Biology subjects

Bach, M. M.

Publications and source records attributed to Bach, M. M..

4 recordsLinked to original sources

bioRxiv2026

The Rhythm of Normality: A Comprehensive Normative Database for TMS-EEG Metrics with Reliability Characterization

BackgroundTranscranial Magnetic Stimulation combined with electroencephalography (TMS-EEG) offers unique insights into cortical excitability and connectivity, yet current analyses are primarily limited to group-level inferences with little validation of individual reliability and feature redundancy. ObjectiveTo construct a comprehensive, open-access, and reliable normative dataset of TMS-EEG features that enables individual-level comparison MethodsWe aggregated TMS-EEG data recorded over the primary motor cortex (M1) from 164 healthy adults (30.8 {+/-} 9.8 years; 88 female) across nine studies using harmonized acquisition and preprocessing pipelines. Reliability analysis was conducted on a test-retest subset (N=57) for 968 extracted features, evaluating systematic bias, absolute error, and relative reliability (Intraclass Correlation Coefficient categorized by the lower bound of the 95% confidence interval). Additionally, feature clustering was performed to quantify redundancy and correlations across the high-dimensional feature space. We then established normative distributions and developed an online benchmarking platform. ResultsReliability analyses (N=57) of the high-dimensional feature set revealed that 525 out of 968 features (54.3%) met at least moderate reliability standards (ICC lower bound > 0.5). Cluster analysis indicated substantial redundancy among metrics, with three distinct clusters having a moderate-to-high internal correlation (|r| = 0.64, 0.48, 0.39, respectively). Finally, normative data from the database identified abnormal results in a test patient, supporting the feasibility of individual-level classification in an open-science framework. ConclusionsHarmonization of data acquisition and analysis pipelines led to the development of a reliable normative M1 TMS-EEG reference. This publicly available resource provides a validated tool for future individual-level classifications and an open platform for ongoing community contributions.

neuroscience↗

bioRxiv2025

REDUCED ALPHA-BAND PHASE COHERENCE AND CORTICAL COMPLEXITY IN FIBROMYALGIA: A TMS-EEG EXPLORATORY STUDY

ObjectivesCortico-spinal excitability of the primary motor cortex (M1) is reduced in fibromyalgia, and repetitive transcranial magnetic stimulation (TMS) targeting M1 normalizes these changes and relieves symptoms. TMS combined with electroencephalography (TMS-EEG) allows the measurement of M1 excitability and its connectivity to other regions, which may help clarify neurophysiological effects in fibromyalgia. We assessed cortical excitability, oscillatory activity, and complexity in individuals with fibromyalgia compared to pain-free healthy controls. MethodsGlobal and local mean field power, peak-to-peak amplitude, event-related spectral perturbation, intertrial coherence (ITC), natural frequency, and perturbational complexity index (PCIst) of the EEG response after left-M1 TMS were compared between groups (n=18 fibromyalgia; n=15 controls). Pain intensity, interference, relief of current therapy, mood, and quality of life were assessed in individuals with fibromyalgia. ResultsCompared with controls, individuals with fibromyalgia showed a reduction in the alpha-band ITC in middle and right parieto-occipital areas (P<0.05). Middle-parieto-occipital ITC negatively correlated with reported pain relief (rho=-0.552, p=0.019). The PCIst was lower in fibromyalgia compared with controls (P<0.01) and correlated with higher pain interference in general activity (rho=-0.486, p=0.042). ConclusionIndividuals with fibromyalgia showed abnormal cortical connectivity compared with asymptomatic controls. SignificanceTMS-EEG measurements may provide insights on brain connectivity relevant for therapy.

neuroscience↗

bioRxiv2023

Mental fatigue has only marginal effects on static balance control in healthy young adults

We examined the influence of mental fatigue on static balance control in healthy young adults to gain greater clarity about this issue than provided in previous research. Based on the prevailing assumption in pertinent literature, we hypothesized that mental fatigue leads to a reduced cognitive regulation of quiet upright standing, as reflected in center of pressure (COP) excursions. More specifically, we hypothesized that the influence of mental fatigue on balance control depends on the attentional effort required by the balance tasks being performed. To test these hypotheses, 44 young adults (24 women and 20 men) were quasi-randomly assigned to either an experimental group that was mentally fatigued (using the TloadDback-task with individualized settings) or a control group (who watched a documentary). Before and after the intervention the participants performed six balance tasks that differed in (attentional) control requirements, while their COP was being recorded. From these time-series sway variability, mean speed, and sample entropy were calculated and analyzed statistically. Additionally, mental fatigue was assessed using VAS scales. Statistical analyses confirmed that the balance tasks differed in control characteristics and that mental fatigue was elevated in the experimental group, but not in the control group. Nevertheless, no significant main effects of mental fatigue were found on any of the COP measures of interest, except for some non-robust and difficult to interpret interaction effects involving the factor group. These results suggest that, in young adults, postural control in static balance tasks is largely automatic and unaffected by mental fatigue.

neuroscience↗

bioRxiv2022

Predicting vertical ground reaction forces from 3D accelerometery using reservoir computers leads to accurate gait event detection

Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high quality. The subsequent foot contact and foot off event detection proved highly accurate when compared to the golden standard based on co-registered ground reaction forces. Our proof-of-concept illustrates the capacity of combining accelerometry with machine learning for detecting isolated gait events irrespective of mode of locomotion.

neuroscience↗