NeuroPM toolbox: integrating Molecular, Neuroimaging and Clinical data for Characterizing Neuropathological Progression and Individual Therapeutic Needs
Bibliographic record
Abstract
Abstract There is a critical need for a better multiscale and multifactorial understanding of neurological disorders, covering from genes to neuroimaging to clinical factors and treatments effects. Here we present NeuroPM-box , a cross-platform, user-friendly and open-access software for characterizing multiscale and multifactorial brain pathological mechanisms and identifying individual therapeutic needs. The implemented methods have been extensively tested and validated in the neurodegenerative context, but there is not restriction in the kind of disorders that can be analyzed. By using advanced analytic modeling of molecular, neuroimaging and/or cognitive/behavioral data, this framework allows multiple applications, including characterization of: (i) the series of sequential states (e.g. transcriptomic, imaging or clinical alterations) covering decades of disease progression, (ii) intra-brain spreading of pathological factors (e.g. amyloid and tau misfolded proteins), (iii) synergistic interactions between multiple brain biological factors (e.g. direct tau effects on vascular and structural properties), and (iv) biologically-defined patients stratification based on therapeutic needs (i.e. optimum treatments for each patient). All models’ outputs are biologically interpretable. A 4D-viewer allows visualization of spatiotemporal brain (dis)organization. Originally implemented in MATLAB, NeuroPM-box is compiled as standalone application for Windows, Linux and Mac environments: neuropm-lab.com/software . In a “regular” workstation, it can analyze over 150 subjects per day, reducing the need for using clusters or High-Performance Computing (HPC) for large-scale datasets. This open-access tool for academic researchers may significantly contribute to a better understanding of complex brain processes and to accelerating the implementation of Precision Medicine (PM) in neurology.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".