Acute two-photon imaging of the neurovascular unit in the cortex of active mice
Bibliographic record
Abstract
In vivo two-photon scanning fluorescence imaging is a powerful technique to observe physiological processes from the millimeter to the micron scale in the intact animal. In neuroscience research, a common approach is to install an acute cranial window and head bar to explore neocortical function under anesthesia before inflammation peaks from the surgery. However, there are few detailed acute protocols for head-restrained and fully awake animal imaging of the neurovascular unit during activity. This is because acutely performed awake experiments are typically untenable when the animal is naïve to the imaging apparatus. Here we detail a method that achieves acute, deep-tissue two-photon imaging of neocortical astrocytes and microvasculature in behaving mice. A week prior to experimentation, implantation of the head bar alone allows mice to train for head-immobilization on an easy-to-learn air-supported ball treadmill. Following just two brief familiarization sessions to the treadmill on separate days, an acute cranial window can subsequently be installed for immediate imaging. We demonstrate how running and whisking data can be captured simultaneously with two-photon fluorescence signals with acceptable movement artifacts during active motion. We also show possible applications of this technique by (1) monitoring dynamic changes to microvascular diameter and red blood cells in response to vibrissa sensory stimulation, (2) examining responses of the cerebral microcirculation to the systemic delivery of pharmacological agents using a tail artery cannula during awake imaging, and (3) measuring Ca(2+) signals from synthetic and genetically encoded Ca(2+) indicators in astrocytes. This method will facilitate acute two-photon fluorescence imaging in awake, active mice and help link cellular events within the neurovascular unit to behavior.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".