Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm
Bibliographic record
Abstract
Rumination, an internal cognitive state characterized by recursive thinking of current self-distress and past negative events, has been found to correlate with the development of depressive disorders. Here, we investigated the feasibility of using connectivity for distinguishing different emotional states induced by a novel free-streaming, subject-driven experimental paradigm. Connectivity between 78 functional regions of interest (ROIs) within 14 large-scale networks and 6 structural ROIs particularly relevant to emotional processing were used for classifying 4 mental states in 19 healthy controls. The 4 mental states comprised: An unconstrained period of mind wandering; a ruminative mental state self-induced by recalling a time of personal disappointment; a euphoric mental state self-induced by recalling what brings the subject joy; and a sequential episodic recollection of the events of the day. A support vector machine achieved accuracies ranging from 89% to 94% in classifying pairs of different mental states. We reported the most significant brain connections that best discriminated these mental states. In particular, connectivity changes involving the amygdala were found to be important for distinguishing the rumination condition from the other mental states. Our results demonstrated that connectivity-based classification of subject-driven emotional states constitutes a novel and effective approach for studying ruminative behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| 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) | 0.000 | 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 teacher head, 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".