Multiblock PLS model for group corticomuscular activity analysis in Parkinson disease
Bibliographic record
Abstract
To explore the cross-information in multi-modal data, several multivariate data fusion techniques have been proposed. Partial least square (PLS) has great potential for neuroimaging studies. However, when performing group analysis with PLS, the presence of inter-subject variability makes the conventional technique of simply pooling data from different subjects problematic. To circumvent this issue, we introduce the idea of multiblock PLS (mbPLS) which incorporates a hierarchical structure into the ordinary two-block PLS. The mbPLS model groups each subject's detail in individual data blocks on the sub-level, while aggregates the sub-level information to obtain a group “consensus” on the super-level. With the hierarchical, two-level design, the mbPLS provides a trade-off between modeling simplicity and preservation of subject specificity. We applied the proposed mbPLS method to concurrent EEG and EMG data collected from normal subjects and patients with Parkinson's disease. The decomposition identifies active brain rhythms and spatial activation pattern involved in the generation of the extracted EEG temporal patterns that show high correlation with EMG signals. The proposed mbPLS framework is a promising technique for performing multi-subject, multi-modal data analysis and it allows for robust group inferences even in the face of large inter-subject variability.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".