Blood-brain barrier leakage in the penumbra is associated with infarction on follow-up imaging in acute ischemic stroke

Blood-brain barrier leakage in the penumbra is associated with infarction on follow-up imaging in acute ischemic stroke | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Blood-brain barrier leakage in the penumbra is associated with infarction on follow-up imaging in acute ischemic stroke View ORCID Profile Felix L Nägele , Lauranne Scheldeman , Anke Wouters , Marlene Heinze , Marvin Petersen , Eckhard Schlemm , Maximilian Schell , Martin Ebinger , Matthias Endres , View ORCID Profile Jochen B Fiebach , View ORCID Profile Jens Fiehler , Ivana Galinovic , Robin Lemmens , View ORCID Profile Keith W Muir , Norbert Nighoghossian , Salvador Pedraza , Josep Puig , Claus Z Simonsen , Vincent Thijs , Götz Thomalla , Bastian Cheng doi: https://doi.org/10.1101/2024.11.19.24317534 Felix L Nägele 1 University Medical Center Hamburg-Eppendorf, Department of Neurology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Felix L Nägele For correspondence: f.naegele{at}uke.de Lauranne Scheldeman 2 University Hospitals Leuven, Department of Neurology , Leuven, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anke Wouters 2 University Hospitals Leuven, Department of Neurology , Leuven, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marlene Heinze 1 University Medical Center Hamburg-Eppendorf, Department of Neurology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marvin Petersen 1 University Medical Center Hamburg-Eppendorf, Department of Neurology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eckhard Schlemm 1 University Medical Center Hamburg-Eppendorf, Department of Neurology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maximilian Schell 1 University Medical Center Hamburg-Eppendorf, Department of Neurology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Ebinger 3 Medical Park Berlin Humboldtmühle, Department of Neurology , Berlin, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthias Endres 4 Charité – Universitätsmedizin Berlin, Department of Neurology , Berlin, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jochen B Fiebach 5 Charité – Universitätsmedizin Berlin, Center for Stroke Research Berlin , Berlin, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jochen B Fiebach Jens Fiehler 6 University Medical Center Hamburg-Eppendorf, Department of Diagnostic and Interventional Neuroradiology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jens Fiehler Ivana Galinovic 5 Charité – Universitätsmedizin Berlin, Center for Stroke Research Berlin , Berlin, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robin Lemmens 2 University Hospitals Leuven, Department of Neurology , Leuven, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Keith W Muir 7 University of Glasgow, Cardiovascular & Metabolic Health , Glasgow, Scotland, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Keith W Muir Norbert Nighoghossian 8 Université Claude Bernard Lyon 1, Department of Stroke Medicine , Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Salvador Pedraza 9 Hospital Clínic Barcelona, Department of Radiology (CDI) and IDIBAPS , Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Josep Puig 9 Hospital Clínic Barcelona, Department of Radiology (CDI) and IDIBAPS , Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Claus Z Simonsen 10 Aarhus University Hospital, Department of Neurology , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vincent Thijs 11 Florey Institute of Neuroscience and Mental Health , Melbourne, Victoria, Australia 12 Florey Department of Neuroscience and Mental Health, University of Melbourne , Melbourne, Victoria, Australia 13 Austin Health, Heidelberg , Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Götz Thomalla 1 University Medical Center Hamburg-Eppendorf, Department of Neurology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bastian Cheng 1 University Medical Center Hamburg-Eppendorf, Department of Neurology , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Blood-brain barrier (BBB) leakage measured with dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) has been associated with hemorrhagic transformation in acute ischemic stroke (AIS). However, the influence of pre-thrombolysis BBB leakage on infarct growth has not been studied. Therefore, we aimed to characterize BBB integrity according to tissue state at admission and tissue fate on follow-up MRI. Methods This is a post-hoc analysis of the Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke (WAKE-UP) trial including 165 randomized patients (mean age 66 years, 38% women, median National Institute of Health Stroke Scale score 6, 53% receiving alteplase) with avail-able baseline DSC-MRI. Ischemic cores were segmented on diffusion weighted imaging at baseline and on fluid-attenuated inversion recovery images at follow-up (22-36h). DSC-MRI provided penumbra masks (T max >6s minus ischemic core) and BBB leakage (extraction frac-tion, EF, z-scored) maps via automated analysis. EF was averaged within the ischemic core, total penumbra, 2 penumbra subtypes (salvaged/infarcted penumbra) and normal tissue. Ad-justed linear mixed effects models tested for differences between tissue types and associations of EF with clinical/imaging outcomes. Complementary voxel-wise analyses were performed. Results Linear models showed significant differences in EF between tissue types ( P <.001). Specifi-cally, EF was increased in the ischemic core and penumbra relative to normally-perfused tis-sue, while differences between total penumbra and ischemic core were statistically non-signif-icant. Penumbral tissue that later infarcted exhibited higher EF than salvaged penumbra, even after adjusting for hypoperfusion severity ( P <.001). Voxel-wise analyses showed a significant association between EF and voxel-level infarction in the placebo group only ( P <.001). Lesional and penumbral EF did not predict hemorrhagic transformation or functional outcomes. Conclusions Our results suggest that penumbral BBB leakage may identify tissue at increased risk of in-farction. Larger, prospective studies are needed to determine the clinical relevance of BBB leakage as an imaging marker of tissue fate. WAKE-UP http://ClinicalTrials.gov number: NCT01525290 ; EudraCT number: 2011-005906-32. INTRODUCTION Advanced brain imaging has been crucial in identifying acute ischemic stroke (AIS) patients who are likely to benefit from intravenous thrombolysis and mechanical throm-bectomy. 1 For example, the diffusion-weighted imaging–fluid-attenuated inversion re-covery (DWI-FLAIR) mismatch, serves as a ‘tissue clock’, thus facilitating the admin-istration of intravenous thrombolysis to patients with an unknown onset of symptoms. 2 Moreover, perfusion imaging with computed tomography (CT) and magnetic reso-nance imaging (MRI) leverages the pathophysiological concept of the ischemic core and penumbra 3 and has been used successfully in the selection of AIS patients for recanalization therapies in the late time window. 4 While recent ‘large core trials’ have challenged the need for conventional perfusion imaging in selecting patients for reperfusion therapies 5 , novel imaging techniques, such as blood-brain barrier (BBB) imaging 6 have gained attention, as they may be useful in the study of novel treatment approaches. BBB disruption builds up within minutes to hours after stroke onset 7 – 9 driven by metabolic failure, neuroinflammation, oxidative stress and increased levels of matrix metalloproteinases. 10 Multiple studies have shown that, following the hyper acute stage (< 6 hours), BBB permeability further aggravates 9 , 11 , subsequently increasing the risk of hemorrhagic transformation in AIS patients. 12 However, the relationship between pre-treatment BBB disruption and sub-sequent infarction or microvascular reperfusion of penumbral tissue remains unex-plored. As a result, the potential application of BBB imaging to identify patients for novel treatment strategies, e.g. targeted at the neurovascular unit, has not been real-ized. The present study aims to bridge this gap by characterizing BBB integrity in relation to pre-treatment tissue state and tissue fate on follow-up MRI after 22-36 hours. To achieve this, we analyzed data of patients enrolled in the WAKE-UP trial (Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke) with available dynamic sus-ceptibility contrast-enhanced (DSC)-MRI. METHODS Reporting follows the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guideline. 13 Study population This post-hoc analysis includes randomized patients from the WAKE-UP trial 2 with available baseline DSC-MRI data. The WAKE-UP trial was a multicenter, randomized, double-blind, placebo-controlled clinical trial to study the efficacy and safety of MRI-guided intravenous thrombolysis with alteplase in patients with an acute stroke of un-known time of onset. The main imaging inclusion criterion was an acute ischemic lesion visible on DWI without corresponding parenchymal hyperintensity on FLAIR, indicating that the stroke most likely had occurred within 4.5 h. Ethics Patients or their legal representatives provided written informed consent according to national and local regulations. There was an exception from explicit informed consent in emergency circumstances in some countries. For each study site, the competent authorities and the corresponding ethics committee approved the trial. The trial was registered at http://ClinicalTrials.gov ( NCT01525290 ) and EudraCT (2011-005906-32). Clinical data For this analysis, we used demographic and stroke characteristics, medical history, National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) scores 90 days after stroke. Imaging data Image acquisition Brain MRI was performed on 1.5T, as well as 3T scanners according to local standards at admission and 22–36 h after randomization. Imaging sequences comprised DWI, FLAIR, susceptibility-weighted (SWI) and DSC-MRI. Image processing An overview of the image processing pipeline is shown in Figure 1 . The analysis code comprising post-processing outside of commercial software can be accessed on GitHub: https://github.com/csi-hamburg/CSIframe/blob/main/pipelines/nice/nice.sh . Download figure Open in new tab Figure 1. Methodological overview of the imaging pipeline (A) First, ischemic cores were segmented semi-automatically on baseline apparent diffusion constant (ADC ≤620 mm²/s) and follow-up fluid-attenuated inversion recovery (FLAIR) images using the SONIA software. Then, pharmacokinetic modelling was performed in two different software suites (B) . 1) Nor-dicIce was used to estimate extraction fraction (EF) as a marker of blood-brain barrier (BBB) leakage, based on delay insensitive singular value deconvolution and residue function based leakage correc-tion using an automatically defined arterial input function. 2) RAPID was used for current state of the art estimation of time to maximum of the tissue residue function (T max ) maps and perfusion deficit masks. (C) After coregistration, different tissue-types-of-interest were defined using the perfusion defi-cit, baseline and follow-up ischemic core masks. Panel (D) illustrates different analytical approaches. We performed both tissue-type-of interest analyses using averaged EF/T max values, and voxel-wise analyses. For simplicity only the salvaged vs. infarcted penumbra contrast is shown here. Ischemic core segmentation Baseline ischemic core lesions were segmented and quantified on apparent diffusion coefficient (ADC) maps calculated from DWI co-registered to FLAIR images using a semi-automated procedure based on an upper ADC threshold of 620 mm 2 /s (in-house Stroke Quantification Tool, SONIA, v1.0) 14 , 15 . The final infarct was defined as acute FLAIR hyperintensity on follow-up images ( Figure 1 Panel A ). Baseline perfusion im-ages were linearly co-registered to FLAIR images using FLIRT (FMRIB’s Linear Image Registration Tool) 16 . The resulting inverse transformation matrices were subsequently applied to the ischemic core lesion masks. Processing of DSC-MRI We performed automated perfusion analysis utilizing the RAPID software (v4.9 and 5.0; iSchemaView, Menlo Park, CA) 17 to derive time to maximum of the tissue residue function (T max ) maps ( Figure 1 Panel B ), along with 4 different perfusion deficit masks: T max >6s, 6-8s, 8-10s and ≥10s. In order to derive penumbra masks, baseline ischemic core lesion masks were subtracted from the T max >6s masks. The resulting penumbra masks were used to define infarcted penumbra and salvaged penumbra masks by multiplying or subtracting the co-registered follow-up ischemic core lesion masks, re-spectively ( Figure 1 Panel C ). Thus, the infarcted and salvaged penumbra regions indicate the follow-up tissue fate of the baseline penumbra. BBB leakage was estimated as extraction fraction (EF) using NordicICE (Version 4.2.0; Nordic-NeuroLab, Bergen, Norway) via delay insensitive singular value deconvolution and residue function-based leakage correction using an automatically defined arterial input function ( Figure 1 Panel B ). EF models the ratio of permeability to perfusion, i.e. the fraction of contrast agent which reaches the extravascular space during first pas-sage. 18 , 19 The signal intensity of EF maps were z-scored to mitigate scanner differ-ences. Average EF and T max values were extracted within different tissue-types-of-interests, i.e., the ischemic core, penumbra (total, infarcted, salvaged), perfusion deficit severi-ties (T max 6-8, 8-10 and ≥10s) and normal tissue. The latter was defined as tissue laying outside of both the ischemic core and perfusion deficit. For a complementary voxel-wise analysis, we extracted EF and T max values for each voxel within the penumbra after applying a Gaussian filter (σ=0.5) to reduce noise, along with the information whether the respective voxel is part of the infarcted-penumbra ( Figure 1 Panel D ). Assessment of hemorrhagic transformation Hemorrhagic transformation was assessed 22–36h after randomization on SWI or CT, if MRI was not feasible, categorized into established radiologic subtypes according to the Heidelberg Bleeding Classification, 20 , 21 and dichotomized for the present analysis in “any hemorrhagic transformation” or “no hemorrhagic transformation”. Image read-ings, that were blind to BBB imaging, were used from the original trial records. Statistical analysis All statistical tests and plotting were performed in python (v. 3.12.1), leveraging numpy (v. 1.26.3), pandas (v. 2.14), tableone (v. 0.8.0), scipy (v. 1.11.4), pymer4 (v. 0.8.1), matplotlib (v. 3.8.0), and seaborn (v. 0.12.2). Testing was performed two-sided with P <.05 considered significant. Missing data were not imputed. The code is publicly available on GitHub: https://github.com/felenae/2024_bbb_wakeup . Clinical and demographic data We report descriptive statistics of the current sample with means (standard deviations), medians (inter-quartile-ranges) and counts (percentages) where applicable. We pro-vide univariate comparisons between randomized patients receiving DSC-MRI and those who did not in the Supplementary Material . Tissue analysis In order to test for differences in mean EF between normal tissue, the ischemic core, and penumbra, we employed a linear mixed effects model (LME) adjusting for age, sex, baseline NIHSS and baseline ischemic core volume. Scanner and subject served as random effects. Following LMEs, Tukey post-hoc tests were performed for pairwise comparisons. Further, an analogous LME modeled differences in EF between perfusion deficit se-verities (T max 6-8s, T max 8-10s, T max ≥10s) with the same covariates and random effects, followed by Tukey post-hoc tests. This analysis was supplemented by a voxel-based LME testing the association between T max and EF values within the penumbra, con-trolling for the same covariates, as well as random effects of T max by scanner and pa-tient. Next, infarcted penumbra and salvaged penumbra served as tissue-types-of-interest to determine whether baseline EF differs according to tissue fate. As severe hypoperfu-sion has been shown to be associated with infarct progression 22 , we adjusted this model for mean T max values within these areas in addition to the previously selected covariates plus treatment status (placebo vs. alteplase). The same random effects were used. Furthermore, a complementary voxel-wise analysis was performed to de-termine the association of penumbral EF with tissue outcome. To this end, we fitted 3 generalized LMEs with the voxel-wise binary outcome future-infarction. Voxel-wise EF, T max , as well as T max along with EF, served as dependent variables for the first, second and third LME, respectively. Age, sex, treatment status, baseline NIHSS and baseline ischemic core-volume were included as covariates. Moreover, we included an interac-tion term of imaging variable(s) by treatment status to test whether EF and/or T max are differentially associated with voxel-wise infarction depending on whether patients re-ceived placebo or alteplase. In case of significant interactions, post-hoc models were run for each treatment group separately. To address potential variability introduced by different scanners, and the repeated measures within each patient, random intercepts and slopes for EF and/or T max by scanner and patient were included. Association of EF with clinical outcomes Our clinical outcomes of interest were dichotomized hemorrhagic transformation on follow-up MRI, excellent functional outcome (mRS 0-1) and NIHSS at 90 days. We fitted separate LMEs for EF within the baseline ischemic core and within the penumbra to predict clinical outcomes. Generalized LMEs were conducted for binary outcomes. Age, sex, baseline NIHSS, baseline ischemic core volume and treatment status served as covariates. Again, an interaction term was modelled for EF by treatment status and follow-up models were conducted for the placebo and alteplase group, separately, in case of significant interactions. To account for potential variability between different scanners, we included a random intercept for scanner and allowed the slope of EF to vary by scanner. Sensitivity analysis We fitted additional LMEs excluding subjects with missing EF data for the respective contrast of tissue analysis. For example, when investigating differences between dif-ferent T max thresholds, this was done only in subjects with complete data for all thresh-olds. Results are reported in the Supplementary Material . RESULTS Sample characteristics Detailed clinical characteristics can be found in Table 1 and Table S1 . Of 503 patients enrolled in the clinical trial, 195 individuals received DSC-MRI. After visual quality con-trol and image processing, 165 patients with a mean age of 65.5 years (37.6% women) had data suitable for the present analysis. Note that only 92 patients had penumbral tissue identified by RAPID; of those, 79 patients had follow-up MRI to delineate the final infarct – necessary for the calculation of infarcted and salvaged penumbra masks. The median NIHSS was 6, 21.2% of patients had a large vessel occlusion, 53.3% pa-tients received alteplase and 21.2% exhibited any hemorrhagic transformation at fol-low-up, while symptomatic intracranial hemorrhages were rare (0.6-8.5% depending on the defining criteria). Demographics, cardiovascular risk factors/comorbidities and stroke characteristics were similar between patients receiving DSC-MRI and those who did not ( P >.05). View this table: View inline View popup Table 1. Sample characteristics Tissue analysis The LME investigating differences in EF between the non-infarcted, normally perfused tissue, acute ischemic core and penumbra showed a significant main effect of tissue-type-of-interest (EF, mean [SD]: normal = -0.15 [0.09], penumbra = 0.20 [0.38], is-chemic core = 0.09 [0.41], P <.001; Table 2 Part A and Figure 2 Panel A ). Post-hoc Tukey tests revealed that the lesion and penumbra exhibited higher EF compared to normal tissue ( P s<.001), while there were no significant differences between the pe-numbra and acute ischemic core ( P =.053). Download figure Open in new tab Figure 2. Box plots and strip plots showing the results of the linear mixed effects models and post-hoc Tukey tests Here, differences in EF between different tissue-types of interest are illustrated with box plots and strip plots. Bars above boxplots indicate the level of significance of pair-wise post-hoc Tukey tests following adjusted linear mixed effects models. Next to each panel, schematics of the corresponding tissue-type-of-interest from Figure 1 are displayed. Panel A refers to the comparison of normal tissue, pe-numbra and ischemic core. Panel B shows differences in EF between varying T max thresholds. Lastly, Panel C demonstrates differences in EF between the infarcted penumbra and salvaged penumbra. Abbreviations : DWI = diffusion weighted imaging, EF = extraction fraction, T max = time to maximum of the issue residue function ns non-significant; * P <.05; ** P <.01, *** P <.001 View this table: View inline View popup Download powerpoint Table 2. Results of the linear mixed effects models investigating differences in EF between tissue-types-of-interest Testing for differences in BBB leakage within the area of perfusion deficit according to the severity of hypoperfusion, we found significantly increasing EF along with longer durations of T max (6-8s = 0.11 [0.32], 8-10s = 0.26 [0.46], ≥10s = 0.52 [0.86], P s<.05; Table 2 Part B and Figure 2 Panel B ). The voxel-wise analysis confirmed a positive linear relationship between EF and T max (standardized estimate [95% CI]: 0.23 [0.16; 0.30], P <.001; Table S2 Part A ). Further, penumbral tissue with infarction on follow-up imaging showed increased mean EF compared to penumbral tissue without future infarction (infarcted penumbra = 0.28 [0.43], salvaged penumbra = 0.17 [0.30], P <.001; Table 2 Part C and Figure 2 Panel C ). The voxel-wise analysis revealed that 20.7% percent of all penumbral voxels showed infarction on follow-up imaging. The generalized LMEs including 74 patients, along with 246,338 voxels/observations, showed a statistically significant positive association of voxel-wise EF and T max with future infarction in separate models (adjusted odds ratio [95%-confidence interval, CI], EF = 1.28 [1.15; 1.42]; T max = 1.87 [1.56; 2.25], P <.001; Table S2 Parts B and C; receiver operating characteristic curves are shown in Figure S1 ). This indicates that for every one standard deviation increase in EF/T max , the odds of infarction increase by 28/87%. In the combined model including both EF and T max , increased EF was still associated with higher odds of voxel infarction (EF = 1.19 [1.07, 1.34], P <.01; T max = 1.76 [1.45; 2.14], P <.001; Table S2 Part D ). Importantly, we ob-served significant interactions between EF and treatment status ( P <.01), signifying that the association of EF and voxel infarction differed between groups. Follow-up models revealed that this relationship was weaker – and statistically not significant – in patients receiving alteplase (N=39) compared to those receiving placebo (N=35) (EF [alteplase] = 1.05 [0.95; 1.15], P =.35; EF [placebo] = 1.35 [1.16; 1.56], P <.001, Table S3 Part A ). The same pattern was observed in the combined EF plus T max models stratified by group ( Table S3 Part B ). There were no significant interactions of T max by treatment status. None of the covariates demonstrated a significant effect on mean or voxel-wise EF as outcome measures ( P >.05). Finally, male sex and higher baseline ischemic core vol-umes were significant predictors of voxel-wise infarction in all models ( P <.05), while treatment, age and NIHSS were not. Sensitivity analysis The sensitivity analysis supported the main findings ( Table S4 ). Of note, non-signifi-cant, nominal differences in EF between the lesion and penumbra reported for the main analysis diminished when including only subjects with complete data, i.e. with penum-bral tissue for this model (EF, mean [SD]: normal = -0.17 [0.09], penumbra = 0.20 [0.38], ischemic core = 0.19 [0.47], P =0.98). Association of EF with clinical outcomes Neither lesional nor penumbral EF were significant predictors of hemorrhagic transfor-mation or functional outcomes in (generalized) LMEs ( Table S5 and S6 ). In terms of covariates, higher baseline NIHSS and ischemic core volume, as well as treatment with alteplase were associated with hemorrhagic transformation ( P <.05). Moreover, both lower baseline NIHSS and ischemic core volume was associated with excellent functional outcome. Finally, female sex, lower baseline NIHSS and smaller baseline ischemic core volumes predicted lower 90-day NIHSS ( P <.05). There were no signifi-cant interactions between treatment status and EF measures. DISCUSSION Utilizing clinical perfusion MRI along with advanced pharmacokinetic modelling, we investigated BBB permeability changes across different pathological tissue states in patients with acute ischemic stroke. Confirming previous studies, we observed BBB disruption in the ischemic core, as defined by tissue with low ADC. In addition, we provide novel evidence of increased EF within penumbral tissue that correlates with hypoperfusion severity and subsequent tissue fate. Specifically, the data suggest that the degree of BBB disruption progresses with increasing severity of hypoperfusion and, most importantly, that elevated EF independently indicates penumbral tissue at greater risk of infarction. No statistically significant associations with clinical outcomes were found. Taken together, these findings imply that BBB imaging offers pathophysiologi-cally meaningful information in addition to hypoperfusion severity. More research is needed to probe the clinical relevance and potential utility of BBB imaging to guide innovative treatment strategies aimed at enhancing BBB integrity in AIS patients. Increased extraction fraction indicates tissue state and fate We demonstrate that the BBB is disrupted in the acute phase of AIS, as indicated by increased EF in both lesional and penumbral tissue compared to normal tissue. Multi-ple studies using DSC, dynamic contrast-enhanced (DCE)-MRI, and CT perfusion have documented contrast extravasation in the ischemic core at various time points. 9 Contrarily, only a few human studies have focused specifically on penumbral (i.e., po-tentially salvageable) tissue, yielding conflicting results. For instance, Dankbaar et al. 23 conducted a CT perfusion study that found significantly higher BBB permeability in the ischemic core and penumbra compared to non-ischemic tissue. In contrast, a study by Horsch and colleagues 24 reported that early non-contrast CT signs were associated with BBB leakage in the ischemic core, but not in the penumbra. Our analyses contrib-ute to our understanding of BBB disruptions in hypoperfused tissue by showing a plau-sible linear relationship between higher EF and increasing T max in both regional and voxel-wise analyses. Notably, this is the first human study to demonstrate that acute BBB disruption is related to future infarction of penumbral tissue, with results consistent across statistical approaches (tissue-type-of-interest/voxel-based), even when control-ling for effects of hypoperfusion, measured as T max . However, the significant interac-tions between treatment status and EF in our voxel-wise models warrant consideration. Post-hoc analyses revealed that the association of EF and infarction was weaker and not statistically significant in patients treated with alteplase, suggestive of the mediating role of alteplase in mechanisms leading from vessel occlusion to infarction 25 , 26 . Placing our results in the context of the current understanding of the ischemic cas-cade 10 , it is conceivable that the observed BBB disruption is a function of the degree of hypoperfusion caused by initial vessel occlusion. BBB leakage in turn may facilitate increased immune cell infiltration which starts around 4 to 6 hours after vessel occlu-sion, causing further damage to the neurovascular unit through mechanisms such as reactive oxygen species production, release of matrix metalloproteinases, and pro-in-flammatory cytokines. 27 Consequently, vasogenic edema 28 develops, potentially hin-dering microcirculation in addition to other factors such as swelling of endothelial cells, vasoconstriction or pericyte contraction. 29 , 30 No-reflow may further exacerbate energy and oxygen depletion, creating a vicious cycle. Intravenous thrombolysis with the re-combinant tissue plasminogen activator alteplase may play a dual role in these pro-cesses. While there is ample evidence from imaging and experimental studies sug-gesting that alteplase is associated with increased BBB leakage 12 , 31 – 33 , its efficacy in restoring blood flow to penumbral tissue might benefit BBB integrity by averting ische-mia of salvageable tissue. This direct influence on the vicious cycle of ischemia may partly explain the lesser association between pre-treatment EF and future infarction in patients treated with alteplase compared to placebo. While our results provide an intriguing perspective, they need replication in future pro-spective AIS trials investigating the link between hypoperfusion, BBB leakage and tis-sue fate, including infarction and the no-reflow phenomenon. Implications for novel treatment approaches Our results support ongoing research into neuroprotective pharmaceuticals aimed at limiting BBB damage. 34 Of note, a recent preclinical monkey study by Debatisse et al. 35 demonstrated the potential use of advanced BBB imaging in monitoring treatment ef-fects, showing reductions in the volume transfer constant K trans after administration of ciclosporin A in a stroke reperfusion model. A complicating factor in developing neuro-protective treatments is that different components of the ischemic cascade may be involved in both damage and repair of the neurovascular unit, depending on the timing relative to ischemia onset. 10 This suggests that it is not only critical to think about the right target, but also about the right timing. Here, the observed BBB changes likely occurred within 4.5 hours after symptom onset 2 , suggesting that interventions targeting BBB preservation may need to be applied within a limited time window to prevent det-rimental downstream effects of BBB leakage such as infarction of penumbral tissue. Blood-brain barrier breakdown, hemorrhagic transformation, and functional out-comes Hemorrhagic transformation can occur in up to 30-40% of AIS patients 36 , while symp-tomatic intracranial hemorrhage is reported in about 5% of patients treated with rtPA 37 . Unlike previous studies 12 , 38 , we did not observe a significant association between in-creased BBB leakage and hemorrhagic transformation. A likely explanation lies in dif-fering methodologies of BBB leakage estimation including the region-of-interest in which the measurement was conducted. Here, we calculated mean EF within each tissue-type-of-interest in order to obtain an objective measure in an automated manner without selecting a region based on qualitative interpretation. On the other hand, this could have diluted observable effects considering that only few voxels may show ex-treme BBB leakage associated with subsequent hemorrhage. Moreover, although we tried to mitigate heterogeneity in EF values across scanners by z-scoring image inten-sities and conducting mixed-effects models, the multi-center/multi-scanner design might have affected our ability to uncover between subject effects. Besides the known risk of hemorrhagic transformation conveyed by increased BBB leakage, associations with clinical, i.e. functional, outcomes are less well established. Some studies have shown benefit in incorporating CT or MRI derived BBB leakage estimates in the prediction of NIHSS or mRS 39 , 40 which is conceptually in line with our finding that higher EF was found in penumbral tissue at greater risk of infarction. Nev-ertheless, we did not find significant associations between average pre-treatment le-sional or penumbral BBB leakage and clinical outcomes, consistent with other re-ports. 24 , 41 , 42 In our study, the average ischemic core volumes of patients were small. Taken together with the high rate of excellent outcomes (49%; median NIHSS 1 after 90 days) this could have obscured any relationship between BBB leakage and func-tional outcomes (and/or hemorrhagic transformation) potentially observable in more severely affected patients. Again, varying methodologies in terms of BBB measure-ments (timing in relation to stroke-onset, imaging modality, pharmacokinetic model, region-of-interest, leakage estimation method) may in part explain discrepancies with other studies. More research is needed to find an optimal approach to elucidate asso-ciations between disturbed BBB integrity and functional outcomes in AIS patients. Study strengths and limitations The strengths of our study include a novel perspective of analyzing detrimental effects of BBB in a large sample of AIS patients with longitudinal imaging data, robust statisti-cal analyses accounting for random effects, stability of results across complemen-tary/sensitivity analyses, and a clinically feasible imaging protocol. In terms of limita-tions, we have to acknowledge that, despite the relatively large sample size compared to previous studies 12 , 39 , 40 , we did not find associations between BBB leakage and clin-ical outcomes or hemorrhagic transformation, which may be due to our measurement approach, a sample of predominantly mild strokes with small ischemic core volumes, and/or a lack of power to which noise introduced by different scanners may have con-tributed. Of technical note, one has to bear in mind the limitation of modelling BBB disruptions in the ischemic core which may contain areas of extremely low cerebral blood flow (i.e., where no or negligible amounts of contrast agent arrive). Further, the reliance on commercial software – owing to the absence of open-source alternatives – limits the replicability of our findings. Lastly, the specifics of the current stroke sample (unknown symptom-onset, DWI-FLAIR mismatch, small infarcts, no thrombectomy performed) may limit the generalizability of our results for other stroke populations. Conclusion We demonstrate that the degree of BBB disruption in the penumbra of the ischemic core is associated with future infarction. This extends our perspective of detrimental consequences of BBB leakage in AIS, which has been focused on the risk of hemor-rhagic transformation so far. Taken together, we suggest that BBB imaging provides valuable pathological insights into AIS, and underline the need for prospective studies to investigate its clinical utility. Data Availability All data produced in the present study are available upon reasonable request to the authors. DISCLOSURES F.L.N., L.S., A.W., M.H., M.P., E.S., M.S., M. Ebinger, R.L., N.N., S.P., J.P., V.T., and B.C. declare no conflicts of interest in relation to this study. L.S. reports fees for congress participation from Angelini Pharma outside the submitted work. M. Endres reports grants from Bayer and fees paid to the Charité from Amgen, Astra-Zeneca, Bayer Healthcare, Boehringer Ingelheim, BMS, Daiichi Sankyo, Sanofi, Pfizer, all outside the submitted work. J.B.F. reports consulting, lecture and advisory board fees from AbbVie, AC Immune, Alzheon, Arte-mida, BioClinica/Clario, Biogen, Bristol Myers Squibb, Brainomix, Cerevast, C2N Diagnostics, Daiichi-Sankyo, EISAI, Eli Lilly, F. Hoffmann-LaRoche AG, Forma Therapeutics, GlaxoSmithKline, Guerbet, Ionis Pharmaceuticals, IQVIA, Janssen, Julius Clinical, jung diagnostics, Lantheus Medical Imaging, Merck, Novo Nordisk, Octapharma AG, Premier Research, ProPharma Group, Prothena Bioscienc-es, Regeneron Pharmaceuticals, Roche, Syneos, Tau Rx, Vertex Pharmaceuticals, and Worldwide Clinical Trials, all outside the submitted work. J.F. reports receiving personal fees from Acandis, Cerenovus, Microvention, Med-tronic, Phenox, and Penumbra; receiving grants from Stryker and Route 92; being managing director of eppdata; and owning shares in Tegus and Vastrax; all outside the submitted work. I.G. reports grants from European Union 7th Framework Program during the conduct of the study. C.Z.S. reports speakers fee from Pfizer outside the submitted work. K.W.M. reports consulting and lecture fees from Boehringer Ingelheim, lecture fees from Brainomix and IschemaView, research support from Boehringer Ingelheim all out-side the submitted work. G.T. reports consulting or lecture fees from Acandis, Alexion, Amarin, Bayer, Boehringer Ingelheim, BristolMyersSquibb/Pfizer, Daichi Sankyo, Portola, and Stryker outside the submitted work. ACKNOWLEDGMENTS We thank the patients and their families for participating in the original trial. SOURCES OF FUNDING The WAKE-UP Trial was supported by a grant (278276) from the European Union Seventh Framework Program. Footnotes First author’s twitter handle: @fe_le_nae Lab’s twitter handle: @CSI_Lab REFERENCES 1. ↵ Thomalla G , Gerloff C . Acute imaging for evidence-based treatment of ischemic stroke . Curr. Opin. Neurol . 2019 ; 32 : 521 – 529 . OpenUrl PubMed 2. ↵ Thomalla G , Simonsen CZ , Boutitie F , Andersen G , Berthezene Y , Cheng B , Cheripelli B , Cho T-H , Fazekas F , Fiehler J , et al. MRI-Guided Thrombolysis for Stroke with Unknown Time of Onset . N. Engl. J. Med . 2018 ; 379 : 611 – 622 . OpenUrl CrossRef PubMed 3. ↵ Astrup J , Siesjö BK , Symon L . Thresholds in cerebral ischemia -the ischemic pe-numbra . Stroke . 1981 ; 12 : 723 – 725 . OpenUrl FREE Full Text 4. ↵ Jovin TG , Nogueira RG , Lansberg MG , Demchuk AM , Martins SO , Mocco J , Ribo M , Jadhav AP , Ortega-Gutierrez S , Hill MD , et al. Thrombectomy for anterior cir-culation stroke beyond 6 h from time last known well (AURORA): a systematic re-view and individual patient data meta-analysis . The Lancet . 2022 ; 399 : 249 – 258 . OpenUrl 5. ↵ Pensato U , Lun R , Demchuk A . Thrombectomy in Medium to Large Ischemic Core: Do Patients Still Need to Be SELECTed? JAMA . 2024 ; 331 : 736 – 738 . OpenUrl PubMed 6. ↵ Quarles CC , Bell LC , Stokes AM . Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI . NeuroImage . 2019 ; 187 : 32 – 55 . OpenUrl PubMed 7. ↵ Arba F , Leigh R , Inzitari D , Warach SJ , Luby M , Lees KR . Blood-brain barrier leakage increases with small vessel disease in acute ischemic stroke . Neurology . 2017 ; 89 : 2143 – 2150 . OpenUrl PubMed 8. Bernardo-Castro S , Sousa JA , Brás A , Cecília C , Rodrigues B , Almendra L , Ma-chado C , Santo G , Silva F , Ferreira L , et al. Pathophysiology of Blood–Brain Bar-rier Permeability Throughout the Different Stages of Ischemic Stroke and Its Im-plication on Hemorrhagic Transformation and Recovery . Front. Neurol. [Internet] . 2020 [cited 2024 Jul 8];11. Available from: https://www.frontiersin.org/jour-nals/neurology/articles/10.3389/fneur.2020.594672/full 9. ↵ Bernardo-Castro S , Sousa JA , Martins E , Donato H , Nunes C , d’Almeida OC , Castelo-Branco M , Abrunhosa A , Ferreira L , Sargento-Freitas J . The evolution of blood–brain barrier permeability changes after stroke and its implications on clini-cal outcome: A systematic review and meta-analysis . Int. J. Stroke . 2023 ; 18 : 783 – 794 . OpenUrl PubMed 10. ↵ Candelario-Jalil E , Dijkhuizen RM , Magnus T. Neuroinflammation, Stroke, Blood-Brain Barrier Dysfunction, and Imaging Modalities . Stroke . 2022 ; 53 : 1473 – 1486 . OpenUrl CrossRef PubMed 11. ↵ Khatri R , McKinney AM , Swenson B , Janardhan V . Blood–brain barrier, reper-fusion injury, and hemorrhagic transformation in acute ischemic stroke . Neurol-ogy . 2012 ; 79 : S52 – S57 . OpenUrl CrossRef PubMed 12. ↵ Arba F , Rinaldi C , Caimano D , Vit F , Busto G , Fainardi E . Blood–Brain Barrier Disruption and Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis . Front. Neurol. [Internet ]. 2021 ; 11 . Available from: https://pubmed.ncbi.nlm.nih.gov/33551955/ 13. ↵ Vandenbroucke JP , Elm E von , Altman DG , Gøtzsche PC , Mulrow CD , Pocock SJ , Poole C , Schlesselman JJ , Egger M , Initiative for the S. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration . PLOS Med . 2007 ; 4 : e297 . OpenUrl CrossRef PubMed 14. ↵ Cheng B , Boutitie F , Nickel A , Wouters A , Cho T-H , Ebinger M , Endres M , Fie-bach JB , Fiehler J , Galinovic I , et al. Quantitative Signal Intensity in Fluid-Attenu-ated Inversion Recovery and Treatment Effect in the WAKE-UP Trial . Stroke . 2020 ; 51 : 209 – 215 . OpenUrl PubMed 15. ↵ Forkert ND , Cheng B , Kemmling A , Thomalla G , Fiehler J . ANTONIA perfusion and stroke: A software tool for the multi-purpose analysis of MR perfusion-weighted datasets and quantitative ischemic stroke assessment . Methods Inf. Med . 2014 ; 53 : 469 – 481 . OpenUrl CrossRef PubMed 16. ↵ Jenkinson M , Beckmann CF , Behrens TEJ , Woolrich MW , Smith SM . FSL . NeuroImage . 2012 ; 62 : 782 – 790 . OpenUrl CrossRef PubMed Web of Science 17. ↵ Straka M , Albers GW , Bammer R . Real-time diffusion-perfusion mismatch analysis in acute stroke . J. Magn. Reson. Imaging . 2010 ; 32 : 1024 – 1037 . OpenUrl CrossRef PubMed 18. ↵ Bjornerud A , Sorensen AG , Mouridsen K , Emblem KE . T1-and T2-dominant extravasation correction in DSC-MRI: Part I-theoretical considerations and impli-cations for assessment of tumor hemodynamic properties . J. Cereb. Blood Flow Metab . 2011 ; 31 : 2041 – 2053 . OpenUrl CrossRef PubMed 19. ↵ Emblem KE , Bjornerud A , Mouridsen K , Borra RJ , Batchelor TT , Jain RK , Sorensen AG . T1-and T2*-Dominant Extravasation Correction in DSC-MRI: Part II—Predicting Patient Outcome after a Single Dose of Cediranib in Recurrent Gli-oblastoma Patients . J. Cereb. Blood Flow Metab . 2011 ; 31 : 2054 – 2064 . OpenUrl CrossRef PubMed 20. ↵ von Kummer R , Broderick JP , Campbell BCV , Demchuk A , Goyal M , Hill MD , Treurniet KM , Majoie CBLM , Marquering HA , Mazya MV , et al. The Heidelberg Bleeding Classification . Stroke . 2015 ; 46 : 2981 – 2986 . OpenUrl FREE Full Text 21. ↵ Jensen M , Schlemm E , Cheng B , Lettow I , Quandt F , Boutitie F , Ebinger M , Endres M , Fiebach JB , Fiehler J , et al. Clinical Characteristics and Outcome of Patients With Hemorrhagic Transformation After Intravenous Thrombolysis in the WAKE-UP Trial . Front. Neurol. [Internet] . 2020 [cited 2024 Mar 22];11. Available from: https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2020.00957/full 22. ↵ Olivot JM , Mlynash M , Inoue M , Marks MP , Wheeler HM , Kemp S , Straka M , Zaharchuk G , Bammer R , Lansberg MG , et al. Hypoperfusion Intensity Ratio Pre-dicts Infarct Progression and Functional Outcome in the DEFUSE 2 Cohort . Stroke . 2014 ; 45 : 1018 – 1023 . OpenUrl Abstract / FREE Full Text 23. ↵ Dankbaar JW , Hom J , Schneider T , Cheng SC , Bredno J , Lau BC , van der Schaaf IC , Wintermark M . Dynamic perfusion-CT assessment of early changes in blood brain barrier permeability of acute ischaemic stroke patients . J. Neuroradiol . 2011 ; 38 : 161 – 166 . OpenUrl CrossRef PubMed 24. ↵ Horsch AD , Dankbaar JW , van Seeters T , Niesten JM , Luitse MJA , Vos PC , van der Schaaf IC , Biessels GJ , van der Graaf Y , Kappelle LJ , et al. Relation be-tween stroke severity, patient characteristics and CT-perfusion derived blood-brain barrier permeability measurements in acute ischemic stroke . Clin. Neuro-radiol . 2016 ; 26 : 415 – 421 . OpenUrl PubMed 25. ↵ Wu O , Christensen S , Hjort N , Dijkhuizen RM , Kucinski T , Fiehler J , Thomalla G , Röther J , Østergaard L . Characterizing physiological heterogeneity of infarc-tion risk in acute human ischaemic stroke using MRI . Brain . 2006 ; 129 : 2384 – 2393 . OpenUrl CrossRef PubMed Web of Science 26. ↵ Scheldeman L , Wouters A , Bertels J , Dupont P , Cheng B , Ebinger M , Endres M , Fiebach JB , Gerloff C , Muir KW , et al. Reversibility of Diffusion-Weighted Im-aging Lesions in Patients With Ischemic Stroke in the WAKE-UP Trial . Stroke . 2023 ; 54 : 1560 – 1568 . OpenUrl PubMed 27. ↵ Bani-Sadr A , Mechtouff L , De Bourguignon C , Mauffrey A , Boutelier T , Cho T- H , Cappucci M , Ameli R , Hermier M , Derex L , et al. Blood-Brain Barrier Permea-bility and Kinetics of Inflammatory Markers in Acute Stroke Patients Treated With Thrombectomy . Neurology . 2023 ; 101 : e502 – e511 . OpenUrl PubMed 28. ↵ Yang C , Hawkins KE , Doré S , Candelario-Jalil E . Neuroinflammatory mecha-nisms of blood-brain barrier damage in ischemic stroke . Am. J. Physiol.-Cell Physiol . 2019 ; 316 : C135 – C153 . OpenUrl CrossRef PubMed 29. ↵ Ng FC , Churilov L , Yassi N , Kleinig TJ , Thijs V , Wu TY , Shah DG , Dewey HM , Sharma G , Desmond PM , et al. Microvascular Dysfunction in Blood-Brain Barrier Disruption and Hypoperfusion Within the Infarct Posttreatment Are Associated With Cerebral Edema . Stroke . 2022 ; 53 : 1597 – 1605 . OpenUrl CrossRef PubMed 30. ↵ Kloner RA , King KS , Harrington MG . No-reflow phenomenon in the heart and brain . Am. J. Physiol.-Heart Circ. Physiol . 2018 ; 315 : H550 – H562 . OpenUrl CrossRef PubMed 31. ↵ Sun F , Zhou J , Chen X , Yang T , Wang G , Ge J , Zhang Z , Mei Z . No-reflow af-ter recanalization in ischemic stroke: From pathomechanisms to therapeutic strat-egies . J. Cereb. Blood Flow Metab . 2024 ; 44 : 857 – 880 . OpenUrl PubMed 32. Kassner A , Roberts TPLL , Moran B , Silver FL , Mikulis DJ . Recombinant tissue plasminogen activator increases blood-brain barrier disruption in acute ischemic stroke: An MR imaging permeability study. Am . J. Neuroradiol . 2009 ; 30 : 1864 – 1869 . OpenUrl 33. ↵ Kastrup A , Gröschel K , Ringer TM , Redecker C , Cordesmeyer R , Witte OW , Terborg C . Early Disruption of the Blood–Brain Barrier After Thrombolytic Ther-apy Predicts Hemorrhage in Patients With Acute Stroke . Stroke . 2008 ; 39 : 2385 – 2387 . OpenUrl Abstract / FREE Full Text 34. ↵ Suzuki Y , Nagai N , Umemura K. A Review of the Mechanisms of Blood-Brain Barrier Permeability by Tissue-Type Plasminogen Activator Treatment for Cere-bral Ischemia . Front. Cell. Neurosci. [Internet] . 2016 [cited 2024 Aug 9];10. Avail-able from: https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2016.00002/full 35. ↵ Fisher M , Savitz SI . Pharmacological brain cytoprotection in acute ischaemic stroke — renewed hope in the reperfusion era . Nat. Rev. Neurol . 2022 ; 18 : 193 – 202 . OpenUrl CrossRef PubMed 36. ↵ Debatisse J , Eker OF , Wateau O , Cho T-H , Wiart M , Ramonet D , Costes N , Mérida I , Léon C , Dia M , et al. PET-MRI nanoparticles imaging of blood–brain barrier damage and modulation after stroke reperfusion . Brain Commun. [Inter-net ]. 2020 ; 2 . Available from: https://pubmed.ncbi.nlm.nih.gov/33305265/ 37. ↵ Balami JS , Chen R-L , Grunwald IQ , Buchan AM . Neurological complications of acute ischaemic stroke . Lancet Neurol . 2011 ; 10 : 357 – 371 . OpenUrl CrossRef PubMed Web of Science 38. ↵ Graham GD . Tissue Plasminogen Activator for Acute Ischemic Stroke in Clini-cal Practice . Stroke . 2003 ; 34 : 2847 – 2850 . OpenUrl Abstract / FREE Full Text 39. ↵ Suh CH , Jung SC , Cho SJ , Woo DC , Oh WY , Lee JG , Kim KW . MRI for pre-diction of hemorrhagic transformation in acute ischemic stroke: a systematic re-view and meta-analysis . Acta Radiol . 2020 ; 61 : 964 – 972 . OpenUrl PubMed 40. ↵ Liu N , Chen H , Wu B , Li Y , Wintermark M , Jackson A , Hu J , Zhang Y , Su Z , Zhu G , et al. CT Permeability Imaging Predicts Clinical Outcomes in Acute Is-chemic Stroke Patients Treated with Intra-arterial Thrombolytic Therapy . Mol. Neurobiol . 2017 ; 54 : 2539 – 2546 . OpenUrl PubMed 41. ↵ Nadareishvili Z , Simpkins AN , Hitomi E , Reyes D , Leigh R . Post-stroke blood-brain barrier disruption and poor functional outcome in patients receiving throm-bolytic therapy . Cerebrovasc. Dis . 2019 ; 47 : 135 – 142 . OpenUrl PubMed 42. ↵ Müller S , Kufner A , Dell’Orco A , Rackoll T , Mekle R , Piper SK , Fiebach JB , Villringer K , Flöel A , Endres M , et al. Evolution of Blood-Brain Barrier Permeability in Subacute Ischemic Stroke and Associations With Serum Biomarkers and Func-tional Outcome . Front. Neurol . 2021 ; 12 : 730923 . 43. Li Y , Xia Y , Chen H , Liu N , Jackson A , Wintermark M , Zhang Y , Hu J , Wu B , Zhang W , et al. Focal low and global high permeability predict the possibility, risk, and location of hemorrhagic transformation following intra-arterial thrombolysis therapy in acute stroke. Am . J. Neuroradiol . 2017 ; 38 : 1730 – 1736 . OpenUrl View the discussion thread. Back to top Previous Next Posted November 20, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Blood-brain barrier leakage in the penumbra is associated with infarction on follow-up imaging in acute ischemic stroke Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Blood-brain barrier leakage in the penumbra is associated with infarction on follow-up imaging in acute ischemic stroke Felix L Nägele , Lauranne Scheldeman , Anke Wouters , Marlene Heinze , Marvin Petersen , Eckhard Schlemm , Maximilian Schell , Martin Ebinger , Matthias Endres , Jochen B Fiebach , Jens Fiehler , Ivana Galinovic , Robin Lemmens , Keith W Muir , Norbert Nighoghossian , Salvador Pedraza , Josep Puig , Claus Z Simonsen , Vincent Thijs , Götz Thomalla , Bastian Cheng medRxiv 2024.11.19.24317534; doi: https://doi.org/10.1101/2024.11.19.24317534 Share This Article: Copy Citation Tools Blood-brain barrier leakage in the penumbra is associated with infarction on follow-up imaging in acute ischemic stroke Felix L Nägele , Lauranne Scheldeman , Anke Wouters , Marlene Heinze , Marvin Petersen , Eckhard Schlemm , Maximilian Schell , Martin Ebinger , Matthias Endres , Jochen B Fiebach , Jens Fiehler , Ivana Galinovic , Robin Lemmens , Keith W Muir , Norbert Nighoghossian , Salvador Pedraza , Josep Puig , Claus Z Simonsen , Vincent Thijs , Götz Thomalla , Bastian Cheng medRxiv 2024.11.19.24317534; doi: https://doi.org/10.1101/2024.11.19.24317534 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (864) Anesthesia (302) Cardiovascular Medicine (4449) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1515) Epidemiology (15240) Forensic Medicine (30) Gastroenterology (1130) Genetic and Genomic Medicine (6614) Geriatric Medicine (669) Health Economics (1000) Health Informatics (4550) Health Policy (1372) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1268) Infectious Diseases (except HIV/AIDS) (15928) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (668) Neurology (6622) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1147) Occupational and Environmental Health (957) Oncology (3343) Ophthalmology (977) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1695) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5460) Public and Global Health (9249) Radiology and Imaging (2205) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1197) Rheumatology (597) Sexual and Reproductive Health (715) Sports Medicine (530) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a02966f22fdc8c8b',t:'MTc3OTkzMDg2Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();