A systematic review of neuroimaging findings in subjective cognitive decline
Bibliographic record
Abstract
Abstract Background Individuals with subjective cognitive decline (SCD), who self‐report changes in cognition, but are within the normal range on neuropsychological testing, are thought to be the earliest along the cognitive continuum between healthy aging and Alzheimer’s disease (Jessen et al., 2014). This study aimed to synthesize findings of neuroimaging studies using various modalities to investigate changes in the brain in those with SCD. Method PubMed and PsycINFO databases were searched for neuroimaging studies of individuals with SCD. Quality assessment was completed using the Appraisal tool for Cross‐Sectional Studies (Downes, Brennan, Williams, & Dean, 2016). Result In total, 108 neuroimaging studies investigating SCD samples were identified. Specifically, 45 studies used MRI, 8 used EEG, 5 used MEG, 3 used CT, 26 used PET, 2 used SPECT, and 19 studies used multi‐modal neuroimaging methods. Many studies investigated differences between healthy controls and those with SCD. Across imaging modalities, findings revealed significant differences in brain structure and function between these groups. Conclusion It is valuable to synthesize the results of studies found in this area to identify neuroimaging biomarkers present in those with SCD. Identifying changes in the brain using objective and physiologically based measures at this early clinical stage will help to characterize the progression of Alzheimer’s disease. In future neuroimaging investigations of SCD, it would be useful for studies to include larger sample sizes, examine groups longitudinally, and use multi‐modal neuroimaging methods. Incorporating these components in future studies could provide a better understanding of changes in the brain that are associated with subsequent conversion to mild cognitive impairment or Alzheimer’s disease.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".