Abnormal Brain Function in Systemic Lupus Erythematosus: A Meta-Analysis of Resting-State Functional Magnetic Resonance Imaging

By Xi Sun¹, Dongmei Fu², Yuting Mao³, Zuanfang Li^4,5, Yinghong Lin⁶, Jiaqiu Lin²

Affiliations

1. School of Information Engineering, Nanyang Institute of Technology, Nanyang, China
2. The Third People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, China
3. College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
4. Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
5. Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fuzhou, China
6. College of Integrated Chinese and Western Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China

doi: 10.29271/jcpsp.2025.06.761

ABSTRACT
This meta-analysis investigated the neurophysiological underpinnings of systemic lupus erythematosus (SLE) by examining resting-state functional magnetic resonance imaging (rs-fMRI) profiles of SLE patients in comparison to healthy controls. Rs-fMRI studies up to November 10, 2022 were extracted from five principal databases: Medline, Web of Science, Embase, CNKI, and WanFang. The analysis included data from 13 distinct studies involving 934 participants. Applying the activation likelihood estimation (ALE) method, this study extracted coordinate data detailing functional disparities between the SLE cohort and controls. The findings indicated reduced activity in the right precentral gyrus (coordinates X = 26, Y = -6, Z = 72; ALE = 0.02155 and X = 26, Y = -18, Z = 68; ALE = 0.01767) and the right parahippocampal gyrus (X = 24, Y = -54, Z = 4; ALE = 0.01978). Conversely, increased activity was observed in the right middle frontal gyrus (X = 36, Y = 42, Z = 32; ALE = 0.01997) and the left lentiform nucleus (X = -18, Y = 14, Z = -8; ALE = 0.02057). These insights enhanced the understanding of the neurophysiological mechanisms in SLE, providing valuable information for diagnostic and therapeutic imaging strategies.

Key Words: Systemic lupus erythematosus, Meta-analysis, Activation likelihood estimation, Resting-state, Functional magnetic resonance imaging.

INTRODUCTION

Systemic lupus erythematosus (SLE) is a prevalent autoimmune disorder that impacts multiple organs and bodily systems.¹ Emerging research suggested that a considerable segment of those diagnosed with SLE, ranging from 21 to 95%, experienced detrimental effects on their central nervous system, which markedly diminished their quality of life.² The spectrum of neuropsychiatric manifestations in SLE patients commonly includes migraines, seizures, cognitive abnormalities, episodes of psychosis, and movement irregularities.³ Existing studies have established a link between these neurological impairments and broader nervous system dysfunction in SLE sufferers. However, the definitive processes driving this connection are yet to be fully elucidated.^4,5

A more comprehensive investigation into these neural functional deviations has the potential to improve the diagnostic accuracy, assessment, and therapeutic interventions for SLE.

Resting-state functional magnetic resonance imaging (rs-fMRI), a crucial tool in assessing cerebral functioning, has been prevalently used in the early identification of neuropsychiatric disorders.^6-8 Rs-fMRI capitalises on the blood oxygen level dependent (BOLD) signal to offer an indirect measurement of neural activity.⁹ Given its non-intrusive nature, reliability, and precision in identifying changes in cerebral activity, rs-fMRI has emerged as a cornerstone in neurobehavioural disorder investigations, encompassing research on SLE.^10,11

Prior researches utilising rs-fMRI have revealed varied functional disruptions across cortical and subcortical regions in individuals with SLE.^12-19 While these observations have significantly advanced the understanding of cerebral anomalies in SLE, discrepancies persisted regarding the specific regions of functional abnormality. For instance, one study reported enhanced activity in the bilateral hippocampus and parahippocampal regions in SLE patients,¹⁸ whereas another documented reduced activity, specifically in the right parahippocampal gyrus.¹⁹ Such variability could stem from differences in sample sizes, MRI protocols, or analytical methods.

This meta-analysis aimed to investigate the neurophysiological alterations associated with SLE by analysing rs-fMRI profiles. By comparing the rs-fMRI data of SLE patients to that of healthy controls, this study aimed to elucidate the intricate brain functions impacted by SLE. Employing the activation likelihood estimation (ALE) method, the research synthesised coordinate data from multiple studies to objectively identify and quantify functional disparities. The insights garnered from this analysis were expected to deepen the understanding of how SLE affected brain function, potentially shaping future diagnostic and therapeutic approaches for this multifaceted disorder.

METHODOLOGY

To conduct this meta-analysis, the study adhered to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines.²⁰ The protocol was registered on PROSPERO with the registration ID: CRD42021272667. Independent research was conducted by two researchers across five major databases: Medline, Web of Science, Embase, CNKI, and WanFang. The search terms used were systemic lupus erythematosus, resting state, and functional magnetic resonance imaging. Specifically, in the Medline database, the search strategy was as follows: ((fMRI OR functional MRI OR functional magnetic resonance imaging) AND Rest* AND (SLE OR systemic lupus erythematosus OR lupus erythematosus, systemic)). The searches spanned from each database's inception to November 10, 2022.

Studies were included which utilised rs-fMRI. Their participant group included individuals diagnosed with SLE as well as healthy controls (HC). Data of the studies provided Montreal Neurological Institute (MNI) or Talairach (Tal) coordinates for the targeted population. The study employed whole-brain imaging techniques. Only observational studies were considered for inclusion. The exclusion criteria for this study were rigorously defined to ensure the integrity and relevance of the data analysed. Specifically, the study excluded any forms of non-original research, such as reviews, letters, and case studies that utilised task-state fMRI; Studies that were conducted without including a healthy control group, that did not provide available MNI and Tal coordinates in the primary research, supplementary materials, or as provided by the authors; studies that exclusively focused on specific brain regions; unpublished data; and studies for which full-length articles were not accessible.

Two authors systematically extracted demographic characteristics, fMRI parameters, and outcome indicators from each study included in this meta-analysis. This comprehensive extraction covered demographic factors such as primary authorship, publication year, population demographics, sample size, number of female participants, age range, educational duration, disease duration, MOCA, SLEDAI, HAMA, HAMD, serum C3 and C4 levels, and quality scores. Rs-fMRI data included analytical methodologies, imaging apparatus, coordinate type, FWHM values, software utilised for data interpretation, and statistical threshold. Outcome metrics from rs-fMRI encompassed the inclusion of MNI or Tal coordinates and peak differential t-values. In cases of extraction disputes, a third adjudicator intervened to ensure consensus, which was supported by the majority and subsequently finalised.

The evaluation of literature quality was conducted using a 20-point checklist derived from a prior study.²¹ The maximum score for this tool is 20, divided into two categories: Population features (10 points) and methodological/reporting aspects (10 points). Population features were scored on adherence to a standardised diagnostic benchmark for participants (1 point); disclosure of pivotal demographic parameters such as age and gender (2 points); inclusion of healthy comparatives, assessed for potential psychiatric or medical conditions, with relevant demographic details documented (1 point); reporting of important clinical data, including illness tenure, MoCA evaluation, SLEDAI, HAMA, HAMD, and serum markers such as C3 and C4 (4 points) and ensuring a minimum group size of more than 10 subjects (2 points). Methodology and reporting were scored as thorough brain examination using automated processes to avoid initial region-specific biases (3 points); employment of imaging devices with a magnet capacity equal to or surpassing 1.5T (1 point); capture of imaging intervals exceeding 5 minutes during the resting state (1 point); total brain coverage in the rs-fMRI scans (1 point); clear delineation of data acquisition and preprocessing steps to enhance reproducibility for future research (1 point); reported coordinates in alignment with the established standard space (1 point); application of correction of statistical methodologies, including but not restricted to FDR, GRF, or permutation-centric methods, for significant results (1 point); and detailed discussion encompassing both the limitations of the study and verification that the conclusions reflect the findings accurately (1 point).

The Ginger ALE software package was employed to discern patterns in rs-fMRI data from patients diagnosed with SLE.^22,23 In instances where studies reported distinct subtypes of SLE¹⁶ or applied varying methodologies or bandpass filters to the same subjects, each variation was treated as a separate entity, and coordinates were collected accordingly. Voxel coordinates obtained from individual studies were treated as probabilistic distributions, facilitating the creation of an updated ALE distribution map. The X, Y, and Z coordinates from each rs-fMRI outcome, capturing both augmentation and diminution, were analysed within the MNI space. These coordinates served as inputs for the probabilistic distribution. For the ALE analysis, a cluster-level inference threshold of p <0.05 (FWE corrected) with 5000 permutations (p <0.001) was employed. The visualisation of the results was performed using MRIcroGL.

For this meta-analysis, data were derived exclusively from previously conducted studies. No new participants were recruited, and there was no involvement of the general public.

RESULTS

The current study commenced by identifying 186 articles from the following five databases: Medline, Web of Science, CNKI, Embase, and WanFang. After the removal of duplicates, 110 articles remained. Initial screening based on titles and abstracts led to the exclusion of 68 articles due to irrelevance. Further application of inclusion and exclusion criteria resulted in the dismissal of an additional 15 articles. A detailed examination of the remaining full-texts excluded 14 more articles, culminating in a final cohort of 13 articles eligible for review. Among these, an article employed three kinds of methodology,¹⁷ another article utilised 4 methodologies,⁴ and Chen et al. applied 4 methodologies along with 3 different bandpass filters.¹² In addition, different subgroups of SLE were compared with HC, respectively, and were regarded as independent data samples.¹⁶ Finally, methodologies including ReHo, ALFF, mALFF, mReHo, PerAF, mPerAF, fALFF, dReHo, dfALFF, and VMHC were included. The ALE meta-analysis ultimately included 13 studies, encompassing 934 participants. The methodological flow and data extraction process were illustrated in Figure 1.

The demographic characteristics and clinical indicators of the 13 studies included in this meta-analysis were comprehensively evaluated, detailing the first author's name, year of publication, and other relevant details. These attributes were systematically organised and were presented in Table I, II. Additionally, Table III offered detailed information on the methodological aspects of the rs-fMRI parameters, encompassing analysis techniques, imaging equipment, and data processing tools employed. Notably, the quality scores of each research incorporated in this investigation exceeded 17, indicating the considerable validity and reliability of the findings, as depicted in Figure 2.

The ALE analysis revealed significant neurophysiological differences between patients with SLE and HC. Patients with SLE exhibited decreased activity across multiple regions, yet displayed increased activity in specific areas (Figure 3). Notably, decreased activity was observed in the body of right precentral gyrus [ (X = 26, Y = -6, Z = 72, ALE value = 0.02155 Brodmann area (BA) 6 and X = 26, Y = -18, Z = 68, ALE value = 0.01767, BA 4], and the right parahippocampal gyrus (X = 24, Y = -54, Z = 4, ALE value = 0.01978, BA 30). Conversely, enhanced activity was detected in the right middle frontal gyrus (X = 36, Y = 42, Z = 32, ALE value = 0.01997, BA 9), and left lentiform nucleus (X = -18, Y = 14, Z = -8, ALE value = 0.02057). Detailed coordinates and peak activation  values  for  these  regions  were  documented  in  Table  IV. 

 

Figure  1:  Flow  diagram  of  literature  retrieval  and  selection  procedure.

Figure 2: Assessment of literature quality. Coloured circles indicate levels of description clarity. Green denotes clear information presentation within the study. Yellow suggests ambiguous or partially clear description. Red signifies absence of the information in the study. Table I: Demographic and clinical characteristics of SLE and HC groups.
 

References	Group	Sample / female	Age (years)	Education (years)	Disease duration (months)	MMSE (score)	MoCA (score)
Lin et al.13 2011	Non-NPSLE	31/31	34.4 ± 7.6	11.9	60	NA	NA
	Control	23/23	31.5	12.9	NA	NA	NA
Zhang et al.18 2017	Non-NPSLE	26/23	30.46 ± 11.51	11.19 ± 3.50	47.67 ± 61.01	28.5 ± 1.45	26.58 ± 2.69
	Control	35/29	28.37 ± 10.43	11.74 ± 2.67	NA	29.43 ± 1.26	28.29 ± 2.24
Liu et al.14 2018	Non-NPSLE	118/98	28.6 ± 7.7	NA	19.2 ± 20.8	NA	NA
	Control	81/67	29.0 ± 7.9	NA	NA	NA	NA
Zhang et al.24 2018	NPSLE	42/42	37.04 ± 12.49	11.29 ± 2.66	NA	NA	NA
	Control	42/42	33.96 ± 11.22	10.38 ± 2.65	NA	NA	NA
Yu et al.17 2019	Non-NPSLE	36/35	36.05 ± 10.30	10.16 ± 3.54	64.08 ± 65.46	28.44 ± 1.08	28.00 ± 1.28
	Control	30/29	34.43 ± 10.37	10.57 ± 3.22	NA	29.20 ± 0.66	29.07 ± 0.83
Yu et al.25 2019	Non-NPSLE	28/28	35:54 ± 7:38	12:21 ± 3:61	7:71 ± 5:58	28:86 ± 1:35	NA
	Control	17/17	33:53 ± 8:35	14:35 ± 3:24	NA	29:29 ± 0:69	NA
Chen et al.12 2021	Non-NPSLE	28/28	35.54 ± 7.38	12.21 ± 3.61	7.71 ± 5.58	28.86 ± 1.35	NA
	Control	20/20	34.60 ± 8.25	13.60 ± 3.97	NA	29.20 ± 0.70	NA
Wang et al.16 2022	NPSLE	27/25	33.81 ± 12.83	12.46 ± 2.28	39.56 ± 42.70	NA	23.56 ± 3.11
	Non-NPSLE	35/26	30.54 ± 9.37	11.97 ± 2.58	41.3 ± 56.06	NA	26.63 ± 1.21
	Control	35/26	28.51 ± 7.29	13.14 ± 2.11	NA	NA	28.62 ± 1.17
Zhang et al.19 2022	SjS-SLE	16/16	46.13 ± 16.10	10.44 ± 4.08	5.13 ± 3.78	28 (27-28)	NA
	Control	17/17	47.00 ± 10.50	13.35 ± 2.62	NA	29 (29-30)	NA
Li et al.26  2021 a	Non-NPSLE	28/28	49 (40.5, 53.8)	9 (6.0, 12.0)	6.5 (5.0, 10.0)	27 (25, 29)	NA
	Control	27/27	45 (41.0, 45.0)	12 (9.0, 15.0)	NA	30 (29, 30)	NA
Li et al.27  2021 b	Non-NPSLE	28/28	46.20 ± 10.00	9.71 ± 3.44	8.07 ± 6.14	26.89 ± 6.77	NA
	Control	27/27	45.10 ± 4.70	12.26 ± 3.82	NA	29.59 ± 0.57	NA
Piao et al.28 2021	Non-NPSLE	23/18	35.29 ± 12.79	NA	NA	NA	NA
	Control	28/21	30.25 ± 10.09	NA	NA	NA	NA
Su et al.29 2022	NPSLE	30/30	32.5 (12.8)	NA	66.0 (12.0, 168.0)	NA	NA
	Non-NPSLE	24/24	29.1 (10.0)	NA	28.0 (10.5, 54.0)	NA	NA
	Control	32/32	NA	NA	NA	NA	NA
MMSE; Mini-mental state examination, MoCA; Montreal cognitive assessment, NPSLE; Neuropsychiatric systemic lupus erythematosus, NA; Not applicable.

Table II: Clinical characteristics and quality scores of SLE and HC groups.

References	Group	SLEDAI (score)	HAMA (score)	HAMD (score)	C3 (g/L)	C4 (mg/L)	Quality scores
Lin et al.13 2011	Non-NPSLE	6.3	NA	NA	NA	NA	17
	Control	NA	NA	NA	NA	NA
Zhang et al.18 2017	Non-NPSLE	14.27 ± 5.59	NA	NA	0.42 ± 0.18	0.07 ± 0.04	20
	Control	NA	NA	NA	NA	NA
Liu et al.14 2018	Non-NPSLE	10.3 ± 6.8	7.1 ± 5.3	8.9 ± 5.8	NA	NA	19
	Control	NA	NA	NA	NA	NA
Zhang et al.24  2018	NPSLE	9.79	NA	NA	NA	NA	19
	Control	NA	NA	NA	NA	NA
Yu et al.17 2019	Non-NPSLE	5.38 ± 5.90	NA	NA	286.02 ± 167.26	476.15 ± 396.97	20
	Control	NA	NA	NA	NA	NA
Yu et al.25 2019	Non-NPSLE	1.25 ± 1.17	NA	NA	0.70 ± 0.20	119.68 ± 65.23	20
	Control	NA	NA	NA	NA	NA
Chen et al.12 2021	Non-NPSLE	1.25 ± 1.17	NA	NA	0.70 ± 0.20	119.68 ± 65.23	20
	Control	NA	NA	NA	NA	NA
Wang et al.16 2022	NPSLE	11.70 ± 7.97	17.44 ± 6.99	15.22 ± 4.69	0.69 ± 0.28	0.65 ± 0.38	20
	Non-NPSLE	12.17 ± 5.87	5.43 ± 4.27	6.97 ± 2.94	0.17 ± 0.14	0.17 ± 0.14
	Control	NA	2.31 ± 1.97	2.54 ± 2.59	NA	NA
Zhang et al.19 2022	SjS-SLE	NA	7 (3-10)	4.69 ± 3.65	NA	NA	18
	Control	NA	3 (2-5)	2.18 ± 1.98	NA	NA
Li et al.26  2021 a	Non-NPSLE	3.0 (1.0,5.5)	6 (4, 11)	6 (2, 8)	0.95 (0.69, 1.04)	0.13 (0.09, 0.20)	18
	Control	NA	NA	NA	NA	NA
Li et al.27  2021 b	Non-NPSLE	NA	7.39 ± 5.01	5.75 ± 4.33	0.89 ± 0.23	0.16 ± 0.71	20
	Control	NA	NA	NA	NA	NA
Piao et al.28 2021	Non-NPSLE	NA	NA	NA	NA	NA	18
	Control	NA	NA	NA	NA	NA
Su et al.29 2022	NPSLE	6.0 (3.0, 12.0)	NA	NA	NA	NA	19
	Non-NPSLE	4.0 (1.0, 7.5)	NA	NA	NA	NA
	Control	NA	NA	NA	NA	NA
SLEDAI; Systemic lupus erythematosus disease activity index, HAMA; Hamilton anxiety rating scale, HAMD; Hamilton depression rating scale, NPSLE; Neuropsychiatric systemic lupus erythematosus, NA; Not applicable.

Table III: Parameters and analysis methods of studies included in the meta-analysis.

References	Methodology of analysis	Scanner	Coordinates	FWHM	Data processing	Statistical threshold
Lin et al.13 2011	ReHo	3.0T	MNI	NA	SPM8, REST	p <0.05, Alphasim corrected
Zhang et al.18 2017	ALFF	3.0T	MNI	NA	SPM8	p <0.05, AlphaSim corrected
Liu et al.14 2018	ReHo	1.5T	MNI	10 mm	SPM8	p <0.05, FDR corrected
Zhang et al.24 2018	ReHo	3.0T	MNI	8 mm	DPARSFA	p <0.05
Yu et al.17 2019	ALFF	3.0T	MNI	4 mm	SPM8, DPARSFA	p <0.05, FDR corrected
Yu et al.25  2019	mALFF, PerAF, and mPerAF	3.0T	MNI	6 mm	SPM12, RESTplus	p <0.05, two-tailed corrected
Chen et al.12 2021	ReHo, Falff, dReHo, and dfALFF	3.0T	MNI	6 mm	SPM12, RESTplus	p <0.05, GRF corrected
Wang et al.16 2022	VMHC	3.0T	MNI	6 mm	SPM8, DPABI	p <0.05, GRF corrected
Zhang et al.19 2022	ALFF	3.0T	MNI	8 mm	SPM12	p <0.05, 3dClustSim corrected
Li et al.26  2021 a	ALFF	3.0T	MNI	NA	RESTplus	p <0.05, AlphaSim corrected
Li et al.27 2021 b	mReHo	3.0T	MNI	NA	RESTplus	p <0.05, AlphaSim corrected
Piao et al.28 2021	ALFF and fALFF	3.0T	MNI	6 mm	DPABI	p <0.05, GRF corrected
Su et al.29 2022	ReHo and fALFF	3.0T	MNI	8 mm	SPM12	p <0.05, FDR corrected
FWHM; Full width at half maxima, ReHo; Regional homogeneity, ALFF; Amplitude of low frequency fluctuation, mALFF standardised ALFF, PerAF; Percent amplitude of fluctuation, mPerAF standardised PerAF, fALFF; Fractional ALFF, dReHo dynamic ReHo, dfALFF; Dynamic fractional ALFF, VMHC; Voxel-mirrored homotopic connectivity, mReHo standardised ReHo, MNI; Montreal neurological institute, SPM; Statistical parametric mapping, PARSFA data processing assistant for resting-state fMRI advanced edition, DPABI; Data processing and analysis for brain imaging, FDR; False discovery rate, GRF; Gaussian random field.

Table IV: Resting-state anomalies in SLE patients compared with healthy controls.

Cluster	Brain region	BA	Peak voxel coordinates			ALE value
			x	y	z
Increased regional activity
	Right middle frontal gyrus	BA9	36	42	32	0.019971276
	Left lentiform nucleus		-18	14	-8	0.020571021
Decreased regional activity
	Right precentral gyrus	BA6	26	-6	72	0.021553122
		BA4	26	-18	68	0.017667238
	Right parahippocampal gyrus	BA30	24	-54	4	0.019775692
BA; Brodmann area, ALE; Activation likelihood estimation.

Figure 3: Deviations in SLE brain function visualised via ALE analyses. Anomalies are projected onto a standard MNI space template. The color spectrum illustrates ALE values: Warmer hues indicate increased activation, while cooler shades signify reduced activation in SLE.
L: Left; R: Right.

DISCUSSION

In this comprehensive meta-analysis, ALE was employed to examine the distinct rs-fMRI patterns in patients with SLE compared to HC. The analysis, which synthesised data from 13 studies, identified consistent alterations in rs-fMRI among patients with SLE. Notably, a decrease in activity across multiple regions was observed, especially in the right precentral gyrus (PreCG) and right parahippocampal gyrus (PHG). In contrast, enhanced activity was detected in the right middle frontal gyrus (MFG) and left lentiform nucleus (LLN). These findings may potentially highlight the neuro-psychiatric manifestations commonly observed in SLE cohorts, suggesting potential underlying neurophysiological disruptions associated with the disease.

Brain regions such as the PreCG, MFG, and superior frontal gyrus (SFG), which exhibited abnormal functional activity in this meta-analysis, are integral components of the executive control network (ECN). These areas are crucial for integra- ting memory and sensory inputs, facilitating the coordination of behavioural and cognitive processes.³⁰ In addition, the PHG and superior temporal gyrus (STG) are part of the default mode network (DMN), which is essential for processing of episodic memory, cognition, and affective regulation.³¹ The findings of this research suggested that SLE may primarily disrupt the functioning of both the ECN and DMN, which correlated with the cognitive impairments observed in individuals affected by SLE.

This study revealed that the patients with SLE exhibited reduced functional activity in the right PreCG, a key component of the ECN, as detected through rs-fMRI. This finding aligned with previous research by Giovacchini et al., who used single photon emission computed tomography (SPECT) to examine regional cerebral blood flow (CBF) in patients with SLE and reported significantly reduced CBF in the right PreCG among patients exhibiting depressive symptoms.³² Findings of the present study further underscore the PreCG as a potential neural hub for depressive symptoms in SLE. Furthermore, Zhao et al. investigated the brain white matter architectures in patients with non-neuropsychiatric systemic lupus erythematosus (NPSLE) and reported a notable reduction in nodal efficiency in the PreCG region.³³ Given its associations with motor control, executive functions, and working memory, the disruption of the PreCG might hold significant importance in the emergence of neuropsychiatric manifestations in SLE.

Previous research has shown inconsistent results concerning brain function in patients with SLE during rs-fMRI. Some studies reported elevated ALFF in the bilateral hippocampal gyrus/PHG in SLE patients compared to HC.¹⁸ However, other research noted a decrease in ALFF, specifically in the right PHG in these patients.¹⁹ This disparity highlighted the complexity of neurophysiological changes in SLE. The current meta-analysis found a general decrease in functional activity in the right PHG among SLE patients. Supporting this, Zhu et al. observed that the activation magnitude in the bilateral hippocampal gyrus/PHG was diminished in SLE patients compared to HC.³⁴ Furthermore, Kozora et al., using diffusion tensor imaging (DTI), reported reductions in metrics associated with the structural integrity of the PHG in SLE patients.³⁵ Both resting and task fMRI studies revealed low activation intensity in the PHG of SLE patients, suggesting that the PHG may play a crucial role in the coupling of function and structure in SLE. In addition, the PHG is recognised as part of DMN, which governs multiple cognitive tasks, encompassing episodic memory retrieval, cognitive processing, and emotional responses.³⁶ The findings of this study implied that the right PHG may serve as a key neural region asso-ciated with cognitive impairment in patients with SLE.

This study identified a significant elevation in functional activity within the MFG among patients diagnosed with SLE, a region integral to the ECN.²⁰ The observation suggested that the function of execution and control may be closely linked to MFG. Prior studies using rs-fMRI by Yu et al.¹⁷ and Bonacchi et al.⁹ also reported enhanced functional connectivity in the MFG among patients with SLE. Additionally, numerous task-based fMRI studies have consistently shown increased activation in the MFG.^37,38 Nonetheless, the specific mechanisms underlying these abnormalities in the MFG remained unclear. It is noteworthy that the observed increase in functional activity might act as a compensatory mechanism to facilitate cogni-tive tasks. Recent research indicated that the application of transcranial magnetic stimulation (TMS) could potentially enhance overall excitability in individuals with cognitive impairment.³⁹ The findings of the present study could provide valuable targets for TMS aimed at enhancing cortical excitability and synaptic plasticity in SLE patients.

The ALE analysis identified a significant increase in func-tional activity within the LLN in patients diagnosed with SLE. LLN is an important part of the left striatum. Previous studies have demonstrated increased striatal activity in SLE patients, indicating a link with impaired decision-making networks.⁴⁰ This study provided a more precise localisation of the LLN within the striatum compared to earlier studies. Additionally, an earlier meta-analysis utilising DTI has noted a significant decrease in fractional anisotropy within the left striatum of SLE patients.¹⁹ This reduction may be related to the observed increased activity in the LLN, potentially serving as a compensatory mechanism.

This study was subjected to several limitations. First, there was potential heterogeneity among the included studies, notably in terms of the demographic characteristics of patients and the methodologies employed in rs-fMRI research. Additionally, treating distinct functional imaging experiments on the same population as independent studies may inadvertently increase the influence of this population on the meta-analysis results. Given that all participants in this study were Chinese, further validation was required to determine whether these findings can be generalised to other populations. Moreover, due to limited sample size, this study encountered difficulties in comparing two subgroups: Non-neuropsychiatric SLE and neuropsychiatric SLE. Consequently, it was unable to analyse the disparities in disease progression. Therefore, larger, multicentre rs-fMRI studies are required to more comprehensively examine whether the identified abnormal activity pattern could serve as effective diagnostic and prognostic markers of neuro-psychiatric manifestations in SLE.

The findings of the data retrieval revealed that all 13 studies included in this meta-analysis were conducted on parti-cipants from China. The retrieval process was meticulously rechecked, confirming the integrity of the data collection. Moreover, numerous studies on SLE employing both task-based and resting-state fMRI have been conducted worldwide. However, the inclusion and exclusion criteria of this research excluded studies that utilised task- and rest-fMRI without reported coordinates in the results, as well as rest-fMRI studies focused on specific regions of interest rather than the entire brain. Consequently, studies from various global locations were excluded from this analysis. This delineation underscores that the utilisation of rs-fMRI to explore brain changes in SLE patients was not confined to China, reflecting a broader international research effort.

CONCLUSION

This meta-analysis revealed that patients with SLE exhibited notable abnormalities in resting brain activity within the DMN and ECN compared to healthy individuals. These findings provided crucial insights into the neurobiological processes underlying neuropsychiatric manifestations in SLE patients, contributing to a more comprehensive under- standing of the disorder. This enhanced knowledge could pave the way for the development of targeted therapies and interventions specifically designed to mitigate the neurological impacts of SLE, potentially improving patient outcomes.

FUNDING:
This work was supported by the Natural Science Foundation of Henan (222300420250). Cooperative Scientific Research Project of the Chunhui Plan of the Ministry of Education of China (202200696). Scientific Research Foundation for the High-level Talents funded by Fujian University of Traditional Chinese Medicine (X2019014-talents). Municipal Science and Technology Plan Project of Nanyang City (JCQY009). Doctoral Research Start-up Fund of Nanyang Institute of Technology (NGBJ-2020-11), Interdisciplinary Sciences Project of Nanyang Institute of Technology.

COMPETING INTEREST:
The authors declared no conflict of interest.

AUTHORS’ CONTRIBUTION:
XS: Principal investigator, conception, design, investigations of work, and write-up.
DF: Conception, design, and supervision
YM: Manuscript correction and data analysis.
ZL: Data analysis and interpretation.
YL: Data and SPSS analysis.
JL: Supervisor, critical analysis, data analysis, and interpre-tation.
All authors approved the final version of the manuscript to be published.

REFERENCES

1. Tsokos GC. Systemic lupus erythematosus. N Engl J Med 2011; 365(22):2110-21. doi: 10.1056/NEJMra1100359.
2. Ramirez GA, Lanzani C, Bozzolo EP, Citterio L, Zagato L, Casamassima N, et al. TRPC6 gene variants and neuro-psychiatric lupus. J Neuroimmunol 2015; 288:21-4. doi: 10.1016/j.jneuroim.2015.08.015.
3. Kiriakidou M, Ching CL. Systemic lupus erythematosus. Ann Intern Med 2020; 172(11):ITC81-94. doi: 10.7326/aitc20 2006020.
4. Chen J, Liu ML, Sun DL, Jin Y, Wang TR, Ren C. Effectiveness and neural mechanisms of home-based telerehabilitation in patients with stroke based on fMRI and DTI: A study protocol for a randomized controlled trial. Medicine (Baltimore) 2018; 97(3):e9605. doi: 10.1097/md.0000000000009605.
5. Levy DM, Kamphuis S. Systemic lupus erythematosus in children and adolescents. Pediatr Clin North Am 2012; 59(2):345-64. doi: 10.1016/j.pcl.2012.03.007.
6. He F, Li Y, Li C, Fan L, Liu T, Wang J. Repeated anodal high-definition transcranial direct current stimulation over the left dorsolateral prefrontal cortex in mild cognitive impairment patients increased regional homogeneity in multiple brain regions. Plos One 2021; 16(8):e0256100. doi: 10.1371/ journal.pone.0256100.
7. Logothetis NK. What we can do and what we cannot do with fMRI. Nature 2008; 453(7197):869-78. doi: 10.1038/nature 06976.
8. Yokoyama S, Okamoto Y, Takagaki K, Okada G, Takamura M, Mori A, et al. Effects of behavioural activation on default mode network connectivity in subthreshold depression: A preliminary resting-state fMRI study. J Affect Disord 2018; 227:156-63. doi: 10.1016/j.jad.2017.10.021.
9. Bonacchi R, Rocca MA, Ramirez GA, Bozzolo EP, Canti V, Preziosa P, et al. Resting state network functional connectivity abnormalities in systemic lupus erythematosus: correlations with neuropsychiatric impairment. Mol Psychiatry 2021; 26(7):3634-45. doi: 10.1038/s41380-020- 00907-z.
10. Antypa D, Simos NJ, Kavroulakis E, Bertsias G, Fanouriakis A, Sidiropoulos P, et al. 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. Lupus Sci Med 2021; 8(1):e000473. doi: 10.1136/lupus-2020-000473.
11. Smyser CD, Neil JJ. Use of resting-state functional MRI to study brain development and injury in neonates. Semin Perinatol 2015; 39(2):130-40. doi: 10.1053/j.semperi. 2015.01.006.
12. Chen L, Sun J, Wang Q, Hu L, Zhang Y, Ma H, et al. Altered temporal dynamics of brain activity in multiple-frequency bands in non-neuropsychiatric systemic lupus erythe-matosus patients with inactive disease. Neuropsychiatr Dis Treat 2021; 17:1385-95. doi: 10.2147/ndt.S292302.
13. Lin Y, Zou QH, Wang J, Wang Y, Zhou DQ, Zhang RH, et al. Localisation of cerebral functional deficits in patients with non-neuropsychiatric systemic lupus erythematosus. Human Brain Mapping 2011; 32(11):1847-55. doi: 10.1002/hbm.21158.
14. Liu S, Cheng Y, Xiel Z, Lai A, Lyl Z, Zhao Y, et al. A conscious resting state fMRI study in SLE patients without major neuropsychiatric manifestations. Front Psychiatry 2018; 9:677. doi: 10.3389/fpsyt.2018.00677.
15. Mu S, Lin Y, Xu Y, Wei X, Zeng Z, Lin K, et al. A novel rat model for cerebral venous sinus thrombosis: Verification of similarity to human disease via clinical analysis and experimental validation. J Transl Med 2022; 20(1):174. doi: 10.1186/s12967-022-03374-y.
16. Wang YL, Jiang ML, Huang LX, Meng X, Li S, Pang XQ, et al. Disrupted resting-state interhemispheric functional connectivity in systemic lupus erythematosus patients with and without neuropsychiatric lupus. Neuroradiology 2022; 64(1):129-40. doi: 10.1007/s00234-021-02750-7.
17. Yu H, Qiu X, Zhang YQ, Deng Y, He MY, Zhao YT, et al. Abnormal amplitude of low frequency fluctuation and functional connectivity in non-neuropsychiatric systemic lupus erythematosus: A resting-state fMRI study. Neuro-radiology 2019; 61(3):331-40. doi: 10.1007/s00234-018-2138-6.
18. Zhang XD, Jiang XL, Cheng Z, Zhou Y, Xu Q, Zhang ZQ, et al. Decreased coupling between functional connectivity density and amplitude of low frequency fluctuation in non-neuro-psychiatric systemic lupus erythematosus: A resting-stage functional MRI study. Mol Neurobiol 2017; 54(7):5225-35. doi: 10.1007/s12035-016-0050-9.
19. Zhang XD, Ke J, Li JL, Su YY, Zhou JM, Zhao LR, et al. Different cerebral functional segregation in Sjogren's syndrome with or without systemic lupus erythematosus revealed by amplitude of low-frequency fluctuation. Acta Radiol 2022; 63(9):1214-22. doi: 10.1177/02841851211032441.
20. Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 2015; 350:g7647. doi: 10. 1136/bmj.g7647.
21. Pan PL, Zhang Y, Liu Y, Zhang H, Guan DN, Xu Y. Abnormalities of regional brain function in Parkinson's disease: A meta-analysis of resting state functional magnetic resonance imaging studies. Sci Rep 2017; 7: 40469. doi: 10.1038/srep40469.
22. Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: A random-effects approach based on empirical estimates of spatial uncertainty. Human Brain Mapping 2009; 30(9):2907-26. doi: 10.1002/hbm.20718.
23. Laird AR, Fox PM, Price CJ, Glahn DC, Uecker AM, Lancaster JL, et al. ALE meta-analysis: Controlling the false discovery rate and performing statistical contrasts. Human Brain Mapping 2005; 25(1):155-64. doi: 10.1002/hbm.20136.
24. Zhang Y, Huang JB, Qin M, Zhou JY, Zhang HY. Study on rs-fMRI about brain of patients with NPSLE based on ReHo. China Medical Equipment 2018; 15(08):48-52. doi: 10. 3969/J.ISSN.1672-8270.2018.08.015.
25. Yu Y, Chen L, Wang Q, Hu L, Ding Q, Jia X, et al. Altered amplitude of low-frequency fluctuations in inactive patients with non-neuropsychiatric systemic lupus erythematosus. Neural Plast 2019; 2019:9408612. doi: 10.1155/2019/ 9408612.
26. Li XL, Zhou WS, Zhang P, Tian WZ, Xia JG, Zhou HM. Amplitude of low-frequency fluctuations and functional connectivity in non-neuropsychiatric systemic lupus erythematosus: A resting-state functional magnetic resonance imaging study. Chinese J Med Imaging 2021; 29(12):1170-6. doi: 10.3969/j.issn.1005-5185.2021.12.002.
27. Li XL, Xia JG, Zou HM, Zhou WS, Zhang P. The changes of brain regional homogeneity in non-NPSLE patients: A resting-state functional magnetic resonance imaging study. Radiol Practice 2021; 36(12):1467-07. doi: 10.13609/j.cnki. 1000-0313.2021.12.003.
28. Piao S, Wang R, Qin H, Hu B, Du J, Wu H, Geng D. Alterations of spontaneous brain activity in systematic lupus erythematosus patients without neuropsychiatric symptoms: A resting-functional MRI study. Lupus 2021; 30(11):1781-9. doi: 10.1177/09612033211033984.
29. Su L, Zhuo Z, Duan Y, Huang J, Qiu X, Li M, Liu Y, Zeng X. Structural and functional characterization of gray matter alterations in female patients with neuropsychiatric systemic lupus. Front Neurosci 2022; 2(16):839194. doi: 10.3389/fnins.2022.839194.
30. Dajani DR, Uddin LQ. Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience. Trends in Neurosci 2015; 38(9):571-8. doi: 10.1016/j.tins. 2015.07.003.
31. Gabrieli SW, Ford JM. Default mode network activity and connectivity in psychopathology. Ann Rev Clin Psychol 2012; 8:49-76. doi: 10.1146/annurev-clinpsy-032511- 143049.
32. Giovacchini G, Mosca M, Manca G, Porta MD, Neri C, Bombardieri S, et al. Cerebral blood flow in depressed patients with systemic lupus erythematosus. J Rheumatol 2010; 37(9):1844-51. doi: 10.3899/jrheum.100121.
33. Zhao L, Tan X, Wang J, Han K, Niu M, Xu J, et al. Brain white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus. Brain Imaging and Behav 2018; 12(1):142-55. doi: 10.1007/ s116 82-017-9681-3.
34. Zhu CM, Ma Y, Xie L, Huang JZ, Sun ZB, Duan SX, et al. Spatial Working Memory Impairment in patients with non-neuropsychiatric systemic lupus erythematosus: A blood-oxygen-level dependent functional magnetic resonance imaging study. J Rheumatol 2017; 44(2):201-8. doi: 10. 3899/jrheum.160290.
35. Kozora E, Ulug AM, Erkan D, Vo A, Filley CM, Ramon G, et al. Functional magnetic resonance imaging of working memory and executive dysfunction in systemic lupus erythematosus and antiphospholipid antibody-positive patients. Arthritis Care Res (Hoboken) 2016; 68(11): 1655-63. doi: 10.1002/ acr.22873.
36. Yuan B, Chen J, Gong L, Shu H, Liao W, Wang Z, et al. Mediation of episodic memory performance by the executive function network in patients with amnestic mild cognitive impairment: A resting-state functional MRI study. Oncotarget 2016; 7(40):64711-25. doi: 10.18632/onco target.11775.
37. Hou J, Lin Y, Zhang W, Song L, Wu W, Wang J, et al. Abnormalities of frontal-parietal resting-state functional connectivity are related to disease activity in patients with systemic lupus erythematosus. Plos One 2013; 8(9):e74530. doi: 10.1371/journal.pone.0074530.
38. Mak A, Ren T, Fu EHY, Cheak AAC, Ho RCM. A Prospective functional MRI study for executive function in patients with systemic lupus erythematosus without neuropsychiatric symptoms. Seminars in Arthritis Rheum 2012; 41(6): 849-58. doi: 10.1016/j.semarthrit.2011.11.010.
39. Cantone M, Lanza G, Fisicaro F, Pennisi M, Bella R, Lazzaro VD, et al. Evaluation and treatment of vascular cognitive impairment by transcranial magnetic stimulation. Neural Plast 2020; 2020:8820881. doi: 10.1155/2020/8820881.
40. Wu BB, Ma Y, Xie L, Huang JZ, Sun ZB, Hou ZD, et al. Impaired decision-making and functional neuronal network activity in systemic lupus erythematosus. J Magn Reson Imaging 2018; 48(6):1508-17. doi: 10.1002/jmri.26006.