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Record W2610363304 · doi:10.3791/52642

A Large Lateral Craniotomy Procedure for Mesoscale Wide-field Optical Imaging of Brain Activity

2017· article· en· W2610363304 on OpenAlexafffund
Michael Kyweriga, Jianjun Sun, Sunny Wang, Richard Kline, Majid H. Mohajerani

Bibliographic record

VenueJournal of Visualized Experiments · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Health Solutions
KeywordsCraniotomyOptogeneticsNeuroscienceMedicineBiomedical engineeringAnatomyBiologySurgery

Abstract

fetched live from OpenAlex

The craniotomy is a commonly performed procedure to expose the brain for in vivo experiments. In mouse research, most labs utilize a small craniotomy, typically 3 mm x 3 mm. This protocol introduces a method for creating a substantially larger 7 mm x 6 mm cranial window exposing most of a cerebral hemisphere over the mouse temporal and parietal cortices (e.g., bregma 2.5 - 4.5 mm, lateral 0 - 6 mm). To perform this surgery, the head must be tilted approximately 30° and much of the temporal muscle must be retracted. Due to the large amount of bone removal, this procedure is intended only for acute experiments with the animal anesthetized throughout the surgery and experiment. The main advantage of this innovative large lateral cranial window is to provide simultaneous access to both medial and lateral areas of the cortex. This large unilateral cranial window can be used to study the neural dynamics between cells, as well as between different cortical areas by combining multi-electrode electrophysiological recordings, imaging of neuronal activity (e.g., intrinsic or extrinsic imaging), and optogenetic stimulation. Additionally, this large craniotomy also exposes a large area of cortical blood vessels, allowing for direct manipulation of the lateral cortical vasculature.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.425
Teacher spread0.389 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2017
Admission routes2
Has abstractyes

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