Biology subjects

Bethus, I.

Publications and source records attributed to Bethus, I..

6 recordsLinked to original sources

bioRxiv2026

Reinforced Hippocampus-Accumbens pathway marks a shift towards cognitive decline in aging

Aging triggers early functional alterations in brain circuits that precede cognitive impairment, yet the underlying mechanisms remain unclear. Spatial navigation and memory, among the earliest affected, depend on hippocampus and downstream targets such as the nucleus accumbens (NAc), which integrates spatial and motivational information to guide goal-directed behaviors. Here we identify age-related, sex-dependent changes of the dorsal hippocampus-NAc (dHPC[->]NAc) pathway. Combining optogenetics with electrophysiology, chemogenetics, and behavioral analyses in mice, we show that aging shifts excitation-inhibition balance in dorsal CA1 toward pyramidal neuron hyperexcitability, strengthening hippocampal outputs. This selectively enhances synaptic drive onto D1 receptor-expressing medium spiny neurons and involves a previously unrecognized long-range parvalbumin-expressing glutamatergic projection. Reducing dHPC[->]NAc excitability improves memory in aged mice. In humans, fMRI reveals heightened posterior hippocampal activity and stronger pHPC-NAc coupling during navigation. Together, we uncover a targetable pathway acting in an intervention-sensitive window of aging to correct negative cognitive trajectories.

neuroscience↗

bioRxiv2025

AETA peptide drives Alzheimer's disease signature of synapse dysfunction

Alzheimers disease (AD), the leading cause of dementia, is characterized by early synaptic dysfunction that precedes overt cognitive decline. While amyloid-{beta} and Tau remain central to AD pathogenesis, molecular triggers of synapse weakening remain unclear. Here, we investigated AETA, a novel brain-secreted peptide derived from amyloid precursor protein (APP), as a potential mediator of synapse dysfunction in AD. We previously identified AETA as a unique modulator of NMDA receptor activity in the healthy brain; however, its role in AD etiology was yet to be explored. Post-mortem analyses of human hippocampal and prefrontal cortex tissues revealed significantly elevated AETA levels in AD patients, particularly in females. To further explore the contribution of AETA to AD synaptic pathology, we analyzed a new mouse model, the AETA-m mouse, exhibiting chronically increased brain AETA expression. Hippocampi of female AETA-m mice display an increase in the number of astrocyte and microglia, but no overt neuroinflammation. RNA sequencing of female AETA-m hippocampi revealed alterations in synaptic gene expression that closely paralleled those observed in vulnerable human AD brain regions, most notably in the hippocampus. These two phenotypes were absent in males. Functionally, hippocampal neurons from AETA-m mice displayed impaired NMDA receptor signaling, dendritic spine loss, and memory deficits especially in females, mirroring early AD-associated synaptic dysfunction. Together, these findings identify AETA as a novel key contributor of synaptic vulnerability in AD and associated memory processing, especially in females. Targeting AETA signaling may therefore offer new therapeutic avenues for preventing or mitigating synaptic and cognitive decline in AD.

neuroscience↗

bioRxiv2025

Cortico-striatal dynamics across working memory stages

Working memory depends on the temporary retention and manipulation of information, bridging the gap between short-term memory and information processing functions. However, when the same working memory task is repeated over several days, it raises the question of whether the rule or task set becomes automated (or proceduralized). The medial prefrontal cortex (mPFC) is crucial for working memory. Yet, the role of the dorsolateral striatum (DLS) in the automation (proceduralization) of rules or task sets remains to be clarified. Using a longitudinal approach of the "delay non-match to place" (DNMP) task in a T-maze combined to chemogenetic inhibition of the mPFC or DLS in mice, we show that the mPFC becomes less critical in the maintenance phase of the task as behaviour progressively shifts toward automation. During this phase, the DLS facilitates automated processing. Accordingly, silencing through chemogenetic inhibition of the DLS during maintenance triggers an adaptation in learning strategies, reactivating a goal-directed behaviour. Our findings strengthen memory traces as a dynamic reorganization of neural networks, challenging the classical view of information migration between brain structures. We here propose that the memory trace remains in a dormant state--less energy-consuming for the system--while still allowing for rapid flexibility in case of task modification.

neuroscience↗

bioRxiv2024

Inferring Time-Varying Internal Models of Agents Through Dynamic Structure Learning

Reinforcement learning (RL) models usually assume a stationary internal model structure of agents, which consists of fixed learning rules and environment representations. However, this assumption does not allow accounting for real problem solving by individuals who can exhibit irrational behaviors or hold inaccurate beliefs about their environment. In this work, we present a novel framework called Dynamic Structure Learning (DSL), which allows agents to adapt their learning rules and internal representations dynamically. This structural flexibility enables a deeper understanding of how individuals learn and adapt in real-world scenarios. The DSL framework reconstructs the most likely sequence of agent structures--sourced from a pool of learning rules and environment models--based on observed behaviors. The method provides insights into how an agents internal structure model evolves as it transitions between different structures throughout the learning process. We applied our framework to study rat behavior in a maze task. Our results demonstrate that rats progressively refine their mental map of the maze, evolving from a suboptimal representation associated with repetitive errors to an optimal one that guides efficient navigation. Concurrently, their learning rules transition from heuristic-based to more rational approaches. These findings underscore the importance of both credit assignment and representation learning in complex behaviors. By going beyond simple reward-based associations, our research offers valuable insights into the cognitive mechanisms underlying decision-making in natural intelligence. DSL framework allows better understanding and modeling how individuals in real-world scenarios exhibit a level of adaptability that current AI systems have yet to achieve.

animal behavior and cognition↗

bioRxiv2024

Capturing Optimal and Suboptimal behavior Of Agents Via Structure Learning Of Their Internal Model

This study introduces a novel framework for understanding the cognitive underpinnings of individual behavior which often deviates from rational decision-making aimed at maximizing rewards in real-life scenarios. We propose a structure learning approach to infer an agents internal model, composed of a learning rule and an internal representation of the environment. Crucially, the combined contribution of these components -- rather than their individual contribution -- determines the overall optimality of the agents behavior. By exploring various combinations of learning rules and environment representations, we identify the most probable agent model structure for each individual. We apply this framework to analyze rats learning behavior in a free-choice task within a T-maze with return arms, evaluating different internal models based on optimal and suboptimal learning rules, along with multiple possible representations of the T-maze decision graph. Identifying the most likely agent model structure based on the rats behavioral data reveals that slower learning rats employed either a suboptimal or a moderately optimal agent model, whereas fast learners employ an optimal agent model. Using the inferred agent models for each rat, we explore the qualitative differences in their individual learning processes. Policy entropy, derived from the inferred agent models, also highlights variations in the balance between exploration and exploitation strategies among the rats. Traditional reinforcement learning approaches to addressing suboptimal behavior focus separately on either suboptimal learning rules or flawed environment representations. Our approach jointly models these components, revealing that suboptimal decisions can arise from complex interactions between learning rules and environment representations within an agents internal model. This provides deeper insights into the cognitive mechanisms underlying real-world decision-making.

animal behavior and cognition↗

bioRxiv2023

Coding dynamics of the striatal networks during learning

The rat dorsomedial (DMS) and dorsolateral striatum (DMS), equivalent to caudate nucleus and putamen in primates, are generally required for goal-directed and habit behaviour, respectively. However, it is still unclear whether and how this functional dychotomy emerges in the course of learning. In this study we investigated this issue by recording DMS and DLS single neuron activity in rats performing a continuous spatial alternation task, from the acquisition to optimized performance. We first applied a classical analytical approach to identify task-related activity based on the modifications of single neuron firing rate in relation to specific task events or maze trajectories. We then used an innovative approach based on Hawkes process to reconstruct a directed connectivity graph of simultaneously recorded neurons, that was used to decode animal behavior. This approach enabled us to better unravel the role of DMS and DLS neural networks across learning stages. We showed that DMS and DLS display different task-related activity throughout learning stages, and the proportion of coding neurons over time decreases in the DMS and increases in the DLS. Despite theses major differences, the decoding power of both networks increases during learning. These results suggest that DMS and DLS neural networks gradually reorganize in different ways in order to progressively increase their control over the behavioral performance.

neuroscience↗