Different patterns of cerebral perfusion in SLE patients with and without neuropsychiatric manifestations

Systemic lupus in the era of machine learning medicine

Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

Implications of Inflammatory Processes on a Developing Central Nervous System in Childhood-Onset Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that is increasingly affecting pediatric and adult populations. Neuropsychiatric manifestations (ie, cognitive dysfunction and mood disorders) appear to occur with greater severity and poorer prognosis in childhood-onset SLE (cSLE) versus adult-onset SLE, negatively impacting school function, self-management, and psychosocial health, as well as lifelong health-related quality of life. In this review, we describe pathogenic mechanisms active in cSLE, such as maladaptive inflammatory processes and ischemia, which are hypothesized to underpin central phenotypes in patients with cSLE, and the role of alterations in protective central nervous system (CNS) barriers (ie, the blood-brain barrier) are also discussed. Recent findings derived from novel neuroimaging approaches are highlighted because the methods employed in these studies hold potential for identifying CNS abnormalities that would otherwise remain undetected with conventional multiple resonance imaging studies (eg, T2-weighted or fluid-attenuated inversion recovery sequences). Furthermore, we propose that a more robust presentation of neuropsychiatric symptoms in cSLE is in part due to the harmful impact of a chronic inflammatory insult on a developing CNS. Although the immature status of the CNS may leave patients with cSLE more vulnerable to harboring neuropsychiatric manifestations, the same property may represent a greater urgency to reverse the maladaptive effects associated with a proneuroinflammatory state, provided that effective diagnostic tools and treatment strategies are available. Finally, considering the crosstalk among the CNS and other organ systems affected in cSLE, we postulate that a finer understanding of this interconnectivity and its role in the clinical presentation in cSLE is warranted.

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

Cerebral Microstructural and Microvascular Changes in Non-Neuropsychiatric Systemic Lupus Erythematosus: A Study Using Diffusion Kurtosis Imaging and 3D Pseudo-Continuous Arterial Spin Labeling

Purpose

The purpose of this study was to observe cerebral microstructure and microcirculation features, as well as changes in white matter (WM) and gray matter (GM) among patients with non-neuropsychiatric systemic lupus erythematosus (non-NPSLE).

Methods

We compared 36 female patients with non-NPSLE and 20 age- and gender-matched healthy controls (HCs) who underwent 3.0T MRI imaging with diffusion kurtosis imaging (DKI) and 3D pseudo-continuous Arterial Spin Labeling (pCASL). Mean kurtosis (MK), mean kurtosis tensor (MKT), and cerebral blood flow (CBF) values were obtained from 25 brain regions, including WM and GM. We analyzed the correlation between imaging indicators and clinical data.

Results

When compared with HCs, patients with non-NPSLE had reduced MK and MKT values in regional WM, deep GM, and the left frontal lobe cortical GM, and increased CBF in the right parietal lobe WM and right semioval center (SOC). The MK and MKT values were weakly correlated with CBF in some regions, including WM and GM. Complement 3 (C3) and Complement 4 (C4) showed a weak positive correlation with MK and MKT in some regions, including WM and deep GM, while platelet (PLT) was positively correlated with MKT in the left frontal lobe WM; dsDNA antibody was correlated negatively with MK in the right occipital lobe WM; and erythrocyte sedimentation rate (ESR) was correlated negatively with CBF in the left SOC.

Conclusion

Our findings revealed the presence of brain microstructural and microvascular abnormalities in non-NPSLE patients, indicating microstructural damage in the cortical GM, which was less commonly reported. We found DKI and pCASL useful in detecting early brain lesions, and MK was a more sensitive and beneficial indicator.

Altered hippocampal connectivity dynamics predicts memory performance in neuropsychiatric lupus: a resting-state fMRI study using cross-recurrence quantification analysis

OBJECTIVE

Τo determine whole-brain and regional functional connectivity (FC) characteristics of patients with neuropsychiatric SLE (NPSLE) or without neuropsychiatric manifestations (non-NPSLE) and examine their association with cognitive performance.

METHODS

Cross-recurrence quantification analysis (CRQA) of resting-state functional MRI (rs-fMRI) data was performed in 44 patients with NPSLE, 20 patients without NPSLE and 35 healthy controls (HCs). Volumetric analysis of total brain and specific cortical and subcortical regions, where significant connectivity changes were identified, was performed. Cognitive status of patients with NPSLE was assessed by neuropsychological tests. Group comparisons on nodal FC, global network metrics and regional volumetrics were conducted, and associations with cognitive performance were estimated (at p<0.05 false discovery rate corrected).

RESULTS

FC in patients with NPSLE was characterised by increased modularity (mean (SD)=0.31 (0.06)) as compared with HCs (mean (SD)=0.27 (0.06); p=0.05), hypoconnectivity of the left (mean (SD)=0.06 (0.018)) and right hippocampi (mean (SD)=0.051 (0.0.16)), and of the right amygdala (mean (SD)=0.091 (0.039)), as compared with HCs (mean (SD)=0.075 (0.022), p=0.02; 0.065 (0.019), p=0.01; 0.14 (0.096), p=0.05, respectively). Hyperconnectivity of the left angular gyrus (NPSLE/HCs: mean (SD)=0.29 (0.26) and 0.10 (0.09); p=0.01), left (NPSLE/HCs: mean (SD)=0.16 (0.09) and 0.09 (0.05); p=0.01) and right superior parietal lobule (SPL) (NPSLE/HCs: mean (SD)=0.25 (0.19) and 0.13 (0.13), p=0.01) was noted in NPSLE versus HC groups. Among patients with NPSLE, verbal episodic memory scores were positively associated with connectivity (local efficiency) of the left hippocampus (r²=0.22, p=0.005) and negatively with local efficiency of the left angular gyrus (r²=0.24, p=0.003). Patients without NPSLE displayed hypoconnectivity of the right hippocampus (mean (SD)=0.056 (0.014)) and hyperconnectivity of the left angular gyrus (mean (SD)=0.25 (0.13)) and SPL (mean (SD)=0.17 (0.12)).

CONCLUSION

By using dynamic CRQA of the rs-fMRI data, distorted FC was found globally, as well as in medial temporal and parietal brain regions in patients with SLE, that correlated significantly and adversely with memory capacity in NPSLE. These results highlight the value of dynamic approaches to assessing impaired brain network function in patients with lupus with and without neuropsychiatric symptoms.

Abnormal cerebral hemodynamics and blood-brain barrier permeability detected with perfusion MRI in systemic lupus erythematosus patients

OBJECTIVE

Dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) has previously shown alterations in cerebral perfusion in patients with systemic lupus erythematosus (SLE). However, the results have been inconsistent, in particular regarding neuropsychiatric (NP) SLE. Thus, we investigated perfusion-based measures in different brain regions in SLE patients with and without NP involvement, and additionally, in white matter hyperintensities (WMHs), the most common MRI pathology in SLE patients.

MATERIALS AND METHODS

We included 3 T MRI images (conventional and DSC) from 64 female SLE patients and 19 healthy controls (HC). Three different NPSLE attribution models were used: the Systemic Lupus International Collaborating Clinics (SLICC) A model (13 patients), the SLICC B model (19 patients), and the American College of Rheumatology (ACR) case definitions for NPSLE (38 patients). Normalized cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) were calculated in 26 manually drawn regions of interest and compared between SLE patients and HC, and between NPSLE and non-NPSLE patients. Additionally, normalized CBF, CBV and MTT, as well as absolute values of the blood-brain barrier leakage parameter (K2) were investigated in WMHs compared to normal appearing white matter (NAWM) in the SLE patients.

RESULTS

After correction for multiple comparisons, the most prevalent finding was a bilateral significant decrease in MTT in SLE patients compared to HC in the hypothalamus, putamen, right posterior thalamus and right anterior insula. Significant decreases in SLE compared to HC were also found for CBF in the pons, and for CBV in the bilateral putamen and posterior thalamus. Significant increases were found for CBF in the posterior corpus callosum and for CBV in the anterior corpus callosum. Similar patterns were found for both NPSLE and non-NPSLE patients for all attributional models compared to HC. However, no significant perfusion differences were revealed between NPSLE and non-NPSLE patients regardless of attribution model. The WMHs in SLE patients showed a significant increase in all perfusion-based metrics (CBF, CBV, MTT and K2) compared to NAWM.

CONCLUSION

Our study revealed perfusion differences in several brain regions in SLE patients compared to HC, independently of NP involvement. Furthermore, increased K2 in WMHs compared to NAWM may indicate blood-brain barrier dysfunction in SLE patients. We conclude that our results show a robust cerebral perfusion, independent from the different NP attribution models, and provide insight into potential BBB dysfunction and altered vascular properties of WMHs in female SLE patients. Despite SLE being most prevalent in females, a generalization of our conclusions should be avoided, and future studies including all sexes are needed.

Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review

OBJECTIVE

We carried out a systematic review (SR) of adherence in diagnostic and prognostic applications of ML in SLE using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.

METHODS

A SR employing five databases was conducted from its inception until December 2021. We identified articles that evaluated the utilization of ML for prognostic and/or diagnostic purposes. This SR was reported based on the PRISMA guidelines. The TRIPOD statement assessed adherence to reporting standards. Assessment for risk of bias was done using PROBAST tool.

RESULTS

We included 45 studies: 29 (64.4%) diagnostic and 16 (35.5%) prognostic prediction- model studies. Overall, articles adhered by between 17% and 67% (median 43%, IQR 37-49%) to TRIPOD items. Only few articles reported the model's predictive performance (2.3%, 95% CI 0.06-12.0), testing of interaction terms (2.3%, 95% CI 0.06-12.0), flow of participants (50%, 95% CI; 34.6-65.4), blinding of predictors (2.3%, 95% CI 0.06-12.0), handling of missing data (36.4%, 95% CI 22.4-52.2), and appropriate title (20.5%, 95% CI 9.8-35.3). Some items were almost completely reported: the source of data (88.6%, 95% CI 75.4-96.2), eligibility criteria (86.4%, 95% CI 76.2-96.5), and interpretation of findings (88.6%, 95% CI 75.4-96.2). In addition, most of model studies had high risk of bias.

CONCLUSIONS

The reporting adherence of ML-based model developed for SLE, is currently inadequate. Several items deemed crucial for transparent reporting were not fully reported in studies on ML-based prediction models.

REVIEW REGISTRATION

PROSPERO ID# CRD42021284881. (Amended to limit the scope).

The conundrum of neuropsychiatric systemic lupus erythematosus: Current and novel approaches to diagnosis

Recognising neuropsychiatric involvement by systemic lupus erythematosus (SLE) is of growing importance, however many barriers to this exist at multiple levels of our currently available diagnostic algorithms that may ultimately delay its diagnosis and subsequent treatment. The heterogeneous and non-specific clinical syndromes, serological and cerebrospinal fluid (CSF) markers and neuroimaging findings that often do not mirror disease activity, highlight important research gaps in the diagnosis of neuropsychiatric SLE (NPSLE). Formal neuropsychological assessments or the more accessible screening metrics may also help improve objective recognition of cognitive or mood disorders. Novel serum and CSF markers, including autoantibodies, cytokines and chemokines have also shown increasing utility as part of diagnosis and monitoring, as well as in distinguishing NPSLE from SLE patients without SLE-related neuropsychiatric manifestations. Novel neuroimaging studies also expand upon our existing strategy by quantifying parameters that indicate microarchitectural integrity or provide an assessment of neuronal function. Some of these novel markers have shown associations with specific neuropsychiatric syndromes, suggesting that future research move away from considering NPSLE as a single entity but rather into its individually recognized neuropsychiatric manifestations. Nevertheless, it is likely that a composite panel of these investigations will be needed to better address the gaps impeding recognition of neuropsychiatric involvement by SLE.

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Background

Application of machine learning (ML) for identification of systemic lupus erythematosus (SLE) has been recently drawing increasing attention, while there is still lack of evidence-based support.

Methods

Systematic review and meta-analysis are conducted to evaluate its diagnostic accuracy and application prospect. PubMed, Embase, Cochrane Library, and Web of Science libraries are searched, in combination with manual searching and literature retrospection, for studies regarding machine learning for identifying SLE and neuropsychiatric systemic lupus erythematosus (NPSLE). Quality Assessment of Diagnostic Accuracy Studies (QUADA-2) is applied to assess the quality of included studies. Diagnostic accuracy of the SLE model and NPSLE model is assessed using the bivariate fixed-effect model, and the data are pooled. Summary receiver operator characteristic curve (SROC) is plotted, and area under the curve (AUC) is calculated.

Results

Eighteen (18) studies are included, in which ten (10) focused on SLE and eight (8) on NPSLE. The AUC of SLE identification is 0.95, the sensitivity is 0.90, the specificity is 0.89, the PLR is 8.4, the NLR is 0.12, and the DOR is 73. AUC of NPSLE identification is 0.89, the sensitivity is 0.83, the specificity is 0.83, the PLR is 5.0, the NLR is 0.20, and the DOR is 25.

Conclusion

Machine learning presented remarkable performance in identification of SLE and NPSLE. Based on the convenience for inclusion factor collection and non-invasiveness of detection, machine learning is expected to be widely applied in clinical practice to assist medical decision making.

Neuropsychiatric lupus erythematosus: Focusing on autoantibodies

Patients with systemic lupus erythematosus (SLE) frequently suffer from nervous system complications, termed neuropsychiatric lupus erythematosus (NPLE). NPLE accounts for the poor prognosis of SLE. Correct attribution of NP events to SLE is the primary principle in managing NPLE. The vascular injuries and neuroinflammation are the fundamental neuropathologic changes in NPLE. Specific autoantibody-mediated central nerve system (CNS) damages distinguish NPLE from other CNS disorders. Though the central antibodies in NPLE are generally thought to be raised from the periphery immune system, they may be produced in the meninges and choroid plexus. On this basis, abnormal activation of microglia and disease-associated microglia (DAM) should be the common mechanisms of NPLE and other CNS disturbances. Improved understanding of both characteristic and sharing features of NPLE might yield further options for managing this disease.

Multidelay ASL of the pediatric brain

Arterial spin labeling (ASL) is a powerful noncontrast MRI technique for evaluation of cerebral blood flow (CBF). A key parameter in single-delay ASL is the choice of postlabel delay (PLD), which refers to the timing between the labeling of arterial free water and measurement of flow into the brain. Multidelay ASL (MDASL) utilizes several PLDs to improve the accuracy of CBF calculations using arterial transit time (ATT) correction. This approach is particularly helpful in situations where ATT is unknown, including young subjects and slow-flow conditions. In this article, we discuss the technical considerations for MDASL, including labeling techniques, quantitative metrics, and technical artefacts. We then provide a practical summary of key clinical applications with real-life imaging examples in the pediatric brain, including stroke, vasculopathy, hypoxic-ischemic injury, epilepsy, migraine, tumor, infection, and metabolic disease.

Noise-immune Extreme Ensemble Learning for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus

Early diagnosis is currently the most effective way of saving the life of patients with neuropsychiatric systemic lupus erythematosus (NPSLE). However, it is rather difficult to detect this terrible disease at the early stage, due to the subtle and elusive symptomatic signals. Recent studies show that the ¹H-MRS (proton magnetic resonance spectroscopy) imaging technique can capture more information reflecting the early appearance of this disease than conventional magnetic resonance imaging techniques. 1H-MRS data, however, also presents more noises that can bring serious diagnosis bias. We hence proposed a noise-immune extreme ensemble learning technique for effectively leveraging 1H-MRS data for advancing the early diagnosis of NPSLE. Our main results are that 1) by developing generalized maximum correntropy criterion in the kernel extreme learning setting, many types of non-Gaussian noises can be distinguished, and 2) weighted recursive feature elimination, using maximal information coefficient to weight feature's importance, helps to further alleviate the bad impact of noises on the diagnosis performance. The proposed method is assessed on a publicly available dataset with 97.5% accuracy, 95.8% sensitivity, and 99.9% specificity, which well demonstrates its efficacy.

A noise-immune reinforcement learning method for early diagnosis of neuropsychiatric systemic lupus erythematosus

The neuropsychiatric systemic lupus erythematosus (NPSLE), a severe disease that can damage the heart, liver, kidney, and other vital organs, often involves the central nervous system and even leads to death. Magnetic resonance spectroscopy (MRS) is a brain functional imaging technology that can detect the concentration of metabolites in organs and tissues non-invasively. However, the performance of early diagnosis of NPSLE through conventional MRS analysis is still unsatisfactory. In this paper, we propose a novel method based on genetic algorithm (GA) and multi-agent reinforcement learning (MARL) to improve the performance of the NPSLE diagnosis model. Firstly, the proton magnetic resonance spectroscopy (1H-MRS) data from 23 NPSLE patients and 16 age-matched healthy controls (HC) were standardized before training. Secondly, we adopt MARL by assigning an agent to each feature to select the optimal feature subset. Thirdly, the parameter of SVM is optimized by GA. Our experiment shows that the SVM classifier optimized by feature selection and parameter optimization achieves 94.9% accuracy, 91.3% sensitivity, 100% specificity and 0.87 cross-validation score, which is the best score compared with other state-of-the-art machine learning algorithms. Furthermore, our method is even better than other dimension reduction ones, such as SVM based on principal component analysis (PCA) and variational autoencoder (VAE). By analyzing the metabolites obtained by MRS, we believe that this method can provide a reliable classification result for doctors and can be effectively used for the early diagnosis of this disease.

The Role of miR-23b in Cancer and Autoimmune Disease

Short-stranded miRNAs are single-stranded RNA molecules involved in the regulation of gene expression. miRNAs are involved in a variety of cellular physiological processes, including cell proliferation, differentiation, and apoptosis. miR-23b have been identified to act both as oncogenes and as tumor suppressors. In addition, miR-23b is related to inflammation resistance to various autoimmune diseases and restrained inflammatory cell migration. The characterization of the specific alterations in the patterns of miR-23b expression in cancer and autoimmune disease has great potential for identifying biomarkers for early disease diagnosis, as well as for potential therapeutic intervention in various diseases. In this review, we summarize the ever-expanding role of miR-23b and its target genes in different models and offer insight into how this multifunctional miRNA modulates tumor cell proliferation and apoptosis or inflammatory cell activation, differentiation, and migration.

Alterations of spontaneous brain activity in systematic lupus erythematosus patients without neuropsychiatric symptoms: A resting-functional MRI study

PURPOSE

To explore the alterations of spontaneous neuronal activity using amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) in non-NPSLE patients and their relationship with the anxiety and depression statuses.

METHODS

Twenty-three non-NPSLE patients and 28 healthy controls were enrolled in this study. Resting-state functional magnetic resonance imaging was firstly analyzed by ALFF, fALFF, and ReHo. The relationships between ALFF/fALFF/ReHo values of abnormal regions and anxiety/depression rating scales, including Self-Rating Anxiety (SAS) and Self-Rating Depression (SDS), were also analyzed.

RESULTS

Compared with HC, non-NPSLE had decreased ALFF values in the bilateral postcentral gyrus, while increased ALFF values in the bilateral inferior temporal gyrus, left putamen, and bilateral precuneus. Non-NPSLE showed reduced fALFF values in the left lingual gyrus, left middle occipital gyrus, right postcentral gyrus, and left superior parietal gyrus, while increased fALFF values were in the left inferior temporal gyrus, right hippocampus, bilateral precuneus, and bilateral superior frontal gyrus. Reduced ReHo values were in the bilateral postcentral gyrus and higher ReHo values were in the left inferior temporal gyrus, left putamen, and bilateral superior frontal gyrus. In the non-NPSLE group, the mean ALFF values of bilateral precuneus were positively correlated with the SAS rating scales (R = 0.5519, p = 0.0176); either were the mean ALFF values of right inferior temporal gyrus and SAS rating scales (R = 0.5380, p = 0.0213). The mean fALFF values of left inferior temporal gyrus were positively correlated with SAS rating scales (R = 0.5700, p = 0.0135). And the mean ReHo values of left putamen were positively correlated with SDS (R = 0.5477, p = 0.0186).

CONCLUSION

Non-NPSLE exhibited abnormal spontaneous neural activity and coherence in several brain regions mainly associated with cognitive and emotional functions. The ALFF values of bilateral PCUN, the right ITG, the fALFF values of left ITG, and the ReHo values of left PUT may be complementary biomarkers for assessing the psychiatric symptoms.

Disrupted resting-state interhemispheric functional connectivity in systemic lupus erythematosus patients with and without neuropsychiatric lupus

PURPOSE

The aim of the study is to explore interhemispheric homotopic functional connectivity alterations in systemic lupus erythematosus (SLE) patients with and without neuropsychiatric lupus (NPSLE and non-NPSLE, respectively) and their potential correlations with clinical characteristics and neuropsychological performance.

METHODS

Based on resting-state functional MRI (rs-fMRI) data collected from SLE patients and matched healthy controls (HCs), the voxel-mirrored homotopic connectivity (VMHC) analysis was conducted to measure functional homotopy. Subsequently, correlations between altered functional homotopy and clinical/neuropsychological data were analyzed.

RESULTS

Compared with the HC group, both NPSLE and non-NPSLE groups showed attenuated homotopic connectivity in middle temporal gyrus (MTG), cuneus (CUN), middle occipital gyrus (MOG), angular gyrus (ANG), and postcentral gyrus (PoCG). NPSLE patients also exhibited decreased homotopic connectivity in inferior parietal gyrus (IPG) and middle frontal gyrus (MFG). Compared with non-NPSLE patients, NPSLE patients showed weaker interhemispheric homotopic functional connectivity in MOG. Decreased homotopic functional connectivity in PoCG, IPG, and MOG were associated with the anxiety state of SLE patients.

CONCLUSIONS

Our findings revealed attenuated functional homotopy in both NPSLE and non-NPSLE groups compared to the HC group, which appeared to be more severe in patients with comorbid neuropsychiatric lupus. Interhemispheric homotopy dysconnectivity may participate in the neuropathology of anxiety symptoms in SLE.

Anxiety and depression severity in neuropsychiatric SLE are associated with perfusion and functional connectivity changes of the frontolimbic neural circuit: a resting-state f(unctional) MRI study

OBJECTIVE

To examine the hypothesis that perfusion and functional connectivity disturbances in brain areas implicated in emotional processing are linked to emotion-related symptoms in neuropsychiatric SLE (NPSLE).

METHODS

Resting-state fMRI (rs-fMRI) was performed and anxiety and/or depression symptoms were assessed in 32 patients with NPSLE and 18 healthy controls (HC). Whole-brain time-shift analysis (TSA) maps, voxel-wise global connectivity (assessed through intrinsic connectivity contrast (ICC)) and within-network connectivity were estimated and submitted to one-sample t-tests. Subgroup differences (high vs low anxiety and high vs low depression symptoms) were assessed using independent-samples t-tests. In the total group, associations between anxiety (controlling for depression) or depression symptoms (controlling for anxiety) and regional TSA or ICC metrics were also assessed.

RESULTS

Elevated anxiety symptoms in patients with NPSLE were distinctly associated with relatively faster haemodynamic response (haemodynamic lead) in the right amygdala, relatively lower intrinsic connectivity of orbital dlPFC, and relatively lower bidirectional connectivity between dlPFC and vmPFC combined with relatively higher bidirectional connectivity between ACC and amygdala. Elevated depression symptoms in patients with NPSLE were distinctly associated with haemodynamic lead in vmPFC regions in both hemispheres (lateral and medial orbitofrontal cortex) combined with relatively lower intrinsic connectivity in the right medial orbitofrontal cortex. These measures failed to account for self-rated, milder depression symptoms in the HC group.

CONCLUSION

By using rs-fMRI, altered perfusion dynamics and functional connectivity was found in limbic and prefrontal brain regions in patients with NPSLE with severe anxiety and depression symptoms. Although these changes could not be directly attributed to NPSLE pathology, results offer new insights on the pathophysiological substrate of psychoemotional symptomatology in patients with lupus, which may assist its clinical diagnosis and treatment.

Advanced neuroimaging in neuropsychiatric systemic lupus erythematosus

Neuropsychiatric lupus (NPSLE) comprises a disparate collection of syndromes affecting the central and peripheral nervous systems. Progress in the attribution of neuropsychiatric syndromes to SLE-related mechanisms and development of targeted treatment strategies has been impeded by a lack of objective imaging biomarkers that reflect specific neuropsychiatric syndromes and/or pathologic mechanisms. The present review addresses recent publications of neuroimaging techniques in NPSLE.

Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

Different patterns of cerebral perfusion in SLE patients with and without neuropsychiatric manifestations

To investigate brain perfusion patterns in systemic lupus erythematosus (SLE) patients with and without neuropsychiatric systemic lupus erythematosus (NPSLE and non-NPSLE, respectively) and to identify biomarkers for the diagnosis of NPSLE using noninvasive three-dimensional (3D) arterial spin labeling (ASL). Thirty-one NPSLE and 24 non-NPSLE patients and 32 age- and sex-matched normal controls (NCs) were recruited. Three-dimensional ASL-MRI was applied to quantify cerebral perfusion. Whole brain, gray (GM) and white matter (WM), and voxel-based analysis (VBA) were performed to explore perfusion characteristics. Correlation analysis was performed to find the relationship between the perfusion measures, lesion volumes, and clinical variables. Receiver operating characteristic (ROC) analysis and support vector machine (SVM) classification were applied to differentiate NPSLE patients from non-NPSLE patients and healthy controls. Compared to NCs, NPSLE patients showed increased cerebral blood flow (CBF) within WM but decreased CBF within GM, while non-NPSLE patients showed increased CBF within both GM and WM. Compared to non-NPSLE patients, NPSLE patients showed significantly reduced CBF in the frontal gyrus, cerebellum, and corpus callosum. CBF within several brain regions such as cingulate and corpus callosum showed significant correlations with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and the Systemic Lupus International Collaborating Clinics (SLICC) damage index scores. ROC analysis showed moderate performance in distinguishing NPSLE from non-NPSLE patients with AUCs > 0.7, while SVM analysis demonstrated that CBF within the corpus callosum achieved an accuracy of 83.6% in distinguishing NPSLE from non-NPSLE patients. Different brain perfusion patterns were observed between NPSLE and non-NPSLE patients. CBF measured by noninvasive 3D ASL could be a useful biomarker for the diagnosis and disease monitoring of NPSLE and non-NPSLE patients.