A Multimodal data fusion approach efficiently predicts disease duration in multiple sclerosis
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) biomarkers of multiple sclerosis (MS), particularly fluid-attenuated inversion-recovery (FLAIR) sequences, have long been investigated. However, advanced analytical methods, capable of fusing results from different MRI modalities, could be more informative to have enabled joint biomarkers. Here we estimate disease duration (DD) in MS subjects (n=47) based on fusion of information from Myelin Water Imaging (MWI), Diffusion Tensor Imaging (DTI), and resting state functional MRI (rsfMRI) modalities, by adapting a joint Multimodal Statistical Analysis Framework. Using this data driven, multimodal, latent variable (LV) approach, common and unique information in each dataset was acquired and their relationship with DD is analyzed through the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The common components between the three modalities, but not the unique components of each modality, accurately predicted DD. To further investigate the regions importance for estimating DD, we separated the data into two groups: “early” and “late” DD depending upon their relationship to the median duration of illness (120 months). In early disease, DTI information in the Right Tapetum and the Fornix jointly with MWI information in the Left Superior Cerebellar Penducle, Right Cerebral Penducle, and Cingulum were most informative. In contrast, rsfMRI demonstrated altered connectivity throughout disease duration. Our results demonstrate the power of multimodal imaging markers in MS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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".