Dynamic changes of amplitude of low-frequency in systemic lupus erythematosus patients with cognitive impairment

Advanced neuroimaging in systemic lupus erythematosus: identifying biomarkers for cognitive dysfunction

BACKGROUND

Cognitive dysfunction (CD) is a common manifestation of central nervous system involvement in patients with systemic lupus erythematosus (SLE). Patients with SLE may develop CD insidiously at an early stage of the disease, and the lack of a standardized diagnostic test poses a major challenge in prompt diagnosis and management of these patients. This review summaries the current application of various magnetic resonance imaging (MRI) techniques for patients with SLE complicated with CD, aiming to identify potential quantitative neuroimaging biomarkers for patients with SLE and CD.

METHODS

We systematically searched several databases between January 2003 to December 2024. We screened retrospective and prospective studies based on search criteria keywords (including structural or functional MRI, cognitive function, lupus, and systemic lupus erythematosus) to identify peer-reviewed articles that reported advanced structural and functional MRI metrics and evaluated CD in human patients with SLE.

RESULTS

123 studies (19 Bold-MRI studies, 9 DTI studies, 2 ASL studies, 4 MTI studies, 5 machine learning, and 84 other studies) were identified. Neuroimaging findings show that patients with CD have abnormal manifestations in the limbic system, hippocampus, corpus callosum, and frontal cortex, and these manifestations are closely related to cognitive functions. The most commonly affected cognitive domains are memory, attention, and executive ability. Multimodal MRI, integrating structural, functional, and perfusion parameters, combined with machine learning, can effectively predict cognitive function.

CONCLUSION

Advanced MRI analysis can identify the abnormalities in the whole brain and local brain regions associated with CD in patients with SLE. The integration of machine learning and multimodal MRI offers new perspectives for early identification and mechanistic studies of CD in SLE patients. More studies are needed to identify potential neuroimaging biomarkers to facilitate early diagnosis, timely treatment, and accurate prognosis for SLE patients with CD.

Early identification of autism spectrum disorder in preschoolers by static and dynamic amplitude of low-frequency fluctuations features

Objectives

Early identification and timely intervention is critical for young children with autism spectrum disorder (ASD). The current study aims to explore potential disparities in static and dynamic intrinsic brain function in preschoolers with ASD, and uncover underlying neural underpinnings that can be used for facilitating the identification of ASD.

Materials and methods

Static and dynamic amplitude of low-frequency fluctuations (ALFF) of 73 ASD preschoolers and 43 age-matched typically developing individuals (TDs) were extracted and compared to identify differences in intrinsic brain local connectivity associated with ASD. The dynamic ALFF (dALFF) utilized a sliding window technique that integrates static ALFF (sALFF) to gauge the variance of local brain activity over time. A receiver operating characteristic (ROC) analysis was conducted to evaluate the potential diagnostic capability of the sALFF and dALFF metrics in identifying ASD.

Results

Compared with TDs, ASD preschoolers exhibited lower levels of sALFF in the left middle temporal gyrus, medial orbitofrontal cortex, precuneus and reduced dALFF values in the left inferior orbitofrontal cortex, middle temporal gyrus. ROC analysis indicated that sALFF and dALFF could distinguish preschoolers with ASD from TDs with the areas under the curve (AUC) of 0.848 and 0.744 (p < 0.001), and their combination showed an increased accuracy with the AUC of 0.866 (p < 0.001). Nevertheless, there were no linear correlation between the ALFF values in children with ASD and clinical scales.

Conclusion

The findings suggest an association of regional left brain dysfunction with ASD in preschoolers. The values of sALFF and dALFF, particularly in the middle temporal gyrus, could act as possible indicators for the early detection of ASD.

Abnormal brain functional networks in systemic lupus erythematosus: a graph theory, network-based statistic and machine learning study

Systemic lupus erythematosus patients' brain functional network impairments are incompletely clarified. This study investigates the brain functional network topological alterations in systemic lupus erythematosus and the application of machine learning to the classification of systemic lupus erythematosus and healthy controls. Resting-state functional MRI data from 127 systemic lupus erythematosus patients and 102 healthy controls were used. The pre-processing process involved using automated anatomical labelling atlas to compute time series data for 116 brain regions. A functional connectivity network was then created by assessing the Pearson correlation between the time series of these brain regions. The GRETNA toolbox was used to compute the difference in topological attributes between groups. Variations in regional networks among groups were evaluated using non-parametric permutation tests that rely on network-based statistical analysis. With the functional connectivity network metrics as features and network-based statistical analysis as the feature selection method, network-based statistical analysis Predict software was used to classify systemic lupus erythematosus from controls by support vector machine. The subnets that contributed the most to systemic lupus erythematosus classification were also identified. For global indicators, the systemic lupus erythematosus group exhibited significantly lower values for the normalized clustering coefficient (P = 0. 0317) and small-world index (P = 0.0364) compared to the healthy controls group. After false discovery rate correction, the differences in Betweeness Centrality, Degree Centrality, Node Efficiency, Node Local Efficiency and other local indexes between the two groups were not retained. No correlation was found between clinical data and network indicators. Systemic lupus erythematosus group had a significantly reduced connection with a 12-node, 11-edge subnetwork (P = 0.024). In conclusion, systemic lupus erythematosus patients exhibit suboptimal global brain functional connectivity network topology and the presence of a subnetwork with abnormally reduced connectivity.

Altered spontaneous brain activity in patients with progressive-stage and end-stage chronic kidney disease: insights from dALFF analysis

Chronic kidney disease (CKD) is a worldwide public health problem. Its association with cognitive impairment has attracted widespread attention in the world. However, the neuropathological mechanisms underlying kidney-brain interactions in CKD remain unclear. Using the dynamic amplitude of low-frequency fluctuations (dALFF) technique, 21 patients with progressive-stage CKD (CKD stages 3-4), 22 patients with end-stage CKD (CKD stage 5, ESKD), and 23 healthy volunteers (HCs) were enrolled to explore the brain regions with dALFF abnormalities in the progressive-stage and end-stage CKD. We used biased correlation analyses to explore the relationship between dALFF values and clinical indicators in patients with progressive-stage and end-stage CKD. Patients with both progressive-stage CKD (stages 3-4) and ESKD had abnormal dALFF values in the right parahippocampus, right inferior temporal gyrus, and left cuneus compared with HCs. In addition, abnormal dALFF were present in the left fusiform gyrus, insula and hippocampus in patients with progressive-stage CKD and in the left inferior temporal gyrus in patients with ESKD. Biased correlation analysis showed that dALFF values in the right parahippocampus, left fusiform gyrus and left insula were positively correlated with serum creatinine concentrations in patients with progressive-stage CKD and that dALFF values in the left inferior temporal gyrus were negatively correlated with MoCA scores in patients with ESKD. Our findings highlight the variability of neuroimaging changes between different stages of CKD and provide new insights and research directions for an in-depth exploration of the neuropathological mechanisms of renal-brain interactions.

Altered structural and functional homotopic connectivity associated with cognitive changes in SLE

OBJECTIVE

Previous studies have revealed functional changes within the cerebral hemispheres of patients with SLE; however the changes between cerebral hemispheres are still unknown. The present study aimed to explore the functional and structural changes between bilateral hemispheres using functional MRI and find their relationship with cognition in patients with SLE.

METHODS

54 patients with SLE and 32 age-matched and sex-matched healthy controls (HCs) underwent MRI scanning and neuropsychological testing, and clinical data was collected in patients with SLE. Voxel-mirrored homotopic connectivity (VMHC) values and grey matter volume were calculated for all subjects. Correlation analysis was established to determine the relationship between VMHC values, grey matter volume and cognitive scores, blood biochemical markers in patients with SLE.

RESULTS

Compared with HCs, patients with SLE showed increased VMHC values in the insula and parahippocampal gyrus, while grey matter volume were reduced in these regions. Correlation analysis demonstrated that the increased VMHC values in insula was negatively correlated with decreased orientation function and positively correlated with decreased attention function. The grey matter volume in insula was negatively correlated with decreased attention and abstraction. The VMHC values and grey matter volume in insula and parahippocampal gyrus were negatively associated with lupus-specific antibodies.

CONCLUSION

The structural and functional changes of insula and parahippocampal gyrus might be potential neuroimaging markers, and specific antibodies associated with lupus might be involved in the pathophysiological mechanisms of brain dysfunction.

TRIAL REGISTRATION NUMBER

NCT06226324.

Brain functional alternation in patients with systemic sclerosis: a resting-state functional magnetic resonance imaging study

BACKGROUND

Neuropsychiatric manifestations, such as cognitive impairment, are relatively prevalent in systemic sclerosis (SSc) patients. This study aimed to investigate the resting state (RS) functional alternations of SSc patients and the potential influenced factors.

METHODS

Forty-four SSc patients (mean age, 46.3 ± 11.4 years; 40 females) and 19 age and sex comparable healthy volunteers (mean age, 42.6 ± 11.3 years; 16 females) were recruited and underwent RS functional MR imaging (fMRI) and neuropsychological assessments. Functional segregation analysis was performed to calculate the amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo). Functional integration analysis was conducted using group independent component analysis to calculate intra-network and inter-network functional connectivity (FC). The fMRI measurements were compared between SSc patients and healthy volunteers using voxel-based pairwise two-sample t-tests. The correlations between clinical characteristics and fMRI measurements were also analyzed.

RESULTS

Compared to healthy volunteers, SSc patients exhibited significantly decreased ALFF and increased ReHo (all P < 0.01, FWE corrected). SSc patients predominantly showed decreased intra-network and inter-network FC in the auditory network, visual network, default mode network, frontoparietal network and attention network (intra-network FC: P < 0.01, uncorrected, cluster size > 30; inter-network FC: P < 0.05, FDR correction). Furthermore, clinical characteristics including disease duration (r value ranged from - 0.31 to 0.36), elevated erythrocyte sedimentation rate (r = 0.35), Montreal Cognitive Assessment score (r = 0.43), and Hamilton Depression Scale score (r = -0.40) were significantly associated with fMRI measurements (all P < 0.05).

CONCLUSIONS

Spontaneous activity and functional connectivity alternations can be seen in SSc patients, which are partially associated with neuropsychiatric manifestations and tend to aggravate with disease duration.

Systemic lupus erythematosus-related brain abnormalities in the default mode network and the limbic system: A resting-state fMRI meta-analysis

BACKGROUND

Systemic lupus erythematosus (SLE) is an immune-mediated and multi-systemic disease which may affect the nervous system, causing neuropsychiatric SLE (NPSLE). Recent neuroimaging studies have examined brain functional alterations in SLE. However, discrepant findings were reported. This meta-analysis aims to identify consistent resting-state functional abnormalities in SLE.

METHODS

PubMed and Web of Science were searched to identify candidate resting-state functional MRI studies assessing SLE. A voxel-based meta-analysis was performed using the anisotropic effect-size version of the seed-based d mapping (AES-SDM). The abnormal intrinsic functional patterns extracted from SDM were mapped onto the brain functional network atlas to determine brain abnormalities at a network level.

RESULTS

Twelve studies evaluating fifteen datasets were included in this meta-analysis, comprising 572 SLE patients and 436 healthy controls (HCs). Compared with HCs, SLE patients showed increased brain activity in the bilateral hippocampus and right superior temporal gyrus, and decreased brain activity in the left superior frontal gyrus, left middle temporal gyrus, bilateral thalamus, left inferior frontal gyrus and right cerebellum. Mapping the abnormal patterns to the network atlas revealed the default mode network and the limbic system as core neural systems commonly affected in SLE.

LIMITATIONS

The number of included studies is relatively small, with heterogeneous analytic methods and a risk of publication bias.

CONCLUSIONS

Brain functional alterations in SLE are predominantly found in the default mode network and the limbic system. These findings uncovered a consistent pattern of resting-state functional network abnormalities in SLE which may serve as a potential objective neuroimaging biomarker.

Altered dynamic and static brain activity and functional connectivity in COVID-19 patients: a preliminary study

This study aimed to investigate the effects of COVID-19 on brain functional activity through resting-state functional MRI (rs-fMRI). fMRI scans were conducted on a cohort of 42 confirmed COVID-19-positive patients and 46 healthy controls (HCs) to assess brain functional activity. A combination of dynamic and static amplitude of low-frequency fluctuations (dALFF/sALFF) and dynamic and static functional connectivity (dFC/sFC) was used for evaluation. Abnormal brain regions identified were then used as feature inputs in the model to evaluate support vector machine (SVM) capability in recognizing COVID-19 patients. Moreover, the random forest (RF) model was employed to verify the stability of SVM diagnoses for COVID-19 patients. Compared to HCs, COVID-19 patients exhibited a decrease in sALFF in the right lingual gyrus and the left medial occipital gyrus and an increase in dALFF in the right straight gyrus. Moreover, there was a decline in sFC between both lingual gyri and the right superior occipital gyrus and a reduction in dFC with the precentral gyrus. The dynamic and static combined ALFF and FC could distinguish between COVID-19 patients and the HCs with an accuracy of 0.885, a specificity of 0.818, a sensitivity of 0.933 and an area under the curve of 0.909. The combination of dynamic and static ALFF and FC can provide information for detecting brain functional abnormalities in COVID-19 patients.

Changes in dynamic and static brain fluctuation distinguish minimal hepatic encephalopathy and cirrhosis patients and predict the severity of liver damage

Purpose

Minimal hepatic encephalopathy (MHE) is characterized by mild neuropsychological and neurophysiological alterations that are not detectable by routine clinical examination. Abnormal brain activity (in terms of the amplitude of low-frequency fluctuation (ALFF) has been observed in MHE patients. However, little is known concerning temporal dynamics of intrinsic brain activity. The present study aimed to investigate the abnormal dynamics of brain activity (dynamic ALFF; dALFF) and static measures [static ALFF; (sALFF)] in MHE patients and to strive for a reliable imaging neuromarkers for distinguishing MHE patients from cirrhosis patients. In addition, the present study also investigated whether intrinsic brain activity predicted the severity of liver damage.

Methods

Thirty-four cirrhosis patients with MHE, 28 cirrhosis patients without MHE, and 33 age-, sex-, and education-matched healthy controls (HCs) underwent resting-state magnetic resonance imaging (rs-fMRI). dALFF was estimated by combining the ALFF method with the sliding-window method, in which temporal variability was quantized over the whole-scan timepoints and then compared among the three groups. Additionally, dALFF, sALFF and both two features were utilized as classification features in a support vector machine (SVM) to distinguish MHE patients from cirrhosis patients. The severity of liver damage was reflected by the Child–Pugh score. dALFF, sALFF and both two features were used to predict Child–Pugh scores in MHE patients using a general linear model.

Results

Compared with HCs, MHE patients showed significantly increased dALFF in the left inferior occipital gyrus, right middle occipital gyrus, and right insula; increased dALFF was also observed in the right posterior lobe of the cerebellum (CPL) and right thalamus. Compared with HCs, noMHE patients exhibited decreased dALFF in the right precuneus. In contrast, compared with noMHE patients, MHE patients showed increased dALFF in the right precuneus, right superior frontal gyrus, and right superior occipital gyrus. Furthermore, the increased dALFF values in the left precuneus were positively associated with poor digit-symbol test (DST) scores (r = 0.356, p = 0.038); however, dALFF in the right inferior temporal gyrus (ITG) was negatively associated with the number connection test–A (NCT-A) scores (r = -0.784, p = 0.000). A significant positive correlation was found between dALFF in the left inferior occipital gyrus (IOG) and high blood ammonia levels (r = 0.424, p = 0.012). Notably, dALFF values yielded a higher classification accuracy than sALFF values in distinguishing MHE patients from cirrhosis patients. Importantly, the dALFF values predicted the Child–Pugh score (r = 0.140, p = 0.030), whereas sALFF values did not in the current dataset. Combining two features had high accuracy in classification in distinguishing MHE patients from cirrhotic patients and yielded prediction in the severity of liver damage.

Conclusion

These findings suggest that combining dALFF and sALFF features is a useful neuromarkers for distinguishing MHE patients from cirrhosis patients and highlights the important role of dALFF feature in predicting the severity of liver damage in MHE.