Giga Connectome: a BIDS-app for time series and functional connectome extraction
Bibliographic record
Abstract
Researchers perform two steps before functional magnetic resonance imaging (fMRI) data analysis: standardised preprocessing and customised denoising.fMRIPrep (Esteban et al., 2019; RRID:SCR_016216), a popular software in the neuroimaging community, is a common choice for preprocessing.fMRIPrep performs minimal preprocessing, leaving a few steps for the end user: smoothing, denoising, and standardisation.The present software, giga-connectome, is a Brain Imaging Data Structure (BIDS; Gorgolewski et al., 2016; RRID:SCR_016124) compliant container image that aims to perform these steps as well as extract time series signals and generate connectomes for machine learning applications.All these steps are implemented with functions from nilearn (Nilearn contributors, 2024; RRID:SCR_001362), a Python library for machine learning in neuroimaging.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.145 | 0.079 |
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".