An Evaluation of the Conductivity Profile in the Somatosensory Barrel Cortex of Wistar Rats
Bibliographic record
Abstract
Microelectrode arrays used to record local field potentials from the brain are being built with increasingly more spatial resolution, ranging from the initially developed laminar arrays to those with planar and three-dimensional (3D) formats. In parallel with such development in recording techniques, current source density (CSD) analyses have recently been expanded up to the continuous-3D form. Unfortunately, the effect of the conductivity profile on the CSD analysis performed with contemporary microelectrode arrays has not yet been evaluated and most of the studies assumed it was homogeneous and isotropic. In this study, we measured the conductivity profile in the somatosensory barrel cortex of Wistar rats. To that end, we combined multisite electrophysiological data recorded with a homemade assembly of silicon-based probes and a nonlinear least-squares algorithm that implicitly assumed that the cerebral cortex of rodents could be locally approximated as a layered anisotropic spherical volume conductor. The eccentricity of the six cortical layers in the somatosensory barrel cortex was evaluated from postmortem histological images. We provided evidence for the local spherical character of the entire barrels field, with concentric cortical layers. We found significant laminar dependencies in the conductivity values with radial/tangential anisotropies. These results were in agreement with the layer-dependent orientations of myelinated axons, but hardly related to densities of cells. Finally, we demonstrated through simulations that ignoring the real conductivity profile in the somatosensory barrel cortex of rats caused considerable errors in the CSD reconstruction, with pronounced effects on the continuous-3D form and charge-unbalanced CSD. We concluded that the conductivity profile must be included in future developments of CSD analysis, especially for rodents.
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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) | 0.000 | 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".