| Title: | A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data |
| Journal: | Aperture Neuro |
| Published: | 13 Jun 2024 |
| DOI: | https://doi.org/10.52294/001c.118576 |
| URL: | https://apertureneuro.org/article/118576.pdf |
Publication 11857
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
The investigation of brain health development is paramount, as a healthy brain underpins cognitive and physical well-being, and mitigates cognitive decline, neurodegenerative diseases, and mental health disorders. This study leverages the UK Biobank dataset containing static functional network connectivity (sFNC) data derived from resting-state functional magnetic resonance imaging (rs-fMRI) and assessment data. We introduce a novel approach to forecasting a brain health index (BHI) by deploying three distinct models, each capitalizing on different modalities for training and testing. The first model exclusively employs psychological assessment measures, while the second model harnesses both neuroimaging and assessment data for training but relies solely on assessment data during testing. The third model encompasses a holistic strategy, utilizing neuroimaging and assessment data for the training and testing phases. The proposed models employ a two-step approach for calculating the BHI. In the first step, the input data is subjected to dimensionality reduction using principal component analysis (PCA) to identify critical patterns and extract relevant features. The resultant concatenated feature vector is then utilized as input to variational autoencoders (VAE). This network generates a low-dimensional representation of the input data used for calculating BHI in new subjects without requiring imaging data. The results suggest that incorporating neuroimaging data into the BHI model, even when predicting from assessments alone, enhances its ability to accurately evaluate brain health. The VAE model exemplifies this improvement by reconstructing the sFNC matrix more accurately than the assessment data. Moreover, these BHI models also enable us to identify distinct behavioral and neural patterns. Hence, this approach lays the foundation for larger-scale efforts to monitor and enhance brain health, aiming to build resilient brain systems.</p>
10 Authors
- Meenu Ajith
- Dawn M. Aycock
- Erin B. Tone
- Jingyu Liu
- Maria B. Misiura
- Rebecca Ellis
- Sergey M. Plis
- Tricia Z. King
- Vonetta M. Dotson
- Vince Calhoun
Enabling scientific discoveries that improve human health