported from Grant Nos. PI050884, PI071029, PS09/01961, PS09/01331, PI12/01306, PI13/01958, PIE14/00034, PI16/00144, PI16/00889, PI16/00950 from the Carlos III Health Institute. PB was supported by the Neuropsychological indexes and innovative treatments for major psychoses (NeuroInno). MBe was supported by PICOS - Ricerca Sanitaria Finalizzata 2004, Giunta Regionale del Veneto with a grant to Mirella Ruggeti; PREVENT & CARIVR - Fondazione Cariverona, Promoting research to improve quality of care; Sotto-obiettivo A9 \u201CDisabilit\u00E0 cognitiva e comportamentale nelle demenze e nelle psicosi\u201D and MANDRAKE - Italian ministry of Health, GR-2010-2319022 \u201CImmune gene expression and white matter pathology in first-manic patients before and after treatment. A multimodal imaging genetic study\u201D. GS-Imaging research initiative was supported and funded by the Welcome Trust Strategic Award \u201CStratifying Resilience and Depression Longitudinally\u201D (Reference 104036/Z/14/Z). OA received support from Research Council of Norway (204966/F20, 223273, 213837); South-Eastern Norway Regional Health Authority (2015073, 2011-080, 2013-123); Kristian Gerhard Jebsen Foundation. JH received support from State Research Funding, Finland, grant #P3848 (Turku TEPS 1 and 2, Turku University Hosipital). The Stradl/GenScot cohort recieved funding from Wellcome Trust [104036/Z/14/Z, 220857/Z/20/Z, and 216767/Z/19/Z], Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6], the Scottish Funding Council [HR03006] & UKRI Award MR/W014386/1.ES is supported through grants by the German Federal Ministry of Education and Research (COMMITMENT (grant 01ZX2204A), BEST (grant 01EK2101B), and IMPLEMENT (grant 01KU1905A) projects). We would like to thank Adriana Herrera for helping us with the data pooling and administrative work while creating the data repository.The ECNP Neuroimaging Network Accessible Data Repository project is a Collaborative project funded by the ECNP. The study design, data collection, analysis and publication submission process were not influenced by the funder.
Funding text 2
ES is supported through grants by the German Federal Ministry of Education and Research (COMMITMENT (grant 01ZX2204A), BEST (grant 01EK2101B), and IMPLEMENT (grant 01KU1905A) projects). We would like to thank Adriana Herrera for helping us with the data pooling and administrative work while creating the data repository.
Funding text 3
NK is supported through grants from NIH (U01MH124639-01; ProNET), the Wellcome Trust, the German Innovation Fund (CARE project), the German Federal Ministry of Education and Research (COMMITMENT and BEST projects), as well as ERA PerMed (IMPLEMENT project). AK is funded through the COMMITMENT project. CS-M and the Barcelona site were supported from Grant Nos. PI050884, PI071029, PS09/01961, PS09/01331, PI12/01306, PI13/01958, PIE14/00034, PI16/00144, PI16/00889, PI16/00950 from the Carlos III Health Institute. PB was supported by the Neuropsychological indexes and innovative treatments for major psychoses (NeuroInno). MBe was supported by PICOS - Ricerca Sanitaria Finalizzata 2004, Giunta Regionale del Veneto with a grant to Mirella Ruggeti; PREVENT & CARIVR - Fondazione Cariverona, Promoting research to improve quality of care; Sotto-obiettivo A9 \u201CDisabilit\u00E0 cognitiva e comportamentale nelle demenze e nelle psicosi\u201D and MANDRAKE - Italian ministry of Health, GR-2010-2319022 \"Immune gene expression and white matter pathology in first-manic patients before and after treatment. A multimodal imaging genetic study\u201D). GS-Imaging research initiative was supported and funded by the Welcome Trust Strategic Award \u201CStratifying Resilience and Depression Longitudinally\u201D (Reference 104036/Z/14/Z). OA received support from Research Council of Norway (204966/F20, 223273, 213837); South-Eastern Norway Regional Health Authority (2015073, 2011-080, 2013-123); Kristian Gerhard Jebsen Foundation). JH received support from State Research Funding, Finland, grant #P3848 (Turku TEPS 1 and 2, Turku University Hosipital). The Stradl/GenScot cohort recieved funding from Wellcome Trust [104036/Z/14/Z, 220857/Z/20/Z, and 216767/Z/19/Z], Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6], the Scottish Funding Council [HR03006] & UKRI Award MR/W014386/1
