Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation.

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aß42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.

Dietary patterns are related to cognitive functioning in elderly enriched with individuals at increased risk for Alzheimer's disease.

PURPOSE: To investigate cross-sectional associations between dietary patterns and cognitive functioning in elderly free of dementia. METHODS: Data of 389 participants from the German DELCODE study (52% female, 69 ± 6 years, mean Mini Mental State Score 29 ± 1) were included. The sample was enriched with elderly at increased risk for Alzheimer's disease (AD) by including participants with subjective cognitive decline, mild cognitive impairment (MCI) and siblings of AD patients. Mediterranean and MIND diets were derived from 148 Food Frequency Questionnaire items, and data-driven patterns by principal component analysis (PCA) of 39 food groups. Associations between dietary patterns and five cognitive domain scores were analyzed with linear regression analyses adjusted for demographics (model 1), and additionally for energy intake, BMI, other lifestyle variables and APOe4-status (model 2). For PCA-derived dietary components, final model 3 included all other dietary components. RESULTS: In fully adjusted models, adherence to Mediterranean and MIND diet was associated with better memory. The 'alcoholic beverages' PCA component was positively associated with most cognitive domains. Exclusion of MCI subjects (n = 60) revealed that Mediterranean and MIND diet were also related to language functions; associations with the alcoholic beverages component were attenuated, but most remained significant. CONCLUSION: In line with data from elderly population samples, Mediterranean and MIND diet and some data-derived dietary patterns were related to memory and language function. Longitudinal data are needed to draw conclusions on the putative effect of nutrition on the rate of cognitive decline, and on the potential of dietary interventions in groups at increased risk for AD.

The influence of current-visit experience within a prey patch on patch persistence.

A critical component of efficient foraging behavior is leaving a feeding site when prey density in that site declines. If ``real time'' hypotheses are to explain patch-leaving mechanisms, we will need to examine the influence that moment-to-moment experience within a current prey patch has on persistence in that patch. We used linear regression to investigate how current experience of patch quality influenced homing pigeons' patch persistence. We did this by examining the amount of variance in persistence that was accounted for by different measures of within-session patch quality. The importance of several measures of the precise sequence of events in individual sessions were assessed with selected averaging algorithms. Mean inter-capture interval and mean number of inter-capture pecks accounted for a significant amount of the variance in giving-up time in three of four, and four of four birds, respectively. Conversely, only one rate measure in one bird showed an influence on patch persistence. In three birds, recent information had a strong influence on giving-up time. Current-visit experience did influence the patch persistence of the pigeons, but the birds' behavior indicated that different individuals used different measures of patch quality, different mechanisms of assessing those measures, and different patch-exit mechanisms.