Biology subjects

Fernandez Slezak, D.

Publications and source records attributed to Fernandez Slezak, D..

3 recordsLinked to original sources

bioRxiv2026

Alzheimers Disease Brain Phenotypes are Age-dependent

A widespread view of neurodegenerative disorders, including Alzheimers Disease (AD), frames their effects as accelerated aging, with the brain-age gap (BAG, the deviation of predicted brain age from chronological age) as a staple biomarker. However, BAG relies on a fundamental, untested assumption: that AD can be identified via age-invariant brain phenotypes. Using invariant representation learning on brain MRI from 44,178 individuals, we created neural representations that optimally convey age information (age-aware) or conversely remove it (age-invariant) while minimizing reconstruction distortion. We provide the first causal evidence that age information is necessary in brain biomarkers for AD detection: age-aware representations achieve competitive state-of-the-art performance and significantly outperform age-invariant ones (0.84 vs. 0.77 AUC, p < 0.001, with external validation). This necessity reveals a conceptual flaw in BAG: by subtracting chronological age, it discards the very information essential for accurate detection. Using conditional decoders to simulate aging trajectories, we found that healthy aging and AD operate along multiple independent anatomical dimensions (deep gray matter, frontoparietal, temporal). AD patients diverge from rather than accelerate healthy aging, showing pathological temporal shifts alongside, remarkably, relative frontoparietal preservation. Furthermore, representational similarity analysis suggests that even models pretrained on non-age tasks (e.g., sex or BMI) implicitly converge toward age-related features when optimized for AD. Given that the AD phenotype cannot be decoupled from age, our results establish a hard limit for age-independent biomarkers and favor multidimensional models that preserve aging structure over unidimensional summaries like BAG.

neuroscience↗

bioRxiv2025

Shared Hierarchical Representations Explain Temporal Correspondence Between Brain Activity and Deep Neural Networks

The visual cortex and artificial neural networks both process images hierarchically, progressing from low-level features to high-level semantic representations. We investigated the temporal correspondence between activations from multiple convolutional and transformer-based neural network models (AlexNet, MoCo, ResNet-50, VGG-19, and ViT) and human EEG responses recorded during visual perception tasks. Leveraging two EEG datasets of images presented at different durations, we assessed whether this correspondence reflects general architectural principles or model-specific computations. Our analysis revealed a robust mapping: early EEG components correlated with activations from initial network layers and low-level visual features, whereas later components aligned with deeper layers and semantic content. Moreover, for images presented during longer times, the extent of correspondence correlated with the semantic contribution to the EEG response. These findings highlight a consistent temporal alignment between biological and artificial vision, suggesting that this correspondence is primarily driven by the hierarchical transformation of visual to semantic representations rather than by idiosyncratic computational features of individual network architectures.

neuroscience↗

bioRxiv2018

An entropic barriers diffusion theory of decision-making in multiple alternative tasks

We present a theory of decision-making in the presence of multiple choices that departs from traditional approaches by explicitly incorporating entropic barriers in a stochastic search process. We analyze response time data from an on-line repository of 15 million blitz chess games, and show that our model fits not just the mean and variance, but the entire response time distribution (over several response-time orders of magnitude) at every stage of the game. We apply the model to show that (a) higher cognitive expertise corresponds to the exploration of more complex solution spaces, and (b) reaction times of users at an on-line buying website can be similarly explained. Our model can be seen as a synergy between diffusion models used to model simple two-choice decision-making and planning agents in complex problem solving.

animal behavior and cognition↗