Pathological Signatures of White Matter Lesions in Multiple Sclerosis versus Stroke: A Synthetic MRI Study

Pathological Signatures of White Matter Lesions in Multiple Sclerosis versus Stroke: A Synthetic MRI 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 Pathological Signatures of White Matter Lesions in Multiple Sclerosis versus Stroke: A Synthetic MRI Study Evangelos Katsarogiannis, Johan Wikström, Johan Virhammar, Shala Ghaderi Berntsson, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8338798/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Neuroradiology → Version 1 posted You are reading this latest preprint version Abstract Purpose To evaluate whether quantitative synthetic MRI (SyMRI) parameters can differentiate non-specific white matter lesions (nsWMLs) in patients with multiple sclerosis (MS) and ischemic stroke, and to assess differences in normal-appearing white matter (NAWM) between these groups. Methods Thirty MS patients and nineteen ischemic stroke patients underwent standardized MRI including SyMRI. Three lesion categories were analyzed: typical MS lesions (MSL), non-specific lesions in MS (nsWML-MS), and non-specific lesions in stroke (nsWML-S). SyMRI-derived parameters (R1, R2, proton density, and myelin content) were extracted from each region of interest (ROI), and one ROI was placed in NAWM per patient. Group differences were evaluated using non-parametric tests. Logistic regression models, both unadjusted and age-adjusted, assessed predictors of MS diagnosis. Results Typical MS lesions showed lower myelin content and R1 and higher proton density than nsWML-MS (all p < 0.0001). Compared with nsWML-S, nsWML-MS demonstrated lower myelin content and higher proton density (p < 0.05), while R1 and R2 values did not differ. NAWM differences between MS and stroke emerged only after age adjustment. Age alone discriminated MS from stroke (AUC 0.83), with modest improvement when NAWM measures were added (AUC 0.86). Conclusion SyMRI captures both lesion-specific and diffuse NAWM differences between MS and stroke. Age strongly influences quantitative white matter measures, and adjusting for age reveals subtle NAWM pathology in MS. SyMRI may support differential diagnosis in patients with ambiguous white matter lesions. Multiple Sclerosis Stroke Quantitative Synthetic MRI White Matter Lesions Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Multiple sclerosis (MS) and stroke are two major neurological disorders contributing significantly to disability worldwide. Although their clinical and epidemiological profiles differ, overlap exists: MS patients have a 2.55-fold higher relative risk of developing stroke [ 1 ]. White matter lesions (WMLs) are a common radiological finding in both MS and stroke. Non-specific white matter lesions (nsWMLs) are defined as T2- or FLAIR-hyperintense regions without clear morphological features that clarify their etiology. They are frequently observed with aging and/or vascular risk factors, have no typical location, and can be observed in periventricular, subcortical as well as deep white matter regions [ 2 , 3 ]. The presence of nsWMLs complicates radiological diagnosis of MS as their ambiguous appearance makes it difficult to distinguish them from ischemic lesions, particularly in older patients and/or those with vascular comorbidities [ 4 – 6 ]. A predominance of non-perivenular lesions, especially subcortical, may signal alternative or comorbid vascular pathology [ 7 ]. nsWMLs may reflect distinct pathogenetic backgrounds in MS and stroke respectively, but it is not excluded that the origin may have common features. Interestingly, recent research reveals that in MS, impaired perfusion, mitochondrial dysfunction, and inflammation create a self-reinforcing hypoxia–inflammation cycle that drives demyelination and disability [ 8 , 9 ]. And in stroke, a secondary demyelination is increasingly recognized as a key driver of long-term deficits. This involves collagen-mediated inhibition of remyelination [ 10 ], astrocytic lipocalin-2 signaling [ 11 ], and persistent myelin deficits that exacerbate neuronal loss [ 12 ]. Thus, both disorders show convergent myelin vulnerability despite different initiating mechanisms. Synthetic MRI (SyMRI) is a quantitative imaging technique that, from one multidynamic multiecho sequence, generates T1-, T2-, and PD-weighted images, volumetric segmentations, and quantitative maps of R1, R2, PD, and myelin content (MyC) [ 13 – 15 ]. In MS imaging, SyMRI matches conventional MRI lesion detection while reducing scan time [ 16 ], and subtraction mapping may enhance the sensitivity for detecting new lesions [ 17 ]. Paramagnetic rim lesions identified with SyMRI may reflect diffuse periplaque damage and ongoing silent progression [ 18 ], and combined PET–SyMRI studies have reported associations between microglial activation, myelin loss, and clinical decline [ 19 ]. In diagnostic imaging of stroke, SyMRI enables estimation of relaxation times with minimal discrepancy compared to conventional MRI [ 20 ]. Synthetic FLAIR performs comparably to standard FLAIR in early ischemia, with reduced scan time [ 21 ]. Quantitative values (R1, R2, PD) can possibly distinguish acute from chronic ischemia [ 22 ] and stratify stroke severity [ 23 ]. Moreover, SyMRI-derived total myelin volume can be linked to 3-month functional outcomes [ 24 ]. One early SyMRI study assessed a small group of patients with MS, stroke and borderline cases [ 14 ]. However, to our knowledge, no previous study has thoroughly explored the role of SyMRI in differentiating nsWMLs. The objectives of the study were to: first, assess the value of quantitative parameters derived from SyMRI for differentiating non-specific white matter lesions in two different patient cohorts, one with MS and one with ischemic stroke; and second, to investigate whether this method can differentiate between typical and non-specific white matter lesions in the MS cohort. Methods Study Population Initially, 32 patients with ischemic stroke aged 60 years or younger were screened for participation. Of these, 13 were excluded because they had only the acute stroke lesion without additional white matter lesions on MRI, or had lesions that did not meet the inclusion criteria, resulting in a final sample of 19 stroke patients. In addition, 30 patients with clinically confirmed multiple sclerosis (MS) were included, yielding a total study population of 49 participants. All participants underwent a single MRI examination at Uppsala University Hospital between 2021 and 2024, utilizing a standardized imaging protocol. Inclusion criteria for all patients included, being ≥ 18 years of age and availability of a complete MRI imaging including fluid-attenuated inversion recovery (FLAIR) and synthetic MRI sequences. For the MS cohort, demographic data, disability scores (EDSS), and data on disease duration, time since last relapse, MS subtypes, and distribution of disease-modifying therapies (DMTs) were collected. EDSS was presented as median (interquartile range, IQR) (Table 1 ). Table 1 Clinical characteristics of patients with multiple sclerosis (MS) Clinical characteristics MS patients Number of MS patients, n 30 Age, mean ± SD (years) Male sex 47.3 ± 7.53 EDSS, median (IQR) 2.0 (1.5–3.0) Time since MS onset, mean ± SD (months) 174.3 ± 88.3 Time since last relapse, mean ± SD (months) 74.8 ± 35.4 MS Type (n/%) RRMS 27 (90.0%) SPMS 3 (10.0%) Type of DMT (n/%) Rituximab 13 (43.0%) HSCT 8 (26.7%) Dimethyl fumarate 3 (10.0%) None 2 (6.7%) Cladribine 1 (3.3%) Alemtuzumab 1 (3.3%) Fingolimod 1 (3.3%) INF 1 (3.3%) As for the patients with ischemic stroke, data on demographic parameters, vascular risk factors, cardiac comorbidities, and stroke subtypes were retrieved from medical records (Table 2 ). Table 2 Clinical characteristics of patients with ischemic stroke. Clinical characteristics Stroke patients Number of patients, n 19 Age, y (mean ± SD) 54.5 ± 4.4 Male sex 13 (68%) BMI > 25, n (%) 3 (15.8%) Hypertension, n (%) 13 (68.4%) Diabetes, n (%) 4 (21.1%) Dyslipidemia, n (%) 6 (31.6%) Atrial Fibrillation, n (%) 2 (10.5%) Smoking, n (%) 2 (10.5%) Chronic heart failure, n (%) 1 (5.3%) Chronic kidney disease, n (%) 1 (5.3%) Patent foramen ovale, n (%) 4 (21.1%) Cryptogenic stroke, n (%) 1 (5.3%) Stroke subtype Large-artery atherosclerosis, n (%) 5 (26.3%) Cardioembolic stroke, n (%) 1 (5.3%) Small-vessel occlusion, n (%) 12 (63.2%) Other cause, n (%) 1 (5.3%) MRI Acquisition All MRI scans were performed on a 3T scanner (Achieva dStream, Philips Medical Systems, Best, The Netherlands) equipped with a 32-channel head coil. The imaging protocol included a three-dimensional FLAIR sequence and a multi-dynamic multi-echo (MDME) sequence optimized for synthetic MRI acquisition. Specific sequence parameters were as follows: FLAIR (TR = [4800] ms, TE = [304] ms, TI = [1650] ms, slice thickness = [1.1] mm) and MDME (TR = [4688] ms, TE = [12.5] ms, slice thickness = [ 4 ] mm). The MDME sequence enabled the generation of quantitative maps of R1, R2 relaxation rates, proton density (PD), and myelin content using the SyMRI software versions (7.1, 2017; 7.3, 2018; 8.0, 2019;11.0.7, 2019; 12.1.11, 2024) Synthetic MR AB, Linköping, Sweden). Lesion Selection and ROI Placement Criteria for typical MS lesions included periventricular or corpus callosum location, ovoid shape, perpendicular orientation of the long axis towards the adjacent lateral ventricle, and size > 5 mm. For each patient, representative non-specific white matter lesions were manually selected based on the FLAIR sequence, adhering to the following inclusion criteria: lesion size > 5 mm in maximum diameter, located at a minimum distance of 10 mm from the ventricular system and 5 mm from the cortical surface (outer edge of FLAIR hyperintensity), absence of contrast-enhancement, and lesions not appearing hypointense on T1-weighted sequences. Ischemic lesions were excluded based on qualitative visual assessment of the DWI sequence. Following lesion selection, FLAIR images were co-registered to synthetic MRI maps, and corresponding regions of interest (ROIs) were manually delineated. During ROI placement, action was taken to maintain a sufficient margin between the ROI borders and the lesion edges, as well as adjacent anatomical boundaries to minimize partial volume effects. Additionally, only lesions providing adequate surrounding white matter space for reliable ROI placement were included in the analysis. One ROI was placed in NAWM for each of the 30 MS and 19 stroke patients. Figures 1 and 2 illustrate examples of ROI placement in lesions from MS and stroke patients, as well as in normal-appearing white matter (NAWM). Quantitative MRI Analysis Quantitative evaluation of the selected lesions was conducted using the SyMRI post-processing software. For each ROI, R1, R2, proton density, and myelin content (MyC) were extracted. In addition to lesion analysis, a ROI was also placed within normal-appearing white matter (NAWM) to obtain reference measurements for comparison. Lesion selection was initially performed by a first rater who was not blinded to the patient group (MS or stroke). A second rater, an experienced neuroradiologist, reviewed all lesions under blinded conditions; however, full blinding could not always be ensured when overt radiological features suggested the underlying diagnosis. Statistics For all analyses, the mean value for the lesions was calculated at the patient level. Comparisons between typical MS lesions (MSL) and non-specific lesions in MS patients (nsWML-MS) were performed using paired Wilcoxon signed-rank tests, while differences between nsWML-MS and non-specific lesions in stroke patients (nsWML-S) were assessed using Mann–Whitney U tests. Logistic regression models, both unadjusted and adjusted for age, were applied to MyC, PD, R1, and R2 values to predict MS status, with results reported as odds ratios (OR) and 95% confidence intervals (CI). Receiver operating characteristic (ROC) curves were generated to evaluate discriminatory ability. Comparisons of normal-appearing white matter (NAWM) between MS and stroke groups were also conducted using Mann–Whitney U tests and age-adjusted logistic regression. Results The demographic and clinical characteristics of the MS and stroke cohorts are summarized in Tables 1 and 2 . In total, 157 white matter lesions were analyzed: 61 typical MS lesions (MSL), 48 non-specific white matter lesions in MS patients (nsWML-MS), and 48 non-specific white matter lesions in stroke patients (nsWML-S). Online Resource Supplementary Table 1 shows the distribution of lesions among patients. Comparison of typical MS Lesions (MSL) and Non-Specific Lesions in MS patients (nsWML-MS) Of the 30 MS patients, 24 had at least one supratentorial MSL that met the size criterion (> 5 mm). These 24 patients represented a total of 61 MSLs and 40 nsWML-MSs. Paired analyses within 24 MS patients (61 MSL and 40 nsWML-MS) showed that the median myelin content (MyC) was markedly lower in MSL compared to nsWML-MS (1.98 vs. 10.63, p < 0.0001), (Fig. 3 ). Proton density (PD) was significantly higher in MSL (89.93 vs. 80.63, p < 0.0001), while R1 and R2 relaxation rates were significantly reduced (both p < 0.0001), (Online Resource Supplementary table 2). Comparison of Non-Specific Lesions in MS (nsWML-MS) and Stroke (nsWML-S) patients A total of 48 nsWML-MS and 48 nsWML-S lesions were included. nsWML-MS had significantly lower MyC than nsWML-S (12.23 vs. 16.40, p = 0.0489), and significantly higher PD (79.11 vs. 74.50, p = 0.0380). In contrast, differences in R1 ( p = 0.1603) and R2 ( p = 0.2520) between the two groups did not reach statistical significance (Table 3 ). Table 3 Comparison between non-specific white matter lesions in MS (nsWML-MS) and stroke (nsWML-S). Values are shown as Median (IQR) and Min–Max. SyMRI Parameters nsWML-MS Median (IQR) nsWML-MS Min–Max nsWML-S Median (IQR) nsWML-S Min–Max P-value MyC 12.23 (4.70–17.50) 1.70–25.80 16.40 (11.80–20.95) 4.85–29.95 0.0489 PD 79.11 (74.20–83.80) 68.40–90.50 74.50 (71.65–76.20) 66.25–86.60 0.0380 R1 0.95 (0.84–0.99) 0.74–1.15 0.97 (0.87–1.04) 0.83–1.16 0.1603 R2 10.40 (9.91–11.10) 8.38–12.99 10.91 (9.81–11.51) 9.38–12.78 0.2520 Logistic Regression Analysis Age-adjusted logistic regression models were fitted using patient-level mean values of MyC, PD, R1, and R2 from nsWML-MS and nsWML-S lesions. In unadjusted analyses, higher PD and lower MyC were associated with increased odds of MS (PD: OR 1.13, 95% CI 1.02–1.28, p = 0.020; MyC: OR 0.91, 95% CI 0.83–1.00, p = 0.038). However, after adjusting for age, these associations were no longer statistically significant. Receiver operating characteristic (ROC) analysis indicated that age alone discriminated MS from stroke with an AUC of 0.83 (95% CI 0.71–0.95). Adding SyMRI parameters to age did not substantially improve discrimination (Online Resource supplementary table 3). Correlation Analysis Pearson’s correlation analysis revealed negative correlations between MyC and PD (r = − 0.97 for nsWML-MS, r = − 0.96 for nsWML-S, both p < 0.0001), suggesting redundancy between these measures. Positive correlations were also observed between MyC and R1, and between PD and R2. In nsWML-MS, MyC showed a moderate positive correlation with age (r = 0.47, p = 0.008), whereas in nsWML-S, no significant correlations with age were observed (Online Resource Supplementary tables 4 and 5). Comparison of Normal-Appearing White Matter (NAWM) NAWM variables did not differ significantly between MS and stroke patients. MyC and PD were marginally lower in the MS group, and R1 showed a trend toward reduction in MS compared to stroke group ( p = 0.068) (Online Resource Supplementary table 6). Logistic regression on NAWM parameters indicated that, after age adjustment, lower MyC (OR 0.76, 95% CI 0.56–0.98, p = 0.031), higher PD (OR 1.52, 95% CI 1.04–2.40, p = 0.032), and reduced R1 (OR 0.25, 95% CI 0.06–0.70, p = 0.007) were significantly associated with MS. ROC analysis showed that age alone achieved an AUC of 0.83, while the addition of NAWM parameters slightly improved classification accuracy (up to AUC 0.86) (Online Resource Supplementary table 7). As shown in Fig. 4 , age alone achieved an AUC of 0.83. When myelin content from lesions was added to the model, the AUC increased modestly to 0.84, and inclusion of myelin content from NAWM reached 0.86, indicating a limited incremental contribution of quantitative myelin measures beyond age. Discussion Within the MS group, typical MS lesions (MSL) differed significantly from non-specific lesions (nsWML-MS). Typical MS lesions showed lower MyC and R1 values, together with higher PD, consistent with more advanced demyelination and tissue loss. In contrast, nsWML-MS retained higher myelin content and relaxation values, closer to normal compared with MSL, suggesting milder demyelination or a different injury pathway. These findings support the view that not all MS-associated white matter lesions are equal and that SyMRI can detect gradations of pathology that conventional imaging cannot capture. When comparing nsWML-MS with nsWML-S, we observed higher MyC and lower PD in stroke patients. These findings may be compatible with differences in the underlying tissue processes of MS and stroke: in MS, involving inflammation-related demyelination, and in stroke, reflecting ischemia-related secondary myelin injury. Similarly, NAWM values showed only subtle differences between MS and stroke patients, with MS tending toward lower myelin content (MyC) and higher proton density (PD). While these differences did not reach significance in direct group comparisons, age-adjusted regression analyses indicated that lower MyC and higher PD were independently associated with MS diagnosis. Previous studies have reported reduced NAWM myelin fractions in MS patients compared to healthy controls. These reductions are correlated to both physical and cognitive disability, underscoring the clinical importance of diffuse white matter damage [ 25 ]. Additional studies have found reduced myelin volume fraction (MVF) and altered relaxometry metrics (R1, R2, PD) in NAWM compared to controls, while further work has demonstrated that SyMRI-derived MVF in NAWM correlates with disease duration, highlighting the method’s sensitivity to disease burden [ 26 , 27 ]. These findings align with longitudinal magnetization transfer studies showing that microstructural alterations in NAWM can precede focal lesion formation, indicating a pre-lesional stage of tissue vulnerability [ 28 ]. This suggests that diffuse NAWM abnormalities captured by quantitative MRI may reflect early pathological changes preceding overt demyelination. In stroke, NAWM undergoes early microstructural alterations, but the underlying mechanism appears distinct. Previous studies indicate that NAWM regions, that later evolved into white matter hyperintensities, already exhibit reduced white-matter–like signals and elevated fluid- and gray-matter–like components early-on, suggesting tissue vulnerability before lesion formation [ 29 ]. A recent study has shown that even beyond visible leukoaraiosis, NAWM exhibits subtle and diffuse FLAIR signal increases, correlating with overall leukoaraiosis burden and age [ 30 ]. Our results extend these observations by suggesting that NAWM metrics might provide additional discriminative power between MS and stroke compared to lesion-based comparisons alone. The lower myelin content and higher PD seen in MS NAWM may reflect widespread inflammatory demyelination, whereas the minimal signal changes observed in stroke-related NAWM may be related to diffuse vascular injury. This distinction suggests a potential role for SyMRI in disentangling the different biological processes underlying NAWM changes, improving diagnostic accuracy when conventional MRI findings are inconclusive. A previous unpublished pilot study from Linköping University Hospital in Sweden explored the use of quantitative MRI with the same SyMRI framework applied in our study to distinguish MS lesions from ischemic lesions in a small cohort of seven MS and seven stroke patients. The study concluded that black holes could be clearly differentiated from ischemic lesions based on R1, R2, and PD values, reflecting the severe tissue destruction typical of chronic MS plaques. However, other white matter lesions showed substantial overlap, making them difficult to classify using these parameters alone. Importantly, no significant differences were found in normal-appearing white matter (NAWM) between the MS and stroke groups, suggesting that more diffuse changes could not be captured in their small sample. In contrast, our study included a larger and prospectively recruited cohort and incorporated myelin-content mapping with age adjustment, revealing trends consistent with mild NAWM myelin loss in the MS group. Together, these findings indicate that SyMRI may help to characterize both advanced and more subtle white matter changes. As expected, age emerged as a strong discriminator in the logistic regression analysis. This finding partly reflects the study design, as patients with stroke were older than those with MS. However, it also mirrors the well-established biological context: vascular-related white matter changes accumulate with advanced age, whereas MS typically presents earlier in life. Importantly, this underscores that SyMRI parameters cannot be interpreted in isolation but need to be evaluated together with demographic and clinical data to improve diagnostic accuracy. NAWM parameters became significant only after age adjustment, suggesting that age exerts a strong influence on white matter tissue properties. Controlling for this effect allowed disease-related differences in MS NAWM to emerge more clearly, implying that a quantitative MRI of NAWM may detect indications of pathology once age-related variance is accounted for. Several advanced imaging techniques have been explored to address the diagnostic challenge of distinguishing MS lesions from non-specific white matter lesions. For instance, biomarkers such as the central vein sign and percentage of perivenous white matter lesions (% PVWML) obtained from 7T SWI/FLAIR imaging can help differentiate MS lesions from other WMLs [ 31 ]. However, their diagnostic accuracy decreases when non-specific white matter lesions exhibit imaging characteristics similar to MS lesions, such as the presence of a central vein or perivenous distribution, creating challenges to differentiation [ 32 , 33 ]. Functional BOLD (blood-oxygen-level-dependent) imaging has shown outward signal reductions in MS lesions compared to nsWMLs [ 34 ], and convolutional neural network models have achieved up to 78% accuracy in separating MS from non-specific WMLs based on MRI data [ 35 ]. Our findings indicate that SyMRI may complement these more specialized methods by providing both quantitative information together with lesion-specific and diffuse NAWM changes in a single, clinically feasible scan, potentially improving diagnostic precision when conventional MRI is inconclusive. Limitations on our study, include the modest sample size, a single-center design, lack of histopathological validation, and absence of a healthy control group for comparison. Lesion selection was performed by one rater and reviewed by a blinded neuroradiologist to ensure consistency. Nevertheless, inter-rater reproducibility was not formally quantified, which may introduce minor subjectivity. Although the median values of SyMRI parameters differed significantly between groups, there was considerable overlap in the distributions, with individual lesions often falling within similar ranges for MS and stroke. This suggests that SyMRI metrics alone are unlikely to provide a definitive diagnostic classification, and their greatest value may lie in combination with clinical and demographic factors. Conclusions To our knowledge, this is the first comprehensive study to use SyMRI to compare non-specific white matter lesions in patients with MS and stroke. Our results suggest that SyMRI-derived parameters may capture differences in both lesions and possibly subtle NAWM variations between the two disorders, potentially reflecting their distinct underlying biological mechanisms. SyMRI also appeared to show modest discriminatory ability when assessing normal-appearing white matter (NAWM), suggesting a potential value in evaluating diffuse white matter changes beyond visible lesions. However, the overlap observed between groups and the modest sample size underline the need for caution in the interpretation of these results. Larger studies may help to validate these findings and establish whether SyMRI can be reliably implemented as a clinical tool for the differential diagnosis of ambiguous white matter lesions. Declarations Ethics approval The study was conducted in accordance with the Declaration of Helsinki. This study was approved by the Regional Ethics Board. Consent to participate We obtained written informed consent for the procedures from all patients. Competing interests The authors declare no competing interests. Author Contribution EK: Conceptualization; study design; data collection; imaging analysis; statistical analysis; interpretation of results; writing – original draft preparation; writing – review and editing; preparation of figures.JW: Methodology; imaging supervision; imaging analysis; interpretation of imaging findings; writing – review and editing.JV: Methodology; imaging analysis; interpretation of imaging findings; writing – review and editing.SGB: Conceptualization; supervision; interpretation of results; writing – review and editing.AML: Conceptualization; writing – review and editing. Acknowledgement Acknowledgements: The authors thank all the participants for their participation in the study. Data Availability Data are available from the corresponding author on reasonable request. References Stefanou MI et al (2024) Prevalence and epidemiology of stroke in patients with multiple sclerosis: a systematic review and meta-analysis. J Neurol 271(7):4075–4085 de Leeuw FE et al (2001) Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 70(1):9–14 Zhang W et al (2023) Prevalence and clinical characteristics of white matter hyperintensities in Migraine: A meta-analysis. Neuroimage Clin 37:103312 Liu S et al (2013) Prevalence of brain magnetic resonance imaging meeting Barkhof and McDonald criteria for dissemination in space among headache patients. Mult Scler 19(8):1101–1105 Seneviratne U, Chong W, Billimoria PH (2013) Brain white matter hyperintensities in migraine: clinical and radiological correlates. Clin Neurol Neurosurg 115(7):1040–1043 Kim SS et al (2014) Limited utility of current MRI criteria for distinguishing multiple sclerosis from common mimickers: primary and secondary CNS vasculitis, lupus and Sjogren's syndrome. Mult Scler 20(1):57–63 Lapucci C et al (2023) Central vein sign and diffusion MRI differentiate microstructural features within white matter lesions of multiple sclerosis patients with comorbidities. Front Neurol 14:1084661 Soroush A, Dunn JF (2025) A Hypoxia-Inflammation Cycle and Multiple Sclerosis: Mechanisms and Therapeutic Implications. Curr Treat Options Neurol 27(1):6 Hollingworth BYA et al (2025) Hypoxic Neuroinflammation in the Pathogenesis of Multiple Sclerosis. Brain Sci, 15(3) Yamazaki R et al (2025) Type I collagen secreted in white matter lesions inhibits remyelination and functional recovery. Cell Death Dis 16(1):285 Huang Z et al (2025) Lipocalin-2 regulates astrocyte-oligodendrocyte interaction to drive post-stroke secondary demyelination. Cell Rep 44(7):115899 Cheng YJ et al (2024) Prolonged myelin deficits contribute to neuron loss and functional impairments after ischaemic stroke. Brain 147(4):1294–1311 Granberg T et al (2016) Clinical Feasibility of Synthetic MRI in Multiple Sclerosis: A Diagnostic and Volumetric Validation Study. AJNR Am J Neuroradiol 37(6):1023–1029 Blystad I et al (2012) Synthetic MRI of the brain in a clinical setting. Acta Radiol 53(10):1158–1163 West J et al (2014) Normal appearing and diffusely abnormal white matter in patients with multiple sclerosis assessed with quantitative MR. PLoS ONE 9(4):e95161 Fujita S et al (2021) 3D Quantitative Synthetic MRI in the Evaluation of Multiple Sclerosis Lesions. AJNR Am J Neuroradiol 42(3):471–478 Schlaeger S et al (2023) Longitudinal Assessment of Multiple Sclerosis Lesion Load With Synthetic Magnetic Resonance Imaging-A Multicenter Validation Study. Invest Radiol 58(5):320–326 Krajnc N et al (2023) Paramagnetic rim lesions lead to pronounced diffuse periplaque white matter damage in multiple sclerosis. Mult Scler 29(11–12):1406–1417 Barletta VT et al (2023) In vivo characterization of microglia and myelin relation in multiple sclerosis by combined (11)C-PBR28 PET and synthetic MRI. J Neurol 270(6):3091–3102 Li CW et al (2020) Reliability of Synthetic Brain MRI for Assessment of Ischemic Stroke with Phantom Validation of a Relaxation Time Determination Method. J Clin Med, 9(6) Benzakoun J et al (2022) Synthetic FLAIR as a Substitute for FLAIR Sequence in Acute Ischemic Stroke. Radiology 303(1):153–159 Andre J, Barrit S, Jissendi P (2022) Synthetic MRI for stroke: a qualitative and quantitative pilot study. Sci Rep 12(1):11552 Huang R et al (2025) Diagnostic and prediction value of synthetic magnetic resonance imaging in acute ischemic stroke patients. Adv Clin Exp Med 34(2):179–186 Toko M et al (2025) Usefulness of Myelin Quantification Using Synthetic Magnetic Resonance Imaging for Predicting Outcomes in Patients With Acute Ischemic Stroke. Stroke 56(3):649–656 Ouellette R et al (2020) Validation of Rapid Magnetic Resonance Myelin Imaging in Multiple Sclerosis. Ann Neurol 87(5):710–724 Ladopoulos T et al (2022) Relaxometry and brain myelin quantification with synthetic MRI in MS subtypes and their associations with spinal cord atrophy. Neuroimage Clin 36:103166 Saccenti L et al (2020) Myelin Measurement Using Quantitative Magnetic Resonance Imaging: A Correlation Study Comparing Various Imaging Techniques in Patients with Multiple Sclerosis. Cells, 9(2) Elliott C et al (2021) Abnormalities in normal-appearing white matter from which multiple sclerosis lesions arise. Brain Commun 3(3):fcab176 Khan W et al (2021) Investigating the microstructural properties of normal-appearing white matter (NAWM) preceding conversion to white matter hyperintensities (WMHs) in stroke survivors. NeuroImage 232:117839 Cougo P et al (2024) Subtle white matter intensity changes on fluid-attenuated inversion recovery imaging in patients with ischaemic stroke. Brain Commun 6(2):fcae089 Hosseini Z et al (2018) Morphology-Specific Discrimination between MS White Matter Lesions and Benign White Matter Hyperintensities Using Ultra-High-Field MRI. AJNR Am J Neuroradiol 39(8):1473–1479 Sati P et al (2016) The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat Rev Neurol 12(12):714–722 Kau T et al (2013) The central vein sign: is there a place for susceptibility weighted imaging in possible multiple sclerosis? Eur Radiol 23(7):1956–1962 Sivakolundu DK et al (2020) BOLD signal within and around white matter lesions distinguishes multiple sclerosis and non-specific white matter disease: a three-dimensional approach. J Neurol 267(10):2888–2896 Amin M, Nakamura K, Ontaneda D (2024) Differentiating multiple sclerosis from non-specific white matter changes using a convolutional neural network image classification model. Mult Scler Relat Disord 82:105420 Additional Declarations No competing interests reported. Supplementary Files Online.Resource.docx Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Neuroradiology → 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. 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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-8338798","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565275561,"identity":"001b8ec6-b1e8-4985-8774-5f81fcab8b19","order_by":0,"name":"Evangelos 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08:49:53","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104800,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8338798/v1/ed0d9e97c06a411580917cb9.html"},{"id":99214543,"identity":"c5c3b6c5-cc59-4a40-8c33-0747101ecc5c","added_by":"auto","created_at":"2025-12-30 08:49:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":406127,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of \u003cem\u003ebefore\u003c/em\u003e and \u003cem\u003eafter\u003c/em\u003e ROI placement (in red) across lesion types in multiple sclerosis (MS).\u003cbr\u003e\n(A–B) Typical MS lesion.\u003cbr\u003e\n(C–D) Non-specific white-matter lesion (nsWML-MS) in an MS patient.\u003c/p\u003e\n\u003cp\u003eLesions are grouped by patient diagnosis for illustration; selection was performed blinded to diagnosis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8338798/v1/53d98ccbe3599ac0cae12846.png"},{"id":99214549,"identity":"5872f9cb-972e-44c9-95c5-336494f39b99","added_by":"auto","created_at":"2025-12-30 08:49:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":378888,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of \u003cem\u003ebefore\u003c/em\u003e and \u003cem\u003eafter\u003c/em\u003e ROI placement (in red) in stroke patients and NAWM.\u003cbr\u003e\n(A–B) Non-specific white-matter lesion (nsWML-S) in a stroke patient.\u003cbr\u003e\n(C–D) Normal-appearing white matter (NAWM) in the same subject.\u003c/p\u003e\n\u003cp\u003eLesions are grouped by patient diagnosis for illustration; selection was performed blinded to diagnosis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8338798/v1/0383e720fcede79ac8456647.png"},{"id":99318237,"identity":"41d5aa2c-95b6-47b0-a28b-de9055ab8d25","added_by":"auto","created_at":"2025-12-31 16:32:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":221944,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots show myelin content (MyC) in typical (MSL) and non-specific lesions (nsWML-MS) in 24 MS patients (Wilcoxon p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8338798/v1/30ed60f02ea169580a2e3982.png"},{"id":99214544,"identity":"2a040bf7-09c5-4748-afb9-1fb729fa7cdb","added_by":"auto","created_at":"2025-12-30 08:49:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDiagnostic performance (ROC analysis) for differentiating multiple sclerosis (MS) from stroke. \u003c/em\u003eAUC values for models using age alone (0.83), age + lesion myelin (0.84), and age + NAWM myelin (0.86). The y-axis is restricted to 0.80–0.90 to enhance visual clarity.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8338798/v1/257d1f943619e6d4155f3cf1.png"},{"id":107928120,"identity":"4e241807-6121-4d67-8a8f-36510bf23117","added_by":"auto","created_at":"2026-04-27 16:08:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1398172,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8338798/v1/6bf9bb50-a223-4cb1-990d-494328ca7cd4.pdf"},{"id":99316892,"identity":"482d916c-9401-4073-a58b-26acd511f30c","added_by":"auto","created_at":"2025-12-31 16:29:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21893,"visible":true,"origin":"","legend":"","description":"","filename":"Online.Resource.docx","url":"https://assets-eu.researchsquare.com/files/rs-8338798/v1/cd2b74b873e0304dafa1a84b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pathological Signatures of White Matter Lesions in Multiple Sclerosis versus Stroke: A Synthetic MRI Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) and stroke are two major neurological disorders contributing significantly to disability worldwide. Although their clinical and epidemiological profiles differ, overlap exists: MS patients have a 2.55-fold higher relative risk of developing stroke [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhite matter lesions (WMLs) are a common radiological finding in both MS and stroke. Non-specific white matter lesions (nsWMLs) are defined as T2- or FLAIR-hyperintense regions without clear morphological features that clarify their etiology. They are frequently observed with aging and/or vascular risk factors, have no typical location, and can be observed in periventricular, subcortical as well as deep white matter regions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The presence of nsWMLs complicates radiological diagnosis of MS as their ambiguous appearance makes it difficult to distinguish them from ischemic lesions, particularly in older patients and/or those with vascular comorbidities [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A predominance of non-perivenular lesions, especially subcortical, may signal alternative or comorbid vascular pathology [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ensWMLs may reflect distinct pathogenetic backgrounds in MS and stroke respectively, but it is not excluded that the origin may have common features. Interestingly, recent research reveals that in MS, impaired perfusion, mitochondrial dysfunction, and inflammation create a self-reinforcing hypoxia\u0026ndash;inflammation cycle that drives demyelination and disability [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. And in stroke, a secondary demyelination is increasingly recognized as a key driver of long-term deficits. This involves collagen-mediated inhibition of remyelination [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], astrocytic lipocalin-2 signaling [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and persistent myelin deficits that exacerbate neuronal loss [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, both disorders show convergent myelin vulnerability despite different initiating mechanisms.\u003c/p\u003e \u003cp\u003eSynthetic MRI (SyMRI) is a quantitative imaging technique that, from one multidynamic multiecho sequence, generates T1-, T2-, and PD-weighted images, volumetric segmentations, and quantitative maps of R1, R2, PD, and myelin content (MyC) [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In MS imaging, SyMRI matches conventional MRI lesion detection while reducing scan time [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and subtraction mapping may enhance the sensitivity for detecting new lesions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Paramagnetic rim lesions identified with SyMRI may reflect diffuse periplaque damage and ongoing silent progression [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and combined PET\u0026ndash;SyMRI studies have reported associations between microglial activation, myelin loss, and clinical decline [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn diagnostic imaging of stroke, SyMRI enables estimation of relaxation times with minimal discrepancy compared to conventional MRI [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Synthetic FLAIR performs comparably to standard FLAIR in early ischemia, with reduced scan time [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Quantitative values (R1, R2, PD) can possibly distinguish acute from chronic ischemia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and stratify stroke severity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, SyMRI-derived total myelin volume can be linked to 3-month functional outcomes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne early SyMRI study assessed a small group of patients with MS, stroke and borderline cases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, to our knowledge, no previous study has thoroughly explored the role of SyMRI in differentiating nsWMLs.\u003c/p\u003e \u003cp\u003eThe objectives of the study were to: first, assess the value of quantitative parameters derived from SyMRI for differentiating non-specific white matter lesions in two different patient cohorts, one with MS and one with ischemic stroke; and second, to investigate whether this method can differentiate between typical and non-specific white matter lesions in the MS cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eInitially, 32 patients with ischemic stroke aged 60 years or younger were screened for participation. Of these, 13 were excluded because they had only the acute stroke lesion without additional white matter lesions on MRI, or had lesions that did not meet the inclusion criteria, resulting in a final sample of 19 stroke patients. In addition, 30 patients with clinically confirmed multiple sclerosis (MS) were included, yielding a total study population of 49 participants.\u003c/p\u003e \u003cp\u003eAll participants underwent a single MRI examination at Uppsala University Hospital between 2021 and 2024, utilizing a standardized imaging protocol. Inclusion criteria for all patients included, being \u0026ge;\u0026thinsp;18 years of age and availability of a complete MRI imaging including fluid-attenuated inversion recovery (FLAIR) and synthetic MRI sequences. For the MS cohort, demographic data, disability scores (EDSS), and data on disease duration, time since last relapse, MS subtypes, and distribution of disease-modifying therapies (DMTs) were collected. EDSS was presented as median (interquartile range, IQR) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of patients with multiple sclerosis (MS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClinical characteristics MS patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of MS patients, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (years)\u003c/p\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDSS, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.5\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since MS onset, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174.3\u0026thinsp;\u0026plusmn;\u0026thinsp;88.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since last relapse, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.8\u0026thinsp;\u0026plusmn;\u0026thinsp;35.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS Type (n/%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (90.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of DMT (n/%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRituximab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimethyl fumarate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCladribine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlemtuzumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFingolimod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs for the patients with ischemic stroke, data on demographic parameters, vascular risk factors, cardiac comorbidities, and stroke subtypes were retrieved from medical records (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of patients with ischemic stroke.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClinical characteristics Stroke patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (68%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026gt;\u0026thinsp;25, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (68.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial Fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic heart failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatent foramen ovale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCryptogenic stroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge-artery atherosclerosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardioembolic stroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall-vessel occlusion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther cause, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMRI Acquisition\u003c/h3\u003e\n\u003cp\u003eAll MRI scans were performed on a 3T scanner (Achieva dStream, Philips Medical Systems, Best, The Netherlands) equipped with a 32-channel head coil. The imaging protocol included a three-dimensional FLAIR sequence and a multi-dynamic multi-echo (MDME) sequence optimized for synthetic MRI acquisition. Specific sequence parameters were as follows: FLAIR (TR = [4800] ms, TE = [304] ms, TI = [1650] ms, slice thickness = [1.1] mm) and MDME (TR = [4688] ms, TE = [12.5] ms, slice thickness = [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] mm). The MDME sequence enabled the generation of quantitative maps of R1, R2 relaxation rates, proton density (PD), and myelin content using the SyMRI software versions (7.1, 2017; 7.3, 2018; 8.0, 2019;11.0.7, 2019; 12.1.11, 2024) Synthetic MR AB, Link\u0026ouml;ping, Sweden).\u003c/p\u003e\n\u003ch3\u003eLesion Selection and ROI Placement\u003c/h3\u003e\n\u003cp\u003eCriteria for typical MS lesions included periventricular or corpus callosum location, ovoid shape, perpendicular orientation of the long axis towards the adjacent lateral ventricle, and size\u0026thinsp;\u0026gt;\u0026thinsp;5 mm.\u003c/p\u003e \u003cp\u003eFor each patient, representative non-specific white matter lesions were manually selected based on the FLAIR sequence, adhering to the following inclusion criteria: lesion size\u0026thinsp;\u0026gt;\u0026thinsp;5 mm in maximum diameter, located at a minimum distance of 10 mm from the ventricular system and 5 mm from the cortical surface (outer edge of FLAIR hyperintensity), absence of contrast-enhancement, and lesions not appearing hypointense on T1-weighted sequences. Ischemic lesions were excluded based on qualitative visual assessment of the DWI sequence. Following lesion selection, FLAIR images were co-registered to synthetic MRI maps, and corresponding regions of interest (ROIs) were manually delineated. During ROI placement, action was taken to maintain a sufficient margin between the ROI borders and the lesion edges, as well as adjacent anatomical boundaries to minimize partial volume effects. Additionally, only lesions providing adequate surrounding white matter space for reliable ROI placement were included in the analysis. One ROI was placed in NAWM for each of the 30 MS and 19 stroke patients. Figures\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrate examples of ROI placement in lesions from MS and stroke patients, as well as in normal-appearing white matter (NAWM).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eQuantitative MRI Analysis\u003c/h3\u003e\n\u003cp\u003eQuantitative evaluation of the selected lesions was conducted using the SyMRI post-processing software. For each ROI, R1, R2, proton density, and myelin content (MyC) were extracted. In addition to lesion analysis, a ROI was also placed within normal-appearing white matter (NAWM) to obtain reference measurements for comparison. Lesion selection was initially performed by a first rater who was not blinded to the patient group (MS or stroke). A second rater, an experienced neuroradiologist, reviewed all lesions under blinded conditions; however, full blinding could not always be ensured when overt radiological features suggested the underlying diagnosis.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eFor all analyses, the mean value for the lesions was calculated at the patient level. Comparisons between typical MS lesions (MSL) and non-specific lesions in MS patients (nsWML-MS) were performed using paired Wilcoxon signed-rank tests, while differences between nsWML-MS and non-specific lesions in stroke patients (nsWML-S) were assessed using Mann\u0026ndash;Whitney U tests. Logistic regression models, both unadjusted and adjusted for age, were applied to MyC, PD, R1, and R2 values to predict MS status, with results reported as odds ratios (OR) and 95% confidence intervals (CI). Receiver operating characteristic (ROC) curves were generated to evaluate discriminatory ability. Comparisons of normal-appearing white matter (NAWM) between MS and stroke groups were also conducted using Mann\u0026ndash;Whitney U tests and age-adjusted logistic regression.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe demographic and clinical characteristics of the MS and stroke cohorts are summarized in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn total, 157 white matter lesions were analyzed: 61 typical MS lesions (MSL), 48 non-specific white matter lesions in MS patients (nsWML-MS), and 48 non-specific white matter lesions in stroke patients (nsWML-S). Online Resource Supplementary Table\u0026nbsp;1 shows the distribution of lesions among patients.\u003c/p\u003e\n\u003ch3\u003eComparison of typical MS Lesions (MSL) and Non-Specific Lesions in MS patients (nsWML-MS)\u003c/h3\u003e\n\u003cp\u003eOf the 30 MS patients, 24 had at least one supratentorial MSL that met the size criterion (\u0026gt;\u0026thinsp;5 mm). These 24 patients represented a total of 61 MSLs and 40 nsWML-MSs.\u003c/p\u003e \u003cp\u003ePaired analyses within 24 MS patients (61 MSL and 40 nsWML-MS) showed that the median myelin content (MyC) was markedly lower in MSL compared to nsWML-MS (1.98 vs. 10.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Proton density (PD) was significantly higher in MSL (89.93 vs. 80.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while R1 and R2 relaxation rates were significantly reduced (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), (Online Resource Supplementary table 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eComparison of Non-Specific Lesions in MS (nsWML-MS) and Stroke (nsWML-S) patients\u003c/h3\u003e\n\u003cp\u003eA total of 48 nsWML-MS and 48 nsWML-S lesions were included. nsWML-MS had significantly lower MyC than nsWML-S (12.23 vs. 16.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0489), and significantly higher PD (79.11 vs. 74.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0380). In contrast, differences in R1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1603) and R2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2520) between the two groups did not reach statistical significance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between non-specific white matter lesions in MS (nsWML-MS) and stroke (nsWML-S). Values are shown as Median (IQR) and Min\u0026ndash;Max.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyMRI\u003c/p\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ensWML-MS Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ensWML-MS Min\u0026ndash;Max\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ensWML-S Median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ensWML-S Min\u0026ndash;Max\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.23 (4.70\u0026ndash;17.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.70\u0026ndash;25.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.40 (11.80\u0026ndash;20.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.85\u0026ndash;29.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.11 (74.20\u0026ndash;83.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.40\u0026ndash;90.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.50 (71.65\u0026ndash;76.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.25\u0026ndash;86.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.84\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.87\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.40 (9.91\u0026ndash;11.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.38\u0026ndash;12.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.91 (9.81\u0026ndash;11.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.38\u0026ndash;12.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLogistic Regression Analysis\u003c/h2\u003e \u003cp\u003eAge-adjusted logistic regression models were fitted using patient-level mean values of MyC, PD, R1, and R2 from nsWML-MS and nsWML-S lesions. In unadjusted analyses, higher PD and lower MyC were associated with increased odds of MS (PD: OR 1.13, 95% CI 1.02\u0026ndash;1.28, p\u0026thinsp;=\u0026thinsp;0.020; MyC: OR 0.91, 95% CI 0.83\u0026ndash;1.00, p\u0026thinsp;=\u0026thinsp;0.038). However, after adjusting for age, these associations were no longer statistically significant. Receiver operating characteristic (ROC) analysis indicated that age alone discriminated MS from stroke with an AUC of 0.83 (95% CI 0.71\u0026ndash;0.95). Adding SyMRI parameters to age did not substantially improve discrimination (Online Resource supplementary table 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analysis revealed negative correlations between MyC and PD (r = \u0026minus;\u0026thinsp;0.97 for nsWML-MS, r = \u0026minus;\u0026thinsp;0.96 for nsWML-S, both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting redundancy between these measures. Positive correlations were also observed between MyC and R1, and between PD and R2. In nsWML-MS, MyC showed a moderate positive correlation with age (r\u0026thinsp;=\u0026thinsp;0.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), whereas in nsWML-S, no significant correlations with age were observed (Online Resource Supplementary tables 4 and 5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Normal-Appearing White Matter (NAWM)\u003c/h2\u003e \u003cp\u003eNAWM variables did not differ significantly between MS and stroke patients. MyC and PD were marginally lower in the MS group, and R1 showed a trend toward reduction in MS compared to stroke group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.068) (Online Resource Supplementary table 6). Logistic regression on NAWM parameters indicated that, after age adjustment, lower MyC (OR 0.76, 95% CI 0.56\u0026ndash;0.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), higher PD (OR 1.52, 95% CI 1.04\u0026ndash;2.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), and reduced R1 (OR 0.25, 95% CI 0.06\u0026ndash;0.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) were significantly associated with MS. ROC analysis showed that age alone achieved an AUC of 0.83, while the addition of NAWM parameters slightly improved classification accuracy (up to AUC 0.86) (Online Resource Supplementary table 7). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, age alone achieved an AUC of 0.83. When myelin content from lesions was added to the model, the AUC increased modestly to 0.84, and inclusion of myelin content from NAWM reached 0.86, indicating a limited incremental contribution of quantitative myelin measures beyond age.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWithin the MS group, typical MS lesions (MSL) differed significantly from non-specific lesions (nsWML-MS). Typical MS lesions showed lower MyC and R1 values, together with higher PD, consistent with more advanced demyelination and tissue loss. In contrast, nsWML-MS retained higher myelin content and relaxation values, closer to normal compared with MSL, suggesting milder demyelination or a different injury pathway. These findings support the view that not all MS-associated white matter lesions are equal and that SyMRI can detect gradations of pathology that conventional imaging cannot capture.\u003c/p\u003e \u003cp\u003eWhen comparing nsWML-MS with nsWML-S, we observed higher MyC and lower PD in stroke patients. These findings may be compatible with differences in the underlying tissue processes of MS and stroke: in MS, involving inflammation-related demyelination, and in stroke, reflecting ischemia-related secondary myelin injury.\u003c/p\u003e \u003cp\u003eSimilarly, NAWM values showed only subtle differences between MS and stroke patients, with MS tending toward lower myelin content (MyC) and higher proton density (PD). While these differences did not reach significance in direct group comparisons, age-adjusted regression analyses indicated that lower MyC and higher PD were independently associated with MS diagnosis. Previous studies have reported reduced NAWM myelin fractions in MS patients compared to healthy controls. These reductions are correlated to both physical and cognitive disability, underscoring the clinical importance of diffuse white matter damage [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additional studies have found reduced myelin volume fraction (MVF) and altered relaxometry metrics (R1, R2, PD) in NAWM compared to controls, while further work has demonstrated that SyMRI-derived MVF in NAWM correlates with disease duration, highlighting the method\u0026rsquo;s sensitivity to disease burden [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These findings align with longitudinal magnetization transfer studies showing that microstructural alterations in NAWM can precede focal lesion formation, indicating a pre-lesional stage of tissue vulnerability [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This suggests that diffuse NAWM abnormalities captured by quantitative MRI may reflect early pathological changes preceding overt demyelination.\u003c/p\u003e \u003cp\u003eIn stroke, NAWM undergoes early microstructural alterations, but the underlying mechanism appears distinct. Previous studies indicate that NAWM regions, that later evolved into white matter hyperintensities, already exhibit reduced white-matter\u0026ndash;like signals and elevated fluid- and gray-matter\u0026ndash;like components early-on, suggesting tissue vulnerability before lesion formation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A recent study has shown that even beyond visible leukoaraiosis, NAWM exhibits subtle and diffuse FLAIR signal increases, correlating with overall leukoaraiosis burden and age [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results extend these observations by suggesting that NAWM metrics might provide additional discriminative power between MS and stroke compared to lesion-based comparisons alone. The lower myelin content and higher PD seen in MS NAWM may reflect widespread inflammatory demyelination, whereas the minimal signal changes observed in stroke-related NAWM may be related to diffuse vascular injury. This distinction suggests a potential role for SyMRI in disentangling the different biological processes underlying NAWM changes, improving diagnostic accuracy when conventional MRI findings are inconclusive.\u003c/p\u003e \u003cp\u003eA previous unpublished pilot study from Link\u0026ouml;ping University Hospital in Sweden explored the use of quantitative MRI with the same SyMRI framework applied in our study to distinguish MS lesions from ischemic lesions in a small cohort of seven MS and seven stroke patients. The study concluded that black holes could be clearly differentiated from ischemic lesions based on R1, R2, and PD values, reflecting the severe tissue destruction typical of chronic MS plaques. However, other white matter lesions showed substantial overlap, making them difficult to classify using these parameters alone. Importantly, no significant differences were found in normal-appearing white matter (NAWM) between the MS and stroke groups, suggesting that more diffuse changes could not be captured in their small sample.\u003c/p\u003e \u003cp\u003eIn contrast, our study included a larger and prospectively recruited cohort and incorporated myelin-content mapping with age adjustment, revealing trends consistent with mild NAWM myelin loss in the MS group. Together, these findings indicate that SyMRI may help to characterize both advanced and more subtle white matter changes.\u003c/p\u003e \u003cp\u003eAs expected, age emerged as a strong discriminator in the logistic regression analysis. This finding partly reflects the study design, as patients with stroke were older than those with MS. However, it also mirrors the well-established biological context: vascular-related white matter changes accumulate with advanced age, whereas MS typically presents earlier in life. Importantly, this underscores that SyMRI parameters cannot be interpreted in isolation but need to be evaluated together with demographic and clinical data to improve diagnostic accuracy. NAWM parameters became significant only after age adjustment, suggesting that age exerts a strong influence on white matter tissue properties. Controlling for this effect allowed disease-related differences in MS NAWM to emerge more clearly, implying that a quantitative MRI of NAWM may detect indications of pathology once age-related variance is accounted for.\u003c/p\u003e \u003cp\u003eSeveral advanced imaging techniques have been explored to address the diagnostic challenge of distinguishing MS lesions from non-specific white matter lesions. For instance, biomarkers such as the central vein sign and percentage of perivenous white matter lesions (% PVWML) obtained from 7T SWI/FLAIR imaging can help differentiate MS lesions from other WMLs [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, their diagnostic accuracy decreases when non-specific white matter lesions exhibit imaging characteristics similar to MS lesions, such as the presence of a central vein or perivenous distribution, creating challenges to differentiation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Functional BOLD (blood-oxygen-level-dependent) imaging has shown outward signal reductions in MS lesions compared to nsWMLs [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and convolutional neural network models have achieved up to 78% accuracy in separating MS from non-specific WMLs based on MRI data [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our findings indicate that SyMRI may complement these more specialized methods by providing both quantitative information together with lesion-specific and diffuse NAWM changes in a single, clinically feasible scan, potentially improving diagnostic precision when conventional MRI is inconclusive.\u003c/p\u003e \u003cp\u003eLimitations on our study, include the modest sample size, a single-center design, lack of histopathological validation, and absence of a healthy control group for comparison. Lesion selection was performed by one rater and reviewed by a blinded neuroradiologist to ensure consistency. Nevertheless, inter-rater reproducibility was not formally quantified, which may introduce minor subjectivity. Although the median values of SyMRI parameters differed significantly between groups, there was considerable overlap in the distributions, with individual lesions often falling within similar ranges for MS and stroke. This suggests that SyMRI metrics alone are unlikely to provide a definitive diagnostic classification, and their greatest value may lie in combination with clinical and demographic factors.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo our knowledge, this is the first comprehensive study to use SyMRI to compare non-specific white matter lesions in patients with MS and stroke. Our results suggest that SyMRI-derived parameters may capture differences in both lesions and possibly subtle NAWM variations between the two disorders, potentially reflecting their distinct underlying biological mechanisms. SyMRI also appeared to show modest discriminatory ability when assessing normal-appearing white matter (NAWM), suggesting a potential value in evaluating diffuse white matter changes beyond visible lesions. However, the overlap observed between groups and the modest sample size underline the need for caution in the interpretation of these results. Larger studies may help to validate these findings and establish whether SyMRI can be reliably implemented as a clinical tool for the differential diagnosis of ambiguous white matter lesions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. This study was approved by the Regional Ethics Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained written informed consent for the procedures from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEK: Conceptualization; study design; data collection; imaging analysis; statistical analysis; interpretation of results; writing \u0026ndash; original draft preparation; writing \u0026ndash; review and editing; preparation of figures.JW: Methodology; imaging supervision; imaging analysis; interpretation of imaging findings; writing \u0026ndash; review and editing.JV: Methodology; imaging analysis; interpretation of imaging findings; writing \u0026ndash; review and editing.SGB: Conceptualization; supervision; interpretation of results; writing \u0026ndash; review and editing.AML: Conceptualization; writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcknowledgements: The authors thank all the participants for their participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStefanou MI et al (2024) Prevalence and epidemiology of stroke in patients with multiple sclerosis: a systematic review and meta-analysis. J Neurol 271(7):4075\u0026ndash;4085\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Leeuw FE et al (2001) Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 70(1):9\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W et al (2023) Prevalence and clinical characteristics of white matter hyperintensities in Migraine: A meta-analysis. Neuroimage Clin 37:103312\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S et al (2013) Prevalence of brain magnetic resonance imaging meeting Barkhof and McDonald criteria for dissemination in space among headache patients. Mult Scler 19(8):1101\u0026ndash;1105\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeneviratne U, Chong W, Billimoria PH (2013) Brain white matter hyperintensities in migraine: clinical and radiological correlates. Clin Neurol Neurosurg 115(7):1040\u0026ndash;1043\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SS et al (2014) Limited utility of current MRI criteria for distinguishing multiple sclerosis from common mimickers: primary and secondary CNS vasculitis, lupus and Sjogren's syndrome. Mult Scler 20(1):57\u0026ndash;63\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLapucci C et al (2023) Central vein sign and diffusion MRI differentiate microstructural features within white matter lesions of multiple sclerosis patients with comorbidities. Front Neurol 14:1084661\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoroush A, Dunn JF (2025) A Hypoxia-Inflammation Cycle and Multiple Sclerosis: Mechanisms and Therapeutic Implications. Curr Treat Options Neurol 27(1):6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHollingworth BYA et al (2025) Hypoxic Neuroinflammation in the Pathogenesis of Multiple Sclerosis. Brain Sci, 15(3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamazaki R et al (2025) Type I collagen secreted in white matter lesions inhibits remyelination and functional recovery. Cell Death Dis 16(1):285\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Z et al (2025) Lipocalin-2 regulates astrocyte-oligodendrocyte interaction to drive post-stroke secondary demyelination. Cell Rep 44(7):115899\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng YJ et al (2024) Prolonged myelin deficits contribute to neuron loss and functional impairments after ischaemic stroke. Brain 147(4):1294\u0026ndash;1311\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGranberg T et al (2016) Clinical Feasibility of Synthetic MRI in Multiple Sclerosis: A Diagnostic and Volumetric Validation Study. AJNR Am J Neuroradiol 37(6):1023\u0026ndash;1029\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlystad I et al (2012) Synthetic MRI of the brain in a clinical setting. Acta Radiol 53(10):1158\u0026ndash;1163\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWest J et al (2014) Normal appearing and diffusely abnormal white matter in patients with multiple sclerosis assessed with quantitative MR. PLoS ONE 9(4):e95161\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujita S et al (2021) 3D Quantitative Synthetic MRI in the Evaluation of Multiple Sclerosis Lesions. AJNR Am J Neuroradiol 42(3):471\u0026ndash;478\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlaeger S et al (2023) Longitudinal Assessment of Multiple Sclerosis Lesion Load With Synthetic Magnetic Resonance Imaging-A Multicenter Validation Study. Invest Radiol 58(5):320\u0026ndash;326\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrajnc N et al (2023) Paramagnetic rim lesions lead to pronounced diffuse periplaque white matter damage in multiple sclerosis. Mult Scler 29(11\u0026ndash;12):1406\u0026ndash;1417\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarletta VT et al (2023) In vivo characterization of microglia and myelin relation in multiple sclerosis by combined (11)C-PBR28 PET and synthetic MRI. J Neurol 270(6):3091\u0026ndash;3102\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi CW et al (2020) Reliability of Synthetic Brain MRI for Assessment of Ischemic Stroke with Phantom Validation of a Relaxation Time Determination Method. J Clin Med, 9(6)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenzakoun J et al (2022) Synthetic FLAIR as a Substitute for FLAIR Sequence in Acute Ischemic Stroke. Radiology 303(1):153\u0026ndash;159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndre J, Barrit S, Jissendi P (2022) Synthetic MRI for stroke: a qualitative and quantitative pilot study. Sci Rep 12(1):11552\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang R et al (2025) Diagnostic and prediction value of synthetic magnetic resonance imaging in acute ischemic stroke patients. Adv Clin Exp Med 34(2):179\u0026ndash;186\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToko M et al (2025) Usefulness of Myelin Quantification Using Synthetic Magnetic Resonance Imaging for Predicting Outcomes in Patients With Acute Ischemic Stroke. Stroke 56(3):649\u0026ndash;656\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuellette R et al (2020) Validation of Rapid Magnetic Resonance Myelin Imaging in Multiple Sclerosis. Ann Neurol 87(5):710\u0026ndash;724\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLadopoulos T et al (2022) Relaxometry and brain myelin quantification with synthetic MRI in MS subtypes and their associations with spinal cord atrophy. Neuroimage Clin 36:103166\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaccenti L et al (2020) Myelin Measurement Using Quantitative Magnetic Resonance Imaging: A Correlation Study Comparing Various Imaging Techniques in Patients with Multiple Sclerosis. Cells, 9(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElliott C et al (2021) Abnormalities in normal-appearing white matter from which multiple sclerosis lesions arise. Brain Commun 3(3):fcab176\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan W et al (2021) Investigating the microstructural properties of normal-appearing white matter (NAWM) preceding conversion to white matter hyperintensities (WMHs) in stroke survivors. NeuroImage 232:117839\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCougo P et al (2024) Subtle white matter intensity changes on fluid-attenuated inversion recovery imaging in patients with ischaemic stroke. Brain Commun 6(2):fcae089\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosseini Z et al (2018) Morphology-Specific Discrimination between MS White Matter Lesions and Benign White Matter Hyperintensities Using Ultra-High-Field MRI. AJNR Am J Neuroradiol 39(8):1473\u0026ndash;1479\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSati P et al (2016) The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat Rev Neurol 12(12):714\u0026ndash;722\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKau T et al (2013) The central vein sign: is there a place for susceptibility weighted imaging in possible multiple sclerosis? Eur Radiol 23(7):1956\u0026ndash;1962\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSivakolundu DK et al (2020) BOLD signal within and around white matter lesions distinguishes multiple sclerosis and non-specific white matter disease: a three-dimensional approach. J Neurol 267(10):2888\u0026ndash;2896\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmin M, Nakamura K, Ontaneda D (2024) Differentiating multiple sclerosis from non-specific white matter changes using a convolutional neural network image classification model. Mult Scler Relat Disord 82:105420\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":true,"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":"Multiple Sclerosis, Stroke, Quantitative Synthetic MRI, White Matter Lesions","lastPublishedDoi":"10.21203/rs.3.rs-8338798/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8338798/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo evaluate whether quantitative synthetic MRI (SyMRI) parameters can differentiate non-specific white matter lesions (nsWMLs) in patients with multiple sclerosis (MS) and ischemic stroke, and to assess differences in normal-appearing white matter (NAWM) between these groups.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThirty MS patients and nineteen ischemic stroke patients underwent standardized MRI including SyMRI. Three lesion categories were analyzed: typical MS lesions (MSL), non-specific lesions in MS (nsWML-MS), and non-specific lesions in stroke (nsWML-S). SyMRI-derived parameters (R1, R2, proton density, and myelin content) were extracted from each region of interest (ROI), and one ROI was placed in NAWM per patient. Group differences were evaluated using non-parametric tests. Logistic regression models, both unadjusted and age-adjusted, assessed predictors of MS diagnosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTypical MS lesions showed lower myelin content and R1 and higher proton density than nsWML-MS (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Compared with nsWML-S, nsWML-MS demonstrated lower myelin content and higher proton density (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while R1 and R2 values did not differ. NAWM differences between MS and stroke emerged only after age adjustment. Age alone discriminated MS from stroke (AUC 0.83), with modest improvement when NAWM measures were added (AUC 0.86).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSyMRI captures both lesion-specific and diffuse NAWM differences between MS and stroke. Age strongly influences quantitative white matter measures, and adjusting for age reveals subtle NAWM pathology in MS. SyMRI may support differential diagnosis in patients with ambiguous white matter lesions.\u003c/p\u003e","manuscriptTitle":"Pathological Signatures of White Matter Lesions in Multiple Sclerosis versus Stroke: A Synthetic MRI Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 08:49:47","doi":"10.21203/rs.3.rs-8338798/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":"937281f2-d1cf-48e1-826c-13d6156eafa8","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:06:21+00:00","versionOfRecord":{"articleIdentity":"rs-8338798","link":"https://doi.org/10.1007/s00234-026-04002-y","journal":{"identity":"neuroradiology","isVorOnly":false,"title":"Neuroradiology"},"publishedOn":"2026-04-22 15:57:34","publishedOnDateReadable":"April 22nd, 2026"},"versionCreatedAt":"2025-12-30 08:49:47","video":"","vorDoi":"10.1007/s00234-026-04002-y","vorDoiUrl":"https://doi.org/10.1007/s00234-026-04002-y","workflowStages":[]},"version":"v1","identity":"rs-8338798","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8338798","identity":"rs-8338798","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}