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

Abstract

BackgroundAlzheimer'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.MethodsIn 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).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.ConclusionOur 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.

Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review

“…Notably, educational background has been found to be a factor that contributes to cognitive resilience, thus serving as a protective measure against the development of dementia. Other factors that can predict cognitive impairment include age, marital status, and instrumental activities of daily living, as well as baseline MMSE [37], personality traits, and state scores [38]. Additionally, certain Neuropsychiatric Symptoms (NPS) proxies, such as the Neuropsychiatric Inventory Questionnaire (NPI-Q) total severity score, NPI-Q total stress score, and Geriatric Depression Scale (GDS) total score, have been found to predict dementia in MCI [39].…”