Biology subjects

McMackin, R.

Publications and source records attributed to McMackin, R..

3 recordsLinked to original sources

bioRxiv2025

Very low amplitude muscle activity increases probability of motor evoked potentials in healthy individuals and in ALS

IntroductionIn many clinical and research settings, transcranial magnetic stimulation (TMS) intensities are standardised based on resting motor threshold (RMT). It is well-established that contraction of the target muscle increases motor evoked potential (MEP) amplitude and correspondingly decreases RMT. As such, when estimating RMT it is crucial to ensure the target muscle is relaxed. Typically trials in which baseline electromyographic (EMG) amplitude exceeds a specified threshold are rejected. The influence of motor activity below typical rejection thresholds on MEP amplitudes has yet to be established. MethodsWe retrospectively analysed TMS-EMG data collected during RMT measurement in 45 healthy controls (1761 datapoints) and 35 people with amyotrophic lateral sclerosis (ALS, 1238 datapoints). Trials where root mean squared (RMS) baseline EMG amplitude exceeded 10{micro}V were rejected. Generalised linear mixed-effects models were used to assess effects of muscle activity below this rejection threshold on probability of evoking an MEP with peak-to-peak amplitude [≥]50{micro}V. ResultsGreater sub-rejection-threshold activity significantly increases MEP probability in controls (p<0.0004) and people with ALS (p=0.0010). Models predicted a 31-38% increase in MEP probability when baseline RMS-EMG amplitude increased from 1{micro}V to 9{micro}V. Sub-rejection- threshold baseline activity was significantly greater in ALS than controls (p=0.0055). DiscussionWe have shown for the first time that higher EMG amplitudes below a typical rejection threshold markedly increase probability of evoking an MEP with peak-to-peak amplitude [≥]50{micro}V. Researchers should take measures to account for effects of sub-rejection-threshold activity on RMT, particularly in populations where baseline activity may be elevated, such as in ALS.

neuroscience↗

bioRxiv2024

TMS-based neurofeedback training of mental finger individuation induces neuroplastic changes in the sensorimotor cortex

Neurofeedback (NF) training based on motor imagery is increasingly used in neurorehabilitation with the aim to improve motor functions. However, the neuroplastic changes underpinning these improvements are poorly understood. Here, we used mental finger individuation, i.e., the selective facilitation of single finger representations without producing overt movements, as a model to study neuroplasticity induced by NF. To enhance mental finger individuation, we used transcranial magnetic stimulation (TMS)-based NF training. During motor imagery of individual finger movements, healthy participants were provided visual feedback on the size of motor evoked potentials, reflecting their finger-specific corticospinal excitability. We found that TMS-NF improved the top-down activation of finger-specific representations. First, intracortical inhibitory circuits in the primary motor cortex were tuned after training such that inhibition was selectively reduced for the finger that was mentally activated. Second, motor imagery finger representations in sensorimotor areas assessed with functional MRI became more distinct after training. Together, our results indicate that the neural underpinnings of finger individuation, a well-known model system for neuroplasticity, can be modified using TMS-NF guided motor imagery training. These findings demonstrate that TMS-NF induces neuroplasticity in the sensorimotor system, highlighting the promise of TMS-NF on the recovery of fine motor function.

neuroscience↗

bioRxiv2019

Non-Parametric Rank Statistics for Spectral Power and Coherence

Despite advances in multivariate spectral analysis of neural signals, the statistical inference of measures such as spectral power and coherence in practical and real-life scenarios remains a challenge. The non-normal distribution of the neural signals and presence of artefactual components make it difficult to use the parametric methods for robust estimation of measures or to infer the presence of specific spectral components above the chance level. Furthermore, the bias of the coherence measures and their complex statistical distributions are impediments in robust statistical comparisons between 2 different levels of coherence. Non-parametric methods based on the median of auto-/cross-spectra have shown promise for robust estimation of spectral power and coherence estimates. However, the statistical inference based on these non-parametric estimates remain to be formulated and tested. In this report a set of methods based on non-parametric rank statistics for 1-sample and 2-sample testing of spectral power and coherence is provided. The proposed methods were demonstrated and tested using simulated neural signals in different conditions. The results show that non-parametric methods provide robustness against artefactual components. Moreover, they provide new possibilities for robust 1-sample and 2-sample testing of the complex coherency function, including both the magnitude and phase, where existing methods fall short of functionality. The utility of the methods were further demonstrated by examples on experimental neural data. The proposed approach provides a new framework for non-parametric spectral analysis of digital signals. These methods are especially suited to neuroscience and neural engineering applications, given the attractive properties such as minimal assumption on distributions, statistical robustness, and the diverse testing scenarios afforded.

bioinformatics↗