Change point based dynamic functional connectivity estimation outperforms sliding window and static estimation for classification of early mild cognitive impairment in resting-state fMRI
Bibliographic record
Abstract
Abstract The most widely used inputs in classification models of brain disorders such as early mild cognitive impairment (eMCI) or Alzheimer’s disease are estimates of static-based functional connectivity (SFC) and sliding window dynamic functional connectivity (swDFC). Although these methods are convenient for estimation and computational purposes, as it keeps the estimation tractable, they present a simplified version of a highly integrated and dynamic phenomenon. Change point dynamic functional connectivity (cpDFC) methods, which are far less commonly used, offer an alternative to swDFC approaches. In this study, we consider a classification task between controls and patients with eMCI using resting-state functional magnetic resonance imaging (fMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) studies, ADNI2 and ADNIGO. Our results indicate that the DFC methods are generally superior to the SFC methods when used as inputs into the classification model. Most importantly, we find that the cpDFC methods are generally superior to the widely used swDFC methods. We discuss how cpDFC methods offer many distinct advantages over swDFC methods, namely, the parsimony of network features and ease of interpretability. We validate the robustness and consistency of our results by testing the methods on an additional resting-state fMRI dataset of mild cognitive impairment patients. These findings call into question the validity of numerous fMRI studies that have utilized inferior approaches, such as SFC and swDFC, as inputs to classification models to predict various brain disorders. Finally, we present an ensemble model of the best models, which achieves an accuracy of 91.17% from leave-one-out cross-validation of subjects with eMCI. Our results suggest that the underlying functional networks are dynamic, multiscale, and that different FC methods capture distinct information for classification efficacy.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".