Modulation of Effective Connectivity in the Default Mode Network at Rest and During a Memory Task
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Bibliographic record
Abstract
It is known that the default mode network (DMN) may be modulated by a cognitive task and by performance level. Changes in the DMN have been examined by investigating resting-state activation levels, but there have been very few studies examining the modulation of effective connectivity of the DMN during a task in healthy older subjects. In this study, the authors examined how effective connectivity changed in the DMN between rest and during a memory task. The authors also investigated whether there was any relationship between effective connectivity modulation in the DMN and memory performance, to establish whether variations in cognitive performance are related to neural network effective connectivity, either at rest or during task performance. Twenty-eight healthy older participants underwent a resting-state functional magnetic resonance imaging scan and an emotional face-name encoding task. Effective connectivity analyses were performed on the DMN to examine the effective connectivity modulation in these two different conditions. During the resting state, there was strong self-influence in the regions of the DMN, while the main regions with statistically significant cross-regional effective connectivity were the posterior cingulate cortex (PCC) and the hippocampus (HP). During the memory task, the self-influence effective connectivities remained statistically significant across the DMN, and there were statistically significant effective connectivities from the PCC, HP, amygdala (AM), and parahippocampal region to other DMN regions. The authors found that effective connectivities from PCC, HP, and AM (in both resting state and during task) were linearly correlated to memory performance. The results suggest that superior memory ability in this older cohort was associated with effective connectivity both at rest and during the memory task of three DMN regions, which are also known to be important for memory function.
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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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it