Network interactions explain effective encoding in the context of medial temporal damage in MCI
Bibliographic record
Abstract
Selective dysfunction in the medial temporal lobe (MTL) in amnestic mild cognitive impairment (MCI) results in a relatively circumscribed impairment in episodic memory. Previously, we found that activation extent in MTL during encoding correlated with subsequent recognition (hit rate) in controls but not in MCI patients (Mandzia et al. [2009]: Neurobiol Aging 30:717-730). Here, we examined whether functional connectivity amongst MTL and cortical regions might better explain differences in subsequent recognition success. Participants underwent fMRI scanning during picture encoding, and multivariate analysis was used to characterize the relationship between network activations and recognition. Both patients and controls activated a canonical MTL encoding network. However, this network correlated with hit rate only for controls. In MCI patients, recognition variability was best explained by the engagement of an additional network including BA 20. We propose that this pattern represents functional reorganization caused by reduced efficiency in the MTL network. Our findings suggest that understanding brain-behavior relationships in neurological disorders requires examination of large-scale networks, even when dysfunction is relatively focal as in MCI.
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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".