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

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Abstract

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.

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Data availability

The data utilized in this study were sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI), available at http://adni.loni.usc.edu/data-samples/access-data, and the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing (AIBL), available at http://aibl.csiro.au.

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Acknowledgements

This study utilized data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, available at http://adni.loni.usc.edu) and the Australian Imaging Biomarkers and Lifestyle study (AIBL, available at http://aibl.csiro.au). While the investigators of ADNI and AIBL played a role in designing and implementing these studies and provided the data, they did not directly contribute to the work presented in this manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (61802330, 61802331), Natural Science Foundation of Shandong Province (ZR2020QH048) and Yantai City Science and Technology Innovation Development Plan (2023XDRH006).

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Author notes

  1. Chuanzhen Zhu and Honglun Li have contributed equally to this study.

Authors and Affiliations

  1. School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005, Shandong, China

    Chuanzhen Zhu, Zhiwei Song, Minbo Jiang, Xuan Wang & Qiang Zheng

  2. Departments of Medical Oncology and Radiology, Affiliated Yantai Yuhuangding Hospital of Qingdao University Medical College, Yantai, 264099, China

    Honglun Li

  3. School of Medical Imaging, Weifang Medical University, Weifang, 261000, China

    Limei Song

  4. Yantaishan Hospital Affiliated to Binzhou Medical University, Yantai, 264003, China

    Lin Li

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  1. Chuanzhen Zhu
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Contributions

Chuanzhen Zhu and Honglun Li: Conceptualization, Methodology, Data curation, Software, Experiments, and Writing—Original Draft. Zhiwei Song: Methodology, Data curation, Writing—Original Draft. Minbo Jiang: Validation, Formal analysis. Limei Song, Lin Li and Xuan Wang: Writing—Original Draft, Investigation. Qiang Zheng: Conceptualization, Supervision, Project administration and Funding acquisition.

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Correspondence to Qiang Zheng.

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Zhu, C., Li, H., Song, Z. et al. Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer’s disease on routinely acquired T1-weighted imaging-based brain network. Health Inf Sci Syst 12, 19 (2024). https://doi.org/10.1007/s13755-023-00269-0

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