Predicting disease progression in behavioral variant frontotemporal dementia

The guaranteed euros: Probabilistic discounting in behavioural-variant frontotemporal dementia

Financial decision-making requires trading off between guaranteed and probabilistic outcomes and between immediate and delayed ones. While research has demonstrated that patients with behavioural-variant frontotemporal dementia (bvFTD) prefer immediate rewards at the expense of future ones (i.e. temporal discounting), little is known about how patients choose between smaller, guaranteed and larger, but probabilistic, outcomes (i.e. probabilistic discounting). We thus investigated probabilistic discounting by inviting 18 patients with bvFTD and 20 control participants to choose between fixed smaller monetary amounts and a fixed larger monetary amount with a variated probability of occurrence (e.g. 'Would you rather have 40€ for sure or a 20% chance of winning 100€?'). Results demonstrated lower scores, indicating higher risk tolerance, on the probabilistic discounting task in patients with bvFTD (while impulsively choosing more immediate rewards on the temporal discounting task) compared to control participants. Probabilistic discounting was significantly correlated with a decline in general cognitive performance in patients with bvFTD. When dealing between smaller, guaranteed, and larger, but probabilistic, rewards, patients with bvFTD tend to prefer guaranteed rewards and discount the uncertain ones.

The pursuit for markers of disease progression in behavioral variant frontotemporal dementia: a scoping review to optimize outcome measures for clinical trials

Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative disorder characterized by diverse and prominent changes in behavior and personality. One of the greatest challenges in bvFTD is to capture, measure and predict its disease progression, due to clinical, pathological and genetic heterogeneity. Availability of reliable outcome measures is pivotal for future clinical trials and disease monitoring. Detection of change should be objective, clinically meaningful and easily assessed, preferably associated with a biological process. The purpose of this scoping review is to examine the status of longitudinal studies in bvFTD, evaluate current assessment tools and propose potential progression markers. A systematic literature search (in PubMed and Embase.com) was performed. Literature on disease trajectories and longitudinal validity of frequently-used measures was organized in five domains: global functioning, behavior, (social) cognition, neuroimaging and fluid biomarkers. Evaluating current longitudinal data, we propose an adaptive battery, combining a set of sensitive clinical, neuroimaging and fluid markers, adjusted for genetic and sporadic variants, for adequate detection of disease progression in bvFTD.

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

INTRODUCTION

Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).

METHODS

Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).

RESULTS

The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.

DISCUSSION

Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.