Enhancing cognitive abilities through transcutaneous auricular vagus nerve stimulation: insights from prefrontal functional connectivity analysis and virtual brain simulation
Bibliographic record
Abstract
During the last years many centers have started investigating the therapeutical target of deep brain stimulation through group analysis, most of them simulating the volume of tissue activated (VTA).Nevertheless, many parameters must be chosen which makes the workflow complex.The aim of the present study was to summarize the different parameters in this workflow and their possible choices.Different workflow and parameter selections have been collected from the relevant literature and summarized for the following workflow steps: 1) VTA simulation for each patient and all available stimulation parameters and link to induced clinical outcome, 2) Patient data transfer to a common reference system and 3) Summary of the available data in form of probabilistic stimulation maps (PSM).For VTA generation the most important criterion was the selected brain model i.e. homogenous, isotropic or anisotropic and the way of assigning the different conductivity values (MRI, diffusion tensor imaging or atlas based) and the tissue types considered (grey, white matter, CSF, blood).As reference space most groups chose the Montreal Neurological Institute template.Cohort specific templates with optimized image registration workflows were rare.The type of image registration, registration settings (number of iterations and of patients), image sequences and subject cohort influence the quality of the final template.For PSM generation, different thresholds (minimum number of patients and simulations per voxel) and statistical methods (e.g.t-test, Wilcoxon test, linear mixed models) were applied.A multitude of parameters exists that must be defined for group analysis.Some of them are influenced by the available clinical data of the cohort such as the image types and stimulation data (intra-or postoperative, best contact or screening data).More studies investigating these influences and establishing guidelines for the less experienced user are necessary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".