Statistics, Data Science, and the Connectome
Bibliographic record
Abstract
Statistics and data science are central to brain connectivity research, enabling extraction of meaningful patterns from complex neuroimaging data and advancing understanding of functional architecture. Connectivity analyses map the intricate networks underlying cognition and disease by revealing how brain regions communicate, synchronize, and influence one another. This article reviews statistical analysis of functional connectivity (i.e., the undirected association between time series from distinct brain regions) and effective connectivity (i.e., the directed causal influence that one region exerts over another). Functional connectivity is often assessed using correlation, coherence, or mutual information, while effective connectivity requires more sophisticated modeling approaches such as Granger causality, dynamic causal modeling, or structural equation models. We highlight key analytic choices in connectivity studies (e.g., preprocessing, parcellation, spatial and temporal resolution across fMRI and EEG), and then describe the metrics and models used to assess functional and effective connectivity. This is followed by recent advances on time-varying, connectivity, high-dimensional graphical networks, and graph theory. We conclude with open problems in brain connectivity research, emphasizing the need for continued statistical innovation and interdisciplinary collaboration.
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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 | 0.021 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| 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".