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 di icult 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 di erent 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 -sample and -sample testing of spectral power and coherence is provided. The proposed methods were demonstrated and tested using simulated neural signals in di erent conditions. The results show that non-parametric methods provide robustness against artefactual components. Moreover, they provide new possibilities for robust -sample and -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 a orded.
Primary lateral sclerosis (PLS) is a slowly progressing disorder, which is characterized primarily by the degeneration of upper motor neurons (UMNs) in the primary motor area (M1). It is not yet clear how the function of sensorimotor networks beyond M1 are affected by PLS. The aim of this study was to use cortico-muscular coherence (CMC) to characterize the oscillatory drives between cortical regions and muscles during a motor task in PLS and to examine the relationship between CMC and the level of clinical impairment. We recorded EEG and EMG from hand muscles in 16 participants with PLS and 18 controls during a pincer-grip task. In PLS, higher CMC was observed over contralateral-M1 (α- and γ-band) and ipsilateral-M1 (β-band) compared with controls. Significant correlations between clinically assessed UMN scores and CMC measures showed that higher clinical impairment was associated with lower CMC over contralateral-M1/frontal areas, higher CMC over parietal area, and both higher and lower CMC (in different bands) over ipsilateral-M1. The results suggest an atypical engagement of both contralateral and ipsilateral M1 during motor activity in PLS, indicating the presence of pathogenic and/or adaptive/compensatory alterations in neural activity. The findings demonstrate the potential of CMC for identifying dysfunction within the sensorimotor networks in PLS.
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