Multimodal Brain Age Prediction Using Machine Learning

Multimodal Brain Age Prediction Using Machine Learning

Overview

This project focuses on developing advanced biomarkers for assessing age-related biological changes in the human brain. We combine structural MRI and 5-HT2AR PET data to improve brain age prediction, which is crucial for understanding neurodegenerative disorders and evaluating neuroprotective interventions. Please visit the GitHub repository for code and documentation:

Key Objectives

1. Predict brain age using 5-HT2AR binding outcomes
2. Compare 5-HT2AR-based predictions to gray matter (GM) volume predictions
3. Investigate the synergy of combining 5-HT2AR and GM volume data for improved prediction accuracy

Methodology

• Data: PET and MR images from 209 healthy individuals (age range: 18-85 years)
• Measures: 5-HT2AR binding and GM volume for 14 cortical and subcortical regions
• Analysis: Applied various machine learning algorithms for age prediction
• Evaluation: Used Mean Absolute Error (MAE) and cross-validation for model comparison

Key Findings

• 5-HT2AR binding predicts chronological age accurately (mean MAE = 6.63 years, std = 0.74 years)
• GM volume also provides accurate predictions (mean MAE = 6.95 years, std = 0.83 years)
• Combining both measures significantly improves prediction accuracy (mean MAE = 5.54 years, std = 0.68)

Publication

• Preprint: bioRxiv
• Journal Article: GeroScience

Citation

```bibtex @article{Doerfel2024, title = {Multimodal Brain Age Prediction Using Machine Learning: Combining Structural and 5- Features}, shorttitle = {Multimodal Brain Age Prediction Using Machine Learning}, author = {D{"o}rfel, Ruben P. and {Arenas-Gomez}, Joan M. and Svarer, Claus and Ganz, Melanie and Knudsen, Gitte M. and Svensson, Jonas E. and {Plav{'e}n-Sigray}, Pontus}, year = {2024}, journal = {GeroScience}, doi = {10.1007/s11357-024-01148-6}, urldate = {2024-05-01}, copyright = {All rights reserved}, langid = {english}, keywords = {5HT2A,brain age,MRI,multimodal,PET} }