Evidence map›Paper›PMID 32269510›Full record

ArticleFrontiers in neuroscience2020

Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate.

Lalith Kumar Shiyam Sundar, Shahira Baajour, Thomas Beyer, Rupert Lanzenberger, Tatjana Traub-Weidinger, Ivo Rausch, Ekaterina Pataraia, Andreas Hahn, Lucas Rischka, Marius Hienert and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 14 citations in OpenAlex.

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  4. Combining CRISPR-Cas9 and brain imaging to study the link from genes to molecules to networks.Proceedings of the National Academy of Sciences of the United States of America · 2022
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  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 2 countries.

Lalith Kumar Shiyam SundarQIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
Shahira BaajourDepartment of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States.
Thomas BeyerQIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
Rupert LanzenbergerDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
Tatjana Traub-WeidingerDivision of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Ivo RauschQIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
Ekaterina PataraiaDepartment of Neurology, Medical University of Vienna, Vienna, Austria.
Andreas HahnDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
Lucas RischkaDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
Marius HienertDepartment of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
Eva-Maria KlebermassDivision of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Otto MuzikDepartment of Pediatrics, Wayne State University School of Medicine, Detroit, MI, United States.
Medical University of Vienna · ATWayne State University · US

Funding

Austrian Science Fund FWF KLI 482
6 · The paper itself

Abstract

In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity.

methodsWe studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully integrated PET/MR system (Siemens Biograph mMR). To validate the non-invasive derivation of an image-derived input function based on combined analysis of PET and MR data, arterial blood samples were obtained. Using the arterial input function (AIF), parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed functional connectivity (dFC) and undirected functional connectivity (uFC) were determined between nodes in six major networks (Default mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R coefficient) or a Multi-Variate AutoRegressive (MVAR) model. In addition, the performance of a regional connectivity measure, the fractional amplitude of low frequency fluctuations (fALFF), was also investigated.

resultsThe average intrasubject variability for CMRGlc values between test and retest was determined as (14 ±8%) with an average inter-subject variability of 25% at test and 15% at retest. The average CMRGlc value (umol/100 g/min) across all networks was 39 ±10 at test and increased slightly to 43 ±6 at retest. The R, MVAR and fALFF coefficients showed relatively large test-retest variability in comparison to the inter-subjects variability, resulting in poor reliability (intraclass correlation in the range of 0.11-0.65). More importantly, no significant relationship was found between the R coefficients (for uFC), MVAR coefficients (for dFC) or fALFF and corresponding CMRGlc values for any of the six major networks. DISCUSSION: Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measured derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.

Indexed as

Cerebral metabolic rate of glucoseglucose metabolic rate variabilityintegrated PET/MRIreal-time fMRIresting-state fMRIstandardization of psychological state

Identifiers

PMID32269510
PMCPMC7111429
OpenAlexW3014056835

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.