P2X7-receptor binding in new-onset and secondary progressive MS – a [11C]SMW139 PET study

P2X7-receptor binding in new-onset and secondary progressive MS – a [11C]SMW139 PET study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article P2X 7 -receptor binding in new-onset and secondary progressive MS – a [ 11 C]SMW139 PET study Jussi Lehto, Richard Aarnio, Jouni Tuisku, Marcus Sucksdorff, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4121612/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background PET imaging of activated microglia has improved our understanding of the pathology behind disability progression in MS, and pro-inflammatory microglia at ‘smoldering’ lesion rims have been implicated as drivers of disability progression. The P2X 7 R is upregulated in the cellular membranes of activated microglia. A single-tissue dual-input model was applied to quantify P2X 7 R binding in the normal appearing white matter, perilesional areas and thalamus among progressive MS patients, healthy controls and newly diagnosed relapsing MS patients. Results Overall, tracer uptake in the MS brain was not significantly higher compared to HCs. In the 3 mm perilesional rim of all T1 lesions, tracer binding was higher among relapsing patients compared to progressive patients. Tracer binding was higher in males compared to females. Disease duration correlated with tracer binding in the normal appearing white matter. Age correlated negatively with tracer binding in the perilesional rims. Conclusions Binding estimates obtained with the dual-input model were consistent with the expected distribution of P2X 7 Rs in the MS brain. According to our study, [ 11 C]SMW139-binding may capture a glial cell phenotype significant in early development of chronic active lesions in relapsing stages of MS. Only tentative evidence for the applicability of [ 11 C]SMW139 to detect MS-related diffuse smoldering inflammation was obtained. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction During the last two decades, extensive use of 18-kDa translocator protein positron emission tomography (TSPO-PET) [ 1 ] in in vivo imaging of activated microglia [ 2 ] has improved our understanding of the pathology behind disability progression [ 3 – 5 ] and treatment outcomes [ 6 – 9 ] in multiple sclerosis (MS). The translational applicability of this method is somewhat limited by its inability to distinctly differentiate between pro-inflammatory and anti-inflammatory phenotypes of activated microglia [ 10 ]. Activation of pro-inflammatory microglia at ‘smoldering’ lesion rims has been implicated as a driver of disability progression in MS [ 11 , 12 ]. The adenosine triphosphate (ATP) -gated cation channel receptor (P2X 7 R) is upregulated in the cellular membranes of activated microglia [ 13 ] and has been identified as a potential therapeutic [ 14 ] and imaging target [ 10 ] in neurodegenerative disease, which offers improved specificity towards microglia that are in the pro-inflammatory end of the phenotype spectrum. The P2X 7 R activates the inflammasome, and results in pro-inflammatory interleukin release and proliferation of activated microglia [ 13 , 15 ]. Compared to non-stimulated and homeostatic microglia in vitro , microglia polarized into an inflammatory phenotype over-express the P2X 7 R 5-folds, whereas a mere 1.5-fold difference is seen in TSPO expression [ 10 ]. In rodents, bacterial lipopolysaccharide –induced neuroinflammation leads to significant brain uptake of P2X 7 R-specific radiotracers [ 16 , 17 ], and uptake is also high at the peak of the MS disease model experimental autoimmune encephalitis (EAE) [ 18 ]. While also found on neurons and other glial cells, P2X 7 R signaling is overwhelmingly associated with activated inflammatory microglia [ 14 , 19 ]. SMW139 is a potent antagonist (Ki 32 nM) of the P2X 7 R [ 20 ]. A mouse biodistribution study with radiolabeled [ 11 C]SMW139 demonstrated rapid brain uptake and clearance via liver metabolism: brain standardized uptake values decreased approximately 80–90 % fom 5 min to 45 min post injection (p.i.) [ 21 ]. According to Akaike information criterion (AIC) [ 22 ], a reversible 90-minute two-tissue compartment model (2TCM) with a blood volume parameter provided the best fit for [ 11 C]SMW139 kinetics in a first in man study with MS patients and healthy control subjects. Increased volumes of distribution (V T ) were observed among MS patients compared to healthy controls throughout the cerebral white matter, cortical grey matter (cGM) and deep gray matter (dGM) including the thalamus [ 23 ]. However, the specific tissue compartment of [ 11 C]SMW139 is small [ 23 ], and rapid metabolism results in a significant fraction of activity from brain-penetrant radiometabolites [ 21 , 24 , 25 ]. It is debatable whether a single-input 2TCM improves the fit by correcting for unspecific radiometabolite activity, rather than providing an accurate estimate of specific parent tracer binding. To address this, Aarnio et al. [ 24 ] utilized a rapid analysis method, which combines thin-layer chromatography with digital autoradiography for the parent fraction analysis, and an ultrafiltration method to analyze the protein-free parent and the protein-free radiometabolite fractions. Using a dual-input (DI) function for [ 11 C]SMW139 kinetic modelling which, by also incorporating protein binding of the parent and its radiometabolites, provided estimates of unbound parent tracer V T . Compared to the 2TCM, the 2TDI model produced a narrow range of V T estimates, and it was concluded that correction for brain-penetrant radiometabolites improves the quantification of specific [ 11 C]SMW139 binding [ 25 ]. In the current study we applied a single tissue compartment dual-input (1TDI) model to obtain estimates for the ratios of parent tracer rate constants between the blood compartment and the tissue compartment (k1p/k2p; V TDI ). We also aimed to further validate [ 11 C]SMW139 for MS studies by imaging progressive MS (PMS) patients with longer disease duration and no recent disease activity, and compared tracer binding in the normal appearing white matter (NAWM), perilesional areas and the thalamus to healthy controls, and to newly diagnosed relapsing MS (RMS). We also tested whether tracer uptake is increased around MS lesions by comparing perilesional V T to lesional and NAWM V T . Methods Subjects and procedures The study was performed at the Turku PET Centre. Recruitment took place at the Turku University Hospital Neurocenter between Feb 2019 and Jun 2022. Inclusion criteria for all MS patients included a confirmed diagnosis according to the 2017 McDonald criteria and a written informed consent. Additional requirements were at least one Gd + lesion of at least 0.5 cm in diameter, and a clinical diagnosis of secondary progression for the RMS and PMS cohorts, respectively. Key exclusion criteria included pregnancy, claustrophobia, and other significant central nervous system pathology besides MS. All MS patients underwent baseline neurological assessments, magnetic resonance imaging (MRI) and [ 11 C]SMW139 PET. Age matched healthy control subjects (HCs) were imaged for comparison. The study protocol was approved by the Ethics Committee of the Hospital District of Southwest Finland. The study was conducted according to the principles of the Declaration of Helsinki. [ 11 C]SMW139 production and PET Irradiations were performed with a TR-19 (ACSI, Richmond, Canada) cyclotron to produce the carbon-11 radioisotope according to previously described procedures [ 8 ]. The complete synthesis procedure of [ 11 C]SMW139 at the Radiopharmaceutical Chemistry Laboratory of Turku PET Centre is described in the supplementary material of Aarnio et al. 2022 [ 24 ]. A transmission scan of 5 minutes followed by a ninety-minute dynamic PET scan was acquired for each subject with a high-resolution research tomograph (HRRT; Siemens Medical Solutions, Knoxville, TN, USA). The mean (SD) injected dose of radioactivity was 402 (12.2) MBq, 404 (13.5) MBq and 404 (10.7) MBq in the RMS, PMS and HC groups, respectively. List mode data was histogrammed into 21 timeframes (3 x 5s, 3 x 10s, 4 x 60s, 2 x 150s, 2 x 300s, 7 x 600s) and reconstructed using an OP-OSEM3D algorithm with 16 subsets and 10 iterations and with point spread function modelling [ 26 ] to reduce the partial volume effect. Reconstructed images were post-processed with a 2.5mm FWHM (full width at half maximum) Gaussian filter. Arterial blood sampling and PET modelling A hematocrit sample was drawn within 1 h before PET imaging. An automated blood pump (ABSS, Allogg AB, Mariefred, Sweden) running at 60 rpm (6 mL/min) was used to obtain a continuous arterial blood time activity curve (TAC) from 0 to 5 min p.i., and manual arterial blood sampling ensued at 5, 10, 20, 40, 60, 75 and 90 min p.i. Arterial plasma activity were then converted to whole blood with individual plasma-to-blood ratio curves. Next, input curves for PET modelling were estimated by fitting the parent fraction with a monoexponential function f(x) = (A-B)e − Cx + B, where A = 1, B > = 0, C > 0, and multiplied with the arterial plasma data to obtain metabolite corrected arterial input time-activity curves (TACs). The resulting curves were subtracted from the uncorrected plasma input curves to obtain the TACs corresponding to the radioactive metabolites in plasma. The differences in appearance times of radioactivity between PET and plasma, whole blood and metabolite TACs were corrected by first estimating the delay of the arterial plasma input TAC, which produced the best fit of two-tissue compartment model to whole brain TAC, and then shifting all other input TACs accordingly. Cerebral blood volume was fixed to 5% in all tested models. The 1TDI model was fitted with five parameters (V B , K 1P , K 2P , K 1M , K 2M ; P = parent, M = metabolite). The distribution volume of free [ 11 C]SMW139 was estimated with the ratio of rate constants of the intact parent tracer between the plasma compartment and the combined tissue compartment; K 1P /K 2P (V TDI ). Additionally, tracer binding was quantified with the total volume of distribution V T2T of a reversible 2TCM, where the model was fitted with five parameters (V B , K 1 , K 2 , K 3 , K 4 ) and where V T2T = K 1 /K 2 (1 + K 3 /K 4 ). The modelling was carried out with in house software (fitk2di and fitk4; http://www.turkupetcentre.net/programs/doc/ ). Parent fraction and plasma protein binding of the parent tracer and its radiometabolites Arterial blood samples were drawn at 0, 5, 10, 20, 40, 60 and 90 min p.i. and the plasma was separated by centrifugation (4°C, 2118 g, 5 min). The plasma proteins were precipitated by adding 700 µL of acetonitrile to 500 µL of plasma, vortexing and centrifuging (3370 g, 3 min). The protein free supernatant was analyzed with high-performance liquid chromatography (HPLC) using a method described in the supplementary material of Brumberg et al. [ 25 ] to obtain fractions of intact [ 11 C]SMW139 and its radioactive metabolites for correcting the plasma TAC. A radioactive standard was prepared by spiking the time point 0 plasma supernatant with [ 11 C]SMW139 in order to analyze the correct peak of the chromatograms to correspond to the parent. Parent and radiometabolite binding to plasma proteins was analyzed for a subset of subjects from blood samples drawn prior to [ 11 C]SMW139 injection and from 20 min p.i. From the time point 0 plasma drawn for in vitro protein binding analysis, 1 mL was frozen for later duplicate analysis. The in vitro plasma and in vivo 20 min parent fraction analysis plasma samples were used to analyze parent and radiometabolite plasma protein binding with separate ultrafiltration membrane corrections. MRI and PET image processing and analysis A 3T MRI (Philips Ingenia/Philips Ingenuity, Best, The Netherlands) was acquired for all study participants with T1, T2, FLAIR, 3DT1, and gadolinium-enhanced T1. The dynamic PET images were smoothed, realigned, and co-registered using statistical parametric mapping (SPM12; Wellcome Trust Center for Neuroimaging, London, UK) according to a previously described procedure [ 6 ]. The images were resliced to match the 1-mm 3 voxel size of the MRI images. The Lesion Segmentation Toolbox (LST) [ 27 ] was used in SPM to create FLAIR masks, which were manually edited to correspond to chronic T1 lesions to create T1 masks following a previously described procedure [ 8 ]. Perilesional masks were created by dilating the lesion mask by 3 mm, and then subtracting the core image from the dilated image. Separate masks were created for the Gd + lesions. NAWM masks were created for each subject by subtracting edited FLAIR lesion masks from segmented white matter. Finally, T1 images were filled with the T1 masks by employing the lesion filling tool of LST in SPM. The filled T1 image was used to segment whole-brain volume (BV) and volumes of different brain areas with FreeSurfer ( https://surfer.nmr.mgh.harvard.edu/ ) for PET assessments. Statistical analysis The statistical analysis was performed with SPSS 28.0 (IBM Corp., Armonk, N.Y., USA). Group level means of V T estimates across different brain areas were compared between MS patients and HCs, and between PMS and RMS with the Wilcoxon rank-sum test. Among MS patients, Group level means of V T estimates were compared between the T1 lesion masks, the 3 mm perilesional rim masks, and the NAWM masks with paired t-tests. Linear correlations between V T estimates, lesion volume, BV, thalamus volume, and demographic variables were measured with the Pearson correlation coefficient. The effects of disease duration and BV on V TDI were estimated with multiple linear regression. All tests were two-tailed, and the alpha was set to 0.05 for all analyses. Results Study subject demographics and other baseline characteristics 26 subjects underwent the study procedures and the final analyzed cohorts consisted of 15 MS patients (n = 6 for RMS, n = 9 for PMS) and 9 HCs. 2 subjects (1 PMS and 1 HC) were excluded from the analysis due to technical issues during the PET visits. All RMS patients were enrolled 10 years (mean 16.9 years, SD 4.3) from diagnosis immediately after inclusion. Compared to patients with MS, the HCs were of comparable age [mean (SD) 47,5 (10.1) vs. 49.6 (14.5) years, p = 0.479, respectively]. Compared to the RMS patients, the PMS cohort had a significantly higher EDSS (median 2.0 vs. 6.0, mean 2.5 vs. 5.1, p = 0.005, respectively), and the two patient cohorts were of comparable age [mean (SD) 45.0 (11.7) vs. 49.2 (9.3) years, p = 0.712, respectively]. The male to female (M/F) ratio was unequal between all cohorts but comparable between all MS and HCs: 6/3 (33% F), 9/6 (40% F), 4/5 (56% F) and 5/1 (17% F) for HCs, MS, PMS and RMS, respectively. At the time of PET imaging, two patients in the RMS cohort had started treatment with i.v. natalizumab, 1 patient had received a single dose of i.v ocrelizumab, and 3 patients had received a single i.v. dose of rituximab. At the time of PET imaging, three PMS patients were treated with rituximab, one with natalizumab, and one with fingolimod. MS lesion loads and other imaging characteristics are displayed in Table 1. Parent fraction and protein binding of [ 11 C]SMW139 The plasma parent fraction decreased steadily down to approximately 45% over the course of the 90 minute sampling time. The mean parent fraction in the PMS group was indicative of slightly faster metabolism, while metabolism of [ 11 C]SMW139 was non-significantly slower among the RMS and HC groups (Fig. 1 ). The plasma protein binding analysis of frozen and fresh plasma samples yielded similar results. The mean (SD) parent free fraction (free parent over all parent in plasma, f P/P ) of [ 11 C]SMW139 was 0.013 (0.004) and 0.013 (0.002) (n = 11 for both) for fresh and frozen samples, respectively, and 0.010 (0.004) (n = 9) for the in vivo 20 min sample. The mean (SD) fraction of free radiometabolites over all radiometabolites was 0.425 (0.132) and the fraction of free radiometabolites over all free radioactivity in plasma was 0.88 (0.06) at 20 min. The mean f P/P was 0.0138 (n = 3), 0.0096 (n = 5) and 0.0093 (n = 3) for HCs, PMS and RMS, respectively. PET Modelling According to visual inspection (Supplement 1), Akaike criterion (AIC) and logarithm of mean residual sum of squares (log (1/n)(model – data) 2 , where n = no. data points) (Supplement 2), both tested models fitted the data well. Coefficients of variation (CoV) of V T estimates were substantially higher with the 2TCM (Supplement 2). Compared to HCs, the overall performance of 1TDI was better among MS patients, and 0–40 min and 0–60 min performed similarly according to AIC (Supplement 3). Finally, 0–60 min data was chosen for the primary analyses based on marginally lower CoV compared to 0–40 min and 0–90 min (Supplement 2–3). Additional exploratory analyses were performed with 0–40 min and 0–90 min data for the 1TDI and 2TCM models, respectively. V T estimates from thalamus (R = 0.800, p < 0.001) and cGM (R = 0.630, p < 0.001) correlated significantly between the models (0–60 min 1TDI and 0–90 min 2TCM), while estimates from NAWM (R = 0.158, p = 0.461) and lesional or perilesional white matter (R= -0.04, p = 0.888) did not. Table 1. MRI variables and [ 11 C]SMW139 V TDI (0–60 min) n Min. Max. Mean SD p Brain Volume (cm 3 ) HC 9 939.20 1267.21 1148.24 109.16 0.665* Brain Volume (cm 3 ) RMS 6 1069.40 1231.09 1172.81 61.34 0.276** Brain Volume (cm 3 ) PMS 9 814.63 1347.18 1055.53 173.10 T1 lesion volume (cm 3 ) RMS 6 2.12 48.17 17.21 18.45 0.940** T1 lesion volume (cm 3 ) PMS 9 2.62 44.14 16.60 12.57 Number of Gd + lesions RMS 6 1 20 8.33 7.97 NA SMW V TDI T1 lesions RMS 6 0.09 0.14 0.11 0.02 0.194** SMW V TDI T1 lesions PMS 9 0.02 0.12 0.09 0.03 SMW V TDI 3mm rim RMS 6 0.11 0.15 0.12 0.02 0.113** SMW V TDI 3mm rim PMS 9 0.03 0.14 0.10 0.03 SMW V TDI Thalamus HC 9 0.03 0.32 0.13 0.09 0.723* SMW V TDI Thalamus RMS 6 0.10 0.19 0.14 0.03 0.071** SMW V TDI Thalamus PMS 9 0.04 0.16 0.11 0.04 SMW V TDI NAWM HC 9 0.04 0.16 0.11 0.04 0.962* SMW V TDI NAWM RMS 6 0.09 0.14 0.12 0.02 0.121** SMW V TDI NAWM PMS 9 0.04 0.14 0.10 0.03 *For the comparison all MS vs. HC **For the comparison RMS vs. PMS V TDI = dual-input distribution volume of parent tracer (K 1P /K 2P ). 3mm rim = T1 perilesional 3 mm rim. NAWM = Normal appearing white matter. HC = Healthy control. RMS = Relapsing MS. PMS = Progressive MS. [ 11 C]SMW139 binding in RMS and PMS patients compared to healthy control subjects Compared to healthy controls, whole-brain, NAWM, thalamic and cGM uptake of [ 11 C]SMW139 was similar in MS patients (n = 15) at group level. After explorative correction for group level mean f P/P (V TDI / f P/P ), mean (SD) NAWM V TDI was significantly higher among all MS compared to HCs [11.15 (3.09) vs 7.69 (2.80), p = 0.012, respectively]. V TDI estimates were somewhat higher in the RMS cohort compared to PMS and HCs, but the differences were not statistically significant (Table 1, Fig. 2 ). In the 3 mm perilesional rim of all T1 lesions, the mean (SD) V TDI was 0.123 (0.02) vs. 0.097 (0.03) (p = 0.113) in the RMS and PMS cohorts, respectively. Exploratory analysis with 0–40 min data revealed a significant difference between the two groups [Fig. 3 ; 0.133 (0.02) vs. 0.101 (0.03), p = 0.049, respectively]. The 2TCM yielded no significant differences between the groups, when all MS was compared to HCs (Supplement 4), or when RMS was compared to PMS (Supplement 5). [ 11 C]SMW139 binding in MS lesions compared with NAWM and the perilesional areas Compared to MS lesion core areas, V TDI estimates were significantly higher in the NAWM and perilesional 3 mm rims among all MS patients (Table 2 , Fig. 4 ). Analyzed separately, perilesional tracer uptake was significantly higher compared to lesions among RMS patients, but not among PMS patients. V TDI estimates within the Gd + lesions and in the 3 mm rim around Gd + lesions were not significantly higher compared to all RMS T1 lesions and the perilesional 3 mm area in general, respectively (Table 2 ). Lesional V T2T estimates were significantly higher compared to the NAWM and the perilesional area among all MS patients. (Supplement 6). Table 2 Lesional, perilesional and NAWM [11C]SMW139 VTDI Comparison Mean SD p 3mm rim - T1 lesions MS 0.0123 0.0140 0.004 3mm rim - NAWM MS 0.0017 0.0124 0.611 NAWM MS - T1 lesions MS 0.0106 0.0176 0.034 3mm rim - T1 lesions PMS 0.0097 0.0144 0.078 3mm rim - NAWM PMS 0.0007 0.0136 0.879 NAWM PMS - T1 lesions PMS 0.0090 0.0178 0.170 3mm rim - T1 lesions RMS 0.0162 0.0138 0.034 3mm rim - NAWM RMS 0.0031 0.0114 0.536 NAWM RMS - T1 lesions RMS 0.0131 0.0185 0.143 Gd + T1 lesions - T1 lesions RMS 0.0067 0.0138 0.403 Gd + 3mm rim – 3mm rim RMS 0.0164 0.0510 0.623 3mm rim = T1 perilesional 3 mm rim, NAWM = Normal appearing white matter RMS = Relapsing MS. PMS = Progressive MS. [ 11 C]SMW139 binding in relation to demographic characteristics and MRI variables Age correlated negatively with V TDI in the 3 mm perilesional area among all MS patients (R= -0.558, p = 0.031; Fig. 5 B), and non-significantly with NAWM V TDI (R= -0.465, p = 0.081), but no correlation with NAWM V TDI was seen among all subjects (n = 24, R = 0.024, p = 0.91). Among all MS patients or among RMS or PMS, V TDI estimates did not correlate with EDSS (results not shown). Among PMS patients, time from diagnosis correlated with NAWM V TDI (Fig. 5 ). Of note, BV or age did not correlate with disease duration among PMS patients (R= -0.259, p = 0.501 and R= -0.350, p = 0.365, respectively), and BV did not significantly correlate with age among all MS (R= -0.414, p = 0.125). Among all subjects, V TDI estimates in the NAWM correlated with BV and the same was true for the 3 mm perilesional rim among all MS (Fig. 5 ). Among PMS with NAWM V TDI as the dependent variable, both disease duration (β = 0.561, t = 2.603, p = 0.041) and BV (β = 0.547, t = 2.536, p = 0.044) added significantly to the prediction (R 2 = 0.855, F(2,6) = 8.166, p = 0.019). V TDI in the perilesional area or NAWM did not correlate with overall T1 lesion volume (results not shown). No significant correlations with V T2T estimates and demographic variables or brain or lesion volume were found with the 2TCM model. V TDI estimates and BV were significantly higher among male subjects compared to females across all examined brain areas. The two groups were of similar age and EDSS, and lesion volumes were comparable, but the time from diagnosis among female subjects was somewhat longer (p > 0.05, Supplement 7). The correlation of NAWM V TDI with BV was abolished (R = 0.076, p = 0.791), when all male subjects (n = 15) were analyzed separately. Discussion It is asserted that compared to a single-tissue model, a two-compartment model likely improves AIC with [ 11 C]SMW139 by fitting radiometabolite build-up between the tissue compartments, and this effect is accentuated with longer fits. Indeed compared to a single-tissue model, a single-input 2TCM yielded superior fits that were further improved by longer p.i. time and by binding the disassociation rate (k4) from the specific tissue compartment to the whole-brain value [ 23 ], which was not done in the present study where exploratory analyses were also performed with a single-input 2TCM. Nonetheless, the V T estimates from thalamus and cGM correlated moderately and strongly between the models, respectively, but estimates from WM did not. Furthermore, significantly lower 2TDI AIC scores have only been reported in association with cGM, and 1TDI was associated with robust fits across all examined regions, and with somewhat lower %SE [ 25 ]. A 2TDI model may be too complex to fit reliably in all cases and regions of interest, while the 1TDI could be more sensitive to inaccuracies in plasma input data. The locus of interest[ 7 , 28 – 30 ] in MS brain imaging is primarily in the NAWM, where the 2TDI has not been shown to outperform the more robust 1TDI, and in the perilesional white matter, where estimates may also be affected by the partial volume effect. In addition to CoV and AIC, the 1TDI 0–60 min fit was preferred based on the theoretically reduced effect of radioactive metabolites, while it was also assumed that fits significantly below 60 min would describe the data inadequately. Beyond pharmacokinetic considerations, PET modelling is also instructed by the model’s ability to measure quantifiable target engagement in line with the known distribution of the tracer’s cognate receptors. In MS, microglia-associated TSPO expression is concentrated to active lesions and chronic lesion rims, and to a lesser extent chronic lesion centers and the NAWM, while inactive lesions and grey matter lesions are relatively devoid of microglia. HLADR + inflammatory microglia are most abundant at chronic lesion rims [ 31 ]. Similarly, P2X 7 R-expression is more prominent in acute active lesions and chronic active lesion rims [ 10 ]. Compared to chronic and chronic active lesions, acute active MS lesions represent a small fraction of overall lesion count [ 32 , 33 ]. The fraction of early active lesions declines rapidly, and has been estimated to represent < 5 % of all lesios at 5–10 years after disease onset [ 32 ]. Based on the above it was expected that at group level, most specific binding of [ 11 C]SMW139 in the WM of MS patients would take place in the immediate area around T1 lesions, and that lesional binding would decline after the initial phase of the disease. In the current study, perilesional binding was significantly higher than lesional binding, but not significantly higher than NAWM binding. In the newly diagnosed cohort of RMS, perilesional tracer binding was increased compared to PMS patients, although it is acknowledged that the groups were not sex matched. Lesions that Gd-enhanced approximately 4 months prior to PET imaging did not exhibit significantly higher tracer uptake, which indicates that disruption of the blood brain barrier did not significantly affect perilesional binding estimates among RMS. According to plasma protein binding analysis, the free fraction of [ 11 C]SMW139 is very low, while radiometabolites contribute significantly to overall radioactivity passing through the blood brain barrier (BBB). 88% of the free radioactivity was due to circulating radiometabolites at 20 min. Thus, estimates obtained by using protein binding to correct V TP are susceptible to minor errors in f P/P analysis, and subject level correction might increase the already considerable variance even further. Correcting for the group level mean of the free parent affected the V T estimates considerably, and our exploratory analysis suggested higher free parent V T among MS patients compared to HCs, but incomplete protein binding data precludes conclusions on group level differences based on this sub-analysis. This approach should be considered in subsequent studies with [ 11 C]SMW139. [ 11 C]SMW139 uptake was significantly increased in male subjects compared to females, which probably affected the strong observed correlation with BV, which was not seen when male subjects were analyzed separately. A similar sex disparity in tracer uptake has been observed with the microglial TSPO tracer [ 11 C]PK11195 [ 34 ]. Interestingly, MS disease duration correlated with tracer uptake, and this correlation was not explained by age or sex, as disease duration was somewhat longer among females. Significant correlations with MS related disability and NAWM uptake of [ 11 C]PK11195 have been reported previously [ 3 , 4 ]. Also considering the higher uptake in RMS, lower tracer uptake in the perilesional rims among older subjects indicates that P2X 7 R-signaling is significant at the early stages of chronic active lesion formation. [ 11 C]SMW139 uptake peaks at the early stage of EAE [ 18 ]. It is concluded that only tentative evidence for the applicability of [ 11 C]SMW139 to detect MS-related smoldering inflammation was obtained, and that overall tracer uptake in the MS brain was not significantly higher compared to HCs in the studied cohort. [ 11 C]SMW139-binding may capture a glial cell phenotype significant in early development of chronic active lesions in relapsing stages of MS. Age and sex are to be matched with a careful emphasis when studies with this tracer are conducted. Further information on the applicability of [ 11 C]SMW139 PET in MS could be obtained by correlating the current results with longitudinal clinical outcomes and progression-related biomarkers. Declarations Author contributions JL: Writing – original draft (lead), Writing - review and editing (lead), Visualization (lead), Investigation (equal), Formal analysis (equal). RA: Writing – original draft (supporting), Investigation (supporting), Writing - review and editing (supporting); Visualization (supporting). JT: Formal analysis (equal), Writing - review and editing (supporting). MS: Investigation (equal); Conceptualization (supporting). EMK:Investigation (equal). MN: Project management (lead); Funding acquisition (supporting), Investigation (supporting). SH: Resources (equal). JR: Resources (equal). JD: Investigation (supporting),Resources (supporting). JG: Investigation (supporting),Resources (supporting). MK: Supervision (supporting), Resources (supporting). VO: Formal analysis (supporting), Conceptualization (supporting). LA: Supervision (lead); Conceptualization (lead); Funding acquisition (lead); Writing – review and editing (supporting). Funding This work was supported by the Finnish Academy (decision number: 330902), the Sigrid Juselius Foundation, the state research funding of the Turku University Hospital expert responsibility area, Finnish Governmental Research Funding (VTR) for Turku University Hospital, and the InFLAMES Flagship Programme of the Academy of Finland (decision number 337530). Conflict of interest statement The authors have nothing to disclose. Data availability statement Any anonymized data used in the preparation of this article will be made available by the request of a qualified investigator. Ethics statement and consent to participate The study was conducted according to the principles of the Declaration of Helsinki.All subjects were adults (>18 years old), and provided written informed consent. The study was approved by the Ethics Committee of the Hospital District of Southwest Finland. Consent to publish Not applicable. Acknowledgements Not applicable. References Chauveau F, Becker G, Boutin H. Have (R)-[11C]PK11195 challengers fulfilled the promise? A scoping review of clinical TSPO PET studies. Eur J Nucl Med Mol Imaging. Springer Science and Business Media Deutschland GmbH; 2021. pp. 201–20. Banati RB, Newcombe J, Gunn RN, Cagnin A, Turkheimer F, Heppner F et al. The peripheral benzodiazepine binding site in the brain in multiple sclerosis: Quantitative in vivo imaging of microglia as a measure of disease activity. Brain [Internet]. 2000;123:2321–37. https://doi.org/10.1093/brain/123.11.2321 . Sucksdorff M, Matilainen M, Tuisku J, Polvinen E, Vuorimaa A, Rokka J et al. Brain TSPO-PET predicts later disease progression independent of relapses in multiple sclerosis. Brain [Internet]. 2020;143:3318–30. https://www.ncbi.nlm.nih.gov/pubmed/33006604 . Rissanen E, Tuisku J, Vahlberg T, Sucksdorff M, Paavilainen T, Parkkola R et al. Microglial activation, white matter tract damage, and disability in MS. Neurol Neuroimmunol Neuroinflamm [Internet]. 2018;5:e443. https://www.ncbi.nlm.nih.gov/pubmed/29520366 . Misin O, Matilainen M, Nylund M, Honkonen E, Rissanen E, Sucksdorff M et al. Innate Immune Cell–Related Pathology in the Thalamus Signals a Risk for Disability Progression in Multiple Sclerosis. Neurology - Neuroimmunology Neuroinflammation [Internet]. 2022;9:e1182. http://nn.neurology.org/content/9/4/e1182.abstract . Sucksdorff M, Tuisku J, Matilainen M, Vuorimaa A, Smith S, Keitilä J et al. Natalizumab treatment reduces microglial activation in the white matter of the MS brain. Neurol Neuroimmunol Neuroinflamm. 2019;6. Lehto J, Sucksdorff M, Nylund M, Raitanen R, Matilainen M, Airas L. PET-measurable innate immune cell activation reduction in chronic active lesions in PPMS brain after rituximab treatment: a case report. J Neurol [Internet]. 2022 [cited 2023 Jan 24];1:1–4. https://link.springer.com/article/ 10.1007/s00415-022-11539-4 . Lehto J, Nylund M, Matilainen M, Sucksdorff M, Vuorimaa A, Rajander J et al. Longitudinal stability of progression-related microglial activity during teriflunomide treatment in patients with multiple sclerosis. Eur J Neurol [Internet]. 2023;n/a. https://doi.org/10.1111/ene.15834 . Sucksdorff M, Rissanen E, Tuisku J, Nuutinen S, Paavilainen T, Rokka J, et al. Evaluation of the effect of fingolimod treatment on microglial activation using serial PET imaging in multiple sclerosis. J Nucl Med. 2017;58:1646–51. Beaino W, Janssen B, Kooij G, van der Pol SMA, van Het Hof B, van Horssen J et al. Purinergic receptors P2Y12R and P2X7R: Potential targets for PET imaging of microglia phenotypes in multiple sclerosis. J Neuroinflammation [Internet]. 2017 [cited 2023 Feb 7];14:1–16. https://jneuroinflammation.biomedcentral.com/articles/ 10.1186/s12974-017-1034-z . Giovannoni G, Popescu V, Wuerfel J, Hellwig K, Iacobeus E, Jensen MB et al. Smouldering multiple sclerosis: the ‘real MS.’ Ther. Adv Neurol Disord. 2022;15. Nylund M, Sucksdorff M, Matilainen M, Polvinen E, Tuisku J, Airas L. Phenotyping of multiple sclerosis lesions according to innate immune cell activation using 18 kDa translocator protein-PET. Brain Commun. 2022;4. Monif M, Burnstock G, Williams DA. Microglia: proliferation and activation driven by the P2X7 receptor. Int J Biochem Cell Biol [Internet]. 2010 [cited 2023 Jun 15];42:1753–6. https://pubmed.ncbi.nlm.nih.gov/20599520/ . Bhattacharya A, Biber K. The microglial ATP-gated ion channel P2X7 as a CNS drug target. Glia [Internet]. 2016 [cited 2023 Jun 15];64:1772–87. https://pubmed.ncbi.nlm.nih.gov/27219534/ . Clark AK, Staniland AA, Marchand F, Kaan TKY, McMahon SB, Malcangio M. P2X7-Dependent Release of Interleukin-1β and Nociception in the Spinal Cord following Lipopolysaccharide. The Journal of Neuroscience [Internet]. 2010 [cited 2023 Jun 15];30:573. /pmc/articles/PMC2880485/ . Fantoni ER, Dal Ben D, Falzoni S, Di Virgilio F, Lovestone S, Gee A. Design, synthesis and evaluation in an LPS rodent model of neuroinflammation of a novel 18F-labelled PET tracer targeting P2X7. EJNMMI Res [Internet]. 2017 [cited 2023 Nov 24];7:1–12. https://link.springer.com/articles/ 10.1186/s13550-017-0275-2 . Territo PR, Meyer JA, Peters JS, Riley AA, McCarthy BP, Gao M et al. Characterization of 11C-GSK1482160 for Targeting the P2X7 Receptor as a Biomarker for Neuroinflammation. J Nucl Med [Internet]. 2017 [cited 2023 Nov 24];58:458–65. https://pubmed.ncbi.nlm.nih.gov/27765863/ . Beaino W, Janssen B, Kooijman E, Vos R, Schuit RC, O’Brien-Brown J et al. PET imaging of P2X7R in the experimental autoimmune encephalomyelitis model of multiple sclerosis using [11C]SMW139. J Neuroinflammation [Internet]. 2020 [cited 2024 Feb 28];17:1–18. https://jneuroinflammation.biomedcentral.com/articles/ 10.1186/s12974-020-01962-7 . Burnstock G. P2X ion channel receptors and inflammation. Purinergic Signalling. 2016 12:1 [Internet]. 2016 [cited 2023 Jun 15];12:59–67. https://link.springer.com/article/10.1007/s11302-015-9493-0 . Wilkinson SM, Barron ML, O’Brien-Brown J, Janssen B, Stokes L, Werry EL et al. Pharmacological Evaluation of Novel Bioisosteres of an Adamantanyl Benzamide P2X7 Receptor Antagonist. ACS Chem Neurosci [Internet]. 2017 [cited 2023 Jun 15];8:2374–80. https://pubs.acs.org/doi/full/10.1021/acschemneuro.7b00272 . Janssen B, Vugts DJ, Wilkinson SM, Ory D, Chalon S, Hoozemans JJM et al. Identification of the allosteric P2X7 receptor antagonist [11C]SMW139 as a PET tracer of microglial activation. Scientific Reports 2018 8:1 [Internet]. 2018 [cited 2023 Jun 15];8:1–10. https://www.nature.com/articles/s41598-018-24814-0 . Akaike H. Information Theory and an Extension of Information the Maximum Theory Likelihood and an Principle Extension of the Maximum Likelihood Principle. Biogeochemistry [Internet]. 1998 [cited 2023 Jun 15];1998:199–213. https://link.springer.com/chapter/ 10.1007/978-1-4612-1694-0_15 . Hagens MHJ, Golla SSV, Janssen B, Vugts DJ, Beaino W, Windhorst AD, et al. The P2X7 receptor tracer [11C]SMW139 as an in vivo marker of neuroinflammation in multiple sclerosis: a first-in man study. Eur J Nucl Med Mol Imaging. 2020;47:379–89. Aarnio R, Alzghool OM, Wahlroos S, O’Brien-Brown J, Kassiou M, Solin O et al. Novel plasma protein binding analysis method for a PET tracer and its radiometabolites: A case study with [11C]SMW139 to explain the high uptake of radiometabolites in mouse brain. J Pharm Biomed Anal. 2022;219. Brumberg J, Aarnio R, Forsberg A, Marjamäki P, Kerstens V, Moein MM et al. Quantification of the purinergic P2X7 receptor with [11C]SMW139 improves through correction for brain-penetrating radiometabolites. J Cereb Blood Flow Metab [Internet]. 2023 [cited 2023 Jun 16];43:258–68. https://pubmed.ncbi.nlm.nih.gov/36163685/ . Sureau FC, Reader AJ, Comtat C, Leroy C, Ribeiro MJ, Buvat I et al. Impact of image-space resolution modeling for studies with the high-resolution research tomograph. J Nucl Med [Internet]. 2008 [cited 2024 Feb 13];49:1000–8. https://pubmed.ncbi.nlm.nih.gov/18511844/ . Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage. 2012;59:3774–83. Airas L, Rissanen E, Rinne JO. Imaging neuroinflammation in multiple sclerosis using TSPO-PET. Clin Transl Imaging. Springer-Verlag Italia s.r.l.; 2015. pp. 461–73. Airas L, Yong VW. Microglia in multiple sclerosis - pathogenesis and imaging. Curr Opin Neurol [Internet]. 2022 [cited 2023 Jan 24];35:299–306. https://pubmed.ncbi.nlm.nih.gov/35674072/ . Bodini B, Tonietto M, Airas L, Stankoff B. Positron emission tomography in multiple sclerosis - straight to the target. Nat Rev Neurol [Internet]. 2021; https://www.ncbi.nlm.nih.gov/pubmed/34545219 . Nutma E, Stephenson JA, Gorter RP, de Bruin J, Boucherie DM, Donat CK et al. A quantitative neuropathological assessment of translocator protein expression in multiple sclerosis. Brain [Internet]. 2019;142:3440–55. https://www.ncbi.nlm.nih.gov/pubmed/31578541 . Frischer JM, Weigand SD, Guo Y, Kale N, Parisi JE, Pirko I et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol [Internet]. 2015 [cited 2024 Jan 5];78:710–21. https://pubmed.ncbi.nlm.nih.gov/26239536/ . Kuhlmann T, Ludwin S, Prat A, Antel J, Brück W, Lassmann H. An updated histological classification system for multiple sclerosis lesions. Acta Neuropathol [Internet]. 2017 [cited 2023 Apr 6];133:13–24. https://pubmed.ncbi.nlm.nih.gov/27988845/ . Laaksonen S, Saraste M, Nylund M, Hinz R, Snellman A, Rinne J et al. Sex-driven variability in TSPO-expressing microglia in MS patients and healthy individuals. Front Neurol [Internet]. 2024 [cited 2024 Feb 28];15:1352116. https://www.frontiersin.org/articles/ 10.3389/fneur.2024.1352116/full . Supplementary Files SupplementData.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4121612","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284506007,"identity":"e5dae0d7-08f6-4709-9a29-8ff366194dc9","order_by":0,"name":"Jussi Lehto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoUlEQVRIiWNgGAWjYBACxgbmBmYGBhsw50MCcVqAmhgS0hh4gOwZRGkB28OQcBiihSgNzDMSGx8X/jifuF8igbHhAVF2zEhsNp6RcDuxB6SFKIcBtbRJ80C0sD8gVkv7b56EcyTawsyTcIAULT0Pm6V50pKNe848bCROi2F78sHPPDZ2su1ARuMPorQ0ICxswKkKBcgTp2wUjIJRMApGNAAA+Xs0pXqyP1oAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2589-2549","institution":"Turku PET Centre: Turun PET keskus","correspondingAuthor":true,"prefix":"","firstName":"Jussi","middleName":"","lastName":"Lehto","suffix":""},{"id":284506008,"identity":"28a8af35-521e-4fb3-8630-e94b5b3690d0","order_by":1,"name":"Richard Aarnio","email":"","orcid":"","institution":"Turku PET Centre: Turun PET keskus","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Aarnio","suffix":""},{"id":284506009,"identity":"66399fb2-e6f2-4dc9-9a64-be408f5d0010","order_by":2,"name":"Jouni Tuisku","email":"","orcid":"","institution":"Turku PET Centre: Turun PET keskus","correspondingAuthor":false,"prefix":"","firstName":"Jouni","middleName":"","lastName":"Tuisku","suffix":""},{"id":284506010,"identity":"78ea4629-900d-45e7-bebe-6bbe479c38f0","order_by":3,"name":"Marcus Sucksdorff","email":"","orcid":"","institution":"Turku PET Centre: Turun PET keskus","correspondingAuthor":false,"prefix":"","firstName":"Marcus","middleName":"","lastName":"Sucksdorff","suffix":""},{"id":284506011,"identity":"5a8fb5f1-ca0e-4865-b510-bed20e28b104","order_by":4,"name":"Esa Mikko Koivumäki","email":"","orcid":"","institution":"Turku PET Centre: Turun PET keskus","correspondingAuthor":false,"prefix":"","firstName":"Esa","middleName":"Mikko","lastName":"Koivumäki","suffix":""},{"id":284506012,"identity":"b07207c6-70ac-46a7-961b-b276d668b4c6","order_by":5,"name":"Marjo Nylund","email":"","orcid":"","institution":"Turku PET Centre: Turun PET keskus","correspondingAuthor":false,"prefix":"","firstName":"Marjo","middleName":"","lastName":"Nylund","suffix":""},{"id":284506013,"identity":"be415f36-e901-445d-8274-bc5e088ec259","order_by":6,"name":"Semi Helin","email":"","orcid":"","institution":"Turku PET Centre: Turun PET keskus","correspondingAuthor":false,"prefix":"","firstName":"Semi","middleName":"","lastName":"Helin","suffix":""},{"id":284506014,"identity":"d6fe964f-a934-4c5d-a091-d4d46f28f192","order_by":7,"name":"Johan Rajander","email":"","orcid":"","institution":"Åbo Akademi University: Abo Akademi","correspondingAuthor":false,"prefix":"","firstName":"Johan","middleName":"","lastName":"Rajander","suffix":""},{"id":284506015,"identity":"949bea6d-f51e-41fc-acb3-9ebd667a1014","order_by":8,"name":"Jonathan Danon","email":"","orcid":"","institution":"The University of Sydney School of Chemistry","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Danon","suffix":""},{"id":284506016,"identity":"53912f8d-e5d8-4e75-9fb1-8d2917cd21a6","order_by":9,"name":"Jayson Gilchrist","email":"","orcid":"","institution":"The University of Sydney School of Chemistry","correspondingAuthor":false,"prefix":"","firstName":"Jayson","middleName":"","lastName":"Gilchrist","suffix":""},{"id":284506017,"identity":"2c113e20-f725-4e6c-af54-482a41a760a1","order_by":10,"name":"Michael Kassiou","email":"","orcid":"","institution":"The University of Sydney School of Chemistry","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Kassiou","suffix":""},{"id":284506018,"identity":"f063894c-77e7-42db-b59b-d20f4e5e386d","order_by":11,"name":"Vesa Oikonen","email":"","orcid":"","institution":"Turku PET Centre: Turun PET keskus","correspondingAuthor":false,"prefix":"","firstName":"Vesa","middleName":"","lastName":"Oikonen","suffix":""},{"id":284506019,"identity":"5d10413f-6426-4082-8c55-2398cfe48650","order_by":12,"name":"Laura Airas","email":"","orcid":"","institution":"Turku PET Centre: Turun PET keskus","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Airas","suffix":""}],"badges":[],"createdAt":"2024-03-18 09:02:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4121612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4121612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53881755,"identity":"27416c90-6d66-47c7-aa70-bc0420bf4620","added_by":"auto","created_at":"2024-04-01 17:54:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96942,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean percentages of unchanged [\u003csup\u003e11\u003c/sup\u003eC]SMW139 of the total radioactivity in the plasma samples (parent fraction) of the healthy controls (HC), relapsing MS (RMS) and progressive MS (PMS)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4121612/v1/96a06b4a71f366464820dc51.png"},{"id":53881757,"identity":"1dcf502c-2a5f-4d72-8f0b-3f2107c85f13","added_by":"auto","created_at":"2024-04-01 17:54:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62389,"visible":true,"origin":"","legend":"\u003cp\u003e[11C]SWM139 V\u003csub\u003eTDI\u003c/sub\u003e\u003cstrong\u003e \u003c/strong\u003e(0-60 min)\u003cstrong\u003e \u003c/strong\u003ein the normal appearing white matter (NAWM), thalamus, cortical gray matter (cGM) and T1 perilesional 3 mm rim (3 mm rim) among healthy controls (HC), relapsing MS (RMS) and progressive MS (PMS)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4121612/v1/dd595843c5b52a61b5a293b2.png"},{"id":53882273,"identity":"110d48cc-5e31-4525-acca-8897e541ed9e","added_by":"auto","created_at":"2024-04-01 18:02:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35626,"visible":true,"origin":"","legend":"\u003cp\u003eT1 perilesional 3mm rim and normal appearing white matter (NAWM) among relapsing MS (RMS) and progressive MS (PMS).\u003cstrong\u003e *\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep=0.049\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4121612/v1/346b3537bbf56df80709c433.png"},{"id":53881761,"identity":"875096b3-7d44-4beb-9034-39c8b2f6ee1a","added_by":"auto","created_at":"2024-04-01 17:54:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71685,"visible":true,"origin":"","legend":"\u003cp\u003e[11C]SWM139 V\u003csub\u003eTDI\u003c/sub\u003e\u003cstrong\u003e \u003c/strong\u003ein the 3 mm perilesional rims, normal appearing white matter (NAWM) and T1 lesions among relapsing MS (RMS), progressive MS (PMS) and all MS. \u003cstrong\u003e*p\u0026lt;0.05\u003c/strong\u003e (Table 2 for pairwise comparisons)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4121612/v1/210a90b71e43e4f3fe85e6ac.png"},{"id":53881758,"identity":"9362f262-8633-4f8d-8cd4-ac1dd7334572","added_by":"auto","created_at":"2024-04-01 17:54:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105468,"visible":true,"origin":"","legend":"\u003cp\u003eV\u003csub\u003eTDI\u003c/sub\u003e in the normal appearing white matter (NAWM) among progressive MS (PMS) correlated with disease duration (A; R=0.666, p=0.05). V\u003csub\u003eTDI\u003c/sub\u003e in the T1 perilesional 3 mm rim among all MS correlated with age (B; R= -0.558, p=0.031). V\u003csub\u003eTDI\u003c/sub\u003e in the NAWM among all subjects correlated with brain volume (C; R=0.577, p=0.003). V\u003csub\u003eTDI\u003c/sub\u003e in the T1 perilesional 3 mm rim among all MS correlated with brain volume (D; R=0.734, p=0.002)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4121612/v1/d6f6c8bd9a30d0518b1fac58.png"},{"id":57254961,"identity":"9f35cbf5-0b43-4435-a29b-0f89e83e5652","added_by":"auto","created_at":"2024-05-28 08:19:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1094817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4121612/v1/93926f46-bbf1-4245-ba2c-f0dd00a3c51c.pdf"},{"id":53881756,"identity":"f5043666-ec92-4a30-a170-a2ea627a245c","added_by":"auto","created_at":"2024-04-01 17:54:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":285638,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementData.docx","url":"https://assets-eu.researchsquare.com/files/rs-4121612/v1/0c48fc847818784ff4aaf9d6.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eP2X\u003csub\u003e7\u003c/sub\u003e-receptor binding in new-onset and secondary progressive MS – a [\u003csup\u003e11\u003c/sup\u003eC]SMW139 PET study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDuring the last two decades, extensive use of 18-kDa translocator protein positron emission tomography (TSPO-PET) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] in \u003cem\u003ein vivo\u003c/em\u003e imaging of activated microglia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] has improved our understanding of the pathology behind disability progression [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and treatment outcomes [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] in multiple sclerosis (MS). The translational applicability of this method is somewhat limited by its inability to distinctly differentiate between pro-inflammatory and anti-inflammatory phenotypes of activated microglia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Activation of pro-inflammatory microglia at \u0026lsquo;smoldering\u0026rsquo; lesion rims has been implicated as a driver of disability progression in MS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe adenosine triphosphate (ATP) -gated cation channel receptor (P2X\u003csub\u003e7\u003c/sub\u003eR) is upregulated in the cellular membranes of activated microglia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and has been identified as a potential therapeutic [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and imaging target [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] in neurodegenerative disease, which offers improved specificity towards microglia that are in the pro-inflammatory end of the phenotype spectrum. The P2X\u003csub\u003e7\u003c/sub\u003eR activates the inflammasome, and results in pro-inflammatory interleukin release and proliferation of activated microglia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Compared to non-stimulated and homeostatic microglia \u003cem\u003ein vitro\u003c/em\u003e, microglia polarized into an inflammatory phenotype over-express the P2X\u003csub\u003e7\u003c/sub\u003eR 5-folds, whereas a mere 1.5-fold difference is seen in TSPO expression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In rodents, bacterial lipopolysaccharide \u0026ndash;induced neuroinflammation leads to significant brain uptake of P2X\u003csub\u003e7\u003c/sub\u003eR-specific radiotracers [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and uptake is also high at the peak of the MS disease model experimental autoimmune encephalitis (EAE) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. While also found on neurons and other glial cells, P2X\u003csub\u003e7\u003c/sub\u003eR signaling is overwhelmingly associated with activated inflammatory microglia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSMW139 is a potent antagonist (Ki 32 nM) of the P2X\u003csub\u003e7\u003c/sub\u003eR [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A mouse biodistribution study with radiolabeled [\u003csup\u003e11\u003c/sup\u003eC]SMW139 demonstrated rapid brain uptake and clearance via liver metabolism: brain standardized uptake values decreased approximately 80\u0026ndash;90 % fom 5 min to 45 min post injection (p.i.) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. According to Akaike information criterion (AIC) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], a reversible 90-minute two-tissue compartment model (2TCM) with a blood volume parameter provided the best fit for [\u003csup\u003e11\u003c/sup\u003eC]SMW139 kinetics in a first in man study with MS patients and healthy control subjects. Increased volumes of distribution (V\u003csub\u003eT\u003c/sub\u003e) were observed among MS patients compared to healthy controls throughout the cerebral white matter, cortical grey matter (cGM) and deep gray matter (dGM) including the thalamus [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the specific tissue compartment of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 is small [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and rapid metabolism results in a significant fraction of activity from brain-penetrant radiometabolites [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It is debatable whether a single-input 2TCM improves the fit by correcting for unspecific radiometabolite activity, rather than providing an accurate estimate of specific parent tracer binding. To address this, Aarnio et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] utilized a rapid analysis method, which combines thin-layer chromatography with digital autoradiography for the parent fraction analysis, and an ultrafiltration method to analyze the protein-free parent and the protein-free radiometabolite fractions. Using a dual-input (DI) function for [\u003csup\u003e11\u003c/sup\u003eC]SMW139 kinetic modelling which, by also incorporating protein binding of the parent and its radiometabolites, provided estimates of unbound parent tracer V\u003csub\u003eT\u003c/sub\u003e. Compared to the 2TCM, the 2TDI model produced a narrow range of V\u003csub\u003eT\u003c/sub\u003e estimates, and it was concluded that correction for brain-penetrant radiometabolites improves the quantification of specific [\u003csup\u003e11\u003c/sup\u003eC]SMW139 binding [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the current study we applied a single tissue compartment dual-input (1TDI) model to obtain estimates for the ratios of parent tracer rate constants between the blood compartment and the tissue compartment (k1p/k2p; V\u003csub\u003eTDI\u003c/sub\u003e). We also aimed to further validate [\u003csup\u003e11\u003c/sup\u003eC]SMW139 for MS studies by imaging progressive MS (PMS) patients with longer disease duration and no recent disease activity, and compared tracer binding in the normal appearing white matter (NAWM), perilesional areas and the thalamus to healthy controls, and to newly diagnosed relapsing MS (RMS). We also tested whether tracer uptake is increased around MS lesions by comparing perilesional V\u003csub\u003eT\u003c/sub\u003e to lesional and NAWM V\u003csub\u003eT\u003c/sub\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects and procedures\u003c/h2\u003e \u003cp\u003eThe study was performed at the Turku PET Centre. Recruitment took place at the Turku University Hospital Neurocenter between Feb 2019 and Jun 2022. Inclusion criteria for all MS patients included a confirmed diagnosis according to the 2017 McDonald criteria and a written informed consent. Additional requirements were at least one Gd\u0026thinsp;+\u0026thinsp;lesion of at least 0.5 cm in diameter, and a clinical diagnosis of secondary progression for the RMS and PMS cohorts, respectively. Key exclusion criteria included pregnancy, claustrophobia, and other significant central nervous system pathology besides MS. All MS patients underwent baseline neurological assessments, magnetic resonance imaging (MRI) and [\u003csup\u003e11\u003c/sup\u003eC]SMW139 PET. Age matched healthy control subjects (HCs) were imaged for comparison. The study protocol was approved by the Ethics Committee of the Hospital District of Southwest Finland. The study was conducted according to the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e[\u003c/b\u003e\u003csup\u003e\u003cb\u003e11\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC]SMW139 production and PET\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIrradiations were performed with a TR-19 (ACSI, Richmond, Canada) cyclotron to produce the carbon-11 radioisotope according to previously described procedures [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The complete synthesis procedure of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 at the Radiopharmaceutical Chemistry Laboratory of Turku PET Centre is described in the supplementary material of Aarnio et al. 2022 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA transmission scan of 5 minutes followed by a ninety-minute dynamic PET scan was acquired for each subject with a high-resolution research tomograph (HRRT; Siemens Medical Solutions, Knoxville, TN, USA). The mean (SD) injected dose of radioactivity was 402 (12.2) MBq, 404 (13.5) MBq and 404 (10.7) MBq in the RMS, PMS and HC groups, respectively. List mode data was histogrammed into 21 timeframes (3 x 5s, 3 x 10s, 4 x 60s, 2 x 150s, 2 x 300s, 7 x 600s) and reconstructed using an OP-OSEM3D algorithm with 16 subsets and 10 iterations and with point spread function modelling [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] to reduce the partial volume effect. Reconstructed images were post-processed with a 2.5mm FWHM (full width at half maximum) Gaussian filter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eArterial blood sampling and PET modelling\u003c/h2\u003e \u003cp\u003eA hematocrit sample was drawn within 1 h before PET imaging. An automated blood pump (ABSS, Allogg AB, Mariefred, Sweden) running at 60 rpm (6 mL/min) was used to obtain a continuous arterial blood time activity curve (TAC) from 0 to 5 min p.i., and manual arterial blood sampling ensued at 5, 10, 20, 40, 60, 75 and 90 min p.i. Arterial plasma activity were then converted to whole blood with individual plasma-to-blood ratio curves. Next, input curves for PET modelling were estimated by fitting the parent fraction with a monoexponential function f(x) = (A-B)e\u003csup\u003e\u0026minus;\u0026thinsp;Cx\u003c/sup\u003e + B, where A\u0026thinsp;=\u0026thinsp;1, B\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0, C\u0026thinsp;\u0026gt;\u0026thinsp;0, and multiplied with the arterial plasma data to obtain metabolite corrected arterial input time-activity curves (TACs). The resulting curves were subtracted from the uncorrected plasma input curves to obtain the TACs corresponding to the radioactive metabolites in plasma. The differences in appearance times of radioactivity between PET and plasma, whole blood and metabolite TACs were corrected by first estimating the delay of the arterial plasma input TAC, which produced the best fit of two-tissue compartment model to whole brain TAC, and then shifting all other input TACs accordingly. Cerebral blood volume was fixed to 5% in all tested models.\u003c/p\u003e \u003cp\u003eThe 1TDI model was fitted with five parameters (V\u003csub\u003eB\u003c/sub\u003e, K\u003csub\u003e1P\u003c/sub\u003e, K\u003csub\u003e2P\u003c/sub\u003e, K\u003csub\u003e1M\u003c/sub\u003e, K\u003csub\u003e2M\u003c/sub\u003e; P\u0026thinsp;=\u0026thinsp;parent, M\u0026thinsp;=\u0026thinsp;metabolite). The distribution volume of free [\u003csup\u003e11\u003c/sup\u003eC]SMW139 was estimated with the ratio of rate constants of the intact parent tracer between the plasma compartment and the combined tissue compartment; K\u003csub\u003e1P\u003c/sub\u003e/K\u003csub\u003e2P\u003c/sub\u003e (V\u003csub\u003eTDI\u003c/sub\u003e). Additionally, tracer binding was quantified with the total volume of distribution V\u003csub\u003eT2T\u003c/sub\u003e of a reversible 2TCM, where the model was fitted with five parameters (V\u003csub\u003eB\u003c/sub\u003e, K\u003csub\u003e1\u003c/sub\u003e, K\u003csub\u003e2\u003c/sub\u003e, K\u003csub\u003e3\u003c/sub\u003e, K\u003csub\u003e4\u003c/sub\u003e) and where V\u003csub\u003eT2T\u003c/sub\u003e = K\u003csub\u003e1\u003c/sub\u003e/K\u003csub\u003e2\u003c/sub\u003e(1\u0026thinsp;+\u0026thinsp;K\u003csub\u003e3\u003c/sub\u003e/K\u003csub\u003e4\u003c/sub\u003e). The modelling was carried out with in house software (fitk2di and fitk4; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.turkupetcentre.net/programs/doc/\u003c/span\u003e\u003cspan address=\"http://www.turkupetcentre.net/programs/doc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParent fraction and plasma protein binding of the parent tracer and its radiometabolites\u003c/h2\u003e \u003cp\u003eArterial blood samples were drawn at 0, 5, 10, 20, 40, 60 and 90 min p.i. and the plasma was separated by centrifugation (4\u0026deg;C, 2118 g, 5 min). The plasma proteins were precipitated by adding 700 \u0026micro;L of acetonitrile to 500 \u0026micro;L of plasma, vortexing and centrifuging (3370 g, 3 min). The protein free supernatant was analyzed with high-performance liquid chromatography (HPLC) using a method described in the supplementary material of Brumberg et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] to obtain fractions of intact [\u003csup\u003e11\u003c/sup\u003eC]SMW139 and its radioactive metabolites for correcting the plasma TAC. A radioactive standard was prepared by spiking the time point 0 plasma supernatant with [\u003csup\u003e11\u003c/sup\u003eC]SMW139 in order to analyze the correct peak of the chromatograms to correspond to the parent.\u003c/p\u003e \u003cp\u003eParent and radiometabolite binding to plasma proteins was analyzed for a subset of subjects from blood samples drawn prior to [\u003csup\u003e11\u003c/sup\u003eC]SMW139 injection and from 20 min p.i. From the time point 0 plasma drawn for \u003cem\u003ein vitro\u003c/em\u003e protein binding analysis, 1 mL was frozen for later duplicate analysis. The \u003cem\u003ein vitro\u003c/em\u003e plasma and \u003cem\u003ein vivo\u003c/em\u003e 20 min parent fraction analysis plasma samples were used to analyze parent and radiometabolite plasma protein binding with separate ultrafiltration membrane corrections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMRI and PET image processing and analysis\u003c/h2\u003e \u003cp\u003e A 3T MRI (Philips Ingenia/Philips Ingenuity, Best, The Netherlands) was acquired for all study participants with T1, T2, FLAIR, 3DT1, and gadolinium-enhanced T1. The dynamic PET images were smoothed, realigned, and co-registered using statistical parametric mapping (SPM12; Wellcome Trust Center for Neuroimaging, London, UK) according to a previously described procedure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The images were resliced to match the 1-mm\u003csup\u003e3\u003c/sup\u003e voxel size of the MRI images.\u003c/p\u003e \u003cp\u003eThe Lesion Segmentation Toolbox (LST) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was used in SPM to create FLAIR masks, which were manually edited to correspond to chronic T1 lesions to create T1 masks following a previously described procedure [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Perilesional masks were created by dilating the lesion mask by 3 mm, and then subtracting the core image from the dilated image. Separate masks were created for the Gd\u0026thinsp;+\u0026thinsp;lesions. NAWM masks were created for each subject by subtracting edited FLAIR lesion masks from segmented white matter. Finally, T1 images were filled with the T1 masks by employing the lesion filling tool of LST in SPM. The filled T1 image was used to segment whole-brain volume (BV) and volumes of different brain areas with FreeSurfer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for PET assessments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed with SPSS 28.0 (IBM Corp., Armonk, N.Y., USA). Group level means of V\u003csub\u003eT\u003c/sub\u003e estimates across different brain areas were compared between MS patients and HCs, and between PMS and RMS with the Wilcoxon rank-sum test. Among MS patients, Group level means of V\u003csub\u003eT\u003c/sub\u003e estimates were compared between the T1 lesion masks, the 3 mm perilesional rim masks, and the NAWM masks with paired t-tests. Linear correlations between V\u003csub\u003eT\u003c/sub\u003e estimates, lesion volume, BV, thalamus volume, and demographic variables were measured with the Pearson correlation coefficient. The effects of disease duration and BV on V\u003csub\u003eTDI\u003c/sub\u003e were estimated with multiple linear regression. All tests were two-tailed, and the alpha was set to 0.05 for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy subject demographics and other baseline characteristics\u003c/h2\u003e \u003cp\u003e26 subjects underwent the study procedures and the final analyzed cohorts consisted of 15 MS patients (n\u0026thinsp;=\u0026thinsp;6 for RMS, n\u0026thinsp;=\u0026thinsp;9 for PMS) and 9 HCs. 2 subjects (1 PMS and 1 HC) were excluded from the analysis due to technical issues during the PET visits. All RMS patients were enrolled\u0026thinsp;\u0026lt;\u0026thinsp;0.5 years (mean 0.12 years, SD 0.06) from diagnosis and PET imaged with [\u003csup\u003e11\u003c/sup\u003eC]SMW139 approximately 4 months thereafter. PMS patients were imaged\u0026thinsp;\u0026gt;\u0026thinsp;10 years (mean 16.9 years, SD 4.3) from diagnosis immediately after inclusion. Compared to patients with MS, the HCs were of comparable age [mean (SD) 47,5 (10.1) vs. 49.6 (14.5) years, p\u0026thinsp;=\u0026thinsp;0.479, respectively]. Compared to the RMS patients, the PMS cohort had a significantly higher EDSS (median 2.0 vs. 6.0, mean 2.5 vs. 5.1, p\u0026thinsp;=\u0026thinsp;0.005, respectively), and the two patient cohorts were of comparable age [mean (SD) 45.0 (11.7) vs. 49.2 (9.3) years, p\u0026thinsp;=\u0026thinsp;0.712, respectively]. The male to female (M/F) ratio was unequal between all cohorts but comparable between all MS and HCs: 6/3 (33% F), 9/6 (40% F), 4/5 (56% F) and 5/1 (17% F) for HCs, MS, PMS and RMS, respectively. At the time of PET imaging, two patients in the RMS cohort had started treatment with i.v. natalizumab, 1 patient had received a single dose of i.v ocrelizumab, and 3 patients had received a single i.v. dose of rituximab. At the time of PET imaging, three PMS patients were treated with rituximab, one with natalizumab, and one with fingolimod. MS lesion loads and other imaging characteristics are displayed in Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParent fraction and protein binding of [\u003csup\u003e11\u003c/sup\u003eC]SMW139\u003c/h2\u003e \u003cp\u003eThe plasma parent fraction decreased steadily down to approximately 45% over the course of the 90 minute sampling time. The mean parent fraction in the PMS group was indicative of slightly faster metabolism, while metabolism of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 was non-significantly slower among the RMS and HC groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe plasma protein binding analysis of frozen and fresh plasma samples yielded similar results. The mean (SD) parent free fraction (free parent over all parent in plasma, f\u003csub\u003eP/P\u003c/sub\u003e) of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 was 0.013 (0.004) and 0.013 (0.002) (n\u0026thinsp;=\u0026thinsp;11 for both) for fresh and frozen samples, respectively, and 0.010 (0.004) (n\u0026thinsp;=\u0026thinsp;9) for the \u003cem\u003ein vivo\u003c/em\u003e 20 min sample. The mean (SD) fraction of free radiometabolites over all radiometabolites was 0.425 (0.132) and the fraction of free radiometabolites over all free radioactivity in plasma was 0.88 (0.06) at 20 min. The mean f\u003csub\u003eP/P\u003c/sub\u003e was 0.0138 (n\u0026thinsp;=\u0026thinsp;3), 0.0096 (n\u0026thinsp;=\u0026thinsp;5) and 0.0093 (n\u0026thinsp;=\u0026thinsp;3) for HCs, PMS and RMS, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePET Modelling\u003c/h2\u003e \u003cp\u003eAccording to visual inspection (Supplement 1), Akaike criterion (AIC) and logarithm of mean residual sum of squares (log (1/n)(model \u0026ndash; data)\u003csup\u003e2\u003c/sup\u003e, where n\u0026thinsp;=\u0026thinsp;no. data points) (Supplement 2), both tested models fitted the data well. Coefficients of variation (CoV) of V\u003csub\u003eT\u003c/sub\u003e estimates were substantially higher with the 2TCM (Supplement 2). Compared to HCs, the overall performance of 1TDI was better among MS patients, and 0\u0026ndash;40 min and 0\u0026ndash;60 min performed similarly according to AIC (Supplement 3). Finally, 0\u0026ndash;60 min data was chosen for the primary analyses based on marginally lower CoV compared to 0\u0026ndash;40 min and 0\u0026ndash;90 min (Supplement 2\u0026ndash;3). Additional exploratory analyses were performed with 0\u0026ndash;40 min and 0\u0026ndash;90 min data for the 1TDI and 2TCM models, respectively. V\u003csub\u003eT\u003c/sub\u003e estimates from thalamus (R\u0026thinsp;=\u0026thinsp;0.800, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cGM (R\u0026thinsp;=\u0026thinsp;0.630, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) correlated significantly between the models (0\u0026ndash;60 min 1TDI and 0\u0026ndash;90 min 2TCM), while estimates from NAWM (R\u0026thinsp;=\u0026thinsp;0.158, p\u0026thinsp;=\u0026thinsp;0.461) and lesional or perilesional white matter (R= -0.04, p\u0026thinsp;=\u0026thinsp;0.888) did not.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;1. MRI variables and [\u003csup\u003e11\u003c/sup\u003eC]SMW139 V\u003csub\u003eTDI\u003c/sub\u003e (0\u0026ndash;60 min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Volume (cm\u003csup\u003e3\u003c/sup\u003e) HC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e939.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1267.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1148.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.665*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Volume (cm\u003csup\u003e3\u003c/sup\u003e) RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1069.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1231.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1172.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.276**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Volume (cm\u003csup\u003e3\u003c/sup\u003e) PMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e814.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1347.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1055.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e173.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 lesion volume (cm\u003csup\u003e3\u003c/sup\u003e) RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.940**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 lesion volume (cm\u003csup\u003e3\u003c/sup\u003e) PMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Gd\u0026thinsp;+\u0026thinsp;lesions RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e T1 lesions RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.194**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e T1 lesions PMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e 3mm rim RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.113**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e 3mm rim PMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e Thalamus HC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.723*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e Thalamus RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.071**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e Thalamus PMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e NAWM HC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.962*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e NAWM RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.121**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMW V\u003csub\u003eTDI\u003c/sub\u003e NAWM PMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*For the comparison all MS vs. HC\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e**For the comparison RMS vs. PMS\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eV\u003csub\u003eTDI\u003c/sub\u003e = dual-input distribution volume of parent tracer (K\u003csub\u003e1P\u003c/sub\u003e/K\u003csub\u003e2P\u003c/sub\u003e). 3mm rim\u0026thinsp;=\u0026thinsp;T1 perilesional 3 mm rim. NAWM\u0026thinsp;=\u0026thinsp;Normal appearing white matter. HC\u0026thinsp;=\u0026thinsp;Healthy control. RMS\u0026thinsp;=\u0026thinsp;Relapsing MS. PMS\u0026thinsp;=\u0026thinsp;Progressive MS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e[\u003csup\u003e11\u003c/sup\u003eC]SMW139 binding in RMS and PMS patients compared to healthy control subjects\u003c/h2\u003e \u003cp\u003eCompared to healthy controls, whole-brain, NAWM, thalamic and cGM uptake of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 was similar in MS patients (n\u0026thinsp;=\u0026thinsp;15) at group level. After explorative correction for group level mean f\u003csub\u003eP/P\u003c/sub\u003e (V\u003csub\u003eTDI\u003c/sub\u003e / f\u003csub\u003eP/P\u003c/sub\u003e), mean (SD) NAWM V\u003csub\u003eTDI\u003c/sub\u003e was significantly higher among all MS compared to HCs [11.15 (3.09) vs 7.69 (2.80), p\u0026thinsp;=\u0026thinsp;0.012, respectively].\u003c/p\u003e \u003cp\u003eV\u003csub\u003eTDI\u003c/sub\u003e estimates were somewhat higher in the RMS cohort compared to PMS and HCs, but the differences were not statistically significant (Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the 3 mm perilesional rim of all T1 lesions, the mean (SD) V\u003csub\u003eTDI\u003c/sub\u003e was 0.123 (0.02) vs. 0.097 (0.03) (p\u0026thinsp;=\u0026thinsp;0.113) in the RMS and PMS cohorts, respectively. Exploratory analysis with 0\u0026ndash;40 min data revealed a significant difference between the two groups [Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; 0.133 (0.02) vs. 0.101 (0.03), p\u0026thinsp;=\u0026thinsp;0.049, respectively]. The 2TCM yielded no significant differences between the groups, when all MS was compared to HCs (Supplement 4), or when RMS was compared to PMS (Supplement 5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e[\u003csup\u003e11\u003c/sup\u003eC]SMW139 binding in MS lesions compared with NAWM and the perilesional areas\u003c/h2\u003e \u003cp\u003eCompared to MS lesion core areas, V\u003csub\u003eTDI\u003c/sub\u003e estimates were significantly higher in the NAWM and perilesional 3 mm rims among all MS patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Analyzed separately, perilesional tracer uptake was significantly higher compared to lesions among RMS patients, but not among PMS patients. V\u003csub\u003eTDI\u003c/sub\u003e estimates within the Gd\u0026thinsp;+\u0026thinsp;lesions and in the 3 mm rim around Gd\u0026thinsp;+\u0026thinsp;lesions were not significantly higher compared to all RMS T1 lesions and the perilesional 3 mm area in general, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Lesional V\u003csub\u003eT2T\u003c/sub\u003e estimates were significantly higher compared to the NAWM and the perilesional area among all MS patients. (Supplement 6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLesional, perilesional and NAWM [11C]SMW139 VTDI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3mm rim - T1 lesions MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3mm rim - NAWM MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAWM MS - T1 lesions MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3mm rim - T1 lesions PMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3mm rim - NAWM PMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAWM PMS - T1 lesions PMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3mm rim - T1 lesions RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3mm rim - NAWM RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAWM RMS - T1 lesions RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGd\u0026thinsp;+\u0026thinsp;T1 lesions - T1 lesions RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGd\u0026thinsp;+\u0026thinsp;3mm rim \u0026ndash; 3mm rim RMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e3mm rim\u0026thinsp;=\u0026thinsp;T1 perilesional 3 mm rim, NAWM\u0026thinsp;=\u0026thinsp;Normal appearing white matter\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRMS\u0026thinsp;=\u0026thinsp;Relapsing MS. PMS\u0026thinsp;=\u0026thinsp;Progressive MS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e[\u003csup\u003e11\u003c/sup\u003eC]SMW139 binding in relation to demographic characteristics and MRI variables\u003c/h2\u003e \u003cp\u003eAge correlated negatively with V\u003csub\u003eTDI\u003c/sub\u003e in the 3 mm perilesional area among all MS patients (R= -0.558, p\u0026thinsp;=\u0026thinsp;0.031; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), and non-significantly with NAWM V\u003csub\u003eTDI\u003c/sub\u003e (R= -0.465, p\u0026thinsp;=\u0026thinsp;0.081), but no correlation with NAWM V\u003csub\u003eTDI\u003c/sub\u003e was seen among all subjects (n\u0026thinsp;=\u0026thinsp;24, R\u0026thinsp;=\u0026thinsp;0.024, p\u0026thinsp;=\u0026thinsp;0.91). Among all MS patients or among RMS or PMS, V\u003csub\u003eTDI\u003c/sub\u003e estimates did not correlate with EDSS (results not shown). Among PMS patients, time from diagnosis correlated with NAWM V\u003csub\u003eTDI\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Of note, BV or age did not correlate with disease duration among PMS patients (R= -0.259, p\u0026thinsp;=\u0026thinsp;0.501 and R= -0.350, p\u0026thinsp;=\u0026thinsp;0.365, respectively), and BV did not significantly correlate with age among all MS (R= -0.414, p\u0026thinsp;=\u0026thinsp;0.125). Among all subjects, V\u003csub\u003eTDI\u003c/sub\u003e estimates in the NAWM correlated with BV and the same was true for the 3 mm perilesional rim among all MS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong PMS with NAWM V\u003csub\u003eTDI\u003c/sub\u003e as the dependent variable, both disease duration (β\u0026thinsp;=\u0026thinsp;0.561, t\u0026thinsp;=\u0026thinsp;2.603, p\u0026thinsp;=\u0026thinsp;0.041) and BV (β\u0026thinsp;=\u0026thinsp;0.547, t\u0026thinsp;=\u0026thinsp;2.536, p\u0026thinsp;=\u0026thinsp;0.044) added significantly to the prediction (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.855, F(2,6)\u0026thinsp;=\u0026thinsp;8.166, p\u0026thinsp;=\u0026thinsp;0.019). V\u003csub\u003eTDI\u003c/sub\u003e in the perilesional area or NAWM did not correlate with overall T1 lesion volume (results not shown). No significant correlations with V\u003csub\u003eT2T\u003c/sub\u003e estimates and demographic variables or brain or lesion volume were found with the 2TCM model. V\u003csub\u003eTDI\u003c/sub\u003e estimates and BV were significantly higher among male subjects compared to females across all examined brain areas. The two groups were of similar age and EDSS, and lesion volumes were comparable, but the time from diagnosis among female subjects was somewhat longer (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Supplement 7). The correlation of NAWM V\u003csub\u003eTDI\u003c/sub\u003e with BV was abolished (R\u0026thinsp;=\u0026thinsp;0.076, p\u0026thinsp;=\u0026thinsp;0.791), when all male subjects (n\u0026thinsp;=\u0026thinsp;15) were analyzed separately.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is asserted that compared to a single-tissue model, a two-compartment model likely improves AIC with [\u003csup\u003e11\u003c/sup\u003eC]SMW139 by fitting radiometabolite build-up between the tissue compartments, and this effect is accentuated with longer fits. Indeed compared to a single-tissue model, a single-input 2TCM yielded superior fits that were further improved by longer p.i. time and by binding the disassociation rate (k4) from the specific tissue compartment to the whole-brain value [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which was not done in the present study where exploratory analyses were also performed with a single-input 2TCM. Nonetheless, the V\u003csub\u003eT\u003c/sub\u003e estimates from thalamus and cGM correlated moderately and strongly between the models, respectively, but estimates from WM did not.\u003c/p\u003e \u003cp\u003eFurthermore, significantly lower 2TDI AIC scores have only been reported in association with cGM, and 1TDI was associated with robust fits across all examined regions, and with somewhat lower %SE [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A 2TDI model may be too complex to fit reliably in all cases and regions of interest, while the 1TDI could be more sensitive to inaccuracies in plasma input data. The locus of interest[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] in MS brain imaging is primarily in the NAWM, where the 2TDI has not been shown to outperform the more robust 1TDI, and in the perilesional white matter, where estimates may also be affected by the partial volume effect. In addition to CoV and AIC, the 1TDI 0\u0026ndash;60 min fit was preferred based on the theoretically reduced effect of radioactive metabolites, while it was also assumed that fits significantly below 60 min would describe the data inadequately.\u003c/p\u003e \u003cp\u003eBeyond pharmacokinetic considerations, PET modelling is also instructed by the model\u0026rsquo;s ability to measure quantifiable target engagement in line with the known distribution of the tracer\u0026rsquo;s cognate receptors. In MS, microglia-associated TSPO expression is concentrated to active lesions and chronic lesion rims, and to a lesser extent chronic lesion centers and the NAWM, while inactive lesions and grey matter lesions are relatively devoid of microglia. HLADR\u0026thinsp;+\u0026thinsp;inflammatory microglia are most abundant at chronic lesion rims [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similarly, P2X\u003csub\u003e7\u003c/sub\u003eR-expression is more prominent in acute active lesions and chronic active lesion rims [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Compared to chronic and chronic active lesions, acute active MS lesions represent a small fraction of overall lesion count [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The fraction of early active lesions declines rapidly, and has been estimated to represent\u0026thinsp;\u0026lt;\u0026thinsp;5 % of all lesios at 5\u0026ndash;10 years after disease onset [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the above it was expected that at group level, most specific binding of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 in the WM of MS patients would take place in the immediate area around T1 lesions, and that lesional binding would decline after the initial phase of the disease. In the current study, perilesional binding was significantly higher than lesional binding, but not significantly higher than NAWM binding. In the newly diagnosed cohort of RMS, perilesional tracer binding was increased compared to PMS patients, although it is acknowledged that the groups were not sex matched. Lesions that Gd-enhanced approximately 4 months prior to PET imaging did not exhibit significantly higher tracer uptake, which indicates that disruption of the blood brain barrier did not significantly affect perilesional binding estimates among RMS.\u003c/p\u003e \u003cp\u003eAccording to plasma protein binding analysis, the free fraction of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 is very low, while radiometabolites contribute significantly to overall radioactivity passing through the blood brain barrier (BBB). 88% of the free radioactivity was due to circulating radiometabolites at 20 min. Thus, estimates obtained by using protein binding to correct V\u003csub\u003eTP\u003c/sub\u003e are susceptible to minor errors in f\u003csub\u003eP/P\u003c/sub\u003e analysis, and subject level correction might increase the already considerable variance even further. Correcting for the group level mean of the free parent affected the V\u003csub\u003eT\u003c/sub\u003e estimates considerably, and our exploratory analysis suggested higher free parent V\u003csub\u003eT\u003c/sub\u003e among MS patients compared to HCs, but incomplete protein binding data precludes conclusions on group level differences based on this sub-analysis. This approach should be considered in subsequent studies with [\u003csup\u003e11\u003c/sup\u003eC]SMW139.\u003c/p\u003e \u003cp\u003e[\u003csup\u003e11\u003c/sup\u003eC]SMW139 uptake was significantly increased in male subjects compared to females, which probably affected the strong observed correlation with BV, which was not seen when male subjects were analyzed separately. A similar sex disparity in tracer uptake has been observed with the microglial TSPO tracer [\u003csup\u003e11\u003c/sup\u003eC]PK11195 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Interestingly, MS disease duration correlated with tracer uptake, and this correlation was not explained by age or sex, as disease duration was somewhat longer among females. Significant correlations with MS related disability and NAWM uptake of [\u003csup\u003e11\u003c/sup\u003eC]PK11195 have been reported previously [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Also considering the higher uptake in RMS, lower tracer uptake in the perilesional rims among older subjects indicates that P2X\u003csub\u003e7\u003c/sub\u003eR-signaling is significant at the early stages of chronic active lesion formation. [\u003csup\u003e11\u003c/sup\u003eC]SMW139 uptake peaks at the early stage of EAE [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is concluded that only tentative evidence for the applicability of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 to detect MS-related smoldering inflammation was obtained, and that overall tracer uptake in the MS brain was not significantly higher compared to HCs in the studied cohort. [\u003csup\u003e11\u003c/sup\u003eC]SMW139-binding may capture a glial cell phenotype significant in early development of chronic active lesions in relapsing stages of MS. Age and sex are to be matched with a careful emphasis when studies with this tracer are conducted. Further information on the applicability of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 PET in MS could be obtained by correlating the current results with longitudinal clinical outcomes and progression-related biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJL: Writing – original draft (lead), Writing - review and editing (lead), Visualization (lead), Investigation (equal), Formal analysis (equal). RA: Writing – original draft (supporting), Investigation (supporting), Writing - review and editing (supporting); Visualization (supporting). JT: Formal analysis (equal), Writing - review and editing (supporting). MS: Investigation (equal); Conceptualization (supporting). EMK:Investigation (equal). MN: Project management (lead); Funding acquisition (supporting), Investigation (supporting). SH:\u0026nbsp;Resources (equal).\u0026nbsp;JR: Resources (equal).\u0026nbsp;JD:\u0026nbsp;Investigation (supporting),Resources (supporting). JG: Investigation (supporting),Resources (supporting).\u0026nbsp;MK: Supervision (supporting),\u0026nbsp;Resources (supporting). VO: Formal analysis (supporting), Conceptualization (supporting). LA: Supervision (lead); Conceptualization (lead); Funding acquisition (lead); Writing – review and editing (supporting).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Finnish Academy (decision number: 330902), the Sigrid Juselius Foundation, the state research funding of the Turku University Hospital expert responsibility area,\u0026nbsp;Finnish Governmental Research Funding (VTR) for Turku University Hospital,\u0026nbsp;and the InFLAMES Flagship Programme of the Academy of Finland (decision number 337530).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAny anonymized data used in the preparation of this article will be made available by the request of a qualified investigator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the principles of the Declaration of Helsinki.All subjects were adults (\u0026gt;18 years old), and provided written informed consent. The study was approved by the Ethics Committee of the Hospital District of Southwest Finland.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChauveau F, Becker G, Boutin H. Have (R)-[11C]PK11195 challengers fulfilled the promise? A scoping review of clinical TSPO PET studies. Eur J Nucl Med Mol Imaging. Springer Science and Business Media Deutschland GmbH; 2021. pp. 201\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanati RB, Newcombe J, Gunn RN, Cagnin A, Turkheimer F, Heppner F et al. The peripheral benzodiazepine binding site in the brain in multiple sclerosis: Quantitative in vivo imaging of microglia as a measure of disease activity. Brain [Internet]. 2000;123:2321\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/brain/123.11.2321\u003c/span\u003e\u003cspan address=\"10.1093/brain/123.11.2321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSucksdorff M, Matilainen M, Tuisku J, Polvinen E, Vuorimaa A, Rokka J et al. Brain TSPO-PET predicts later disease progression independent of relapses in multiple sclerosis. Brain [Internet]. 2020;143:3318\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pubmed/33006604\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pubmed/33006604\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRissanen E, Tuisku J, Vahlberg T, Sucksdorff M, Paavilainen T, Parkkola R et al. Microglial activation, white matter tract damage, and disability in MS. Neurol Neuroimmunol Neuroinflamm [Internet]. 2018;5:e443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pubmed/29520366\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pubmed/29520366\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMisin O, Matilainen M, Nylund M, Honkonen E, Rissanen E, Sucksdorff M et al. Innate Immune Cell\u0026ndash;Related Pathology in the Thalamus Signals a Risk for Disability Progression in Multiple Sclerosis. Neurology - Neuroimmunology Neuroinflammation [Internet]. 2022;9:e1182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://nn.neurology.org/content/9/4/e1182.abstract\u003c/span\u003e\u003cspan address=\"http://nn.neurology.org/content/9/4/e1182.abstract\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSucksdorff M, Tuisku J, Matilainen M, Vuorimaa A, Smith S, Keitil\u0026auml; J et al. Natalizumab treatment reduces microglial activation in the white matter of the MS brain. Neurol Neuroimmunol Neuroinflamm. 2019;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehto J, Sucksdorff M, Nylund M, Raitanen R, Matilainen M, Airas L. PET-measurable innate immune cell activation reduction in chronic active lesions in PPMS brain after rituximab treatment: a case report. J Neurol [Internet]. 2022 [cited 2023 Jan 24];1:1\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/article/\u003c/span\u003e\u003cspan address=\"https://link.springer.com/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00415-022-11539-4\u003c/span\u003e\u003cspan address=\"10.1007/s00415-022-11539-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehto J, Nylund M, Matilainen M, Sucksdorff M, Vuorimaa A, Rajander J et al. Longitudinal stability of progression-related microglial activity during teriflunomide treatment in patients with multiple sclerosis. Eur J Neurol [Internet]. 2023;n/a. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ene.15834\u003c/span\u003e\u003cspan address=\"10.1111/ene.15834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSucksdorff M, Rissanen E, Tuisku J, Nuutinen S, Paavilainen T, Rokka J, et al. Evaluation of the effect of fingolimod treatment on microglial activation using serial PET imaging in multiple sclerosis. J Nucl Med. 2017;58:1646\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaino W, Janssen B, Kooij G, van der Pol SMA, van Het Hof B, van Horssen J et al. Purinergic receptors P2Y12R and P2X7R: Potential targets for PET imaging of microglia phenotypes in multiple sclerosis. J Neuroinflammation [Internet]. 2017 [cited 2023 Feb 7];14:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jneuroinflammation.biomedcentral.com/articles/\u003c/span\u003e\u003cspan address=\"https://jneuroinflammation.biomedcentral.com/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12974-017-1034-z\u003c/span\u003e\u003cspan address=\"10.1186/s12974-017-1034-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiovannoni G, Popescu V, Wuerfel J, Hellwig K, Iacobeus E, Jensen MB et al. Smouldering multiple sclerosis: the \u0026lsquo;real MS.\u0026rsquo; Ther. Adv Neurol Disord. 2022;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNylund M, Sucksdorff M, Matilainen M, Polvinen E, Tuisku J, Airas L. Phenotyping of multiple sclerosis lesions according to innate immune cell activation using 18 kDa translocator protein-PET. Brain Commun. 2022;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonif M, Burnstock G, Williams DA. Microglia: proliferation and activation driven by the P2X7 receptor. Int J Biochem Cell Biol [Internet]. 2010 [cited 2023 Jun 15];42:1753\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/20599520/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/20599520/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattacharya A, Biber K. The microglial ATP-gated ion channel P2X7 as a CNS drug target. Glia [Internet]. 2016 [cited 2023 Jun 15];64:1772\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/27219534/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/27219534/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark AK, Staniland AA, Marchand F, Kaan TKY, McMahon SB, Malcangio M. P2X7-Dependent Release of Interleukin-1β and Nociception in the Spinal Cord following Lipopolysaccharide. The Journal of Neuroscience [Internet]. 2010 [cited 2023 Jun 15];30:573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC2880485/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC2880485/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFantoni ER, Dal Ben D, Falzoni S, Di Virgilio F, Lovestone S, Gee A. Design, synthesis and evaluation in an LPS rodent model of neuroinflammation of a novel 18F-labelled PET tracer targeting P2X7. EJNMMI Res [Internet]. 2017 [cited 2023 Nov 24];7:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/articles/\u003c/span\u003e\u003cspan address=\"https://link.springer.com/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13550-017-0275-2\u003c/span\u003e\u003cspan address=\"10.1186/s13550-017-0275-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerrito PR, Meyer JA, Peters JS, Riley AA, McCarthy BP, Gao M et al. Characterization of 11C-GSK1482160 for Targeting the P2X7 Receptor as a Biomarker for Neuroinflammation. J Nucl Med [Internet]. 2017 [cited 2023 Nov 24];58:458\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/27765863/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/27765863/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaino W, Janssen B, Kooijman E, Vos R, Schuit RC, O\u0026rsquo;Brien-Brown J et al. PET imaging of P2X7R in the experimental autoimmune encephalomyelitis model of multiple sclerosis using [11C]SMW139. J Neuroinflammation [Internet]. 2020 [cited 2024 Feb 28];17:1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jneuroinflammation.biomedcentral.com/articles/\u003c/span\u003e\u003cspan address=\"https://jneuroinflammation.biomedcentral.com/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12974-020-01962-7\u003c/span\u003e\u003cspan address=\"10.1186/s12974-020-01962-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurnstock G. P2X ion channel receptors and inflammation. Purinergic Signalling. 2016 12:1 [Internet]. 2016 [cited 2023 Jun 15];12:59\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/article/10.1007/s11302-015-9493-0\u003c/span\u003e\u003cspan address=\"https://link.springer.com/article/10.1007/s11302-015-9493-0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkinson SM, Barron ML, O\u0026rsquo;Brien-Brown J, Janssen B, Stokes L, Werry EL et al. Pharmacological Evaluation of Novel Bioisosteres of an Adamantanyl Benzamide P2X7 Receptor Antagonist. ACS Chem Neurosci [Internet]. 2017 [cited 2023 Jun 15];8:2374\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubs.acs.org/doi/full/10.1021/acschemneuro.7b00272\u003c/span\u003e\u003cspan address=\"https://pubs.acs.doi/full/10.1021/acschemneuro.7b00272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanssen B, Vugts DJ, Wilkinson SM, Ory D, Chalon S, Hoozemans JJM et al. Identification of the allosteric P2X7 receptor antagonist [11C]SMW139 as a PET tracer of microglial activation. Scientific Reports 2018 8:1 [Internet]. 2018 [cited 2023 Jun 15];8:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nature.com/articles/s41598-018-24814-0\u003c/span\u003e\u003cspan address=\"https://www.nature.com/articles/s41598-018-24814-0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkaike H. Information Theory and an Extension of Information the Maximum Theory Likelihood and an Principle Extension of the Maximum Likelihood Principle. Biogeochemistry [Internet]. 1998 [cited 2023 Jun 15];1998:199\u0026ndash;213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/chapter/\u003c/span\u003e\u003cspan address=\"https://link.springer.com/chapter/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-4612-1694-0_15\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4612-1694-0_15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagens MHJ, Golla SSV, Janssen B, Vugts DJ, Beaino W, Windhorst AD, et al. The P2X7 receptor tracer [11C]SMW139 as an in vivo marker of neuroinflammation in multiple sclerosis: a first-in man study. Eur J Nucl Med Mol Imaging. 2020;47:379\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAarnio R, Alzghool OM, Wahlroos S, O\u0026rsquo;Brien-Brown J, Kassiou M, Solin O et al. Novel plasma protein binding analysis method for a PET tracer and its radiometabolites: A case study with [11C]SMW139 to explain the high uptake of radiometabolites in mouse brain. J Pharm Biomed Anal. 2022;219.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrumberg J, Aarnio R, Forsberg A, Marjam\u0026auml;ki P, Kerstens V, Moein MM et al. Quantification of the purinergic P2X7 receptor with [11C]SMW139 improves through correction for brain-penetrating radiometabolites. J Cereb Blood Flow Metab [Internet]. 2023 [cited 2023 Jun 16];43:258\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/36163685/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/36163685/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSureau FC, Reader AJ, Comtat C, Leroy C, Ribeiro MJ, Buvat I et al. Impact of image-space resolution modeling for studies with the high-resolution research tomograph. J Nucl Med [Internet]. 2008 [cited 2024 Feb 13];49:1000\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/18511844/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/18511844/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt P, Gaser C, Arsic M, Buck D, F\u0026ouml;rschler A, Berthele A, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage. 2012;59:3774\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAiras L, Rissanen E, Rinne JO. Imaging neuroinflammation in multiple sclerosis using TSPO-PET. Clin Transl Imaging. Springer-Verlag Italia s.r.l.; 2015. pp. 461\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAiras L, Yong VW. Microglia in multiple sclerosis - pathogenesis and imaging. Curr Opin Neurol [Internet]. 2022 [cited 2023 Jan 24];35:299\u0026ndash;306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/35674072/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/35674072/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBodini B, Tonietto M, Airas L, Stankoff B. Positron emission tomography in multiple sclerosis - straight to the target. Nat Rev Neurol [Internet]. 2021; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pubmed/34545219\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pubmed/34545219\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNutma E, Stephenson JA, Gorter RP, de Bruin J, Boucherie DM, Donat CK et al. A quantitative neuropathological assessment of translocator protein expression in multiple sclerosis. Brain [Internet]. 2019;142:3440\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pubmed/31578541\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pubmed/31578541\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrischer JM, Weigand SD, Guo Y, Kale N, Parisi JE, Pirko I et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol [Internet]. 2015 [cited 2024 Jan 5];78:710\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/26239536/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/26239536/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuhlmann T, Ludwin S, Prat A, Antel J, Br\u0026uuml;ck W, Lassmann H. An updated histological classification system for multiple sclerosis lesions. Acta Neuropathol [Internet]. 2017 [cited 2023 Apr 6];133:13\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/27988845/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/27988845/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaaksonen S, Saraste M, Nylund M, Hinz R, Snellman A, Rinne J et al. Sex-driven variability in TSPO-expressing microglia in MS patients and healthy individuals. Front Neurol [Internet]. 2024 [cited 2024 Feb 28];15:1352116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2024.1352116/full\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2024.1352116/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4121612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4121612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePET imaging of activated microglia has improved our understanding of the pathology behind disability progression in MS, and pro-inflammatory microglia at \u0026lsquo;smoldering\u0026rsquo; lesion rims have been implicated as drivers of disability progression. The P2X\u003csub\u003e7\u003c/sub\u003eR is upregulated in the cellular membranes of activated microglia. A single-tissue dual-input model was applied to quantify P2X\u003csub\u003e7\u003c/sub\u003eR binding in the normal appearing white matter, perilesional areas and thalamus among progressive MS patients, healthy controls and newly diagnosed relapsing MS patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, tracer uptake in the MS brain was not significantly higher compared to HCs. In the 3 mm perilesional rim of all T1 lesions, tracer binding was higher among relapsing patients compared to progressive patients. Tracer binding was higher in males compared to females. Disease duration correlated with tracer binding in the normal appearing white matter. Age correlated negatively with tracer binding in the perilesional rims.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBinding estimates obtained with the dual-input model were consistent with the expected distribution of P2X\u003csub\u003e7\u003c/sub\u003eRs in the MS brain. According to our study, [\u003csup\u003e11\u003c/sup\u003eC]SMW139-binding may capture a glial cell phenotype significant in early development of chronic active lesions in relapsing stages of MS. Only tentative evidence for the applicability of [\u003csup\u003e11\u003c/sup\u003eC]SMW139 to detect MS-related diffuse smoldering inflammation was obtained.\u003c/p\u003e","manuscriptTitle":"P2X7-receptor binding in new-onset and secondary progressive MS – a [11C]SMW139 PET study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 17:54:18","doi":"10.21203/rs.3.rs-4121612/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"786184f8-4aec-4670-83ed-fd08f77cef9b","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-14T09:30:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 17:54:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4121612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4121612","identity":"rs-4121612","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}