“Neuropathological function estimations”: a user-friendly module for analyzing neural activity in neurological disorders
Bibliographic record
Abstract
Abstract Motivation This work introduces the Neuropathological Function Estimations software, designed to facilitate the study of neuronal activity alterations in neurological disorders without requiring programming expertise. With its user-friendly interface, researchers can input various data types to generate subject-specific functional brain models and decode neuropathological influences. Results The software’s capabilities are validated through its application to Alzheimer’s disease, providing insights into neuronal excitability and disease mechanisms. This tool has the potential to enhance our understanding of the biological basis of in vivo neural activity and contribute to the development of personalized therapeutic interventions. Availability and implementation The latest version of the software and support are freely available for noncommercial users through the Neuroinformatics for Personalized Medicine Lab (NeuroPM Lab) website at McGill University (https://www.neuropm-lab.com/neuropm-box.html). The software is maintained by the NeuroPM team. This publication is linked to version 1.0.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.027 |
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".