Distinct regional brain atrophy pattern in multiple sclerosis and neuropsychiatric systemic lupus erythematosus patients

Abstract

Differentiation of systemic lupus erythematosus (SLE) from multiple sclerosis (MS) can be challenging, especially when neuropsychiatric (NP) symptoms are accompanied by white matter lesions in the brain. Given the lack of discriminative power of currently applied tools for their differentiation, there is an unmet need for other measures that can aid in distinguishing between the two autoimmune disorders. In this study we aimed at exploring whether brain atrophy measures could serve as markers differentiating MS and SLE. Thirty-seven relapsing-remitting MS and 38 SLE patients with nervous system manifestations, matched according to age and disease duration, underwent 1.5 Tesla magnetic resonance imaging (MRI), including volumetric sequences, and clinical assessment. Voxelwise analysis was performed using ANTS-SyN elastic registration protocol, FSL Randomise and Gamma methods. Cortical and subcortical segmentation was performed with Freesurfer 5.3 pipeline using T1-weighted MPRAGE sequence data. Using MRI volumetric markers of general and subcortical gray matter atrophy and clinical variables, we built a stepwise multivariable logistic diagnostic model to identify MRI parameters that best differentiate MS and SLE patients. We found that the best volumetric predictors to distinguish them were: fourth ventricle volume (sensitivity 0.86, specificity 0.57, area under the curve, AUC 0.77), posterior corpus callosum (sensitivity 0.81, specificity 0.57, AUC 0.68), and third ventricle to thalamus ratio (sensitivity 0.42, specificity 0.84, AUC 0.65). The same classifiers were identified in a subgroup analysis that included patients with a short disease duration. In MS brain atrophy and lesion load correlated with clinical disability, while in SLE age was the main determinant of brain volume. This study proposes new imaging parameters for differential diagnosis of MS and SLE with central nervous system involvement. We show there is a different pattern of atrophy in MS and SLE, and the key structural volumes that are differentially affected include fourth ventricle and posterior section of corpus callosum, followed by third ventricle to thalamus ratio. Different correlation patterns between volumetric and clinical data may suggest that while in MS atrophy is driven mainly by disease activity, in SLE it is mostly associated with age. However, these results need further replication in a larger cohort.

Neuropsychiatric manifestations and their attribution to SLE: a retrospective single-center study in the Polish population

“…The white matter and total brain volume were lower in patients with NPSLE experiencing inflammatory phenotype comparing with ischemic phenotype [31]. Higher volume of the left and right caudate nuclei was also correlated with disease activity (measured by SLEDAI-2K) in patients with NPSLE [36]. Advanced MRI techniques may convey more subtle insights on brain alterations, e.g., the severity of depression and anxiety in patients with NPSLE was associated with hemodynamic and functional connectivity changes of the frontolimbic neural circuit [37].…”

The impact of disease modifying therapies on cognitive functions typically impaired in multiple sclerosis patients: a clinician’s review

“…Our team found associations between regional black hole volumes and several cognitive functions ( 20 ). We also managed to identify a distinct regional brain atrophy pattern in multiple sclerosis, as compared with one of the MS mimickers, namely neuropsychiatric systemic lupus erythematosus ( 21 ) and this MS-specific pattern of global and subcortical gray matter atrophy correlated with cognitive impairment ( 22 ).…”

MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients

“…In approximately 60% of cases, symptoms may be independent of brain damage, and instead attributed to chronic pain, altered sleep quality or corticosteroid therapy (Magro-Checa et al, 2016). The broad range of symptoms presented and the absence of a reliable biomarker therefore make diagnosis and management challenging in a large number of cases (Kalinowska-Lyszczarz et al, 2018).…”