Biology subjects

Hinault, T. T.

Publications and source records attributed to Hinault, T. T..

13 recordsLinked to original sources

bioRxiv2026

Aging effects on retrospective and naturalistic encoding of time and their relationship with memory.

In everyday life, attention is not explicitly directed toward time. Yet, we can reconstruct how long an experience lasted from the memory traces it left behind, a process intertwined with episodic memory and subjective experience. As both memory and temporal processing change with advancing age, investigating aging effects could provide critical insights on these reconstructive processes. In this study, 31 younger (aged 24{+/-}3) and 36 older (aged 69{+/-}6) healthy adults completed two EEG sessions: 1) a resting-state recording followed by an unexpected retrospective time estimation, and 2) a video-based prospective duration estimation task where participants encoded and judged the duration of 50 naturalistic videos (2-32s) varying in eventfulness. Using a cycle-by-cycle burst analysis of alpha (: 8-12 Hz), we examined oscillatory correlates of both retrospective temporal judgement and prospective temporal processing. Behaviorally, both groups reconstructed short intervals similarly, but older adults produced longer prospective duration estimates than younger adults for longer video clips (16-32s), particularly under low-eventfulness. Regression analyses also showed that individual differences in older adults' prospective duration judgments were influenced by both their retrospective time estimation and episodic memory abilities, especially for longer videos. At the neural level, resting-state -burst dynamics were higher in older adults and predicted, with -power, longer retrospective estimates in older adults. Additionally, resting-state -burst activity predicted -burst during video encoding in both groups. Together, these findings reveal that individual -power and -burst dynamics could constitute memory-based time-tracking mechanism that aging reshapes in ways that manifest behaviorally under naturalistic conditions.

neuroscience↗

bioRxiv2026

How aging reshapes implicit learning: behavioral and oscillatory evidence from an EEG-adapted Fast Mapping paradigm.

Most of everyday memory formation occurs without deliberate intent, yet the neural mechanisms supporting implicit encoding and their reorganization with age remain poorly understood. The present study investigated age-related changes in implicit learning using an EEG-adapted Fast Mapping paradigm (the Disjunctive Inference Implicit Learning Test, DIILT) in 31 younger and 35 older adults, paired with a comprehensive neuropsychological assessment and a one-month delayed recognition measure. Behaviorally, younger adults rapidly acquired novel implicit associations across the learning phase, whereas older adults showed persistent reliance on familiar items and limited improvement for novel associations. Critically, both groups successfully retained novel associations after one month, with implicitly learned associations showing greater long-term stability than explicitly encoded ones regardless of age. At the neural level, younger adults exhibited early posterior theta engagement for novel compared to familiar items, which disappeared by the end of learning, consistent with novelty-driven associative encoding. Older adults showed no such theta modulation for novel items, pointing to reorganization of the neural mechanisms supporting implicit encoding. Theta dynamics predicted immediate recognition in younger adults but not in older adults who rather showed theta association with processing speed, suggesting an age-related shift from memory-specific binding to domain-general cognitive resource allocation, consistent with neural maintenance frameworks of cognitive aging. These findings demonstrate that implicit encoding produces durable memory traces across the lifespan but relies on reorganized oscillatory dynamics in aging, with implications for understanding how cognitive aging reshapes everyday memory formation.

neuroscience↗

bioRxiv2025

Diurnal dynamics of multilayer brain networks predict cognitive trajectories in aging

ObjectivesResting-state functional connectivity (rsFC) is a highly dynamic process that varies across different times of the day within each individual. Although this variability was long considered to be noise, recent evidence suggests it may allow for an optimal adaptation to changes in the environment. However, the way rsFC is shaped on a circadian scale and its association with cognition are still unclear. MethodsWe analyzed data from 90 late-middle-age participants from the Cognitive Fitness in Aging study (61 women; 50-69y). Participants completed five electroencephalographic (EEG) recordings of spontaneous resting-state activity spread over 20h of prolonged wakefulness. Using a temporal multilayer network approach, we characterized the diurnal variations of the dynamic recruitment and integration of resting-state brain networks. We focused on the theta and gamma frequency bands within the default mode network (DMN), central executive network (CEN), and salience network (SN). Additionally, we investigated the relationship recruitment and integration of these network with baseline cognitive performance and at 7-year longitudinal follow-up, as well as with positron emission tomography (PET) early neuropathological markers of Alzheimers disease such as {beta}-amyloid and tau/neuroinflammation. ResultsDiurnal changes in theta and gamma dynamics were associated with distinct cognitive aspects. Specifically, higher baseline memory performance was associated with higher theta dynamic integration of the SN and the CEN, as well as higher theta dynamic recruitment of the DMN. Moreover, lower longitudinal memory decline at 7-year was associated with higher theta dynamic integration of the SN, CEN, and DMN. In contrast, higher gamma diurnal dynamic integration of the SN and the CEN was associated with lower executive and attentional performance, as well as higher early {beta}-amyloid accumulation, at baseline. DiscussionThese findings suggest that maintaining a balance between network flexibility and stability throughout the diurnal phase of the circadian cycle may play a crucial role in cognitive aging, with stable theta-band connectivity supporting memory, whereas excessive gamma-band stability in the SN and CEN may contribute to executive decline and early amyloid accumulation. These insights highlight the importance of considering time-of-day in brain rsFC studies, calling for a temporal multilayer approach to capture these dynamic patterns more effectively.

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bioRxiv2024

The influence of the head model on magnetoencephalography-derived functional connectivity fingerprinting

Functional connectivity (FC)-based neural fingerprinting is an approach that promises to identify and/or differentiate subjects within a cohort on the basis of the patterns of statistical dependencies between time series recorded mostly if not always noninvasively, with electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). The message is that brain activity is what differentiates subjects, or what makes a neural fingerprint "unique". In EEG- and MEG-derived FC fingerprinting, the activity recorded at the sensors is usually projected back into cortical sources by means of an inverse model depending on head and brain shapes, sensor locations, and tissue conductivity, and further reduced in dimension to obtain time series of regional activity, used to compute FC. We think that the role of the head model in fingerprinting has been so far dismissed by means of suboptimal or incomplete tests. Here we employed a set of experiments aimed to decouple recorded activity and head model for each subject, and we found that the head model has a strong influence on both identification and differentiation.

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bioRxiv2023

The multiscale topological organization of the functional brain network in adolescent PTSD

The experience of an extremely aversive event can produce enduring deleterious behavioral and neural consequences, among which posttraumatic stress disorder (PTSD) is a representative example. In this work, we aim to study the whole-cortex functional organization of adolescents with PTSD without the a priori selection of specific regions of interest or functional networks. To do so, we built on the network neuroscience framework and specifically on multisubject community analysis to study the functional connectivity of the brain. We show, across different topological scales (the number of communities composing the cortex), an increased coupling between regions belonging to unimodal (sensory) regions and a reduced coupling between transmodal (association) regions in the adolescent PTSD group. These results open up an intriguing possibility concerning an altered large-scale cortical organization in adolescent PTSD. Significance StatementThe understanding of brain responses following the experience of a traumatic event and the eventual apparition of posttraumatic stress disorder (PTSD) symptoms during youth remains an active topic of research in clinical neuroscience. We adapted a multisubject community detection algorithm to study the functional brain organization of PTSD from a whole cortex and multiscale topological perspective, thereby taking into account the complex (network) organization of the brain. PTSD patients and control subjects presented a different whole cortex functional organization that remained present across topological scales. PTSD patients showed a decreased interaction in transmodal cortices and, inversely, an enhanced interaction in unimodal cortices. This investigation highlights the importance of studying the brain from a complex and whole cortex perspective.

neuroscience↗

bioRxiv2023

Daily dynamics of resting-state EEG theta and gamma fluctuations are associated with cognitive performance in healthy aging

Healthy age-related cognitive changes are highly heterogeneous across individuals. This variability is increasingly explained through the lens of spontaneous fluctuations of brain activity, now considered as powerful index of age-related changes. However, brain activity is a biological process modulated by circadian rhythms, and how these fluctuations evolve throughout the day is under investigated. Assessing the daily dynamics of brain fluctuations involves the use of techniques measuring the temporal dynamics of synchronized communications between brain regions, such as electroencephalography. We found that theta and gamma daily fluctuations in the salience-control executive inter-network (SN-CEN) are associated with distinct mechanisms underlying cognitive heterogeneity in aging. Higher levels of SN-CEN theta daily fluctuations appear to be deleterious for memory performance and were associated with higher tau/neuroinflammation rates. In contrast, higher levels of gamma daily fluctuations are positively associated with executive performance, and were associated with lower rate of {beta}-amyloid deposition. Thus, accounting for daily EEG fluctuations of brain activity contributes to better understand subtle brain changes underlying individuals cognitive performance in healthy aging.

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bioRxiv2023

Linking structural and functional changes during aging using multilayer brain network analysis

Brain structure and function are intimately linked, however this association remains poorly understood of the complexity of this relationship has remained understudied. Healthy aging is characterized by heterogenous levels of structural integrity changes that influence functional network dynamics. Here, we used the multilayer brain network analysis on structural (diffusion tensor imaging) and functional (magnetoencephalography) data from the Cam-CAN database. We found that the level of similarity of connectivity patterns between brain structure and function in the parietal and temporal regions (alpha frequency band) was associated with cognitive performance in healthy older individuals. These results highlight the impact of structural connectivity changes on the reorganisation of functional connectivity associated with the preservation of cognitive function, and provide a mechanistic understanding of the concepts of brain maintenance and compensation with aging. Investigation of the link between structure and function could thus represent a new marker of individual variability, and of pathological changes.

neuroscience↗

bioRxiv2022

Age-related changes of deep-brain neurophysiological activity

Cognitive decline with age is associated with brain atrophy and reduced brain activations, but the underlying neurophysiological mechanisms are unclear, especially in deeper brain structures primarily affected by healthy aging or neurodegenerative processes. Here, we characterize time-resolved, resting-state magnetoencephalography activity of the hippocampus and subcortical brain regions in a large cohort of healthy young and older volunteers from the Cam-CAN open repository. The data show age-related changes in both rhythmic and arrhythmic signal strength and temporal variability in multiple deeper brain regions, including the hippocampus, striatum, and thalamus. We observe a slowing of neural activity in deeper brain regions, which echoes previous reports of cortical slowing. We also report reduced occipito-parietal alpha peak associated with increased theta-band activity and signal variability in the hippocampus, an effect that may reflect compensatory processes as theta activity and variability were more strongly expressed when cognitive performances are preserved. Overall, this study advances the understanding of the biological nature of inter-individual variability in aging. The data provide new insight of how hippocampus and subcortical neurophysiological activity evolve with biological age, and highlight frequency-specific effects associated with cognitive decline vs. cognitive maintenance.

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bioRxiv2022

Connectivity dynamics and cognitive variability during aging

Aging is associated by cognitive changes, with strong variations across individuals. One way to characterize this individual variability is to use techniques such as magnetoencephalography (MEG) to measure the dynamics of neural synchronization between brain regions, and the variability of this connectivity over time. Indeed, few studies have focused on fluctuations in the dynamics of brain networks over time and their evolution with age. We therefore characterize aging effects on MEG phase synchrony in healthy young and older adults from the Cam-CAN database. Age-related changes were observed, with an increase in the variability of brain synchronization, as well as a reversal of the direction of information transfer in the default mode network (DMN), in the delta frequency band. These changes in functional connectivity were associated with cognitive decline. Results suggest that advancing age is accompanied by a functional disorganization of dynamic networks, with a loss of communication stability and a decrease in the information transmitted. This could be partly due to the loss of integrity of the network structure.

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bioRxiv2021

Dynamic Cognitive States Predict Individual Variability in Behavior and Modulate with EEG Functional Connectivity during Working Memory

Fluctuations in strategy, attention, or motivation can cause large variability in performance across task trials. Typically, this variability is treated as noise, and assumed to cancel out, leaving supposedly stable relationships among behavior, neural activity, and experimental task conditions. Those relationships, however, could change with a participants internal cognitive states, and variability in performance may carry important information regarding those states, which cannot be directly measured. Therefore, we used a mathematical, state-space modeling framework to estimate internal states from measured behavioral data, quantifying each participants sensitivity to factors such as past errors or distractions, to predict their reaction time fluctuations. We show how modeling these states greatly improves trial-by-trial prediction of behavior. Further, we identify EEG functional connectivity features that modulate with each state. These results illustrate the potential of this approach and how it could enable quantification of intra- and inter-individual differences and provide insight into their neural bases. Statement of RelevanceCognitive behavioral performance and its neural bases vary both across individuals and within individuals over time. Understanding this variability may be key to the success of clinical or educational interventions. Internal cognitive states reflecting differences in strategy, attention, and motivation may drive much of these inter- and intra-individual differences, but often cannot be reliably controlled or measured in cognitive neuroscience research. The mathematical modeling framework developed here uses measured data to estimate a participants dynamic, internal cognitive states, with each state derived from specific factors hypothesized to affect attention, motivation or strategy. The results highlight potential sources of behavioral variability and reveal EEG features that modulate with each state. Our method quantifies and characterizes individual behavioral differences and highlights their underlying neural mechanisms, which could be used for future targeted training or neuromodulation therapies to improve cognitive performance.

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bioRxiv2020

Age-related differences in network structure and dynamic synchrony of cognitive control

Cognitive trajectories vary greatly across older individuals, and the neural mechanisms underlying these differences remain poorly understood. Here, we propose a mechanistic framework of cognitive variability in older adults, linking the influence of white matter microstructure on fast and effective communications between brain regions. Using diffusion tensor imaging and electroencephalography, we show that individual differences in white matter network organization are associated with network clustering and efficiency in the alpha and high-gamma bands, and that functional network dynamics partly explain individual cognitive control performance in older adults. We show that older individuals with high versus low structural network clustering differ in task-related network dynamics and cognitive performance. These findings were corroborated by investigating magnetoencephalography networks in an independent dataset. This multimodal brain connectivity framework of individual differences provides a holistic account of how differences in white matter microstructure underlie age-related variability in dynamic network organization and cognitive performance.

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bioRxiv2020

Evidence and urgency-related EEG signals during dynamic decision-making in humans

A successful class of models link decision-making to brain signals by assuming that evidence accumulates to a decision threshold. These evidence accumulation models have identified neuronal activity that appears to reflect sensory evidence and decision variables that drive behavior. More recently, an additional evidence-independent and time-variant signal, named urgency, has been hypothesized to accelerate decisions in the face of insufficient evidence. However, most decision-making paradigms tested with fMRI or EEG in humans have not been designed to disentangle evidence accumulation from urgency. Here we use a face-morphing decision-making task in combination with EEG and a hierarchical Bayesian model to identify neural signals related to sensory and decision variables, and to test the urgency-gating model. We find that an evoked potential time-locked to the decision, the centroparietal positivity, reflects the decision variable from the computational model. We further show that the unfolding of this signal throughout the decision process best reflects the product of sensory evidence and an evidence-independent urgency signal. Urgency varied across subjects, suggesting that it may represent an individual trait. Our results show that it is possible to use EEG to distinguish neural signals related to sensory evidence accumulation, decision variables, and urgency. These mechanisms expose principles of cognitive function in general and may have applications to the study of pathological decision-making as in impulse control and addictive disorders. Significance StatementPerceptual decisions are often described by a class of models that assumes sensory evidence accumulates gradually over time until a decision threshold is reached. In the present study, we demonstrate that an additional urgency signal impacts how decisions are formed. This endogenous signal encourages one to respond as time elapses. We found that neural decision signals measured by EEG reflect the product of sensory evidence and an evidence-independent urgency signal. A nuanced understanding of human decisions, and the neural mechanisms that support it, can improve decision-making in many situations and potentially ameliorate dysfunction when it has gone awry.

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bioRxiv2019

Disrupted neural synchrony mediates the relationship between white matter integrity and cognitive performance in older adults.

Our main goal was to determine the influence of white matter integrity on the dynamic coupling between brain regions and the individual variability of cognitive performance in older adults. EEG was recorded while participants performed a task specifically designed to engage working memory and inhibitory processes, and the associations among functional activity, structural integrity, and cognitive performance were assessed. We found that the association between white matter microstructural integrity and cognitive functioning with aging is mediated by time-varying alpha and gamma phase-locking value (PLV). Specifically, older individuals with better preservation of the inferior fronto-occipital fasciculus showed greater task-related modulations of alpha and gamma long-range PLV between the inferior frontal gyrus and occipital lobe, lower local phase-amplitude coupling in occipital lobes, and better cognitive control performance. Our results help delineate the role of individual variability of white matter microstructure in dynamic synchrony and cognitive performance during normal aging, and show that even small reductions in white matter integrity can lead to altered communications between brain regions, which in turn can result in reduced efficiency of cognitive functioning. Significance statementCognitive aging is associated with large individual differences, as some individuals maintain cognitive performance similar to that of young adults while others are significantly impaired. We hypothesized that individual differences in white matter integrity would influence the functional synchrony between frontal and posterior brain regions, and cognitive performance in older adults. We found that the association between reduced tract integrity and worse cognitive performance in older adults was mediated by task-related modulations of coupling synchrony in the alpha and gamma bands. Results offer a mechanistic explanation for the neural basis of the variability of cognitive performance in older adults who do not have any clinically diagnosable neuropathology, and for the association between structural network integrity and cognition in older adults.

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