Biology subjects

Benchenane, K.

Publications and source records attributed to Benchenane, K..

9 recordsLinked to original sources

bioRxiv2025

Hippocampal reactivation of aversive experience enables safety learning and slow-breathing state for recovery from stress

Adaptive threat responses require both defensive behaviours to minimize danger and recovering from the induced physiological stress. However, the behavioural and neural basis of these recuperative strategies are still elusive. Using a novel two-location fear conditioning paradigm in mice, we have identified a slow-breathing immobility state of recovery that emerges when animals identify safe environments after threat avoidance. This immobile state was characterized by a 2-4 Hz breathing profile and replay of the aversive experience in the hippocampus. Suppressing hippocampal sharp-wave ripples (SWRs) inhibited the emergence of this recovery state, suggesting their role in learning safe locations. Anxiolysis with diazepam directly promoted the recovery state while suppressing SWRs, showing this treatment to be a double-edged sword that facilitates immediate relief but impairs long-term safety learning. These results demonstrate the importance of hippocampal replay for emotional resilience through its role in recovery.

neuroscience↗

bioRxiv2024

Rat anterior cingulate neurons responsive to rule or strategy changes are modulated by the hippocampal theta rhythm and sharp-wave ripples

To better understand neural processing during adaptive learning of stimulus-response-reward contingencies, we recorded synchrony of neuronal activity in anterior cingulate cortex (ACC) with hippocampal rhythms in male rats acquiring and switching between spatial and visual discrimination tasks in a Y-maze. ACC population and single unit activity responded shortly after task rule changes, or just before the rats adopted different task strategies. Hippocampal theta oscillations (associated with memory encoding) modulated an elevated proportion of rule-change responsive neurons (70%), but other neurons that were correlated with strategy-change, strategy value, and reward-rate were not. However, hippocampal sharp wave-ripples modulated significantly higher proportions of rule-change, strategy-change and reward-rate responsive cells during post-session sleep but not pre-session sleep. This suggests an underestimated mechanism for hippocampal mismatch and contextual signals to facilitate ACC detection of contingency changes for cognitive flexibility, a function that is attenuated after it is damaged.

neuroscience↗

bioRxiv2023

EEG and computational aspects of how aging affects sleep slow waves

Sleep slow-waves have been reported to vary with age in human subjects, as well as in mouse, but the underlying mechanisms remain unclear. Here, we perform a precise quantification of the effect of aging on the shape and dynamics of sleep slow waves, in a large cohort of human subjects recorded with the electro-encephalogram (EEG) during sleep. The fine-structure analysis of slow waves reveals that they slow-down, increase of variability and decrease in amplitude with age. We next investigate a computational model of the genesis of slow-wave activity and model the aging by a global decrease of the strength of the external excitatory drive to the network. This simple model reproduces some of the main features observed in the EEG, suggesting that changes of long-range excitatory connection strength may explain the evolution of slow-waves with age.

neuroscience↗

bioRxiv2022

Distinction between slow waves and delta waves sheds light to sleep homeostasis and their association to hippocampal sharp waves ripples

Cortical slow waves and delta waves are hallmarks of NREM sleep and key elements in physiological processes such as memory consolidation and sleep homeostasis. However, no clear distinction has been made between the two types of electrophysiological events. We introduce a new distinction between slow waves, large amplitude waves on filtered LFP cortical signals, and delta waves, characterized by an inversion between deep and superficial layers and the co-occurrence with a cortical down state. Detection of slow waves, as large amplitude waves during NREM sleep, reveals that a large portion of them does not share the properties of delta waves and does not co-occur with cortical down states. Therefore, we distinguish type-1 slow waves, that are associated to a down state, from type-2 slow waves. We show that 1) only type-1 slow waves undergo strong homeostatic regulations and 2) type-2 slow waves create confusion about the temporal link with hippocampal sharp-wave ripples. Thus, we confirm that delta waves detected with our new method can be used as a proxy for down state. Altogether, this confirms the importance of a robust and accurate definition of delta waves to understand the fine neurophysiological mechanisms at stake during NREM sleep.

neuroscience↗

bioRxiv2022

Validation of the EEG signal of the URGOnight neurofeedback device, associated with a new SMR detection method

Sensorimotor (SMR) neurofeedback is a promising therapy for several health disorders but is still not widely used due to the high cost of the equipment. URGOnight offers a low-cost solution to democratize these therapies by providing an at-home EEG headband with dry electrodes connected to a mobile application. The first aim of this study is both to validate the URGOnight EEG signal and to compare it to Enobio-20, a medical grade EEG device. The second aim of the study is to propose a new method to detect SMR rhythm based on its oscillatory properties and discriminate it from alpha oscillations. In our study, we compared the URGOnight headband EEG signal (C3/C4) to Enobio-20 (CP3/CP4), placed on subjects simultaneously equipped with the two headbands. All subjects (n=33) performed a dual blocking task inspired by Kulhman (1978) based on the blocking effect of movement and eyes opening on SMR and alpha respectively. This task was followed by SSVEP stimulations to evaluate the frequency response of the two EEG devices. The performance of the EEG headbands was statistically identical for most of the characteristics of the EEG signal, including the frequency response to SSVEP (from 4Hz to 20Hz). The main difference was a larger amplitude in the 8-15Hz due to the location of the reference in URGOnight that did not impair the detection of both alpha and SMR. In addition, we show that our new method allows to discriminate alpha and SMR rhythms based on their oscillatory properties with a single recording site (C3/C4). The method is fast enough to be used in real time. We show that the detected SMR rhythm is modulated by movement as opposed to the 12-15Hz frequency band often used as indicator of SMR in most neurofeedback studies. Altogether, our results validate the quality of the EEG recordings obtained with URGOnight since it gives similar results as the one obtained with Enobio-20, a validated EEG medical grade system. In addition, we provide a new method allowing the identification and the separation of the alpha and SMR with a single recording site C3/C4. This method opens up a new research lead to improve SMR neurofeedback efficiency and thus of its clinical possibilities by focusing on the reinforcement of the SMR oscillation strictly speaking. Highlights- Validation of the URGOnight EEG device suitable for neurofeedback at home - New method for the detection and the discrimination of alpha rhythm and SMR rhythm with a small number of recording sites - The oscillatory activity related to the SMR displays different properties compared to the 12-15Hz frequency band. - Description of a full validation procedure for wireless EEG devices usable at home for neurofeedback - Comparison of the signal of URGOnight (dry electrodes) with a wet electrode EEG device

neuroscience↗

bioRxiv2022

Social defeat induces REM sleep fragmentation through the PFC-VLPO pathway

The brainstem and hypothalamic structures implementing the daily cycles of wake, REM and NREM sleep have now been identified in remarkable detail. However, sleep structure dynamically adapts to environmental stressors, likely requiring top-down cortical feedback that is as yet unidentified. Here, we investigate the role of projections from prefrontal cortex (PFC), a key hub in stress regulation, to the ventro-lateral preoptic areas (VLPO), a master regulator of sleep states. Using ex vivo optogenetics, we demonstrate that activation of PFC terminals induces monosynaptic excitatory glutamatergic currents in VLPO NREM-promoting neurons. In vivo, activation of PFC-VLPO projections interrupts ongoing REM in favour of NREM, leading to fragmented REM bouts. Remarkably, chemogenetic inhibition of PFC-VLPO projections has no effect in baseline conditions but it blocks the REM fragmentation induced by Social Defeat Stress. Therefore, the PFC-VLPO pathway provides a top-down regulation specifically recruited in stressful conditions to induce short, fragmented REM bouts and favor NREM sleep.

neuroscience↗

bioRxiv2018

Improved sleep scoring in mice reveals human-like stages.

Rodents are the main animal model to study sleep. Yet, in spite of a large consensus on the distinction between rapid-eye-movements sleep (REM) and non-REM sleep (NREM) in both humans and rodent, there is still no equivalent in mice of the NREM subdivision classically described in humans.\n\nHere we propose a classification of sleep stages in mice, inspired by human sleep scoring. By using chronic recordings in medial prefrontal cortex (mPFC) and hippocampus we can classify three NREM stages with a stage N1 devoid of any low oscillatory activity and N3 with a high density of delta waves. These stages displayed the same evolution observed in human during the whole sleep or within sleep cycles. Importantly, as in human, N1 in mice is the first stage observed at sleep onset and is increased after sleep fragmentation in Orexin-/- mice, a mouse model of narcolepsy.\n\nWe also show that these substages are associated to massive modification of neuronal activity. Moreover, considering these stages allows to predict mPFC neurons evolution of firing rates across sleep period. Notably, neurons preferentially active within N3 decreased their activity over sleep while the opposite is seen for those preferentially active in N1 and N2.\n\nOverall this new approach shows the feasibility of NREM sleep sub-classification in rodents, and, in regard to the similarity between sleep in both species, will pave the way for further studies in sleep pathologies given the perturbation of specific sleep substages in human pathologies such as insomnia, somnambulism, night terrors, or fibromyalgia.

neuroscience↗

bioRxiv2018

Dissociation of fear initiation and maintenance by breathing-driven prefrontal oscillations

Does the body play an active role in emotions? Since the original James/Cannon controversy this debate has mainly been fueled by introspective accounts of human experience. Here, we use the animal model to demonstrate a physiological mechanism for bodily feedback and its causal role in the stabilization of emotional states. We report that during fear-related freezing mice breathe at 4Hz and show, using probabilistic modelling, that optogenetic perturbation of this feedback specifically reduces freezing maintenance without impacting its initiation. This rhythm is transmitted by the olfactory bulb to the prefrontal cortex where it organizes neural firing and optogenetic probing of the circuit demonstrates frequency-specific tuning that maximizes prefrontal cortex responsivity at 4Hz, the breathing frequency during freezing. These results point to a brain-body-brain loop in which the initiation of emotional behavior engenders somatic changes which then feedback to the cortex to directly participate in sustaining emotional states.

neuroscience↗

bioRxiv2017

Harnessing sleep/wake state tracking by olfactory bulb oscillations to perform fully brain-based sleep scoring in mice

It has long been thought that sleep scoring could not be achieved with brain signals alone despite the deep neuromodulatory transformations that accompany sleep state changes. Here we demonstrate using multi-site electrophysiological LFP recordings in freely moving mice that gamma power in the olfactory bulb (OB) allows for clear classification of sleep and wake. Coupled with hippocampal theta activity, it allows the construction of a sleep scoring algorithm that relies on brain activity alone. This method reaches over 90% homology with classical methods based on muscular activity (EMG) and video tracking. Moreover, contrary to EMG, OB gamma power allows correct discrimination between sleep and immobility in ambiguous situations such as fear-related freezing. We use the instantaneous power of hippocampal theta oscillation and OB gamma oscillation to construct a 2D phase-space that is highly robust across mice and days. Dynamic analysis of trajectories within this space yields a novel characterization of sleep/wake and wake/sleep transitions as deeply divergent phenomena. Whereas waking up is a fast and direct transition, falling asleep is best described as stochastic and gradual change. Altogether this methodology opens the avenue for multi-timescale characterization of sleep states with high temporal resolution based on brain signals only.

neuroscience↗