Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression

  1. Waschkies, Konrad F.
  2. Soch, Joram
  3. Darna, Margarita
  4. Richter, Anni ORCID logo
  5. Altenstein, Slawek
  6. Beyle, Aline
  7. Brosseron, Frederic
  8. Buchholz, Friederike
  9. Butryn, Michaela
  10. Dobisch, Laura
  11. Ewers, Michael
  12. Fliessbach, Klaus
  13. Gabelin, Tatjana
  14. Glanz, Wenzel
  15. Goerss, Doreen
  16. Gref, Daria
  17. Janowitz, Daniel
  18. Kilimann, Ingo
  19. Lohse, Andrea
  20. Munk, Matthias H.
  21. Rauchmann, Boris-Stephan
  22. Rostamzadeh, Ayda
  23. Roy, Nina
  24. Spruth, Eike Jakob
  25. Dechent, Peter
  26. Heneka, Michael T.
  27. Hetzer, Stefan
  28. Ramirez, Alfredo
  29. Scheffler, Klaus
  30. Buerger, Katharina
  31. Laske, Christoph
  32. Perneczky, Robert ORCID logo
  33. Peters, Oliver
  34. Priller, Josef
  35. Schneider, Anja
  36. Spottke, Annika
  37. Teipel, Stefan
  38. Düzel, Emrah
  39. Jessen, Frank
  40. Wiltfang, Jens
  41. Schott, Björn ORCID logo
  42. Kizilirmak, Jasmin ORCID logo

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Int J Geriat Psychiatry - 2023 - Waschkies - Machine learning‐based classification of Alzheimer s disease and its at‐risk.pdf 1,07MB
WeightNameValue
1000 Titel
  • Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression
1000 Autor/in
  1. Waschkies, Konrad F. |
  2. Soch, Joram |
  3. Darna, Margarita |
  4. Richter, Anni |
  5. Altenstein, Slawek |
  6. Beyle, Aline |
  7. Brosseron, Frederic |
  8. Buchholz, Friederike |
  9. Butryn, Michaela |
  10. Dobisch, Laura |
  11. Ewers, Michael |
  12. Fliessbach, Klaus |
  13. Gabelin, Tatjana |
  14. Glanz, Wenzel |
  15. Goerss, Doreen |
  16. Gref, Daria |
  17. Janowitz, Daniel |
  18. Kilimann, Ingo |
  19. Lohse, Andrea |
  20. Munk, Matthias H. |
  21. Rauchmann, Boris-Stephan |
  22. Rostamzadeh, Ayda |
  23. Roy, Nina |
  24. Spruth, Eike Jakob |
  25. Dechent, Peter |
  26. Heneka, Michael T. |
  27. Hetzer, Stefan |
  28. Ramirez, Alfredo |
  29. Scheffler, Klaus |
  30. Buerger, Katharina |
  31. Laske, Christoph |
  32. Perneczky, Robert |
  33. Peters, Oliver |
  34. Priller, Josef |
  35. Schneider, Anja |
  36. Spottke, Annika |
  37. Teipel, Stefan |
  38. Düzel, Emrah |
  39. Jessen, Frank |
  40. Wiltfang, Jens |
  41. Schott, Björn |
  42. Kizilirmak, Jasmin |
1000 Erscheinungsjahr 2023
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2023-10-06
1000 Erschienen in
1000 Quellenangabe
  • 38(10):e6007
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2023
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1002/gps.6007 |
1000 Ergänzendes Material
  • https://onlinelibrary.wiley.com/doi/10.1002/gps.6007#support-information-section |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • BACKGROUND: Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages. METHODS: In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aβ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). RESULTS: Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets. CONCLUSION: Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.
1000 Sacherschließung
lokal amnestic mild cognitive impairment
lokal machine learning
lokal resting-state
lokal biomarker
lokal subjective cognitive decline
lokal fMRI
lokal Alzheimer's disease
lokal personality
lokal cerebrospinal fluid
lokal support vector machine
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/V2FzY2hraWVzLCBLb25yYWQgRi4=|https://frl.publisso.de/adhoc/uri/U29jaCwgSm9yYW0=|https://frl.publisso.de/adhoc/uri/RGFybmEsIE1hcmdhcml0YQ==|https://orcid.org/0000-0001-9681-6298|https://frl.publisso.de/adhoc/uri/QWx0ZW5zdGVpbiwgU2xhd2Vr|https://frl.publisso.de/adhoc/uri/QmV5bGUsIEFsaW5l|https://frl.publisso.de/adhoc/uri/QnJvc3Nlcm9uLCBGcmVkZXJpYw==|https://frl.publisso.de/adhoc/uri/QnVjaGhvbHosIEZyaWVkZXJpa2U=|https://frl.publisso.de/adhoc/uri/QnV0cnluLCBNaWNoYWVsYQ==|https://frl.publisso.de/adhoc/uri/RG9iaXNjaCwgTGF1cmE=|https://frl.publisso.de/adhoc/uri/RXdlcnMsIE1pY2hhZWw=|https://frl.publisso.de/adhoc/uri/RmxpZXNzYmFjaCwgS2xhdXM=|https://frl.publisso.de/adhoc/uri/R2FiZWxpbiwgVGF0amFuYQ==|https://frl.publisso.de/adhoc/uri/R2xhbnosIFdlbnplbA==|https://frl.publisso.de/adhoc/uri/R29lcnNzLCBEb3JlZW4=|https://frl.publisso.de/adhoc/uri/R3JlZiwgRGFyaWE=|https://frl.publisso.de/adhoc/uri/SmFub3dpdHosIERhbmllbA==|https://frl.publisso.de/adhoc/uri/S2lsaW1hbm4sIEluZ28=|https://frl.publisso.de/adhoc/uri/TG9oc2UsIEFuZHJlYQ==|https://frl.publisso.de/adhoc/uri/TXVuaywgTWF0dGhpYXMgSC4=|https://frl.publisso.de/adhoc/uri/UmF1Y2htYW5uLCBCb3Jpcy1TdGVwaGFu|https://frl.publisso.de/adhoc/uri/Um9zdGFtemFkZWgsIEF5ZGE=|https://frl.publisso.de/adhoc/uri/Um95LCBOaW5h|https://frl.publisso.de/adhoc/uri/U3BydXRoLCBFaWtlIEpha29i|https://frl.publisso.de/adhoc/uri/RGVjaGVudCwgUGV0ZXI=|https://frl.publisso.de/adhoc/uri/SGVuZWthLCBNaWNoYWVsIFQu|https://frl.publisso.de/adhoc/uri/SGV0emVyLCBTdGVmYW4=|https://frl.publisso.de/adhoc/uri/UmFtaXJleiwgQWxmcmVkbw==|https://frl.publisso.de/adhoc/uri/U2NoZWZmbGVyLCBLbGF1cw==|https://frl.publisso.de/adhoc/uri/QnVlcmdlciwgS2F0aGFyaW5h|https://frl.publisso.de/adhoc/uri/TGFza2UsIENocmlzdG9waA==|https://orcid.org/0000-0003-1981-7435|https://frl.publisso.de/adhoc/uri/UGV0ZXJzLCBPbGl2ZXI=|https://frl.publisso.de/adhoc/uri/UHJpbGxlciwgSm9zZWY=|https://frl.publisso.de/adhoc/uri/U2NobmVpZGVyLCBBbmph|https://frl.publisso.de/adhoc/uri/U3BvdHRrZSwgQW5uaWth|https://frl.publisso.de/adhoc/uri/VGVpcGVsLCBTdGVmYW4=|https://frl.publisso.de/adhoc/uri/RMO8emVsLCBFbXJhaA==|https://frl.publisso.de/adhoc/uri/SmVzc2VuLCBGcmFuaw==|https://frl.publisso.de/adhoc/uri/V2lsdGZhbmcsIEplbnM=|https://orcid.org/0000-0002-8237-4481|https://orcid.org/0000-0002-5938-3523
1000 Label
1000 Förderer
  1. Deutsches Zentrum für Neurodegenerative Erkrankungen |
  2. Projekt DEAL |
1000 Fördernummer
  1. BN012
  2. -
1000 Förderprogramm
  1. -
  2. Open Acess funding
1000 Dateien
  1. Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression
    Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Deutsches Zentrum für Neurodegenerative Erkrankungen |
    1000 Förderprogramm -
    1000 Fördernummer BN012
  2. 1000 joinedFunding-child
    1000 Förderer Projekt DEAL |
    1000 Förderprogramm Open Acess funding
    1000 Fördernummer -
1000 Objektart article
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1000 @id frl:6475331.rdf
1000 Erstellt am 2024-04-23T11:23:23.165+0200
1000 Erstellt von 242
1000 beschreibt frl:6475331
1000 Bearbeitet von 337
1000 Zuletzt bearbeitet Wed Jul 03 11:20:31 CEST 2024
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