Identification of Earlier Biomarkers for Alzheimer’s Disease: A Multimodal Neuroimaging Study of Individuals with Subjective Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: Individuals with subjective cognitive decline (SCD) are thought to be the earliest along the cognitive continuum between healthy aging and Alzheimer's disease (AD). OBJECTIVE: The current study used a multi-modal neuroimaging approach to examine differences in brain structure and function between individuals with SCD and healthy controls (HC). METHODS: 3T high-resolution anatomical images and resting-state functional MRI scans were retrieved for 23 individuals with SCD and 23 HC from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. RESULTS: The SCD and HC groups were not significantly different in age or education level. Voxel-based morphometry results did not show significant differences in grey matter volume between the groups. Functional MRI results revealed significantly greater functional connectivity in the default mode network in regions including the bilateral precuneus cortex, bilateral thalamus, and right hippocampal regions in individuals with SCD relative to controls. Conversely, those with SCD showed decreased functional connectivity in the bilateral frontal pole, caudate, angular gyrus, and lingual gyrus, compared to HC. CONCLUSION: Findings revealed differences in brain function but not structure between individuals with SCD and HC. Overall, this study represents a crucial step in characterizing individuals with SCD, a group recognized to be at increased risk for AD. It is imperative to identify biomarkers of AD prior to significant decline on clinical assessment, so that disease-delaying interventions may be delivered at the earliest possible time point.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".