Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder
Bibliographic record
Abstract
Resting-state fMRI (RS-fMRI) has become a useful tool to investigate the connectivity structure of mental health disorders. In the case of major depressive disorder (MDD), recent studies regarding the RS-fMRI have found abnormal connectivity in several regions of the brain, particularly in the default mode network (DMN). Thus, the relevance of the DMN to self-referential thoughts and ruminations has made the use of the resting-state approach particularly important for MDD. The majority of such research has relied on the grand averaged functional connectivity measures based on the temporal correlations between the BOLD time series of various brain regions. We, in our study, investigated the variations in the functional connectivity over time at global and local level using RS-fMRI BOLD time series of 27 MDD patients and 27 healthy control subjects. We found that global synchronization and temporal stability were significantly increased in the MDD patients. Furthermore, the participants with MDD showed significantly increased overall average (static) functional connectivity (sFC) but decreased variability of functional connectivity (vFC) within specific networks. Static FC increased to predominance among the regions pertaining to the default mode network (DMN), while the decreased variability of FC was observed in the connections between the DMN and the frontoparietal network. Hum Brain Mapp 37:2918-2930, 2016. © 2016 Wiley Periodicals, Inc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".