Biology subjects

Quentin, R.

Publications and source records attributed to Quentin, R..

13 recordsLinked to original sources

bioRxiv2025

No evidence of neural feature-specific pre-activation during the prediction of an upcoming stimulus

Our brains constantly make predictions about upcoming events based on prior knowledge of the environment. Although several neural mechanisms have been proposed to support this capacity, it is not yet clear how the brain makes such predictions. A compelling hypothesis is that the brain preactivates a sensory template of a predictable stimulus before it appears. In a recent study, Demarchi et al. had participants listen to sequences of sounds with different levels of predictability. For some sequences, participants could anticipate the next sound (in regular sequences), for others not (in random sequences). Using magnetoencephalography recordings and machine-learning methods to decode sounds from brain signals, Demarchi et al. concluded that auditory predictions pre-activate tone-specific neural templates before the sound onset. In our reanalysis of their data, we demonstrate that their results can be fully explained by a bias induced by the structure of the sequences: because the most likely stimulus also happens to be physically close to the previous one, spurious higher-than-chance decoding performance arises before the sound onset. We provide general criteria to assess whether a study is affected by this confound and requires a reexamination. We conclude that there is no evidence of anticipatory predictive perception in the Demarchi et al. dataset, and that existing evidence for feature-specific pre-activation during prediction in humans remains inconclusive.

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bioRxiv2025

Learning Amidst Noise: The Complementary Roles of Neural Predictive Activity and Representational Changes

The ability to extract structured sensory patterns from a noisy environment is fundamental to cognition, yet how the brain learns complex regularities remains unclear. Using magnetoencephalography during a visuomotor task, we tracked the neural dynamics as humans learned non-adjacent temporal dependencies embedded in noise. We reveal that learning is supported by two temporally dissociable mechanisms. Neural predictive activity emerged rapidly, with stimulus-specific patterns appearing before stimulus onset and preceding measurable behavioral improvements. This is followed by a slower build-up of representational change, characterized by an increased neural pattern similarity between statistically dependent, non-adjacent elements. Both processes are supported by a distributed consortium of networks, with the sensorimotor and dorsal attentional networks playing a central role. These findings suggest that both neural predictive activity and representational changes contribute to learning regularities, revealing a temporal hierarchy in which neural predictive activity precedes behavioral improvement and is followed by neural representational changes, possibly facilitating the gradual consolidation of knowledge into stable neural representations.

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bioRxiv2025

Modulation of error sensitivity during motor learning across time, space and environment variability

We never experience the exact same situation twice. In our dynamic and constantly changing environment, we continuously need to adapt our behavior, either due to external (e.g., a change of wind) or internal factors (e.g., muscle noise). Motor adaptation is the process of recalibration of movements in response to such perturbation. It activates when an agent needs to change their movement in response to a perceived error. It has been proposed that in order to perform motor adaptation, the brain continuously updates an error sensitivity signal that controls how much is learned from a past error. Such error sensitivity reflects the speed of learning, and understanding its dynamics is crucial to promoting efficient learning. In our experiment, healthy participants performed a visuomotor adaptation task requiring reaching multiple targets using a joystick. They experienced two periods of stable perturbations and two periods of random perturbations. In agreement with past research, we found that error sensitivity is lower in unstable environments. Moreover, error sensitivity is higher in a stable environment without perturbation than in a stable environment with perturbations. We observed a continuous increase of the error sensitivity within a stable perturbation environment. Finally, continuous spatial (across target locations) and temporal (across trials with same target) generalization of learning is present not only in stable but also in the random environment.

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bioRxiv2025

Decoding hypnotic consciousness: neural and experiential insights into induced and ideomotor suggestions

Hypnotic induction and ideomotor suggestions provide a powerful framework for investigating the remarkable capacity of verbal influence to reshape conscious experience, cognition, and motor control. We employed a multimodal approach combining high-density EEG, respiratory and behavioral monitoring, and first-person reports across three conditions: baseline resting state, progressive hypnotic induction (Light and Deep states), and an ideomotor task comparing a hypnotically suggested arm catalepsy to a voluntary simulation. EEG results revealed that light hypnosis was associated with early parieto-occipital alpha suppression and increased theta-band activity. As hypnosis deepened, frontoparietal connectivity increased in the theta while parasympathetic activation declined, challenging the view of hypnosis as a passive, low-arousal state and instead pointing to active top-down reorganization of large-scale brain networks. During the ideomotor state, participants exhibited distinct patterns of behavioral responsiveness, classified as tremblers and non-tremblers, despite reporting comparable disruptions in the sense of agency. Phenomenological analyses corroborated these distinctions, revealing that Tremblers attempted to move despite experiencing the action as involuntary or constrained, whereas Non-Tremblers refrained from acting due to a perceived impossibility or an inability to initiate the motor command. EEG connectivity analysis in Tremblers showed an increased frontoparietal gamma activity and reduced delta connectivity, suggesting heightened sensorimotor integration and greater executive monitoring under motor conflict. Together, these findings demonstrate that hypnosis engages dynamic top-down processes that reconfigure both neural connectivity and subjective experience depending on suggestion-types. They support predictive coding accounts of agency disruption and underscore the value of neurophenomenological methods for advancing consciousness science and informing clinical applications.

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bioRxiv2023

Humans progressively feel agency over events triggered before their actions

Artificial Intelligence (AI) has become increasingly efficient in anticipating our behavior. Will this impact, in the near future, how much we feel control over events generated with AI-assistance? This would in turn influence our decisions, actions, mental health and sense of responsibility. In everyday life, our sense of agency over events occurring at various delays after our actions has adapted to accommodate these delays. Here, we investigate whether our sense of agency can also adapt to an unusual situation where a consequence precedes an action. We used an online game in which players aimed to beat the computer at finding and clicking on a target to trigger an animation, while, in fact, an algorithm triggered the animation before the players click. The animation was not randomly controlled by the algorithm, but rather based on the history of the players past movements and on the beginning of their current movement. We used correlational, machine learning decoding, and modelling approaches to capture how players compute their reported sense of agency over the animation. We found evidence that, in less than an hour, players implicitly learned that the timing of the animation was related to their own actions, and adapted their sense of agency accordingly. Such findings may help us to anticipate how humans will integrate AI-assistance to guide their behavior.

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bioRxiv2022

Manipulating the rapid consolidation periods in a learning task affects general skills more than statistical learning

Memory consolidation processes have traditionally been investigated from the perspective of hours or days. However, the latest developments in memory research showed that memory consolidation processes could occur even within seconds, possibly due to the neural replay of just-practiced memory traces during short breaks. Here, we investigate this rapid form of consolidation during statistical learning. We aim to answer (a) whether this rapid consolidation occurs in implicit statistical learning and general skill learning and (b) whether the duration of rest periods affects these two learning types differently. Participants performed a widely used statistical learning task - the Alternating Serial Reaction Time (ASRT) task - that enables us to measure implicit statistical and general skill learning separately. The ASRT task consisted of 25 learning blocks with a rest period between the blocks. In a between-subjects design, the length of the rest periods was fixed at 15 or 30 seconds, or the participants could control the length themselves. We found that the duration of rest periods does not affect the amount of statistical knowledge acquired but does change the dynamics of learning. Shorter rest periods led to better learning during the learning blocks, whereas longer rest periods promoted learning also in the between block rest periods, possibly due to the higher amount of replay. Moreover, we found weaker general skill learning in the self-paced group than in the fixed rest period groups. These results suggest that distinct learning processes are differently affected by the duration of short rest periods.

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bioRxiv2021

Decoding individualized motor cortical excitability states from whole-brain electroencephalography

Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability states. Current approaches deliver TMS during brain states that correlate with motor cortex (M1) excitability at the group level. Here, we hypothesized that machine learning classifiers could successfully discriminate between high and low M1 excitability states in individual participants using information obtained from low-density electroencephalography (EEG) signals. To test this, we analyzed a publicly available dataset that delivered 600 single TMS pulses to the right M1 during EEG and electromyography (EMG) recordings in 20 healthy adults. Multivariate pattern classification was used to discriminate between brain states during which TMS evoked small and large motor-evoked potentials (MEPs). Results show that personalized classifiers successfully discriminated between low and high M1 excitability states in 80% of tested participants. MEPs elicited during classifier-predicted high excitability states were significantly larger than those elicited during classifier-predicted low excitability states in 90% of tested participants. Personalized classifiers did not generalize across participants. Overall, results show that individual participants exhibit unique brain activity patterns which predict low and high M1 excitability states and that these patterns can be efficiently captured using low-density EEG signals. Our findings suggest that deploying individualized classifiers during brain state-dependent TMS may enable fully personalized neuromodulation in the future.

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bioRxiv2021

Consolidation of human skill linked to waking hippocampo-neocortical replay

The introduction of rest intervals interspersed with practice strengthens wakeful consolidation of skill. The mechanisms by which the brain binds discrete action representations into consolidated, highly temporally-resolved skill sequences during waking rest are not known. To address this question, we recorded magnetoencephalography (MEG) during acquisition and rapid consolidation of a sequential motor skill. We report the presence of highly prominent, fast waking neural replay during the same rest periods in which rapid consolidation occurs. The observed replay was temporally compressed by approximately 20x relative to the acquired skill, occurred in both forward and reverse directions, was selective for the trained sequence and predicted the magnitude of skill consolidation. Replay representations extended beyond the hippocampus and entrorhinal cortex to the contralateral sensorimotor cortex. These results document the presence of robust hippocampo-neocortical replay supporting rapid wakeful consolidation of skill.

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bioRxiv2020

Statistical learning occurs during practice while high-order rule learning during rest period

Knowing when the brain learns is crucial for both the comprehension of memory formation and consolidation, and for developing new training and neurorehabilitation strategies in healthy and patient populations. Recently, a rapid form of offline learning developing during short rest periods has been shown to account for most of procedural learning, leading to the hypothesis that the brain mainly learns during rest between practice periods. Nonetheless, procedural learning has several subcomponents not disentangled in previous studies investigating learning dynamics, such as acquiring the statistical regularities of the task, or else the high-order rules that regulate its organization. Here, we analyzed 506 behavioral sessions of implicit visuomotor deterministic and probabilistic sequence learning tasks, allowing the distinction between general skill learning, statistical learning and high-order rule learning. Our results show that the temporal dynamics of apparently simultaneous learning processes differ. While general skill and high-order rule learning are acquired offline, statistical learning is evidenced online. These findings open new avenues on the short-scale temporal dynamics of learning and memory consolidation and reveal a fundamental distinction between statistical and high-order rule learning, the former benefiting from online evidence accumulation and the latter requiring short rest periods for rapid consolidation.

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bioRxiv2020

No evidence for ultra-fast consolidation in probabilistic learning, but time-dependent improvements in general skills

Memory consolidation has mainly been investigated for extended periods, from hours to days. Recent studies suggest that memory consolidation can also occur within shorter periods, from minutes to seconds. Our study aimed at determining (1) whether short rest periods lead to improvements in implicit probabilistic sequence learning and (2) whether length of rest duration influences such offline improvements. Participants performed an implicit probabilistic sequence learning task throughout 45 blocks. Between blocks, participants were allowed to rest and then to continue the task in their pace. The overall reaction times (general skill learning) shortened from pre- to post-rest periods, and this improvement was increased for longer rest durations. However, probabilistic sequences knowledge decreased in these periods, and this decrement was not related to the length of rest duration. These results suggest that (1) general skill learning but not probabilistic sequence knowledge benefits from short rest periods and, possibly, from memory consolidation, (2) ultra-fast offline improvements in general skills, but not forgetting in probabilistic sequence knowledge, are time-dependent. Overall, our findings highlight that ultra-fast consolidation differently affects distinct cognitive processes.

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bioRxiv2019

Voluntary movement initiation is not coupled to optimal sensorimotor oscillatory phases

Voluntary movement requires motor commands to be released from motor cortex (M1) and transmitted to spinal motoneurons and effector muscles. M1 activity oscillates between brief excitatory and inhibitory states that correlate with single neuron spiking rates. Here, we asked if the motor commands needed to produce voluntary, self-paced finger movements are preferentially released from M1 during restricted phases of this ongoing sensorimotor oscillatory activity. 21 healthy adults performed a self-paced finger movement task while EEG and EMG signals were recorded. For each finger movement, we identified the individual sensorimotor mu (8-12 Hz) and beta (13-35 Hz) oscillatory phase at the estimated time of motor command release from M1 by subtracting individually-defined MEP latencies from EMG-determined movement onset times. We report that motor commands were preferentially released at ~120{degrees} along the beta cycle but were released uniformly across the mu cycle. These results suggest that motor commands are preferentially released from M1 near optimal peak phases of endogenous beta rhythms.

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bioRxiv2019

Entrainment of local synchrony reveals a causal role for high-beta right frontal oscillations in human visual consciousness

Prior evidence supports the critical role of oscillatory activity in cognitive function, but are cerebral oscillations simply correlated or causally linked to specific aspects of visual cognition? Here, EEG signals were recorded on humans performing a conscious visual detection task, while they received brief rhythmic or random noninvasive stimulation patterns delivered to the right Frontal Eye Field prior to the onset of a lateralized target. Compared to random patterns, rhythmic high-beta patterns led to greater entrainment of local oscillations (i.e., increased power and phase alignment at the stimulation frequency), and to higher conscious detection of contralateral targets. When stimulation succeeded in enhancing visual detection, the magnitude of oscillation entrainment correlated with visual performance increases. Our study demonstrates a causal link between high-beta oscillatory activity in the Frontal Eye Field and conscious visual perception. Furthermore, it supports future applications of brain stimulation to manipulate local synchrony and improve or restore impaired visual behaviors.

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bioRxiv2018

Differential brain mechanisms of selection and maintenance of information during working memory

Working memory is our ability to select and temporarily hold information as needed for complex cognitive operations. The temporal dynamics of sustained and transient neural activity supporting the selection and holding of memory content is not known. To address this problem, we recorded magnetoencephalography (MEG) in healthy participants performing a retro-cue working memory task in which the selection rule and the memory content varied independently. Multivariate decoding and source analyses showed that selecting the memory content relies on prefrontal and parieto-occipital persistent oscillatory neural activity. By contrast, the memory content was reactivated in a distributed occipito-temporal posterior network, preceding the working memory decision and in a different format that during the visual encoding. These results identify a neural signature of content selection and characterize differentiated spatiotemporal constraints for subprocesses of working memory.

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