Biology subjects

Hill, A. T.

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

18 recordsLinked to original sources

bioRxiv2024

Neural differences in conflict monitoring and stimulus expectancy processes in experienced meditators are likely driven by enhanced attention

ObjectivesMindfulness meditation has been linked to enhanced attention and executive function, likely resulting from practice-related effects on neural activity patterns. In this study, we used an event-related potential (ERP) paradigm to examine brain responses related to conflict monitoring and attention in experienced mindfulness meditators to better understand key factors driving meditation-related effects. MethodsWe measured electroencephalography-derived N2 and P3 ERPs reflecting conflict monitoring and attention processes from 35 meditators and 29 non-meditators across both an easy and a hard Go/Nogo task (50% Nogo and 25% Nogo stimuli, respectively). ResultsMeditators displayed distinct neural activity patterns compared to non-meditators, with enhanced N2 responses in fronto-midline electrodes following hard Nogo trials (pFDR = 0.011, np2 = 0.111). The fronto-midline N2 ERP was also larger following Nogo trials than Go trials, in the harder task condition, and was related to correct responses. Meditators also exhibited a more frontally distributed P3 ERP in the easy task compared to the hard task, while non-meditators showed a more frontally distributed P3 ERP in the hard task (pFDR = 0.015, np2 = 0.079). ConclusionsMindfulness meditation was associated with distinct topographical patterns of neural activity in the attention task, without corresponding increases in global neural activity amplitudes. These meditation-related effects appear to be driven by attention-specific mechanisms, despite the examined neural activity being associated with conflict monitoring and stimulus expectancy. Our findings suggest that the cognitive benefits of meditation may only emerge in tasks that actively engage targeted cognitive processes, such as sustained attention.

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bioRxiv2024

EEG During Dynamic Facial Emotion Processing Reveals Neural Activity Patterns Associated with Autistic Traits in Children

Altered brain connectivity and atypical neural oscillations have been observed in autism, yet their relationship with autistic traits in non-clinical populations remains underexplored. Here, we employ electroencephalography (EEG) to examine functional connectivity, oscillatory power, and broadband aperiodic activity during a dynamic facial emotion processing (FEP) task in 101 typically developing children aged 4-12 years. We investigate associations between these electrophysiological measures of brain dynamics and autistic traits as assessed by the Social Responsiveness Scale, 2nd Edition (SRS-2). Our results revealed that increased FEP-related connectivity across theta (4-7 Hz) and beta (13-30 Hz) frequencies correlated positively with higher SRS-2 scores, predominantly in right-lateralized (theta) and bilateral (beta) cortical networks. Additionally, a steeper 1/f-like aperiodic slope (spectral exponent) across fronto-central electrodes was associated with higher SRS-2 scores. Greater aperiodic-adjusted theta and alpha oscillatory power further correlated with both higher SRS-2 scores and steeper aperiodic slopes. These findings underscore important links between FEP-related brain dynamics and autistic traits in typically developing children. Future work could extend these findings to assess these EEG-derived markers as potential mechanisms underlying behavioural difficulties in autism.

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bioRxiv2024

Experienced meditators show greater forward travelling cortical alpha wave strengths

Mindfulness meditation involves training attention, commonly towards the current sensory experience, with an attitude of non-judgemental awareness. Theoretical perspectives suggest meditation alters the brains predictive processing mechanisms, increasing the synaptic gain and precision with which sensory information is processed, and reducing the generation or elaboration of higher-order beliefs. Recent research suggests that forwards and backwards travelling cortical alpha waves provide an indication of these predictive processing functions. Here, we used electroencephalography (EEG) to test whether the strength of forwards and backwards travelling cortical alpha waves differed between experienced meditators and a matched sample of non-meditators, both during an eyes-closed resting state (N = 97) and during a visual cognitive (Go/No-go) task (N = 126). Our results showed that meditators produced stronger forwards travelling cortical alpha waves compared to non-meditators, both while resting with their eyes closed and during task performance. Meditators also exhibited weaker backwards travelling cortical alpha wave strength while resting with their eyes closed. These results may be indicative of a neural mechanism underpinning enhanced attention associated with meditation practice, as well as a potential neural marker of the reductions in resting mind-wandering that are suggested to be associated with meditation practice. The results also support models of brain function that suggest attention modification can be achieved by mental training aimed at increased processing of sensory information, which might be indexed by greater strength of forwards travelling cortical alpha waves.

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bioRxiv2024

EEG is better when cleaning effectively targets artifacts

Electroencephalography (EEG) is a useful tool to measure neural activity. However, EEG data are usually contaminated with non-neural artifacts, including voltage shifts generated by eye movements and muscle activity, and other artifacts that are less easily characterizable. The confounding influence of artifacts is often addressed by decomposing data into components, subtracting probable artifactual components, then reconstructing data back into the electrode space. This approach is commonly applied using independent component analysis (ICA). Here, we demonstrate the counterintuitive finding that due to imperfect component separation, component subtraction can artificially inflate effect sizes for event-related potentials (ERPs) and connectivity measures, bias source localisation estimates, and remove neural signals. To address this issue, we developed a method that targets cleaning to the artifact periods of eye movement components and artifact frequencies of muscle components. When tested across different EEG systems and cognitive tasks, our results showed that the targeted artifact reduction method is effective in cleaning artifacts while also reducing the artificial inflation of ERP and connectivity effect sizes and minimizing source localisation biases. Our results suggest EEG pre-processing is better when targeted cleaning is applied, as this improves preservation of neural signals and mitigates effect size inflation and source localisation biases that result from approaches which subtract artifact components across the entire time-series. These improvements enhance the reliability and validity of EEG data analysis. Our method is provided in the freely available RELAX pipeline, which includes a graphical user interface for ease of use and is available as an EEGLAB plugin (https://github.com/NeilwBailey/RELAX).

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bioRxiv2024

RELAX-Jr: An Automated Pre-Processing Pipeline for Developmental EEG Recordings

Automated EEG pre-processing pipelines provide several key advantages over traditional manual data cleaning approaches; primarily, they are less time-intensive and remove potential experimenter error/bias. Automated pipelines also require fewer technical expertise as they remove the need for manual artifact identification. We recently developed the fully automated Reduction of Electroencephalographic Artifacts (RELAX) pipeline and demonstrated its performance in cleaning EEG data recorded from adult populations. Here, we introduce the RELAX-Jr pipeline, which was adapted from RELAX to be designed specifically for pre- processing of data collected from children. RELAX-Jr implements multi-channel Wiener filtering (MWF) and/or wavelet-enhanced independent component analysis (wICA) combined with the adjusted-ADJUST automated independent component classification algorithm to identify and reduce all artifacts using algorithms adapted to optimally identify artifacts in EEG recordings taken from children. Using a dataset of resting-state EEG recordings (N = 136) from children spanning early-to-middle childhood (4-12 years), we assessed the cleaning performance of RELAX-Jr using a range of metrics including signal-to-error ratio, artifact-to-residue ratio, ability to reduce blink and muscle contamination, and differences in estimates of alpha power between eyes-open and eyes-closed recordings. We also compared the performance of RELAX- Jr against four publicly available automated cleaning pipelines. We demonstrate that RELAX-Jr provides strong cleaning performance across a range of metrics, supporting its use as an effective and fully automated cleaning pipeline for neurodevelopmental EEG data.

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bioRxiv2023

TMS-EEG shows mindfulness meditation is associated with an altered excitation/inhibition balance in the dorsolateral prefrontal cortex

ObjectiveMindfulness meditation is associated with functional brain changes in regions subserving higher order cognitive processes such as attention. However, no research to date has causally probed these areas in meditators using combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG). This study aimed to investigate whether cortical reactivity to TMS differs in a community sample of experienced mindfulness meditators when compared to matched controls MethodsTMS was applied to the left and right dorsolateral prefrontal cortices (DLPFC) of 19 controls and 15 meditators while brain responses were measured using EEG. TMS-evoked potentials (P60 and N100) were analysed, and exploratory analyses using the whole EEG scalp field were performed to test whether TMS-evoked global neural response strength or the distribution of neural activity differed between groups. ResultsMeditators were found to have statistically larger P60/N100 ratios in response to left and right hemisphere DLPFC stimulation compared to controls (pFDR = 0.004, BF10 > 39). No differences were observed in P60 or N100 amplitudes when examined in isolation. We also found preliminary evidence for differences in the distribution of neural activity 269-332ms post stimulation. ConclusionThese findings demonstrate differences in cortical reactivity to TMS in meditators. Differences in the distribution of neural activity approximately 300ms following stimulation suggest differences in cortico-subcortical reverberation in meditators that may be indicative of greater inhibitory activity in frontal regions. This research contributes to our current understanding of the neurophysiology of mindfulness and highlights opportunities for further exploration into the mechanisms underpinning the benefits of mindfulness meditation.

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bioRxiv2023

Resting state aperiodic slope predicts Research Domain Criteria Social Processes in young typically developing adults

The aperiodic exponent of the electrophysiological signal has been utilised to demonstrate differences in brain excitation-inhibition in ageing, cognition, and neuro- and psycho-pathology. Furthermore, excitation-inhibition imbalance has been associated with social communication difficulties in clinical and non-clinical cohorts. No work to date, however, has explored the association between aperiodic activity and social communication difficulties. A total of 40 neurotypical young adults aged 18-24 (24 female; age mean = 21.6, SD = 1.9) each underwent 5 minutes of eyes open and eyes closed resting state electroencephalography using a 64-channel HydroCel Geodesic Sensor Net. Participants also completed Research Domain Criteria social processes measures: Social Responsiveness Scale, Reading the Mind in the Eyes task, and Penn Emotional Recognition Task. Sex differences in aperiodic exponent and offset were observed, with larger exponent and greater offset observed in males, suggestive of greater inhibitory tone. Whole-brain, as well as left and right social brain, aperiodic exponent were moderately correlated with Social Responsiveness Scale Social Communication Index, but not Reading the Mind in the Eyes or Penn Emotional Recognition tasks. However, the correlations between aperiodic exponent and Social Responsiveness Scale Social Communication Index did not survive multiple comparisons correction, and regression models demonstrated that neither aperiodic exponent or offset were significantly predictive of social processes outcomes. These preliminary data provide some evidence that increased aperiodic activity may be associated with social communication difficulties, however, larger samples are needed to validate these initial findings.

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bioRxiv2023

Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction

Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. Here we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67%, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.

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bioRxiv2023

Experienced Meditators Show Multifaceted Attention-Related Differences in Neural Activity

ObjectivesMindfulness meditation (MM) is suggested to improve attention. Research has explored this using the attentional-blink (AB) task, where stimuli are rapidly presented, and a second target stimulus (T2) is often missed if presented [~]300ms after an initial target stimulus (T1). This research showed improved task-accuracy and altered neural activity after an intensive 3-month MM retreat. We tested whether these results replicated in a community sample of typical meditators. MethodsThirty-one mindfulness meditators and 30 non-meditators completed an AB task while electroencephalography (EEG) was recorded. Between-group comparisons were made for task-accuracy, event-related potential activity (posterior-N2 and P3b), theta and alpha oscillatory phase synchronisation to stimuli presentation, and alpha-power. Primary aims examined effects within time windows reported by previous research. Additional exploratory aims assessed effects across broader time windows. ResultsNo differences were detected in task-accuracy or neural activity within our primary hypotheses. However, exploratory analyses showed posterior-N2 and theta phase synchronisation effects indicating meditators prioritised attending to T2 stimuli (p < 0.01). Meditators also showed more alpha-phase synchronisation, and lower alpha-power when processing T2 stimuli (p < 0.025). ConclusionsOur results showed multiple differences in neural activity that suggested enhanced attention in meditators. The neural activity patterns in meditators aligned with theoretical perspectives on activity associated with enhanced cognitive performance. These include enhanced alpha gating mechanisms, increased oscillatory synchronisation to stimuli, and more equal allocation of neural activity across stimuli. However, meditators did not show higher task-accuracy, nor did effects align with our primary hypotheses or previous research. PreregistrationThis study was not preregistered.

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bioRxiv2022

Meditators probably show increased behaviour-monitoring related neural activity

ObjectivesMindfulness meditation is associated with better attention function. Performance monitoring and error-processing are important aspects of attention. We investigated whether experienced meditators showed different neural activity related to performance monitoring and error-processing. Previous research has produced inconsistent results. This study used more rigorous analyses and a larger sample to resolve the inconsistencies. MethodsWe used electroencephalography (EEG) to measure the error-related negativity (ERN) and error positivity (Pe) following correct and incorrect responses to a Go/Nogo task from 27 experienced meditators and 27 non-meditators. ResultsNo differences were found in the ERN (all p > 0.05). Meditators showed larger global field potentials (GFP) in the Pe after both correct responses and errors, indicating stronger neural responses (p = 0.0190, FDR-p = 0.152, np2 = 0.0951, BFincl = 2.691). This effect did not pass multiple comparison controls. However, single electrode analysis of the Pe did pass multiple comparison controls (p = 0.002, FDR-p = 0.016, np2 = 0.133, BFincl = 220.659). Meditators also showed a significantly larger Pe GFP for errors only, which would have passed multiple comparison controls, but was not a primary analysis (p = 0.0028, np2 = 0.1493, BF10 = 9.999). ConclusionsMeditation may strengthen neural responses related to performance monitoring (measured by the Pe), but not specifically to error monitoring (although measurements of the Pe after errors may be more sensitive to group differences). However, only the single electrode analysis passed multiple comparison controls, while analysis including all electrodes did not, so this conclusion remains tentative.

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bioRxiv2022

Right Anterior Theta Connectivity Predicts Autistic Social Traits in Neurotypical Children

Growing evidence supports functional network alterations in autism spectrum disorder, however much less is known about the neural mechanisms underlying autistic traits in typically developing children. Using resting-state electroencephalographic (EEG) recordings, we examined whether functional connectivity could predict autistic trait expression in 127 children aged between 4-12 years. Regression models showed that right anterior theta connectivity was a significant predictor of autistic traits (p = 0.013), with increased connectivity in this region associated with greater autistic trait expression. These results corroborate similar recent findings in adults, extending this observation to a cohort of children spanning early-to-middle childhood. These findings further highlight EEG-derived functional connectivity as a sensitive physiological correlate of autistic trait expression in neurotypical children.

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bioRxiv2022

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and Application to Oscillations

Electroencephalographic (EEG) data is typically contaminated with non-neural artifacts which can confound the results of experiments. Artifact cleaning approaches are available, but often require time-consuming manual input and significant expertise. Advancements in artifact cleaning often only address a single artifact, are only compared against a small selection of pre-existing methods, and seldom assess whether a proposed advancement improves experimental outcomes. To address these issues, we developed RELAX (the Reduction of Electroencephalographic Artifacts), an automated EEG cleaning pipeline implemented within EEGLAB that reduces all artifact types. RELAX cleans continuous data using Multiple Wiener filtering [MWF] and/or wavelet enhanced independent component analysis [wICA] applied to artifacts identified by ICLabel [wICA_ICLabel]). Several versions of RELAX were tested using three datasets containing a mix of cognitive and resting recordings (N = 213, 60 and 23 respectively). RELAX was compared against six commonly used EEG cleaning approaches across a wide range of artifact cleaning quality metrics, including signal-to-error and artifact-to-residue ratios, measures of remaining blink and muscle activity, and the amount of variance explained by experimental manipulations after cleaning. RELAX with MWF and wICA_ICLabel showed amongst the best performance for cleaning blink and muscle artifacts while still preserving neural signal. RELAX with wICA_ICLabel (and no MWF) may perform better at detecting the effect of experimental manipulations on alpha oscillations in working memory tasks. The pipeline is easy to implement in MATLAB and freely available on GitHub. Given its high cleaning performance, objectivity, and ease of use, we recommend RELAX for data cleaning across EEG studies.

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bioRxiv2022

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 2: Application to Event-Related Potentials

Electroencephalography (EEG) is commonly used to examine neural activity time-locked to the presentation of a stimulus, referred to as an Event-Related Potential (ERP). However, EEG is also influenced by non-neural artifacts, which can confound ERP comparisons. Artifact cleaning can reduce artifacts, but often requires time-consuming manual decisions. Most automated cleaning methods require frequencies <1Hz to be filtered out of the data, so are not recommended for ERPs (which often contain <1Hz frequencies). In our companion article, we introduced RELAX (the Reduction of Electroencephalographic Artifacts), an automated and modular cleaning pipeline that reduces artifacts with Multiple Wiener Filtering (MWF) and/or wavelet enhanced independent component analysis (wICA) applied to artifact components detected with ICLabel (wICA_ICLabel) (Bailey et al., 2022). To evaluate the suitability of RELAX for data cleaning prior to ERP analysis, multiple versions of RELAX were compared to four commonly used EEG cleaning pipelines. Cleaning performance was compared across a range of artifact cleaning metrics and in the amount of variance in ERPs explained by different conditions in a Go-Nogo task. RELAX with MWF and wICA_ICLabel cleaned the data the most effectively and produced amongst the most dependable ERP estimates. RELAX with wICA_ICLabel only or MWF_only may detect experimental effects better for some ERP measures. Importantly, RELAX can high-pass filter data at 0.25Hz, so is applicable to analyses involving ERPs. The pipeline is easy to implement via EEGLAB in MATLAB and is freely available on GitHub. Given its performance, objectivity, and ease of use, we recommend RELAX for EEG data cleaning. The MATLAB code, the supplementary materials, and a simple instruction manual explaining how to implement the RELAX pipeline can be downloaded from https://github.com/NeilwBailey/RELAX/releases. A condition of use of the pipeline is that the version of the pipeline used is referred to as RELAX_[pipeline], for example "RELAX_MWF_wICA" or "RELAX_wICA_ICLabel", and that the current paper be cited, as well as the dependencies used. These dependencies are likely to include: EEGLAB (Delorme & Makeig, 2004), fieldtrip (Oostenveld et al., 2011), the MWF toolbox (Somers et al., 2019), fastICA (Hyvarinen, 1999), wICA (Castellanos & Makarov, 2006), ICLabel (Pion-Tonachini et al., 2019), and PREP (Bigdely-Shamlo et al., 2015) See our companion article for the application of RELAX to the study of oscillatory power (Bailey et al., 2022).

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bioRxiv2021

Periodic and Aperiodic Neural Activity Displays Age-Dependent Changes Across Early-to-Middle Childhood

The neurodevelopmental period spanning early-to-middle childhood represents a time of significant growth and reorganisation throughout the cortex. Such changes are critical for the emergence and maturation of a range of social and cognitive processes. Here, we utilised both eyes open and eyes closed resting-state electroencephalography (EEG) to examine maturational changes in both oscillatory (i.e., periodic) and non-oscillatory (aperiodic, 1/f-like) activity in a large cohort of participants ranging from 4-to-12 years of age (N=139, average age=9.41 years, SD=1.95). The EEG signal was parameterised into aperiodic and periodic components, and linear regression models were used to evaluate if chronological age could predict aperiodic exponent and offset, as well as well as peak frequency and power within the alpha and beta ranges. Exponent and offset were found to both decrease with age, while aperiodic-adjusted alpha peak frequency increased with age; however, there was no association between age and peak frequency for the beta band. Age was also unrelated to aperiodic-adjusted spectral power within either the alpha or beta bands, despite both frequency ranges being correlated with the aperiodic signal. Overall, these results highlight the capacity for both periodic and aperiodic features of the EEG to elucidate age-related functional changes within the developing brain.

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bioRxiv2021

A single- and paired-pulse TMS-EEG investigation of the N100 and long interval cortical inhibition in autism spectrum disorder

BackgroundThere is evidence to suggest a disruption of gamma-aminobutyric acid (GABA) in autism spectrum disorder (ASD), but findings are mixed. Concurrent electroencephalography and transcranial magnetic stimulation (TMS-EEG) provides a novel method by which to probe GABA-mediated cortical inhibition. MethodsWith a particular focus on GABAB-ergic mechanisms, we investigated the N100 peak of the TMS evoked potential (TEP), as well as long interval cortical inhibition (LICIEEG) in adults with ASD (n = 23; 12 female) without intellectual disability, and a neurotypical comparison group (n =22; 12 female) matched for age, sex, and IQ. Seventy-five single-(spTMS) and 75 paired-(ppTMS; 100 ms inter-stimulus-interval) pulses were applied to the right primary motor cortex (M1), right temporoparietal junction (TPJ), and right dorsolateral prefrontal cortex (DLPFC) while EEG was recorded from 20 scalp electrodes. Additionally, electromyography (EMG) was used to investigate corticospinal inhibition following ppTMS to M1 (LICIEMG). ResultsThere were no group differences in the N100 amplitude or latency following spTMS. LICI outcomes following ppTMS, as measured by either EEG or EMG, also did not differ between groups. These findings were further supported by Bayesian analyses, which provided weak-moderate support for the null hypothesis. LimitationsData presented here reflect adults without intellectual disability, and the generalisability of these results is therefore limited. ConclusionsThe findings of this study argue against GABAB-ergic impairment in adults with ASD without intellectual disability, at least at the cortical regions examined. Further research investigating these mechanisms in ASD at various ages, with varying degrees of symptomatology, and at different brain sites is an important factor in understating the role of GABA in the neuropathophysiology of ASD.

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bioRxiv2020

Assessing cerebellar-cortical connectivity using concurrent TMS-EEG: A Feasibility Study

BackgroundCombined single-pulse transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been used to probe the features of local networks in the cerebral cortex. Here we investigate whether we can use this approach to explore long-range connections between the cerebellum and cerebral cortex. ObjectiveTo assess the feasibility of using cerebellar TMS-EEG for the exploration of cerebellar-cerebral network dynamics. MethodsTen healthy adults received single-pulse suprathreshold TMS to the cerebellum and an occipital/parietal control site with double-cone and figure-of-eight coils while cerebral activity was recorded. A multisensory electrical control condition was used to simulate the sensation of the double-cone coil at the cerebellar site. Two cleaning pipelines were compared, and the spatiotemporal relationships of the EEG output between conditions were examined at sensor and source levels. ResultsCerebellar stimulation with the double-cone coil resulted in large artefacts in the EEG trace. The addition of SOUND filtering to the cleaning pipeline improved the signal such that further analyses could be undertaken. The cortical potentials evoked by the active TMS conditions showed strong relationships with the responses to the multisensory control condition after ~50 ms. A distinct parietal component at ~42 ms was found following cerebellar double-cone stimulation. ConclusionsCerebellar double-cone stimulation produces large artefacts in the EEG. Cerebellar-specific responses could not be reliably differentiated from sensory evoked potentials after ~50 ms. While evoked potentials differed across conditions at early latencies, it is unclear as to whether these represented TMS-related network activation of the cerebellarthalamocortical tract, or whether components were dominated by sensory contamination and/or coil-driven artefacts. Further work will be required to clarify the specific contribution of cerebellar-cortical connectivity to the observed early latency signals.

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bioRxiv2019

Mindfulness meditators show enhanced working memory performance concurrent with different brain region engagement patterns during recall

Mindfulness meditation has been shown to improve working memory (WM). However, the altered brain activity underpinning these improvements is underexplored. In non-meditating individuals, modulation of theta and alpha oscillations and 1/f aperiodic activity during WM has been found to be related to WM performance. Resting theta and alpha oscillations have been found to differ in meditators, but WM related oscillation changes and 1/f aperiodic activity have not yet been examined. Additionally, WM event-related-potentials (ERPs) are modulated by attention, which is also enhanced by meditation, so these neural measures are candidates for exploring neural activity underpinning WM improvement in meditators. We recorded EEG from 29 controls and 29 meditators during a modified Sternberg WM task and compared theta, alpha, and 1/f aperiodic activity during the WM delay, and ERPs time-locked to the WM probe. Meditators responded more accurately (p = 0.008, Cohens d = 0.688). Meditators also showed different ERP distributions with earlier left-temporal activation and more frontal distribution of activity (FDR-p = 0.0186, {eta}2 = 0.0903), as well as a reduction in overall neural response strength (FDR-p = 0.0098, {eta}2 = 0.1251). While a higher proportion of meditators showed theta oscillations during the WM delay, no other differences in theta, alpha or 1/f aperiodic activity were present. These results suggest that increased WM performance in meditators might not be the result of higher amplitudes of typical WM activity, but instead due to an alternative neural strategy during WM decision making, which may allow more accurate responses with less neural activation.\n\nHighlights- Long term mindfulness meditators showed improved working memory (WM) accuracy\n- This was concurrent with earlier left temporal activation following probe stimuli\n- As well as a more frontal distribution and reduced overall neural response strength\n- No oscillation differences were present in the working memory delay period\n- Improved WM from altered neural strategy rather than increased neural activity

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bioRxiv2019

Experienced meditators exhibit no differences to demographically-matched controls in theta phase synchronisation, P200, or P300 during an auditory oddball task

ObjectivesLong-term meditation practice affects the brains ability to sustain attention. However, how this occurs is not well understood. Electroencephalography (EEG) studies have found that during dichotic oddball listening tasks, experienced meditators displayed altered attention-related neural markers including theta phase synchronisation (TPS) and event-related potentials (ERP; P200 and P300) to target tones while meditating compared to resting, and compared to non-meditators after intensive meditation interventions. Research is yet to establish whether the changes in the aforementioned neural markers are trait changes which may be observable in meditators irrespective of practice setting.\n\nMethodThe present study expanded on previous research by comparing EEG measures from a dichotic oddball task in a sample of community-based mindfulness meditators (n=22) to healthy controls with no meditation experience (n=22). To minimise state effects, neither group practiced meditation during / immediately prior to the EEG session.\n\nResultsNo group differences were observed in behavioural performance or either the global amplitude or distribution of theta phase synchronisation, P200 or P300. Bayes Factor analysis suggested evidence against group differences for the P200 and P300.\n\nConclusionsThe results suggest that increased P200, P300 and TPS do not reflect trait-related changes in a community sample of mindfulness meditators. The present study used a larger sample size than previous research and power analayses suggested the study was suficiently powered to detect differences. These results add nuance to our understanding of which processes are affected by meditation and the amount of meditation required to generate differences in specific neural processes.

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