Modular Organization of Electrical Fluctuations in the Mouse Brain
Bibliographic record
Abstract
Abstract A powerful computational feature of the brain is its ability to compartmentalize different functions in a way that can be flexibly recombined. Experimental evidence for such modularity arose from cytoarchitecture, connectivity, and electroencephalograms, while magnetic resonance imaging could attest that this modularity is dynamic. More spatially precise and more widely recordable, the local field potential (LFP) has an unknown brain wide organization. Here, we developed deep electrospectroscopy, a method to assess nonlinear spectral similarity between the LFP of different areas. A brain-wide application of this technique to the mouse brain revealed an organization composed of groups of spectrally similar areas. Such communities were mostly found in the fore-brain and showed an organization that did not strictly follow the cytoarchitecture. For instance, visual parts of the cortex and the visual parts of the colliculus were found in the same community. These electrospectral communities reshaped with context, showing splitting and merging operations, growing in size around brain areas required for a task. In particular, upper and lower limb parts of the somatosensory cortex were primarily in separate communities but merged during a task that required turning a wheel. Similarly, oculomotor reflexes and associative parts of the thalamus merged during the visuo-motor task. These analyzes show that LFPs are organized in a modular fashion, offering a window onto subcortical and layer-specific contributions to the compositionality of cortical brain functions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".