Publications

Objective: To investigate the effects of single-session premotor and primary motor tDCS in chronic stroke patients with relation to possible inter-hemispheric interactions. Methods: Anodal tDCS of either M1 or premotor cortex of the side contralateral to the paretic hand, cathodal tDCS of the premotor cortex of the side ipsilateral to the paretic hand and sham stimulation were performed in 12 chronic stroke patients with mild hand paresis in a balanced cross-over design. The Jebsen-Taylor Hand Function test, evaluating the time required for performance of everyday motor tasks, was employed. Results: The repeated-measure ANOVA with Greenhouse-Geisser correction showed significant influence of the stimulation type (factor SESSION; F(2.6, 28.4) = 47.3, p < 0.001), the test performance time relative to stimulation (during or after tDCS; factor TIME, F(1.0, 11.0) = 234.5, p < 0.001) with higher effect after the stimulation and the interaction SESSION*TIME (F(1.7, 1.2) = 30.5, p < 0.001). All active conditions were effective for the modulation of JTT performance, though the highest effect was observed after anodal tDCS of M1, followed by effects after anodal stimulation of the premotor cortex contralateral to the paretic hand. Based on the correlation patterns, the inhibitory input to M1 from premotor cortex of another hemisphere and an excitatory input from the ipsilesional premotor cortex were suggested. Conclusion: The premotor cortex is a promising candidate area for transcranial non-invasive stimulation of chronic stroke patients. © 2022 The Author(s)

JTD Keywords: areas, contralateral primary motor, dorsal premotor, excitability, jtt, lateral premotor, object manipulation, premotor cortex, recovery, stroke, tdcs, time-course, transcranial direct current stimulation, Jtt, Noninvasive brain-stimulation, Premotor cortex, Stroke, Tdcs, Transcranial direct current stimulation

The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity. © 2022 American Physical Society.

JTD Keywords: brain, entropy, epileptogenic networks, functional connectivity, hilbert transform, seizure onset, surrogate data, synchronization, time-series, Biomedical signal processing, Brain areas, Brain dynamics, Dynamics, Electroencephalographic signals, Electroencephalography, Electrophysiology, Intracranial eeg signals, Localisation, Neurological disorders, Neurology, Phase based, Phase coherence, Signal detection, Simple++, Univariate, Velocity, World population