Biology subjects

Rogasch, N. C.

Publications and source records attributed to Rogasch, N. C..

At least 19 recordsLinked to original sources

bioRxiv2026

Repetitive suprathreshold paired-pulse transcranial magnetic stimulation alters corticospinal excitability

Background: Suprathreshold paired-pulse transcranial magnetic stimulation (TMS) at interstimulus intervals (ISIs) of 20-30 ms evokes cumulative online increases in corticospinal excitability. Whether these changes persist beyond stimulation remains unclear. This study investigated whether repetitive paired-pulse TMS targeting periods of net excitation or inhibition induces offline changes in corticospinal excitability. Methods: Nineteen healthy participants completed three sessions, separated by at least one week, involving 40 pairs of repetitive paired-pulse TMS delivered to the left primary motor cortex at an ISI of 20 ms (rPPS20) or 100 ms (rPPS100), or a single-pulse control condition (rSPS). Offline changes in corticospinal excitability were assessed using motor evoked potential (MEP) amplitudes recorded before and after stimulation. Results: During stimulation, MEP amplitudes decreased across trials in the rPPS100 condition, although online changes did not differ significantly between conditions. Following stimulation, MEP amplitudes were significantly reduced for up to 40 minutes after rPPS100 (p = 0.017) but were not changed following rPPS20 or rSPS (all p > 0.05). Conclusion: Contrary to our hypothesis, repetitive paired-pulse TMS targeting periods of net excitation did not induce offline changes in corticospinal excitability. In contrast, stimulation targeting long-interval intracortical inhibition (LICI) produced sustained reductions in corticospinal excitability, suggesting that repetitive LICI-inducing stimulation can induce lasting plasticity within the corticospinal system.

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bioRxiv2026

An Integrated EEG, TMS-EEG and Behavioural Dataset for Investigating the Neural Correlates of Working Memory

Working memory is a fundamental process that underlies cognition. Accordingly, the neural mechanisms that support working memory performance are of great interest in cognitive neuroscience. We present a publicly accessible dataset comprising 123 healthy adults (mean age = 28.58 years, SD = 7.56; age range = 18-46 years; 75 females) to facilitate investigation of the neural mechanisms underlying working memory performance in humans. Across two days of assessment, participants underwent a battery of cognitive tasks assessing working memory and other cognitive domains (day 1), followed by 62-channel EEG recordings (day 2). EEG data were acquired at rest, during a visual working memory task, and following single-pulse transcranial magnetic stimulation (TMS-EEG) targeting key brain regions involved in working memory. This article provides a detailed overview of the study design, methodology, and data characteristics.

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bioRxiv2026

Characterising and Minimising Step and Filtering Artifacts in TMS-EEG Recordings

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) enables direct measurement of cortical reactivity via TMS-evoked potentials (TEPs). Interpretation of early TEP components however, is highly sensitive to stimulation and hardware-related artifacts. We identified and characterised a persistent, non-neural step-drift artifact unexpectedly present in recent TMS-EEG recordings from our group. We show that the artifact is distinct from previously described TMS pulse and discharge/decay artifacts and likely reflects a hardware interaction phenomenon. We demonstrated that amplifier settings, but not TMS pulse shape, substantially influenced artifact expression, with DC-coupled recordings with no online high-pass filter reducing step amplitude compared with AC-coupled recordings with a high-pass filter. Simulations additionally revealed that filtering over the step-drift artifact introduced pronounced ringing and edge artifacts, highlighting the need to address this artifact prior to data processing. We propose a processing pipeline incorporating robust polynomial detrending and a modified Butterworth filter with autoregressive extrapolation that minimised TEP distortion in both simulated and real data containing the step-drift artifact. Together, these findings provide practical recommendations for both preventing and correcting step-drift artifacts and underscore the need for formal definition and routine recognition of this artifact to improve reproducibility and data quality in TMS-EEG research.

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bioRxiv2026

Non-Invasive Brain Stimulation Data Analysis Structure (NIBS-DAS): A Template for the Layout, Management, and Analysis of NIBS Data

Currently, there is no consensus about how investigators should format their NIBS data for sharing. This presents a barrier to the advancement of big data analyses because it requires time-consuming operations to generate consistent formats across different shared datasets. Recently, we launched Big non-invasive brain stimulation data (Big NIBS data), an open-access platform and repository for NIBS data (https://www.bignibsdata.com/), providing a structured mechanism for researchers to share NIBS data. However, the reusability and interoperability of data uploaded to Big NIBS data is restricted by the absence of a common data structure. The current paper addresses this problem by creating the NIBS data analysis structure (NIBS-DAS), a template pipeline for the layout, management, and analysis of collated NIBS outcome data. While its primary purpose is to provide a template layout for uploading collated data to the Big NIBS data repository, NIBS-DAS also offers guidelines for the management and analysis of collated NIBS data, thereby forming a data analysis pipeline that can be freely used by the NIBS field in general. We anticipate that NIBS-DAS will serve to facilitate data sharing on the Big NIBS data platform and promote greater standardisation of data management and analytical practices in the NIBS field.

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bioRxiv2025

Cumulative timing-dependent changes in corticospinal excitability during suprathreshold paired-pulse transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is widely used to assess inhibitory and facilitatory circuits within the primary motor cortex. However, accumulating evidence suggests that even brief TMS paradigms may induce unintended changes in corticospinal excitability. Here, we examined whether suprathreshold paired-pulse TMS delivered at inter-stimulus intervals (ISIs) associated with intracortical facilitation or long-interval cortical inhibition (LICI) elicits cumulative changes in motor-evoked potential (MEP) amplitude. In experiment 1, we reanalysed data from 17 participants who received 20 suprathreshold paired-pulses at eight ISIs (10-200 ms) and 40 unconditioned single pulses. Stimulation was pseudo-randomised and distributed evenly across four blocks. A linear mixed-effects model assessed trial-wise changes in MEP amplitude across ISIs. We replicated the design in an independent sample (n=10, experiment 2). A significant trial-by-ISI interaction was observed in both cohorts. Specifically, MEP amplitudes increased across trials for ISIs of 20 and 30 ms (p<0.05), but remained stable at LICI-related ISIs (100-150 ms). A similar increase was also seen with single-pulse TMS. These findings demonstrate that suprathreshold paired-pulse TMS at short ISIs can cumulatively enhance corticospinal excitability during stimulation. Furthermore, the results suggest potential for using these protocols not just for probing cortical circuits, but also as interventions to modulate motor system excitability.

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bioRxiv2025

Investigating the Influence of Anti-Seizure Medications on Aperiodic EEG Activity

ObjectiveElectroencephalography (EEG) signals comprise both oscillatory (periodic) and non-oscillatory (aperiodic) components. Aperiodic activity forms the 1/f-like background of the EEG power spectrum and can be characterised by parameters describing the offset and slope (1/f exponent). This study examined the effects of two antiseizure medications (ASMs), which reduce cortical excitability through different mechanisms of action, on aperiodic and periodic EEG activity. MethodsResting EEG was recorded with eyes open and closed from 13 healthy male volunteers at baseline and two hours after administration of lamotrigine (300 mg), levetiracetam (3000 mg), or placebo. Power spectra were computed using Welchs method. Aperiodic parameters were estimated using the specparam algorithm, and periodic activity was quantified after subtraction of the aperiodic component. ResultsIn the eyes-open condition, lamotrigine significantly reduced both aperiodic offset and exponent relative to placebo, consistent with a flattening of the aperiodic spectrum, whereas levetiracetam reduced the exponent without significantly altering the offset. Neither drug significantly altered aperiodic exponent or offset during eyes-closed. Lamotrigine reduced corrected theta power in both conditions and alpha power during eyes-open, whereas levetiracetam increased beta power in both conditions and reduced gamma power during eyes-closed. ConclusionsBoth lamotrigine and levetiracetam altered aperiodic and periodic EEG activity, with aperiodic effects observed only during eyes-open. SignificanceAperiodic EEG measures may provide a non-invasive approach for characterising ASM-related neurophysiological effects and may help elucidate how distinct pharmacological mechanisms influence large-scale cortical activity.

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bioRxiv2025

Mild traumatic brain injury alters function in the dorsolateral prefrontal cortex: a TMS-EEG study.

Numerous studies highlight alterations in motor cortical neurophysiology after mild traumatic brain injury (mTBI). In contrast, understanding of effects in cognitive brain areas, which potentially underpin more impactful effects of injury, remains limited. The current study addressed this limitation by using electroencephalography to index responses to transcranial magnetic stimulation (TMS-EEG) applied over the dorsolateral prefrontal cortex (DLPFC). In 18 mTBI patients and 22 healthy controls, TMS-EEG was used to index cortical inhibition and long-term depression (LTD)-like plasticity within DLPFC. These were assessed using paired-pulse TMS (short-[SICI] and long-[LICI] interval intracortical inhibition) and continuous theta burst stimulation (cTBS), respectively. Functional effects of injury were assessed via the N-back and Trail Making Test. Responses were quantified in both temporal (i.e., TMS-evoked potentials; TEPs) and spectral (i.e., TMS-related oscillations; TROs) domains. For TEPs, measures of inhibition and plasticity both tended to be reduced by mTBI. In contrast, TRO data suggested a contradictory increased response of patients to both paired-pulse stimulation and cTBS that was specific to high-frequency bands. In addition, while TMT performance was reduced in the mTBI group, this was unrelated to neurophysiological effects of injury. These findings highlight novel effects of mTBI on DLPFC neurophysiology and emphasise the importance of considering both evoked and induced activity when examining neurophysiological effects of mTBI.

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bioRxiv2024

Default mode and motor networks facilitate early learning of implicit motor sequences: a multimodal MR spectroscopy and fMRI study

Learning new motor skills is a fundamental process that involves the sequencing of actions. Skill develops with practice and time, and manifests as performance that is fast and accurate. Although we know that learning can occur through an implicit process in the absence of conscious awareness, and across multiple temporal scales, the precise neural mechanisms mediating implicit motor sequence learning remain poorly understood. Similarly, the capacity for interventions with known influence on learning and memory, such as cardiovascular exercise, to facilitate implicit learning is yet to be clearly established. Here, we investigated the neuroplasticity of implicit motor sequence learning and the effect of acute exercise priming. Healthy adults (39.5% female) aged 22.55 {+/-} 2.69 years were allocated to either a high-intensity exercise (HIIT) group (n = 16) or to a very low-intensity control group (n = 17). Following exercise, participants performed a serial reaction time task. MR spectroscopy estimates of sensorimotor gamma-aminobutyric acid (GABA) were acquired before and after exercise, and during task performance, and resting-state fMRI was acquired at the end of the protocol. We show that early stages of learning are linked to default mode network connectivity, while the overall degree of learning following sustained practice is associated with motor network connectivity. Sensorimotor GABA concentration was linked to the early stages of learning, and GABA concentration was modulated following HIIT, although the two were not related. Overall, via integration of multiple neuroimaging modalities we demonstrate that interactions between hippocampal and motor networks underlie implicit motor sequence learning. Key points summary- Motor learning occurs across different temporal scales and can arise implicitly in the absence of conscious awareness. - Explicit motor learning is linked to the brains primary inhibitory neurotransmitter, GABA, and interactions across motor and hippocampal networks. - Whether these same neural mechanisms are implicated in implicit learning is unclear. Similarly, the capacity to influence learning via priming with cardiovascular exercise is yet to be clearly established. - We show that early implicit learning is underpinned by default mode network connectivity and sensorimotor GABA concentration, while total learning following sustained practice is linked to motor network connectivity. We also found that HIIT exercise elevated sensorimotor GABA concentration, but not the magnitude of implicit learning. - Overall, our results highlight shared involvement of default mode and motor networks in implicit motor sequence learning.

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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

Supplementary motor area disinhibition during motor sequence learning: A TMS-EEG study

BackgroundIn primary motor cortex, changes in excitatory and inhibitory neurotransmission (E:I balance) accompany motor sequence learning. In particular, there is an early reduction in inhibition (i.e., disinhibition). The supplementary motor area (SMA) is a key brain region involved in the learning of sequences, however the neurophysiological mechanisms within SMA which support motor sequence learning remain poorly understood. Disinhibition may also occur in SMA, but this possibility remains unexamined. ObjectiveWe investigated disinhibition within SMA during motor sequence learning using combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG). MethodsTwenty-nine healthy adults practiced a sequential motor task. TMS-evoked potentials (TEPs) resulting from SMA stimulation were measured with EEG before, during, and after practice. The N45 TEP peak was our primary measure of disinhibition. Furthermore, the slope of aperiodic EEG activity was included as an additional E:I balance measure. ResultsSignificant improvements in task performance (i.e., learning) occurred with practice. We observed smaller N45 amplitudes during early learning relative to baseline (both p < .01), indicative of disinhibition. Intriguingly, aperiodic exponents increased as learning progressed and were associated with greater sequence learning (p < .05). ConclusionOur results show disinhibition within SMA during the planning phase of motor sequence learning and thus provide novel understanding on the neurophysiological mechanisms within higher-order motor cortex that accompany new sequence learning.

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bioRxiv2023

Characterising the contribution of auditory and somatosensory inputs to TMS-evoked potentials following stimulation of prefrontal, premotor and parietal cortex

Transcranial magnetic stimulation (TMS) results in a series of evoked potentials (TEPs) in electroencephalography (EEG) recordings. However, it remains unclear whether these responses reflect neural activity resulting from transcranial stimulation of the cortex, or from the sensory experiences of TMS. Across three experiments (total n = 135), we recorded EEG activity following TMS to the dorsolateral prefrontal cortex, premotor cortex, and parietal cortex as well as a sensory control condition (stimulation of the shoulder or electrical stimulation of the scalp with a click sound). We found that TEPs showed a stereotypical frontocentral N100/P200 complex following TMS of all cortical sites and control conditions, regardless of TMS intensity or the type of sensory control. In contrast, earlier TEPs (<60 ms) showed site-specific characteristics which were largest at the site of stimulation. Self-reported sensory experiences differed across sites, with prefrontal stimulation resulting in stronger auditory (click sound perception) and somatosensory input (scalp muscle twitch, discomfort) than premotor or parietal stimulation, a pattern that was reflected in the amplitude of later (N100/P200), but not earlier (<60 ms) TEP peak amplitudes. Later TEPs were also larger in individuals who experienced stronger click sound perception and, to a lesser extent, TMS-evoked scalp muscle twitches. Increasing click sound perception by removing auditory masking increased N100/P200 amplitudes without altering earlier peaks, an effect which was more prominent at sites with more successful masking. Together, these findings suggest that the frontocentral N100/P200 complex represents a generalised sensory response resulting from TMS-related auditory and somatosensory input. In contrast, early TEP peaks likely reflect activity resulting from transcranial stimulation of the cortex. The results have important implications for designing and interpreting TEP studies, especially when comparing TEPs between stimulation sites and participant groups showing differences in sensory experiences following TMS.

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bioRxiv2022

Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

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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

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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bioRxiv2022

Utilising TMS-EEG to assess the response to cerebellar-brain inhibition

BackgroundCerebellar-brain inhibition (CBI) is a transcranial magnetic stimulation (TMS) paradigm indexing excitability of cerebellar projections to motor cortex (M1). Stimulation involved with CBI is often considered to be uncomfortable, and alternative ways to index connectivity between cerebellum and the cortex would be valuable. Utilising electroencephalography in conjunction with TMS (combined TMS-EEG) to record the response to CBI has the potential to achieve this, but has not been attempted previously. ObjectiveTo investigate the utility of TMS-EEG for characterising cerebellar-cortical interactions recruited by CBI. MethodsA total of 33 volunteers (25.7 {+/-} 4.9 years, 20 females) participated across three experiments. These investigated EEG responses to CBI induced with a figure-of-eight (F8; experiment 1) or double cone (DC; experiment 2) conditioning coil over cerebellum, in addition to multisensory sham stimulation (experiment 3). ResultsBoth F8 and DC coils suppressed early TMS-evoked EEG potentials (TEPs) produced by TMS to M1 (P < 0.05). Furthermore, the TEP produced by CBI stimulation was related to the motor inhibitory response to CBI recorded in a hand muscle (P < 0.05), but only when using the DC coil. Multisensory sham stimulation failed to modify the M1 TEP. ConclusionsCerebellar conditioning produced changes in the M1 TEP that were not apparent following sham stimulation, and that were related to the motor inhibitory effects of CBI. Our findings therefore suggest it is possible to index the response to CBI using TMS-EEG. In addition, while both F8 and DC coils appear to recruit cerebellar projections, the nature of these may be different.

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bioRxiv2022

Task-related changes in aperiodic activity are related to visual working memory capacity independent of event-related potentials and alpha oscillations

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 decreased (i.e., slope flattened) in lateral parieto-occipital electrodes but 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.

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