# Comparison of single and multitask learning for predicting cognitive decline based on MRI data

arXiv: https://arxiv.org/abs/2109.10266 · HTML: https://mlsanity.com/paper/2109.10266/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 76 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 1 of 1)

- [Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)](https://mlsanity.com/paper/1802.08561/) (2018)

## Built on by (most cited first, 5 of 5)

- [Multi-task longitudinal forecasting with missing values on Alzheimer's Disease](https://mlsanity.com/paper/2201.05040/) (2022)
- [Multi-Objective Genetic Algorithm for Multi-View Feature Selection](https://mlsanity.com/paper/2305.18352/) (2023)
- [Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach](https://mlsanity.com/paper/2401.06546/) (2024)
- [A Weighted Vision Transformer-Based Multi-Task Learning Framework for Predicting ADAS-Cog Scores](https://mlsanity.com/paper/2508.17613/) (2025)
- [Improving Interpretability in Alzheimer's Prediction via Joint Learning of ADAS-Cog Scores](https://mlsanity.com/paper/2508.17619/) (2025)

## Abstract (arXiv)

The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes in ADAS-Cog scores could help in timing therapeutic interventions in dementia and at-risk populations. In the present work, we compared single and multitask learning approaches to predict the changes in ADAS-Cog scores based on T1-weighted anatomical magnetic resonance imaging (MRI). In contrast to most machine learning-based prediction methods ADAS-Cog changes, we stratified the subjects based on their baseline diagnoses and evaluated the prediction performances in each group. Our experiments indicated a positive relationship between the predicted and observed ADAS-Cog score changes in each diagnostic group, suggesting that T1-weighted MRI has a predictive value for evaluating cognitive decline in the entire AD continuum. We further studied whether correction of the differences in the magnetic field strength of MRI would improve the ADAS-Cog score prediction. The partial least square-based domain adaptation slightly improved the prediction performance, but the improvement was marginal. In summary, this study demonstrated that ADAS-Cog change could be, to some extent, predicted based on anatomical MRI. Based on this study, the recommended method for learning the predictive models is a single-task regularized linear regression due to its simplicity and good performance. It appears important to combine the training data across all subject groups for the most effective predictive models.