# Neurosolve

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Steven Yang](https://cerebralvalley.ai/u/stevenyintech), [Siddharth Radhakrishnan](https://cerebralvalley.ai/u/Overthrowing)
- **GitHub:** https://github.com/stevenybuilder/NeuroAD
- **Demo video:** https://drive.google.com/drive/folders/1_7ffBVwyPmRaLIaexN5qHlO9dVIjoPTw?usp=sharing
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/232

Neuroscience MRI research is slowed by silent failures - proprietary MATLAB tooling, out-of-memory crashes, and hundreds of preprocessing steps that never fully remove scanner artifacts. The result is data where a model's "signal" often tracks which scanner took the scan more than the patient, and adding more data only introduces more scanner-specific artifacts. These failures surface only after hours or days of wasted training. Downstream, biomarker discovery for early detection of Alzheimer's, Parkinson's, and related diseases is bottlenecked further: the labs that collect scans and the labs that link them to biomarkers don't share tooling, so a wet lab inherits preprocessed data it can't re-derive or correct.
NeuroAD is a plugin for Claude Science that accelerates biomarker discovery for drug development. You research in Claude Science, call NeuroAD to quality-control and analyze your data, then step back in to keep going. Under the hood, we freeze NeuroJEPA - a foundation model strong across various tasks - and train a small network on top, tuned to your dataset and hypothesis, to weigh candidate biomarkers against the scans. The output is a ranked set of biological pathways a wet-lab researcher can act on. Before that data is trusted, we run the Silent-Failure Guard's tools directly on the MRI scans - registration, skull-stripping, anomaly and volume sanity, and silent-mirroring checks - catching confounded scans before time is wasted. The result: a verified, provenance-tracked loop from scan to wet-lab candidate.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/232. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt