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Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging 1/2022

26.08.2022 | Original Article

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

verfasst von: Min Wang, Michael Schutte, Timo Grimmer, Aldana Lizarraga, Thomas Schultz, Dennis M. Hedderich, Janine Diehl-Schmid, Axel Rominger, Sybille Ziegler, Nassir Navab, Zhuangzhi Yan, Jiehui Jiang, Igor Yakushev, Kuangyu Shi

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 1/2022

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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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Literatur
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Metadaten
Titel
Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach
verfasst von
Min Wang
Michael Schutte
Timo Grimmer
Aldana Lizarraga
Thomas Schultz
Dennis M. Hedderich
Janine Diehl-Schmid
Axel Rominger
Sybille Ziegler
Nassir Navab
Zhuangzhi Yan
Jiehui Jiang
Igor Yakushev
Kuangyu Shi
Publikationsdatum
26.08.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 1/2022
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-022-05949-9

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