Biology subjects

Bekinschtein, T. A.

Publications and source records attributed to Bekinschtein, T. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Multisensory perceptual decision-making persists as alertness fades, even in the absence of an overt response

Momentary declines in alertness are common in everyday life, yet we must continue to make rapid and accurate decisions from multiple sensory signals that may converge or conflict. Here we investigated multisensory perceptual decision-making across the wake-sleep transition. Participants performed an audio-tactile spatial localisation task while electroencephalography tracked spontaneous fluctuations in alertness. Although drowsiness slowed responses and increased omissions, participants remained largely accurate, and the behavioural influence of congruent and conflicting tactile information persisted. Behavioural modelling revealed slower evidence accumulation and increased decision boundaries. Stimulus- and response-related neural patterns remained robustly decodable across alertness states, despite changes in their temporal dynamics but multisensory congruency was no longer reliably decoded during drowsiness. Brain signatures of lateralized motor preparation persisted during behavioural omissions and, under multisensory conflict, might even favour the competing response. Declining alertness does not uniformly degrade multisensory perceptual decision-making but selectively alters the processes linking sensory information to action.

neuroscience↗

bioRxiv2026

Thalamic and cortical signals synergistically represent auditory prediction errors

Prediction errors (PEs) are commonly described as cortical signals generated within sensory hierarchies, but whether the thalamus participates in their encoding and transmission remains unclear. We recorded Local Field Potentials (LFP) from the medial and lateral geniculate nuclei and electrocorticography (ECoG) from multiple cortical regions in three awake cats during two auditory prediction tasks. Mutual information (MI) analyses revealed PE encoding in both thalamic and cortical signals. Co-information (co-I) analyses showed off-diagonal temporal synergy between early and later thalamic response components, consistent with an early response inducing a neural state change that shaped the informational content of subsequent activity. Multivariate co-information (MVCo-I) further revealed that thalamic and cortical population activity carried complementary PE information unavailable from either thalamic or cortical areas alone. These synergistic interactions were reliable across animals for violations of structured auditory sequences and weaker for repetition-based deviants. These findings show that auditory PEs are not simply relayed or duplicated across the thalamocortical hierarchy. Instead, they emerge through state-dependent transformations within the thalamus and complementary interactions between thalamic and cortical populations, identifying the thalamus as an active node of context-dependent PE processing.

neuroscience↗

bioRxiv2026

Attention reshapes the information dynamics of thalamic and cortical prediction-error learning

Prediction errors (PEs) drive perceptual learning by updating internal models of the sensory environment, yet it remains unclear how attention reshapes their representation across distributed thalamocortical circuits. Using intracranial stereoelectroencephalography (sEEG) from 17 patients performing a roving auditory oddball task under attended and unattended conditions, we quantified PE encoding using mutual information and co-information to capture redundant and synergistic PE representations. Attention modulated PE encoding in both the thalamus and the temporal cortex, but with distinct informational dynamics. Thalamic encoding showed a stable reduction of PE information during distraction, consistent with state-dependent thalamocortical gating. In contrast, the temporal cortex expressed two opposing learning trajectories during attended listening that converged once attention was diverted, revealing distinct cortical learning regimes rather than a uniform attentional effect. Attention further reorganized the informational content of cortical PE representations by altering the balance between redundant and synergistic information. A biologically constrained neural network showed that attention-dependent changes in inhibition and long-range connectivity reproduced these dynamics through Hebbian learning. Together, these findings suggest that attention regulates predictive learning not simply by changing the strength of PE responses, but by reshaping how distributed thalamocortical circuits represent and integrate sensory evidence over time.

neuroscience↗

bioRxiv2026

Drowsiness alters the neural dynamics but not the core computations of multisensory integration

Fluctuations in alertness shape perception and behaviour, yet how they affect the brains ability to integrate information across senses remains poorly understood. Here we investigated whether multisensory integration is preserved as humans transition from wakefulness to sleep. Participants (18 females, 8 males) performed an audio-tactile detection task while electroencephalography tracked spontaneous declines in alertness. Behaviourally, multisensory stimulation continued to facilitate responses during drowsiness, despite slowing and increased omissions. Although race-model predictions were consistent with preserved evidence for multisensory integration, event-related signatures of multisensory interactions were attenuated or no longer detectable during drowsiness. Crucially, multivariate decoding showed that multisensory neural representations remained detectable, although less temporally stable, and early cross-state generalization revealed shared representational structure across alertness levels, indicating preserved core multisensory computations. These findings show that multisensory integration remains functional but dynamically altered as alertness declines, revealing how the brain maintains adaptive behaviour during the wake-to-sleep transition. Significance StatementMultisensory integration has often been described as a stable and automatic perceptual process. However, growing evidence indicates that it can be modulated by ongoing brain states and cognitive demands. Here, we ask whether this computation remains invariant as alertness declines. We show that multisensory integration is neither abolished nor fully invariant during drowsiness. Instead, behavioural benefits are maintained despite less stable neural dynamics. Although event-related responses are attenuated, core multisensory representations remain shared across levels of alertness. These findings indicate that multisensory integration is preserved across fluctuations in alertness, while their neural dynamics are flexibly altered as alertness decreases.

neuroscience↗

bioRxiv2023

High arousal disrupts the neural signatures of conflict processing but not behavioural performance

Physical activation fluctuates throughout the day. Previous studies have shown that during periods of reduced activation, cognitive control remains resilient due to neural compensatory mechanisms. In this study, we in-vestigated the effects of high physical activation on both behavioural and neural markers of cognitive control. We hypothesized that while behavioural measures of cognitive control would remain intact during periods of high activation, there would be observable changes in neural correlates. In our electroencephalography study, we manipulated levels of physical activation through physical exercise. Although we observed no significant impact on behavioural measures of cognitive conflict, both univariate and multivariate time-frequency markers proved unreliable under conditions of high activation. Moreover, there was no modulation of whole-brain connectivity measures by physical activation. We suggest that this dissociation between behavioural and neural measures indicates that the human cognitive control system remains resilient even at high activation, possibly due to underlying neural compensatory mechanisms.

neuroscience↗

bioRxiv2023

Brain network dynamics in transitions of consciousness reorganize according to task engagement

Substantial changes in behavior, physiology, and brain function occur when alertness decreases 1- 5. These changes in brain function involve increased synchronization between cortical areas 6,7 as well as alterations in sensory processing pathways and networks connecting the thalamus and cortex 5,8-11. Cognitive tasks engage overlapping functional networks with sensory pathways facilitating information processing 12,13, and thalamocortical and corticocortical networks supporting task performance 14,15. Frontoparietal circuits play a crucial role in cognitive tasks 16 and states of decreased consciousness 17. To develop an integrated framework of consciousness and cognition, it is important to understand how fluctuations in alertness and cognitive processing interact in these shared circuits 18. Our hypothesis is that during periods of low alertness, individuals who actively maintain task engagement would recruit additional frontoparietal and sensory processing networks, while thalamocortical dynamics that typically change during sleep onset would remain unaffected. Our findings demonstrated that as alertness decreased, passively listening to auditory tones led to increased synchronization in the parietal lobe, whereas actively performing an auditory task resulted in increased long-range frontoparietal synchronization. During decreasing alertness, passive listening (but not active task engagement) was associated with widespread increased synchronization between the thalamus and cortex. In contrast, active task engagement (but not passive listening) led to increased synchronization between the auditory cortex and the rest of the brain. These results reveal the functional mechanisms of the brains flexible reorganization during transitions of consciousness when individuals are actively engaged in cognitive processes.

neuroscience↗

bioRxiv2023

Staying in control: characterising the mechanisms underlying cognitive control in high and low arousal states

Throughout the day, humans show natural fluctuations in arousal that impact cognitive function. To study the behavioural dynamics of cognitive control during high and low arousal states, healthy participants performed an auditory conflict task during high-intensity physical exercise (N = 39) or drowsiness (N = 33). In line with the pre-registered hypotheses, conflict and conflict adaptation effects were preserved during both altered arousal states. Overall task performance was markedly poorer during low arousal, but not for high arousal. Modelling behavioural dynamics with drift-diffusion analysis revealed evidence accumulation and non-decision time decelerated, and decisional boundaries became wider during low arousal, whereas high arousal was unexpectedly associated with a decrease in the interference of task-irrelevant information processing. These findings show how arousal differentially modulates cognitive control at both sides of normal alertness, and further validates drowsiness and physical exercise as key experimental models to disentangle the interaction between physiological fluctuations on cognitive dynamics.

neuroscience↗

bioRxiv2023

Neural and behavioural dynamics of the individual and collective self(s)

Usually considered as internal representations of self-concepts, the individual-self and the collective-self have been primarily studied in social and personality psychology while the experimental and theoretical advances of the cognitive and neurophysiological mechanisms of these self-representations are poorly understood. Two competing hypotheses emerge to understand their structure: first, each self-representation corresponds to a specific and independent dimension of self-concept and is therefore conceptualized as separate cognitive components and different brain networks are predicted; and second, both "selfs", collective and individual, are part of the same structure and interdependent, sharing similar networks and showing a hierarchical organization from a core-self. Both perspectives have some support from current theories in social psychology but are still speculative and faintly supported by empirical evidence. To test this, we designed an experiment using sentences that would activate the individual or collective-self representations in 80 healthy right-handed participants. We use reaction times during a decision-making task, in combination with individualism/collectivism scales and characterize the neural dynamics throughout the experiment using event-related potentials and fronto-parietal informational connectivity networks. Participants reacted slower to the collective than individual self conditions and showed differences in neural activity and information Integration level that distinguished between each type of self. More importantly, the neural integration measure representing the core-self (subtraction between individual and collective wSMI) was only associated with the individualism scores but not collectivism, lending further support for the core-self perspective. We interpret that the collective self, in the broader sense, is a part of the self-concept and therefore probably assembled from the core-self.

animal behavior and cognition↗

bioRxiv2022

Can neural correlates of encoding explain the context dependence of reward-enhanced memory?

Selective encoding can be studied by manipulating how valuable it is for participants to remember specific stimuli, for instance by varying the monetary reward participants receive for recalling a particular stimulus in a subsequent memory test. It would be reasonable for participants to strategically attend more to high-reward items compared to low-reward items in mixed list contexts, but to attend both types of items equally in pure list contexts, where all items are of equal value. Reward-enhanced memory may be driven by automatic dopaminergic interactions between reward circuitry and the hippocampus and thus be insensitive to list context; or it may be driven by meta-cognitive strategies, and thus context-dependent. We contrasted these alternatives by manipulating list composition, and tracked selective encoding through multiple EEG measures of attention and rehearsal. Behavioural results were context-dependent, such that recall of high-reward items was increased only in mixed lists. This result and aspects of the recall dynamics confirm predictions of the eCMR (emotional Context Maintenance and Retrieval) model. The power of ssVEPs was lower for high-reward items regardless of list composition, suggesting decreased visual processing of high-reward stimuli and that ssVEPs may index the modulation of context-to-item associations predicted by eCMR. By contrast, reward modulated the amplitude of Late Positive Potential and Frontal Slow Wave only in mixed lists. Taken together the results provide evidence that reward-enhanced memory is caused by an interplay between strategic processes applied when high- and low-reward items compete for cognitive resources during encoding and context-dependent mechanisms operating during recall.

neuroscience↗

bioRxiv2021

Direct brain recordings reveal continuous encoding of structure in random stimuli

The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to the creation of sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where this modeling takes place is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Linking the frame-works of statistical learning and predictive processing, our work illuminates an implicit process that can be crucial for the swift detection of patterns and unexpected events in the environment.

neuroscience↗

bioRxiv2021

Fluctuations in Neural Complexity During Wakefulness Relate To Conscious Level and Cognition

There has been considerable recent progress in measuring conscious level using neural complexity measures. For instance, such measures can reliably distinguish healthy awake from asleep subjects and vegetative state patients. However, this line of research has never explored the dynamics of conscious level during normal wakefulness. Being able to capture meaningful differences in conscious level during wakefulness may provide a vital new insight into the nature of consciousness, by demonstrating what biological, behavioural and cognitive factors relate to such differences. Here we take advantage of a large MEG and fMRI dataset of healthy adults, to examine within-subject conscious level fluctuations during resting state and tasks, by using a range of complexity measures. We first establish the validity of this approach in both neuroimaging domains by relating neural complexity measures to pre-existing techniques for capturing transitions of consciousness from full wakefulness into drowsiness and the earliest stages of sleep, finding decreased complexity as participants become increasingly drowsy. We further demonstrate that neural complexity measures in both MEG and fMRI change both within and between tasks, and relate to performance on an executive task, with higher complexity associated with better performance and faster reaction times. This approach provides a powerful new route to further explore the cognitive and neural underpinnings of consciousness.

neuroscience↗

bioRxiv2021

Ketamine and sleep modulate neural complexity dynamics in cats

There is increasing evidence that level of consciousness can be captured by neural informational complexity: for instance, complexity, as measured by the Lempel Ziv (LZ) compression algorithm, decreases during anesthesia and non-rapid eye movement (NREM) sleep in humans and rats, when compared to LZ in awake and REM sleep. In contrast, LZ is higher in humans under the effect of psychedelics, including subanesthetic doses of ketamine. However, it is both unclear how this result would be modulated by varying ketamine doses, and whether it would extend to other species. Here we studied LZ with and without auditory stimulation during wakefulness and different sleep stages in 5 cats implanted with intracranial electrodes, as well as under subanesthetic doses of ketamine (5, 10, and 15 mg/kg i.m.). In line with previous results, LZ was lowest in NREM sleep, but similar in REM and wakefulness. Furthermore, we found an inverted U-shaped curve following different levels of ketamine doses in a subset of electrodes, primarily in prefrontal cortex. However, it is worth noting that the variability in the ketamine dose-response curve across cats and cortices was larger than that in the sleep-stage data, highlighting the differential local dynamics created by two different ways of modulating conscious state. These results replicate previous findings, both in humans and other species, demonstrating that neural complexity is highly sensitive to capture state changes between wake and sleep stages while adding a local cortical description. Finally, this study describes the differential effects of ketamine doses, replicating a rise in complexity for low doses, and further fall as doses approach anesthetic levels in a differential manner depending on the cortex. In briefPrevious studies have shown that Lempel Ziv complexity (LZ) decreases during anesthesia and non-rapid eyes movement (NREM) sleep in humans and rats whereas it increases in REM sleep and under the effect of psychedelics. In this work we show that in the cat, LZ is lowest in NREM sleep, but similar in REM and wakefulness. We also found a ketamine inverted U-shape dose-response curve only in the auditory and prefrontal cortex, with a much larger variability in the ketamine across cats and cortices when compared to the sleep cycle. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/449513v2_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@1bab0d0org.highwire.dtl.DTLVardef@1ae33dborg.highwire.dtl.DTLVardef@1cea0f6org.highwire.dtl.DTLVardef@a9b1e0_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Different underlying mechanisms for high and low arousal in probabilistic learning in humans

Humans are uniquely capable of adapting to highly changing environments by updating relevant information and adjusting ongoing behaviour accordingly. Here we show how this ability --termed cognitive flexibility-- is differentially modulated by high and low arousal fluctuations. We implemented a probabilistic reversal learning paradigm in healthy participants as they transitioned towards sleep or physical extenuation. The results revealed, in line with our pre-registered hypotheses, that low arousal leads to diminished behavioural performance through increased decision volatility, while performance decline under high arousal was attributed to increased perseverative behaviour. These findings provide evidence for distinct patterns of maladaptive decision-making on each side of the arousal inverted u-shaped curve, differentially affecting participants ability to generate stable evidence-based strategies, and introduces wake-sleep and physical exercise transitions as complementary experimental models for investigating neural and cognitive dynamics.

neuroscience↗

bioRxiv2020

Alertness modulates perceptual decision-making

The ability to make decisions based on external information, prior knowledge and evidence, is a crucial aspect of cognition and may determine the success and survival of an organism. Despite extensive work on decision-making mechanisms/models, understanding the effects of alertness on neural and cognitive processes remain limited. Here we use electroencephalography and behavioural modelling to characterise cognitive and neural dynamics of perceptual decision-making in awake/low alertness periods in humans (14 male, 18 female) and characterise the compensatory mechanisms as alertness decreases. Well-rested human participants, changing between full-wakefulness and low alertness, performed an auditory tone-localisation task and its behavioural dynamics was quantified with psychophysics, signal detection theory and drift-diffusion modelling, revealing slower reaction times, inattention to the left side of space, and a lower rate of evidence accumulation in periods of low alertness. Unconstrained multivariate pattern analysis (decoding) showed a ~280ms delayed onset driven by low alertness of the neural signatures differentiating between left and right decision, with a spatial reconfiguration from centro-parietal to lateral frontal regions 150-360ms. To understand the neural compensatory mechanisms with decreasing alertness, we connected the evidence-accumulation behavioural parameter to the neural activity, showing in the early periods (125-325ms) a shift in the associated patterns from right parietal regions in awake, to right fronto-parietal during low alertness. This change in the neurobehavioural dynamics for central accumulation related cognitive processes define a clear reconfiguration of the brain networks regions and dynamics needed for the implementation of decision-making, revealing mechanisms of resilience of cognition when challenged by decreased alertness. Significance statementMost living organisms make multiple daily decisions and these require a degree of evidence from both the environment and the internal milieu. Such decisions are usually studied under sequential sampling models and involve making a behavioural choice based on sensory encoding, central accumulation, and motor implementation processes. Since there is little research on how decreasing alertness affects such cognitive processes, this study has looked at the cognitive and neural dynamics of perceptual decision-making in people while fully awake and in drowsy periods. Using computational modelling of behaviour and neural dynamics on human participants performing an auditory tone-localisation task, we reveal how low alertness modulates evidence accumulation related processes and its corresponding compensatory neural signatures.

neuroscience↗

bioRxiv2019

Decreased alertness reconfigures cognitive control networks

Humans remarkable capacity to flexibly adapt their behaviour based on rapid situational changes is termed cognitive control. Intuitively, cognitive control is thought to be affected by the state of alertness, for example, when drowsy we feel less capable of adequately implementing effortful cognitive tasks. Although scientific investigations have focused on the effects of sleep deprivation and circadian time, little is known about how natural daily fluctuations in alertness in the regular awake state affect cognitive control. Here we combined a conflict task in the auditory domain with EEG neurodynamics to test how neural and behavioural markers of conflict processing are affected by fluctuations in alertness. Using a novel computational method, we segregated alert and drowsy trials from two testing sessions and observed that, although participants (both sexes) were generally sluggish, the typical Conflict Effect reflected in slower responses to conflicting information compared to non-conflicting information was still intact, as well as the moderating effect of previous conflict (Conflict Adaptation). However, the typical neural markers of cognitive control-local midfrontal-theta band power changes-that participants show during full alertness were no longer noticeable when alertness decreased. Instead, when drowsy, we found an increase in long-range information sharing (connectivity) between brain regions in the same frequency band. These results show the resilience of the human cognitive control system when affected by internal fluctuations of alertness, and suggest neural compensatory mechanisms at play in response to physiological pressure during diminished alertness. Significance StatementThe normal variability in alertness we experience in daily tasks is rarely taking into account in cognitive neuroscience. Here we studied neurobehavioral dynamics of cognitive control with decreasing alertness. We used the classic Simon Task where participants hear the word "left" or "right" in the right or left ear, eliciting slower responses when the word and the side are incongruent - the conflict effect. Participants performed the task both while fully awake and while getting drowsy, allowing for the characterisation of alertness modulating cognitive control. The changes in the neural signatures of conflict from local theta oscillations to a long-distance distributed theta network suggests a reconfiguration of the underlying neural processes subserving cognitive control when affected by alertness fluctuations.

neuroscience↗

bioRxiv2018

Neural signatures of classical conditioning during human sleep

Recent evidence indicate that humans can learn entirely new information during sleep. To elucidate the neural dynamics underlying sleep-learning we investigated brain activity during auditory-olfactory discriminatory associative learning in human sleep. We found that learning-related delta and sigma neural changes are involved in early acquisition stages, when new associations are being formed. In contrast, learning-related theta activity emerged in later stages of the learning process, after tone-odour associations were already established. These findings suggest that learning new associations during sleep is signalled by a dynamic interplay between slow-waves, sigma and theta activity.

neuroscience↗

bioRxiv2017

Transient topographical dynamics of the electroencephalogram predict brain connectivity and behavioural responsiveness during drowsiness

As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of frontoparietal alpha networks and the emergence of frontoparietal theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth.\n\nAuthor summaryHow do we lose responsiveness as we fall asleep? As we become sleepy, our ability to react to external stimuli disappears gradually. Here we sought to understand the rapid fluctuations in brain electrical activity that predict the loss of responsiveness as participants fell asleep while performing a word classification task. We analysed the patterns of connectivity between anterior and posterior brain regions observed during wakefulness in alpha band and showed that this connectivity shifted to slower theta frequencies as participants became unresponsive. We also investigated the dynamics of brain electrical microstates, which represent an alphabet of quasi-stable global brain states with lifetimes of 10-100 milliseconds, and found that the temporal dynamics of microstates slowed down when participants became unresponsive. Using machine learning, we further showed that microstate dynamics prior to a stimulus predict whether subjects will respond to it. We integrated microstates and connectivity for the first time to show that a specific microstate captures connectivity patterns correlated with unresponsiveness during this transition. We conclude that falling asleep is accompanied by a millisecond-level interplay between distinct brain networks, and suggest a renewed focus on fine-grained temporal scales in the study of transitions between levels of consciousness.

neuroscience↗

bioRxiv2017

Tracking wakefulness as it fades: micro-measures of Alertness

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method based on frequency and sleep graphoelements, which is capable of detecting micro variations in alertness. The validity of the automated method was verified by training and testing the algorithm using a dataset where participants are known to fall asleep. In addition, we tested generalizability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience.

neuroscience↗