Neural Correlates of Sustained Attention During Disengagement From Repetitive Thought
Bibliographic record
Abstract
Background: Repetitive thought (e.g., worry; rumination) is a major transdiagnostic symptom and mechanism of internalizing psychopathology. Ability to regulate RT is particularly clinically important. Prior work implicates the default mode network (DMN) in self-generated and memory-driven cognitions including RT. However, few studies have investigated the neural correlates corresponding to successful RT regulation.Method: Participants were 57 (32 female, 4 nonbinary, M age = 30) community-residing adults aged 20-45 oversampled for severe RT. 79% met criteria for one or more DSM-5 disorders. Participants completed an fMRI RT disengagement task during which they first viewed their own self-identified RT stimuli and perseverated as normal, then shifted attention to a validated sustained attention task, and finally responded to thought probes assessing momentary RT disengagement success. Study design, hypotheses, and analytic plan were preregistered on OSF (https://osf.io/qy4df).Results: More difficulty disengaging from RT corresponded to worse attention task performance (slower responding with no increase in accuracy); reduced activity in the salience, ventral and dorsal attention networks; and reduced connectivity between the DMN (posterior cingulate cortex) and dorsal attention network; but not reduced activity in or connectivity of DMN.Conclusions and relevance: Successful regulation of repetitive thought may rely on the effective recruitment of attentional resources, but downregulation of DMN may not be necessary. Clinically, these findings suggest potential benefit from interventions that focus more on upregulating attentional functioning than on downregulating networks believed to subserve repetitive thought.
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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".