Glial Laminar Cortical Architecture Matches Metabolic Demand
Bibliographic record
Abstract
The link between increased neural activity and cerebral blood flow is well established (Roy and Sherrington, 1890); yet, the mechanism by which blood flow is matched to neuronal activity both spatially and temporally is unclear. Based on their morphology, gray matter (protoplasmic) astrocytes are ideally situated to translate regional synaptic activity to hyperemia (increased flow) as they extend processes that envelop both synapses and vasculature (reviewed in Iadecola and Nedergaard, 2007). Indeed, evidence from in vitro and in vivo two-photon imaging has suggested that stimulus-induced elevations in astrocyte intracellular calcium are positioned to modulate vascular tone (Zonta et al, 2003; Takano et al, 2006; Mulligan and MacVicar, 2004; Winship et al, 2007). These local interactions are purported to occur at the astrocytic endfoot–blood vessel interface (reviewed in Iadecola and Nedergaard, 2007). Regionally, the spatial organization of astrocytes and vasculature should also have implications for matching blood flow to activity. While previous works have examined the interrelationships between astrocytes and microvasculature across cortical lamina, these studies have been conducted using histology and thus may not always faithfully represent the living tissue (White et al, 1981; Tsai et al, 2009). The present work of McCaslin et al (2010) has taken an important step to extend these findings in the live murine brain.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".