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

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, Aß42/40 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.

Selective attention to stimulus representations in perception and memory: commonalities and differences.

It has been proposed that the deployment of selective attention to perceptual and memory representations might be governed by similar cognitive processes and neural resources. However, evidence for this simple and appealing proposal remains inconclusive, which might be due to a considerable divergence in tasks and cognitive demands when comparing attentional selection in memory versus perception. To examine whether selection in both domains share common attentional processes and only differ in the stimuli they act upon (external vs. internal), we compared behavioral costs or benefits between selection domains. In both domains, participants had to attend a target stimulus from a set of simultaneously presented stimuli or simultaneously active memory representations, respectively, with set, target, or both, being repeated or changed across trials. The results of two experiments delineated principal similarities and differences of selection processes in both domains: While positive priming from stimulus repetition was found in both selection domains, we found no consistent effects of negative priming when shifting the focus of attention to a previously to-be-ignored stimulus. However, priming in the perception task was mainly due to repetitions of the target feature (here: color), whereas for the memory task, repetition of the same set of stimulus representations was most important. We propose that the differences can be attributed to a reduced cognitive effort when the now relevant memory representation had already been pre-activated (even as a distractor) in the previous trial. Additionally, our experiments both underscore the importance of taking stimulus-response associations into account, which may be a hidden factor behind differences between domains. We conclude that any attempt of comparing internal versus external attentional selection has to consider inherent differences in selection dynamics across representational domains.