The human brain is a complex system, where closed-loop mechanisms are vital in maintaining stability, synchronization, and adaptability of brain function. An important component of these mechanisms are neural oscillations (brain waves). Abnormal neural oscillation patterns are associated with psychiatric and neurological disorders. Brain stimulation techniques are being explored as a treatment option that can alter these abnormal oscillatory patterns. In closed-loop brain stimulation, an electric current or magnetic field stimulates brain activity, while being dynamically adjusted using feedback from ongoing brain activity captured through real-time electroencephalogram (EEG) measurements, with the aim to enhance the effectiveness of stimulation at modulating the target neural activity. Developing these treatment strategies is challenging due to the complex nonlinear dynamics of brain activity, so treatment development benefits from computer simulations. However, existing simulation tools do not integrate brain dynamics with transcranial stimulation to conduct closed-loop simulations, restricting the application of advanced methods such as reinforcement learning (RL), which refer to machine and deep learning algorithms capable of identifying complex patterns in data to discover novel decision-making strategies. Therefore, in this study, we first introduce a framework named NeuroStimEnv for simulating closed-loop brain stimulation. The tool provides the capability for researchers to integrate different models of neural circuits (exhibiting characteristics observed in Alzheimer's disease or depression), configure different electrode montage setups for stimulation and EEG measurement, and simulate treatment strategies. We have released the code as open source under the MIT license. Next, we simulate a depression microcircuit to demonstrate the feasibility of using RL to discover a transcranial alternating current stimulation treatment strategy. We demonstrate promising initial results on using RL-based algorithms for treatment selection, to successfully shift neural circuits representative of depression towards circuits representative of healthy individuals. The proposed simulation framework provides valuable insights into transcranial stimulation and enables the application of RL methods as shown in the case study.

Background:The motor threshold (MT) plays a central role in probing brain excitability and individualizing transcranial magnetic stimulation (TMS). Previously, we proposed stochastic approximation (SA) as a new method for determining TMS MT and demonstrated its excellent speed and accuracy via simulations. SA also has low computational requirements and is theoretically robust to potential model flaws. Objective:This project aimed to develop a practical SA thresholding method and assess its performance in clinical studies. Methods:The SA thresholding method was implemented as an online software application-SAMT (Stochastic Approximator of MT)-that incorporates features for warning against likely inaccurate MT estimates. Two clinical studies used SAMT and collected 365 small hand muscle MTs from 179 participants to date. SAMT's misestimation warning method marked MTs of 7 thresholding trials as likely inaccurate, and SAMT's performance in the remaining 358 trials was assessed by comparing the MT at each step to the threshold estimated by fitting a sigmoidal probability distribution to the complete muscle response data from the session using maximum likelihood estimation (MLE). Results:By the 25th TMS pulse, 99% of the SAMT MTs differed by less than 3.0% (relative) and 1.3% of maximum stimulator output (absolute) from the corresponding fitted MLE sigmoid thresholds and were within the 95% confidence intervals of the MLE thresholds. Conclusions:We provide the TMS community with a new thresholding tool, SAMT. Combined with the prior simulation results, the experimental assessment presented here supports the practicality and accuracy of the SA thresholding method and the SAMT software.

OBJECTIVE:Mindfulness-based interventions (MBIs) show promise in managing chronic pain but often require substantial time commitments, leading to high attrition and concerns about acceptability. This meta-analysis evaluated attrition rates in MBIs for chronic pain and examined moderators contributing to participant withdrawal. METHODS:Following PRISMA guidelines, we searched relevant databases for studies of MBIs for pain. Eligible studies included randomised controlled trials, controlled trials, and quasi-experimental designs that reported attrition data for adults (≥18 y) with chronic pain lasting over 3 months. Data extraction covered attrition metrics, program characteristics, and participant demographics. Statistical analyses included random-effects meta-analyses of proportions, sensitivity analyses, meta-regression, and publication bias assessments. RESULTS:Forty-four studies (45 intervention conditions) were included. The pooled attrition rate was 30.1% (95% CI: 24.5%- 37.3%) with substantial heterogeneity ( I ²=89.0%). Attrition increased with stricter completion thresholds (minimum sessions required for programme completion status) ( P <0.001, R ²=28.1%): 18.0% (≥3 to 4 sessions), 31.6% (≥5 to 6 sessions), and 49.7% (>6 sessions). Online delivery showed higher attrition (51.0%) than in-person delivery (25.6%, P =0.002, R ²=17.1%). Individually delivered MBIs were also associated with higher attrition than group formats (β=0.216, P =0.039, R ²=5.5%). Publication bias analyses suggested minor influence on the pooled effect, which remained robust after adjustment. DISCUSSION:Attrition rates for MBIs in chronic pain vary widely. Higher attrition is associated with stricter completion criteria, online delivery, and individual formats. These findings highlight the need to optimise MBI programme structure for management of pain.

OBJECTIVES:Electroencephalography (EEG) can be used to assess functional brain connectivity (FC). However, there is considerable variability in the methods used for FC measurement across different studies, which may contribute to heterogeneity in research outcomes. We aimed to assess how different EEG pre-processing steps impact EEG-FC measurement when applied to real EEG data. METHODS:Using the BrainClinics.com open-source EEG data repository we investigated how different pre-processing steps impacted the ability to detect age-related differences in alpha band FC and the test-retest reliability of FC measures. The pre-processing steps tested included artifact reduction techniques (Independent Component Analysis (ICA), wavelet-enhanced ICA (wICA), and Multi-channel Wiener Filters (MWF)), different epoch lengths (epochs that were 2 s versus 6 s in length), and different re-referencing montages (the common average reference (CAR) versus current source density (CSD) re-referencing). We also assessed different FC metrics including imaginary coherence (iCOH), real magnitude squared coherence (rMSC), and weighted phase lag index (wPLI) metrics. RESULTS:The best performing pipeline at detecting age-related differences in alpha FC and providing high test-retest reliability included artifact reduction by ICA or wICA, data re-referenced using the CSD method, and FC measured by rMSC. CONCLUSION & SIGNIFICANCE:This paper presents evidence for an EEG pre-processing pipeline that provides good detection of meaningful effects and high test-retest reliability for sensor space EEG alpha frequency FC.

Objective: Despite many decades of experimental studies and clinical trials involving a variety of psychedelic agents, we still lack a comprehensive understanding of the effects of these substances on psychological experiences. As such, we designed and conducted a study to comprehensively characterise the effects of both psilocybin and 3,4-Methylenedioxymethamphetamine (MDMA) on a range of psychological outcomes in a substantive non-clinical population. Methods: This study involved a single dose administration of psilocybin or MDMA in healthy individuals in a group setting (2-4 people per session). All participants underwent a single preparation session, a drug exposure session, and an integration session within 72 hours of dosing. Outcome assessments were conducted at a pre-dosing baseline, 1-3 days post dose (side effects only), one week post dose and at 3 month follow up (the later time point data is not included here). Results: Of 48 participants, 25 initially received MDMA and 23 psilocybin. Ten cross-over participants received MDMA and then psilocybin and six participants received both in the reverse order: making a total of 31 MDMA and 33 psilocybin dosing sessions. In the week after dosing, we found significant changes in personality (a reduction in neuroticism and increase in extraversion), mindfulness, and connectedness following the administration of psilocybin but not MDMA. Psilocybin also produced significantly stronger mystical experiences compared to MDMA, and there was a significant correlation between the magnitude of these mystical experiences and changes in connectedness and mindfulness (but not changes in personality). Of note, participants seemed more comfortable with, and preferred, larger group sizes when being administered MDMA than psilocybin. Discussion: Our results identified a range of short-term psychological effects in non-clinical participants following a single dose of psilocybin, that were not reported following a single dose of MDMA. Notably, our results indicate that these effects following psilocybin may be moderated through its induction of mystical experiences, as has been previously hypothesised. Although preliminary, our results also suggest that larger group dosing sessions seem more feasible with MDMA than psilocybin. ### Competing Interest Statement In the last 3 years PBF has received equipment for research from Neurosoft, Nexstim and Brainsway Ltd. He has served on scientific advisory boards for Magstim and LivaNova and received speaker fees from Otsuka. He has also acted as a founder and board member for TMS Clinics Australia and Resonance Therapeutics. PBF is supported by a National Health and Medical Research Council of Australia Investigator grant (1193596). The other authors declare that they have no conflicts of interest. ### Clinical Trial ACTRN12622001535763p ### Funding Statement This study was funded through a donation to the Australian National University from Mind Medicine Australia. PBF is supported by a National Health and Medical Research Council of Australia Practitioner Fellowship (6069070). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: ACT Health Human Ethics Committee gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The de-identified data that support the findings of this study are available from the corresponding author, PBF, upon reasonable request.

OBJECTIVE:Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression, but not for all patients. Accurate treatment response prediction could lower treatment burden. Research suggests machine learning trained with electroencephalography (EEG) data may predict response, but a limited range of features have been tested. We tested whether a combination of > 7000 time-series features were predictive of response in training and test and independent datasets. METHODS:Pre-treatment EEG from 188 patients with depression treated with rTMS were decomposed into five principal components (PCs). The highly comparative time-series analysis toolbox was used to extract 7304 time-series features from each participant and PC. A classification algorithm was trained to predict responders from these features separately for each PC. The classifier was applied to an independent dataset (N = 58) to test generalizability. RESULTS:Within the training and test dataset, the third PC showed above-chance classification accuracy (69.4 %, pFDR = 0.005). The model generalized to the independent dataset with above-chance accuracy (60 %, p = 0.046). Analysis of feature-clusters suggested responders showed more high frequency relative power relative, and a more negative skew in the distribution of time-series values. CONCLUSIONS:Results suggest our methods could be used to inform treatment selection. SIGNIFICANCE:Our methods may enable better outcomes than 'one-size-fits-all' treatment approaches.

Previous research has shown that word-finding difficulties in older age are associated with functional and structural brain changes. Functional brain networks, measured through electroencephalography, reflect the brain's neurophysiological organisation. However, the utility of functional brain networks, to predict word-finding in older and younger adults has not yet been investigated. This study utilised eyes-closed resting-state electroencephalography data (61 channels) from the Leipzig Study for Mind-Body-Emotion Interactions dataset (Babayan et al., 2019) to investigate the relationship between functional brain networks and word-finding ability in 53 healthy right-handed younger (aged 20-35) and 53 (aged 59-77) older adults. Brain segregation reflects the efficiency of localised brain regions to process information, while brain integration reflects the efficiency of global information processing between distant brain regions. Word-finding ability was quantified as the number of orally produced words during a semantic and letter fluency task. Multiple linear regression revealed that, in older adults, greater synchronised brain activity was associated with lower semantic fluency. Irrespective of age, greater brain segregation was related to lower semantic fluency. Increased brain integration corresponded to greater semantic fluency in older adults. Both older and younger participants with a more optimised balance between brain segregation and integration performed better on semantic fluency. These findings suggest that word-finding ability seems to be related to brain segregation and integration, possibly indicating alterations in cognitive control or compensatory changes in brain activity. The article further provides a discussion on neural dedifferentiation, hyper-synchronisation, study limitations, and directions for future research.

Objectives: Personality traits must relate to stable neural processes, yet few robust neural correlates of personality have been discovered. Recent methodological advances enable measurement of cortical travelling waves, which likely underpin information flow between brain regions. Here, we explore whether cortical travelling waves relate to personality traits from the "Big Five" taxonomy. Method: We assessed personality traits and recorded resting electroencephalography (EEG) from 300 participants. We computed travelling wave strength using a 3D fast Fourier transform and explored relationships between alpha travelling waves and personality traits. Results: Trait Agreeableness and Openness/Intellect had significant relationships to travelling waves that passed multiple-comparison controls (pFDR = 0.019, pFDR = 0.036). Agreeableness related to interhemispheric waves travelling from the right hemisphere along central lines (rho = 0.263, p < 0.001, BF10 = 356.350). This relationship was unique to the compassion aspect (t = 3.719, p <0.001) rather than politeness aspect of Agreeableness (t = 0.897, p = 0.370). Openness/Intellect related to backwards travelling waves along midline electrodes (rho = 0.197, p < 0.001, BF10 = 13.800), which was confirmed for the Openness aspect (rho = 0.216, p < 0.001, BF10 = 26.444) but not the Intellect aspect (rho = 0.093, p = 0.109, BF10 = 0.344). Conclusions: Greater cortical travelling wave strength from right temporal regions may partly underpin variation in trait compassion, and backwards travelling wave strength along midline electrodes may mark trait openness. Further research is needed to investigate the mechanistic role of travelling waves in personality traits and other individual differences. ### Competing Interest Statement The authors have declared no competing interest.

Research using electroencephalography (EEG) has shown that individual differences in visual working memory capacity are related to slow-wave event-related potentials (ERPs) and suppression of alpha-band oscillatory power during the delay period of memory tasks. However, recent evidence suggests that changes in non-oscillatory (aperiodic) features of the EEG signal are related to working memory performance. We assessed several features of task-related changes in aperiodic activity including its spatial distribution, the effect of memory load, and the relationships between aperiodic activity, memory capacity, slow-wave ERPs, and alpha suppression. Eighty-four healthy individuals performed a continuous recall working memory (WM) task consisting of 2, 4 or 6 coloured squares while EEG was recorded. Aperiodic activity during a baseline and WM delay period was quantified by fitting a model to the background of the EEG power spectra, which returned parameters describing the slope (exponent) and broadband offset of the spectra. The aperiodic exponent increased (i.e., slope steepened) in fronto-central electrodes during the WM delay period, whereas the offset decreased over parieto-occipital electrodes. These task-related changes in aperiodic activity did not differ between memory loads. Larger increases in the aperiodic exponent were associated with higher working memory capacity measured from both the WM task and a separate battery of complex span tasks, a relationship that was independent of slow-wave ERPs and alpha suppression. Our findings suggest that WM task-related changes in aperiodic activity are region specific and reflect an independent neural mechanism that is important for general working memory ability.

Objective Mindfulness 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 Methods TMS 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. Results Meditators were found to have statistically larger P60/N100 ratios in response to left and right hemisphere DLPFC stimulation compared to controls ( p FDR = 0.004, BF 10 > 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. Conclusion These 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.

OBJECTIVE:Electroencephalography (EEG) data are contaminated by a range of non-neural artifacts. The confounding influence of artifacts is often addressed by using independent component analysis (ICA) to decompose data into components, subtracting artifactual components, then reconstructing data into the electrode space. Due to imperfect component separation, this common approach can remove neural signals as well as artifacts. Here, we demonstrate the counterintuitive finding that this can artificially inflate event-related potential and connectivity effect sizes and bias source localisation estimates, while also removing neural signals. METHODS:We developed a novel method that targets cleaning to artifact periods of eye movement components and artifact frequencies of muscle components, and tested our method across different EEG systems and cognitive tasks. RESULTS:Our targeted artifact reduction method was effective in cleaning artifacts while also reducing the artificial inflation of effect sizes and minimizing source localisation biases. CONCLUSIONS:EEG pre-processing of Go/No-go and N400 task data is better when targeted cleaning is applied, which better preserves neural signals and mitigates effect size inflation and source localisation biases that result from subtracting artifact components. SIGNIFICANCE:These improvements enhance the reliability and validity of EEG analyses. Our method is provided in the RELAX pipeline, which is freely available as an EEGLAB plugin (https://github.com/NeilwBailey/RELAX).

Mindfulness meditation involves training attention, commonly toward sensory experiences, with nonjudgmental awareness. Theoretical perspectives propose that meditation increases the precision of sensory processing and reduces the generation/elaboration of top-down expectations. Research suggests forward traveling cortical alpha waves may reflect bottom-up inhibition to enhance signal-to-noise ratios of sensory processing, while backward traveling alpha waves may reflect top-down inhibition based on expectations. We used electroencephalography to test whether the strength of forward and backward traveling cortical alpha waves differed between meditators and a matched sample of nonmeditators during eyes-closed resting (N = 97) and during a visual cognitive (Go/No-go) task (N = 126). Our results showed meditators produced stronger forward traveling waves compared to nonmeditators while resting with their eyes closed and during task performance. Meditators also exhibited weaker backward traveling waves while resting with their eyes closed. These results may indicate a neural mechanism underpinning enhanced attention associated with meditation, as well as a potential neural marker of reductions in mind-wandering, suggested to be associated with meditation. The results also support models of brain function that suggest attention modification is achievable through mental training to increase sensory awareness, which might be indexed by the greater strength of forward traveling cortical waves.

Altered brain connectivity and atypical neural oscillations have been observed in autism, yet their relationship with autistic traits in nonclinical populations remains underexplored. Here, we employ electroencephalography to examine functional connectivity, oscillatory power, and broadband aperiodic activity during a dynamic facial emotion processing task in 101 typically developing children aged 4 to 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 facial emotion processing-related connectivity across theta (4 to 7 Hz) and beta (13 to 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 facial emotion processing-related brain dynamics and autistic traits in typically developing children. Future work could extend these findings to assess these electroencephalography-derived markers as potential mechanisms underlying behavioral difficulties in autism.

OBJECTIVE:Functional brain connectivity (FC) can be estimated using electroencephalography (EEG). However, there is considerable variability across studies in the FC measures used and in data (pre-)processing methods, leading to difficulties comparing and amalgamating results between studies. Thus, standardisation of EEG (pre-)processing for the measurement and reporting of FC is needed.We aimed to assess differences in FC estimates produced by different settings across multiple EEG pre-processing steps, (including re-referencing and epoching) to validate a reliable methodological pipeline for assessing EEG-FC in simulated EEG data. METHODS:We simulated EEG-FC data where the 'ground truth' of the connections is known and compared estimates of FC from this ground truth data across multiple FC measures and variations in multiple pre-processing steps. RESULTS:Our results indicated that pre-processing steps that included segmenting the data into 40 or more epochs that were 6 s or more in length provided the most accurate estimation of the simulated FC. With regards to the data re-referencing, the Reference Electrode Standardization Technique or the common average re-referencing appeared best when used in conjunction with imaginary coherence and weighted phase lag index metrics. However, the magnitude-squared coherence FC measure performed best with the Current Source Density reference free techniques. CONCLUSIONS & SIGNIFICANCE:Our paper provides an evidence-base for the influence of referencing, epoch length and number, controls for volume conduction, and different FC metrics on EEG-FC measurement. Using this evidence, we present an initial and promising account of the best performing (pre-)processing choices for robust EEG-FC assessment.

Personality traits must relate to stable neural processes, yet few robust neural correlates of personality have been discovered. Recent methodological advances enable measurement of cortical travelling waves, which likely underpin information flow between brain regions. Here, we explore whether cortical travelling waves relate to personality traits from the “Big Five” taxonomy. We assessed personality traits and recorded resting electroencephalography (EEG) from 300 participants. We computed travelling wave strength using a 3D fast Fourier transform and explored relationships between alpha travelling waves and personality traits. Trait Agreeableness and Openness/Intellect had significant relationships to travelling waves that passed multiple-comparison controls (pFDR = 0.019 and pFDR = 0.036 respectively). Agreeableness related to interhemispheric waves travelling from the right hemisphere along central lines (rho = 0.263, p < 0.001, BF10 = 356.350). This relationship was unique to the compassion aspect (t = 3.719, p <0.001) rather than politeness aspect of Agreeableness (t = 0.897, p = 0.370). Openness/Intellect related to backwards travelling waves along midline electrodes (rho = 0.197, p < 0.001, BF10 = 13.800), which was confirmed for the Openness aspect (rho = 0.216, p < 0.001, BF10 = 26.444) but not the Intellect aspect (rho = 0.093, p = 0.109, BF10 = 0.344). Greater cortical travelling wave strength from right temporal regions was associated with higher trait compassion, and backwards travelling wave strength along midline electrodes was associated with trait openness. Further research is needed to investigate the mechanistic role of travelling waves in personality traits and other individual differences.

Objectives: Mindfulness 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. Methods: We 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). Results: Meditators 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). Conclusions: Mindfulness 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. ### Competing Interest Statement In the last 3 years PBF has received equipment for research from Neurosoft, Nexstim and Brainsway Ltd. He has served on scientific advisory boards for Magstim and LivaNova and received speaker fees from Otsuka. He has also acted as a founder and board member for TMS Clinics Australia and Resonance Therapeutics. PBF is supported by a National Health and Medical Research Council of Australia Investigator grant (1193596). The other authors declare that they have no conflicts of interest.