Multiple patterns of structural brain change trajectories in AD: MRI deep learning verification with enhanced sequential pattern analysis
Bibliographic record
Abstract
Abstract Background Our recently proposed interpretable Ensemble 3DCNN deep learning (DL) technique has allowed the derivation of a P‐score based on brain structural magnetic resonance imaging (sMRI) modelling to predict patterned neurodegeneration in AD (https://doi.org/10.1002/advs.202204717). Sequential association rules with a support rate greater than 18% have been used to define a neurodegenerative progression pattern. To more prudently test the meaningfulness of the patterns found, we further calculated the lift values of the various sequential patterns underlying the suggested longitudinal sMRI changes. Method The lift value of an association rule is defined as the rule’s confidence rate divided by the support rate of the rule consequent, i.e., the support rate of the rule divided by the product of the support values of the rule consequent and the rule antecedent. For example, with {L.NAC}→{L.NAC, R.NAC} (https://doi.org/10.1002/advs.202204717), among the 167 AD subjects employed for analyzing neurodegenerative progression patterns, the number (N) of the subjects with at least an sMRI image containing both L.NAC and R.NAC as neurodegenerative regions at all time points examined was firstly calculated. Secondly, among the above‐mentioned 167 AD subjects, the number (M) of the subjects with at least an sMRI image containing only L.NAC as neurodegenerative regions at all time points examined was counted and the time point (P) corresponding to the selected (earlier/earliest) sMRI image was saved. Thirdly, among the above selected M subjects, the number (K) of the subjects with at least an sMRI image containing both L.NAC and R.NAC as neurodegenerative regions at all time points latter than the P was computed. Thus, lift value = (K/M)/(N/167). Result The lift values of the fifty sequential association rules reported in the recent paper (https://doi.org/10.1002/advs.202204717) ranged between 1.27 and 2.59(i.e., all > 1; Table 1), further confirming the longitudinal sMRI change patterns in AD. Conclusion This study analyzing the lift values of the sequential association rules further verified the multiple patterns of longitudinal sMRI trajectories in AD brains, as reported recently. The research demonstrated featured and heterogeneous whole‐brain structural degeneration with AD progression.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".