Exploring functional brain networks using independent component analysis:functional brain networks connectivity
Bibliographic record
Abstract
Functional communication between brain regions is likely to play a key role in complex cognitive processes that require continuous integration of information across different regions of the brain.This makes the studying of functional connectivity in the human brain of high importance.It also provides new insights into the hierarchical organization of the human brain regions.Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels.A growing number of ICA studies have reported altered functional connectivity in clinical populations.In the current work, it was hypothesized that ICA model order selection influences characteristics of RSNs as well as their functional connectivity.In addition, it was suggested that high ICA model order could be a useful tool to provide more detailed functional connectivity results.RSNs' characteristics, i.e. spatial features, volume and repeatability of RSNs, were evaluated, and also differences in functional connectivity were investigated across different ICA model orders.ICA model order estimation had a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks.Notably, at low model orders neuroanatomically and functionally different units tend to aggregate into large singular RSN components, while at higher model orders these units become separate RSN components.Disease-related differences in functional connectivity also seem to alter as a function of ICA model order.The volume of between-group differences reached maximum at high model orders.These findings demonstrate that fine-grained RSNs can provide detailed, diseasespecific functional connectivity alterations.Finally, in order to overcome the multiple comparisons problem encountered at high ICA model orders, a new framework for group-ICA analysis was introduced.The framework involved concatenation of IC maps prior to permutation tests, which enables statistical inferences from all selected RSNs.In SAD patients, this new correction enabled the detection of significantly increased functional connectivity in eleven RSNs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".