MOSAICS: An open-source Python platform for brain stimulation mapping analysis
Bibliographic record
Abstract
Subthalamic (STN) rhythmic activity in the beta band (11e30 Hz range) is considered the most promising biomarker for controlling novel adaptive deep brain stimulation (aDBS) therapies in Parkinson's disease (PD).aDBS typically follows the amplitude (or power) of beta oscillations, which is sensitive to the clinical and dopaminergic state of the patient.An implicit assumption of this approach is that the frequency of the oscillation is fixed and thus does not convey any useful information about the state of the patient.Here we challenged this assumption by quantifying the temporal structure of both the amplitude and the frequency of STN beta oscillations, extracted from local field potential (LFP) recordings in patients OFF and ON medication.We specifically calculated the temporal variance of instantaneous amplitude (amplitude modulation, AM) and the temporal variance of instantaneous frequency (frequency modulation, FM).We found that beta AM decreases while and beta FM increases with dopaminergic medication, with significant within-and between-subjects interactions.Furthermore, two components of the instantaneous frequency signal (i.e. its slow part and the phase slips) differently contribute to the dopaminedependent interactions between AM and FM.These findings suggest that both AM and FM of STN beta oscillations jointly define the dopaminergic state of the patient and promote FM as a possible novel biomarker for aDBS in PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.019 |
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".