Biomarker changes associated with fornix deep brain stimulation in Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Deep brain stimulation of the fornix (fx-DBS) is being investigated for treatment of Alzheimer's disease (AD). The therapy aims at alleviating memory and cognitive circuit dysfunction. In preclinical models of AD, electrical stimulation of the memory circuit has demonstrated a possible disease-modifying potential. Here we examined changes resulting from fx-DBS in hippocampal atrophy and amyloid accumulation in AD patients with fx-DBS. METHODS: Repeated magnetic resonance imaging and positron emission tomography (PET) images acquired over the course of 12 months were used to assess changes in hippocampal volume in 36 ADvance trial patients compared to 40 matched untreated AD patients from the Alzheimer's Disease Neuroimaging Initiative, and in 10 separate patients with repeated flutemetamol PET and cerebrospinal fluid (CSF) markers. RESULTS: We observed a reduction of hippocampal atrophy and amyloid beta (Aβ) PET binding, and an increase in the CSF Aβ/total-tau ratio in DBS patients. DISCUSSION: These findings highlight the potential of fornix deep brain stimulation to modify AD biomarkers and possibly progression in some patients. HIGHLIGHTS: Fornix deep brain stimulation (fx-DBS) is being investigated to treat Alzheimer's disease (AD). Results show that fx-DBS modifies imaging and cerebrospinal fluid (CSF) markers. It reduces hippocampal atrophy and increases the amyloid beta/total-tau CSF ratio. These findings highlight the potential of fx-DBS to modify AD.
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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 | 0.000 |
| 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".