Evidence map›Paper›PMID 36018359›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2022

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

Min Wang, Michael Schutte, Timo Grimmer, Aldana Lizarraga, Thomas Schultz, Dennis M Hedderich, Janine Diehl-Schmid, Axel Rominger, Sybille Ziegler, Nassir Navab and 4 more

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Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 58% of its field
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed, 2 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

14 authors at 6 institutions in 3 countries.

Min WangInstitute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China.
Michael SchutteThe Bonn-Aachen International Center for Information Technology (b-it) and Institute of Computer Science II, University of Bonn, Bonn, Germany.
Timo GrimmerDepartment of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Aldana LizarragaDepartment of Nuclear Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
Thomas SchultzDepartment for Visual Computing, University of Bonn, Bonn, Germany.
Dennis M HedderichDepartment of Nuclear Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
Janine Diehl-SchmidDepartment of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Axel RomingerDepartment of Nuclear Medicine, University of Bern, Bern, Switzerland.
Sybille ZieglerDepartment of Nuclear Medicine, Ludwig Maximilian University of Munich, Munich, Germany.
Nassir NavabComputer-Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany.
Zhuangzhi YanInstitute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China.
Jiehui JiangInstitute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China. jiangjiehui@shu.edu.cn.ORCID http://orcid.org/0000-0003-4948-3683
Igor YakushevDepartment of Nuclear Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany. igor.yakushev@tum.de.
Kuangyu ShiComputer-Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany.
TUM Klinikum · DEShanghai University · CNUniversity of Bern · CHUniversity of Bonn · DELudwig-Maximilians-Universität München · DETechnical University of Munich · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSparse inverse covariance estimation (SICE) is increasingly utilized to estimate inter-subject covariance of FDG uptake (FDG

methodsWe 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.

resultsCompared 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).

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

Indexed as

Alzheimer DiseaseFrontotemporal DementiaFrontotemporal Lobar DegenerationBrainBrain MappingFluorodeoxyglucose F18HumansMagnetic Resonance ImagingPositron-Emission TomographyRetrospective StudiesFluorodeoxyglucose F18Metabolic connectivityPositron emission tomographySparse inverse covariance estimationStructure-weighted regularization

Identifiers

PMID36018359
OpenAlexW4293230405

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.