ORCID Profile
0000-0001-5775-2367
Current Organisations
Shanghai Jiao Tong University
,
Northwestern University Feinberg School of Medicine
,
University of South Australia
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Publisher: Center for Open Science
Date: 23-03-2023
Abstract: Sleep is a daily experience across humans and other species, yet our understanding of how and why we sleep is presently incomplete. This is particularly prevalent in research examining the online neurophysiological measurement of sleepiness in humans, where several electroencephalographic (EEG) phenomena have been linked with prolonged wakefulness. This leaves researchers without a solid basis for the measurement of sleep need in the in idual and complicates our understanding of the nature of sleep. Recent theoretical and technical advances have allowed for a greater understanding of the neurobiological basis of sleep need: sleep need may result from increases in neuronal excitability and shifts in excitation/inhibition balance in neuronal circuits, and this can be directly measured via the aperiodic component of the EEG. Here, we review the literature on EEG-derived markers of sleepiness in humans and argue that changes in these may actually result from changes in aperiodic markers of neural activity. We argue for the use of aperiodic markers derived from the EEG in predicting sleepiness and suggest areas for future research based on these.
Publisher: Frontiers Media SA
Date: 31-01-2018
Publisher: Cold Spring Harbor Laboratory
Date: 24-08-2022
DOI: 10.1101/2022.08.23.505024
Abstract: Memory is critical for many cognitive functions, from remembering facts, to learning complex environmental rules. While memory encoding occurs during wake, memory consolidation is associated with sleep-related neural activity. Further, research suggests that in idual differences in alpha frequency during wake (∼ 7 – 13 Hz) modulate memory processes, with higher in idual alpha frequency (IAF) associated with greater memory performance. However, the relationship between wake-related EEG in idual differences, such as IAF, and sleep-related neural correlates of memory consolidation has been largely unexplored, particularly in a complex rule-based memory context. Here, we aimed to investigate whether wake-derived IAF and sleep neurophysiology interact to influence rule learning in a s le of 35 healthy adults (16 males mean age = 25.4, range: 18 – 40). Participants learned rules of a modified miniature language prior to either 8hrs of sleep or wake, after which they were tested on their knowledge of the rules in a grammaticality judgement task. Results indicate that sleep neurophysiology and wake-derived IAF do not interact but modulate memory for complex linguistic rules separately. Phase- litude coupling between slow oscillations and spindles during non-rapid eye-movement (NREM) sleep also promoted memory for rules that were analogous to the canonical English word order. As an exploratory analysis, we found that rapid eye-movement (REM) sleep theta power at posterior regions interacts with IAF to predict rule learning and proportion of time in REM sleep predicts rule learning differentially depending on grammatical rule type. Taken together, the current study provides behavioural and electrophysiological evidence for a complex role of NREM and REM sleep neurophysiology and wake-derived IAF in the consolidation of rule-based information.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Cold Spring Harbor Laboratory
Date: 11-11-2017
DOI: 10.1101/218123
Abstract: We hypothesise a beneficial influence of sleep on the consolidation of the combinatorial mechanisms underlying incremental sentence comprehension. These predictions are grounded in recent work examining the effect of sleep on the consolidation of linguistic information, which demonstrate that sleep-dependent neurophysiological activity consolidates the meaning of novel words and simple grammatical rules. However, the sleep-dependent consolidation of sentence-level combinatorics has not been studied to date. Here, we propose that dissociable aspects of sleep neurophysiology consolidate two different types of combinatory mechanisms in human language: sequence-based (order-sensitive) and dependency-based (order-insensitive) combinatorics. The distinction between the two types of combinatorics is motivated both by cross-linguistic considerations and the neurobiological underpinnings of human language. Unifying this perspective with principles of sleep-dependent memory consolidation, we posit that a function of sleep is to optimise the consolidation of sequence-based knowledge (the when ) and the establishment of semantic schemas of unordered items (the what ) that underpin cross-linguistic variations in sentence comprehension. This hypothesis builds on the proposal that sleep is involved in the construction of predictive codes, a unified principle of brain function that supports incremental sentence comprehension. Finally, we discuss neurophysiological measures (EEG/MEG) that could be used to test these claims, such as the quantification of neuronal oscillations, which reflect basic mechanisms of information processing in the brain.
Publisher: Elsevier BV
Date: 07-2021
Publisher: Cold Spring Harbor Laboratory
Date: 31-08-2021
DOI: 10.1101/2021.08.29.456571
Abstract: Effective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more in iduals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty in iduals 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., in idual 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.
Publisher: Emerald Publishing Limited
Date: 22-11-2021
Publisher: Cold Spring Harbor Laboratory
Date: 24-03-2022
DOI: 10.1101/2022.03.23.485424
Abstract: 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 in idual information processing and performance capabilities. Previous research has identified electroencephalographic resting-state characteristics (most prominently: the in idual 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 in idual 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.
Publisher: Royal Society of Chemistry (RSC)
Date: 2019
DOI: 10.1039/C9NR05874E
Abstract: 3D halos were fabricated from co-assembly of Fe 3 O 4 and Au NPs of different sizes and shapes by a bottom-up strategy, creating new opportunities to explore assembled materials with enriched functionalities.
Publisher: Frontiers Media SA
Date: 09-05-2022
DOI: 10.3389/FNHUM.2022.821191
Abstract: 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 2 h 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.
Publisher: Elsevier BV
Date: 08-2021
Publisher: Cold Spring Harbor Laboratory
Date: 14-02-2020
DOI: 10.1101/2020.02.13.948539
Abstract: Language is one of the most defining human capabilities, involving the coordination of brain networks that generalise the meaning of linguistic units of varying complexity. On a neural level, neocortical slow oscillations and thalamic spindles during sleep facilitate the reactivation of newly encoded memory traces, manifesting in distinct oscillatory activity during retrieval. However, it is currently unknown if the effect of sleep on memory extends to the generalisation of the mechanisms that subserve sentence comprehension. We address this question by analysing electroencephalogram data recorded from 35 participants during an artificial language learning task and an 8hr nocturnal sleep period. We found that a period of sleep was associated with increased alpha/beta power and improved behavioural performance. Phase litude coupling analyses also revealed that spindle-slow oscillation coupling predicted the consolidation of sequence-based word orders, which was associated with distinct patterns of oscillatory activity during sentence processing. Taken together, this study presents converging behavioural and neurophysiological evidence for a role of sleep in the consolidation of higher order language learning and associated oscillatory neural activity.
Publisher: Cold Spring Harbor Laboratory
Date: 08-06-2022
DOI: 10.1101/2022.06.07.495229
Abstract: In idual differences in second language (L2) learning can offer insights into the neurobiological bases of learning aptitude. One neurophysiological marker of inter-in idual differences in cognition is the in idual alpha frequency (IAF), a trait-like measure correlated with cognition. Further, the N400 is an electrophysiological marker indexing stimulus irregularity and has been used to study L2 learning however, its relationship with IAF and L2 learning remains unknown. To examine the relation between IAF and L2 learning (indexed by N400 litude), we report data from a modified miniature language learning study. After a vocabulary learning period, participants ( N = 38, M age = 25.3, SD = 7.13) judged the grammaticality of classifier-noun pairs, with mixed-effects modelling revealing lower IAF in iduals were better than higher IAF in iduals at grammaticality judgements. N400 litude also reduced across the experiment in low relative to high IAF in iduals, indicating the relationship between IAF and language learning is more complex than initially postulated.
Publisher: Springer Science and Business Media LLC
Date: 28-09-2022
DOI: 10.1038/S41598-022-20704-8
Abstract: Effective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more in iduals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty in iduals 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., in idual 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.
Publisher: American Association for Cancer Research (AACR)
Date: 08-2023
DOI: 10.1158/1055-9965.23814249.V1
Abstract: Median folate intake and mandatory folate fortification status, OCAC studies.
Publisher: Springer Science and Business Media LLC
Date: 06-10-2021
Publisher: Oxford University Press (OUP)
Date: 25-04-2023
Abstract: Sleep supports memory consolidation as well as next-day learning. The influential “Active Systems” account of offline consolidation suggests that sleep-associated memory processing paves the way for new learning, but empirical evidence in support of this idea is scarce. Using a within-subjects (n = 30), crossover design, we assessed behavioral and electrophysiological indices of episodic encoding after a night of sleep or total sleep deprivation in healthy adults (aged 18–25 years) and investigated whether behavioral performance was predicted by the overnight consolidation of episodic associations from the previous day. Sleep supported memory consolidation and next-day learning as compared to sleep deprivation. However, the magnitude of this sleep-associated consolidation benefit did not significantly predict the ability to form novel memories after sleep. Interestingly, sleep deprivation prompted a qualitative change in the neural signature of encoding: Whereas 12–20 Hz beta desynchronization—an established marker of successful encoding—was observed after sleep, sleep deprivation disrupted beta desynchrony during successful learning. Taken together, these findings suggest that effective learning depends on sleep but not necessarily on sleep-associated consolidation.
Publisher: Cold Spring Harbor Laboratory
Date: 11-08-2022
DOI: 10.1101/2022.08.07.503118
Abstract: Current 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. We 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). While 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. Our 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.
Publisher: Elsevier BV
Date: 02-2023
Publisher: Cold Spring Harbor Laboratory
Date: 10-03-2022
DOI: 10.1101/2020.03.10.984971
Abstract: Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli considerably less is known about the oscillatory correlates of complex rule learning, as in language. Further, recent work has shown that non-oscillatory (1/ f ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 females, 18 males), we show for the first time that 1/ f and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Flexible word order rules were associated with a steeper 1/ f slope, while fixed word order rules were associated with a shallower slope. We also show that increased theta and alpha power predicts fixed relative to flexible word order rule learning and behavioural performance. Together, these results suggest that 1/ f activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word order permutations, which manifest in distinct oscillatory profiles.
Publisher: Cold Spring Harbor Laboratory
Date: 18-11-2021
DOI: 10.1101/2021.11.16.468870
Abstract: Sleep supports memory consolidation as well as next-day learning. The influential Active Systems account of offline consolidation suggests that sleep-associated memory processing paves the way for new learning, but empirical evidence in support of this idea is scarce. Using a within-subjects (N = 30), crossover design, we assessed behavioural and electrophysiological indices of episodic encoding after a night of sleep or total sleep deprivation in healthy adults (aged 18-25 years), and investigated whether behavioural performance was predicted by the overnight consolidation of episodic associations formed the previous day. Sleep supported memory consolidation and next-day learning, as compared to sleep deprivation. However, the magnitude of this sleep-associated consolidation benefit did not significantly predict the ability to form novel memories after sleep. Interestingly, sleep deprivation prompted a qualitative change in the neural signature of encoding: whereas 12-20 Hz beta desynchronization – an established marker of successful encoding – was observed after sleep, sleep deprivation disrupted beta desynchrony during successful learning. Taken together, these findings suggest that effective learning depends on sleep, but not necessarily sleep-associated consolidation.
Publisher: Cold Spring Harbor Laboratory
Date: 08-05-2023
DOI: 10.1101/2023.05.08.539915
Abstract: The present study investigated the extent of prediction in language by reanalysing 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 (EEG) was recorded. We measured pre-stimulus and N400 event-related activity and calculated lexical surprisal using Generative Pre-trained Transformer-2 (GPT-2) models. Results demonstrate increases in N400 litude as article surprisal increased, supporting DeLong et al.’s (2005) findings. Strikingly, N400 litudes for surprising articles were reduced when prior word surprisal was high, suggesting that surprising input reduces prediction precision for upcoming words. The magnitude of prediction error effects was additionally modulated by inter-in idual differences (in idual alpha frequency and aperiodic slope of resting EEG). These findings indicate that prediction in language is a flexible mechanism that is adaptive across contexts and in iduals. They further support the assumption that prediction is a unified mechanism of cognition.
Publisher: MIT Press
Date: 2022
DOI: 10.1162/JOCN_A_01878
Abstract: Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low-frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli considerably less is known about the oscillatory correlates of complex rule learning, as in language. Furthermore, recent work has shown that nonoscillatory (1/ƒ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 women, 18 men), we show for the first time that 1/ƒ and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Flexible word-order rules were associated with a steeper 1/ƒ slope, whereas fixed word-order rules were associated with a shallower slope. We also show that increased theta and alpha power predicts fixed relative to flexible word-order rule learning and behavioral performance. Together, these results suggest that 1/ƒ activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word-order permutations, which manifest in distinct oscillatory profiles.
Publisher: Cold Spring Harbor Laboratory
Date: 12-10-2017
DOI: 10.1101/202176
Abstract: Sleep promotes memory consolidation through unique neuromodulatory activity. However, little is known about the impact of attention during pre-sleep memory encoding on later memory performance. The current study aimed to address the question of whether attentional state prior to encoding, as indexed by alpha oscillatory activity, modulates the consolidation of images across periods of sleep and wake. 22 participants aged 18 – 41 years (mean age = 27.3) viewed 120 emotionally valenced images (positive, negative, neutral) before a 2hr afternoon sleep opportunity and an equivalent period of wake. Following the sleep and wake conditions, participants were required to distinguish between 120 previously seen (target) images and 120 new (distractor) images. Relative alpha power – adjusted according to participants’ in idual alpha frequency – was computed to index attentional state prior to the learning phase. Generalised linear mixed-effects modelling revealed memory performance was modulated by attention, such that greater pre-encoding alpha power preferentially promoted memory consolidation during wake compared to sleep. There was no difference in memory performance between positive, negative and neutral stimuli. Modulations in alpha oscillatory activity may help to coordinate the flow of information between task-relevant cortical regions and a thalamo-cortical loop that preferentially subserves the formation of memory during times of wake relative to sleep.
Publisher: Cold Spring Harbor Laboratory
Date: 09-04-2031
Location: United States of America
No related grants have been discovered for Zachariah Cross.