Contributions to a conference proceedings

Experimental research on cognition needs more realistic paradigms to achieve ecological validity as wellas to transfer and eventually generalize the results to clinical practice. Selective attention was found to behighly related to memory and training of attentional filtering enhanced memory performance. [...]

The segmentation of brain tumors using magnetic resonance imaging (MRI) data is fundamental for precise diagnosis, effective treatment planning, and continuous monitoring of patient outcomes, and poses significant challenges as a result of variations in tumor morphology, intensity, variable size, and complex structure of tumor regions. Most existing deep learning approaches rely on task-specific architectures, requiring substantial effort to redesign and optimize models for each new imaging application. [...]

Magnetic resonance imaging (MRI) inherently suffers from noise, which limits downstream medical analyses. In MRI, noise-free images are unobtainable; therefore, existing denoising approaches formulate surrogate training objectives, compromising between preserving detail and concealing noise, causing domain shifts or incomplete de-noising. [...]

This study investigates how to improve the reliability of probability estimates produced by deep learning models for the detection of Alzheimer’s disease using MRI data. Although convolutional neural networks (CNNs) can accurately classify neurodegenerative diseases, their softmax outputs often misrepresent true classification probabilities. [...]

Accurately predicting age at symptom onset in genetic frontotemporal dementia (FTD) remains challenging due to substantial inter-individual variability, even among carriers of distinct pathogenic variants within the same gene. Time-to-event models provide a natural framework to address this problem while accounting for right censoring in presymptomatic individuals. [...]

Growing interest in perivascular spaces (PVS) quantification has highlighted the need for accurate and robust methods, particularly in magnetic resonance imaging (MRI). The Frangi filter is widely used to enhance tubular structures, including PVS, however its performance is highly dependent on scale selection. [...]

In many biomedical segmentation tasks, the preservation of elongated structure continuity and length is more important than voxel-wise accuracy. We propose two novel loss functions, Negative Centerline Loss and Simplified Topology Loss, that, applied to Convolutional Neural Networks (CNNs), help preserve connectivity of output instances. [...]

Alzheimer's disease (AD), a progressive neurodegenerative disorder, is projected to affect over 130 million people globally by 2050. While extensive efforts have focused on targeting molecular hallmarks such as amyloid-beta (Aβ) plaques and tau pathology, network-level dysfunction remains a critical but underexplored component of AD progression. [...]

Learning and memory are fundamental brain functions governed by rhythmic oscillatory activity, which synchronizes neural communication and modulates network dynamics. Distinct oscillatory patterns-theta (θ), beta (β), gamma (γ), and sharp wave-ripples (SWR)-coordinate neural ensemble activity, particularly in the hippocampal CA1-CA3 regions, where they play a crucial role in learning and memory. [...]

Unraveling the complexity of network-level synaptic plasticity remains a challenge due to the dynamic and interconnected nature of neural circuits. In this study, we employ network motifs-recurrent, functionally specialized patterns of connectivity-as a framework to dissect long-term potentiation (LTP)-induced reorganization in hippocampal CA1-CA3 networks. [...]