Problems using structural MRIs from the oldest‐old, and some solutions: Lessons learned from The 90+ Study and ADNI
Bibliographic record
Abstract
Abstract Background The oldest‐old (≥85 years) have the highest rates of dementia. However, because of pronounced brain atrophy, standard neuroimaging programs face unique challenges in this cohort. Here, using data from both The 90+ Study and Alzheimer’s Disease Neuroimaging Initiative (ADNI), we report the prevalence of errors when processing brain MRIs from oldest‐old participants using standard approaches and present some strategies for ameliorating these issues. Method First, we analyzed image quality issues and segmentation errors (FreeSurfer) for the last available scans by age‐groups (oldest‐old, ≥85 years, vs younger‐old, 55 to 85 years) in ADNI. Second, we evaluated the utility of an age‐specific template (the 90+ T1w Template) for realigning MRIs from the oldest‐old compared to a standard template (Montreal Neurological Institute T1w template, MNI) using linear and nonlinear registration (FSL) and evaluating alignment quality using visual and deformation (Jacobian) measures for oldest‐old participants (≥90 years) in both The 90+ Study and ADNI. Lastly, we evaluated whether stringent preprocessing (bias field removal, denoising, brain extraction, intensity normalization using Computational Anatomy Toolbox (CAT12)) helped reduce FreeSurfer segmentation errors in frontal, parietal, and temporal regions in MRIs from the oldest‐old. Comparisons were performed using Chi‐square tests. Result In ADNI data (Table‐1), MRIs from oldest‐old participants (∼10% of cohort) were around twice as likely to have image quality issues (20% vs 13%, P<0.001) and brain segmentation errors (45% vs 29%, P<0.001) compared to younger‐old participants. Figure‐1 shows a histogram of image quality issues and segmentation errors, which become more common with greater age. Compared to the MNI template, MRIs from participants aged 90 and older from both The 90+ Study (N=163, Table‐2) and ADNI (N=75) had fewer registration errors (∼5% vs ∼23%, P<0.001 for all) and less severe deformation when using the 90+ T1w Template (Figure‐2). Our proposed preprocessing pipeline (see Figure‐3 for example) roughly halved segmentation errors in the oldest‐old (P<0.001 for all regions, Figure‐4). Conclusion Our results show that adapting pipelines specifically for processing brain MRIs of oldest‐old participants minimizes errors, enabling more effective use of neuroimaging to study neural correlates of neurodegeneration and dementia in this important portion of our aging population.
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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 | 0.085 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".