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Objectives: To examine trajectories of depression and apathy over a 5-year followup period in (prodromal) Alzheimer's disease (AD), and to relate these trajectories to AD biomarkers. Methods: The trajectories of depression and apathy (measured with the Neuropsychiatric Inventory or its questionnaire) were separately modeled using growth mixture models for two cohorts (National Alzheimer's Coordinating Center, NACC, n = 22 760 and Alzheimer's Disease Neuroimaging Initiative, ADNI, n = 1 733). The trajectories in ADNI were associated with baseline CSF AD biomarkers (Aβ 42, t-tau, and p-tau) using bias-corrected multinomial logistic regression. Results: Multiple classes were identified, with the largest classes having no symptoms over time. Lower Aβ 42 and higher tau (ie, more AD pathology) was associated with increased probability of depression and apathy over time, compared to classes without symptoms. Lower Aβ 42 (but not tau) was associated with a steep increase of apathy, whereas higher tau (but not Aβ 42) was associated with a steep decrease of apathy.

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Objective To investigate whether automatic analysis of the Semantic Verbal Fluency test (SVF) is reliable and can extract additional information that is of value for identifying neurocognitive disorders. In addition, the associations between the automatically derived speech and linguistic features and other cognitive domains were explored. Method We included 135 participants from the memory clinic of the Maastricht University Medical Center+ (with Subjective Cognitive Decline [SCD; N = 69] and Mild Cognitive Impairment [MCI]/dementia [N = 66]). The SVF task (one minute, category animals) was recorded and processed via a mobile application, and speech and linguistic features were automatically extracted. The diagnostic performance of the automatically derived features was investigated by training machine learning classifiers to differentiate SCD and MCI/dementia participants. Results The intraclass correlation for interrater reliability between the clinical total score (golden standard) and automatically derived total word count was 0.84. The full model including the total word count and the automatically derived speech and linguistic features had an Area Under the Curve (AUC) of 0.85 for differentiating between people with SCD and MCI/dementia. The model with total word count only and the model with total word count corrected for age showed an AUC of 0.75 and 0.81, respectively. Semantic switching correlated moderately with memory as well as executive functioning. Conclusion The one-minute SVF task with automatically derived speech and linguistic features was as reliable as the manual scoring and differentiated well between SCD and MCI/dementia. This can be considered as a valuable addition in the screening of neurocognitive disorders and in clinical practice.