Gradient of brain perfusion variation predicts thrombosis risk for patients with systemic lupus erythematosus

Gradient of brain perfusion variation predicts thrombosis risk for patients with systemic lupus erythematosus | 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 Gradient of brain perfusion variation predicts thrombosis risk for patients with systemic lupus erythematosus Na Wang, Zhengye Cao, Renjun Xu, Song'an Shang, Hongying Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8393739/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Systemic lupus erythematosus (SLE) is a chronic systemic autoimmune disease that can lead to intracranial thrombosis. Early detection and intervention are crucial for improving prognosis. However, misdiagnosis often occurs due to the lack of specificity in clinical symptoms and atypical nature of brain imaging. Method: We engaged a cohort of 36 SLE patients and 45 normal controls (NC). First, CBF values of brain were compared between patients and NC. Based on the normative perfusion pattern, gradient of CBF variation was obtained for each patient. Then, the significance of the correspondence between gradient of CBF variation and immunity indices were estimated using correlation analysis. Finally, moderation analysis was performed to test whether cognitive status of patients could moderate the relationship between gradient of CBF variation and immunity indices. Results: SLE patients displayed abnormal CBF alterations with increase distributed in left putamen and decrease distributed in right superior temporal gyrus. We found that gradient of CBF variation of these brain regions showed significant correlations with anti-cardiolipin antibody IgM in patients. When Mini-mental state examination (MMSE) score was used as the moderator, we observed no significant interaction between gradient of CBF variation and MMSE. Conclusion: CBF perfusion of SLE patients can be severely disrupted. The gradient of CBF variation in patients has a predictive effect on thrombosis formation. And this predictive power cannot be affected by patients’ cognition status. Anti-cardiolipin antibody Cerebral blood flow Cognition Gradient Systemic lupus erythematosus Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Systemic lupus erythematosus (SLE) is a chronic, systemic autoimmune disease characterized by abnormal immune system attacks on patient's own tissues, which can affect multiple organs 1 , 2 . After abnormal activation of the immune system, autoantibodies and immune complexes may deposit in cerebral blood vessels or tissues, resulting in intracranial inflammation, thrombosis, and neurological damage 3 , 4 . Therefore, early detection of thrombosis in SLE patients and early intervention treatment are crucial for improving prognosis. However, the symptoms of intracranial thrombosis caused by SLE lack specificity and are difficult to distinguish from SLE disease activity in the early stages, making it prone to misdiagnosis 5 , 6 . In addition, some patients have no obvious abnormalities in brain imaging, or their autoantibody testing does not match clinical symptoms, which also increases the difficulty of diagnosis. Cerebral perfusion abnormalities often appear earlier before the formation of intracranial thrombosis 7 , 8 . The endothelial damage and hypercoagulability caused by SLE first lead to changes in cerebral vascular blood flow velocity and a decrease in local blood flow, resulting in abnormal cerebral perfusion 9 , 10 . Therefore, understanding the cerebral perfusion status of SLE patients is key to predicting intracranial thrombosis. The core advantages of arterial spin labeling (ASL) imaging are non-invasive, quantitative, and contrast free, especially suitable for diseases such as SLE that require long-term monitoring 11 , 12 . Numerous studies have shown that significant changes in cerebral blood flow (CBF) perfusion can occur in SLE patients during the early stages of the disease 11 , 13 , 14 . However, due to the strong heterogeneity of inflammation in invading intracranial blood vessels, quantifying individual heterogeneity of CBF variation is an urgent issue that needs to be addressed. Recently, normative modeling was introduced as a useful statistical method for mapping the heterogeneity in imaging features at the individual level among patients 15 , 16 . For a specific imaging feature, it can provide statistical inferences to the degree to which each individual deviates from the normative pattern and has been successfully applied to quantify heterogeneity in many disorders at the individual level 17 , 18 . Based on this, we can explore the relationship between perfusion heterogeneity and clinical indicators in SLE patients. In this study, we focused on the relationships between CBF heterogeneity and clinical indicators led by anti-cardiolipin antibodies (ACA), which are the key high-risk factors for thrombosis (including intracranial thrombosis) in SLE patients 19 , 20 . It should be noted that approximately 30–60% of SLE patients may experience cognitive decline throughout the entire disease process 21 , 22 . Previous studies have shown that cognition and cerebral perfusion have a bidirectional regulatory effect, with the core being the mutual influence of "cerebral perfusion supporting cognitive function" and "cognitive activity regulating cerebral perfusion" through mechanisms such as neurovascular coupling 23 , 24 . If the thrombus formation risk of patients is closely related to changes in their cerebral perfusion, further exploration of whether the relationship between the two is regulated by cognition would be interesting. Therefore, another goal of this study wanted to reveal if and how cognitive status of SLE patients moderate the relationship between CBF heterogeneity and ACA. To address these issues above, our study first investigated changes in the CBF between 36 SLE patients and 45 age-matched normal controls (NC); then achieved individual CBF heterogeneity by using normative modeling; following that, focused on the relationships between CBF heterogeneity and ACA in SLE patients; finally, explored the moderation role of patients’ cognitive status between their relationship (Fig. 1 ). Materials and methods Subjects We included 36 patients with SLE and 45 NC, recruited from our medical imaging center. Demographic information of all subjects was detailed in Table 1 . All SLE patients was diagnosed based on the 1982 American College of Rheumatology Revised Criteria 25 . Individuals enrolled in the NC group were cognitively normal, free of neurological and psychiatric disorders, and had no family history of SLE. All subjects performed both T1-weighted structural MRI scanning and ASL functional MRI scanning. Mini-mental state examination (MMSE) was evaluated in all subjects. And immunity indices including complement 3 (C3), complement 4 (C4), anti-dsDNA antibody (A-dsDNA), anti-nucleosome antibody (ANuA), anti-cardiolipin antibody IgM (ACA-IgM), anti-cardiolipin antibody IgG (ACA-IgG), anti-β2-Glycoprotein I antibody (aβ2GpIA), and anti-ribosomal P protein antibody ‌(ARPA) were also tested on SLE patients. Our human participant study was based on the Helsinki Declaration and had been reviewed and approved by the institution review committee of local hospital. Informed consent forms were signed from all subjects before experiments. Table 1 Demographic information of SLE and NC. Clinical information SLE NC T/χ2 p Age (years) 37.81 ± 1.874 34.58 ± 1.339 1.437 0.1548 Gender 1/35 (M/F) 1/44 (M/F) χ2 = 0.025 0.8728 MMSE 24.81 ± 0.6157 28.22 ± 0.3096 5.257 < 0.0001 Course of disease(months) 84.71 ± 14.13 Handedness 34/2(R/L) 44/1(R/L) χ2 = 0.623 0.4299 A-dsDNA (IU/ml) 651.8 ± 451.3 ANuA (RU/ml) 33.81 ± 6.126 ACA-IgG (U/ml) 9.600 ± 1.590 ACA-IgM (U/ml) 5.843 ± 0.9361 aβ2GpIA (RU/ml) 10.97 ± 1.645 APRP 16/20 (Positive/Negative) C3 (mg/ml) 0.7073 ± 0.04567 C4 (g/l) 0.1394 ± 0.01419 T-tests were performed on age and MMSE. Chi-square tests were performed on gender and handedness. Anti-nuclear antibody = ANA, Anti-dsDNA antibody = A-dsDNA, Anti-nucleosome antibody = ANuA, Anti-cardiolipin antibody IgM = ACA-IgM, Anti-cardiolipin antibody IgG = ACA-IgG, Anti-β2-Glycoprotein I antibody = aβ2GpIA, Anti-ribosomal P protein antibody = ARPA, Complement 3 = C3, Complement 4 = C4, and MMSE = Mini-mental state examination. Imaging data acquisition All participants were scanned on a GE Discovery MR750 3.0T MRI scanner. Earplugs and foam pads were used to reduce noise and head motion. Before scanning, subjects were told to keep their eyes closed, remain motionless, and not fall asleep. The ASL functional data was collected using a pseudocontinuous sequence. Three-dimensional fast spin-echo acquisition and background suppression were used for this imaging protocol. Scan parameters were as follows: Repetition Time = 4.9 ms, Echo Time = 10.5 ms, Flip Angle = 111°, Slice Thickness = 4 mm without gap, Field of View = 240 × 240 mm 2 , Matrix size = 128 × 128, Labeling Duration = 1,500 ms, Post-labeling Delay = 2,025 ms, Number of Excitations = 3, Number of Slices = 36, 8 arms with 512 points per arm, Total scan time: 284 seconds, and Units: ml/100 g/min. This sequence also included a fluid-suppressed proton density acquisition with the same image dimensions as the pseudocontinuous ASL but without radio frequency labeling for CBF quantitation and image registration. The structural T1 data were acquired using a whole-brain 3D brain volume imaging sequence as follows: Repetition Time = 12 ms, Echo Time = 5.1 ms, Inversion Time = 450 ms, Flip Angle = 15°, Field of View = 240 × 240 mm 2 , Matrix size = 256 × 256, Slice Thickness = 1 mm without gaps, Voxel Size: 1 × 1 × 1 mm 3 , and 172 continuous sagittal slices. The total scan time: 320 seconds. Imaging processing of CBF Individual CBF images were acquired using FuncTool software (Version 4.6, GE Medical Systems, Milwaukee, WI, USA) based on a general kinetic model for ASL and then preprocessed using SPM 12 running in MATLAB R2025a 26 . Before CBF preprocessing, individual CBF images were carefully viewed to ensure superior imaging quality. Then, motion and magnetic field B0 inhomogeneity was corrected by removing subjects who translated and rotated higher than 2 mm and 2°. Spatially normalization of the CBF images was via T1 structural image transformation (co-registration and segmentation) in the form of non-linearly co-registered to the MNI space resampling to 1 × 1 × 1 mm 3 . Finally, standardizing the normalized CBF images by using z-score and smoothing the z-scored CBF maps by using a 6 × 6 × 6 mm 3 FWHM Gaussian kernel to reduce the hemodynamic effects and increase the signal/noise ratio. Gradient of CBF variation In our study, individual CBF variation was defined as the deviation degree of an individual from the normal perfusion covariance pattern. As illustrated in the flowchart (Fig. 3 -A), we first parcellated the whole brain by using the Automated Anatomic Labeling (AAL) atlas. CBF values of 116 brain regions were extracted. Then, a normal perfusion covariance pattern was first constructed, which was obtained by calculating the Pearson correlation between CBF values for each pair of brain regions across the NC subjects. Subsequently, one SLE patient (patient k) was added to the group of NC subjects. Repeating the above calculation method, a mixed perfusion covariance pattern was constructed by using the n + 1 subjects. Finally, the CBF variation of patient k could be acquired by calculating the difference between the mixed pattern and the normal pattern. Using the process above, CBF variation matrix of each SLE patient was acquired. Next, gradient of CBF variation was analyzed by using BrainSpace 27 . Via diffusion map embedding, gradient was derived from CBF variation matrix 28 . This identified spatial axes of variation in connectivity across different brain areas, whereby regions that were strongly interconnected were closer together and regions with little or no interconnectivity were farther apart 28 , 29 . We used normalized angle as a metric of distance similarity (values range from 0 to 1, with 1 denoting identical angle, and 0 opposing angle). The formula for calculating the normalized angle (A (i, j)) between two brain regions i and j was as follows: $$\:\varvec{A}(\varvec{i},\varvec{j})=1-\frac{{\varvec{cos}}^{-1}(\varvec{c}\varvec{o}\varvec{s}\varvec{s}\varvec{i}\varvec{m}({\varvec{v}}_{\varvec{i}},{\varvec{v}}_{\varvec{j}}\left)\right)}{\varvec{\pi\:}}$$ where cossim was the cosine similarity function, v i and v j were the rows in the matrix of CBF variation corresponding to brain regions i and j , respectively. We performed Procrustes alignment to align the gradient of each subject to the group template 30 . Then, gradient defined in connectivity space were mapped back onto the brain areas (the AAL atlas). Statistical analysis Statistical analyses in this study were performed using the SPSS statistical software (version 28: IBM Corp.) and MATLAB (version R2025a: MathWorks, Inc.). Demographics information was compared between SLE and NC using t-tests or Mann–Whitney and chi-squared test (the D’Agostino-Pearson omnibus normality test was used to check the data normality). Statistical significance was set at p <0.05 (two tailed). For group CBF comparisons, we used general linear models (GLM), with age and gender as covariates. We controlled multiple comparisons by using Family Wise Error (FWE) correction ( p < 0.05). To further explore the disease effects implied by abnormal perfusion, the significance of the correspondence between gradient of CBF variation and immunity indices were estimated using Spearman correlation analysis. And the threshold for significance was set as p < 0.05. Moderation analysis of cognition Previous studies had shown that there might be a bidirectional effect between cognition and brain perfusion 23 , 24 . In fact, the brain regions with abnormal perfusion in SLE patients in our results did involve the putamen and the superior temporal gyrus which were associated with cognition (See Results part). Therefore, an exploratory moderation analysis was performed to test whether cognitive status of patients could moderate the relationship between gradient of CBF variation and immunity indices (Fig. 4 -C). MMSE scores were used as the moderator. Then, the significance of the interaction effect between cognitive status and gradient of CBF variation was assessed based on GLM. Statistical significance was set at p <0.05. Results Demographics and clinical variables The demographic and clinical information were compared between the groups. We found that the SLE cohort had significantly lower MMSE scores than the NC cohort ( p 0.05) ( Table 1 ) . Abnormal CBF changes occur in SLE First, we examined how CBF changes in SLE patients. When compared with NC, the CBF value of the right superior temporal gyrus in SLE patients was significantly decreased, while the CBF value of the left putamen in SLE patients was significantly increased (adjusting for age and gender, p = 1.7589×10 − 6 , FWE corrected p < 0.05) (Fig. 2 ). Relationships between gradient of CBF variation in SLE and immunity indices Subsequently, we continued to investigate the disease effects implied by these abnormal brain perfusions in SLE patients. By adopting the concept of normative modeling, we first calculated and obtained the deviation degree of CBF for each SLE patient. Then, gradient of CBF variation was extracted for the right superior temporal gyrus and the left putamen of patient, respectively. We investigated the relationship between immunity indices and the gradient of CBF variation. We found that the gradient of right superior temporal gyrus and left putamen in SLE showed significant correlations with ACA-IgM ( p < 0.05) (Fig. 3 -B &C ). Specifically, the gradient of right superior temporal gyrus in SLE was found positively correlated with ACA-IgM ( r = 0.4, p = 0.02). And negative correlation was also found between the gradient of left putamen in SLE and ACA-IgM ( r = -0.5, p = 0.004). No significant relationships were detected between the gradient of right superior temporal gyrus and left putamen and C3, C4, A-dsDNA, ANuA, ACA-IgG, aβ2GpIA, and ARPA in SLE patients ( p > 0.05). Non-existent moderation role of cognition Moderation analysis was specifically to test if and how patient’s cognitive status moderates the relationship between the gradient of right superior temporal gyrus and left putamen and the ACA-IgM (Fig. 4 -C). It should be noted that, consistent with previous studies, our study had indeed confirmed the mutual influence between cognition and brain perfusion 14 , 31 (Fig. 4 -A &B ). Specifically, we found that there was a significant positive correlation between the MMSE scores and the gradient of right superior temporal gyrus in patient ( r = 0.42, p = 0.01). And significant negative correlation was also found between the MMSE scores and the gradient of left putamen in patient ( r = -0.39, p = 0.018). However, when MMSE score was used as the moderator, we observed no significant interaction between gradient of right superior temporal gyrus and MMSE ( β = 0.257, standard error (SE) = 3.779, p = 0.205); no significant interaction between gradient of left putamen and MMSE either ( β = -0.219, standard error (SE) = 2.183, p = 0.199) (Table 2 ). Table 2 The moderation analysis of cognition (MMSE). SE T p β SE T p β Gradient of CBF variation (Left putamen) 8.4 -2.124 0.043 -0.385 Gradient of CBF variation (Right superior temporal gyrus) 13.582 0.777 0.444 0.166 MMSE 0.248 0.686 0.498 0.123 MMSE 0.258 1.45 0.158 0.269 Gradient of CBF variation*MMSE -1.316 -1.316 0.199 -0.219 Gradient of CBF variation*MMSE 3.779 1.298 0.205 0.257 R 2 0.308 R 2 0.244 F 4.001 F 2.905 Moderation analysis was performed to test whether cognitive status could moderate the relationship between gradient of CBF variation and ACA-IgM. When MMSE scores were used as moderator, we observed no significant interaction between gradient of CBF variation and MMSE in both left putamen and right superior temporal gyrus. SE = Standard error, MMSE = Mini-mental state examination. Discussion In the present study, we revealed how CBF changes in patients with SLE, proved that the gradient of CBF variation in SLE patients had the significance of predicting the ACA-IgM indicator (which has been previously shown to be associated with thrombosis), and further clarified that the relationship between them could not be moderated by patients’ cognition status. Due to the abnormal activation of autoimmune response in SLE patients, immune attacks on intracranial vascular walls often lead to extensive vascular inflammation and damage 2 , 32 . ASL imaging is non-invasive and can quantify blood flow perfusion in different brain regions. It can sensitively identify local perfusion increase (such as vascular dilation during acute inflammation) or decrease (such as microcirculatory arrest or mild luminal stenosis), thus having certain advantages in exploring intracranial inflammatory damage caused by SLE 11 , 33 . In our study, the CBF of the left putamen was found significantly increased in SLE patients. The blood supply to the putamen is extremely abundant, directly supplied by the deep perforating branch of the middle cerebral artery (the lenticulostriate artery) 34 , 35 . Therefore, when blood flow resistance occurred in patient’s brain, such as increased blood viscosity or enhanced platelet aggregation caused by inflammation, some viewpoints suggested that to maintain normal brain metabolism, the body might initiate "compensatory vasodilation", which lead to a further significant CBF increase in the putamen 36 – 38 . We also found that the CBF of the right superior temporal gyrus in SLE patients was significantly decreased. This might be due to the dense small blood vessels in the superior temporal gyrus and the relatively large area of endothelial cell leakage, making it more susceptible to inflammatory attacks 39 , 40 . In addition, the superior temporal gyrus is also an important cognitive functional area of the cerebral cortex, involved in advanced functions such as language comprehension, auditory processing, and memory encoding. It has vigorous metabolic activity and relatively high demand for blood oxygen and energy supply 41 , 42 . Neurons in a high metabolic state are prone to energy metabolism disorders, which can lead to oxidative stress, apoptosis, and functional and structural damage - this "high demand low supply" contradiction makes the superior temporal gyrus more susceptible to inflammatory damage than brain regions with lower metabolic rates 43 , 44 . Recently, normative modeling was introduced as a useful statistical method for mapping the heterogeneity in imaging features at the individual level among patients 17 . By borrowing this framework that is already used in our study, we could provide statistical inferences for understanding the degree of deviation from normal cerebral perfusion patterns in each SLE patient. This allowed us to further explore the disease effects represented by cerebral perfusion abnormalities in SLE patients. In our study, we found that the gradient of CBF variation which distributed in right superior temporal gyrus and left putamen showed significant correlations with ACA-IgM indicator in SLE patients. ACA-IgM is one of the important risk factors for thrombosis 45 . Numerous studies have shown that when ACA-IgM remained positive, individuals had a significantly increased risk of developing thrombosis 46 – 48 . Our study indirectly suggested that the gradient of CBF variation had a predictive effect on the risk of thrombosis for SLE patients. The relationship between cognitive function and cerebral perfusion is believed to have bidirectional mutual regulation 23 , 24 . Insufficient cerebral perfusion may lead to ischemia and hypoxia in brain tissue, damage neurons, and result in cognitive decline. For example, one study on moyamoya disease provided evidence that cognition could be impaired by chronic hypoperfusion 49 . On the other hand, cognitive impairment (such as dementia) may be accompanied by a decrease in CBF autoregulation ability, making it difficult to flexibly adjust perfusion according to cognitive needs, further exacerbating the vicious cycle between cerebral perfusion and cognition 50 , 51 . Therefore, it is also worth exploring whether the cognitive status of patients can moderate the relationship between the gradient of CBF variation and the ACA-IgM indicator. However, no moderation role of cognition was observed in our study. This seemed to indicate a strong causal relationship by using the gradient of CBF variation to predict the risk of thrombosis for SLE patients. Several methodological limitations needed to be considered when interpreting the results of this study. First, some SLE patients had received treatment before the experiment, which could affect the accuracy of our analysis. In addition, some treatments might have long-term effects, and even stopping medication might interfere with the stability of experimental indicators. Second, our relatively small sample size and lack of differentiation among SLE subtypes also limited our ability to detect moderation effect of cognition in depth. Specifically, we did not have sufficient power to explore whether potential individual cognitive differences in neuropsychiatric SLE (NPSLE) patients might moderate the strength of gradient of CBF variation on predicting the ACA-IgM indicator. Finally, our study focused on cross-sectional data, although this provided useful insights, longitudinal studies in SLE patients were still needed to provide further insights into the temporal pattern of the gradient of CBF variation in SLE. Conclusion Taking together, our findings showed that CBF perfusion was severely disrupted in patients with SLE, with even more pronounced alteration in right superior temporal gyrus and left putamen. We clarified the predictability of the gradient of CBF variation on thrombosis in SLE patients. And we even proved that this predictive power could not be affected by patients’ cognition status. Abbreviations Anti-dsDNA antibody=A-dsDNA, Anti-nucleosome antibody=ANuA, Anti-cardiolipin antibody IgM=ACA-IgM, Anti-cardiolipin antibody IgG=ACA-IgG, Anti-β2-Glycoprotein I antibody=a β 2GpIA, Anti-ribosomal P protein antibody=ARPA, Automated Anatomic Labeling =AAL, Arterial spin labeling=ASL, Cerebral blood flow=CBF, Complement 3=C3, Complement 4=C4, Family Wise Error=FWE, General linear models=GLM, Mini-mental State Examination=MMSE, NC=Normal control, and Systemic lupus erythematosus=SLE. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of Northern Jiangsu People's Hospital (2022-KY-007) and written informed consent was obtained from all subjects prior to inclusion. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China (82202120), Science and Technology Project of Yangzhou (YZ2022071, YZ2023082), Medical Research Program of Jiangsu Health Commission (M2022059), and Clinical research project of Clinical Medical College, Yangzhou University (SBLC22004). Acknowledgement We thank all the volunteers participating in our study cohort and their relatives. Data and code availability statement Some or all data, models, or code generated or used during the study are available from the corresponding author by request. References Tsokos GC, Lo MS, Costa Reis P, Sullivan KE. New insights into the immunopathogenesis of systemic lupus erythematosus. Nat Rev Rheumatol 2016;12:716-730. Schwartz N, Stock AD, Putterman C. Neuropsychiatric lupus: new mechanistic insights and future treatment directions. Nat Rev Rheumatol 2019;15:137-152. Nishida H, Wakida K, Sakurai T. Cerebral venous thrombosis as a complication of neuropsychiatric systemic lupus erythematosus. Intern Med 2015;54:837-841. Carroll KR, Mizrachi M, Simmons S, et al. Lupus autoantibodies initiate neuroinflammation sustained by continuous HMGB1:RAGE signaling and reversed by increased LAIR-1 expression. Nat Immunol 2024;25:671-681. Alboudi A, Sarathchandran P, Alrukn S, Al Madani A. Cerebral venous thrombosis presenting with subdural haematoma as first presentation for systemic lupus erythematosus with negative antiphospholipid antibodies. BMJ Case Rep 2014;2014. Wang L, Chen H, Zhang Y, et al. Clinical Characteristics of Cerebral Venous Sinus Thrombosis in Patients with Systemic Lupus Erythematosus: A Single-Centre Experience in China. J Immunol Res 2015;2015:540738. Wang W, Han X. Teaching NeuroImages: Reversible Cerebral Perfusion Abnormalities in Deep Cerebral Venous Thrombosis. Neurology 2024;103:e210127. Göb V, Voll MG, Zimmermann L, et al. Infarct growth precedes cerebral thrombosis following experimental stroke in mice. Sci Rep 2021;11:22887. Wiseman SJ, Bastin ME, Jardine CL, et al. Cerebral Small Vessel Disease Burden Is Increased in Systemic Lupus Erythematosus. Stroke 2016;47:2722-2728. Ambler WG, Kaplan MJ. Vascular damage in systemic lupus erythematosus. Nat Rev Nephrol 2024;20:251-265. Jia J, Xie J, Li H, et al. Cerebral blood flow abnormalities in neuropsychiatric systemic lupus erythematosus. Lupus 2019;28:1128-1133. Álvarez MGM, Madhuranthakam AJ, Udayakumar D. Quantitative non-contrast perfusion MRI in the body using arterial spin labeling. Magma 2024;37:681-695. Shen YY, Kao CH, Ho YJ, Lee JK. Regional cerebral blood flow in patients with systemic lupus erythematosus. J Neuroimaging 1999;9:160-164. Liu H, Liu H, Tian B, Yang P, Fan G. Alterations in cerebral perfusion and corresponding brain functional networks in systemic lupus erythematosus with cognitive impairment. Sci Rep 2025;15:1310. Marquand AF, Rezek I, Buitelaar J, Beckmann CF. Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies. Biol Psychiatry 2016;80:552-561. Liu Z, Palaniyappan L, Wu X, et al. Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Mol Psychiatry 2021;26:7719-7731. Lv J, Di Biase M, Cash RFH, et al. Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort. Mol Psychiatry 2021;26:3512-3523. Wolfers T, Doan NT, Kaufmann T, et al. Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models. JAMA Psychiatry 2018;75:1146-1155. Domingues V, Magder LS, Petri M. Assessment of the independent associations of IgG, IgM and IgA isotypes of anticardiolipin with thrombosis in SLE. Lupus Sci Med 2016;3:e000107. Wahl DG, Guillemin F, de Maistre E, Perret C, Lecompte T, Thibaut G. Risk for venous thrombosis related to antiphospholipid antibodies in systemic lupus erythematosus--a meta-analysis. Lupus 1997;6:467-473. Peretti CS, Peretti CR, Kozora E, Papathanassiou D, Chouinard VA, Chouinard G. Cognitive impairment in systemic lupus erythematosus women with elevated autoantibodies and normal single photon emission computerized tomography. Psychother Psychosom 2012;81:276-285. Petri M, Naqibuddin M, Carson KA, et al. Cognitive function in a systemic lupus erythematosus inception cohort. J Rheumatol 2008;35:1776-1781. Sun L, Peng XL, Zi HX, et al. Globally patterned locus coeruleus-norepinephrine neuron-pericyte coupling orchestrates brain-wide vascular dynamics. Neuron 2025. Krolak T, Kaplan L, Navas K, et al. Brain endothelial gap junction coupling enables rapid vasodilation propagation during neurovascular coupling. Cell 2025;188:5003-5019.e5022. Tan EM, Cohen AS, Fries JF, et al. The 1982 revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum 1982;25:1271-1277. Buxton RB, Frank LR, Wong EC, Siewert B, Warach S, Edelman RR. A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn Reson Med 1998;40:383-396. Vos de Wael R, Benkarim O, Paquola C, et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol 2020;3:103. Margulies DS, Ghosh SS, Goulas A, et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A 2016;113:12574-12579. Coifman RR, Lafon S, Lee AB, et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc Natl Acad Sci U S A 2005;102:7426-7431. Langs G, Golland P, Ghosh SS. Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion. Med Image Comput Comput Assist Interv 2015;9350:313-320. Oh DH, Kim SH, Jung S, et al. Precuneus hypoperfusion plays an important role in memory impairment of patients with systemic lupus erythematosus. Lupus 2011;20:855-860. Moore E, Huang MW, Putterman C. Advances in the diagnosis, pathogenesis and treatment of neuropsychiatric systemic lupus erythematosus. Curr Opin Rheumatol 2020;32:152-158. Lapucci C, Boffa G, Massa F, et al. Could arterial spin labelling perfusion imaging uncover the invisible in N-methyl-d-aspartate receptor encephalitis? Eur J Neurol 2019;26:e86-e87. Rieke GK, Bowers DE, Jr., Penn P. Vascular supply pattern to rat caudatoputamen and globus pallidus: scanning electronmicroscopic study of vascular endocasts of stroke-prone vessels. Stroke 1981;12:840-847. Umansky F, Gomes FB, Dujovny M, et al. The perforating branches of the middle cerebral artery. A microanatomical study. J Neurosurg 1985;62:261-268. Wang X, Huang L, Guo W, et al. Cerebral Microstructural and Microvascular Changes in Non-Neuropsychiatric Systemic Lupus Erythematosus: A Study Using Diffusion Kurtosis Imaging and 3D Pseudo-Continuous Arterial Spin Labeling. J Inflamm Res 2023;16:5465-5475. Iadecola C. Neurovascular regulation in the normal brain and in Alzheimer's disease. Nat Rev Neurosci 2004;5:347-360. Dirnagl U, Iadecola C, Moskowitz MA. Pathobiology of ischaemic stroke: an integrated view. Trends Neurosci 1999;22:391-397. Verheggen ICM, de Jong JJA, van Boxtel MPJ, et al. Increase in blood-brain barrier leakage in healthy, older adults. Geroscience 2020;42:1183-1193. van Lanen RH, Haeren RH, Staals J, et al. Cerebrovascular glycocalyx damage and microcirculation impairment in patients with temporal lobe epilepsy. J Cereb Blood Flow Metab 2023;43:1737-1751. Mathiesen C, Caesar K, Akgören N, Lauritzen M. Modification of activity-dependent increases of cerebral blood flow by excitatory synaptic activity and spikes in rat cerebellar cortex. J Physiol 1998;512 ( Pt 2):555-566. Ances BM, Leontiev O, Perthen JE, Liang C, Lansing AE, Buxton RB. Regional differences in the coupling of cerebral blood flow and oxygen metabolism changes in response to activation: implications for BOLD-fMRI. Neuroimage 2008;39:1510-1521. Wang M, He A, Kang Y, et al. Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer's disease: transcriptomics combined with machine learning analysis. Neural Regen Res 2025;20:3620-3634. Guo JD, Zhao X, Li Y, Li GR, Liu XL. Damage to dopaminergic neurons by oxidative stress in Parkinson's disease (Review). Int J Mol Med 2018;41:1817-1825. Harris EN, Gharavi AE, Boey ML, et al. Anticardiolipin antibodies: detection by radioimmunoassay and association with thrombosis in systemic lupus erythematosus. Lancet 1983;2:1211-1214. Rajendran A, Eudy AM, Balevic SJ, Clowse MEB. The importance of pregnancy planning in lupus pregnancies. Lupus 2021;30:741-751. Miyakis S, Lockshin MD, Atsumi T, et al. International consensus statement on an update of the classification criteria for definite antiphospholipid syndrome (APS). J Thromb Haemost 2006;4:295-306. Hansen KE, Kong DF, Moore KD, Ortel TL. Risk factors associated with thrombosis in patients with antiphospholipid antibodies. J Rheumatol 2001;28:2018-2024. Nakamizo A, Kikkawa Y, Hiwatashi A, Matsushima T, Sasaki T. Executive function and diffusion in frontal white matter of adults with moyamoya disease. J Stroke Cerebrovasc Dis 2014;23:457-461. Zazulia AR, Videen TO, Morris JC, Powers WJ. Autoregulation of cerebral blood flow to changes in arterial pressure in mild Alzheimer's disease. J Cereb Blood Flow Metab 2010;30:1883-1889. Niwa K, Kazama K, Younkin L, Younkin SG, Carlson GA, Iadecola C. Cerebrovascular autoregulation is profoundly impaired in mice overexpressing amyloid precursor protein. Am J Physiol Heart Circ Physiol 2002;283:H315-323. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8393739","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572169490,"identity":"8687adee-cd53-4439-859a-c96dd5253bad","order_by":0,"name":"Na Wang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Wang","suffix":""},{"id":572169491,"identity":"5f2f2d18-cf9a-4ffb-aeef-829b22937038","order_by":1,"name":"Zhengye Cao","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhengye","middleName":"","lastName":"Cao","suffix":""},{"id":572169492,"identity":"34e98eee-d47f-4938-b98d-f30b492b694f","order_by":2,"name":"Renjun Xu","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Renjun","middleName":"","lastName":"Xu","suffix":""},{"id":572169493,"identity":"3cf6e4c8-da4e-446e-9fc2-d78f50829ec1","order_by":3,"name":"Song'an Shang","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Song'an","middleName":"","lastName":"Shang","suffix":""},{"id":572169494,"identity":"d17c4fd1-5b59-4a07-ab67-d9dc883cbfa9","order_by":4,"name":"Hongying Zhang","email":"","orcid":"","institution":"Northern Jiangsu People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongying","middleName":"","lastName":"Zhang","suffix":""},{"id":572169495,"identity":"27786087-cb31-4b93-a798-094bd8fa5f40","order_by":5,"name":"Jieting Jia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACxmYGBoMEBgYeILPhQEKFhJw8kVoMgFqYDz74cMbC2LCBOMsMgJgt2XBmW0UiwwECapnbmR8UPKj4I2POv8ZMmneeRAJjA/PDRzfwOozNwCDhjAGP5Yw3QC3bJPLYGdiMjXPw+8XAILHNgMfgxhmwlmLGBh42afxa2D8YJP6DaZkjkdhwgKAWHqAtDUAt59uA3m8gTkuBQcIxY6AtoEA+JmFs2EzAL4b9x7cZ/qiRszc4fxAYlTV1cvLszQ8f49XSwMBmAGZJJECFmPEoBwFg8mB+AGbxHyCgdBSMglEwCkYsAAAcwktTFTuYTgAAAABJRU5ErkJggg==","orcid":"","institution":"Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Jieting","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2025-12-18 09:38:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8393739/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8393739/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100094174,"identity":"c5918ec8-3581-4017-bb98-a5bc58a70a23","added_by":"auto","created_at":"2026-01-13 01:28:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1567513,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/088f6c95bb2fa1e21de9aa5d.docx"},{"id":100094173,"identity":"8760d3b8-6870-4b0e-b06a-d9a0c72ab29d","added_by":"auto","created_at":"2026-01-13 01:28:56","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7701,"visible":true,"origin":"","legend":"","description":"","filename":"8b1951f75e1e4a22a7f6b484e0f89f9e.json","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/8f336a7c6193fd1c6a1bf39e.json"},{"id":100094175,"identity":"ab6743dd-1a83-40b1-8517-2f9674b14834","added_by":"auto","created_at":"2026-01-13 01:28:56","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126888,"visible":true,"origin":"","legend":"","description":"","filename":"8b1951f75e1e4a22a7f6b484e0f89f9e1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/4f92c684ede987ec5d1ef7e2.xml"},{"id":100365919,"identity":"66587d3a-753e-4b62-981f-6dee51e78a24","added_by":"auto","created_at":"2026-01-16 07:55:44","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":421620,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/0bd3b758e243eeb20b122c16.jpeg"},{"id":100364408,"identity":"c5be3b5b-41f6-4bc5-a428-b0d0d306d22d","added_by":"auto","created_at":"2026-01-16 07:53:38","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":480896,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/471e76f149a462a7e713addb.jpeg"},{"id":100365385,"identity":"7d901f90-9b90-4874-8000-78bcd448f32d","added_by":"auto","created_at":"2026-01-16 07:55:10","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":396200,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/138e6736cee6a105a50fd218.jpeg"},{"id":100364467,"identity":"5c7b0950-004c-4be8-99d2-42da4797b0a1","added_by":"auto","created_at":"2026-01-16 07:53:46","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":284634,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/dcb68f930f1974340f1d352b.jpeg"},{"id":100094180,"identity":"e7b20377-a836-4af7-8f58-4fb60ab5675b","added_by":"auto","created_at":"2026-01-13 01:28:56","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62184,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/87449c8b9d5e9667de2f3fdf.png"},{"id":100365469,"identity":"bbf4df7f-7bec-45e4-b7b1-796bb2cade1c","added_by":"auto","created_at":"2026-01-16 07:55:14","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":58184,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/f113d0b537031ea633d59ad6.png"},{"id":100364886,"identity":"c89cf6b5-5b96-4c11-89a6-713b05ee93b0","added_by":"auto","created_at":"2026-01-16 07:54:27","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56717,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/39679b45e3a52d67a3e31be3.png"},{"id":100094184,"identity":"73a02fbf-1031-411f-a6be-8f454f140586","added_by":"auto","created_at":"2026-01-13 01:28:56","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46464,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/bc28bc69e31c3a7625595e6c.png"},{"id":100365937,"identity":"3b641c01-d38d-49ce-a848-6536c2b28dad","added_by":"auto","created_at":"2026-01-16 07:55:46","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123550,"visible":true,"origin":"","legend":"","description":"","filename":"8b1951f75e1e4a22a7f6b484e0f89f9e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/2c084eb069d613266c2eb6a8.xml"},{"id":100094185,"identity":"37289c46-f51b-4558-8bfa-f9becc3220e2","added_by":"auto","created_at":"2026-01-13 01:28:56","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143563,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/b1e2b870e0bd83607c558408.html"},{"id":100094171,"identity":"09d21e0a-75ce-49fa-877c-09f9190bbee3","added_by":"auto","created_at":"2026-01-13 01:28:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eOverview of the study methodology.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we first investigated changes in the CBF between 36 SLE patients and 45 age-matched NC \u003cstrong\u003eA\u003c/strong\u003e; then achieved individual CBF heterogeneity by using normative modeling \u003cstrong\u003eB\u003c/strong\u003e; following that, focused on the relationships between CBF heterogeneity and ACA in SLE patients \u003cstrong\u003eC\u003c/strong\u003e; finally, explored the moderation role of patient’s cognitive status between their relationship \u003cstrong\u003eD\u003c/strong\u003e. Normal controls=NC, Anti-cardiolipin antibody IgM=ACA-IgM, and Cerebral blood flow=CBF.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/5238cac99e0d016876543f2e.png"},{"id":100094172,"identity":"c256859f-76dc-42b1-82e8-17e5d73c25ec","added_by":"auto","created_at":"2026-01-13 01:28:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCBF comparisons between SLE and NC.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e The violin plot showed significant intergroup differences in CBF at the left putamen and right superior temporal gyrus.\u003cstrong\u003e B \u003c/strong\u003eWhen compared with NC, the CBF value of the right superior temporal gyrus in SLE patients was significantly decreased, while the CBF value of the left putamen in SLE patients was significantly increased (adjusting for age and gender, p = 1.7589×10-6, FWE corrected p \u0026lt; 0.05). Cerebral blood flow=CBF, and Family Wise Error=FEW.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/da1b2adc78f9ff70fe701071.png"},{"id":100365379,"identity":"85df7904-1bd7-4d48-b6be-d331cf36dec6","added_by":"auto","created_at":"2026-01-16 07:55:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":214831,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCorrelation analysis between gradient of CBF variation and ACA-IgM.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Detailed steps for obtaining gradient of CBF variation in SLE patients. \u003cstrong\u003eB\u003c/strong\u003e The correlation analysis between gradient of CBF variation and clinical indicator. In our study, we found that the gradient of right superior temporal gyrus and left putamen in SLE showed significant correlations with ACA-IgM (p \u0026lt; 0.05). Specifically, the gradient of right superior temporal gyrus in SLE was found positively correlated with ACA-IgM (r = 0.4, p = 0.02). And negative correlation was also found between the gradient of left putamen in SLE and ACA-IgM (r = -0.5, p = 0.004). Anti-cardiolipin antibody IgM=ACA-IgM, and Cerebral blood flow=CBF.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/6a9438c126fdbecaca9013a9.png"},{"id":100094178,"identity":"8f32ddac-3bd8-42ae-a478-e8ad27d4f059","added_by":"auto","created_at":"2026-01-13 01:28:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":130248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThe moderation analysis of cognition.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThere may be a bidirectional effect between cognition and brain perfusion\u003cstrong\u003e. \u003c/strong\u003eAs we can see, the brain regions with abnormal CBF perfusion in SLE patients did involve the putamen and the superior temporal gyrus which were associated with cognition. \u003cstrong\u003eA-B\u003c/strong\u003e Consistent with previous studies, our study had indeed confirmed the mutual influence between cognition and brain perfusion. Specifically, the gradient of right superior temporal gyrus in SLE was found positively correlated with ACA-IgM (r = 0.4, p = 0.02). And negative correlation was also found between the gradient of left putamen in SLE and ACA-IgM (r = -0.5, p = 0.004).\u003cstrong\u003e C\u003c/strong\u003e An exploratory moderation analysis was performed to test whether cognitive status of patients could moderate the relationship between gradient of CBF variation and immunity indices (ACA-IgM, which is the key high-risk factors for thrombosis). MMSE scores were used as the moderator. Anti-cardiolipin antibody IgM=ACA-IgM, and Mini-mental State Examination=MMSE.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/3e8b2400c667ed499493ba6b.png"},{"id":105327479,"identity":"ef245809-9166-418d-bdd5-a55ca999de62","added_by":"auto","created_at":"2026-03-24 19:25:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1632023,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8393739/v1/37783522-ff49-4247-b2e6-bab541c23a44.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gradient of brain perfusion variation predicts thrombosis risk for patients with systemic lupus erythematosus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSystemic lupus erythematosus (SLE) is a chronic, systemic autoimmune disease characterized by abnormal immune system attacks on patient's own tissues, which can affect multiple organs \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. After abnormal activation of the immune system, autoantibodies and immune complexes may deposit in cerebral blood vessels or tissues, resulting in intracranial inflammation, thrombosis, and neurological damage \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Therefore, early detection of thrombosis in SLE patients and early intervention treatment are crucial for improving prognosis. However, the symptoms of intracranial thrombosis caused by SLE lack specificity and are difficult to distinguish from SLE disease activity in the early stages, making it prone to misdiagnosis \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition, some patients have no obvious abnormalities in brain imaging, or their autoantibody testing does not match clinical symptoms, which also increases the difficulty of diagnosis.\u003c/p\u003e \u003cp\u003eCerebral perfusion abnormalities often appear earlier before the formation of intracranial thrombosis \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The endothelial damage and hypercoagulability caused by SLE first lead to changes in cerebral vascular blood flow velocity and a decrease in local blood flow, resulting in abnormal cerebral perfusion \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, understanding the cerebral perfusion status of SLE patients is key to predicting intracranial thrombosis. The core advantages of arterial spin labeling (ASL) imaging are non-invasive, quantitative, and contrast free, especially suitable for diseases such as SLE that require long-term monitoring \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Numerous studies have shown that significant changes in cerebral blood flow (CBF) perfusion can occur in SLE patients during the early stages of the disease \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, due to the strong heterogeneity of inflammation in invading intracranial blood vessels, quantifying individual heterogeneity of CBF variation is an urgent issue that needs to be addressed. Recently, normative modeling was introduced as a useful statistical method for mapping the heterogeneity in imaging features at the individual level among patients \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. For a specific imaging feature, it can provide statistical inferences to the degree to which each individual deviates from the normative pattern and has been successfully applied to quantify heterogeneity in many disorders at the individual level \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Based on this, we can explore the relationship between perfusion heterogeneity and clinical indicators in SLE patients. In this study, we focused on the relationships between CBF heterogeneity and clinical indicators led by anti-cardiolipin antibodies (ACA), which are the key high-risk factors for thrombosis (including intracranial thrombosis) in SLE patients \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt should be noted that approximately 30\u0026ndash;60% of SLE patients may experience cognitive decline throughout the entire disease process \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that cognition and cerebral perfusion have a bidirectional regulatory effect, with the core being the mutual influence of \"cerebral perfusion supporting cognitive function\" and \"cognitive activity regulating cerebral perfusion\" through mechanisms such as neurovascular coupling \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. If the thrombus formation risk of patients is closely related to changes in their cerebral perfusion, further exploration of whether the relationship between the two is regulated by cognition would be interesting. Therefore, another goal of this study wanted to reveal if and how cognitive status of SLE patients moderate the relationship between CBF heterogeneity and ACA.\u003c/p\u003e \u003cp\u003eTo address these issues above, our study first investigated changes in the CBF between 36 SLE patients and 45 age-matched normal controls (NC); then achieved individual CBF heterogeneity by using normative modeling; following that, focused on the relationships between CBF heterogeneity and ACA in SLE patients; finally, explored the moderation role of patients\u0026rsquo; cognitive status between their relationship (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eWe included 36 patients with SLE and 45 NC, recruited from our medical imaging center. Demographic information of all subjects was detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All SLE patients was diagnosed based on the 1982 American College of Rheumatology Revised Criteria \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Individuals enrolled in the NC group were cognitively normal, free of neurological and psychiatric disorders, and had no family history of SLE. All subjects performed both T1-weighted structural MRI scanning and ASL functional MRI scanning. Mini-mental state examination (MMSE) was evaluated in all subjects. And immunity indices including complement 3 (C3), complement 4 (C4), anti-dsDNA antibody (A-dsDNA), anti-nucleosome antibody (ANuA), anti-cardiolipin antibody IgM (ACA-IgM), anti-cardiolipin antibody IgG (ACA-IgG), anti-β2-Glycoprotein I antibody (aβ2GpIA), and anti-ribosomal P protein antibody \u0026zwnj;(ARPA) were also tested on SLE patients. Our human participant study was based on the Helsinki Declaration and had been reviewed and approved by the institution review committee of local hospital. Informed consent forms were signed from all subjects before experiments.\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\u003eDemographic information of SLE and NC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClinical information\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSLE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eT/χ2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e37.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.874\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e34.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.339\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.437\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.1548\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1/35 (M/F)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1/44 (M/F)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eχ2\u0026thinsp;=\u0026thinsp;0.025\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.8728\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e24.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6157\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e28.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3096\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e5.257\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCourse of disease(months)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e84.71\u0026thinsp;\u0026plusmn;\u0026thinsp;14.13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHandedness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e34/2(R/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e44/1(R/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eχ2\u0026thinsp;=\u0026thinsp;0.623\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.4299\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA-dsDNA (IU/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e651.8\u0026thinsp;\u0026plusmn;\u0026thinsp;451.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANuA (RU/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e33.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.126\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eACA-IgG (U/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e9.600\u0026thinsp;\u0026plusmn;\u0026thinsp;1.590\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eACA-IgM (U/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e5.843\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9361\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eaβ2GpIA (RU/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.645\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e16/20 (Positive/Negative)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC3 (mg/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.7073\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04567\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC4 (g/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.1394\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01419\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eT-tests were performed on age and MMSE. Chi-square tests were performed on gender and handedness. Anti-nuclear antibody\u0026thinsp;=\u0026thinsp;ANA, Anti-dsDNA antibody\u0026thinsp;=\u0026thinsp;A-dsDNA, Anti-nucleosome antibody\u0026thinsp;=\u0026thinsp;ANuA, Anti-cardiolipin antibody IgM\u0026thinsp;=\u0026thinsp;ACA-IgM, Anti-cardiolipin antibody IgG\u0026thinsp;=\u0026thinsp;ACA-IgG, Anti-β2-Glycoprotein I antibody\u0026thinsp;=\u0026thinsp;aβ2GpIA, Anti-ribosomal P protein antibody\u0026thinsp;=\u0026thinsp;ARPA, Complement 3\u0026thinsp;=\u0026thinsp;C3, Complement 4\u0026thinsp;=\u0026thinsp;C4, and MMSE\u0026thinsp;=\u0026thinsp;Mini-mental state examination.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImaging data acquisition\u003c/h3\u003e\n\u003cp\u003eAll participants were scanned on a GE Discovery MR750 3.0T MRI scanner. Earplugs and foam pads were used to reduce noise and head motion. Before scanning, subjects were told to keep their eyes closed, remain motionless, and not fall asleep. The ASL functional data was collected using a pseudocontinuous sequence. Three-dimensional fast spin-echo acquisition and background suppression were used for this imaging protocol. Scan parameters were as follows: Repetition Time\u0026thinsp;=\u0026thinsp;4.9 ms, Echo Time\u0026thinsp;=\u0026thinsp;10.5 ms, Flip Angle\u0026thinsp;=\u0026thinsp;111\u0026deg;, Slice Thickness\u0026thinsp;=\u0026thinsp;4 mm without gap, Field of View\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 mm\u003csup\u003e2\u003c/sup\u003e, Matrix size\u0026thinsp;=\u0026thinsp;128 \u0026times; 128, Labeling Duration\u0026thinsp;=\u0026thinsp;1,500 ms, Post-labeling Delay\u0026thinsp;=\u0026thinsp;2,025 ms, Number of Excitations\u0026thinsp;=\u0026thinsp;3, Number of Slices\u0026thinsp;=\u0026thinsp;36, 8 arms with 512 points per arm, Total scan time: 284 seconds, and Units: ml/100 g/min. This sequence also included a fluid-suppressed proton density acquisition with the same image dimensions as the pseudocontinuous ASL but without radio frequency labeling for CBF quantitation and image registration. The structural T1 data were acquired using a whole-brain 3D brain volume imaging sequence as follows: Repetition Time\u0026thinsp;=\u0026thinsp;12 ms, Echo Time\u0026thinsp;=\u0026thinsp;5.1 ms, Inversion Time\u0026thinsp;=\u0026thinsp;450 ms, Flip Angle\u0026thinsp;=\u0026thinsp;15\u0026deg;, Field of View\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 mm\u003csup\u003e2\u003c/sup\u003e, Matrix size\u0026thinsp;=\u0026thinsp;256 \u0026times; 256, Slice Thickness\u0026thinsp;=\u0026thinsp;1 mm without gaps, Voxel Size: 1 \u0026times; 1 \u0026times; 1 mm\u003csup\u003e3\u003c/sup\u003e, and 172 continuous sagittal slices. The total scan time: 320 seconds.\u003c/p\u003e\n\u003ch3\u003eImaging processing of CBF\u003c/h3\u003e\n\u003cp\u003eIndividual CBF images were acquired using FuncTool software (Version 4.6, GE Medical Systems, Milwaukee, WI, USA) based on a general kinetic model for ASL and then preprocessed using SPM 12 running in MATLAB R2025a \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Before CBF preprocessing, individual CBF images were carefully viewed to ensure superior imaging quality. Then, motion and magnetic field B0 inhomogeneity was corrected by removing subjects who translated and rotated higher than 2 mm and 2\u0026deg;. Spatially normalization of the CBF images was via T1 structural image transformation (co-registration and segmentation) in the form of non-linearly co-registered to the MNI space resampling to 1 \u0026times; 1 \u0026times; 1 mm\u003csup\u003e3\u003c/sup\u003e. Finally, standardizing the normalized CBF images by using z-score and smoothing the z-scored CBF maps by using a 6 \u0026times; 6 \u0026times; 6 mm\u003csup\u003e3\u003c/sup\u003e FWHM Gaussian kernel to reduce the hemodynamic effects and increase the signal/noise ratio.\u003c/p\u003e\n\u003ch3\u003eGradient of CBF variation\u003c/h3\u003e\n\u003cp\u003eIn our study, individual CBF variation was defined as the deviation degree of an individual from the normal perfusion covariance pattern. As illustrated in the flowchart (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A), we first parcellated the whole brain by using the Automated Anatomic Labeling (AAL) atlas. CBF values of 116 brain regions were extracted. Then, a normal perfusion covariance pattern was first constructed, which was obtained by calculating the Pearson correlation between CBF values for each pair of brain regions across the NC subjects. Subsequently, one SLE patient (patient k) was added to the group of NC subjects. Repeating the above calculation method, a mixed perfusion covariance pattern was constructed by using the n\u0026thinsp;+\u0026thinsp;1 subjects. Finally, the CBF variation of patient k could be acquired by calculating the difference between the mixed pattern and the normal pattern. Using the process above, CBF variation matrix of each SLE patient was acquired.\u003c/p\u003e \u003cp\u003eNext, gradient of CBF variation was analyzed by using BrainSpace \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Via diffusion map embedding, gradient was derived from CBF variation matrix \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This identified spatial axes of variation in connectivity across different brain areas, whereby regions that were strongly interconnected were closer together and regions with little or no interconnectivity were farther apart \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. We used normalized angle as a metric of distance similarity (values range from 0 to 1, with 1 denoting identical angle, and 0 opposing angle). The formula for calculating the normalized angle (A (i, j)) between two brain regions i and j was as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{A}(\\varvec{i},\\varvec{j})=1-\\frac{{\\varvec{cos}}^{-1}(\\varvec{c}\\varvec{o}\\varvec{s}\\varvec{s}\\varvec{i}\\varvec{m}({\\varvec{v}}_{\\varvec{i}},{\\varvec{v}}_{\\varvec{j}}\\left)\\right)}{\\varvec{\\pi\\:}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ecossim\u003c/em\u003e was the cosine similarity function, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e were the rows in the matrix of CBF variation corresponding to brain regions \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, respectively. We performed Procrustes alignment to align the gradient of each subject to the group template \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Then, gradient defined in connectivity space were mapped back onto the brain areas (the AAL atlas).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses in this study were performed using the SPSS statistical software (version 28: IBM Corp.) and MATLAB (version R2025a: MathWorks, Inc.). Demographics information was compared between SLE and NC using t-tests or Mann\u0026ndash;Whitney and chi-squared test (the D\u0026rsquo;Agostino-Pearson omnibus normality test was used to check the data normality). Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 (two tailed).\u003c/p\u003e \u003cp\u003eFor group CBF comparisons, we used general linear models (GLM), with age and gender as covariates. We controlled multiple comparisons by using Family Wise Error (FWE) correction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To further explore the disease effects implied by abnormal perfusion, the significance of the correspondence between gradient of CBF variation and immunity indices were estimated using Spearman correlation analysis. And the threshold for significance was set as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModeration analysis of cognition\u003c/h2\u003e \u003cp\u003ePrevious studies had shown that there might be a bidirectional effect between cognition and brain perfusion \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In fact, the brain regions with abnormal perfusion in SLE patients in our results did involve the putamen and the superior temporal gyrus which were associated with cognition (See Results part). Therefore, an exploratory moderation analysis was performed to test whether cognitive status of patients could moderate the relationship between gradient of CBF variation and immunity indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e-C). MMSE scores were used as the moderator. Then, the significance of the interaction effect between cognitive status and gradient of CBF variation was assessed based on GLM. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and clinical variables\u003c/h2\u003e \u003cp\u003eThe demographic and clinical information were compared between the groups. We found that the SLE cohort had significantly lower MMSE scores than the NC cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. And no significant differences were found in age, handedness, and gender between the SLE cohort and the NC cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAbnormal CBF changes occur in SLE\u003c/h2\u003e \u003cp\u003eFirst, we examined how CBF changes in SLE patients. When compared with NC, the CBF value of the right superior temporal gyrus in SLE patients was significantly decreased, while the CBF value of the left putamen in SLE patients was significantly increased (adjusting for age and gender, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.7589\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, FWE corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between gradient of CBF variation in SLE and immunity indices\u003c/h2\u003e \u003cp\u003eSubsequently, we continued to investigate the disease effects implied by these abnormal brain perfusions in SLE patients. By adopting the concept of normative modeling, we first calculated and obtained the deviation degree of CBF for each SLE patient. Then, gradient of CBF variation was extracted for the right superior temporal gyrus and the left putamen of patient, respectively.\u003c/p\u003e \u003cp\u003eWe investigated the relationship between immunity indices and the gradient of CBF variation. We found that the gradient of right superior temporal gyrus and left putamen in SLE showed significant correlations with ACA-IgM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B\u003cb\u003e\u0026amp;C\u003c/b\u003e). Specifically, the gradient of right superior temporal gyrus in SLE was found positively correlated with ACA-IgM (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). And negative correlation was also found between the gradient of left putamen in SLE and ACA-IgM (\u003cem\u003er\u003c/em\u003e = -0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). No significant relationships were detected between the gradient of right superior temporal gyrus and left putamen and C3, C4, A-dsDNA, ANuA, ACA-IgG, aβ2GpIA, and ARPA in SLE patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNon-existent moderation role of cognition\u003c/h2\u003e \u003cp\u003eModeration analysis was specifically to test if and how patient\u0026rsquo;s cognitive status moderates the relationship between the gradient of right superior temporal gyrus and left putamen and the ACA-IgM (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e-C). It should be noted that, consistent with previous studies, our study had indeed confirmed the mutual influence between cognition and brain perfusion \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A\u003cb\u003e\u0026amp;B\u003c/b\u003e). Specifically, we found that there was a significant positive correlation between the MMSE scores and the gradient of right superior temporal gyrus in patient (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). And significant negative correlation was also found between the MMSE scores and the gradient of left putamen in patient (\u003cem\u003er\u003c/em\u003e = -0.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). However, when MMSE score was used as the moderator, we observed no significant interaction between gradient of right superior temporal gyrus and MMSE (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.257, standard error (SE)\u0026thinsp;=\u0026thinsp;3.779, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.205); no significant interaction between gradient of left putamen and MMSE either (\u003cem\u003eβ\u003c/em\u003e = -0.219, standard error (SE)\u0026thinsp;=\u0026thinsp;2.183, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.199) (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\u003eThe moderation analysis of cognition (MMSE).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGradient of CBF variation\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(Left putamen)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e8.4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-2.124\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-0.385\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eGradient of CBF variation\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(Right superior temporal gyrus)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e13.582\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.777\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e0.444\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.166\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.248\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.686\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.498\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.123\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eMMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e0.258\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e1.45\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e0.158\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.269\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGradient of CBF variation*MMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-1.316\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-1.316\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.199\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-0.219\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eGradient of CBF variation*MMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e3.779\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e1.298\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e0.205\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.257\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.308\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e0.244\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e4.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e2.905\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModeration analysis was performed to test whether cognitive status could moderate the relationship between gradient of CBF variation and ACA-IgM. When MMSE scores were used as moderator, we observed no significant interaction between gradient of CBF variation and MMSE in both left putamen and right superior temporal gyrus. SE\u0026thinsp;=\u0026thinsp;Standard error, MMSE\u0026thinsp;=\u0026thinsp;Mini-mental state examination.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we revealed how CBF changes in patients with SLE, proved that the gradient of CBF variation in SLE patients had the significance of predicting the ACA-IgM indicator (which has been previously shown to be associated with thrombosis), and further clarified that the relationship between them could not be moderated by patients\u0026rsquo; cognition status.\u003c/p\u003e \u003cp\u003eDue to the abnormal activation of autoimmune response in SLE patients, immune attacks on intracranial vascular walls often lead to extensive vascular inflammation and damage \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. ASL imaging is non-invasive and can quantify blood flow perfusion in different brain regions. It can sensitively identify local perfusion increase (such as vascular dilation during acute inflammation) or decrease (such as microcirculatory arrest or mild luminal stenosis), thus having certain advantages in exploring intracranial inflammatory damage caused by SLE \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In our study, the CBF of the left putamen was found significantly increased in SLE patients. The blood supply to the putamen is extremely abundant, directly supplied by the deep perforating branch of the middle cerebral artery (the lenticulostriate artery) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Therefore, when blood flow resistance occurred in patient\u0026rsquo;s brain, such as increased blood viscosity or enhanced platelet aggregation caused by inflammation, some viewpoints suggested that to maintain normal brain metabolism, the body might initiate \"compensatory vasodilation\", which lead to a further significant CBF increase in the putamen \u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. We also found that the CBF of the right superior temporal gyrus in SLE patients was significantly decreased. This might be due to the dense small blood vessels in the superior temporal gyrus and the relatively large area of endothelial cell leakage, making it more susceptible to inflammatory attacks \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In addition, the superior temporal gyrus is also an important cognitive functional area of the cerebral cortex, involved in advanced functions such as language comprehension, auditory processing, and memory encoding. It has vigorous metabolic activity and relatively high demand for blood oxygen and energy supply \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Neurons in a high metabolic state are prone to energy metabolism disorders, which can lead to oxidative stress, apoptosis, and functional and structural damage - this \"high demand low supply\" contradiction makes the superior temporal gyrus more susceptible to inflammatory damage than brain regions with lower metabolic rates \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecently, normative modeling was introduced as a useful statistical method for mapping the heterogeneity in imaging features at the individual level among patients \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. By borrowing this framework that is already used in our study, we could provide statistical inferences for understanding the degree of deviation from normal cerebral perfusion patterns in each SLE patient. This allowed us to further explore the disease effects represented by cerebral perfusion abnormalities in SLE patients. In our study, we found that the gradient of CBF variation which distributed in right superior temporal gyrus and left putamen showed significant correlations with ACA-IgM indicator in SLE patients. ACA-IgM is one of the important risk factors for thrombosis \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Numerous studies have shown that when ACA-IgM remained positive, individuals had a significantly increased risk of developing thrombosis \u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Our study indirectly suggested that the gradient of CBF variation had a predictive effect on the risk of thrombosis for SLE patients.\u003c/p\u003e \u003cp\u003eThe relationship between cognitive function and cerebral perfusion is believed to have bidirectional mutual regulation \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Insufficient cerebral perfusion may lead to ischemia and hypoxia in brain tissue, damage neurons, and result in cognitive decline. For example, one study on moyamoya disease provided evidence that cognition could be impaired by chronic hypoperfusion \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. On the other hand, cognitive impairment (such as dementia) may be accompanied by a decrease in CBF autoregulation ability, making it difficult to flexibly adjust perfusion according to cognitive needs, further exacerbating the vicious cycle between cerebral perfusion and cognition \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Therefore, it is also worth exploring whether the cognitive status of patients can moderate the relationship between the gradient of CBF variation and the ACA-IgM indicator. However, no moderation role of cognition was observed in our study. This seemed to indicate a strong causal relationship by using the gradient of CBF variation to predict the risk of thrombosis for SLE patients.\u003c/p\u003e \u003cp\u003eSeveral methodological limitations needed to be considered when interpreting the results of this study. First, some SLE patients had received treatment before the experiment, which could affect the accuracy of our analysis. In addition, some treatments might have long-term effects, and even stopping medication might interfere with the stability of experimental indicators. Second, our relatively small sample size and lack of differentiation among SLE subtypes also limited our ability to detect moderation effect of cognition in depth. Specifically, we did not have sufficient power to explore whether potential individual cognitive differences in neuropsychiatric SLE (NPSLE) patients might moderate the strength of gradient of CBF variation on predicting the ACA-IgM indicator. Finally, our study focused on cross-sectional data, although this provided useful insights, longitudinal studies in SLE patients were still needed to provide further insights into the temporal pattern of the gradient of CBF variation in SLE.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTaking together, our findings showed that CBF perfusion was severely disrupted in patients with SLE, with even more pronounced alteration in right superior temporal gyrus and left putamen. We clarified the predictability of the gradient of CBF variation on thrombosis in SLE patients. And we even proved that this predictive power could not be affected by patients\u0026rsquo; cognition status.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eAnti-dsDNA antibody=A-dsDNA, Anti-nucleosome antibody=ANuA, Anti-cardiolipin antibody IgM=ACA-IgM, Anti-cardiolipin antibody IgG=ACA-IgG, Anti-β2-Glycoprotein I antibody=a\u003c/em\u003e\u003cem\u003eβ\u003c/em\u003e\u003cem\u003e2GpIA, Anti-ribosomal P protein antibody=ARPA, Automated Anatomic Labeling\u003c/em\u003e\u003cem\u003e=AAL,\u003c/em\u003e \u003cem\u003eArterial spin labeling=ASL, Cerebral blood flow=CBF, Complement 3=C3, Complement 4=C4,\u0026nbsp;\u003c/em\u003e\u003cem\u003eFamily Wise Error=FWE, General linear models=GLM,\u0026nbsp;\u003c/em\u003e\u003cem\u003eMini-mental State Examination=MMSE, NC=Normal control, and Systemic lupus erythematosus=SLE.\u003c/em\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe study was approved by the Ethics Committee of Northern Jiangsu People's Hospital (2022-KY-007) and written informed consent was obtained from all subjects prior to inclusion.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that they have no competing interests.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis work was supported by the National Natural Science Foundation of China (82202120), Science and Technology Project of Yangzhou (YZ2022071, YZ2023082), Medical Research Program of Jiangsu Health Commission (M2022059), and Clinical research project of Clinical Medical College, Yangzhou University (SBLC22004).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWe thank all the volunteers participating in our study cohort and their relatives.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData and code availability statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSome or all data, models, or code generated or used during the study are available from the corresponding author by request.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eTsokos GC, Lo MS, Costa Reis P, Sullivan KE. New insights into the immunopathogenesis of systemic lupus erythematosus. Nat Rev Rheumatol 2016;12:716-730.\u003c/li\u003e\n \u003cli\u003eSchwartz N, Stock AD, Putterman C. Neuropsychiatric lupus: new mechanistic insights and future treatment directions. Nat Rev Rheumatol 2019;15:137-152.\u003c/li\u003e\n \u003cli\u003eNishida H, Wakida K, Sakurai T. Cerebral venous thrombosis as a complication of neuropsychiatric systemic lupus erythematosus. Intern Med 2015;54:837-841.\u003c/li\u003e\n \u003cli\u003eCarroll KR, Mizrachi M, Simmons S, et al. Lupus autoantibodies initiate neuroinflammation sustained by continuous HMGB1:RAGE signaling and reversed by increased LAIR-1 expression. Nat Immunol 2024;25:671-681.\u003c/li\u003e\n \u003cli\u003eAlboudi A, Sarathchandran P, Alrukn S, Al Madani A. Cerebral venous thrombosis presenting with subdural haematoma as first presentation for systemic lupus erythematosus with negative antiphospholipid antibodies. BMJ Case Rep 2014;2014.\u003c/li\u003e\n \u003cli\u003eWang L, Chen H, Zhang Y, et al. Clinical Characteristics of Cerebral Venous Sinus Thrombosis in Patients with Systemic Lupus Erythematosus: A Single-Centre Experience in China. J Immunol Res 2015;2015:540738.\u003c/li\u003e\n \u003cli\u003eWang W, Han X. Teaching NeuroImages: Reversible Cerebral Perfusion Abnormalities in Deep Cerebral Venous Thrombosis. Neurology 2024;103:e210127.\u003c/li\u003e\n \u003cli\u003eG\u0026ouml;b V, Voll MG, Zimmermann L, et al. Infarct growth precedes cerebral thrombosis following experimental stroke in mice. Sci Rep 2021;11:22887.\u003c/li\u003e\n \u003cli\u003eWiseman SJ, Bastin ME, Jardine CL, et al. Cerebral Small Vessel Disease Burden Is Increased in Systemic Lupus Erythematosus. Stroke 2016;47:2722-2728.\u003c/li\u003e\n \u003cli\u003eAmbler WG, Kaplan MJ. Vascular damage in systemic lupus erythematosus. Nat Rev Nephrol 2024;20:251-265.\u003c/li\u003e\n \u003cli\u003eJia J, Xie J, Li H, et al. Cerebral blood flow abnormalities in neuropsychiatric systemic lupus erythematosus. Lupus 2019;28:1128-1133.\u003c/li\u003e\n \u003cli\u003e\u0026Aacute;lvarez MGM, Madhuranthakam AJ, Udayakumar D. Quantitative non-contrast perfusion MRI in the body using arterial spin labeling. Magma 2024;37:681-695.\u003c/li\u003e\n \u003cli\u003eShen YY, Kao CH, Ho YJ, Lee JK. Regional cerebral blood flow in patients with systemic lupus erythematosus. J Neuroimaging 1999;9:160-164.\u003c/li\u003e\n \u003cli\u003eLiu H, Liu H, Tian B, Yang P, Fan G. Alterations in cerebral perfusion and corresponding brain functional networks in systemic lupus erythematosus with cognitive impairment. Sci Rep 2025;15:1310.\u003c/li\u003e\n \u003cli\u003eMarquand AF, Rezek I, Buitelaar J, Beckmann CF. Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies. Biol Psychiatry 2016;80:552-561.\u003c/li\u003e\n \u003cli\u003eLiu Z, Palaniyappan L, Wu X, et al. Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Mol Psychiatry 2021;26:7719-7731.\u003c/li\u003e\n \u003cli\u003eLv J, Di Biase M, Cash RFH, et al. Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort. Mol Psychiatry 2021;26:3512-3523.\u003c/li\u003e\n \u003cli\u003eWolfers T, Doan NT, Kaufmann T, et al. Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models. JAMA Psychiatry 2018;75:1146-1155.\u003c/li\u003e\n \u003cli\u003eDomingues V, Magder LS, Petri M. Assessment of the independent associations of IgG, IgM and IgA isotypes of anticardiolipin with thrombosis in SLE. Lupus Sci Med 2016;3:e000107.\u003c/li\u003e\n \u003cli\u003eWahl DG, Guillemin F, de Maistre E, Perret C, Lecompte T, Thibaut G. Risk for venous thrombosis related to antiphospholipid antibodies in systemic lupus erythematosus--a meta-analysis. Lupus 1997;6:467-473.\u003c/li\u003e\n \u003cli\u003ePeretti CS, Peretti CR, Kozora E, Papathanassiou D, Chouinard VA, Chouinard G. Cognitive impairment in systemic lupus erythematosus women with elevated autoantibodies and normal single photon emission computerized tomography. Psychother Psychosom 2012;81:276-285.\u003c/li\u003e\n \u003cli\u003ePetri M, Naqibuddin M, Carson KA, et al. Cognitive function in a systemic lupus erythematosus inception cohort. J Rheumatol 2008;35:1776-1781.\u003c/li\u003e\n \u003cli\u003eSun L, Peng XL, Zi HX, et al. Globally patterned locus coeruleus-norepinephrine neuron-pericyte coupling orchestrates brain-wide vascular dynamics. Neuron 2025.\u003c/li\u003e\n \u003cli\u003eKrolak T, Kaplan L, Navas K, et al. Brain endothelial gap junction coupling enables rapid vasodilation propagation during neurovascular coupling. Cell 2025;188:5003-5019.e5022.\u003c/li\u003e\n \u003cli\u003eTan EM, Cohen AS, Fries JF, et al. The 1982 revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum 1982;25:1271-1277.\u003c/li\u003e\n \u003cli\u003eBuxton RB, Frank LR, Wong EC, Siewert B, Warach S, Edelman RR. A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn Reson Med 1998;40:383-396.\u003c/li\u003e\n \u003cli\u003eVos de Wael R, Benkarim O, Paquola C, et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol 2020;3:103.\u003c/li\u003e\n \u003cli\u003eMargulies DS, Ghosh SS, Goulas A, et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A 2016;113:12574-12579.\u003c/li\u003e\n \u003cli\u003eCoifman RR, Lafon S, Lee AB, et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc Natl Acad Sci U S A 2005;102:7426-7431.\u003c/li\u003e\n \u003cli\u003eLangs G, Golland P, Ghosh SS. Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion. Med Image Comput Comput Assist Interv 2015;9350:313-320.\u003c/li\u003e\n \u003cli\u003eOh DH, Kim SH, Jung S, et al. Precuneus hypoperfusion plays an important role in memory impairment of patients with systemic lupus erythematosus. Lupus 2011;20:855-860.\u003c/li\u003e\n \u003cli\u003eMoore E, Huang MW, Putterman C. Advances in the diagnosis, pathogenesis and treatment of neuropsychiatric systemic lupus erythematosus. Curr Opin Rheumatol 2020;32:152-158.\u003c/li\u003e\n \u003cli\u003eLapucci C, Boffa G, Massa F, et al. Could arterial spin labelling perfusion imaging uncover the invisible in N-methyl-d-aspartate receptor encephalitis? Eur J Neurol 2019;26:e86-e87.\u003c/li\u003e\n \u003cli\u003eRieke GK, Bowers DE, Jr., Penn P. Vascular supply pattern to rat caudatoputamen and globus pallidus: scanning electronmicroscopic study of vascular endocasts of stroke-prone vessels. Stroke 1981;12:840-847.\u003c/li\u003e\n \u003cli\u003eUmansky F, Gomes FB, Dujovny M, et al. The perforating branches of the middle cerebral artery. A microanatomical study. J Neurosurg 1985;62:261-268.\u003c/li\u003e\n \u003cli\u003eWang X, Huang L, Guo W, et al. Cerebral Microstructural and Microvascular Changes in Non-Neuropsychiatric Systemic Lupus Erythematosus: A Study Using Diffusion Kurtosis Imaging and 3D Pseudo-Continuous Arterial Spin Labeling. J Inflamm Res 2023;16:5465-5475.\u003c/li\u003e\n \u003cli\u003eIadecola C. Neurovascular regulation in the normal brain and in Alzheimer\u0026apos;s disease. Nat Rev Neurosci 2004;5:347-360.\u003c/li\u003e\n \u003cli\u003eDirnagl U, Iadecola C, Moskowitz MA. Pathobiology of ischaemic stroke: an integrated view. Trends Neurosci 1999;22:391-397.\u003c/li\u003e\n \u003cli\u003eVerheggen ICM, de Jong JJA, van Boxtel MPJ, et al. Increase in blood-brain barrier leakage in healthy, older adults. Geroscience 2020;42:1183-1193.\u003c/li\u003e\n \u003cli\u003evan Lanen RH, Haeren RH, Staals J, et al. Cerebrovascular glycocalyx damage and microcirculation impairment in patients with temporal lobe epilepsy. J Cereb Blood Flow Metab 2023;43:1737-1751.\u003c/li\u003e\n \u003cli\u003eMathiesen C, Caesar K, Akg\u0026ouml;ren N, Lauritzen M. Modification of activity-dependent increases of cerebral blood flow by excitatory synaptic activity and spikes in rat cerebellar cortex. J Physiol 1998;512 ( Pt 2):555-566.\u003c/li\u003e\n \u003cli\u003eAnces BM, Leontiev O, Perthen JE, Liang C, Lansing AE, Buxton RB. Regional differences in the coupling of cerebral blood flow and oxygen metabolism changes in response to activation: implications for BOLD-fMRI. Neuroimage 2008;39:1510-1521.\u003c/li\u003e\n \u003cli\u003eWang M, He A, Kang Y, et al. Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer\u0026apos;s disease: transcriptomics combined with machine learning analysis. Neural Regen Res 2025;20:3620-3634.\u003c/li\u003e\n \u003cli\u003eGuo JD, Zhao X, Li Y, Li GR, Liu XL. Damage to dopaminergic neurons by oxidative stress in Parkinson\u0026apos;s disease (Review). Int J Mol Med 2018;41:1817-1825.\u003c/li\u003e\n \u003cli\u003eHarris EN, Gharavi AE, Boey ML, et al. Anticardiolipin antibodies: detection by radioimmunoassay and association with thrombosis in systemic lupus erythematosus. Lancet 1983;2:1211-1214.\u003c/li\u003e\n \u003cli\u003eRajendran A, Eudy AM, Balevic SJ, Clowse MEB. The importance of pregnancy planning in lupus pregnancies. Lupus 2021;30:741-751.\u003c/li\u003e\n \u003cli\u003eMiyakis S, Lockshin MD, Atsumi T, et al. International consensus statement on an update of the classification criteria for definite antiphospholipid syndrome (APS). J Thromb Haemost 2006;4:295-306.\u003c/li\u003e\n \u003cli\u003eHansen KE, Kong DF, Moore KD, Ortel TL. Risk factors associated with thrombosis in patients with antiphospholipid antibodies. J Rheumatol 2001;28:2018-2024.\u003c/li\u003e\n \u003cli\u003eNakamizo A, Kikkawa Y, Hiwatashi A, Matsushima T, Sasaki T. Executive function and diffusion in frontal white matter of adults with moyamoya disease. J Stroke Cerebrovasc Dis 2014;23:457-461.\u003c/li\u003e\n \u003cli\u003eZazulia AR, Videen TO, Morris JC, Powers WJ. Autoregulation of cerebral blood flow to changes in arterial pressure in mild Alzheimer\u0026apos;s disease. J Cereb Blood Flow Metab 2010;30:1883-1889.\u003c/li\u003e\n \u003cli\u003eNiwa K, Kazama K, Younkin L, Younkin SG, Carlson GA, Iadecola C. Cerebrovascular autoregulation is profoundly impaired in mice overexpressing amyloid precursor protein. Am J Physiol Heart Circ Physiol 2002;283:H315-323.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anti-cardiolipin antibody, Cerebral blood flow, Cognition, Gradient, Systemic lupus erythematosus","lastPublishedDoi":"10.21203/rs.3.rs-8393739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8393739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground: \u003c/strong\u003e\u003c/em\u003eSystemic lupus erythematosus (SLE) is a chronic systemic autoimmune disease that can lead to intracranial thrombosis. Early detection and intervention are crucial for improving prognosis. However, misdiagnosis often occurs due to the lack of specificity in clinical symptoms and atypical nature of brain imaging.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethod: \u003c/strong\u003e\u003c/em\u003eWe engaged a cohort of 36 SLE patients and 45 normal controls (NC). First, CBF values of brain were compared between patients and NC. Based on the normative perfusion pattern, gradient of CBF variation was obtained for each patient. Then, the significance of the correspondence between gradient of CBF variation and immunity indices were estimated using correlation analysis. Finally, moderation analysis was performed to test whether cognitive status of patients could moderate the relationship between gradient of CBF variation and immunity indices.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/em\u003e SLE patients displayed abnormal CBF alterations with increase distributed in left putamen and decrease distributed in right superior temporal gyrus. We found that gradient of CBF variation of these brain regions showed significant correlations with anti-cardiolipin antibody IgM in patients. When Mini-mental state examination (MMSE) score was used as the moderator, we observed no significant interaction between gradient of CBF variation and MMSE.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003e\u003c/em\u003eCBF perfusion of SLE patients can be severely disrupted. The gradient of CBF variation in patients has a predictive effect on thrombosis formation. And this predictive power cannot be affected by patients’ cognition status.\u003c/p\u003e","manuscriptTitle":"Gradient of brain perfusion variation predicts thrombosis risk for patients with systemic lupus erythematosus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 01:28:51","doi":"10.21203/rs.3.rs-8393739/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":"613606d5-2fa6-4788-b8ad-3d4eac56726e","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T14:40:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 01:28:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8393739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8393739","identity":"rs-8393739","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}