Biology subjects

Polosecki, P.

Publications and source records attributed to Polosecki, P..

5 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↗

bioRxiv2024

The control of overt and covert attention across two nodes of the attention-control network

Attention is a central cognitive capability whose focus is thought to be directed by a spatial map coding behavioral priority. Here we tested the three defining properties of priority map theory with electrophysiological recordings from two attentional control areas. Both areas, lateral intraparietal area LIP and dorsal posterior inferotemporal cortex PITd, selected behaviorally relevant locations even in the absence of visual stimuli (cognitive sustainment principle). Second, priority signals are thought to arise from the summation of multiple spatial signals (superposition principle). LIP approximated linear summation for visual stimuli, spatial attention, and eye movements, while PITd did not. Third, the same priority signal should guide different behaviors (agnosticity principle). LIP, instead, used separable processing channels for representing attentional focus and eye position, while PITd coded attentional focus only. Thus primate attentional control circuits implement multiple priority maps, whose functional diversity and dimensionality increase the computational capacity of attentional selection. SIGNIFICANCE STATEMENTA central theory of attention poses that the brain computes a priority map to highlight spatial locations of relevance for behavior. Here we tested the hypothesis and its key predictions about how priority signals are assembled and used, through electrophysiological single-unit recordings from two nodes of the attention control network, localized by functional magnetic resonance imaging. We found both areas to highlight locations even in the absence of a stimulus, and that each assembled spatial signals differently and provided spatial information in different forms. These findings force a revision of how and where spatial attention is controlled in the brain.

neuroscience↗

bioRxiv2023

Multimodal fusion of brain signals for robust prediction of psychosis transition

Psychosis symptoms are often evident before diagnosis, suggesting the underlying biology of high-risk status may predict later disease outcomes. However, a single predictor remains unknown, indicating a need for algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n=74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC=0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.

neuroscience↗

bioRxiv2021

MVD-Fuse: Detection of White Matter Degeneration via Multi-View Learning of Diffusion Microstructure

Detecting neuro-degenerative disorders in early-stage and asymptomatic patients is challenging. Diffusion MRI (dMRI) has shown great success in generating biomarkers for cellular organization at the microscale level using complex biophysical models, but there has never been a consensus on a clinically usable standard model. Here, we propose a new framework (MVD-Fuse) to integrate measures of diverse diffusion models to detect alterations of white matter microstructure. The spatial maps generated by each measure are considered as a different diffusion representation (view), the fusion of these views being used to detect differences between clinically distinct groups. We investigate three different strategies for performing intermediate fusion: neural networks (NN), multiple kernel learning (MKL) and multi-view boosting (MVB). As a proof of concept, we applied MVD-Fuse to a classification of premanifest Huntingtons disease (pre-HD) individuals and healthy controls in the TRACK-ON cohort. Our results indicate that the MVD-Fuse boosts predictive power, especially with MKL (0.90 AUC vs 0.85 with the best single diffusion measure). Overall, our results suggest that an improved characterization of pathological brain microstructure can be obtained by combining various measures from multiple diffusion models.

neuroscience↗

bioRxiv2018

Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM

Many real-world data sets, especially in biology, are produced by highly multivariate and nonlinear complex dynamical systems. In this paper, we focus on brain imaging data, including both calcium imaging and functional MRI data. Standard vector-autoregressive models are limited by their linearity assumptions, while nonlinear general-purpose, large-scale temporal models, such as LSTM networks, typically require large amounts of training data, not always readily available in biological applications; furthermore, such models have limited interpretability. We introduce here a novel approach for learning a nonlinear differential equation model aimed at capturing brain dynamics. Specifically, we propose a variable-projection optimization approach to estimate the parameters of the multivariate (coupled) van der Pol oscillator, and demonstrate that such a model can accurately represent nonlinear dynamics of the brain data. Furthermore, in order to improve the predictive accuracy when forecasting future brain-activity time series, we use this analytical model as an unlimited source of simulated data for pretraining LSTM; such model-specific data augmentation approach consistently improves LSTM performance on both calcium and fMRI imaging data.

neuroscience↗