Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests
Bibliographic record
Abstract
Background. Psychophysiological and cognitive tests as well as other functional studies can detect pre-symptomatic stages of dementia. When assembled with structural data, cognitive tests diagnose NDs more reliably thus becoming a multimodal diagnostic tool. Objective. Our main goal is to improve screening for dementia by studying an association between the brain structure and its function. Hypothetically, the brain structure-function association has features specific for either diseaserelated cognitive deterioration or normal neurocognitive slowing while aging. Materials and methods. We studied a total number of 287 cognitively normal cases, 646 of mild cognitive impairment, and 369 of Alzheimer’s disease. To work out a new marker of neurodegeneration, we created a convolutional neural network-based regression model and predicted the cognitive status of the cognitively preserved examinee from the brain MRI data. This was a model of normal aging. A big deviation from the model suggests a high risk of accelerated cognitive decline. Results. The deviation from the model of normal aging can accurately distinguish cognitively normal subjects from MCI patients (AUC = 0.9957). We also achieved creditable performance in the MCI-versus-AD classification (AUC = 0.9793). We identified a considerable difference in the MMSE test between A-positive and A-negative demented individuals according to ATN-criteria (6.27±1.82 vs 5.32±1.9; p < 0.05). Conclusion. The deviation from the model of normal aging can be potentially used as a marker of dementia and as a tool for differentiating Alzheimer’s disease from non-Alzheimer’s dementia. To find and justify a reliable threshold levels, further research is required.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".