Biology subjects

Doyere, V.

Publications and source records attributed to Doyere, V..

6 recordsLinked to original sources

bioRxiv2026

The Appetite for Freediving differs between Sprague-Dawley and Long Evans Rats.

Freediving in rats has emerged as a relevant model to study physiology and neural adaptation underlying submersion mechanisms. However, despite well-established strain-dependent differences in behaviour and physiological responses, most studies about freediving rely on Sprague Dawley rats. As the choice of strain could significantly shape experimental results depending on the field of research, we conducted a behavioural comparative study between Long Evans (LE) rats, genetically closer to the wild Norway rat, with the commonly used Sprague Dawley (SD) strain. We developed an 11-week progressive voluntary freediving protocol involving four distances (from 5 to 11 meters), and assessed the rats' natural willingness to dive and swim, and identified several parameters for evaluation of their confidence (waiting time before diving, speed), performance capacity (freediving time) and population variability. We found that Long Evans rats were naturally more willing to dive and more confident, compared to Sprague Dawley rats: they showed better performance with longer time underwater and slower diving speed. We also uncover differences in their variability, at trial-to-trial intra-individual and population inter-individual levels, which can guide the choice of one strain over the other, depending on the aim of the scientific inquiry.

animal behavior and cognition↗

bioRxiv2026

Differential Contributions of Anterior Cingulate and Orbito-Frontal Cortex to action timing and its self-monitoring in rats

Adaptive behavior requires the ability to monitor the accuracy of self-generated actions, including the production of precise time intervals. Here, we investigated the neural mechanisms underlying temporal error monitoring in rats by combining a time production and error-reporting task with selective pharmacological inactivation of the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC). We found that OFC inhibition impaired the production of time intervals in a dose-dependent manner, indicating its critical role in generating temporally precise actions, whereas ACC inhibition left time production intact but caused a systematic overestimation of temporal errors and increased overconfident responses on incorrect trials. Analyses of choice behavior revealed that ACC inactivation disrupted the use of trial history and shifted decision thresholds, suggesting that ACC implements a hierarchical read-out of ongoing temporal performance. These results support a functional dissociation in which OFC provides the temporal signal for action, while ACC evaluates errors and confidence. Our findings establish a causal link between prefrontal circuits and self-monitoring of time, offering a model for hierarchical temporal control and evaluation in the rodent brain.

neuroscience↗

bioRxiv2021

Dorsal striatum and the temporal expectancy of an aversive event in Pavlovian odor fear learning

Interval timing, the ability to encode and retrieve the memory of intervals from seconds to minutes, guides fundamental animal behaviors across the phylogenetic tree. In Pavlovian fear conditioning, an initially neutral stimulus (conditioned stimulus, CS) predicts the arrival of an aversive unconditioned stimulus (US, generally a mild foot-shock) at a fixed time interval. Although some studies showed that temporal relations between CS and US events are learned from the outset of conditioning, the question of the memory of time and its underlying neural network in fear conditioning is still poorly understood. The aim of the present study was to investigate the role of the dorsal striatum in timing intervals in odor fear conditioning in male rats. To assess the animals interval timing ability in this paradigm, we used the respiratory frequency. This enabled us to detect the emergence of temporal patterns related to the odor-shock time interval from the early stage of learning, confirming that rats are able to encode the odor-shock time interval after few training trials. We carried out reversible inactivation of the dorsal striatum before the acquisition session and before a shift in the learned time interval, and measured the effects of this treatment on the temporal pattern of the respiratory rate. In addition, using intracerebral microdialysis, we monitored extracellular dopamine level in the dorsal striatum throughout odor-shock conditioning and in response to a shift of the odor-shock time interval. Contrary to our initial predictions based on the existing literature on interval timing, we found evidence suggesting that transient inactivation of the dorsal striatum may favor a more precocious buildup of the respiratory frequencys temporal pattern during the odor-shock interval in a manner that reflected the duration of the interval. Our data further suggest that the conditioning and the learning of a novel time interval were associated with a decrease in dopamine level in the dorsal striatum, but not in the nucleus accumbens. These findings prompt a reassessment of the role of the striatum and striatal dopamine in interval timing, at least when considering Pavlovian aversive conditioning.

neuroscience↗

bioRxiv2020

Respiration and brain neural dynamics associated with interval timing during odor fear learning in rats

In fear conditioning, where a conditioned stimulus predicts the arrival of an aversive stimulus, the animal encodes the time interval between the two stimuli. Freezing, the most used index to assess learned fear, lacks the temporal resolution required to investigate interval timing at the early stages of learning. Here we monitored respiration to visualize anticipatory behavioral responses in an odor fear conditioning in rats, while recording theta (5-15Hz) and gamma (40-80Hz) brain oscillatory activities in the medial prefrontal cortex (mPFC), basolateral amygdala (BLA), dorsomedial striatum (DMS) and olfactory piriform cortex (PIR). We investigated the temporal patterns of respiration frequency and of theta and gamma activity power during the odor-shock interval. We found that akin to respiration patterns, theta temporal curves were modulated by the duration of the odor-shock interval in the four recording sites, and respected scalar property in mPFC and DMS. In contrast, gamma temporal curves were modulated by the interval duration only in the mPFC, and in a manner that did not respect scalar property. This suggests a preferential role for theta rhythm in interval timing. In addition, our data bring the novel idea that the respiratory rhythm might take part in the setting of theta activity dynamics.

neuroscience↗

bioRxiv2017

Oscillatory multiplexing indexes precision

Oscillatory coupling has been implicated in the representation and in the processing of information in the brain. Specific hypotheses suggest that oscillatory coupling may be relevant for the temporal coding of information but to which extent this may translate to conscious timing is unknown. Here, we tested the hypothesis that the temporal precision of self-generated timed actions may be controlled by phase-amplitude coupling (PAC). Using a timing task, we show the existence of significant alpha-beta (-{beta}) PAC, robust at the individual level, and specific to temporal production. Second, an increase in the strength of -{beta} PAC was associated with a smaller variance in time production, i.e. an increased precision in timing, but there was no correlation with the duration of the produced interval. Our results suggest an active role for -{beta} coupling in maintaining the precision of the endogenous temporal goal during time production: specifically, oscillations may maintain the content of current cognitive states, thus securing the endogenous temporal code for duration estimation instantiated in {beta} band. Oscillatory multiplexing may thus index the variance of neuronal computations, which translates into the precision of behavioral performance.

neuroscience↗

bioRxiv2017

Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model \"goodness of fit\" via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling.\n\nAuthor SummaryNeural oscillations synchronize information across brain areas at various anatomical and temporal scales. Of particular relevance, slow fluctuations of brain activity have been shown to affect high frequency neural activity, by regulating the excitability level of neural populations. Such cross-frequency-coupling can take several forms. In the most frequently observed type, the power of high frequency activity is time-locked to a specific phase of slow frequency oscillations, yielding phase-amplitude-coupling (PAC). Even when readily observed in neural recordings, such non-linear coupling is particularly challenging to formally characterize. Typically, neuroscientists use band-pass filtering and Hilbert transforms with ad-hoc correlations. Here, we explicitly address current limitations and propose an alternative probabilistic signal modeling approach, for which statistical inference is fast and well-posed. To statistically model PAC, we propose to use non-linear auto-regressive models which estimate the spectral modulation of a signal conditionally to a driving signal. This conditional spectral analysis enables easy model selection and clear hypothesis-testing by using the likelihood of a given model. We demonstrate the advantage of the model-based approach on three datasets acquired in rats and in humans. We further provide novel neuroscientific insights on previously reported PAC phenomena, capturing two mechanisms in PAC: influence of amplitude and directionality estimation.

neuroscience↗