Michael Hornberger | Searcharxiv

arXiv2026

Spatial navigation in preclinical Alzheimer's disease: A review

Alzheimer's disease (AD) develops over a prolonged preclinical phase, during which neuropathological changes accumulate long before cognitive symptoms appear. Identifying cognitive functions affected at early stages is critical for the preclinical detection of asymptomatic individuals at-risk of AD. Early risk identification could enable timely interventions aimed at mitigating the development of significant future cognitive impairment. While episodic memory decline typically appears after substantial medial temporal lobe damage, spatial navigation has emerged as a particularly sensitive cognitive function in preclinical AD. In this review, we provide an overview of spatial navigation computations and the tasks used to assess them, highlighting how spatial navigation relies on neural circuits corresponding to the earliest sites of AD pathology. We synthesize evidence from cognitively unimpaired individuals with AD biomarkers, i.e. individuals at-risk of AD, and discuss future research directions. Overall, performance on spatial navigation tasks, particularly path integration and wayfinding, correlates with plasma and CSF biomarkers of AD pathology, notably p-tau. Spatial navigation assessment can represent a sensitive and scalable approach for early detection of individuals at-risk of AD in preclinical stages, and will inform future interventions to mitigate the progression toward clinically significant cognitive impairment.

q-bio.NC↗

arXiv2020

Capturing and Explaining Trajectory Singularities using Composite Signal Neural Networks

Spatial trajectories are ubiquitous and complex signals. Their analysis is crucial in many research fields, from urban planning to neuroscience. Several approaches have been proposed to cluster trajectories. They rely on hand-crafted features, which struggle to capture the spatio-temporal complexity of the signal, or on Artificial Neural Networks (ANNs) which can be more efficient but less interpretable. In this paper we present a novel ANN architecture designed to capture the spatio-temporal patterns characteristic of a set of trajectories, while taking into account the demographics of the navigators. Hence, our model extracts markers linked to both behaviour and demographics. We propose a composite signal analyser (CompSNN) combining three simple ANN modules. Each of these modules uses different signal representations of the trajectory while remaining interpretable. Our CompSNN performs significantly better than its modules taken in isolation and allows to visualise which parts of the signal were most useful to discriminate the trajectories.

cs.LG↗