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

被引：3

作者：

Waschkies, Konrad F. ^{[1
,2
]}

Soch, Joram ^{[1
,3
]}

Darna, Margarita ^{[1
,4
]}

Richter, Anni ^{[4
,5
,6
]}

Altenstein, Slawek ^{[7
,8
]}

Beyle, Aline ^{[9
,10
]}

Brosseron, Frederic ^{[9
]}

Buchholz, Friederike ^{[7
,11
,12
,13
,14
]}

Butryn, Michaela ^{[15
,16
]}

Dobisch, Laura ^{[15
]}

Ewers, Michael ^{[17
,18
]}

Fliessbach, Klaus ^{[9
,19
]}

Gabelin, Tatjana

Glanz, Wenzel ^{[15
,16
]}

Goerss, Doreen ^{[20
,21
]}

Gref, Daria ^{[11
]}

Janowitz, Daniel ^{[18
]}

Kilimann, Ingo ^{[20
,21
]}

Lohse, Andrea ^{[8
]}

Munk, Matthias H. ^{[22
,23
,24
]}

Rauchmann, Boris-Stephan ^{[25
,26
,27
]}

Rostamzadeh, Ayda ^{[28
]}

Roy, Nina ^{[9
]}

Spruth, Eike Jakob ^{[7
,8
]}

Dechent, Peter ^{[29
]}

Heneka, Michael T. ^{[9
]}

Hetzer, Stefan ^{[30
]}

Ramirez, Alfredo ^{[9
,19
,31
,32
,33
,34
,35
]}

Scheffler, Klaus ^{[36
]}

Buerger, Katharina ^{[17
,18
]}

Laske, Christoph ^{[22
,23
,24
]}

Perneczky, Robert ^{[17
,25
,26
,37
,38
]}

Peters, Oliver ^{[7
,11
,12
,13
,14
]}

Priller, Josef ^{[7
,8
,39
,40
,41
]}

Schneider, Anja ^{[9
,19
]}

Spottke, Annika ^{[9
,10
]}

Teipel, Stefan ^{[20
,21
]}

Duezel, Emrah ^{[15
,16
]}

Jessen, Frank ^{[9
,28
,31
]}

Wiltfang, Jens ^{[1
,2
,42
]}

Schott, Bjoern H. ^{[1
,2
,4
]}

Kizilirmak, Jasmin M. ^{[1
,43
]}

机构：

[1] German Ctr Neurodegenerat Dis DZNE, Gottingen, Germany

[2] Univ Med Ctr Gottingen, Dept Psychiat & Psychotherapy, Gottingen, Germany

[3] Bernstein Ctr Computat Neurosci, Berlin, Germany

[4] Leibniz Inst Neurobiol, Magdeburg, Germany

[5] German Ctr Mental Hlth DZPG, Munich, Germany

[6] Ctr Intervent & Res Adapt & Maladapt Brain Circui, Jena, Germany

[7] German Ctr Neurodegenerat Dis DZNE, Berlin, Germany

[8] Charite, Dept Psychiat & Psychotherapy, Berlin, Germany

[9] German Ctr Neurodegenerat Dis DZNE, Bonn, Germany

[10] Univ Bonn, Dept Neurol, Bonn, Germany

[11] Charite Univ Med Berlin, Berlin, Germany

[12] Free Univ Berlin, Berlin, Germany

[13] Humboldt Univ, Berlin, Germany

[14] Berlin Inst Psychiat & Psychotherapy, Berlin, Germany

[15] German Ctr Neurodegenerat Dis DZNE, Magdeburg, Germany

[16] Otto von Guericke Univ, Inst Cognit Neurol & Dementia Res IKND, Magdeburg, Germany

[17] German Ctr Neurodegenerat Dis DZNE, Munich, Germany

[18] Ludwig Maximilians Univ Munchen, Univ Hosp, Inst Stroke & Dementia Res ISD, Munich, Germany

[19] Univ Bonn, Med Ctr, Dept Neurodegenerat Dis & Geriatr Psychiat Psychi, Bonn, Germany

[20] German Ctr Neurodegenerat Dis DZNE, Rostock, Germany

[21] Rostock Univ, Med Ctr, Dept Psychosomat Med, Rostock, Germany

[22] German Ctr Neurodegenerat Dis DZNE, Tubingen, Germany

[23] Univ Tubingen, Sect Dementia Res, Hertie Inst Clin Brain Res, Tubingen, Germany

[24] Univ Tubingen, Dept Psychiat & Psychotherapy, Tubingen, Germany

[25] Ludwig Maximilians Univ Munchen, Dept Psychiat & Psychotherapy, Univ Hosp, Munich, Germany

[26] Univ Sheffield, Sheffield Inst Translat Neurosci SITraN, Sheffield, S Yorkshire, England

[27] Univ Hosp LMU, Dept Neuroradiol, Munich, Germany

[28] Univ Cologne, Fac Med, Dept Psychiat, Cologne, Germany

[29] Georg August Univ Goettingen, Dept Cognit Neurol, MR Res Neurosci, Gottingen, Germany

[30] Charite Univ Med Berlin, Berlin Ctr Adv Neuroimaging, Berlin, Germany

[31] Univ Cologne, Excellence Cluster Cellular Stress Responses Agin, Cologne, Germany

[32] Univ Cologne, Div Neurogenet & Mol Psychiat, Dept Psychiat & Psychotherapy, Fac Med, Cologne, Germany

[33] Univ Cologne, Univ Hosp Cologne, Cologne, Germany

[34] Dept Psychiat, San Antonio, TX USA

[35] Glenn Biggs Inst Alzheimers & Neurodegenerat Dis, San Antonio, TX USA

[36] Univ Tubingen, Dept Biomed Magnet Resonance, Tubingen, Germany

[37] Munich Cluster Syst Neurol SyNergy Munich, Munich, Germany

[38] Imperial Coll London, Sch Publ Hlth, Ageing Epidemiol Res Unit AGE, London, England

[39] Tech Univ Munich, Dept Psychiat & Psychotherapy, Sch Med, Munich, Germany

[40] Univ Edinburgh, Edinburgh, Midlothian, Scotland

[41] UK DRI, Edinburgh, Midlothian, Scotland

[42] Univ Aveiro, Dept Med Sci, Inst Biomed iBiMED, Neurosci & Signaling Grp, Aveiro, Portugal

[43] Univ Hildesheim, Inst Psychol, Neurodidact & NeuroLab, Hildesheim, Germany

来源：

INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY | 2023年 / 38卷 / 10期

关键词：

Alzheimer's disease; amnestic mild cognitive impairment; biomarker; cerebrospinal fluid; fMRI; machine learning; personality; resting-state; subjective cognitive decline; support vector machine; MILD COGNITIVE IMPAIRMENT; ASSOCIATION WORKGROUPS; DIAGNOSTIC GUIDELINES; NATIONAL INSTITUTE; CEREBROSPINAL-FLUID; DEFAULT MODE; DEMENTIA; RECOMMENDATIONS; ANOSOGNOSIA; SYMPTOMS;

D O I：

10.1002/gps.6007

中图分类号：

R592 [老年病学]; C [社会科学总论];

学科分类号：

03 ; 0303 ; 100203 ;

摘要：

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, A beta 42/40 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).ResultsMean 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.

引用

页数：14

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