Evidence map›Paper›PMID 38719607›Full record

ArticleAJNR. American journal of neuroradiology2024

Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma.

Nicholas S Cho, Chencai Wang, Kathleen Van Dyk, Francesco Sanvito, Sonoko Oshima, Jingwen Yao, Albert Lai, Noriko Salamon, Timothy F Cloughesy, Phioanh L Nghiemphu and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in AJNR. American journal of neuroradiology, 2024. 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
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Nicholas S ChoFrom the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California.ORCID 0000-0002-8686-0575
Chencai WangFrom the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California.
Kathleen Van DykDepartment of Psychiatry and Biobehavioral Sciences (K.V.D, B.M.E.), David Geffen School of Medicine, Semel Institute, University of California Los Angeles, Los Angeles, California.
Francesco SanvitoFrom the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California.ORCID 0000-0003-3379-9958
Sonoko OshimaFrom the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California.
Jingwen YaoFrom the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California.ORCID 0000-0002-8828-1841
Albert LaiUCLA Neuro-Oncology Program (A.L., T.F.C., P.L.N.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.ORCID 0000-0002-7265-2986
Noriko SalamonDepartment of Radiological Sciences (N.S.C., C.W., F.S., S.O., J.Y., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.ORCID 0000-0002-3520-9467
Timothy F CloughesyUCLA Neuro-Oncology Program (A.L., T.F.C., P.L.N.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.ORCID 0000-0002-8656-7483
Phioanh L NghiemphuUCLA Neuro-Oncology Program (A.L., T.F.C., P.L.N.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.ORCID 0000-0003-4064-6079
Benjamin M EllingsonFrom the UCLA Brain Tumor Imaging Laboratory (BTIL) (N.S.C., C.W., F.S., S.O., J.Y., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California.ORCID 0000-0002-2764-6640

Funding

UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
UCLA SPORE in Brain CancerP50CA211015 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GANG LI · 2017 to 2026
$25.2M
Role of decorin and diffusion MRI in anti-VEGF efficacy for recurrent glioblastomaR01CA270027 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TIMOTHY CLOUGHESY, Benjamin M. Ellingson · 2022 to 2026
$3.0M
Quantitative molecular MR-PET imaging of glycolysis in glioblastomaR01CA279984 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Benjamin M. Ellingson, David A. Nathanson · 2023 to 2026
$2.4M
Brain Health in Breast Cancer Survivors: Interaction of Menopause and Endocrine TherapyK08CA241337 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI VAN DYK, KATHLEEN · 2019 to 2023
$925k
Molecular MR-PET to characterize malignant transformation and identify therapeutic vulnerabilities in human IDH-mutant gliomasF30CA284809 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHO, NICHOLAS S. · 2023 to 2025
$136k
NCI NIH HHS F30 CA284809NCI NIH HHS K08 CA241337NCI NIH HHS P50 CA211015NCI NIH HHS R01 CA270027NCI NIH HHS R01 CA279984NIGMS NIH HHS T32 GM008042
6 · The paper itself

Abstract

background and purposeResting-state functional MRI (rs-fMRI) can be used to estimate functional connectivity (FC) between different brain regions, which may be of value for identifying cognitive impairment in patients with brain tumors. Unfortunately, neither rs-fMRI nor neurocognitive assessments are routinely assessed clinically, mostly due to limitations in examination time and cost. Since DSC perfusion MRI is often used clinically to assess tumor vascularity and similarly uses a gradient-echo-EPI sequence for T2*-sensitivity, we theorized a "pseudo-rs-fMRI" signal could be derived from DSC perfusion to simultaneously quantify FC and perfusion metrics, and these metrics can be used to estimate cognitive impairment in patients with brain tumors. MATERIALS AND

methodsTwenty-four consecutive patients with gliomas were enrolled in a prospective study that included DSC perfusion MRI, resting-sate functional MRI (rs-fMRI), and neurocognitive assessment. Voxelwise modeling of contrast bolus dynamics during DSC acquisition was performed and then subtracted from the original signal to generate a residual "pseudo-rs-fMRI" signal. Following the preprocessing of pseudo-rs-fMRI, full rs-fMRI, and a truncated version of the full rs-fMRI (first 100 timepoints) data, the default mode, motor, and language network maps were generated with atlas-based ROIs, Dice scores were calculated for the resting-state network maps from pseudo-rs-fMRI and truncated rs-fMRI using the full rs-fMRI maps as reference. Seed-to-voxel and ROI-to-ROI analyses were performed to assess FC differences between cognitively impaired and nonimpaired patients.

resultsDice scores for the group-level and patient-level (mean±SD) default mode, motor, and language network maps using pseudo-rs-fMRI were 0.905/0.689 ± 0.118 (group/patient), 0.973/0.730 ± 0.124, and 0.935/0.665 ± 0.142, respectively. There was no significant difference in Dice scores between pseudo-rs-fMRI and the truncated rs-fMRI default mode (

conclusionsDSC perfusion MRI-derived pseudo-rs-fMRI data can be used to perform typical rs-fMRI FC analyses that may identify cognitive decline in patients with brain tumors while still simultaneously performing perfusion analyses.

Indexed as

Brain NeoplasmsCognitive DysfunctionGliomaMagnetic Resonance ImagingAdultAgedContrast MediaFemaleHumansMagnetic Resonance AngiographyMaleMiddle AgedProspective StudiesContrast Media

Identifiers

PMID38719607
PMCPMC11448991

What OpenQuestion holds

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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.