Brainways: An AI-based Tool for Automated Registration, Quantification and Generation of Brain-wide Activity Networks Based on Fluorescence in Coronal Slices
Bibliographic record
Abstract
A central current trend in neuroscience involves the identification of brain-wide neural circuits associated with complex behavior. A major challenge for this approach involves the laborious process for registration and quantification of fluorescence on histological brain slices, as well as the difficulty of deriving functional insight from the complex resulting datasets. As a solution, we developed Brainways, a simple-to-use AI-based open-source software for the identification of neural networks involved in a specific behavior, from digital images to network analysis. Brainways offers automatic registration of coronal slices to any 3D brain atlas, and provides quantification of fluorescent markers (e.g. activity marker, tracer) per region, as well as statistical comparisons with visual mapping of contrasts between conditions. A built-in partial least squares task analysis provides the neural patterns associated with a specific contrast, as well as network graph analysis representing functional connectivity. Trained on atlases for rats and mice, Brainways currently provides above 80% atlas registration accuracy and allows the user to easily adjust the outputs for better fit. Below, a case study validation of Brainways is demonstrated on a previously published data set describing the neural correlates of empathic helping behavior in rats. The original results were successfully replicated and expanded upon, due to the exponentially larger sample size that covered over a 100 times more brain tissue compared to the original manual sampling. Brainways thus provides a fast, accurate solution for quantification of large-scale projects and facilitates novel neurobiological insights about the structural and functional neural networks involved in complex behavior. Brainways has a highly accessible GUI and is functionality exposed through a Python-based API, which can be enhanced for different applications.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".