Transformer attention-based neural network for cognitive score estimation from sMRI data.
Authors
Affiliations (5)
Affiliations (5)
- College of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu, 610225, China; National Intelligent Society Comprehensive Governance Experimental Base (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu, 610225, China.
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State, Georgia Tech, Emory), Atlanta, GA, 30303, USA.
- Klinikum Rechts der Isar der Technischen Universität München, Munchen, 81675, Bayern, Germany.
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, CB3 0WA, UK.
- College of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu, 610225, China; National Intelligent Society Comprehensive Governance Experimental Base (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu, 610225, China. Electronic address: [email protected].
Abstract
Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia and forecasting Alzheimer's disease (AD). Some existing deep learning methods often depend on anatomical priors, overlooking individual-specific structural differences during AD progression. To address these limitations, this work proposes a deep neural network that incorporates Transformer attention to jointly predict multiple cognitive scores, including ADAS, CDRSB, and MMSE. The architecture first employs a 3D convolutional neural network backbone to encode sMRI, capturing preliminary local structural information. Then an improved Transformer attention block integrated with 3D positional encoding and 3D convolutional layer to adaptively capture discriminative imaging features across the brain, thereby focusing on key cognitive-related regions effectively. Finally, an attention-aware regression network enables the joint prediction of multiple clinical scores. Experimental results demonstrate that our method outperforms some existing traditional and deep learning methods based on the ADNI dataset. Further qualitative analysis reveals that the dementia-related brain regions identified by the model hold important biological significance, effectively enhancing the performance of cognitive score prediction. Our code is publicly available at: https://github.com/lshsx/CTA_MRI.