Dataset Information

MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients.

ABSTRACT:

Introduction

Systemic lupus erythematosus (SLE) is an autoimmune connective tissue disease affecting multiple organs in the human body, including the central nervous system. Recently, an artificial intelligence method called BrainAGE (Brain Age Gap Estimation), defined as predicted age minus chronological age, has been developed to measure the deviation of brain aging from a healthy population using MRI. Our aim was to evaluate brain aging in SLE patients using a deep-learning BrainAGE model.

Methods

Seventy female patients with a clinical diagnosis of SLE and 24 healthy age-matched control females, were included in this post-hoc analysis of prospectively acquired data. All subjects had previously undergone a 3 T MRI acquisition, a neuropsychological evaluation and a measurement of neurofilament light protein in plasma (NfL). A BrainAGE model with a 3D convolutional neural network architecture, pre-trained on the 3D-T1 images of 1,295 healthy female subjects to predict their chronological age, was applied on the images of SLE patients and controls in order to compute the BrainAGE. SLE patients were divided into 2 groups according to the BrainAGE distribution (high vs. low BrainAGE).

Results

BrainAGE z-score was significantly higher in SLE patients than in controls (+0.6 [±1.1] vs. 0 [±1.0], p = 0.02). In SLE patients, high BrainAGE was associated with longer reaction times (p = 0.02), lower psychomotor speed (p = 0.001) and cognitive flexibility (p = 0.04), as well as with higher NfL after adjusting for age (p = 0.001).

Conclusion

Using a deep-learning BrainAGE model, we provide evidence of increased brain aging in SLE patients, which reflected neuronal damage and cognitive impairment.

SUBMITTER: Kuchcinski G

PROVIDER: S-EPMC10622955 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients.

Kuchcinski Grégory G Rumetshofer Theodor T Zervides Kristoffer A KA Lopes Renaud R Gautherot Morgan M Pruvo Jean-Pierre JP Bengtsson Anders A AA Hansson Oskar O Jönsen Andreas A Sundgren Pia C Maly PCM

Frontiers in aging neuroscience 20231020

<h4>Introduction</h4>Systemic lupus erythematosus (SLE) is an autoimmune connective tissue disease affecting multiple organs in the human body, including the central nervous system. Recently, an artificial intelligence method called BrainAGE (Brain Age Gap Estimation), defined as predicted age minus chronological age, has been developed to measure the deviation of brain aging from a healthy population using MRI. Our aim was to evaluate brain aging in SLE patients using a deep-learning BrainAGE m ...[more]

PMID: 37927336

Similar Datasets

Project description:ObjectiveDynamic 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 methodsWe 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.ResultsAfter 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.ConclusionOur 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.

Project description:IntroductionThe pathogenesis of neuropsychiatric systemic lupus erythematosus (NPSLE) is widely unknown, and the role of autoantibodies is still undetermined.MethodsTo identify brain-reactive autoantibodies possibly related to NPSLE, immunofluorescence (IF) and transmission electron microscopy (TEM) on rat and human brains were performed. ELISA was used to reveal the presence of known circulating autoantibodies, while western blot (WB) was applied to characterize potential unknown autoantigen(s).ResultsWe enrolled 209 subjects, including patients affected by SLE (n=69), NPSLE (n=36), Multiple Sclerosis (MS, n=22), and 82 age- and gender-matched healthy donors (HD). Autoantibody reactivity by IF was observed in almost the entire rat brain (cortex, hippocampus, and cerebellum) using sera from NPSLE and SLE patients and was virtually negative in MS and HD. NPSLE showed higher prevalence (OR 2.4; p = 0.047), intensity, and titer of brain-reactive autoantibodies than SLE patients. Most of the patient sera with brain-reactive autoantibodies (75%) also stained human brains. Double staining experiments on rat brains mixing patients' sera with antibodies directed against neuronal (NeuN) or glial markers showed autoantibody reactivity restricted to NeuN-containing neurons. Using TEM, the targets of brain-reactive autoantibodies were located in the nuclei and, to a lesser extent, in the cytoplasm and mitochondria. Given the high degree of colocalization between NeuN and brain-reactive autoantibodies, we assumed NeuN was a possible autoantigen. However, WB analysis with HEK293T cell lysates expressing or not expressing the gene encoding for NeuN protein (RIBFOX3) showed that patients' sera carrying brain-reactive autoantibodies did not recognize the NeuN corresponding band size. Among the panel of NPSLE-associated autoantibodies (e.g., anti-NR2, anti-P-ribosomal protein, antiphospholipid) investigated by ELISA assay, only the anti-β2-glycoprotein-I (aβ2GPI) IgG was exclusively found in those sera containing brain-reactive autoantibodies.ConclusionIn conclusion, SLE and NPSLE patients possess brain-reactive autoantibodies but with higher frequency and titers found in NPSLE patients. Although many target antigens of brain-reactive autoantibodies are still undetermined, they likely include β2GPI.

Project description:Introduction/purposeSystemic lupus erythematosus (SLE) is a chronic auto-immune disease with a broad spectrum of clinical presentations, including heterogeneous neuropsychiatric (NP) syndromes. Structural brain abnormalities are commonly found in SLE and NPSLE, but their role in diagnosis is limited, and their usefulness in distinguishing between NPSLE patients and patients in which the NP symptoms are not primarily attributed to SLE (non-NPSLE) is non-existent. Self-supervised contrastive learning algorithms proved to be useful in classification tasks in rare diseases with limited number of datasets. Our aim was to apply self-supervised contrastive learning on T1-weighted images acquired from a well-defined cohort of SLE patients, aiming to distinguish between NPSLE and non-NPSLE patients.Subjects and methodsWe used 3T MRI T1-weighted images of 163 patients. The training set comprised 68 non-NPSLE and 34 NPSLE patients. We applied random geometric transformations between iterations to augment our data sets. The ML pipeline consisted of convolutional base encoder and linear projector. To test the classification task, the projector was removed and one linear layer was measured. Validation of the method consisted of 6 repeated random sub-samplings, each using a random selection of a small group of patients of both subtypes.ResultsIn the 6 trials, between 79% and 83% of the patients were correctly classified as NPSLE or non-NPSLE. For a qualitative evaluation of spatial distribution of the common features found in both groups, Gradient-weighted Class Activation Maps (Grad-CAM) were examined. Thresholded Grad-CAM maps show areas of common features identified for the NPSLE cohort, while no such communality was found for the non-NPSLE group.Discussion/conclusionThe self-supervised contrastive learning model was effective in capturing common brain MRI features from a limited but well-defined cohort of SLE patients with NP symptoms. The interpretation of the Grad-CAM results is not straightforward, but indicates involvement of the lateral and third ventricles, periventricular white matter and basal cisterns. We believe that the common features found in the NPSLE population in this study indicate a combination of tissue loss, local atrophy and to some extent that of periventricular white matter lesions, which are commonly found in NPSLE patients and appear hypointense on T1-weighted images.

Dataset Information

MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients.

Introduction

Methods

Results

Conclusion

Publications

MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets