Brain structures and networks responsible for stimulation‐induced memory flashbacks during forniceal deep brain stimulation for Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Fornix deep brain stimulation (fx-DBS) is under investigation for treatment of Alzheimer's disease (AD). We investigated the anatomic correlates of flashback phenomena that were reported previously during acute diencephalic stimulation. METHODS: Thirty-nine patients with mild AD who took part in a prior fx-DBS trial (NCT01608061) were studied. After localizing patients' implanted electrodes and modeling the volume of tissue activated (VTA) by DBS during systematic stimulation testing, we performed (1) voxel-wise VTA mapping to identify flashback-associated zones; (2) machine learning-based prediction of flashback occurrence given VTA overlap with specific structures; (3) normative functional connectomics to define flashback-associated brain-wide networks. RESULTS: A distinct diencephalic region was associated with greater flashback likelihood. Fornix, bed nucleus of stria terminalis, and anterior commissure involvement predicted memory events with 72% accuracy. Flashback-inducing stimulation exhibited greater functional connectivity to a network of memory-evoking and autobiographical memory-related sites. DISCUSSION: These results clarify the neuroanatomical substrates of stimulation-evoked flashbacks.
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 | 0.001 |
| 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".