The Brain Imaging and Neurophysiology Database: BINDing multimodal neural data into a large-scale repository
Bibliographic record
Abstract
1 The Brain Imaging and Neurophysiology Database (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositories, comprising 1.8 million brain scans from 38,945 patients, linked to neurophysiological recordings. This comprehensive dataset addresses critical limitations in neuroimaging research by providing unprecedented scale and diversity across pathologies and health. BIND integrates de-identified data from Massachusetts General Hospital, Brigham and Women's Hospital, and Stanford University, including 1,723,699 MRI scans (1.5 Tesla, 3 Tesla, and 7 Tesla), 54,137 CT scans, 5,093 PET scans, and 526 SPECT scans, converted to standardized NIfTI format following BIDS organization. The database spans the full age spectrum (newborn to 106 years) and encompasses diverse neurological conditions alongside healthy patients. We deployed Bio-Medical Large Language Models to extract structured clinical metadata from 84,960 brain-related reports, categorizing findings into standardized pathology classifications. All imaging data are linked to previously published EEG and polysomnography recordings from the Harvard Electroencephalography Database, enabling unprecedented multimodal analyses. BIND is freely accessible for academic research through the Brain Data Science Platform ( https://bdsp.io/ ). This resource facilitates large-scale neuroimaging studies, machine learning applications, and multimodal brain research to accelerate discoveries in clinical neuroscience.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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".