Increased Cerebral Metabolism After 1 Year of Deep Brain Stimulation in Alzheimer Disease
Bibliographic record
Abstract
BACKGROUND: The importance of developing unique, neural circuitry-based treatments for the cognitive and neuropsychiatric symptoms of Alzheimer disease (AD) was the impetus for a phase I study of deep brain stimulation (DBS) in patients with AD that targeted the fornix. OBJECTIVE: To test the hypotheses that DBS would increase cerebral glucose metabolism in cortical and hippocampal circuits and that increased metabolism would be correlated with better clinical outcomes. DESIGN: Open-label trial. SETTING: Academic medical center. PATIENTS: A total of 5 patients with mild, probable AD (1 woman and 4 men, with a mean [SD] age of 62.6 [4.2] years). INTERVENTION: Deep brain stimulation of the fornix. MAIN OUTCOME MEASURES: All patients underwent clinical follow-up and high-resolution positron emission tomography studies of cerebral glucose metabolism after 1 year of DBS. RESULTS: Functional connectivity analyses revealed that 1 year of DBS increased cerebral glucose metabolism in 2 orthogonal networks: a frontal-temporal-parietal-striatal-thalamic network and a frontal-temporal-parietal-occipital-hippocampal network. In similar cortical regions, higher baseline metabolism prior to DBS and increased metabolism after 1 year of DBS were correlated with better outcomes in global cognition, memory, and quality of life. CONCLUSIONS: Increased connectivity after 1 year of DBS is observed, which is in contrast to the decreased connectivity observed over the course of AD. The persistent cortical metabolic increases after 1 year of DBS were associated with better clinical outcomes in this patient sample and are greater in magnitude and more extensive in the effects on cortical circuitry compared with the effects reported for pharmacotherapy over 1 year in 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 | 0.001 |
| 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".