Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer’s disease on routinely acquired T1-weighted imaging-based brain network

• Published in: Health information science and systems 2024-03, Vol.12 (1), p.19, Article 19
• Main Authors: Zhu, Chuanzhen, Li, Honglun, Song, Zhiwei, Jiang, Minbo, Song, Limei, Li, Lin, Wang, Xuan, Zheng, Qiang
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Artificial neural networks; Bioinformatics; Brain; Classification; Computational Biology/Bioinformatics; Computer Science; Constraints; Data acquisition; Diagnosis; Ganglia; Health Informatics; Image acquisition; Information Systems and Communication Service; Magnetic resonance imaging; Medical imaging; Neurons; Regularization; Representations; Similarity
• ISSN: 2047-2501; 2047-2501
• DOI: 10.1007/s13755-023-00269-0

Background Radiomics-based morphological brain networks (radMBN) constructed from routinely acquired structural MRI (sMRI) data have gained attention in Alzheimer's disease (AD). However, the radMBN suffers from limited characterization of AD because sMRI only characterizes anatomical changes and is not a direct measure of neuronal pathology or brain activity. Purpose To establish a group sparse representation of the radMBN under a joint constraint of group-level white matter fiber connectivity and individual-level sMRI regional similarity (JCGS-radMBN). Methods Two publicly available datasets were adopted, including 120 subjects from ADNI with both T1-weighted image (T1WI) and diffusion MRI (dMRI) for JCGS-radMBN construction, 818 subjects from ADNI and 200 subjects solely with T1WI from AIBL for validation in early AD diagnosis. Specifically, the JCGS-radMBN was conducted by jointly estimating non-zero connections among subjects, with the regularization term constrained by group-level white matter fiber connectivity and individual-level sMRI regional similarity. Then, a triplet graph convolutional network was adopted for early AD diagnosis. The discriminative brain connections were identified using a two-sample t-test, and the neurobiological interpretation was validated by correlating the discriminative brain connections with cognitive scores. Results The JCGS-radMBN exhibited superior classification performance over five brain network construction methods. For the typical NC vs. AD classification, the JCGS-radMBN increased by 1–30% in accuracy over the alternatives on ADNI and AIBL. The discriminative brain connections exhibited a strong connectivity to hippocampus, parahippocampal gyrus, and basal ganglia, and had significant correlation with MMSE scores. Conclusion The proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI.