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Search: WFRF:(Paul Elisabeth) > (2020-2024) > Functional connecti...

  • Gallo, SeleneAmsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands (author)

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

  • Article/chapterEnglish2023

Publisher, publication year, extent ...

  • 2023-02-15
  • SPRINGERNATURE,2023
  • electronicrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:liu-192503
  • https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-192503URI
  • https://doi.org/10.1038/s41380-023-01977-5DOI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]
  • The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • El-Gazzar, AhmedAmsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands (author)
  • Zhutovsky, PaulAmsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands (author)
  • Thomas, Rajat M.Amsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands (author)
  • Javaheripour, NooshinJena Univ Hosp, Germany (author)
  • Li, MengJena Univ Hosp, Germany (author)
  • Bartova, LucieMed Univ Vienna, Austria (author)
  • Bathula, DeeptiIndian Inst Technol IIT, India (author)
  • Dannlowski, UdoUniv Munster, Germany (author)
  • Davey, ChristopherUniv Melbourne, Australia (author)
  • Frodl, ThomasOtto von Guericke Univ, Germany; German Ctr Mental Hlth, Germany (author)
  • Gotlib, IanStanford Univ, CA 94305 USA (author)
  • Grimm, SimoneCharite Univ Med Berlin, Germany (author)
  • Grotegerd, DominikUniv Munster, Germany (author)
  • Hahn, TimUniv Munster, Germany (author)
  • Hamilton, Paul J.,1970-Linköpings universitet,Centrum för social och affektiv neurovetenskap,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV(Swepub:liu)pauha77 (author)
  • Harrison, Ben J.Univ Melbourne, Australia (author)
  • Jansen, AndreasUniv Marburg, Germany (author)
  • Kircher, Tilo (author)
  • Meyer, BernhardMed Univ Vienna, Austria (author)
  • Nenadic, IgorUniv Marburg, Germany (author)
  • Olbrich, SebastianUniv Hosp Zurich, Switzerland (author)
  • Paul, Elisabeth,1991-Linköpings universitet,Centrum för social och affektiv neurovetenskap,Medicinska fakulteten(Swepub:liu)elipa45 (author)
  • Pezawas, LukasMed Univ Vienna, Austria (author)
  • Sacchet, Matthew D.Harvard Med Sch, MA USA (author)
  • Saemann, PhilippMax Planck Inst Psychiat, Germany (author)
  • Wagner, GerdJena Univ Hosp, Germany (author)
  • Walter, HenrikCharite Univ Med Berlin, Germany (author)
  • Walter, MartinOtto von Guericke Univ, Germany; German Ctr Mental Hlth, Germany (author)
  • PsyMRI, Guido (author)
  • van Wingen, GuidoAmsterdam UMC, Netherlands; Amsterdam Neurosci, Netherlands (author)
  • Amsterdam UMC, Netherlands; Amsterdam Neurosci, NetherlandsJena Univ Hosp, Germany (creator_code:org_t)

Related titles

  • In:Molecular Psychiatry: SPRINGERNATURE28:7, s. 3013-30221359-41841476-5578

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