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Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Sparse inverse covariance estimation (SICE) is increasingly utilized to estimate inter-subject covariance of FDG uptake (FDGcov) as proxy of metabolic brain connectivity. However, this statistical method suffers from the lack of robustness in the connectivity estimation. Patterns of FDGcov were observed to be spatially similar with patterns of structural connectivity as obtained from DTI imaging. Based on this similarity, we propose to regularize the sparse estimation of FDGcov using the structural connectivity.

Methods

We retrospectively analyzed the FDG-PET and DTI data of 26 healthy controls, 41 patients with Alzheimer’s disease (AD), and 30 patients with frontotemporal lobar degeneration (FTLD). Structural connectivity matrix derived from DTI data was introduced as a regularization parameter to assign individual penalties to each potential metabolic connectivity. Leave-one-out cross validation experiments were performed to assess the differential diagnosis ability of structure weighted SICE approach. A few approaches of structure weighted were compared with the standard SICE.

Results

Compared to the standard SICE, structural weighting has shown more stable performance in the supervised classification, especially in the differentiation AD vs. FTLD (accuracy of 89–90%, while unweighted SICE only 85%). There was a significant positive relationship between the minimum number of metabolic connection and the robustness of the classification accuracy (r = 0.57, P < 0.001). Shuffling experiments showed significant differences between classification score derived with true structural weighting and those obtained by randomized structure (P < 0.05).

Conclusion

The structure-weighted sparse estimation can enhance the robustness of metabolic connectivity, which may consequently improve the differentiation of pathological phenotypes.

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Funding

This work was supported by the National Natural Science Foundation of China (82020108013) and the research project of Shanghai Health Commission (2020YJZX0111).

Author information

Authors and Affiliations

  1. Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China

    Min Wang, Zhuangzhi Yan & Jiehui Jiang

  2. Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany

    Min Wang, Nassir Navab & Kuangyu Shi

  3. The Bonn-Aachen International Center for Information Technology (b-it) and Institute of Computer Science II, University of Bonn, Bonn, Germany

    Michael Schutte

  4. Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany

    Timo Grimmer & Janine Diehl-Schmid

  5. Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany

    Aldana Lizarraga, Dennis M. Hedderich & Igor Yakushev

  6. Department for Visual Computing, University of Bonn, Bonn, Germany

    Thomas Schultz

  7. Department of Nuclear Medicine, University of Bern, Bern, Switzerland

    Axel Rominger & Kuangyu Shi

  8. Department of Nuclear Medicine, Ludwig Maximilian University of Munich, Munich, Germany

    Sybille Ziegler

Authors
  1. Min Wang
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  2. Michael Schutte
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  11. Zhuangzhi Yan
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Corresponding authors

Correspondence to Jiehui Jiang or Igor Yakushev.

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This study was performed in line with the principles of the Declaration of Helsinki and with ethical standards of the institutional and/or national research committee. Informed consent was obtained from all individual participants included in the study.

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The authors declare no competing interests.

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This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence).

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Wang, M., Schutte, M., Grimmer, T. et al. Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach. Eur J Nucl Med Mol Imaging 50, 80–89 (2022). https://doi.org/10.1007/s00259-022-05949-9

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