Alterations in cerebral perfusion and corresponding brain functional networks in NPSLE with cognitive impairment

Alterations in cerebral perfusion and corresponding brain functional networks in NPSLE with cognitive impairment | 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 Article Alterations in cerebral perfusion and corresponding brain functional networks in NPSLE with cognitive impairment Huiyang Liu, Hu Liu, Bailing Tian, Zhen Sun, Wen Xiong, Pingting Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4480752/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: Cognitive impairment (CI) frequently occurs in patients with systemic lupus erythematosus (SLE) and may result from neuroinflammation processes and vascular changes in the brain. The cerebral hemodynamics underlying SLE with CI (SLE-CI) remain unclear. We aimed to explore changes in cerebral blood flow (CBF) and intrinsic functional connectivity (FC) in patients with SLE-CI. Methods: We enrolled 97 patients with systemic lupus erythematosus (SLE) and 51 heathy controls (HCs) matched for age and gender. The CI status of patients was measured using the Montreal Cognitive Assessment (MoCA). Based on the findings, the patients were subdivided into two subgroups, the SLE-CI (n = 40) and SLE-NC (n = 57) subgroups. Sagittal three-dimensionT1-weighted (3D-T1), arterial spin labeling (ASL) and resting-state functional (rs-fMRI) sequences were obtained. Seed-based FC was calculated using the CBF results as regions of interest (ROIs). Correlation analysis was performed for further examination of differences in alterations and clinical scores between the patient subgroups. Results: Compared with patients with SLE-NC, patients with SLE-CI had higher CBF in the left hippocampus, thalamus, and cerebellum crus II and lower CBF in the left frontal lobe. Left hippocampus gray matter (GM) atrophy was detected in patients with SLE-CI but not in patients with SLE-NC. Secondary analyses revealed that compared with patients with SLE-NC, patients with SLE-CI had increased FC of the left insula gyrus when the left cerebellum crus II was set as the seed region and decreased FC in the homolateral parahippocampus when the left hippocampus was set as the seed region. Correlation analysis revealed that CBF in the left hippocampus, cerebellum, and thalamus was negatively associated with MoCA and memory scores and slightly positively associated with Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) scores. CBF in the left frontal lobe was positively associated with MoCA and visual space execution capability scores and slightly negatively associated with SLEDAI scores and serum double-stranded DNA (dsDNA) titer. Conclusion: The changes of structure, function, and network in hippocampus gray matter may be biomarkers of cognitive impairment in patients with NPSLE. Biological sciences/Neuroscience/Cognitive neuroscience Biological sciences/Neuroscience/Diseases of the nervous system neuropsychiatric systemic lupus cognitive impairment arterial spin labeling functional connectivity Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Cognitive impairment (CI) is 1 of the 19 most common syndromes of neuropsychiatric lupus (NPSLE) according to the American College of Rheumatology (ACR) 1,2 . The prevalence of CI in patients with SLE varies widely, ranging from 5–80%, which is ascribed to differences in testing tools, small sample sizes, and comorbidities 3 . CI in SLE affects multiple cognitive domains, including attention, executive function, verbal and non-verbal learning, working memory, and psychomotor function 4–6 . Previous studies have shown that deficits in attention, memory, and verbal fluency, the most severely affected CI domains in SLE, adversely impact patient quality of life over time 5,7–9 . Cognitive impairment in SLE (SLE-CI) is recognized as predominantly arising from inflammatory-neurotoxic microvascular ischemia mediated by complement activation; increased permeability of the blood-brain barrier (BBB); intrathecal migration of auto-antibodies; local production of pro-inflammatory cytokines and other inflammatory mediators; and subsequent recruitment of resident brain cells, in particular microglia 10–13 . Autopsies in NPSLE brain pathology studies have provided evidence of extensive vasculopathy 14–16 . The pathological mechanisms of this neuroinflammation and consequential neurovascular impairment can cause changes in cerebral perfusion 17,18 . Therefore, changes in cerebral blood flow (CBF) perfusion models might reveal a potential mechanism of SLE-CI onset. Previous studies used positron emission tomography (PET), single photon emission computed tomography (SPECT), and dynamic contrast-enhanced MRI (DCE-MRI) to assess regional brain perfusion. The PET and SPECT studies revealed that several perfusion-altered brain areas, such as the hippocampus and frontal cortex, are related to NPSLE 19–22 . In DCE-MRI studies, patients with SLE showed changed perfusion in the putamen and thalamus independent of NP involvement 23 . Several studies also using DCE-MRI techniques found that assessment of partial blood flow values in the frontal lobe might improve NPSLE diagnosis 18,24 . However, PET, SPECT, and DSC-MRI are not suitable for every patient due to irradiation or contrast medium injection contraindications. Arterial spin labeling (ASL) enables the quantitative measurement of CBF by using endogenous arterial blood water magnetization as a noninvasive tracer and provides effectiveness comparable to PET 25 . CBF-associated exchange of oxygen and nutrients is considered an indicator of glucose metabolism and neuronal activity 26,27 . Previous studies using ASL found different cerebral perfusion patterns in patients with SLE and healthy controls (HCs) 28,29 . This technique has not been used in the study of patients with SLE-CI, as it can only reflect the brain function of the region. Altered focal CBF may underlie correlation disruptions with other brain regions, in this study we combined the use of ASL with the resting state index to reflect the overall level of brain function changes in the patients with SLE-CI. Changes in neural microvascular perfusion can affect resting-state functional MRI (rs-fMRI) indices 30 . Both neurovascular perfusion and perfusion-related spontaneous neural activity lead to changes in functional connectivity (FC), an index that can show internal connections between isolated brain regions. FC is widely used in the study of other neurological diseases, such as Parkinson's disease and Alzheimer's disease 31,32 . In this study, we used the combination of noninvasive quantitative ASL and high temporal resolution rs-fMRI 33 to examine local and integrative brain function in SLE-CI. 2 Methods 2.1 Participants This study mainly assessed the relationship between cognitive function and cerebral blood flow. To reduce the influence of confounding factors, only participants under 55 years of age who were right-handed and had more than 9 years of education were included. Ninety-seven patients with SLE, 40 with SLE-CI and 57 with SLE-NC, who fulfilled the revised American College of Rheumatology (ACR) criteria for SLE (i.e., had at least four of the classification criteria) were recruited from the First Hospital of China Medical University between October 2021 and January 2024. Clinical exclusion criteria were (a) current or past diagnosed primary mental illness; (b) secondary NPSLE due to infection, electrolyte disturbance, hypertension, or other causes; (c) severe circulatory system (cardiac function grade NYHA III or above or hypertension grade II for more than 5 years) dysfunction; (4) use of anti-depressant drugs; and (5) acute confusional state. Concurrently, 51 HCs matched for gender, age, and education with no history of neuropsychiatric disease were recruited. The study was approved by the Ethics Committee of the First Hospital of China Medical University (No. 2022-306-02), and informed consent was obtained in writing from each participant. For patients with SLE, demographic variables, previous NP events and attribution 34 , medications, and Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) 35 were documented. Laboratory variables included routine blood and urine, anti-ribosomal P + and anti-Ro + antibody (i.e., rheumatology 9 score), anti-double stranded DNA antibody (anti-dsDNA ab + ), anti-phospholipid antibody (aPL), and complement 3 and 4 (C3, C4) levels. Clinical and cognitive assessments, blood collection, and MRI scanning were performed within 3 days before and after scanning. Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) damage index (SDI) scores were not included because some patients in the sample were not followed up for six months. 2.2 Cognitive function evaluation All participants underwent the Montreal Cognitive Assessment (MoCA), and those with an MoCA score under 28 (under 27 for those with less than 12 years of education) were diagnosed with SLE-CI. Due to the relatively young age of the patients, the cognitive classification criterion differed from the guideline score of 25, as studies have shown that the optimal screening threshold is under 28 in patients with SLE 3,6 . 2.3 Image acquisition MRI scans were performed with a 3.0T Pioneer GE MRI (GE Healthcare, Chicago, IL, USA) using foam pads to reduce head motion and scanner noise. The following brain images were acquired from all participants: (a) three-dimensional T1: repetition time (TR) = 7.8 ms, echo time (TE) = 3.0 ms, field of view (FOV) = 24 cm × 24 cm, matrix = 240 × 240, slice thickness = 1.0 mm, number of slices = 176, and no gap; (b) ASL: TR = 5344 ms, TE = 10.9 ms, FOV = 24 cm × 24 cm, points = 512, arms = 8, slice thickness = 4.0 mm, number of slices = 36, no gap, effective resolution = 3.79, post label delay = 2525 ms, and number of excitations (NEX) = 3; and (c) blood oxygenation level dependent (BOLD): TR = 2000 ms, TE = 30 ms, FA = 90°, FOV = 24 × 24 cm, matrix size = 640 × 640, pixel size = 3.8 × 3.8 mm 2 , slice thickness = 3.5 mm, gap = 0.7, and number of slices = 34. Additionally, T2-weighted imaging (T2WI), susceptibility weighted imaging (SWI), and diffusion-weighted imaging (DWI) in common MRI (cMRI) were acquired simultaneously to exclude subjects with clear structural abnormalities. 2.4 Image processing: All patients with SLE were clinically evaluated by a rheumatologist and a neurologist regarding their ongoing or history of NP events attributable to SLE. Patients with lesions larger than 1.5 cm on cMRI were excluded. Whole-brain voxel-based morphometry (VBM) analysis was performed to detect gray matter volume (GMV) reduction among groups using the Computational Anatomy Toolbox (CAT12) in the Statistical Parametric Mapping (SPM12) software package ( http://www.fil.ion.ucl.ac.uk/spm/ ). T1 images were spatially registered to the Montreal Neurological Institute (MNI) space and segmented into white matter (WM), GM, and cerebro-spinal fluid (CSF). Segmented images of the GM were modulated and the modulated normalized GM maps smoothed using an 8-mm full-width at half-maximum (FWHM) Gaussian kernel for further analysis before the total intracranial volume (TIV) was estimated for each participant. Voxel-wise statistical analysis for comparison of VBM imaging data between the SLE-CI and SLE-NC groups was performed using two-sample t-tests with SPM12 software with Gaussian random field (GRF) correction. The cluster-level statistical threshold was set at P < 0.05 and the voxel-level threshold at P 20 voxels) with age and TIV as covariates. ASL data were preprocessed and further analyzed with SPM12 and the Data Processing and Analysis for Brain Imaging (DPABI) toolbox version 3.0 ( www.restfmri.net ). The preprocessing steps were as follows: (a) the ASL images were analyzed on a GE post-processing workstation to generate CBF images, (b) the individual 3D-T1 structure images were co-registered to the corresponding CBF brain maps, (c) all the images were standardized by mean decision in DPABI using GM templates, and (d) spatial smoothing was performed with an isotropic Gaussian kernel at a full width and half maximum (FWHM) of 6 mm. Rs-fMRI data were preprocessed and subsequently analyzed with SPM12 and DPABI. The preprocessing steps for BOLD MRI were as follows: (a) time handling by discarding the first 10 time points of each participant to avoid magnetic saturation effects and to allow participants to adapt to the scanning noise; (b) slice timing; (c) head movement correction, excluding patients with more than a 2-mm displacement in any of the x, y, or z directions or with angular rotation; (d) space normalization by Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) using new T1 image segments; (e) spatial smoothing with an isotropic Gaussian kernel at s full width and half maximum (FWHM) of 6 mm; (f) detrending; (g) data filtration at 0.01 to 0.08 Hz to reduce the influence of noise; (h) nuisance covariate regression; and (i) seed-based functional connectivity analysis. Pearson's correlation coefficients were calculated between the seed-ROI time courses and the rest of the brain in a voxel-wise manner. The correlation coefficients were used for Fisher r-to-z transformation to transform single FC mapping into z-FC mapping and hence improve the normality of each participant. After adjustment for head movement and other reasons, the final cohort in the Rs-fMRI study included 74 patients with SLE, 29 with SLE-CI and 45 with SLE-NC. Voxel-wise statistical analysis for ASL imaging data was performed for comparison of the SLE-CI, SLE-NC, and HC groups using one-way analysis of variance (ANOVA), in SPM12 and DPABI (Gaussian random field [GRF] correction, cluster P < 0.05, voxel, P 20 voxels), with age and sex as covariates. After masks were made of the positive brain regions, post hoc t -tests were performed to identify differences between each pair of groups within the union mask (GRF correction, cluster P < 0.05, voxel P 20 voxels) with age as a covariate. Based on the ASL results, peak MNI coordinates of the 4 seed regions (the left hippocampus, thalamus, cerebellum_crus II, and frontal lobe gyrus) were chosen, as these are differential brain areas between SLE-CI and SLE-NC groups. Using a radius of 6 mm as seed regions, seed-based FC analysis was performed (GRF corrected, voxel P < 0.001, and cluster P < 0.05). 2.5 Statistical analysis Non-fMRI data were analyzed using SPSS 27 (IBM Software Analytics, Armonk, NY, USA). The age distribution of the groups was compared with ANOVA and post hoc analyses, and sex-related differences were detected using the chi-square test. The two-sample t -test was used to estimate differences in the clinical data, including duration of disease, clinical indicators, MoCA scores, and SLEDAI scores, between the SLE-CI and SLE-NC groups with a two-tailed P < 0.05 as the statistical significance level. For the fMRI results, the DPABI function was used to generate a mask for each differential brain region and extract the specific values within the mask (CBF and FC values). Pearson and Spearman correlation coefficients were calculated to examine possible associations between fMRI values and clinical scores (SLEDAI, MoCA and its components, and clinical indicators) in the SLE subgroups with a two-tailed P < 0.05 as the statistical significance level. 3 Results 3.1 Demographic, clinical, and MRI findings Table 1 summarizes the main demographic, clinical, and MRI variables of the 97 patients in the SLE group and the 51 patients in the HC group. The 97 patients in the SLE group were further divided into 40 patients in the SLE-CI group and 57 patients in the SLE NC group, yielding an SLE-CI, an SLE-NC, and a HC group. The results of one-way ANOVA revealed no statistical differences in age, sex, or education level among the three groups. The MoCA scores and their components were significantly lower in the SLE-CI group than in the SLE-NC group, and the SLE-CI group showed significant differences in scores in all six cognitive fields, among which the T value showed the largest difference in memory scores. There were no significant differences in disease duration, SLEDAI score, or daily glucocorticoid dose among the patients with SLE. Table 2 shows additional data obtained from rheumatological evaluation. Among the 97 patients with SLE, 10 had experienced recent intracranial infarction, 26 had experienced intracranial microhemorrhage, and 55 had experienced white matter hyperintensity (WMH) changes, which were mostly distributed in the parietal ventricles, deep cortical non-marginal areas, and subcortical marginal areas. In terms of clinical manifestations, 59 patients had a history of NP events. In the SLE-NC group, 19 patients had experienced at least 1 NP event, 10 patients had been hospitalized for transient coma or convulsion, and 4 patients had been hospitalized for headache. A total of 25 patients with SLE were excluded from the original cohort due to having conventional MRI lesions larger than 1.5 cm. On cMRI, compared with the SLE-NC group, the SLE-CI group showed extensive GMV atrophy in the large and cerebellar cerebellum, including in the bilateral orbitofrontal cortex, right thalamus, and right temporal lobe. A noteworthy finding was a decrease in GMV in the left hippocampus, which is consistent with the CBF results ( Supplementary Fig. 1 ). 3.2 ASL results One-way ANOVA revealed significant differences in CBF among the three groups controlled for age. Post hoc analysis showed that compared with the HC group, the SLE-CI and SLE-NC groups showed decreased CBF in the bilateral medial prefrontal cortex (MPFC) and insula gyrus and increased CBF in the bilateral basal ganglia, thalamus, and cerebellum posterior lobe ( Fig. 1 ) . These findings show that the abnormal (as compared with the HC group) CBF range in the SLE-CI group was significantly larger than that of the SLE-NC group, especially in the hippocampal region, in which increased CBF was observed in the in SLE-CI but not the SLE-NC group. Compared with the SLE-NC group, the SLE-CI group had higher CBF values in the left hippocampus, thalamus, and left cerebellum_crus II and lower CBF values in the left frontal lobe ( Table 3 and Fig. 2 ) . 3.3 FC results Compared with the SLE-NC group, the SLE-CI group showed increased FC of the left insula gyrus when the left cerebellum_crus II was set as the seed region and decreased FC in the homolateral parahippocampus when the left hippocampus was set as the seed region. When the thalamus and frontal lobe were used as seed points, no significant group differences were identified (Table 4 and Fig. 3 ). The FC value of the left insular lobe was negatively correlated with the MoCA score (r = − 0.433, P = 0.001) but not with any other regions. 3.4 Correlation analysis results Data that did not conform to normal distribution in the correlation analysis were all analyzed by Spearman correlation analysis. Left hippocampal CBF was negatively correlated with MoCA score (r = − 0.418, P = 0.001) and memory score (r = − 0.502, P = 0.001), and slightly positively correlated with SLEDAI (r = 0.234, P = 0.021). Left cerebellar crus II CBF was negatively correlated with MoCA score (r = − 0.373, P = 0.001) and memory score (r = − 0.332, P = 0.001), and was slightly positively correlated with SLEDAI (r = 0.253, P = 0.012). Left thalamus CBF was negatively correlated with MoCA score (r = − 0.436, P = 0.001) and memory score (r = − 0.470, P = 0.001), and was slightly positively correlated with SLEDAI score (r = 0.217, P = 0.033). Due to the absence of dsDNA clinical data, this analysis included 70 patients with SLE. Left frontal CBF was positively correlated with MoCA score (r = 0.391, P = 0.001) and visuospatial executive ability (r = 0.346, P = 0.001), and was slightly negatively correlated with the antibody titer of anti-dsDNA (r = − 0.341, P = 0.005) and SLEDAI score (r = − 0.247, P = 0.015); Fig. 4 shows the correlation results for P levels less than 0.01 (Fig. 4 ). 4 Discussion We investigated a well-validated quantitative MRI approach (sagittal 3D-T1, ASL, and seed-based FC) to investigate the structural and functional characteristics of GM. The cognitive classification criterion that we used differed from the guideline (a score of 25) that is used in the vast majority of neurodegenerative diseases, including Parkinson's and Alzheimer's disease, due to the relatively young age (mean age approximately 30 years of all groups) of our patients 3,6 . Among the three groups, we observed CBF alteration in patients with SLE-CI, leading us to further investigate whether neurovascular dysfunction affects neural activity in these patients. One finding was that compared with HCs, patients with SLE shared common features of CBF, but the range of abnormalities in the SLE-CI group was significantly larger than that in the SLE-NC group. Within the SLE subgroups, we found that the SLE-CI group had increased CBF in the left hippocampus, thalamus, and cerebellum_crus II and decreased CBF in the left frontal lobe compared with the SLE-NC group. Another finding was that the SLE-CI group had decreased GM volume in the left hippocampus compared with the SLE-NC group. Secondary FC analysis revealed that compared with the SLE-NC group, the SLE-CI group had increased FC in the left insula gyrus when we set the left cerebellum_crus II as the seed region and decreased FC in the homolateral parahippocampus when we set the left hippocampus as the seed region. Interestingly, we found abnormal alterations in the left hippocampal region in the SLE-CI group, including in the CBF, VBM and FC. Our findings indicate that altered structural and functional characteristics of the hippocampal gyrus and the cerebello-cerebral and hippocampus-parahippocampus networks may be image biomarkers of cognitive impairment in patients with NPSLE. In terms of ASL sequence parameters, the clinical post label delay (PLD) values are typically set between 1000–3000 milliseconds and 2000 ms in normal adults. Additional temporal measurements with longer PLD may be desired in cases of regional or global vascular compromise. NPSLE belongs to the diffuse cranial microangiopathy 36 , we chose 2525 ms as the PLD. 4.1 Changes in structural and functional hippocampus indices in SLE Compared with the SLE-NC group, the SLE-CI group showed multiple abnormalities in the left hippocampus, including increased CBF, decreased CM volume, and decreased FC in the parahippocampal gyrus. Compared with that of the HC group, the left hippocampal CBF increased in the SLE-CI group but not the SLE-NC group, which may have been due to SLE-related neurovascular changes and neuroinflammation, whose clinical manifestations of cognitive impairment only appear after the injury exceeds a certain threshold 37 . Abnormal hippocampal indices have been reported in patients with SLE 21,38-43 . In their study of patients with SLE, especially those with epilepsy, Toyota et al. reported hippocampal sclerosis with neuronal loss and gliosis 41 . Other studies reported that hippocampal GM atrophy in patients with SLE is associated with disease duration and independent of a diagnosis of NPSLE 42,43 . When Chi et al. used DSC-MRI to evaluate the changes in the BBB in patients with SLE, they found that the permeability of the BBB in the hippocampus of the patient group increased. Specifically, they observed that the intravascular to extravascular/extracellular space (K trans ) value increased and the K trans value was significantly positively correlated with CBF value. Such findings suggest that the CBF value can partly reflect changes in BBB permeability 40,44 . Previous studies reported that alteration of the hippocampal region in patients with SLE is related to increased production of specific antibodies, such as anti-N-methyl-D-aspartate receptor (NMDAR) antibodies, anti-ribosomal P, and neuronal surface P-antigen 39,45 , which target cells in the hippocampus and lead to abnormal hypermetabolism and atrophy 21,40 . In murine models, both anti-ribosomal P antibodies and NMDAR antibodies have neurotoxic effects, leading to enhanced calcium ion influx and neuronal dysfunction or death 46,47 . Activation of autoantibodies causes an inflammatory response to recruit microglia. Many previous studies of the mechanisms of NPSLE have identified an important role for microglia in inflammation, which has been associated with cognitive impairment in animal studies 48-50 . Kathleen et al. found that cerebellar and hippocampal microglia exist in a more immune-vigilant state in mice 51 . In a translator protein (TSPO)-PET imaging study, Wang found a decreased specific contrast agent distribution in the cerebellum and hippocampus of patients with SLE compared with HCs, especially in cognitively normal SLE subjects, and pseudo-normalization in cognitively impaired patients with SLE, which may be caused by glial-cell activation 52 . The hippocampal gyrus is an important part of the limbic system that plays crucial roles in learning, memory processes, and spatial navigation 53 , and the hippocampus is the center for memory processing and storage 54 . In this study, patients with SLE were grouped according to cognitive impairment. The results suggest that the change in hippocampal volume in the SLE-CI group was the result of nerve damage and that the change in blood flow was a neural compensatory mechanism, as would be the aggregation of microglia to the site of neuronal necrosis. Consistent with this finding, Mackay et al. found that the high metabolism of the hippocampus was associated with decline in cognitive function in patients with SLE 21 . In FC analysis, we observed decreased FC between the left hippocampus and parahippocampus gyrus in the SLE-CI group compared with the SLE-NC group. When Pentari et al. used cross­recurrence quantification analysis, they observed hypoconnectivity in medial temporal structures in patients with NPSLE that adversely affected their memory capacity 55 . 4.2 Changes in frontal and insula functional indices in SLE We found an increasing trend in perfusion values in MPFC ranging from the lowest values in the HC group to intermediate values in the SLE-NC group and the highest values in the SLE-CI group. Compared with the HC group, the SLE group, regardless of cognitive status, had widely reduced bilateral insular lobe CBF, a finding consistent with previous studies 56,57 . However, there were no significant differences between the SLE-NC and SLE-CI groups in bilateral insular lobe CBF. Histopathological studies have revealed that nonspecific focal vasculopathy occurs in SLE, which could be the pathological basis for perfusion abnormalities 58-62 . Using ASL, Jia et al. reported asymmetric, reduced perfusion in the frontal and temporal cortices in both the NPSLE and non-NPSLE groups compared with HCs. This funding, which was validated by quantitative CBF analyses, suggests the existence of a subclinical process in patients who have not experienced NP events 29 . Regional hypermetabolism in the orbitofrontal cortex and insular lobe has been associated with abnormal inter-regional metabolic processes that are associated with impaired cognitive performance 21 .Using fMRI, several previous studies have also found alterations in the frontal, temporal, and insular cortices along with cognition-related clinical indicators in patients with SLE 30,49,63 . The insular cortex is part of the salience network (SN), a cognitive and psychiatric network of the brain 64 . We found that CBF in the insula was reduced in patients with SLE, regardless of cognitive state, compared with HCs. The insula is involved in attention and salience processing, social cognition, and speech, which may account for the low cognitive scores in the combined SLE patient groups compared with the HCs. A previous study that used rs-fMRI to identify changes in the FC (rs-FC study) of patients with SLE found that altered FC in the insula is correlated with depression scores, which accords with our findings 63 . Using MR spectroscopy (MRS), Cagnoli found that the insula N-acetylaspartate /creatine (NAA/Cr) ratio is negatively correlated with SLE activity 65 , which is consistent with our findings. Taken together, these findings indicate that change in insular perfusion may indicate the occurrence and severity of disease. Task-based MRI studies have shown that patients with SLE have a decreased ability to suppress the default mode network (DMN), especially in the medial prefrontal cortex 66,67 . This clearly implies that the prefrontal lobe is correlated with cognition in these patients, corroborating our finding that CBF in the subregion positively correlates with MoCA score and visuospatial executive ability scores.This correlation suggests that changes in the prefrontal network play an important role in the processing of cognitive tasks in patients with SLE, confirming that CBF in this region is positively correlated with MoCA and visuospatial executive function scores. Our findings and those of previous studies indicate that perfusion changes in the frontal and insular cortex in patients with SLE are correlated with clinical indicators to varying degrees, suggesting that these brain regions are susceptible to hemodynamic changes. The different degree of clinical presentation in the patient groups in our study may have been due to the different disease stages of the two SLE groups. The insular lobe changes did not differ significantly between these subgroups, suggesting that cerebral perfusion occurs as early as the onset of SLE rather than later in the disease process when accompanied by clinical symptoms 68 . 4.4 Differences in cerebellar functional index changes in SLE Compared with that of the SLE-NC group, the CBF of the left cerebellum crus_II of the SLE-CI group increased, which may indicate the compensatory effect of the cerebellum in patients with SLE-CI. As this increase is negatively correlated with cognitive score, it may also indicate that the cerebellum is in the decompensated stage. Alterations in SPECT images were observed in the cerebellum before treatment, with various levels of recovery after therapy in patients with NPSLE 69,70 . In a previous study, CBF significantly increased in the GM in cerebellum regions in patients with non-NPSLE compared with HCs, and the main differences between the NPSLE and non-NPSLE subgroups were in the GM of cerebellum 28 . The cerebellum is mostly associated with motor function, having roles in regulating muscle tension and body balance. Many neuroimaging neuropsychology studies have attempted to determine the involvement of the cerebellum in high-level cognitive functions, including studies researching a range of psychiatric and developmental disorders 71,72 . Disorders caused by non-motor cerebellar dysfunction are the basis of the “dysmetria of thought (DoT)” theory 73 . Posterior and lateral cerebellar regions, including the lateral lobule VI, crus I, crus II, and lobule VIIB, are active during cognitive tasks 74 . However, the low number of MRI reports on the cerebellum may be the fact that perfusion metrics are calibrated through the cerebellum 75-77 . Alterations in cerebellar regions and their relationship to metabolism, including the activation of microglia, should be investigated in future studies. In FC analysis, we observed increased FC values between the left cerebellar crus II and the left insular gyrus and decreased values between the left hippocampus and parahippocampus gyrus in the SLE-CI group compared with the SLE-NC group. Using rs-fMRI to identify differences between patients with SLE with or without NP events and HCs, Su et al. found increased compensatory functional connectivity in cerebello-cerebral networks in the patients with SLE 78 . FC analysis by Cao et al. indicated that decreased synergy between the cerebrum and the cerebellum may be one cause of cognitive dysfunction in patients with SLE 79 . These results suggest that abnormal cerebello-cerebral networks are possible biomarkers of cognitive impairments in patients with SLE-CI, for which further evidence has been provided by other studies of patients with cognitive impairment 80,81 . Limitations As with any scientific research, our study is subject to certain limitations that we acknowledge here. Two limitations were our examination of a small sample and use of a cross-sectional study design, which may limit the generalizability of our findings. Overcoming these limitations requires analysis of larger samples and use of longitudinal study designs. Another limitation was that despite there being no significant differences in the daily dose of glucocorticoids administered to the SLE subgroups, the type and dose of hormone therapy used by different patients may have had some influence on their outcomes. A final limitation was that most patients with SLE were in the active stage of disease, which may have affected the results despite there being no significant differences in SLEDAI scores between the groups. Therefore, further follow-up of these patients is required. Conclusion Our results suggest that in patients with SLE-CI, decrease in hippocampal GMV may be the result of inflammatory injury, increase in CBF may be an inflammatory response to SLE, and increase in both CBF in the crus II region of the posterior cerebellar lobe and in FC in the ipsilateral insular lobe may be compensatory mechanisms. These findings suggest that the insular and medial frontal lobes may be more susceptible to hemodynamic damage and that changes in their function and network may be biomarkers of cognitive impairment in patients with SLE-CI. Larger longitudinal studies are needed to validate these findings. Declarations The sources of support : This study was supported by the foundation from the Chinese National Key Technology R&D Program (2021YFC2501303 to Pingting Yang) and the Doctoral Fund of Liaoning Province (No.2022-BS-141 to Bailing Tian). Conflict of interest : The authors have no competing interests to declare. Data availability statement ： The study was approved by the Ethics Committee of the First Hospital of China Medical University (No.2022-306-02), and informed consent was obtained in writing from each participant. The datasets during the current study available from the corresponding author on reasonable request. References Ainiala H, Loukkola J, Peltola J, Korpela M, Hietaharju A. The prevalence of neuropsychiatric syndromes in systemic lupus erythematosus. Neurology 2001;57:496-500. Brey RL, Holliday SL, Saklad AR, et al. Neuropsychiatric syndromes in lupus: prevalence using standardized definitions. Neurology 2002;58:1214-20. Rayes HA, Tani C, Kwan A, et al. 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Tables Table 1 Demographic and clinical characteristics of SLE patients and HCs HCs （n=51） SLE-CI （n=40） SLE-NC （n=57） F/c 2 /T P Age, mean (SD), years Gender (male/female) Education (years) Duration (months) MoCA score Visuospatial executive ability Named ability attention Language Abstract Memory directive force SLEDAI score 33.3(10.72) 6/45 10.48（4.45） - - - - - - - - - - 29.35(11.85) 2/38 10.43（3.75） 48.46(64.01) 21.80(4.76) 3.08(1.64) 2.65(0.62) 4.78(1.31) 2.17(0.78) 1.48(0.60) 1.85(1.12) 5.53(1.04) 15.4(6.62) 33.24(10.86) 2/55 11.31（2.36） 32.27(47.34) 28.25(1.106) 4.74(0.48) 3.00(0.00) 5.88(0.33) 2.75(0.51) 1.89(0.36) 4.00(0.93) 5.98(0.13) 12.51(12.51) 2.177 3.181 3.84 1.189 8.403 7.234 3.343 5.204 4.117 3.954 10.310 2.773 1.817 0.117 0.204 0.254 0.237 <0.001 * <0.001 * 0.002 * <0.001 * <0.001 * <0.001 * <0.001 * 0.008 * 0.072 GC（mg/day） - 59.01(65.59) 62.28(83.08) 0.204 0.839 Note: HCs healthy control group; SLE-CI lupus with cognitive impairment group; SLE-NC lupus cognition normal group; MoCA Montreal Cognitive Assessment Scale; SLEDAI Systemic lupus erythematosus Activity Index; GC glucocorticoid; Among the three groups, one-way analysis of variance and Chi-square test were used for gender. The above data is represented by "mean (standard deviation)"; P<0.05 Use * indicates a statistically significant difference. Table 2 Imaging and clinical data of the SLE patient group SLE-CI（n=40） SLE-NC（n=57） Common MRI Imaging Recent intracranial infarction (DWI) n(%) 5(12.5) 5(8.8) Intracranial microhemorrhage (SWI) n(%) 12(30) 14(24.6) White matter hyperintensity n(%) Clinical indicators SLEDAI score ≥ 5 n(%) Complement decreases n(%) 28(70) 38(95) 21(53.5) 27(47.4) 37(65) 30(52.6) LN n(%) 15(37.5) 30(52.6) aPL(+) n(%) 7(17.5) 13(22.8) Anti-dsDNA (+) n(%) Anti-ribosomal P (+) n(%) Hypertension n(%) The first diagnosis SLE n(%) 22(55) 14(35) 7(17.5) 10(25) 30(52) 27(47) 3(5.2) 15(26.3) Manifestations of NP Epilepsia n(%) Seizure disorder n(%) Psychosis n(%) Severe headache n(%) 7(17.5) 9(22.5) 3(7.5) 10(25) 0(0) 10(17.5) 0(0) 4(7.0) Note: DWI: diffusion imaging; SWI: magnetic sensitive imaging; LN: lupus nephritis; aPLs: anti-phospholipid antibody; dsDNA: Double-stranded DNA. Table 3 CBF differences between SLE patient groups cluster size (voxels) Peak MNI coordinate (x, y, z) Peak T SLE-CI>SLE-NC Cerebellum_Crus2_L Hippocampus_L Thalamus_L SLE-NC>SLE-CI 61 42 26 (-36,-76,-48) (-26,-20,-16) (-20,-24,0) 4.26 4.77 4.06 Frontal_Med_Orb_L 36 (-4,50,-10) -4.04 SLE-CI: systemic lupus group with cognitive impairment; SLE-NC: lupus cognitively normal group; MNI Montreal Neurological Institute; L: left; T value after Gaussian random field corrected, pSLE-NC) Cluster size, voxels Peak MNI coordinate (x, y, z) T value Seed：Cerebellum_Crus2_L Insula_L 22 (-42,-3,6) 4.2654 Seed：Hippocampus_L ParaHippocampal_L 27 (-42,-36,-12) -3.9157 SLE-CI: systemic lupus group with cognitive impairment; SLE-NC: lupus cognitively normal group; MNI Montreal Neurological Institute; L:left; T value after Gaussian random field corrected, p<0.05. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4480752","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":315380620,"identity":"8a35b597-6701-46c7-8441-dfae05f3a930","order_by":0,"name":"Huiyang Liu","email":"","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huiyang","middleName":"","lastName":"Liu","suffix":""},{"id":315380621,"identity":"d8d50a20-5585-49b1-86f2-14620c8bc9d3","order_by":1,"name":"Hu Liu","email":"","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hu","middleName":"","lastName":"Liu","suffix":""},{"id":315380622,"identity":"0c68416a-0fa7-4d18-85f3-eaa395eb9ef4","order_by":2,"name":"Bailing Tian","email":"","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bailing","middleName":"","lastName":"Tian","suffix":""},{"id":315380623,"identity":"f164d24f-9a18-4e25-8e6b-e170daf62569","order_by":3,"name":"Zhen Sun","email":"","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Sun","suffix":""},{"id":315380624,"identity":"c852ca68-bd12-45f2-82c9-7870852705f1","order_by":4,"name":"Wen Xiong","email":"","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Xiong","suffix":""},{"id":315380625,"identity":"8fd573e1-065c-44b2-9527-160deb38b5ad","order_by":5,"name":"Pingting Yang","email":"","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pingting","middleName":"","lastName":"Yang","suffix":""},{"id":315380626,"identity":"fb9c4639-b988-4730-9b3d-1774a79c717d","order_by":6,"name":"Guoguang Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBAC9gYILcfATKwWngMQ2ph0LYkNxOpg4GE/e/g1T0Vt+vx23oMfGGpsoglr4clLs+Y5czx3w2G+ZAmGY2m5BK2zZ8gxM+ZtO5a7gZnHQIKx4TBhLTz8b8Ba0uWbeYx/EKdFIsf4MW9bTQLDYR4zIm2ReGPGOOfMAcMNQC0WCcT4hYc/x/jDm4o6efn+M8Y3PtTYENYCBGwSDAyHIcwEIpSDAPMHBoY6ItWOglEwCkbBiAQA4905uYMfvkIAAAAASUVORK5CYII=","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guoguang","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2024-05-26 16:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4480752/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4480752/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58588402,"identity":"2aadeaa2-63bb-4a8b-9ce4-002b82e046fb","added_by":"auto","created_at":"2024-06-18 14:50:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146934,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical maps showing cerebral blood flow (CBF) differences in the patient groups relative to healthy controls.\u003c/p\u003e\n\u003cp\u003eNote: (a) Brain region maps with significantly changed CBF in SLE-CI group compared with HCs group; (b) Brain region maps of significant changes in CBF in SLE-NC group compared with HCs group; (GRF correction, cluster \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, voxel-level \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) The warm color block represents the area where the CBF of the patient group is higher than that of normal people, and the cool color block represents the area where the CBF of the patient group is lower than that of normal people. The color bar chart below represents the T-value, that is, the degree of CBF change; The left side of all the above images is the left side of the patient.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4480752/v1/4ffeba80994483e9cb660453.png"},{"id":58588404,"identity":"1c25a541-490c-46f2-9f72-7c3149a21972","added_by":"auto","created_at":"2024-06-18 14:50:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99441,"visible":true,"origin":"","legend":"\u003cp\u003eBrain region map with significant changes in CBF in the SLE-CI group compared with the SLE-NC group; Axial display map of the first behavioral brain region, The left side of the image is the left side of the patient; The warm color block represents the area of the CBF increase in SLE-CI group compared with SLE-NC group, The cold color block represents the region of CBF reduction in SLE-CI group compared with SLE-NC group, T value colorbar; Two groups of CBF violin charts (the left darker is SLE-CI group Vs the right lighter is SLE-CI group), The white circle in the figure represents the median, The black diamond represents the interquartile spacing (25-75%).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4480752/v1/49e07b7428a11e6e791f4b6b.png"},{"id":58588405,"identity":"35e5829e-0070-4f50-8582-e64636c0596e","added_by":"auto","created_at":"2024-06-18 14:50:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113962,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of brain regions with significantly altered functional connectivity in SLE-CI compared to SLE-NC group, with a scatter plot of correlation analysis on the right.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4480752/v1/904969fb5c57c987ab625e76.png"},{"id":58588406,"identity":"f4f8250d-0f79-4b53-8c4d-13fcae6d7d05","added_by":"auto","created_at":"2024-06-18 14:50:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96103,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation analysis map of CBF changes in SLE-CI group compared with SLE-NC group; the first column is the brain region axis + crown / vector display map, the left side of the patient is the left side; a: left hippocampus, b: left cerebellar crus II, c: left thalamus, d: left frontal lobe; the second column is the scatter plot of Spearman correlation analysis, and the r value and P value are indicated on the right side of the picture.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4480752/v1/2e7d58e3842b9d54e77075d3.png"},{"id":58589955,"identity":"92f2fdc2-30fb-44d3-8894-9bf295d855b4","added_by":"auto","created_at":"2024-06-18 15:06:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1109410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4480752/v1/4969193a-e07a-49ea-9f77-f5a916fb62ff.pdf"},{"id":58588403,"identity":"359692d2-a22b-4a19-8512-939e7d41faf4","added_by":"auto","created_at":"2024-06-18 14:50:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":497246,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4480752/v1/42fd2cb2f796b61a7d9eaee3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Alterations in cerebral perfusion and corresponding brain functional networks in NPSLE with cognitive impairment","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCognitive impairment (CI) is 1 of the 19 most common syndromes of neuropsychiatric lupus (NPSLE) according to the American College of Rheumatology (ACR) \u003csup\u003e1,2\u003c/sup\u003e. The prevalence of CI in patients with SLE varies widely, ranging from 5\u0026ndash;80%, which is ascribed to differences in testing tools, small sample sizes, and comorbidities \u003csup\u003e3\u003c/sup\u003e. CI in SLE affects multiple cognitive domains, including attention, executive function, verbal and non-verbal learning, working memory, and psychomotor function \u003csup\u003e4\u0026ndash;6\u003c/sup\u003e. Previous studies have shown that deficits in attention, memory, and verbal fluency, the most severely affected CI domains in SLE, adversely impact patient quality of life over time \u003csup\u003e5,7\u0026ndash;9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCognitive impairment in SLE (SLE-CI) is recognized as predominantly arising from inflammatory-neurotoxic microvascular ischemia mediated by complement activation; increased permeability of the blood-brain barrier (BBB); intrathecal migration of auto-antibodies; local production of pro-inflammatory cytokines and other inflammatory mediators; and subsequent recruitment of resident brain cells, in particular microglia \u003csup\u003e10\u0026ndash;13\u003c/sup\u003e. Autopsies in NPSLE brain pathology studies have provided evidence of extensive vasculopathy \u003csup\u003e14\u0026ndash;16\u003c/sup\u003e. The pathological mechanisms of this neuroinflammation and consequential neurovascular impairment can cause changes in cerebral perfusion \u003csup\u003e17,18\u003c/sup\u003e. Therefore, changes in cerebral blood flow (CBF) perfusion models might reveal a potential mechanism of SLE-CI onset.\u003c/p\u003e \u003cp\u003ePrevious studies used positron emission tomography (PET), single photon emission computed tomography (SPECT), and dynamic contrast-enhanced MRI (DCE-MRI) to assess regional brain perfusion. The PET and SPECT studies revealed that several perfusion-altered brain areas, such as the hippocampus and frontal cortex, are related to NPSLE \u003csup\u003e19\u0026ndash;22\u003c/sup\u003e. In DCE-MRI studies, patients with SLE showed changed perfusion in the putamen and thalamus independent of NP involvement \u003csup\u003e23\u003c/sup\u003e. Several studies also using DCE-MRI techniques found that assessment of partial blood flow values in the frontal lobe might improve NPSLE diagnosis \u003csup\u003e18,24\u003c/sup\u003e. However, PET, SPECT, and DSC-MRI are not suitable for every patient due to irradiation or contrast medium injection contraindications.\u003c/p\u003e \u003cp\u003eArterial spin labeling (ASL) enables the quantitative measurement of CBF by using endogenous arterial blood water magnetization as a noninvasive tracer and provides effectiveness comparable to PET \u003csup\u003e25\u003c/sup\u003e. CBF-associated exchange of oxygen and nutrients is considered an indicator of glucose metabolism and neuronal activity \u003csup\u003e26,27\u003c/sup\u003e. Previous studies using ASL found different cerebral perfusion patterns in patients with SLE and healthy controls (HCs) \u003csup\u003e28,29\u003c/sup\u003e. This technique has not been used in the study of patients with SLE-CI, as it can only reflect the brain function of the region. Altered focal CBF may underlie correlation disruptions with other brain regions, in this study we combined the use of ASL with the resting state index to reflect the overall level of brain function changes in the patients with SLE-CI.\u003c/p\u003e \u003cp\u003eChanges in neural microvascular perfusion can affect resting-state functional MRI (rs-fMRI) indices \u003csup\u003e30\u003c/sup\u003e. Both neurovascular perfusion and perfusion-related spontaneous neural activity lead to changes in functional connectivity (FC), an index that can show internal connections between isolated brain regions. FC is widely used in the study of other neurological diseases, such as Parkinson's disease and Alzheimer's disease \u003csup\u003e31,32\u003c/sup\u003e. In this study, we used the combination of noninvasive quantitative ASL and high temporal resolution rs-fMRI \u003csup\u003e33\u003c/sup\u003e to examine local and integrative brain function in SLE-CI.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Participants\u003c/h2\u003e\n \u003cp\u003eThis study mainly assessed the relationship between cognitive function and cerebral blood flow. To reduce the influence of confounding factors, only participants under 55 years of age who were right-handed and had more than 9 years of education were included. Ninety-seven patients with SLE, 40 with SLE-CI and 57 with SLE-NC, who fulfilled the revised American College of Rheumatology (ACR) criteria for SLE (i.e., had at least four of the classification criteria) were recruited from the First Hospital of China Medical University between October 2021 and January 2024. Clinical exclusion criteria were (a) current or past diagnosed primary mental illness; (b) secondary NPSLE due to infection, electrolyte disturbance, hypertension, or other causes; (c) severe circulatory system (cardiac function grade NYHA III or above or hypertension grade II for more than 5 years) dysfunction; (4) use of anti-depressant drugs; and (5) acute confusional state. Concurrently, 51 HCs matched for gender, age, and education with no history of neuropsychiatric disease were recruited. The study was approved by the Ethics Committee of the First Hospital of China Medical University (No. 2022-306-02), and informed consent was obtained in writing from each participant.\u003c/p\u003e\n \u003cp\u003eFor patients with SLE, demographic variables, previous NP events and attribution \u003csup\u003e34\u003c/sup\u003e, medications, and Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) \u003csup\u003e35\u003c/sup\u003e were documented. Laboratory variables included routine blood and urine, anti-ribosomal P\u003csup\u003e+\u003c/sup\u003e and anti-Ro\u003csup\u003e+\u003c/sup\u003e antibody (i.e., rheumatology 9 score), anti-double stranded DNA antibody (anti-dsDNA ab\u003csup\u003e+\u003c/sup\u003e), anti-phospholipid antibody (aPL), and complement 3 and 4 (C3, C4) levels. Clinical and cognitive assessments, blood collection, and MRI scanning were performed within 3 days before and after scanning. Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) damage index (SDI) scores were not included because some patients in the sample were not followed up for six months.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Cognitive function evaluation\u003c/h2\u003e\n \u003cp\u003eAll participants underwent the Montreal Cognitive Assessment (MoCA), and those with an MoCA score under 28 (under 27 for those with less than 12 years of education) were diagnosed with SLE-CI. Due to the relatively young age of the patients, the cognitive classification criterion differed from the guideline score of 25, as studies have shown that the optimal screening threshold is under 28 in patients with SLE \u003csup\u003e3,6\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Image acquisition\u003c/h2\u003e\n \u003cp\u003eMRI scans were performed with a 3.0T Pioneer GE MRI (GE Healthcare, Chicago, IL, USA) using foam pads to reduce head motion and scanner noise. The following brain images were acquired from all participants: (a) three-dimensional T1: repetition time (TR)\u0026thinsp;=\u0026thinsp;7.8 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;3.0 ms, field of view (FOV)\u0026thinsp;=\u0026thinsp;24 cm \u0026times; 24 cm, matrix\u0026thinsp;=\u0026thinsp;240 \u0026times; 240, slice thickness\u0026thinsp;=\u0026thinsp;1.0 mm, number of slices\u0026thinsp;=\u0026thinsp;176, and no gap; (b) ASL: TR\u0026thinsp;=\u0026thinsp;5344 ms, TE\u0026thinsp;=\u0026thinsp;10.9 ms, FOV\u0026thinsp;=\u0026thinsp;24 cm \u0026times; 24 cm, points\u0026thinsp;=\u0026thinsp;512, arms\u0026thinsp;=\u0026thinsp;8, slice thickness\u0026thinsp;=\u0026thinsp;4.0 mm, number of slices\u0026thinsp;=\u0026thinsp;36, no gap, effective resolution\u0026thinsp;=\u0026thinsp;3.79, post label delay\u0026thinsp;=\u0026thinsp;2525 ms, and number of excitations (NEX)\u0026thinsp;=\u0026thinsp;3; and (c) blood oxygenation level dependent (BOLD): TR\u0026thinsp;=\u0026thinsp;2000 ms, TE\u0026thinsp;=\u0026thinsp;30 ms, FA\u0026thinsp;=\u0026thinsp;90\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;24 \u0026times; 24 cm, matrix size\u0026thinsp;=\u0026thinsp;640 \u0026times; 640, pixel size\u0026thinsp;=\u0026thinsp;3.8 \u0026times; 3.8 mm\u003csup\u003e2\u003c/sup\u003e, slice thickness\u0026thinsp;=\u0026thinsp;3.5 mm, gap\u0026thinsp;=\u0026thinsp;0.7, and number of slices\u0026thinsp;=\u0026thinsp;34. Additionally, T2-weighted imaging (T2WI), susceptibility weighted imaging (SWI), and diffusion-weighted imaging (DWI) in common MRI (cMRI) were acquired simultaneously to exclude subjects with clear structural abnormalities.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Image processing:\u003c/h2\u003e\n \u003cp\u003eAll patients with SLE were clinically evaluated by a rheumatologist and a neurologist regarding their ongoing or history of NP events attributable to SLE. Patients with lesions larger than 1.5 cm on cMRI were excluded. Whole-brain voxel-based morphometry (VBM) analysis was performed to detect gray matter volume (GMV) reduction among groups using the Computational Anatomy Toolbox (CAT12) in the Statistical Parametric Mapping (SPM12) software package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk/spm/\u003c/span\u003e\u003c/span\u003e). T1 images were spatially registered to the Montreal Neurological Institute (MNI) space and segmented into white matter (WM), GM, and cerebro-spinal fluid (CSF). Segmented images of the GM were modulated and the modulated normalized GM maps smoothed using an 8-mm full-width at half-maximum (FWHM) Gaussian kernel for further analysis before the total intracranial volume (TIV) was estimated for each participant.\u003c/p\u003e\n \u003cp\u003eVoxel-wise statistical analysis for comparison of VBM imaging data between the SLE-CI and SLE-NC groups was performed using two-sample t-tests with SPM12 software with Gaussian random field (GRF) correction. The cluster-level statistical threshold was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and the voxel-level threshold at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (threshold k\u0026thinsp;\u0026gt;\u0026thinsp;20 voxels) with age and TIV as covariates. ASL data were preprocessed and further analyzed with SPM12 and the Data Processing and Analysis for Brain Imaging (DPABI) toolbox version 3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.restfmri.net\u003c/span\u003e\u003c/span\u003e). The preprocessing steps were as follows: (a) the ASL images were analyzed on a GE post-processing workstation to generate CBF images, (b) the individual 3D-T1 structure images were co-registered to the corresponding CBF brain maps, (c) all the images were standardized by mean decision in DPABI using GM templates, and (d) spatial smoothing was performed with an isotropic Gaussian kernel at a full width and half maximum (FWHM) of 6 mm.\u003c/p\u003e\n \u003cp\u003eRs-fMRI data were preprocessed and subsequently analyzed with SPM12 and DPABI. The preprocessing steps for BOLD MRI were as follows: (a) time handling by discarding the first 10 time points of each participant to avoid magnetic saturation effects and to allow participants to adapt to the scanning noise; (b) slice timing; (c) head movement correction, excluding patients with more than a 2-mm displacement in any of the x, y, or z directions or with angular rotation; (d) space normalization by Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) using new T1 image segments; (e) spatial smoothing with an isotropic Gaussian kernel at s full width and half maximum (FWHM) of 6 mm; (f) detrending; (g) data filtration at 0.01 to 0.08 Hz to reduce the influence of noise; (h) nuisance covariate regression; and (i) seed-based functional connectivity analysis. Pearson\u0026apos;s correlation coefficients were calculated between the seed-ROI time courses and the rest of the brain in a voxel-wise manner. The correlation coefficients were used for Fisher r-to-z transformation to transform single FC mapping into z-FC mapping and hence improve the normality of each participant. After adjustment for head movement and other reasons, the final cohort in the Rs-fMRI study included 74 patients with SLE, 29 with SLE-CI and 45 with SLE-NC.\u003c/p\u003e\n \u003cp\u003eVoxel-wise statistical analysis for ASL imaging data was performed for comparison of the SLE-CI, SLE-NC, and HC groups using one-way analysis of variance (ANOVA), in SPM12 and DPABI (Gaussian random field [GRF] correction, cluster \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, voxel, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and threshold k\u0026thinsp;\u0026gt;\u0026thinsp;20 voxels), with age and sex as covariates. After masks were made of the positive brain regions, post hoc \u003cem\u003et\u003c/em\u003e-tests were performed to identify differences between each pair of groups within the union mask (GRF correction, cluster \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, voxel \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and threshold k\u0026thinsp;\u0026gt;\u0026thinsp;20 voxels) with age as a covariate. Based on the ASL results, peak MNI coordinates of the 4 seed regions (the left hippocampus, thalamus, cerebellum_crus II, and frontal lobe gyrus) were chosen, as these are differential brain areas between SLE-CI and SLE-NC groups. Using a radius of 6 mm as seed regions, seed-based FC analysis was performed (GRF corrected, voxel \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and cluster \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eNon-fMRI data were analyzed using SPSS 27 (IBM Software Analytics, Armonk, NY, USA). The age distribution of the groups was compared with ANOVA and post hoc analyses, and sex-related differences were detected using the chi-square test. The two-sample \u003cem\u003et\u003c/em\u003e-test was used to estimate differences in the clinical data, including duration of disease, clinical indicators, MoCA scores, and SLEDAI scores, between the SLE-CI and SLE-NC groups with a two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the statistical significance level.\u003c/p\u003e\n \u003cp\u003eFor the fMRI results, the DPABI function was used to generate a mask for each differential brain region and extract the specific values within the mask (CBF and FC values). Pearson and Spearman correlation coefficients were calculated to examine possible associations between fMRI values and clinical scores (SLEDAI, MoCA and its components, and clinical indicators) in the SLE subgroups with a two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the statistical significance level.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic, clinical, and MRI findings\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the main demographic, clinical, and MRI variables of the 97 patients in the SLE group and the 51 patients in the HC group. The 97 patients in the SLE group were further divided into 40 patients in the SLE-CI group and 57 patients in the SLE NC group, yielding an SLE-CI, an SLE-NC, and a HC group. The results of one-way ANOVA revealed no statistical differences in age, sex, or education level among the three groups. The MoCA scores and their components were significantly lower in the SLE-CI group than in the SLE-NC group, and the SLE-CI group showed significant differences in scores in all six cognitive fields, among which the T value showed the largest difference in memory scores. There were no significant differences in disease duration, SLEDAI score, or daily glucocorticoid dose among the patients with SLE.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows additional data obtained from rheumatological evaluation. Among the 97 patients with SLE, 10 had experienced recent intracranial infarction, 26 had experienced intracranial microhemorrhage, and 55 had experienced white matter hyperintensity (WMH) changes, which were mostly distributed in the parietal ventricles, deep cortical non-marginal areas, and subcortical marginal areas. In terms of clinical manifestations, 59 patients had a history of NP events. In the SLE-NC group, 19 patients had experienced at least 1 NP event, 10 patients had been hospitalized for transient coma or convulsion, and 4 patients had been hospitalized for headache. A total of 25 patients with SLE were excluded from the original cohort due to having conventional MRI lesions larger than 1.5 cm.\u003c/p\u003e \u003cp\u003eOn cMRI, compared with the SLE-NC group, the SLE-CI group showed extensive GMV atrophy in the large and cerebellar cerebellum, including in the bilateral orbitofrontal cortex, right thalamus, and right temporal lobe. A noteworthy finding was a decrease in GMV in the left hippocampus, which is consistent with the CBF results (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ASL results\u003c/h2\u003e \u003cp\u003eOne-way ANOVA revealed significant differences in CBF among the three groups controlled for age. Post hoc analysis showed that compared with the HC group, the SLE-CI and SLE-NC groups showed decreased CBF in the bilateral medial prefrontal cortex (MPFC) and insula gyrus and increased CBF in the bilateral basal ganglia, thalamus, and cerebellum posterior lobe \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These findings show that the abnormal (as compared with the HC group) CBF range in the SLE-CI group was significantly larger than that of the SLE-NC group, especially in the hippocampal region, in which increased CBF was observed in the in SLE-CI but not the SLE-NC group. Compared with the SLE-NC group, the SLE-CI group had higher CBF values in the left hippocampus, thalamus, and left cerebellum_crus II and lower CBF values in the left frontal lobe \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 FC results\u003c/h2\u003e \u003cp\u003eCompared with the SLE-NC group, the SLE-CI group showed increased FC of the left insula gyrus when the left cerebellum_crus II was set as the seed region and decreased FC in the homolateral parahippocampus when the left hippocampus was set as the seed region. When the thalamus and frontal lobe were used as seed points, no significant group differences were identified (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The FC value of the left insular lobe was negatively correlated with the MoCA score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.433, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) but not with any other regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation analysis results\u003c/h2\u003e \u003cp\u003eData that did not conform to normal distribution in the correlation analysis were all analyzed by Spearman correlation analysis. Left hippocampal CBF was negatively correlated with MoCA score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.418, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and memory score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.502, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and slightly positively correlated with SLEDAI (r\u0026thinsp;=\u0026thinsp;0.234, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). Left cerebellar crus II CBF was negatively correlated with MoCA score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.373, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and memory score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.332, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and was slightly positively correlated with SLEDAI (r\u0026thinsp;=\u0026thinsp;0.253, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). Left thalamus CBF was negatively correlated with MoCA score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.436, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and memory score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.470, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and was slightly positively correlated with SLEDAI score (r\u0026thinsp;=\u0026thinsp;0.217, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). Due to the absence of dsDNA clinical data, this analysis included 70 patients with SLE. Left frontal CBF was positively correlated with MoCA score (r\u0026thinsp;=\u0026thinsp;0.391, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and visuospatial executive ability (r\u0026thinsp;=\u0026thinsp;0.346, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and was slightly negatively correlated with the antibody titer of anti-dsDNA (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.341, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and SLEDAI score (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.247, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015); Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the correlation results for \u003cem\u003eP\u003c/em\u003e levels less than 0.01 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eWe investigated a well-validated quantitative MRI approach (sagittal 3D-T1, ASL, and seed-based FC) to investigate the structural and functional characteristics of GM. The cognitive classification criterion that we used differed from the guideline (a score of 25) that is used in the vast majority of neurodegenerative diseases, including Parkinson's and Alzheimer's disease, due to the relatively young age (mean age approximately 30 years of all groups) of our patients \u003csup\u003e3,6\u003c/sup\u003e. Among the three groups, we observed CBF alteration in patients with SLE-CI, leading us to further investigate whether neurovascular dysfunction affects neural activity in these patients. One finding was that compared with HCs, patients with SLE shared common features of CBF, but the range of abnormalities in the SLE-CI group was significantly larger than that in the SLE-NC group. Within the SLE subgroups, we found that the SLE-CI group had increased CBF in the left hippocampus, thalamus, and cerebellum_crus II and decreased CBF in the left frontal lobe compared with the SLE-NC group. Another finding was that the SLE-CI group had decreased GM volume in the left hippocampus compared with the SLE-NC group.\u003c/p\u003e\n\u003cp\u003eSecondary FC analysis revealed that compared with the SLE-NC group, the SLE-CI group had increased FC in the left insula gyrus when we set the left cerebellum_crus II as the seed region and decreased FC in the homolateral parahippocampus when we set the left hippocampus as the seed region. Interestingly, we found abnormal alterations in the left hippocampal region in the SLE-CI group, including in the CBF, VBM and FC. Our findings indicate that altered structural and functional characteristics of the hippocampal gyrus and the cerebello-cerebral and hippocampus-parahippocampus networks may be image biomarkers of cognitive impairment in patients with NPSLE.\u003c/p\u003e\n\u003cp\u003eIn terms of ASL sequence parameters, the clinical post label delay (PLD) values are typically set between 1000–3000 milliseconds and 2000 ms in normal adults. Additional temporal measurements with longer PLD may be desired in cases of regional or global vascular compromise. NPSLE belongs to the diffuse cranial microangiopathy\u0026nbsp;\u003csup\u003e36\u003c/sup\u003e, we chose 2525 ms as the PLD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e4.1 Changes in structural and functional hippocampus indices in SLE\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eCompared with the SLE-NC group, the SLE-CI group showed multiple abnormalities in the left hippocampus, including increased CBF, decreased CM volume, and decreased FC in the parahippocampal gyrus. Compared with that of the HC group, the left hippocampal CBF increased in the SLE-CI group but not the SLE-NC group, which may have been due to SLE-related neurovascular changes and neuroinflammation, whose clinical manifestations of cognitive impairment only appear after the injury exceeds a certain threshold \u003csup\u003e37\u003c/sup\u003e. Abnormal hippocampal indices have been reported in patients with SLE \u003csup\u003e21,38-43\u003c/sup\u003e. In their study of patients with SLE, especially those with epilepsy, Toyota et al. reported hippocampal sclerosis with neuronal loss and gliosis \u003csup\u003e41\u003c/sup\u003e. Other studies reported that hippocampal GM atrophy in patients with SLE is associated with disease duration and independent of a diagnosis of NPSLE \u003csup\u003e42,43\u003c/sup\u003e. When Chi et al. used DSC-MRI to evaluate the changes in the BBB in patients with SLE, they found that the permeability of the BBB in the hippocampus of the patient group increased. Specifically, they observed that the intravascular to extravascular/extracellular space (K\u003csup\u003etrans\u003c/sup\u003e) value increased and the K\u003csup\u003etrans\u003c/sup\u003e value was significantly positively correlated with CBF value. Such findings suggest that the CBF value can partly reflect changes in BBB permeability \u003csup\u003e40,44\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePrevious studies reported that alteration of the\u0026nbsp;hippocampal region\u0026nbsp;in patients with SLE is related to increased production of specific antibodies, such as anti-N-methyl-D-aspartate receptor (NMDAR) antibodies, anti-ribosomal P, and neuronal surface P-antigen\u0026nbsp;\u003csup\u003e39,45\u003c/sup\u003e, which target cells in the hippocampus and lead to abnormal hypermetabolism and atrophy\u0026nbsp;\u003csup\u003e21,40\u003c/sup\u003e. In murine models, both anti-ribosomal P antibodies and NMDAR antibodies have neurotoxic effects, leading to enhanced calcium ion influx and neuronal dysfunction or death\u0026nbsp;\u003csup\u003e46,47\u003c/sup\u003e. Activation of autoantibodies causes an inflammatory response to recruit microglia. Many previous studies of the mechanisms of NPSLE have identified an important role for microglia in inflammation, which has been associated with cognitive impairment in animal studies \u003csup\u003e48-50\u003c/sup\u003e.\u0026nbsp;Kathleen et al. found that cerebellar and hippocampal microglia exist in a more immune-vigilant state in mice\u0026nbsp;\u003csup\u003e51\u003c/sup\u003e. In a translator protein (TSPO)-PET imaging study, Wang found a decreased specific contrast agent distribution in the cerebellum and hippocampus of patients with SLE compared with HCs, especially in cognitively normal SLE subjects, and pseudo-normalization in cognitively impaired patients with SLE, which may be caused by glial-cell activation\u0026nbsp;\u003csup\u003e52\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe hippocampal gyrus is an important part of the limbic system that plays crucial roles in learning, memory processes, and spatial navigation\u0026nbsp;\u003csup\u003e53\u003c/sup\u003e, and the hippocampus is the center for memory processing and storage\u0026nbsp;\u003csup\u003e54\u003c/sup\u003e. In this study, patients with SLE were grouped according to cognitive impairment. The results suggest that the change in hippocampal volume in the SLE-CI group was the result of nerve damage and that the change in blood flow was a neural compensatory mechanism, as would be the aggregation of microglia to the site of neuronal necrosis. Consistent with this finding, Mackay et al. found that the high metabolism of the hippocampus was associated with decline in cognitive function in patients with SLE\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e. In FC analysis, we observed decreased FC between the left hippocampus and parahippocampus gyrus in the SLE-CI group compared with the SLE-NC group.\u0026nbsp;When Pentari et al. used cross­recurrence quantification analysis, they observed hypoconnectivity in medial temporal structures in patients with NPSLE that adversely affected their memory capacity\u0026nbsp;\u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e4.2 Changes in frontal and insula functional indices in SLE\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe found an increasing trend in perfusion values in MPFC ranging from the lowest values in the HC group to intermediate values in the SLE-NC group and the highest values in the SLE-CI group. Compared with the HC group, the SLE group, regardless of cognitive status, had widely reduced bilateral insular lobe CBF, a finding consistent with previous studies \u003csup\u003e56,57\u003c/sup\u003e. However, there were no significant differences between the SLE-NC and SLE-CI groups in bilateral insular lobe CBF. Histopathological studies have revealed that nonspecific focal vasculopathy occurs in SLE, which could be the pathological basis for perfusion abnormalities \u003csup\u003e58-62\u003c/sup\u003e. Using ASL, Jia et al. reported asymmetric, reduced perfusion in the frontal and temporal cortices in both the NPSLE and non-NPSLE groups compared with HCs. This funding, which was validated by quantitative CBF analyses, suggests the existence of a subclinical process in patients who have not experienced NP events \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRegional hypermetabolism in the orbitofrontal cortex and insular lobe has been associated with abnormal inter-regional metabolic processes that are associated with impaired cognitive performance \u003csup\u003e21\u003c/sup\u003e.Using fMRI, several previous studies have also found alterations in the frontal, temporal, and insular cortices along with cognition-related clinical indicators in patients with SLE \u003csup\u003e30,49,63\u003c/sup\u003e. The insular cortex is part of the salience network (SN), a cognitive and psychiatric network of the brain \u003csup\u003e64\u003c/sup\u003e. We found that CBF in the insula was reduced in patients with SLE, regardless of cognitive state, compared with HCs. The insula is involved in attention and salience processing, social cognition, and speech, which may account for the low cognitive scores in the combined SLE patient groups compared with the HCs. A previous study that used rs-fMRI to identify changes in the FC (rs-FC study) of patients with SLE found that altered FC in the insula is correlated with depression scores, which accords with our findings \u003csup\u003e63\u003c/sup\u003e. Using MR spectroscopy (MRS), Cagnoli found that the insula N-acetylaspartate /creatine (NAA/Cr) ratio is negatively correlated with SLE activity \u003csup\u003e65\u003c/sup\u003e, which is consistent with our findings. Taken together, these findings indicate that change in insular perfusion may indicate the occurrence and severity of disease.\u003c/p\u003e\n\u003cp\u003eTask-based MRI studies have shown that patients with SLE have a decreased ability to suppress the default mode network (DMN), especially in the medial prefrontal cortex \u003csup\u003e66,67\u003c/sup\u003e. This clearly implies that the prefrontal lobe is correlated with cognition in these patients, corroborating our finding that CBF in the subregion positively correlates with MoCA score and visuospatial executive ability scores.This correlation suggests that changes in the prefrontal network play an important role in the processing of cognitive tasks in patients with SLE, confirming that CBF in this region is positively correlated with MoCA and visuospatial executive function scores. Our findings and those of previous studies indicate that perfusion changes in the frontal and insular cortex in patients with SLE are correlated with clinical indicators to varying degrees, suggesting that these brain regions are susceptible to hemodynamic changes. The different degree of clinical presentation in the patient groups in our study may have been due to the different disease stages of the two SLE groups. The insular lobe changes did not differ significantly between these subgroups, suggesting that cerebral perfusion occurs as early as the onset of SLE rather than later in the disease process when accompanied by clinical symptoms \u003csup\u003e68\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e4.4 Differences in cerebellar functional index changes in SLE\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eCompared with that of the SLE-NC group, the CBF of the left cerebellum crus_II of the SLE-CI group increased, which may indicate the compensatory effect of the cerebellum in patients with SLE-CI. As this increase is negatively correlated with cognitive score, it may also indicate that the cerebellum is in the decompensated stage. Alterations in SPECT images were observed in the cerebellum before treatment, with various levels of recovery after therapy in patients with NPSLE\u0026nbsp;\u003csup\u003e69,70\u003c/sup\u003e. In a previous study, CBF significantly increased in the GM in cerebellum regions in patients with non-NPSLE compared with HCs, and the main differences between the NPSLE and non-NPSLE subgroups were in the GM of cerebellum\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe cerebellum is mostly associated with motor function, having roles in regulating muscle tension and body balance. Many neuroimaging neuropsychology studies have attempted to determine the involvement of the cerebellum in high-level cognitive functions, including studies researching a range of psychiatric and developmental disorders\u0026nbsp;\u003csup\u003e71,72\u003c/sup\u003e. Disorders caused by non-motor cerebellar dysfunction are the basis of the “dysmetria of thought (DoT)” theory\u0026nbsp;\u003csup\u003e73\u003c/sup\u003e. Posterior and lateral cerebellar regions, including the lateral lobule VI, crus I, crus II, and lobule VIIB, are active during cognitive tasks\u0026nbsp;\u003csup\u003e74\u003c/sup\u003e. However, the low number of MRI reports on the cerebellum may be the fact that perfusion metrics are calibrated through the cerebellum\u0026nbsp;\u003csup\u003e75-77\u003c/sup\u003e. Alterations in cerebellar regions and their relationship to metabolism, including the activation of microglia, should be investigated in future studies.\u003c/p\u003e\n\u003cp\u003eIn FC analysis, we observed increased FC values between the left cerebellar crus II and the left insular gyrus and decreased values between the left hippocampus and parahippocampus gyrus in the SLE-CI group compared with the SLE-NC group. Using rs-fMRI to identify differences between patients with SLE with or without NP events and HCs, Su et al. found increased compensatory functional connectivity in cerebello-cerebral networks in the patients with SLE \u003csup\u003e78\u003c/sup\u003e. FC analysis by Cao et al. indicated that decreased synergy between the cerebrum and the cerebellum may be one cause of cognitive dysfunction in patients with SLE \u003csup\u003e79\u003c/sup\u003e. These results suggest that abnormal cerebello-cerebral networks are possible biomarkers of cognitive impairments in patients with SLE-CI, for which further evidence has been provided by other studies of patients with cognitive impairment \u003csup\u003e80,81\u003c/sup\u003e.\u003c/p\u003e\n\u003ch1\u003eLimitations\u003c/h1\u003e\n\u003cp\u003eAs with any scientific research, our study is subject to certain limitations that we acknowledge here. Two limitations were our examination of a small sample and use of a cross-sectional study design, which may limit the generalizability of our findings. Overcoming these limitations requires analysis of larger samples and use of longitudinal study designs. Another limitation was that despite there being no significant differences in the daily dose of glucocorticoids administered to the SLE subgroups, the type and dose of hormone therapy used by different patients may have had some influence on their outcomes. A final limitation was that most patients with SLE were in the active stage of disease, which may have affected the results despite there being no significant differences in SLEDAI scores between the groups. Therefore, further follow-up of these patients is required.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results suggest that in patients with SLE-CI, decrease in hippocampal GMV may be the result of inflammatory injury, increase in CBF may be an inflammatory response to SLE, and increase in both CBF in the crus II region of the posterior cerebellar lobe and in FC in the ipsilateral insular lobe may be compensatory mechanisms. These findings suggest that the insular and medial frontal lobes may be more susceptible to hemodynamic damage and that changes in their function and network may be biomarkers of cognitive impairment in patients with SLE-CI. Larger longitudinal studies are needed to validate these findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eThe sources of support\u003c/strong\u003e: This study was supported by the foundation from the Chinese National Key Technology R\u0026amp;D Program (2021YFC2501303 to Pingting Yang) and the\u0026nbsp;Doctoral Fund of Liaoning Province (No.2022-BS-141 to Bailing Tian).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: The authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe study was approved by the Ethics Committee of the First Hospital of China Medical University (No.2022-306-02), and informed consent was obtained in writing from each participant. The datasets during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n \u003cli\u003eAiniala H, Loukkola J, Peltola J, Korpela M, Hietaharju A. The prevalence of neuropsychiatric syndromes in systemic lupus erythematosus. Neurology 2001;57:496-500.\u003c/li\u003e\n \u003cli\u003eBrey RL, Holliday SL, Saklad AR, et al. Neuropsychiatric syndromes in lupus: prevalence using standardized definitions. Neurology 2002;58:1214-20.\u003c/li\u003e\n \u003cli\u003eRayes HA, Tani C, Kwan A, et al. What is the prevalence of cognitive impairment in lupus and which instruments are used to measure it? A systematic review and meta-analysis. Semin Arthritis Rheum 2018;48:240-55.\u003c/li\u003e\n \u003cli\u003eThe American College of Rheumatology nomenclature and case definitions for neuropsychiatric lupus syndromes. 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JCI Insight 2019;4.\u003c/li\u003e\n \u003cli\u003eWang Y, Coughlin JM, Ma S, et al. Neuroimaging of translocator protein in patients with systemic lupus erythematosus: a pilot study using [(11)C]DPA-713 positron emission tomography. Lupus 2017;26:170-8.\u003c/li\u003e\n \u003cli\u003eSalomonsson T, Rumetshofer T, J\u0026ouml;nsen A, et al. Abnormal cerebral hemodynamics and blood-brain barrier permeability detected with perfusion MRI in systemic lupus erythematosus patients. NeuroImage: Clinical 2023;38.\u003c/li\u003e\n \u003cli\u003eKamintsky L, Beyea SD, Fisk JD, et al. Blood-brain barrier leakage in systemic lupus erythematosus is associated with gray matter loss and cognitive impairment. Ann Rheum Dis 2020;79:1580-7.\u003c/li\u003e\n \u003cli\u003eWolf RL, Detre JA. Clinical neuroimaging using arterial spin-labeled perfusion magnetic resonance imaging. Neurotherapeutics 2007;4:346-59.\u003c/li\u003e\n \u003cli\u003eMusiek ES, Chen Y, Korczykowski M, et al. 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Blood-brain barrier disruption and neuroinflammation as pathophysiological mechanisms of the diffuse manifestations of neuropsychiatric systemic lupus erythematosus. Autoimmunity Reviews 2019;18:426-32.\u003c/li\u003e\n \u003cli\u003eChi JM, Mackay M, Hoang A, et al. Alterations in Blood-Brain Barrier Permeability in Patients with Systemic Lupus Erythematosus. AJNR Am J Neuroradiol 2019;40:470-7.\u003c/li\u003e\n \u003cli\u003eToyota T, Akamatsu N, Tanaka A, et al. Mesial temporal lobe epilepsy as a neuropsychiatric syndrome of systemic lupus erythematosus. Epilepsia 2013;54:e33-6.\u003c/li\u003e\n \u003cli\u003eAppenzeller S, Carnevalle AD, Li LM, Costallat LT, Cendes F. Hippocampal atrophy in systemic lupus erythematosus. Ann Rheum Dis 2006;65:1585-9.\u003c/li\u003e\n \u003cli\u003eLiu S, Cheng Y, Zhao Y, et al. Hippocampal Atrophy in Systemic Lupus Erythematosus Patients without Major Neuropsychiatric Manifestations. J Immunol Res 2020;2020:2943848.\u003c/li\u003e\n \u003cli\u003eJoseph CR. Utilizing 3D Arterial Spin Labeling to Identify Cerebrovascular Leak and Glymphatic Obstruction in Neurodegenerative Disease. Diagnostics 2021;11.\u003c/li\u003e\n \u003cli\u003eSchwarting A, Mockel T, Lutgendorf F, et al. Fatigue in SLE: diagnostic and pathogenic impact of anti-N-methyl-D-aspartate receptor (NMDAR) autoantibodies. Ann Rheum Dis 2019;78:1226-34.\u003c/li\u003e\n \u003cli\u003eBravo-Zehnder M, Toledo EM, Segovia-Miranda F, et al. Anti-ribosomal P protein autoantibodies from patients with neuropsychiatric lupus impair memory in mice. Arthritis Rheumatol 2015;67:204-14.\u003c/li\u003e\n \u003cli\u003eSegovia-Miranda F, Serrano F, Dyrda A, et al. Pathogenicity of lupus anti-ribosomal P antibodies: role of cross-reacting neuronal surface P antigen in glutamatergic transmission and plasticity in a mouse model. Arthritis Rheumatol 2015;67:1598-610.\u003c/li\u003e\n \u003cli\u003eZarfeshani A, Carroll KR, Volpe BT, Diamond B. Cognitive Impairment in SLE: Mechanisms and Therapeutic Approaches. Curr Rheumatol Rep 2021;23:25.\u003c/li\u003e\n \u003cli\u003eMikdashi JA. Altered functional neuronal activity in neuropsychiatric lupus: A systematic review of the fMRI investigations. Semin Arthritis Rheum 2016;45:455-62.\u003c/li\u003e\n \u003cli\u003eDeczkowska A, Keren-Shaul H, Weiner A, Colonna M, Schwartz M, Amit I. Disease-Associated Microglia: A Universal Immune Sensor of Neurodegeneration. Cell 2018;173:1073-81.\u003c/li\u003e\n \u003cli\u003eGrabert K, Michoel T, Karavolos MH, et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat Neurosci 2016;19:504-16.\u003c/li\u003e\n \u003cli\u003eWang Y, Coughlin JM, Ma S, et al. Neuroimaging of translocator protein in patients with systemic lupus erythematosus: a pilot study using [11C]DPA-713 positron emission tomography. Lupus 2017;26:170-8.\u003c/li\u003e\n \u003cli\u003eNwaubani P, Cercignani M, Colasanti A. In vivo quantitative imaging of hippocampal inflammation in autoimmune neuroinflammatory conditions: a systematic review. Clin Exp Immunol 2022;210:24-38.\u003c/li\u003e\n \u003cli\u003eReagh ZM, Ranganath C. What does the functional organization of cortico-hippocampal networks tell us about the functional organization of memory? Neurosci Lett 2018;680:69-76.\u003c/li\u003e\n \u003cli\u003ePentari A, Simos N, Tzagarakis G, et al. Altered hippocampal connectivity dynamics predicts memory performance in neuropsychiatric lupus: a resting-state fMRI study using cross-recurrence quantification analysis. Lupus Sci Med 2023;10.\u003c/li\u003e\n \u003cli\u003eMackay M, Tang CC, Volpe BT, et al. Brain metabolism and autoantibody titres predict functional impairment in systemic lupus erythematosus. Lupus Sci Med 2015;2:e000074.\u003c/li\u003e\n \u003cli\u003eZhuo Z, Su L, Duan Y, et al. Different patterns of cerebral perfusion in SLE patients with and without neuropsychiatric manifestations. Hum Brain Mapp 2020;41:755-66.\u003c/li\u003e\n \u003cli\u003eAppenzeller S, Cendes F, Costallat LTL. Cognitive impairment and employment status in systemic lupus erythematosus: a prospective longitudinal study. Arthritis Rheum 2009;61:680-7.\u003c/li\u003e\n \u003cli\u003eKozora E, Ellison MC, West S. Depression, fatigue, and pain in systemic lupus erythematosus (SLE): relationship to the American College of Rheumatology SLE neuropsychological battery. Arthritis Rheum 2006;55:628-35.\u003c/li\u003e\n \u003cli\u003eLangensee L, M\u0026aring;rtensson J, J\u0026ouml;nsen A, et al. Cognitive performance in systemic lupus erythematosus patients: a cross-sectional and longitudinal study. BMC Rheumatol 2022;6:22.\u003c/li\u003e\n \u003cli\u003eOta Y, Srinivasan A, Capizzano AA, et al. Central Nervous System Systemic Lupus Erythematosus: Pathophysiologic, Clinical, and Imaging Features. Radiographics 2022;42:212-32.\u003c/li\u003e\n \u003cli\u003eHanly JG, Kozora E, Beyea SD, Birnbaum J. Review: Nervous System Disease in Systemic Lupus Erythematosus: Current Status and Future Directions. Arthritis Rheumatol 2019;71:33-42.\u003c/li\u003e\n \u003cli\u003eBonacchi R, Rocca MA, Ramirez GA, et al. Resting state network functional connectivity abnormalities in systemic lupus erythematosus: correlations with neuropsychiatric impairment. Mol Psychiatry 2021;26:3634-45.\u003c/li\u003e\n \u003cli\u003eSeeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 2007;27:2349-56.\u003c/li\u003e\n \u003cli\u003eCagnoli P, Harris RE, Frechtling D, et al. Reduced Insular Glutamine and N-acetylaspartate in systemic lupus erythematosus: a single-voxel (1)H-MR spectroscopy study. Acad Radiol 2013;20:1286-96.\u003c/li\u003e\n \u003cli\u003eBarraclough M, McKie S, Parker B, et al. Altered cognitive function in systemic lupus erythematosus and associations with inflammation and functional and structural brain changes. Ann Rheum Dis 2019;78:934-40.\u003c/li\u003e\n \u003cli\u003eBarraclough M, McKie S, Parker B, Elliott R, Bruce IN. The effects of disease activity on neuronal and behavioural cognitive processes in systemic lupus erythematosus. Rheumatology (Oxford) 2021;61:195-204.\u003c/li\u003e\n \u003cli\u003eSarbu N, Alobeidi F, Toledano P, et al. Brain abnormalities in newly diagnosed neuropsychiatric lupus: systematic MRI approach and correlation with clinical and laboratory data in a large multicenter cohort. Autoimmun Rev 2015;14:153-9.\u003c/li\u003e\n \u003cli\u003eLiu FY, Huang WS, Kao CH, Yen RF, Wang JJ, Ho ST. Usefulness of Tc-99m ECD brain SPECT to evaluate the effects of methylprednisolone pulse therapy in lupus erythematosus with brain involvement: a preliminary report. Rheumatol Int 2003;23:182-5.\u003c/li\u003e\n \u003cli\u003eTrevisan AC, Alexandre-Santos L, Assad RL, et al. Temporal and spatial changes in cerebral blood flow in neuropsychiatric systemic lupus erythematosus: a subtraction brain spect study. Eur J Hybrid Imaging 2021;5:19.\u003c/li\u003e\n \u003cli\u003eKing M, Hernandez-Castillo CR, Poldrack RA, Ivry RB, Diedrichsen J. Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nat Neurosci 2019;22:1371-8.\u003c/li\u003e\n \u003cli\u003eSchmahmann JD, Guell X, Stoodley CJ, Halko MA. The Theory and Neuroscience of Cerebellar Cognition. Annu Rev Neurosci 2019;42:337-64.\u003c/li\u003e\n \u003cli\u003eSchmahmann JD. An emerging concept. The cerebellar contribution to higher function. Arch Neurol 1991;48:1178-87.\u003c/li\u003e\n \u003cli\u003eSchmahmann JD, Guell X, Stoodley CJ, Halko MA. The Theory and Neuroscience of Cerebellar Cognition. Annu Rev Neurosci 2019;42:337-64.\u003c/li\u003e\n \u003cli\u003eSalomonsson T, Rumetshofer T, J\u0026ouml;nsen A, et al. Abnormal cerebral hemodynamics and blood-brain barrier permeability detected with perfusion MRI in systemic lupus erythematosus patients. Neuroimage Clin 2023;38:103390.\u003c/li\u003e\n \u003cli\u003ePapadaki E, Fanouriakis A, Kavroulakis E, et al. Neuropsychiatric lupus or not? Cerebral hypoperfusion by perfusion-weighted MRI in normal-appearing white matter in primary neuropsychiatric lupus erythematosus. Ann Rheum Dis 2018;77:441-8.\u003c/li\u003e\n \u003cli\u003ePapadaki E, Kavroulakis E, Bertsias G, et al. Regional cerebral perfusion correlates with anxiety in neuropsychiatric SLE: evidence for a mechanism distinct from depression. Lupus 2019;28:1678-89.\u003c/li\u003e\n \u003cli\u003eSu L, Zhuo Z, Duan Y, et al. Structural and Functional Characterization of Gray Matter Alterations in Female Patients With Neuropsychiatric Systemic Lupus. Front Neurosci 2022;16:839194.\u003c/li\u003e\n \u003cli\u003eCao Z-Y, Wang N, Jia J-T, et al. Abnormal topological organization in systemic lupus erythematosus: a resting-state functional magnetic resonance imaging analysis. Brain Imaging Behav 2021;15:14-24.\u003c/li\u003e\n \u003cli\u003eLi Y, Liu H, Yu H, et al. Alterations of voxel-wise spontaneous activity and corresponding brain functional networks in multiple system atrophy patients with mild cognitive impairment. Hum Brain Mapp 2023;44:403-17.\u003c/li\u003e\n \u003cli\u003eChen F, Gong J, Chen G, et al. Shared and specific characteristics of regional cerebral blood flow and functional connectivity in unmedicated bipolar and major depressive disorders. J Affect Disord 2022;309:77-84.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographic and clinical characteristics of SLE patients and HCs\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.697183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.661971830985916%\" valign=\"top\"\u003e\n \u003cp\u003eHCs\u003c/p\u003e\n \u003cp\u003e（n=51）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003eSLE-CI\u003c/p\u003e\n \u003cp\u003e（n=40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eSLE-NC\u003c/p\u003e\n \u003cp\u003e（n=57）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"top\"\u003e\n \u003cp\u003eF/c\u003csup\u003e2\u003c/sup\u003e/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.697183098591548%\" valign=\"top\"\u003e\n \u003cp\u003eAge, mean (SD), years\u003c/p\u003e\n \u003cp\u003eGender (male/female)\u003c/p\u003e\n \u003cp\u003eEducation (years)\u003c/p\u003e\n \u003cp\u003eDuration (months)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMoCA score\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eVisuospatial executive ability\u003c/p\u003e\n \u003cp\u003eNamed ability\u003c/p\u003e\n \u003cp\u003eattention\u003c/p\u003e\n \u003cp\u003eLanguage\u003c/p\u003e\n \u003cp\u003eAbstract\u003c/p\u003e\n \u003cp\u003eMemory\u003c/p\u003e\n \u003cp\u003edirective force\u003c/p\u003e\n \u003cp\u003eSLEDAI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.661971830985916%\" valign=\"top\"\u003e\n \u003cp\u003e33.3(10.72)\u003c/p\u003e\n \u003cp\u003e6/45\u003c/p\u003e\n \u003cp\u003e10.48（4.45）\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e29.35(11.85)\u003c/p\u003e\n \u003cp\u003e2/38\u003c/p\u003e\n \u003cp\u003e10.43（3.75）\u003c/p\u003e\n \u003cp\u003e48.46(64.01)\u003c/p\u003e\n \u003cp\u003e21.80(4.76)\u003c/p\u003e\n \u003cp\u003e3.08(1.64)\u003c/p\u003e\n \u003cp\u003e2.65(0.62)\u003c/p\u003e\n \u003cp\u003e4.78(1.31)\u003c/p\u003e\n \u003cp\u003e2.17(0.78)\u003c/p\u003e\n \u003cp\u003e1.48(0.60)\u003c/p\u003e\n \u003cp\u003e1.85(1.12)\u003c/p\u003e\n \u003cp\u003e5.53(1.04)\u003c/p\u003e\n \u003cp\u003e15.4(6.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003e33.24(10.86)\u003c/p\u003e\n \u003cp\u003e2/55\u003c/p\u003e\n \u003cp\u003e11.31（2.36）\u003c/p\u003e\n \u003cp\u003e32.27(47.34)\u003c/p\u003e\n \u003cp\u003e28.25(1.106)\u003c/p\u003e\n \u003cp\u003e4.74(0.48)\u003c/p\u003e\n \u003cp\u003e3.00(0.00)\u003c/p\u003e\n \u003cp\u003e5.88(0.33)\u003c/p\u003e\n \u003cp\u003e2.75(0.51)\u003c/p\u003e\n \u003cp\u003e1.89(0.36)\u003c/p\u003e\n \u003cp\u003e4.00(0.93)\u003c/p\u003e\n \u003cp\u003e5.98(0.13)\u003c/p\u003e\n \u003cp\u003e12.51(12.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"top\"\u003e\n \u003cp\u003e2.177\u003c/p\u003e\n \u003cp\u003e3.181\u003c/p\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003cp\u003e1.189\u003c/p\u003e\n \u003cp\u003e8.403\u003c/p\u003e\n \u003cp\u003e7.234\u003c/p\u003e\n \u003cp\u003e3.343\u003c/p\u003e\n \u003cp\u003e5.204\u003c/p\u003e\n \u003cp\u003e4.117\u003c/p\u003e\n \u003cp\u003e3.954\u003c/p\u003e\n \u003cp\u003e10.310\u003c/p\u003e\n \u003cp\u003e2.773\u003c/p\u003e\n \u003cp\u003e1.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.697183098591548%\" valign=\"top\"\u003e\n \u003cp\u003eGC（mg/day）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.661971830985916%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e59.01(65.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003e62.28(83.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"top\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: HCs healthy control group; SLE-CI lupus with cognitive impairment group; SLE-NC lupus cognition normal group; MoCA Montreal Cognitive Assessment Scale; SLEDAI Systemic lupus erythematosus Activity Index; GC glucocorticoid; Among the three groups, one-way analysis of variance and Chi-square test were used for gender. The above data is represented by \u0026quot;mean (standard deviation)\u0026quot;; P\u0026lt;0.05 Use * indicates a statistically significant difference.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Imaging and clinical data of the SLE patient group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.27124773960217%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.412296564195298%\" valign=\"top\"\u003e\n \u003cp\u003eSLE-CI（n=40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\" valign=\"top\"\u003e\n \u003cp\u003eSLE-NC（n=57）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.27124773960217%\" valign=\"top\"\u003e\n \u003cp\u003eCommon MRI Imaging\u003c/p\u003e\n \u003cp\u003eRecent intracranial infarction (DWI)\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.412296564195298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.27124773960217%\" valign=\"top\"\u003e\n \u003cp\u003eIntracranial microhemorrhage (SWI)\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.412296564195298%\" valign=\"top\"\u003e\n \u003cp\u003e12(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\" valign=\"top\"\u003e\n \u003cp\u003e14(24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.27124773960217%\" valign=\"top\"\u003e\n \u003cp\u003eWhite matter hyperintensity\u0026nbsp;n(%)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eClinical indicators\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSLEDAI score \u0026ge; 5\u0026nbsp;n(%)\u003c/p\u003e\n \u003cp\u003eComplement decreases n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.412296564195298%\" valign=\"top\"\u003e\n \u003cp\u003e28(70)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38(95)\u003c/p\u003e\n \u003cp\u003e21(53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\" valign=\"top\"\u003e\n \u003cp\u003e27(47.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37(65)\u003c/p\u003e\n \u003cp\u003e30(52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.27124773960217%\" valign=\"top\"\u003e\n \u003cp\u003eLN n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.412296564195298%\" valign=\"top\"\u003e\n \u003cp\u003e15(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\" valign=\"top\"\u003e\n \u003cp\u003e30(52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.27124773960217%\" valign=\"top\"\u003e\n \u003cp\u003eaPL(+) \u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.412296564195298%\" valign=\"top\"\u003e\n \u003cp\u003e7(17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\" valign=\"top\"\u003e\n \u003cp\u003e13(22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.27124773960217%\" valign=\"top\"\u003e\n \u003cp\u003eAnti-dsDNA (+) n(%)\u003c/p\u003e\n \u003cp\u003eAnti-ribosomal P (+) n(%)\u003c/p\u003e\n \u003cp\u003eHypertension n(%)\u003c/p\u003e\n \u003cp\u003eThe first diagnosis SLE n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.412296564195298%\" valign=\"top\"\u003e\n \u003cp\u003e22(55)\u003c/p\u003e\n \u003cp\u003e14(35)\u003c/p\u003e\n \u003cp\u003e7(17.5)\u003c/p\u003e\n \u003cp\u003e10(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\" valign=\"top\"\u003e\n \u003cp\u003e30(52)\u003c/p\u003e\n \u003cp\u003e27(47)\u003c/p\u003e\n \u003cp\u003e3(5.2)\u003c/p\u003e\n \u003cp\u003e15(26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.27124773960217%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eManifestations of NP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eEpilepsia n(%)\u003c/p\u003e\n \u003cp\u003eSeizure disorder n(%)\u003c/p\u003e\n \u003cp\u003ePsychosis n(%)\u003c/p\u003e\n \u003cp\u003eSevere headache n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.412296564195298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7(17.5)\u003c/p\u003e\n \u003cp\u003e9(22.5)\u003c/p\u003e\n \u003cp\u003e3(7.5)\u003c/p\u003e\n \u003cp\u003e10(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003cp\u003e10(17.5)\u003c/p\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003cp\u003e4(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: DWI: diffusion imaging; SWI: magnetic sensitive imaging; LN: lupus nephritis; aPLs: anti-phospholipid antibody; dsDNA: Double-stranded DNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u0026nbsp; CBF differences between SLE patient groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"555\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.339350180505416%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003ecluster size (voxels)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.783393501805055%\" valign=\"top\"\u003e\n \u003cp\u003ePeak MNI coordinate\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(x, y, z)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.60649819494585%\" valign=\"top\"\u003e\n \u003cp\u003ePeak T\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.339350180505416%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSLE-CI\u0026gt;SLE-NC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCerebellum_Crus2_L\u003c/p\u003e\n \u003cp\u003eHippocampus_L\u003c/p\u003e\n \u003cp\u003eThalamus_L\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSLE-NC\u0026gt;SLE-CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.783393501805055%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-36,-76,-48)\u003c/p\u003e\n \u003cp\u003e(-26,-20,-16)\u003c/p\u003e\n \u003cp\u003e(-20,-24,0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.60649819494585%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.339350180505416%\" valign=\"top\"\u003e\n \u003cp\u003eFrontal_Med_Orb_L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.270758122743683%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.783393501805055%\" valign=\"top\"\u003e\n \u003cp\u003e(-4,50,-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.60649819494585%\" valign=\"top\"\u003e\n \u003cp\u003e-4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSLE-CI: systemic lupus group with cognitive impairment; SLE-NC: lupus cognitively normal group; MNI Montreal Neurological Institute; L: left; T value after Gaussian random field corrected, p\u0026lt;0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e FC differences between SLE patient groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.50704225352113%\" valign=\"top\"\u003e\n \u003cp\u003eRegion (SLE-CI\u0026gt;SLE-NC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eCluster size, voxels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.408450704225352%\" valign=\"top\"\u003e\n \u003cp\u003ePeak MNI coordinate\u003c/p\u003e\n \u003cp\u003e(x, y, z)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43661971830986%\" valign=\"top\"\u003e\n \u003cp\u003eT\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.50704225352113%\" valign=\"top\"\u003e\n \u003cp\u003eSeed：Cerebellum_Crus2_L\u003c/p\u003e\n \u003cp\u003eInsula_L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.408450704225352%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-42,-3,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43661971830986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.2654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.50704225352113%\" valign=\"top\"\u003e\n \u003cp\u003eSeed：Hippocampus_L\u003c/p\u003e\n \u003cp\u003eParaHippocampal_L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.408450704225352%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-42,-36,-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43661971830986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-3.9157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSLE-CI: systemic lupus group with cognitive impairment; SLE-NC: lupus cognitively normal group; MNI Montreal Neurological Institute; L:left; T value after Gaussian random field corrected, p\u0026lt;0.05.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"neuropsychiatric systemic lupus, cognitive impairment, arterial spin labeling, functional connectivity","lastPublishedDoi":"10.21203/rs.3.rs-4480752/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4480752/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cognitive impairment (CI) frequently occurs in patients with systemic lupus erythematosus (SLE) and may result from neuroinflammation processes and vascular changes in the brain. The cerebral hemodynamics underlying SLE with CI (SLE-CI) remain unclear. We aimed to explore changes in cerebral blood flow (CBF) and intrinsic functional connectivity (FC) in patients with SLE-CI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We enrolled 97 patients with systemic lupus erythematosus (SLE) and 51 heathy controls (HCs) matched for age and gender. The CI status of patients was measured using the Montreal Cognitive Assessment (MoCA). Based on the findings, the patients were subdivided into two subgroups, the SLE-CI (n = 40) and SLE-NC (n = 57) subgroups. Sagittal three-dimensionT1-weighted (3D-T1), arterial spin labeling (ASL) and resting-state functional (rs-fMRI) sequences were obtained. Seed-based FC was calculated using the CBF results as regions of interest (ROIs). Correlation analysis was performed for further examination of differences in alterations and clinical scores between the patient subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Compared with patients with SLE-NC, patients with SLE-CI had higher CBF in the left hippocampus, thalamus, and cerebellum crus II and lower CBF in the left frontal lobe. Left hippocampus gray matter (GM) atrophy was detected in patients with SLE-CI but not in patients with SLE-NC. Secondary analyses revealed that compared with patients with SLE-NC, patients with SLE-CI had increased FC of the left insula gyrus when the left cerebellum crus II was set as the seed region and decreased FC in the homolateral parahippocampus when the left hippocampus was set as the seed region. Correlation analysis revealed that CBF in the left hippocampus, cerebellum, and thalamus was negatively associated with MoCA and memory scores and slightly positively associated with Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) scores. CBF in the left frontal lobe was positively associated with MoCA and visual space execution capability scores and slightly negatively associated with SLEDAI scores and serum double-stranded DNA (dsDNA) titer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe changes of structure, function, and network in hippocampus gray matter may be biomarkers of cognitive impairment in patients with NPSLE.\u003c/p\u003e","manuscriptTitle":"Alterations in cerebral perfusion and corresponding brain functional networks in NPSLE with cognitive impairment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-18 14:49:56","doi":"10.21203/rs.3.rs-4480752/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":"d25cea85-f996-4331-af2f-9ac465db8f75","owner":[],"postedDate":"June 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33348928,"name":"Biological sciences/Neuroscience/Cognitive neuroscience"},{"id":33348929,"name":"Biological sciences/Neuroscience/Diseases of the nervous system"}],"tags":[],"updatedAt":"2024-06-18T14:49:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-18 14:49:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4480752","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4480752","identity":"rs-4480752","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}