| Title: | Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults |
| Journal: | Sensors |
| Published: | 30 Mar 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37050682/ |
| DOI: | https://doi.org/10.3390/s23073622 |
| URL: | https://www.mdpi.com/1424-8220/23/7/3622/pdf?version=1680244628 |
| Citations: | 11 (11 in last 2 years) as of 8 Aug 2024 |
Publication 7887
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Abstract
Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.6 Authors
- Min Xiong
- Lan Lin
- Yue Jin
- Wenjie Kang
- Shuicai Wu
- Shen Sun
1 Application
| Application ID | Title |
|---|---|
| 68382 | Effects of environmental and behavioural factors on the development of aging brain via multi-modal imaging |
Enabling scientific discoveries that improve human health