@article{pmid41962851,

title = {Excessive inhibition in the medial prefrontal cortex contributes to cognitive susceptibility in aging},

author = {Iason Keramidis and Patrick Desrosiers and Andrée-Anne Verreault and Romain Sansonetti and Reza Hazrati and Antoine G Godin and Yves De Koninck},

doi = {10.1016/j.nbd.2026.107385},

issn = {1095-953X},

year  = {2026},

date = {2026-04-01},

journal = {Neurobiol Dis},

volume = {224},

pages = {107385},

abstract = {Aging is the most impactful risk factor for cognitive decline. The prefrontal cortex (PFC), a brain region essential for higher-order cognition, undergoes age-related synaptic alterations which are postulated to yield an excitation/inhibition imbalance within the PFC. Here, we assessed cognitive performance in young and aged mice using a battery of behavioral tests linked to PFC function. Behavioral variability within the aged cohort suggested structured heterogeneity in cognitive performance. Using a data-driven analytical framework integrating behavioral dimensionality reduction, consensus clustering, and spectral embedding we achieved robust and stable behavioral subgroup separation within the aged population. This approach revealed a subset of "cognitively susceptible" aged mice with pronounced memory and novelty-directed exploration deficits, distinct from both "resilient" aged mice and young controls. Unlike resilient aged mice, susceptible mice showed intact social preference. Susceptible aged mice exhibited elevated levels of the inhibitory synaptic proteins Gephyrin and VGAT in the PFC, a pattern absent in resilient aged mice. Notably, the increase in Gephyrin was accounted for by a higher density of inhibitory synapses, indicating a structural shift toward inhibition. Consistent with this mechanism, optogenetically enhancing inhibition in the PFC of young mice was sufficient to recapitulate the memory and novelty-exploration deficits observed in susceptible aged mice. Altogether, these findings implicate excessive prefrontal inhibition as a mechanistic substrate contributing to cognitive susceptibility in unsuccessful aging.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41169497,

title = {Chronic optogenetic activation of hippocampal CA1 neurons triggers Alzheimer's disease-like proteomic remodeling},

author = {Iason Keramidis and Martina Samiotaki and Romain Sansonetti and Johanna Alonso and Patrick Desrosiers and Katerina Papanikolopoulou and Yves De Koninck},

doi = {10.1016/j.isci.2025.113454},

issn = {2589-0042},

year  = {2025},

date = {2025-10-01},

journal = {iScience},

volume = {28},

number = {10},

pages = {113454},

abstract = {Neuronal overexcitability triggers synaptic changes, leading to neural hyperactivity, network disruption, and is postulated to trigger neurodegeneration in Alzheimer's disease (AD). However, the sequence of synaptic changes from excessive activity remains unclear. We employed optogenetics to induce sustained neuronal hyperactivity in the hippocampi of wild-type and AD-like 5xFAD mice. After a month of daily optogenetic stimulation, the proteomic profiles of photoactivated wild-type and 5xFAD mice exhibited remarkable similarity. Proteins involved in translation, protein transport, autophagy, and notably in the AD pathology were upregulated in wild-type mice. Conversely, both glutamatergic and GABAergic synaptic proteins were downregulated. These hippocampal proteomic and signaling alterations in wild-type mice resulted in spatial memory loss and augmented Αβ42 secretion. Collectively, these findings indicate that sustained neuronal hyperactivity alone replicates proteome changes seen in AD-like mutant mice. Therefore, prolonged neuronal hyperactivity may contribute to synaptic transmission disruption, memory deficits and the neurodegenerative process associated with AD.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40644546,

title = {Structural and genetic determinants of zebrafish functional brain networks},

author = {Antoine Légaré and Mado Lemieux and Vincent Boily and Sandrine Poulin and Arthur Légaré and Patrick Desrosiers and Paul De Koninck},

doi = {10.1126/sciadv.adv7576},

issn = {2375-2548},

year  = {2025},

date = {2025-07-01},

journal = {Sci Adv},

volume = {11},

number = {28},

pages = {eadv7576},

abstract = {Network science has revealed universal brain connectivity principles across species. However, several macroscopic network features established in human neuroimaging studies remain underexplored at cellular scales in small animal models. Here, we use whole-brain calcium imaging in larval zebrafish to investigate the structural and genetic basis of functional brain networks. Mesoscopic functional connectivity (FC) robustly captures the individuality of larvae and reflects structural connectivity (SC) derived from single-neuron reconstructions. Several connectome properties, including diffusion mechanisms and indirect pathways, predict interregional correlations. SC and FC share a hierarchical modular architecture, with structural modules shaping spontaneous and stimulus-driven activity patterns. Visual stimuli and tail monitoring reveal a functional gradient that coincides with sensorimotor functions. Last, regional expression levels of specific genes predict interregional FC. Our findings reproduce key mammalian brain network features, demonstrating larval zebrafish as a powerful model for studying large-scale network phenomena in a small and optically accessible vertebrate brain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40161997,

title = {Firing rate distributions in plastic networks of spiking neurons},

author = {Marina Vegué and Antoine Allard and Patrick Desrosiers},

doi = {10.1162/netn_a_00442},

issn = {2472-1751},

year  = {2025},

date = {2025-01-01},

journal = {Netw Neurosci},

volume = {9},

number = {1},

pages = {447--474},

abstract = {In recurrent networks of leaky integrate-and-fire neurons, the mean-field theory has been instrumental in capturing the statistical properties of neuronal activity, like firing rate distributions. This theory has been applied to networks with either homogeneous synaptic weights and heterogeneous connections per neuron or vice versa. Our work expands mean-field models to include networks with both types of structural heterogeneity simultaneously, particularly focusing on those with synapses that undergo plastic changes. The model introduces a spike trace for each neuron, a variable that rises with neuron spikes and decays without activity, influenced by a degradation rate   and the neuron's firing rate . When the ratio  = /  is significantly high, this trace effectively estimates the neuron's firing rate, allowing synaptic weights at equilibrium to be determined by the firing rates of connected neurons. This relationship is incorporated into our mean-field formalism, providing exact solutions for firing rate and synaptic weight distributions at equilibrium in the high  regime. However, the model remains accurate within a practical range of degradation rates, as demonstrated through simulations with networks of excitatory and inhibitory neurons. This approach sheds light on how plasticity modulates both activity and structure within neuronal networks, offering insights into their complex behavior.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38657971,

title = {The Determining Role of Covariances in Large Networks of Stochastic Neurons},

author = {Vincent Painchaud and Patrick Desrosiers and Nicolas Doyon},

doi = {10.1162/neco_a_01656},

issn = {1530-888X},

year  = {2024},

date = {2024-05-01},

journal = {Neural Comput},

volume = {36},

number = {6},

pages = {1121--1162},

abstract = {Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38796449,

title = {Duality between predictability and reconstructability in complex systems},

author = {Charles Murphy and Vincent Thibeault and Antoine Allard and Patrick Desrosiers},

doi = {10.1038/s41467-024-48020-x},

issn = {2041-1723},

year  = {2024},

date = {2024-05-01},

journal = {Nat Commun},

volume = {15},

number = {1},

pages = {4478},

abstract = {Predicting the evolution of a large system of units using its structure of interaction is a fundamental problem in complex system theory. And so is the problem of reconstructing the structure of interaction from temporal observations. Here, we find an intricate relationship between predictability and reconstructability using an information-theoretical point of view. We use the mutual information between a random graph and a stochastic process evolving on this random graph to quantify their codependence. Then, we show how the uncertainty coefficients, which are intimately related to that mutual information, quantify our ability to reconstruct a graph from an observed time series, and our ability to predict the evolution of a process from the structure of its interactions. We provide analytical calculations of the uncertainty coefficients for many different systems, including continuous deterministic systems, and describe a numerical procedure when exact calculations are intractable. Interestingly, we find that predictability and reconstructability, even though closely connected by the mutual information, can behave differently, even in a dual manner. We prove how such duality universally emerges when changing the number of steps in the process. Finally, we provide evidence that predictability-reconstruction dualities may exist in dynamical processes on real networks close to criticality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37798311,

title = {Predicting cognitive decline in a low-dimensional representation of brain morphology},

author = {Rémi Lamontagne-Caron and Patrick Desrosiers and Olivier Potvin and Nicolas Doyon and Simon Duchesne},

doi = {10.1038/s41598-023-43063-4},

issn = {2045-2322},

year  = {2023},

date = {2023-10-01},

journal = {Sci Rep},

volume = {13},

number = {1},

pages = {16793},

abstract = {Identifying early signs of neurodegeneration due to Alzheimer's disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, normal aging cortical decline and high inter-individual variability complicate the comparison and statistical determination of the impact of AD-related neurodegeneration on trajectories. In this paper, we computed trajectories in a 2D representation of a 62-dimensional manifold of individual cortical thickness measures. To compute this representation, we used a novel, nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We trained two embeddings, one on cortical thickness measurements of 6237 cognitively healthy participants aged 18-100 years old and the other on 233 mild cognitively impaired (MCI) and AD participants from the longitudinal database, the Alzheimer's Disease Neuroimaging Initiative database (ADNI). Each participant had multiple visits ([Formula: see text]), one year apart. The first embedding's principal axis was shown to be positively associated ([Formula: see text]) with participants' age. Data from ADNI is projected into these 2D spaces. After clustering the data, average trajectories between clusters were shown to be significantly different between MCI and AD subjects. Moreover, some clusters and trajectories between clusters were more prone to host AD subjects. This study was able to differentiate AD and MCI subjects based on their trajectory in a 2D space with an AUC of 0.80 with 10-fold cross-validation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37786400,

title = {Zebrafish brain atlases: a collective effort for a tiny vertebrate brain},

author = {Antoine Légaré and Mado Lemieux and Patrick Desrosiers and Paul De Koninck},

doi = {10.1117/1.NPh.10.4.044409},

issn = {2329-423X},

year  = {2023},

date = {2023-10-01},

journal = {Neurophotonics},

volume = {10},

number = {4},

pages = {044409},

abstract = {In the past two decades, digital brain atlases have emerged as essential tools for sharing and integrating complex neuroscience datasets. Concurrently, the larval zebrafish has become a prominent vertebrate model offering a strategic compromise for brain size, complexity, transparency, optogenetic access, and behavior. We provide a brief overview of digital atlases recently developed for the larval zebrafish brain, intersecting neuroanatomical information, gene expression patterns, and connectivity. These atlases are becoming pivotal by centralizing large datasets while supporting the generation of circuit hypotheses as functional measurements can be registered into an atlas' standard coordinate system to interrogate its structural database. As challenges persist in mapping neural circuits and incorporating functional measurements into zebrafish atlases, we emphasize the importance of collaborative efforts and standardized protocols to expand these resources to crack the complex codes of neuronal activity guiding behavior in this tiny vertebrate brain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37215634,

title = {Dimension reduction of dynamics on modular and heterogeneous directed networks},

author = {Marina Vegué and Vincent Thibeault and Patrick Desrosiers and Antoine Allard},

doi = {10.1093/pnasnexus/pgad150},

issn = {2752-6542},

year  = {2023},

date = {2023-05-01},

journal = {PNAS Nexus},

volume = {2},

number = {5},

pages = {pgad150},

abstract = {Dimension reduction is a common strategy to study nonlinear dynamical systems composed by a large number of variables. The goal is to find a smaller version of the system whose time evolution is easier to predict while preserving some of the key dynamical features of the original system. Finding such a reduced representation for complex systems is, however, a difficult task. We address this problem for dynamics on weighted directed networks, with special emphasis on modular and heterogeneous networks. We propose a two-step dimension-reduction method that takes into account the properties of the adjacency matrix. First, units are partitioned into groups of similar connectivity profiles. Each group is associated to an observable that is a weighted average of the nodes' activities within the group. Second, we derive a set of equations that must be fulfilled for these observables to properly represent the original system's behavior, together with a method for approximately solving them. The result is a reduced adjacency matrix and an approximate system of ODEs for the observables' evolution. We show that the reduced system can be used to predict some characteristic features of the complete dynamics for different types of connectivity structures, both synthetic and derived from real data, including neuronal, ecological, and social networks. Our formalism opens a way to a systematic comparison of the effect of various structural properties on the overall network dynamics. It can thus help to identify the main structural driving forces guiding the evolution of dynamical processes on networks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}