Converging on consistent functional connectomics
Bibliographic record
Abstract
Abstract Functional interactions between brain regions can be viewed as a network, empowering neuroscientists to leverage network science to investigate distributed brain function. However, obtaining a brain network from functional neuroimaging data involves multiple steps of data manipulation, which can drastically affect the organisation and validity of the estimated brain network and its properties. Here, we provide a systematic evaluation of 576 unique data-processing pipelines for functional connectomics from resting-state functional MRI, obtained from all possible recombinations of popular choices for brain atlas type and size, connectivity definition and selection, and global signal regression. We use the portrait divergence, an information-theoretic measure of differences in network topology across scales, to quantify the influence of analytic choices on the overall organisation of the derived functional connectome. We evaluate each pipeline across an entire battery of criteria, seeking pipelines that (i) minimise spurious test-retest discrepancies of network topology, while simultaneously (ii) mitigating motion confounds, and being sensitive to both (iii) inter-subject differences and (iv) experimental effects of interest, as demonstrated by propofol-induced general anaesthesia. Our findings reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Choice of the wrong data-processing pipeline can lead to results that are not only misleading, but systematically so, distorting the functional connectome more drastically than the passage of several months. We also found that the majority of pipelines failed to meet at least one of our criteria. However, we identified 8 candidates satisfying all criteria across each of four independent datasets spanning minutes, weeks, and months, ensuring the generalisability of our recommendations. Our results also generalise to alternative acquisition parameters and preprocessing and denoising choices. By providing the community with a full breakdown of each pipeline’s performance across this multi-dataset, multi-criteria, multi-scale and multi-step approach, we establish a comprehensive set of benchmarks to inform future best practices in functional connectomics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".