Improved methods for chronic light-based motor mapping in mice: automated movement tracking with accelerometers, and chronic EEG recording in a bilateral thin-skull preparation
Bibliographic record
Abstract
Optogenetic stimulation of the mouse cortex can be used to generate motor maps that are similar to maps derived from electrode-based stimulation. Here we present a refined set of procedures for repeated light-based motor mapping in ChR2-expressing mice implanted with a bilateral thinned-skull chronic window and a chronically implanted electroencephalogram (EEG) electrode. Light stimulation is delivered sequentially to over 400 points across the cortex, and evoked movements are quantified on-line with a three-axis accelerometer attached to each forelimb. Bilateral maps of forelimb movement amplitude and movement direction were generated at weekly intervals after recovery from cranial window implantation. We found that light pulses of ~2 mW produced well-defined maps that were centered approximately 0.7 mm anterior and 1.6 mm lateral from bregma. Map borders were defined by sites where light stimulation evoked EEG deflections, but not movements. Motor maps were similar in size and location between mice, and maps were stable over weeks in terms of the number of responsive sites, and the direction of evoked movements. We suggest that our method may be used to chronically assess evoked motor output in mice, and may be combined with other imaging tools to assess cortical reorganization or sensory-motor integration.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".