Signatures of covert neuron loss in the local field potential of motor cortex
Bibliographic record
Abstract
Abstract Background Covert stroke is understudied despite occurring ten times for every symptomatic stroke and contributing to stroke’s enormous global disease burden. For instance, it is unknown whether covert stroke replicates the peri-infarct neuroelectrophysiological frequency spectrum impact of symptomatic stroke. This motivated the use of our novel electrolytic lesioning platform to explore the spectral consequences of covert neuron loss. Methods During a multi-month period of participation in an arm reaching task, neuron loss was induced via electrolytic lesions to the motor cortex of two large animals (U: n = 4; H: n = 7). Behavioral metrics were paired with spectrum estimates from local field potential recordings. These spectra were represented as bandpowers, aperiodic/periodic parameters, and decomposed time-frequency tensors. Lesion impact was assessed using nonparametric permutation tests of next-day effects and state space model parameters. Results Task success rate was unaffected by lesions, but shifts in aperiodic structure reduced next-day γ bandpower (30 – 100 Hz; U: −1.38µV 2 , p < 1 × 10 − 3 ; H: −1.66µV 2 , p = 0.001) while sensorimotor rhythms spanning 8 – 45 Hz (Σ SMR ) were amplified (Monkey U: 8.68µV 2 , p < 1 × 10 − 3 ; Monkey H: 2.40µV 2 , p = 0.004). Additionally, state space modeling showed that spectral perturbations to γ and Σ SMR outlasted any behavioral impact of the lesions (Monkey U: Behavior=1 d, γ =2 d, Σ SMR =2 d; Monkey H: Behavior=0 d, γ =3 d, Σ SMR =1 d). Finally, tensor decomposition revealed interpretable, animal-specific perturbations to time-frequency dynamics. Conclusion Covert neuron loss induced multi-day spectral perturbations similar to those observed after symptomatic stroke. The neural spectrum is thus more sensitive to neuron loss than previously understood and could be responsive to neuron loss caused by covert stroke. This work also motivates the use of electrolytic lesions to bridge covert and symptomatic regimes of neuron loss, advancing our causal understanding of post-stroke interactions between spectrum and behavior.
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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 | 0.001 |
| 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) | 0.001 | 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 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".