IC‐P‐157: Novel Toolbox for Performing Voxel‐Wise Generalized Linear Regression With Mulitple Volumetric Covariates in Longitudinal Data
Bibliographic record
Abstract
Many research studies have shifted their focus towards longitudinal data analysis. However, the currently available statistical software have not welcomed this shift as they are still lacking support for longitudinal study design, multiple imaging covariates or generalized linear regression analysis. Here, we introduce a novel statistical software written in Matlab which can perform longitudinal generalized regression analysis with multiple imaging covariates. The biggest challenge encountered to incorporate complex statistical models and multiple imaging covariates is the required time and memory complexity. This has been dealt by utilizing data parallelism techniques through the Matlab parallel computing toolbox and the Matlab distributed computing server. The function that performs generalized linear regression supports binomial, normal, poisson, gamma and inverse gaussian response variable distributions and can accommodate any number of imaging variables in the regression model and repeated measurements for longitudinal study designs. To illustrate the voxel-wise generalized linear regression functionality, neuroimaging data ([F]FDG PET, T1-MRI) were acquired for 219 individuals from the ADNI database. Demographic and MMSE scores were also obtained for the same individuals to be included in the regression models. T1 data were processed using the CIVET image processing pipeline while the PET data were processed with an established image processing pipeline. The statistical model included a logistic regression analysis to evaluate the contribution from the interaction of grey matter density and glucose metabolism for developing Alzheimer’s dementia in a cohort of MCI patients. Figure 1. shows the brain regions with highest statistical significance to increase the odds of developing Alzheimer’s dementia within 24 months from MCI. Reduced glucose metabolism in the temporal brain structures and the precuneus show significant contribution toward increasing the odds of developing AD, while the interaction of reducing glucose metabolism and reducing grey matter density in the superior gyrus of the temporal lobe increases the odds of developing AD. This novel software enables rapid prototyping and testing of sophisticated image based hypotheses particularly involving longitudinal data with multispectral neuroimaging resources expanding the existing methods for neuroimage analysis. T-statistical maps for the FDG (a), VBM (b) and FDG* VBM (c) in logistic regression model for Convertion to AD in 24 months form MCI.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.085 | 0.036 |
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".