Tetherless miniaturized point detector device for monitoring cortical surface hemodynamics in mice
Bibliographic record
Abstract
SignificanceSeveral miniaturized optical neuroimaging devices for preclinical studies mimicking benchtop instrumentation have been proposed in the past. However, they are generally relatively large, complex, and power-hungry, limiting their usability for long-term measurements in freely moving animals. Further, there is limited research in the development of algorithms to analyze long-term signals.AimWe aim to develop a cost-effective, easy-to-use miniaturized intrinsic optical monitoring system (TinyIOMS) that can be reliably used to record spontaneous and stimulus-evoked hemodynamic changes and further cluster brain states based on hemodynamic features.ApproachWe present the design and fabrication of TinyIOMS (8 mm×13 mm×9 mm3, 1.2 g with battery). A standard camera-based widefield system (WFIOS) is used to validate the TinyIOMS signals. Next, TinyIOMS is used to continuously record stimulus-evoked activity and spontaneous activity for 7 h in chronically implanted mice. We further show up to 2 days of intermittent recording from an animal. An unsupervised machine learning algorithm is used to analyze the TinyIOMS signals.ResultsWe observed that the TinyIOMS data is comparable to the WFIOS data. Stimulus-evoked activity recorded using the TinyIOMS was distinguishable based on stimulus magnitude. Using TinyIOMS, we successfully achieved 7 h of continuous recording and up to 2 days of intermittent recording in its home cage placed in the animal housing facility, i.e., outside a controlled lab environment. Using an unsupervised machine learning algorithm (k-means clustering), we observed the grouping of data into two clusters representing asleep and awake states with an accuracy of ∼91%. The same algorithm was then applied to the 2-day-long dataset, where similar clusters emerged.ConclusionsTinyIOMS can be used for long-term hemodynamic monitoring applications in mice. Results indicate that the device is suitable for measurements in freely moving mice during behavioral studies synchronized with behavioral video monitoring and external stimuli.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".