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<meta name="DC.Title" content="Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8327" />
<meta name="DC.Date" content="2024-10-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2024 by American Journal of Neuroradiology" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Resting-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 METHODS: Twenty-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.

RESULTS: Dice 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 ( P = .97) or language networks ( P = .30), but there was a difference in motor networks ( P = .02). A multiple logistic regression classifier applied to ROI-to-ROI FC networks using pseudo-rs-fMRI could identify cognitively impaired patients (sensitivity = 84.6%, specificity = 63.6%, receiver operating characteristic area under the curve (AUC) = 0.7762 ± 0.0954 (standard error), P = .0221) and performance was not significantly different from full rs-fMRI predictions (AUC = 0.8881 ± 0.0733 (standard error), P = .0013, P = .29 compared with pseudo-rs-fMRI).

CONCLUSIONS: DSC 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.

ASL
:   arterial spin-labeling
AUC
:   area under curve
BOLD
:   blood oxygenation level–dependent
FC
:   functional connectivity
FDR
:   false discovery rate
FWE
:   family-wise error
MNI
:   Montreal Neurological Institute
ROC
:   receiver operating characteristic
rs-fMRI
:   resting-state functional MRI" />
<meta name="DC.Contributor" content="Nicholas S. Cho" />
<meta name="DC.Contributor" content="Chencai Wang" />
<meta name="DC.Contributor" content="Kathleen Van Dyk" />
<meta name="DC.Contributor" content="Francesco Sanvito" />
<meta name="DC.Contributor" content="Sonoko Oshima" />
<meta name="DC.Contributor" content="Jingwen Yao" />
<meta name="DC.Contributor" content="Albert Lai" />
<meta name="DC.Contributor" content="Noriko Salamon" />
<meta name="DC.Contributor" content="Timothy F. Cloughesy" />
<meta name="DC.Contributor" content="Phioanh L. Nghiemphu" />
<meta name="DC.Contributor" content="Benjamin M. Ellingson" />
<meta name="article:published_time" content="2024-10-01" />
<meta name="article:section" content="Brain Tumor Imaging" />
<meta name="citation_title" content="Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Resting-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.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Twenty-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.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Dice 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 (&lt;i&gt;P&lt;/i&gt; = .97) or language networks (&lt;i&gt;P&lt;/i&gt; = .30), but there was a difference in motor networks (&lt;i&gt;P&lt;/i&gt; = .02). A multiple logistic regression classifier applied to ROI-to-ROI FC networks using pseudo-rs-fMRI could identify cognitively impaired patients (sensitivity = 84.6%, specificity = 63.6%, receiver operating characteristic area under the curve (AUC) = 0.7762 ± 0.0954 (standard error), &lt;i&gt;P&lt;/i&gt; = .0221) and performance was not significantly different from full rs-fMRI predictions (AUC = 0.8881 ± 0.0733 (standard error), &lt;i&gt;P&lt;/i&gt; = .0013, &lt;i&gt;P&lt;/i&gt; = .29 compared with pseudo-rs-fMRI).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;DSC 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.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
<meta name="citation_publisher" content="American Journal of Neuroradiology" />
<meta name="citation_publication_date" content="2024/10/01" />
<meta name="citation_mjid" content="ajnr;45/10/1552" />
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<meta name="citation_volume" content="45" />
<meta name="citation_issue" content="10" />
<meta name="citation_article_type" content="Research Article" />
<meta name="citation_section" content="Brain Tumor Imaging" />
<meta name="citation_firstpage" content="1552" />
<meta name="citation_lastpage" content="1561" />
<meta name="citation_author" content="Nicholas S. Cho" />
<meta name="citation_author_institution" content="aFrom 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" />
<meta name="citation_author_institution" content="bDepartment 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" />
<meta name="citation_author_institution" content="cDepartment of Bioengineering (N.S.C., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California" />
<meta name="citation_author_institution" content="dMedical Scientist Training Program (N.S.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California" />
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<meta name="citation_author" content="Sonoko Oshima" />
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<meta name="twitter:title" content="Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Resting-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 METHODS: Twenty-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.

RESULTS: Dice 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 ( P = .97) or language networks ( P = .30), but there was a difference in motor networks ( P = .02). A multiple logistic regression classifier applied to ROI-to-ROI FC networks using pseudo-rs-fMRI could identify cognitively impaired patients (sensitivity = 84.6%, specificity = 63.6%, receiver operating characteristic area under the curve (AUC) = 0.7762 ± 0.0954 (standard error), P = .0221) and performance was not significantly different from full rs-fMRI predictions (AUC = 0.8881 ± 0.0733 (standard error), P = .0013, P = .29 compared with pseudo-rs-fMRI).

CONCLUSIONS: DSC 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.

ASL
:   arterial spin-labeling
AUC
:   area under curve
BOLD
:   blood oxygenation level–dependent
FC
:   functional connectivity
FDR
:   false discovery rate
FWE
:   family-wise error
MNI
:   Montreal Neurological Institute
ROC
:   receiver operating characteristic
rs-fMRI
:   resting-state functional MRI" />
<meta name="og-title" property="og:title" content="Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/45/10/1552" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Resting-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 METHODS: Twenty-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.

RESULTS: Dice 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 ( P = .97) or language networks ( P = .30), but there was a difference in motor networks ( P = .02). A multiple logistic regression classifier applied to ROI-to-ROI FC networks using pseudo-rs-fMRI could identify cognitively impaired patients (sensitivity = 84.6%, specificity = 63.6%, receiver operating characteristic area under the curve (AUC) = 0.7762 ± 0.0954 (standard error), P = .0221) and performance was not significantly different from full rs-fMRI predictions (AUC = 0.8881 ± 0.0733 (standard error), P = .0013, P = .29 compared with pseudo-rs-fMRI).

CONCLUSIONS: DSC 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.

ASL
:   arterial spin-labeling
AUC
:   area under curve
BOLD
:   blood oxygenation level–dependent
FC
:   functional connectivity
FDR
:   false discovery rate
FWE
:   family-wise error
MNI
:   Montreal Neurological Institute
ROC
:   receiver operating characteristic
rs-fMRI
:   resting-state functional MRI" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node110796--5" data-node-nid="110796" id="top-node-110796--62002838778" data-pisa="ajnr;45/10/1552" data-pisa-master="ajnr;ajnr.A8327" data-apath="/ajnr/45/10/1552.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-jcore-article-title-complete clearfix has-author-tooltip highwire-citation-highwire-article-top-a">
  
  
      <div class="highwire-cite-overline"><span  class="highwire-cite-metadata-article-type highwire-cite-metadata">Research Article</span><span class="separator-pipe"></span><span  class="highwire-cite-metadata-art-cat highwire-cite-metadata"><span class="wrapper">Brain Tumor Imaging</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Nicholas S. Cho</span>, <span class="highwire-citation-author" data-delta="1">Chencai Wang</span>, <span class="highwire-citation-author" data-delta="2">Kathleen Van Dyk</span>, <span class="highwire-citation-author" data-delta="3">Francesco Sanvito</span>, <span class="highwire-citation-author" data-delta="4">Sonoko Oshima</span>, <span class="highwire-citation-author" data-delta="5">Jingwen Yao</span>, <span class="highwire-citation-author" data-delta="6">Albert Lai</span>, <span class="highwire-citation-author" data-delta="7">Noriko Salamon</span>, <span class="highwire-citation-author" data-delta="8">Timothy F. Cloughesy</span>, <span class="highwire-citation-author" data-delta="9">Phioanh L. Nghiemphu</span> and <span class="highwire-citation-author" data-delta="10">Benjamin M. Ellingson</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">October 2024,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1552-1561; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8327 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-110796--62002838778" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Nicholas S. Cho </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From 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</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department 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</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Bioengineering (N.S.C., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Medical Scientist Training Program (N.S.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Nicholas%2BS.%2BCho%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Cho%20NS&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3ANicholas%2BS.%2BCho%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-8686-0575" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Nicholas S. Cho</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Chencai Wang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From 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</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department 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</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Chencai%2BWang%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Wang%20C&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AChencai%2BWang%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Kathleen Van Dyk </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>Department 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</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Kathleen%2BVan%2BDyk%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Van%20Dyk%20K&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AKathleen%2BVan%2BDyk%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Francesco Sanvito </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From 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</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department 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</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Francesco%2BSanvito%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Sanvito%20F&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a 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California, Los Angeles, Los Angeles, California</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Sonoko%2BOshima%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Oshima%20S&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3ASonoko%2BOshima%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">Jingwen Yao </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From 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</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department 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</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Jingwen%2BYao%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Yao%20J&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" 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class="author-tooltip-7"><div class="author-tooltip-name">Noriko Salamon </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department 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</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Noriko%2BSalamon%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Salamon%20N&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a 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Ellingson </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From 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</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department 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</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Bioengineering (N.S.C., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California</div><div class='author-affiliation'><span class='nlm-sup'>e</span>Department 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</div><div class='author-affiliation'><span class='nlm-sup'>h</span>Department of Neurosurgery (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Benjamin%2BM.%2BEllingson%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Ellingson%20BM&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3ABenjamin%2BM.%2BEllingson%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-2764-6640" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Benjamin M. 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 45</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 10 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Oct 2024 </div></div>
      
      
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      <div class="highwire-cite-title">Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma</div>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">Nicholas S.</span> <span class="nlm-surname">Cho</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Chencai</span> <span class="nlm-surname">Wang</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Kathleen</span> <span class="nlm-surname">Van Dyk</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Francesco</span> <span class="nlm-surname">Sanvito</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Sonoko</span> <span class="nlm-surname">Oshima</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Jingwen</span> <span class="nlm-surname">Yao</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Albert</span> <span class="nlm-surname">Lai</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Noriko</span> <span class="nlm-surname">Salamon</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Timothy F.</span> <span class="nlm-surname">Cloughesy</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Phioanh L.</span> <span class="nlm-surname">Nghiemphu</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">Benjamin M.</span> <span class="nlm-surname">Ellingson</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Oct 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1552-1561; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8327 </span></div>
  
  
  
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">Nicholas S.</span> <span class="nlm-surname">Cho</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Chencai</span> <span class="nlm-surname">Wang</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Kathleen</span> <span class="nlm-surname">Van Dyk</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Francesco</span> <span class="nlm-surname">Sanvito</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Sonoko</span> <span class="nlm-surname">Oshima</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Jingwen</span> <span class="nlm-surname">Yao</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Albert</span> <span class="nlm-surname">Lai</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Noriko</span> <span class="nlm-surname">Salamon</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Timothy F.</span> <span class="nlm-surname">Cloughesy</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Phioanh L.</span> <span class="nlm-surname">Nghiemphu</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">Benjamin M.</span> <span class="nlm-surname">Ellingson</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Oct 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1552-1561; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8327 </span></div>
  
  
  
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