Functional and Effective Hippocampal–Neocortical Connectivity During Construction and Elaboration of Autobiographical Memory Retrieval
Bibliographic record
Abstract
Autobiographical memory (AM) provides the opportunity to study interactions among brain areas that support the search for a specific episodic memory (construction), and the later experience of mentally reliving it (elaboration). While the hippocampus supports both construction and elaboration, it is unclear how hippocampal-neocortical connectivity differs between these stages, and how this connectivity involves the anterior and posterior segments of the hippocampus, as these have been considered to support the retrieval of general concepts and recollection processes, respectively. We acquired fMRI data in 18 healthy participants during an AM retrieval task in which participants were asked to access a specific AM (construction) and then to recollect it by recovering as many episodic details as possible (elaboration). Using multivariate analytic techniques, we examined changes in functional and effective connectivity of hippocampal-neocortical interactions during these phases of AM retrieval. We found that the left anterior hippocampus interacted with frontal areas during construction and bilateral posterior hippocampi with visual perceptual areas during elaboration, indicating key roles for both hippocampi in coordinating transient neocortical networks at both AM stages. Our findings demonstrate the importance of direct interrogation of hippocampal-neocortical interactions to better illuminate the neural dynamics underlying complex cognitive tasks such as AM retrieval.
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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 | 0.002 |
| 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".