Connecting Invasive and Noninvasive Brain Stimulation (S52.002)
Bibliographic record
Abstract
OBJECTIVE: To identify brain diseases in which both invasive and noninvasive brain stimulation have shown evidence of efficacy and determine whether the stimulation sites are different nodes within the same brain network. BACKGROUND: Invasive deep brain stimulation (DBS) and noninvasive transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are increasingly being applied to treat a variety of brain diseases. Although generally considered separate in terms of mechanism and clinical indication, invasive and noninvasive approaches share the ability to modify brain activity at the stimulated site, impact regions remote from the site of stimulation, and the challenge of knowing where to stimulate to optimize therapeutic effect. Here we determine whether these two forms of stimulation are linked through brain networks. DESIGN/METHODS: Diseases with evidence of efficacy for both invasive and noninvasive brain stimulation were identified using a pubmed search. For each disease, resting state functional connectivity with the most effective DBS site was assessed using a previously collected MRI dataset from 1000 normal subjects. Connectivity to noninvasive brain stimulation sites was compared to that expected by chance. RESULTS: Thirteen brain diseases were identified with reports of efficacy for both invasive and noninvasive brain stimulation including Parkinson’s, dystonia, epilepsy, Alzheimer’s, and disorders of consciousness. Across diseases, DBS sites were functionally connected to noninvasive brain stimulation sites at a level much greater than chance (p < 0.005). CONCLUSIONS: Resting state functional connectivity links invasive and noninvasive brain stimulation sites across diseases. This suggests that these two types of brain stimulation may exert their therapeutic effect by modulating different nodes in the same brain network. Identifying such networks may prove valuable in determining the optimal targets for brain stimulation and represents a potential therapeutic application of the human connectome. Study Supported by: National Institutes of Health (K23NS083741), the AAN / American Brain Foundation, and the National Center for Research Resources: Harvard Clinical and Translational Science Center (UL1 RR025758).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.109 | 0.016 |
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".