Atrophy-centered subtyping of mild cognitive impairment
Bibliographic record
Abstract
Abstract Mild cognitive impairment (MCI) is considered as the transitional phase between normal cognitive aging and Alzheimer’s disease (AD). Nevertheless, trajectories of cognitive decline vary considerably among individuals with MCI. To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogenous subgroups and ultimately improving prognostic outcomes. To date, subtyping of MCI has been based primarily on cognitive performance measures, often resulting in indistinct boundaries between the proposed subgroups and limited validity. The degree to which markers of neurodegeneration such as brain atrophy can be used to subtype MCI into biologically and clinically meaningful subgroups remains unclear. Here we introduce and validate a data-driven subtyping method for MCI based solely upon measures of atrophy derived from structural magnetic resonance imaging (MRI). We trained a dense convolutional neural network to differentiate between patients with AD and age-matched cognitively normal (CN) subjects based on whole brain MRI features. We then deployed the trained model to classify individuals with MCI, as MCI-CN or MCI-AD, based on the degree to which their whole brain gray matter volume resembles CN-like or AD-like patterns. We subsequently validated the model-based subgroups using cognitive, clinical, fluid biomarker, and molecular neuroimaging data. Namely, we observed marked differences between the MCI-CN and MCI-AD groups in baseline and longitudinal cognitive and clinical rating scales, disease-free survival, cerebrospinal fluid (CSF) levels of amyloid beta and tau, fluorodeoxyglucose (FDG) and amyloid PET. Overall, the results suggest that patterns of atrophy in MCI are sufficiently distinct and heterogeneous, and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".