The use of tACS on movement-related brain waves
Bibliographic record
Abstract
Background. MEG and EEG are techniques used to study the electrical activity from different brain areas. The signal arises from synchronized postsynaptic potentials of neurons that generate electrophysiological oscillations in different frequency bands. During movement process, the EEG/MEG spectral power within the beta range (15–29 Hz) decreases in amplitude and this is termed as Event-Related Desynchronization (ERD) [1]. It has been revealed that ERD is related with increased excitability of neurons in sensorimotor areas [2], [3]. Moreover, ERD amplitudes at baseline and during movement vary between age groups of young and old [4]. Abnormal increases in beta EEG power at baseline ERD are also well documented in e.g. Parkinson’s diseases and stroke [5], [6].There is converging evidence suggesting an association of cortical oscillations in the motor cortex with neuroplasticity events underlying motor memory consolidation [7], [8]. Using EEG, positive effects of exercise on motor learning by quantifying the modulation of ERD has been studied [9]. It was revealed that improvements in motor learning was associated with a significant acute decrease in beta-band ERD in sensorimotor areas measured during a grip task. Based on previous research studies, non-invasive brain stimulation (NIBS) protocols can induce neuroplasticity changes[10]–[12]. For instance, tACS allows for inducing direct cortical alterations in the underlying intrinsic neural oscillations by modulating cortical excitability through electrodes placed on the surface of the scalp [11]. Therefore, NIBS is considered as promising therapy for patients with motor deficits caused by neurodegenerative diseases.Objective. Since NIBS could induce changes in neuroplasticity and ERD is closely related with motor performance, the objective of this study is to determine the effects of NIBS protocols (tDCS and tACS) on baseline ERD.Hypothesis. We will use of a novel HD-EEG cap which allows for simultaneous EEG recording and NIBS (tDCS or tACS); this will increase spatial accuracy of stimulation and eliminate the delay between stimulation and EEG recordings. We hypothesize that tDCS applied through a custom electrode configuration in the HD-EEG cap will decrease baseline ERD, whereas tACS could induce an increase in baseline ERD. Successful outcome will further help to design NIBS therapy for patients with motor deficits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".