Despite heightened risk of cognitive decline, no evidence of local atrophy in people with subjective cognitive decline compared to normal controls in ADNI
Bibliographic record
Abstract
Abstract Background People with subjective cognitive decline (SCD) are at increased risk for developing Alzheimer’s disease (AD). SCD may thus be a very early clinical manifestations of AD. However, identifying which individuals with SCD will develop AD is difficult with current biomarker techniques. To predict whether someone with SCD will progress to AD, it is necessary to determine whether people with SCD display neurodegeneration in brain regions associated with AD. Method We included 1769 baseline and follow‐up MRI scans for 447 participants (177 normal controls, NC; 100 SCD; and 170 early mild cognitive impairment, eMCI) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). All scans were pre‐processed through a previously validated pipeline. Deformation‐based morphometry (DBM) was performed to examine the pattern of volumetric change over time. An atlas‐based approach was also used to examine mean volume differences for regions of interest (ROIs; lateral ventricles, entorhinal cortex, and amygdala from CerebrA atlas (Manera et al., 2020)) estimated by integrating the Jacobian of the deformation field within the ROI. A previously validated MRI analysis method (SNIPE) was used to determine volume and grading of the hippocampus (Coupe et al., 2012). We applied a linear mixed effects model for all analysis (volume ∼Diagnostic Group +Age +Sex +Amyloid Positivity +APOE e4 +Education +1|ID). Result Longitudinal volume analysis showed slight atrophy in eMCI compared to NC and SCD. ROI analysis revealed that eMCI had smaller volumes than SCD and NC in the amygdala (NC & SCD: p<.001) and entorhinal cortex (NC: p<.001; SCD: p=.01), and larger lateral ventricles (NC & SCD: p=.01). SNIPE volume and grading analysis revealed that eMCI hippocampal volume differed from both NC and SCD (p<.001). SCD and NC did not differ in any of the analyses. With respect to demographics, people with SCD had 2 years more education (p<0.001) than eMCI and NC. Conclusion The structural differences observed in eMCI may act as an early biomarker for AD. Although SCD participants’ brain volumes significantly differed from eMCI, they did not differ from NC. However, lack of SCD:NC differences may be due to confounds (e.g., greater education in SCD) within the sample.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".