Bibliographic record
Abstract
This thesis consists of four chapters. In this thesis we explored associative plasticity of human motor cortex with the use of noninvasive transcranial magnetic stimulation (TMS). Paired Associative Stimulation (PAS) has grown in popularity because of its potential clinical applications. We used TMS techniques in combination with electromyographic (EMG) measurements to study cortical excitability and kinematic features of arm movement. This work has focused in a cohesive approach to answer certain fundamental questions about a) the rules of cortical plasticity and mechanism of PAS, b) the interaction between the state of neuronal excitability at the targeted cortical network and the effects of PAS, and c) translation of these effects into obvious measurable kinematic changes starting from network level changes and ending up with the behavioral modulation of arm movement. First we explored the role of GABAergic intracortical networks and intracortical facilitation on modulation of cortical excitability by showing for the first time that PAS can be conditioned by these inhibitory and facilitatory intracortical networks. Next, using standard indirect approaches utilizing peripheral EMG measures, we showed a graded excitability response for the PAS technique and showed that interactions of PAS with motor learning depends on the degree as well as the state of cortical excitability. Rules governing the interactions of brain stimulation techniques and motor learning are important because brain stimulation techniques can be used to modify, improve or disrupt motor adaptation and skill learning with great potential for clinical applications such as facilitation of recovery after stroke. TMS provide us with a unique opportunity to study the rules of plasticity at a systems level, which is a combination of synaptic and nonsynaptic (metaplastic) changes. These changes can occur either in the direction to limit the physiological range of neuronal functioning (homeostatic) or against the direction established state of neurons.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.007 | 0.001 |
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".