A.2 Cognitive outcomes of deep brain stimulation depend upon subiculum connectivity
Bibliographic record
Abstract
Background: Recent research has demonstrated that DBS sites in Alzheimer’s (AD) and Parkinson’s (PD) influencing cognition are functionally connected to the subiculum. However, the results are mixed, and it is unclear how or if DBS site-subiculum connectivity can be optimized to improve patient cognition. Methods: We studied how subiculum connectivity influenced cognitive outcomes in both PD (subthalamic nucleus) and AD (fornix) DBS patients (total n = 110). We first confirmed DBS site-subiculum connectivity had opposite cognitive effects in each disease. We next investigated patient factors underlying these opposing effects. Lastly, we related our findings back to clinical practice to guide DBS programming in PD and AD. Results: DBS site-subiculum connectivity correlated with cognitive improvement in AD but decline in PD. This was dependent upon hippocampal atrophy; such that higher subiculum connectivity was beneficial when the hippocampus was atrophic but deleterious when it was intact. Finally, we related our findings back to anatomy with cadaveric dissections and present how DBS stimulation can be optimized to improve patient cognition. Conclusions: DBS site-subiculum connectivity influences cognition but depends on patient factors. Thus, to optimize cognition based on patient factors, DBS electrodes can be programmed to stimulate subregions with higher or lower subiculum connectivity.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".