Biology subjects

Chatburn, A.

Publications and source records attributed to Chatburn, A..

15 recordsLinked to original sources

bioRxiv2026

Using the aperiodic slope across learning to predict sleep-based memory consolidation

Homeostatic mechanisms adjust synaptic strengths to regulate neuronal activity across wake-sleep cycles. Although sleep supports memory consolidation, limited research has explored how sleep-induced homeostatic changes influence memory outcomes in humans. The EEG-derived aperiodic slope (1/f) could be a candidate measure of homeostatic memory processes through its reflection of neural excitation-inhibition balance. In this study, 27 participants learned object-word pairs, and completed an immediate recognition test before either a nap or equivalent wakeful period. Resting-state EEG was recorded, both before and after learning and the interval, to derive changes in aperiodic slope values. A delayed recognition test then assessed whether changes in the aperiodic slope (i.e., homeostatic activity) across learning and the interval predicted memory accuracy and N400 amplitude, an index of stimulus expectancy and recognition. Our study found evidence to suggest homeostatic renormalisation, for the nap condition only, indicated by a shift towards inhibition across sleep which was inversely related to the slope change across learning. Although the aperiodic slope change across the nap and wake intervals did not predict delayed memory accuracy, a shift towards inhibition during learning was beneficial for memory accuracy, only in the nap condition. Similarly, a shift towards inhibition across learning, followed by a shift towards excitation in the nap condition, predicted reduced N400 amplitudes. This suggests that homeostatic renormalisation across the nap influenced model updating and stimulus expectancy. This study provides evidence for a link between synaptic homeostatic processes and memory consolidation, emphasising the importance of encoding-related aperiodic activity in this relationship.

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bioRxiv2026

From flexible to anticipatory processing: alpha and beta oscillatory signatures of feedback-guided strategy adaptation and memory updating

When humans learn under conditions of uncertainty, they dynamically adjust how they prepare for and respond to feedback. In navigating uncertain environments, the brain minimizes error by continuously refining internal models via memory updating (MU). Feedback is critical for MU, and anticipatory neural mechanisms shape how feedback is processed, likely reflecting learned environmental certainty. However, the literature has largely focused on post-feedback activity, leaving pre-feedback certainty-related mechanisms less understood. The present study aims to address this gap by examining how certainty modulates anticipatory states, preceding feedback and subsequent MU. We examined oscillatory activity prior to performance feedback in a reanalysis of EEG data previously published by Hassall and colleagues (2023). Twenty-one participants (16 female, Mage = 25.81 years) predicted the strength of cartoon characters with varying predictability levels which were learned through exposure. Feedback on prediction accuracy was presented via an animated rising bar. Results revealed that theta power is modulated by accumulative feedback. Linear mixed-effects models revealed an interaction between predictability-related certainty and learning stage: in late learning, higher performance was associated with increased pre-feedback alpha and beta power for low-certainty trials, whereas in early learning, higher performance was associated with decreased beta power. These learning-related modulations in alpha and beta power suggest that initial learning is marked by adaptable exploratory processing. Subsequent learning exhibited increased alpha-mediated inhibition and beta-related anticipatory activity for lower certainty trials, indicative of dynamic strategy refinement and selective engagement of task-relevant information. These results demonstrate that certainty shapes preparatory oscillatory activity associated with MU.

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bioRxiv2026

Phase similarity between similar objects indicates representational merging across retrieval training but not sleep

Retrieval training (i.e., cued recall) is theorised to induce rapid memory consolidation, similarly to sleep. Across consolidation, related neural representations become increasingly similar; yet, this representational change has never been directly compared between sleep and retrieval training. In this study, 30 subjects (27F, 18-34, M=22.17) completed four separate sessions in which they (1) learnt object-word pairs, followed by (2) immediate recognition testing, (3) one of four 120-min interventions (retrieval training, restudy, sleep, or wake), and (4) delayed recognition testing. We compared EEG phase similarity between similar and different objects to assess the time, frequency, and anatomical distribution of representational similarity across encoding (learning to immediate recognition), and each intervention (immediate to delayed recognition). We hypothesised that EEG phase patterns for similar objects would become more similar (i.e., representational merging) across retrieval training and sleep interventions, and predict a greater endorsement of similar-object lures. Indeed, we found increased representational similarity between similar objects across the encoding shift in the theta-band and occipital sources. Crucially, additional representational merging was only observed across the retrieval training intervention, in the alpha-band and parieto-occipital sources. Despite retrieval training leading to reduced performance in discriminating similar-objects lures, greater representational merging across retrieval training predicted greater discrimination of similar-object lures. Together, these findings suggest that sleep and retrieval training induce different memory transformations across the same timescale. Retrieval training may generally provoke rapid gist extraction, with greater neocortical integration supporting episodic discrimination. Conversely, sleep may selectively maintain task-relevant episodic and semantic details in the short-term.

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bioRxiv2025

A Comparison of Sleep-Based and Retrieval-Mediated Memory Consolidation Using Sigma-Band Activity

Sleep and retrieval training are reported to promote memory consolidation and are thought to have similar underlying brain-based mechanisms; however, these mechanisms have yet to be directly compared side-by-side, across equivalent timescales. The current study aimed to see if we could replicate sleep spindles enhancement of weakly encoded memories, across both sleep-based and retrieval-mediated memory consolidation, using EEG sigma power ([~]12-16 Hz) as a proxy for sleep spindles (i.e., sigma-band activity). Thirty subjects (27F, 18-34, M=22.17) participated in four separate sessions where they learnt different sets of 104 object-word pairs. Subjects were then tested on their recognition accuracy of the pairs before and after one of four 120-min memory interventions where EEG was recorded: retrieval training (i.e., cued recall practice), restudy (i.e., pair re-exposure), a nap opportunity, or a wakeful rest. Our results did not replicate an enhancement of weakly encoded memories, moderated by either sleep spindles or sigma power. Instead, posterior regions of sigma power negatively related to subsequent memory outcomes. We also detected a prioritisation to enhance memory outcomes for strongly encoded memories and greater memory outcomes following retrieval training and restudy compared to sleep and wake interventions. These results shed new light upon the role of sigma-band activity to predict subsequent memory outcomes and inform how future studies should measure encoding strength and sigma power. Importantly, our study provides a methodological approach to comparing sleep-based and retrieval-mediated memory consolidation that should be explored across different memory paradigms in future studies.

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bioRxiv2025

Retrieval-mediated and sleep-based memory consolidation provoke different neural and behavioural markers of memory generalisation.

Across sleep-based and retrieval-mediated consolidation, memories typically become generalised and less dependent on their episodic components for their recollection. However, memory transformations across sleep and retrieval training have not been directly compared. The current study aims to compare how sleep and retrieval training impact the endorsement of semantically similar and different lures, as well as their episodic recollection using the parietal old/new effect on the late positive component (LPC) in subjects EEG. Thirty subjects (27F, 18-34, M=22.17) attended four sessions where they learnt different sets of 104 object-word pairs and completed one of four 120-minute memory interventions: retrieval training (i.e., cued recall practice), restudy (i.e., pair re-exposure), a nap opportunity, or a wakeful rest. EEG was recorded while subjects were tested on their recognition accuracy in an old/new paradigm with similar- and different-object lures. Our results revealed that retrieval training, but not sleep, lead to greater accuracy for identifying old pairs, but worse similar-lure discrimination. Whilst the parietal old/new effect did not differ between conditions, retrieval had lower LPC amplitudes for similar- than different-object false alarms, whilst restudy demonstrated the opposite. Sleep and wake demonstrated no differences in LPC amplitudes between hits and different false alarm types. Together, our study demonstrates evidence for gist-abstraction across retrieval training, and a task-relevant selective maintenance of episodic details across sleep. These results challenge theories that retrieval training replicates sleep-based consolidation mechanisms, instead acting as a fast route to semanticization regardless of the context.

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bioRxiv2024

Predictive influences on memory encoding: investigating oscillations and the N400 event-related potential component

To effectively function in an ever-changing environment, the brain is proposed to make predictions about upcoming information. However, the association between prediction and memory formation and the role of between-subject neural variability in this relationship is unclear. To shed light on the relationship between prediction and memory, the present study reanalysed data from Jano, Chatburn, and colleagues (2024). In the original experiment, participants were exposed to naturalistic images in predictable and unpredictable four-item sequences, after which their memory was tested using an old/new paradigm. In the present analysis (N = 46), N400 amplitude and oscillatory power during learning was measured to gauge processes related to prediction error and memory encoding, respectively. This activity was compared with subsequent memory outcomes and individual alpha frequency (IAF) calculated at rest. Linear mixed-effects regressions revealed an alpha power subsequent memory effect that was not related to the amplitude of the N400, suggesting that memory encoding may occur independently of the level of prediction error. Notably, IAF influenced the relationship between theta power, N400 amplitude and subsequent memory, implying that the electrophysiological conditions for successful memory formation differ between individuals. Consequently, the degree to which prediction errors (presumably gauged via the N400) drive memory encoding could depend on inter-individual variability in intrinsic neural activity. These findings emphasise the flexible nature of memory, whilst having potential implications for prediction error-driven accounts of learning.

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bioRxiv2023

Individual alpha frequency modulates prediction error-driven memory encoding

Prediction and memory are strongly intertwined, with predictions relying on memory retrieval, whilst also influencing memory encoding. However, it is unclear how predictability influences explicit memory performance, and how individual neural factors may modulate this relationship. The current study sought to investigate the effect of predictability on memory processing with an analysis of the N400 event-related potential in a context extending beyond language. Participants (N = 48, females = 33) completed a study-test paradigm where they first viewed predictable and unpredictable four-item ABCD sequences of outdoor scene images, whilst their brain activity was recorded using electroencephalography (EEG). Subsequently, their memory for the images was tested, and N400 patterns during learning were compared with memory outcomes. Behavioural results revealed better memory for images in predictable sequences in contrast to unpredictable sequences. Memory was also strongest for predictable images in the B position, suggesting that when processing longer sequences, the brain may prioritise the data deemed most informative. Strikingly, greater N400 amplitudes during learning were associated with enhanced memory at test for individuals with low versus high individual alpha frequencies (IAFs). In light of the relationship between the N400 and stimulus predictability, this finding may imply that predictive processing differs between individuals to influence the extent of memory encoding. Finally, exploratory analyses provided evidence for a later positivity that was predictive of subsequent memory performance. Ultimately, the results highlight the complex and interconnected relationship between predictive processing and memory, whilst shedding light on the accumulation of predictions across longer sequences.

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bioRxiv2023

Expecting the unexpected: A reanalysis of a multi-laboratory study provides evidence for prediction in language

The extent to which the brain predicts upcoming information during language processing remains controversial. To shed light on this debate, the present study reanalysed Nieuwland and colleagues (2018) replication of DeLong et al. (2005). Participants (N = 356) viewed sentences containing articles and nouns of varying predictability, while their electroencephalogram was recorded. We measured event-related potentials (ERPs) preceding the critical words (namely the semantic prediction potential; SPP), in conjunction with post-word N400 patterns and individual neural metrics. ERP activity was compared with two measures of word predictability: cloze probability and lexical surprisal. In contrast to prior literature, SPP amplitudes did not increase as cloze probability increased, suggesting that the component may not reflect prediction during natural language processing. Initial N400 results at the article provided evidence against phonological prediction in language, in line with Nieuwland and colleagues findings. Strikingly however, when the surprisal of the prior words in the sentence was included in the analysis, increases in article surprisal were associated with increased N400 amplitudes, consistent with prediction accounts. This relationship between surprisal and N400 amplitude was not observed when the surprisal of the two prior words was low, suggesting that expectation violations at the article may be overlooked under highly predictable conditions. Individual alpha frequency (IAF) also modulated the relationship between article surprisal and the N400, emphasising the importance of individual neural factors for prediction. The present study extends upon existing neurocognitive models of language and prediction more generally, by illuminating the flexible and subject-specific nature of predictive processing.

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bioRxiv2022

Resting-state aperiodic neural activity as a novel objective marker of daytime somnolence

Study ObjectivesCurrent assessment of excessive daytime somnolence (EDS) requires subjective measurements such as the Epworth Sleepiness Scale (ESS), and/or resource intensive sleep laboratory investigations. Recent work 1,2 has called for more non-performance-based measures of EDS. One promising non-performance-based measure of EDS is the aperiodic component of electroencephalography (EEG). Aperiodic (non-oscillatory) activity reflects excitation/inhibition ratios of neural populations and is altered in various states of consciousness, and thus may be a potential biomarker of hypersomnolence. MethodsWe retrospectively analysed EEG data from patients who underwent a Multiple Sleep Latency Test (MSLT) and determined whether aperiodic neural activity is predictive of EDS. Participants having undergone laboratory polysomnogram and next day MSLT were grouped into MSLT+ (n = 26) and MSLT- (n = 33) groups (mean sleep latency of < 8min and > 10min, respectively) and compared against a non-clinical (Control) group of participants (n = 26). ResultsWhile the MSLT+ and MSLT- groups did not differ in their aperiodic activity, the Control group had a significantly flatter slope and larger offset compared to both MSLT+ and MSLT- groups. Logistic regression machine learning predicted group status (i.e., symptomatic, non-symptomatic) with 90% accuracy based on the aperiodic slope while controlling for age. Slow oscillation-spindle coupling was also significantly stronger in the Control group relative to MSLT+ and MSLT- groups. ConclusionsOur results provide first evidence that aperiodic neural dynamics and sleep-based cross-frequency coupling is predictive of EDS, thereby providing a novel avenue for basic and applied research in the study of sleepiness.

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bioRxiv2022

Neural and cognitive correlates of performance in dynamic multi-modal settings

The endeavour to understand human cognition has largely relied upon investigation of task-related brain activity. However, resting-state brain activity can also offer insights into individual information processing and performance capabilities. Previous research has identified electroencephalographic resting-state characteristics (most prominently: the individual alpha frequency; IAF) that predict cognitive function. However, it has largely overlooked a second component of electrophysiological signals: aperiodic 1/f activity. The current study examined how both oscillatory and aperiodic resting-state EEG measures, alongside traditional cognitive tests, can predict performance in a dynamic and complex, semi-naturalistic cognitive task. Participants resting-state EEG was recorded prior to engaging in a Target Motion Analysis (TMA) task in a simulated submarine control room environment (CRUSE), which required participants to integrate dynamically changing information over time. We demonstrated that the relationship between IAF and cognitive performance extends from simple cognitive tasks (e.g., digit span) to complex, dynamic measures of information processing. Further, our results showed that individual 1/f parameters (slope and intercept) differentially predicted performance across practice and testing sessions, whereby flatter slopes were associated with improved performance during learning, while higher intercepts were linked to better performance during testing. In addition to the EEG predictors, we demonstrate a link between cognitive skills most closely related to the TMA task (i.e., spatial imagery) and subsequent performance. Overall, the current study highlights (1) how resting-state metrics - both oscillatory and aperiodic - have the potential to index higher-order cognitive capacity, while (2) emphasising the importance of examining these electrophysiological components within more dynamic settings and over time.

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bioRxiv2021

Theta activity during encoding interacts with NREM sleep oscillations to predict memory generalization

Relatively little is known regarding the interaction between encoding-related neural activity and sleep-based memory consolidation. One suggestion is that a function of encoding-related theta power may be to tag memories for subsequent processing during sleep. This study aimed to extend previous work on the relationships between sleep spindles, slow oscillation-spindle coupling and task-related theta activity with a combined Deese-Roediger-McDermott (DRM) and nap paradigm. This allowed us to examine the influence of task- and sleep-related oscillatory activity on the recognition of both encoded list words and associative theme words. Thirty-three participants (29 females, mean age = 23.2 years) learned and recognised DRM lists separated by either a 2hr wake or sleep period. Mixed-effects modelling revealed the sleep condition endorsed more associative theme words and fewer list words in comparison to the wake group. Encoding-related theta power was also found to influence sleep spindle density, and this interaction was predictive of memory outcomes. The influence of encoding-related theta was specific to sleep spindle density, and did not appear to influence the strength of slow oscillation-spindle coupling as it relates to memory outcomes. The finding of interactions between wakeful and sleep oscillatory-related activity in promoting memory and learning has important implications for theoretical models of sleep-based memory consolidation.

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bioRxiv2021

Sleep spindles track cortical learning patterns for memory consolidation

Memory consolidation, the transformation of labile memory traces into stable long-term representations, is facilitated by post-learning sleep. Computational and biophysical models suggest that sleep spindles may play a key mechanistic role for consolidation, igniting structural changes at cortical sites involved in prior learning. Here we tested the resulting prediction that spindles are most pronounced over learning-related cortical areas and that the extent of this learning-spindle overlap predicts behavioural measures of memory consolidation. Using high-density scalp Electroencephalography (EEG) and Polysomnography (PSG) in healthy volunteers, we first identified cortical areas engaged during a temporospatial associative memory task (power decreases in the alpha/beta frequency range, 6-20 Hz). Critically, we found that participant-specific topographies (i.e., spatial distributions) of post-learning sleep spindle amplitude correlated with participant-specific learning topographies. Importantly, the extent to which spindles tracked learning patterns further predicted memory consolidation across participants. Our results provide empirical evidence for a role of post-learning sleep spindles in tracking learning networks, thereby facilitating memory consolidation.

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bioRxiv2021

Neural signatures of teamwork: Evidence from real-world military training

Effective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more individuals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty individuals paired as twenty dyads and recorded dual-EEG at rest and during realistic training scenarios of increasing complexity using virtual simulation systems. We estimated markers of intrinsic brain activity (i.e., individual alpha frequency and aperiodic activity), as well as task-related theta and alpha oscillations. Using nonlinear modelling and a logistic regression machine learning model, we found that resting-state EEG predicts performance and can also reliably differentiate between members within a dyad. Task-related theta and alpha activity during easy training tasks predicted later performance on complex training to a greater extent than prior behaviour. These findings complement laboratory-based research on both oscillatory and aperiodic activity in higher-order cognition and provide evidence that theta and alpha activity play a critical role in complex task performance in team environments.

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bioRxiv2021

Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning

An emerging body of work has demonstrated that resting-state non-oscillatory, or aperiodic, 1/f neural activity is a functional and behaviorally relevant marker of cognitive function capacity. In the motor domain, previous work has only applied 1/f analyses to description of action coordination and performance. The value of aperiodic resting-state neural dynamics as a marker of individual visuomotor performance capacity remains unknown. Accordingly, the aim of this work was to investigate if individual 1/f intercept and slope parameters of aperiodic resting-state neural activity predict reaction time and perceptual sensitivity in an immersive virtual reality marksmanship task. The marksmanship task required speeded selection of target stimuli and avoidance of non-target stimuli selection. Motor and perceptual demands were incrementally increased across task blocks and participants performed the task across three training sessions spanning one week. When motor demands were high, steeper individual 1/f slope predicted shorter reaction time. This relationship did not change with practice. Increased 1/f intercept and a steeper 1/f slope were associated with higher perceptual sensitivity, measured as d. However, this association was only observed under the highest levels of perceptual demand and only in the initial exposure to these conditions. Individuals with a lower 1/f intercept and a shallower 1/f slope demonstrated the greatest gains in perceptual sensitivity from task practice. These findings demonstrate that individual differences in motor and perceptual performance can be accounted for with resting-state aperiodic neural dynamics. The 1/f aperiodic parameters are most informative in predicting visuomotor performance under complex and demanding task conditions. In addition to predicting capacity for high visuomotor performance with a novel task, 1/f aperiodic parameters might also be useful in predicting which individuals might derive the most improvements from practice.

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bioRxiv2020

Sleep Enhances Neural Representations of True and False Memories: An Event-Related Potential Study

Episodic memory is reconstructive and is thus prone to false memory formation. Although false memories are proposed to develop via associative processes, the nature of their neural representations, and the effect of sleep on false memory processing is currently under debate. The present research employed the Deese-Roediger-McDermott (DRM) paradigm and a daytime nap to determine whether semantic false memories and true memories could be differentiated using event-related potentials (ERPs). We also sought to illuminate the role of sleep in memory formation and learning, with the use of a daytime nap. Healthy participants (N = 34, 28F, mean age = 23.23, range = 18-33) completed the DRM task with the learning and recognition phases separated by either a 2hr daytime nap or an equivalent wake period. Linear mixed modelling revealed larger LPC amplitudes for true memories in contrast to false memories, and larger P300 amplitudes for false compared to true memories. Those in the nap group also exhibited larger LPC and P300 amplitudes than participants in the wake group. Additionally, a larger P300 amplitude at delayed recognition (following a consolidation opportunity) was associated with increased true memory accuracy. These findings are argued to be reflective of sleeps ability to promote pattern separation and pattern completion, with true memories arising from distinct memory traces, and false memories arising from thematic extraction and overlap in neural representations. The present research supports the perspective that both true and false memories are reflective of adaptive memory processes, whilst also suggesting that P300 amplitude affects episodic memory accuracy.

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