Mesoscale Transcranial Spontaneous Activity Mapping in GCaMP3 Transgenic Mice Reveals Extensive Reciprocal Connections between Areas of Somatomotor Cortex
Bibliographic record
Abstract
Transgenic mice expressing genetically encoded activity indicators are an attractive means of mapping mesoscopic regional functional cortical connectivity given widespread stable and cell-specific expression compatible with chronic recordings. Cortical functional connectivity was evaluated using wide-field imaging in lightly anesthetized Emx1-creXRosa26-GCaMP3 mice expressing calcium sensor in cortical neurons. Challenges exist because green fluorescence signals overlap with endogenous activity-dependent autofluorescence and are affected by changes in blood volume and oxygenation. Under the conditions used for imaging and analysis (0.1-1 Hz frequency band), autofluorescence and hemodynamic effects contributed 3% and 8% of the SD of spontaneous activity-dependent GCaMP3 fluorescence when signals were recorded through intact bone. To evaluate the accuracy and sensitivity of this approach, the topology of functional connections between somatomotor cortex (primary S1 and secondary S2 somatosensory, and primary motor cortex M1) was estimated. During sequences of spontaneous activity, calcium signals recorded at each location of area S1 were correlated with activity in contralateral area S1, ipsilateral area S2, and bilateral areas M1. Reciprocal results were observed when "seed pixels" were placed in S2 and M1. Coactivation of areas implies functional connections but could also be attributed to both regions receiving common upstream drive. These apparent connections revealed during spontaneous activity coactivation by GCaMP3 were confirmed by intracortical microstimulation but were more difficult to detect using intrinsic signals from reflected red light. We anticipate GCAMP wide-field imaging will enable longitudinal studies during plasticity paradigms or after models of CNS disease, such as stroke, where the weighting within these connectivity maps may be altered.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".