Cortical Intra‐Layer Hypersynchronization in Levodopa‐Induced Dyskinesia Mouse Model
Bibliographic record
Abstract
BACKGROUND: Levodopa (l-dopa)-induced dyskinesia (LID) is a common and difficult complication in Parkinson's disease (PD) patients. It may result from hyperactivation of the primary motor cortex (M1) due to hypoactivation of the basal ganglia (BG) output nuclei. Electrophysiological evidences are sparse, mainly due to technological limitations related to the poor ability to simultaneously acquire data from many neurons of the different involved regions. We exploited the Neuropixels technology to overcome these obstacles. METHODS: Extracellular Neuropixels recordings were acquired from awake head-restrained mice in wild-type (WT), parkinsonian, and LID conditions. Activity was recorded from M1 and the motor striatum simultaneously and compared for each mouse in four conditions: control (WT with and without l-dopa), hemiparkinsonian (6-hydroxydopamine model), and LID states. RESULTS: Neural firing rates in M1 were decreased in PD and increased in LID as expected. Focusing on the quiet periods, the firing rates between the different conditions were similar. LID was associated with cortical intra-layer hypersynchronization, a phenomenon not previously described. The overall synchrony was significantly increased between neurons of Layers 2, 3, and 5 in M1 in LID compared to PD state. Inter-layer cross-correlation was increased in LID, compared to PD state, between Layer 5 of M1 and the striatum. These changes in functional connectivity were absent in WT mice receiving l-dopa. CONCLUSIONS: Our single-cell recordings from thousands of neurons provide insight into cortical network changes in LID. We found that LID is associated with intra-layer hypersynchronization of neurons within the motor cortex, which may be an intrinsic network feature within the cortico-BG loop.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".