Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach.
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...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 50; no. 1; pp. 80 - 90 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160255979&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160255979 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Dec2022 vid: 50 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160255979 158717064 10.1007/s00259-022-05949-9 160255979 ppf: 80 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach. aug: au: Wang, Min Schutte, Michael Grimmer, Timo Lizarraga, Aldana Schultz, Thomas Hedderich, Dennis M. Diehl-Schmid, Janine Rominger, Axel Ziegler, Sybille Navab, Nassir Yan, Zhuangzhi Jiang, Jiehui Yakushev, Igor Shi, Kuangyu affil: Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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