Biology subjects

Falco-Roget, J.

Publications and source records attributed to Falco-Roget, J..

6 recordsLinked to original sources

bioRxiv2025

Emergence of Value and Action Codes in Bimodular Spiking Actor-Critic Networks

Decision-making depends on coordinated computations distributed across dorsal and ventral circuits, often described as actor-critic systems. We examined how this division of labor gives rise to value- and decision-related representations by training a bimodular recurrent network on an economic choice task and transferring it to an expanded gated spiking model that preserved its latent dynamics while stabilizing the underlying neural representations. The two modules assumed complementary roles: the actor encoded action-value differences and spatial contingencies required for selecting between actions, whereas the critic represented object values predictive of reward. Neurons across modules were selective for object, action, total, and difference values, supporting decision- and confidence-related activity. Low-dimensional analyses revealed structured trajectories reflecting temporal evolution, spatial configuration, and value-dependent divergence. These results clarify how distributed circuits can jointly implement valuation and action selection, providing a foundation for linking reinforcement-trained recurrent models with anatomically inspired dorsal-ventral frameworks.

neuroscience↗

bioRxiv2025

Atypical functional connectome in congenitally blind humans

The human cortex is organized along continuous functional gradients that capture systematic transitions in functional connectivity across the brain. These gradients describe large-scale organizational principles, including hierarchical transitions from unimodal to transmodal regions. Here, we provide the first characterization of cortical gradients in a large sample of congenitally blind (n = 41) and sighted (n = 44) adults to assess the relative contributions of intrinsic (genetic) and experiential factors to cortical gradient organization. Using resting-state fMRI, we compared functional connectome gradients and their association with cortical structure. Both groups exhibited similar principal gradients: unimodal to transmodal, somatosensory to visual, and frontoparietal segregation, demonstrating that the fundamental scaffold of cortical organization emerges largely independently of visual experience. However, blindness altered specific features of the functional connectome: the visual network was more segregated from the sensorimotor network and more integrated with transmodal and frontoparietal networks. Moreover, blind individuals showed reduced canonical hierarchical ordering within early visual areas, weaker structure-function coupling in visual and temporal regions, and altered functional areal boundaries in V1. These findings suggest that the development of large-scale cortical gradients reflects a genetically guided scaffold that is subsequently refined by sensory experience.

neuroscience↗

bioRxiv2024

Exploring the Impact of Ischemic Stroke on Brain Function: Insights from Functional Connectivity Gradients and Temporal Lag Correction

Ischemic brain stroke disrupts blood flow, leading to functional and structural changes associated with behavioral deficits. Importantly, despite this disruption occurring in localized regions, the resulting changes in the functional organization are both high-dimensional and widespread across the human cortex. However, the mechanisms with which these global patterns emerge and the subsequent behavioral deficits they entail, remain largely unexplored. Functional connectivity gradients provide consistent, reproducible, and robust low-dimensional representations of brain function that can be explored to reduce brain heterogeneity to a handful of axes along which brain function is organized. Here, we investigated how stroke disrupts this canonical gradient space by aligning each patient to a control-averaged gradient embedding and computing the distances to the "correct" positions to quantify functional deviations and their contribution to behavioral deficits. Importantly, we explicitly corrected these gradients for stroke-induced hemodynamic lags to further study their contribution. We found that lag correction enhanced the functional connectivity gradients most prominently in the second gradient, on which visual and somatomotor function is concentrated. Additionally, we identified significant functional deviations primarily within somatomotor, visual, and ventral attention networks, correlating with behavioral impairments. We studied the hemispheric asymmetries of these deviations finding that intact hemispheres preserve comparable patterns of asymmetry while damaged ones presented important changes. Lastly, right-sided lesions displayed more localized functional deviations than their contralateral lesions. Overall, we provide evidence that 1) correcting for hemodynamic lags improves gradient accuracy, as indicated by increased percentages of explained variance, and 2) behavioral impairments and hemispheric asymmetries result from a repositioning of region-based connectivity profiles in a low-dimensional, interpretable space. This suggests that large-scale brain function alterations manifest in slight, predictable movements largely confined to the visual-somatomotor axis.

neuroscience↗

bioRxiv2023

Directed networks and resting-state effective brain connectivity with state-space reconstruction using reservoir computing causality

Causality theory is a complex field involving philosophy, mathematics, and computer science. It relies on the temporal precedence of cause over a consequence or unidirectional propagation of changes. Despite these simple principles, normative modeling of causal relationships is conceptually and computationally challenging. Model-free approaches provide insights into large, complex, and chaotic networks, but suffer from false positive and false negative inferences caused by meaningless statistical and temporal correlations. Machine learning advancements have extended these data-driven methods to nonlinear systems, yet inherited similar drawbacks as linear approaches. Interestingly, newer proposals within this model-free paradigm reverse the temporal precedence using the internal structure of the driven variable to recover information from the driving one. Efficient machine learning models combined with these state space reconstruction methods automate part of the process, potentially reducing inductive biases during training and inference. However, their translation into neuroscience, especially neuroimaging, is limited due to complex interpretations and a lack of systematic analyses of the results. Here, we exploited these methods combining them with normative analyses to reconstruct chaotic relationships and networks emerging from neuroimaging data. We validated the proposed scores with a chaotic yet solved system and rebuilt brain networks both in synthetic and real scenarios. We compared our method and heuristics with well-established alternatives providing a comprehensive and transparent benchmark. We obtained higher accuracies and reduced false inferences compared to Granger causality in tasks with known ground truth. When tested to unravel directed influences in brain networks meaningful predictions were found to exist between nodes from the default mode network. The presented framework explores reservoir computing for causality detection, offering a conceptual detour from traditional premises and has the potential to provide theoretical guidance opening perspectives for studying cognition and neuropathologies. Author summaryIn sciences, reliable methods to distinguish causes from consequences are crucial. Despite some progress, researchers are often unsatisfied with the current understanding of causality modeling and its predictions. In neuroscience, causality detection requires imposing world models or assessing statistical utility to predict future values. These approaches, known as model-based and model-free, have advantages and drawbacks. A recent model-free approach augmented with artificial networks tries to autonomously explore the internal structure of the system, (i.e, the state space), to identify directed predictions from consequences to causes but not the other way around. This has not been extensively studied in large networks nor in the human brain, and systematic attempts to reveal its capabilities and inferences are lacking. Here, the proposal is expanded to large systems and further validated in chaotic systems, challenging neuronal simulations, and networks derived from real brain activity. Although the manuscript does not claim true causality, it presents new ideas in the context of current trends in data-driven causality theory. Directed networks encoding causality are hypothesized to contain more information than correlation-based relationships. Hence, despite its evident difficulties, causality detection methods can hold the key to new and more precise discoveries in brain health and disease.

neuroscience↗

bioRxiv2022

Community Detection in Brain Connectome using Quantum Annealer Devices

Recent advancements in network neuroscience are pointing in the direction of considering the brain as a small-world system with segregated regions integrated to facilitate different cognitive tasks and functions. In this context, community detection is a pivotal issue in computational neuroscience. In this paper we explore community detection within brain connectomes using the power of quantum annealers, and in particular the Leaps Hybrid Solver. Our results shows that quantum annealers can achieve higher modularity index compared to classical annealer while computing communities of brain connectomes. Those promising preliminary results points out that quantum annealers might be the better choice compared to classical computing optimization process.

neuroscience↗

bioRxiv2022

Structural Reorganization Following a Brain Tumor: A Machine Learning Study Considering Desynchronized Functional Oscillations

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and oedema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find that intra-tumor signals do exist and are correlated with alterations present both in healthy tissue and resting-state networks. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.

neuroscience↗