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Article

Microstructural Changes in the Corpus Callosum in Systemic Lupus Erythematous

by
Paulo Rogério Julio
1,2,
Thais Caldeira
3,
Gustavo Retuci Pinheiro
3,
Carla Helena Capello
1,2,
Renan Bazuco Fritolli
2,4,
Roberto Marini
5,
Fernando Cendes
6,
Paula Teixeira Fernandes
7,
Lilian T. L. Costallat
8,
Leticia Rittner
3 and
Simone Appenzeller
2,8,*
1
Graduate Student at Child and Adolescent Health Program, School of Medical Science, University of Campinas, Campinas 13083-970, SP, Brazil
2
Autoimmunity Laboratory, School of Medical Science, University of Campinas, Campinas 13083-970, SP, Brazil
3
Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas 13083-852, SP, Brazil
4
Graduate Program Physiopathology Program, School of Medical Science, University of Campinas, Campinas 13083-970, SP, Brazil
5
Department of Pediatrics, School of Medical Science, University of Campinas, Campinas 13083-970, SP, Brazil
6
Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas 13083-970, SP, Brazil
7
Department of Sport Sciences, Faculty of Physical Education, University of Campinas, Campinas 13083-970, SP, Brazil
8
Rheumatology Unit, Department of Orthopedics, Rheumatology and Traumatology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas 13083-970, SP, Brazil
*
Author to whom correspondence should be addressed.
Cells 2023, 12(3), 355; https://doi.org/10.3390/cells12030355
Submission received: 13 October 2022 / Revised: 2 November 2022 / Accepted: 21 November 2022 / Published: 18 January 2023
(This article belongs to the Special Issue Neuropsychiatric Lupus from Bench to Bedside)
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Abstract

:
Central nervous system (CNS) involvement in childhood-onset systemic lupus erythematosus (cSLE) occurs in more than 50% of patients. Structural magnetic resonance imaging (MRI) has identified global cerebral atrophy, as well as the involvement of the corpus callosum and hippocampus, which is associated with cognitive impairment. In this cross-sectional study we included 71 cSLE (mean age 24.7 years (SD 4.6) patients and a disease duration of 11.8 years (SD 4.8) and two control groups: (1) 49 adult-onset SLE (aSLE) patients (mean age of 33.2 (SD 3.7) with a similar disease duration and (2) 58 healthy control patients (mean age of 29.9 years (DP 4.1)) of a similar age. All of the individuals were evaluated on the day of the MRI scan (Phillips 3T scanner). We reviewed medical charts to obtain the clinical and immunological features and treatment history of the SLE patients. Segmentation of the corpus callosum was performed through an automated segmentation method. Patients with cSLE had a similar mid-sagittal area of the corpus callosum in comparison to the aSLE patients. When compared to the control groups, cSLE and aSLE had a significant reduction in the mid-sagittal area in the posterior region of the corpus callosum. We observed significantly lower FA values and significantly higher MD, RD, and AD values in the total area of the corpus callosum and in the parcels B, C, D, and E in cSLE patients when compared to the aSLE patients. Low complement, the presence of anticardiolipin antibodies, and cognitive impairment were associated with microstructural changes. In conclusion, we observed greater microstructural changes in the corpus callosum in adults with cSLE when compared to those with aSLE. Longitudinal studies are necessary to follow these changes, however they may explain the worse cognitive function and disability observed in adults with cSLE when compared to aSLE.

1. Introduction

Childhood-onset systemic lupus erythematosus (cSLE) is a complex, inflammatory autoimmune disease characterized by the production of autoantibodies, with the involvement of several organs and tissues [1,2,3,4]. Compared to adult-onset (aSLE), cSLE patients generally have a more severe disease and more frequent neuropsychiatric manifestations [5,6]. Long-term disability has been described, including cognitive impairment in different domains, leading to difficulties in academic performance and employment [7,8,9].
Magnetic resonance imaging (MRI) has shown regional and diffuse brain atrophy, white matter (WM) lesions, and functional changes in SLE, independently of the age of disease onset. Disease activity, disease duration, autoantibodies, corticosteroid use, and CNS manifestations are associated with structural brain abnormalities [3,10,11].
The corpus callosum is the main axonal commissure formed by dense myelinated fibers, which interconnect the homologous territories of the two cerebral hemispheres [12,13,14,15]. Several pathologies including those of inflammatory, vascular, and autoimmune origin may affect the corpus callosum in a peculiar way, either in isolation or in association with other brain alterations. Recent studies have shown atrophy in a significant number of patients with cSLE, which is associated with low complement levels and an acute confusional state [16].
Diffusion tensor is an advanced MRI technique that is based on measurements of water diffusion in cellular compartments [3,17]. Water diffusion depends on orientation, spacing, and structural barriers, such as myelin and cellular membranes, in the brain tissue [18,19]. By analyzing the structure and integrity, DTI shows microstructural abnormalities that are not visualized by traditional MRI techniques. In SLE, DTI has shown lower fractional anisotropy (FA) and increased mean diffusivity (MD) in different brain structures, especially in SLE patients with neuropsychiatric manifestations [19]. However, the majority of studies focused on identifying microstructural abnormalities in SLE with neuropsychiatric manifestations when compared to SLE patients without these manifestations or healthy controls. More recently, our group has shown that DTI-based scalar maps allow parcellation of the corpus callosum in five different areas, corresponding to fibers received from different brain regions [20]. Since different cognitive functions are impaired in SLE, we hypothesized that the corpus callosum is not uniformly affected.
Therefore, the objective of this study was to analyze the microstructural abnormalities of the corpus callosum in adults with SLE with disease onset during childhood (cSLE) and during adulthood (aSLE) with similar disease durations. In addition, we analyzed the different areas of the corpus callosum to determine which areas are most affected by clinical, immunological, and treatment features and different durations of the disease.

2. Materials and Methods

2.1. Patients

We included consecutive SLE patients followed regularly at the pediatric and adult rheumatology outpatient clinic of the University of Campinas between October 2017 and December 2019. For inclusion in the present study, SLE patients had to: (1) fulfill the American College of Rheumatology (ACR) criteria for SLE [21,22] and the ACR/European League Against Rheumatism (EULAR) [23]; (2) be under 18 years of age at disease onset for the cSLE group and over the age of 18 for aSLE group, and (3) have a minimum follow-up period of 6 months. Patients that were unable to undergo MRI (claustrophobia (3 patient) and braces (10 patients)) and patients with CNS manifestations unrelated to SLE were excluded.

2.2. Controls

We invited the friends (without a personal or family history of autoimmune disease) of the SLE patients to be part of the control groups to assure similar sex and socio-demographic backgrounds. Control subjects with a prior history of a psychiatric disorder diagnosed by a medical practitioner, or those who had received psychotropic medications, were excluded (3 controls).

2.3. Ethics Approval

This study was approved by the local ethics committee (CAAE 72889717.8.0000.5404). All patients and controls (or legal guardians of minors) provided written informed consent.

2.4. Clinical Evaluation

A computer database was used to store the clinical and serologic characteristics of each patient. The following variables were included in this database: onset of the disease (age at which the first symptoms clearly attributable to SLE occurred); age at diagnosis (age at which the patients fulfilled 4 or more of the 1982 revised criteria for the classification of SLE [21]); and follow-up time (time from disease onset until date of MRI). Clinical and laboratory manifestations retrieved from medical charts were defined according to current guidelines [21,24,25].

2.5. Disease Activity/Cumulative Damage Evaluation

The Systemic Lupus Erythematous Disease Activity Index (SLEDAI) was used to define disease activity and a score of ≥3 was considered to represent active disease [26,27]. The systemic Lupus International Collaborating Clinics/ACR Damage Index (SDI) was used to define damage [28].

2.6. Neuropsychiatric (NP) Evaluation

All patients and controls underwent complete NP evaluation [29,30]. NP manifestations were categorized as being present at disease onset (≤6 months of disease) or during the follow-up period (>6 months of disease).
The instrument used to screen for cognitive impairment was the Montreal Cognitive Assessment (MoCa) in the translated version and validated for the Brazilian population [31]. The MoCa instrument evaluates eight domains, with scores from 0 to 30 points, where the higher the score, the better the cognitive function of the individual. The score is divided into short-term memory (5 points), visuospatial skills (4 points), executive function (4 points), attention, concentration and working memory (6 points), language (5 points), orienteering time (3 points), and space orientation (3 points) [31]. Beck Depression Inventory (BDI) [32,33] and Beck Anxiety Inventory (BAI) were applied to each individual [34].

2.7. MRI Acquisition

All of the subjects underwent MRI examination with a Philips 3T scanner. Sagittal T1-weighted images (3-dimensional acquisitions obtained in the sagittal T1-weighted gradient echo plane with 1 mm thickness, an angle of excitation of 35, a repetition time of 22 ms, a time to echo of 9 ms, a matrix of 240 × 240 pixels, a field of view of 230 × 250 cm, 1 × 1 pixel).
Diffusion weighted images (DWI) were acquired in the same scan with a 1 × 1 mm spatial resolution and 2 mm slice thickness in the axial plane, along 32 directions (b-value = 1000 s/mm2 repetition time 8.5 s, time to echo 61 ms), dimensions of 256 × 256 × 70, and interpolated by the MRI machine from an image with the dimensions 128 × 128 × 70. All data, initially in DICOM format, were anonymized using the GDCM toolbox [35] and processed using the FSL tool [36] for correction of eddy currents, registration of the DWI volumes, calculation of the diffusion tensor image (DTI), its eigenvalues and eigenvectors and conversion to the NIFTI format. Moreover, using the eigenvalues and eigenvectors we obtained scalar maps of the fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). The mid-callosal plane, a better alternative to the midsagittal plane when dealing with corpus callosum studies, was computed directly from the DTI data using a previously proposed method that considers the highly organized fibers within the corpus callosum structure [18].
The corpus callosum was then segmented automatically using a method proposed by Rittner based on the watershed transform, a widespread technique often used for medical imaging segmentation [37]. Furthermore, the corpus callosum was parcellated (divided into parts accordingly with connected cortical regions) using the Cover scheme, a data-driven method based on DTI data within the corpus callosum segmented area [20]. It works by finding the corpus callosum medial line and using the k-means algorithm to cluster two hundred points in this line into five groups based on their FA values. The central point of each cluster is then used as seed for the watershed transform. Both mentioned segmentation and parcellation steps were performed using an open-source software called inCCsight [38], that also performed an automated quality check based on shape analysis [39].
Using the scalar maps, we obtained the average and standard deviation of FA, MD, AD, and RD for the segmented corpus callosum and each parcel (Figure 1A,B).

2.8. Statistics

Descriptive statistics were performed using continuous variables presented as the mean and standard deviation. Categorical variables were compared by the chi-square test. For the comparison of DTI-based scalar maps between groups and categorical variables, an ANOVA test with a p value < 0.05 was used. The correlations between DTI parameters and continuous variables were explored by Spearman’s rank correlation. Bonferroni corrections for multiple comparisons was completed when indicated. Statistical analysis was performed with IBM SPSS statistics version 20, Armonk, NY: IBM Corp.

3. Results

We included 71 cSLE patients (mean age = 24.6 (4.6); 64 (90.1%) women) and 49 aSLE patients (mean age = 33.2 (3.72); 46 (89.8%) women) matched for sex and disease duration (Table 1). Fifty-eight controls (29.9 (3.6); 42 (72.4%) women) were included. Clinical characteristics and immunological and treatment features are shown in Table 1.
We observed similar mid-sagittal corpus callosum areas in the cSLE and aSLE patients (Figure 2A). When we compared the aSLE patients and the controls and the cSLE patients and the controls, we observed a reduced mid-sagittal area in the posterior aspects of the corpus callosum (Figure 2B,C).
We observed significantly lower FA values in the total corpus callosum when comparing the aSLE patients to the controls, as well as higher MD and RD values (Table 2). No differences between the cSLE patients and the controls was observed.
We observed significantly lower FA values in the total corpus callosum area in cSLE patients (mean = 0.63603; SD ± 0.0665) when compared to the aSLE patints (mean = 0.677624; SD ± 0.08920; p = 0.002). When comparing cSLE and aSLE patients, we also observed significantly higher MD (mean = 0.00096; SD ± 0.00014 vs. mean = 0.00096; SD ± 0.00014; p = 0.002), RD (mean = 0.00057; SD ± 0.00015 vs. when compared with the aSLE group, mean = 0.00048; SD ± 0.00019; p = 0.002), and AD (mean = 0.0017; SD ± 0.00014 vs. 0.0016, SD ± 0.00011; p = 0.024) in cSLE (Table 2).
When we analyzed the different areas of the corpus callosum, we observed significant differences in the parcels B, C, D, and E. In all these parcels, we observed significantly lower FA and significantly higher MD, RD, and AD values in the cSLE patients when compared to the aSLE patients (Table 2).
We observed an association of lower C4 and FA, MD, RD, and D at the entire corpus callosum and with all four DTI-based scalar maps in parcels A, B, and C (p < 0.05). In parcel D, only associations between C4 and FA, and MD and RD (p < 0.05) were observed. We observed an association between DTI-based scalar maps and the presence of anticardiolipin and antiphospholipid antibodies (Table 3). No other association with immunological variables was observed.
We observed a correlation between DTI-based scalar maps and the age at disease onset (r = 0.71; p < 0.001) and disease duration (r = 0.481; p < 0.001). No association with current corticosteroid dose, hydroxychloroquine use, or antiplatelet use was observed. We observed a correlation of MoCA scores and MD values in the entire corpus callosum (r = −0.207; p = 0.03), as well as in all different parcels (p < 0.05). No other associations between DTI-based scalar maps and clinical and neuropsychiatric manifestations were observed (p > 0.05).

4. Discussion

Our study is the first to show that the corpus callosum is not uniformly affected in SLE and that microstructural changes occur predominately in the posterior region and are influenced by age of disease-onset.
In this study analyzing the mid-sagittal area of the corpus callosum, we observed significant differences in the posterior region of the corpus callosum when comparing the area between the cSLE patients and the controls and between the aSLE patients and the controls. However, no difference was observed when comparing the cSLE patients and aSLE patients. Since previous studies have analyzed the volumetric measurements of the entire corpus callosum, no data are available in the literature for comparison. Previous studies on DTI have analyzed different brain areas and observed a significant reduction in DTI-based scalar maps in SLE patients with and without neuropsychiatric manifestations and controls [40,41,42,43,44,45,46,47,48,49,50,51,52].
Our segmentation is based on a careful automated model that uses DTI parameters to differentiate corpus callosum from other structures [20]. The segmentation of the different parcels of the corpus callosum is also based on DTI parameters and has been shown to correlate with fibers received from different brain areas [12,13,14,15].
When analyzing different parcels, we observed more microstructural changes in the parcels B to D, with no changes in parcel A.
Children with SLE have a more severe disease and more disability in adulthood when compared to patients whose disease onset starts in in adulthood. The exact mechanism is not known, but inflammation and the presence of autoantibodies in the developing brain have been postulated as possible causes [3,10,11]. In this study, we have shown that cSLE patients have more microstructural damage than SLE patients with disease onset during adulthood, despite a similar disease duration. An association of DTI-based scalar maps and disease duration has been observed, indicating greater changes in longer disease duration. No significant differences in the cumulative clinical manifestations have been observed between the groups, except for a higher frequency of anxiety in aSLE patients. We did not observe any association of DTI-based scalar maps with clinical or neuropsychiatric manifestations (except cognitive impairment).
aSLE patients also had more frequently positive anti-Sm and anti-Ro antibodies. The presence of these antibodies was not associated with differences in the DTI-based scalar maps. We observed that low C4 values and the presence of anticardiolipin antibodies were associated with changes in DTI-based scalar maps. SLE patients with antiphospholipid syndrome also had more changes in DTI-based scalar maps.
No association between DTI-based scalar maps and corticosteroid use was observed. In volumetric studies, corticosteroids have been associated with cerebral atrophy [10,16,53,54].
The use of neuroimaging has significantly improved the understanding of the brain’s involvement in the disease, identifying structural and functional changes in both patients with neuropsychiatric manifestations and subclinical involvement [8,9]. However, a significant proportion of SLE patients present with normal structural MRI or unspecific findings, making care in clinical practice challenging [55,56]. The use of advanced MRI techniques and automatic segmentations allow the identification of microstructural changes. Longitudinal studies are necessary to determine if these abnormalities are risk factors for corpus callosum and brain atrophy or if they are reversible with disease remission.
Cognitive deficit is well described in the literature when related to SLE, even when neuropsychiatric manifestations are not present [57]. Cognitive deficit in SLE patients is still a challenge and several risk factors, such as neuronal autoantibodies, antiphospholipid antibodies, neuropsychiatric manifestations, and corticosteroid use have been reported as risk factors [7]. Several MRI studies, including structural and functional MRI, have identified the brain areas associated with the presence of cognitive impairment [9,10,19]. Since executive function is one of the cognitive domains more often involved in SLE, we have hypothesized that the anterior parcel of the corpus callosum would have greater microstructural abnormalities [51]. We observed a correlation of MoCA scores only with MD values in the entire corpus callosum and different parcels. No other association was observed.
This study has limitations. We were not able to retrieve the total corticosteroid dose of the patients. Longitudinal studies are necessary to determine the clinical significance of the non-uniform involvement of the corpus callosum and the use of DTI-scalar maps as biomarkers for corpus callosum atrophy.

5. Conclusions

We observed that the corpus callosum of adults with cSLE has more microstructural changes when compared to aSLE patients with a similar disease duration. These changes were more frequently observed in the mid and posterior area of the corpus callosum. Low complement, anticardiolipin antibodies, and cognitive impairment were associated with microstructural changes. Longitudinal studies are necessary to determine if these changes will progress to structural abnormalities or if they are reversible.

Author Contributions

Conceptualization, R.B.F., F.C., P.T.F., L.R. and S.A.; data curation, P.R.J., R.B.F. and S.A.; formal analysis, P.R.J., T.C., G.R.P., L.R. and S.A.; funding acquisition, F.C., L.R. and S.A.; investigation, P.R.J., R.M., P.T.F. and L.T.L.C.; methodology, T.C., G.R.P., C.H.C., R.M., F.C., L.R. and S.A.; project administration, C.H.C., R.B.F. and S.A.; resources, F.C., L.R. and S.A.; software, T.C. and L.R.; supervision, L.R. and S.A.; validation, C.H.C.; visualization, T.C., G.R.P. and L.R.; writing—original draft, P.R.J., T.C., L.R. and S.A.; writing—review and editing, P.R.J., T.C., G.R.P., C.H.C., R.B.F., R.M., P.T.F., L.T.L.C. and S.A. All authors have read and agreed to the published version of the manuscript.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Financial Code 001; Fundação Apoio À Pesquisa Estado São Paulo-Brasil (FAPESP—2019/06632-5, 13/07559-3). Conselho Nacional Pesquisa Desenvolvimento-Brasil CNPq (306723/2019-0).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of University of Campinas (CAAE 72889717.8.0000.5404).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon specific request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lateef, A.; Petri, M. Unmet medical needs in systemic lupus erythematosus. Arthritis Res. Ther. 2012, 14 (Suppl. 4), 4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Jarukitsopa, S.; Hoganson, D.D.; Crowson, C.S.; Sokumbi, O.; Davis, M.D.; Michet, C.J.; Matteson, E.L.; Kremers, H.M.; Chowdhary, V.R. Epidemiology of systemic lupus erythematosus and cutaneous lupus in a predominantly white population in the United States. Arthritis Care Res. 2015, 67, 817–828. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Postal, M.; Lapa, A.T.; Reis, F.; Rittner, L.; Appenzeller, S. Magnetic resonance imaging in neuropsychiatric systemic lupus erythematosus: Current state of the art and novel approaches. Lupus 2017, 26, 517–521. [Google Scholar] [CrossRef] [PubMed]
  4. Fernandes, H.; Brito, I. Juvenile Systemic Lupus Erythematosus: Neuropsychiatric manifestations. Acta Reumatol Port. 2012, 37, 117–125. [Google Scholar] [PubMed]
  5. Silva, C.A.; Avčin, T.; Brunner, H.I. Taxonomy for systemic lupus erythematosus with onset before adulthood. Arthritis Care Res. 2012, 64, 1787–1793. [Google Scholar] [CrossRef] [Green Version]
  6. Tucker, L.; Uribe, A.; Fernández, M.; Vilá, L.; McGwin, G.; Apte, M.; Fessler, B.; Bastian, H.; Reveille, J.; Alarcón, G. Adolescent onset of lupus results in more aggressive disease and worse outcomes: Results of a nested matched case-control study within LUMINA, a multiethnic US cohort (LUMINA LVII). Lupus 2008, 17, 314–322. [Google Scholar] [CrossRef] [Green Version]
  7. Kozora, E.; Filley, C.M. Cognitive dysfunction and white matter abnormalities in systemic lupus erythematosus. J. Int. Neuropsychol. Soc. 2011, 17, 385–392. [Google Scholar] [CrossRef]
  8. Frittoli, R.B.; de Oliveira, P.K.; Bellini, B.S.; Marini, R.; Fernandes, P.T.; Appenzeller, S. Association between academic performance and cognitive dysfunction in patients with juvenile systemic lupus erythematosus. Revista Brasileira Reumatologia 2016, 56, 252–257. [Google Scholar] [CrossRef] [Green Version]
  9. Klein-Gitelman, M.; Brunner, H.I. The impact and implications of neuropsychiatric systemic lupus erythematosus in adolescents. Curr. Rheumatol. Rep. 2009, 11, 212–217. [Google Scholar] [CrossRef]
  10. Appenzeller, S.; Bonilha, L.; Rio, P.A.; Min, L.L.; Costallat, L.T.; Cendes, F. Longitudinal analysis of gray and white matter loss in patients with systemic lupus erythematosus. NeuroImage 2007, 34, 694–701. [Google Scholar] [CrossRef]
  11. Petri, M.; Naqibuddin, M.; Carson, K.A.; Wallace, D.J.; Weisman, M.H.; Holliday, S.L.; Sampedro, M.; Narayana, S.; Fox, P.; Franklin, C.; et al. Brain magnetic resonance imaging in newly diagnosed systemic lupus erythematosus. J. Rheumatol. 2008, 35, 2348–2354. [Google Scholar] [CrossRef]
  12. Park, J.-S.; Yoon, U.; Kwak, K.-C.; Seo, S.W.; Kim, S.I.; Na, D.L.; Lee, J.-M. The relationships between extent and microstructural properties of the midsagittal corpus callosum in human brain. NeuroImage 2011, 56, 174–184. [Google Scholar] [CrossRef]
  13. Bourekas, E.C.; Varakis, K.; Bruns, D.; Christoforidis, G.A.; Baujan, M.; Slone, H.W.; Kehagias, D. Lesions of the corpus callosum: MR imaging and differential considerations in adults and children. AJR Am. J. Roentgenol. 2002, 179, 251–257. [Google Scholar] [CrossRef]
  14. Kazi, A.Z.; Joshi, P.C.; Kelkar, A.B.; Mahajan, M.S.; Ghawate, A.S. MRI evaluation of pathologies affecting the corpus callosum: A pictorial essay. Indian J. Radiol. Imaging. 2013, 23, 321–332. [Google Scholar] [CrossRef]
  15. Engelhardt, E.; Moreira, D.M. A substância branca cerebral: Localização dos principais feixes com anisotropia fracionada direcional. Rev. Bras. Neurol. 2008, 44, 19–34. [Google Scholar]
  16. Lapa, A.T.; Postal, M.; Sinicato, N.A.; Ferreira, W.G.; Bellini, B.S.; Fernandes, P.T.; Rittner, L.; Marini, R.; Cendes, F.; Appenzeller, S. Reduction of cerebral and corpus callosum volumes in childhood-onset systemic lupus erythematosus: A volumetric magnetic resonance imaging analysis. Arthritis Rheumatol. 2016, 68, 2193–2199. [Google Scholar] [CrossRef]
  17. Gyori, N.G.; Clark, C.A.; Alexander, D.C.; Kaden, E. On the potential for mapping apparent neural soma density via a clinically viable diffusion MRI protocol. Neuroimage 2021, 239, 118303. [Google Scholar] [CrossRef]
  18. Pinheiro, G.R.; Cover, G.S.; Bento, M.P.; Rittner, L. Automatic callosal fiber convergence plane computation through DTI-based divergence map. In Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; SPIE: Bellingham, WA, USA, 2018; Volume 10578, pp. 256–264. [Google Scholar] [CrossRef]
  19. Costallat, B.L.; Ferreira, D.M.; Lapa, A.T.; Rittner, L.; Costallat, L.T.L.; Appenzeller, S. Brain diffusion tensor MRI in systematic lupus erythematosus: A systematic review. Autoimmun. Rev. 2018, 17, 36–43. [Google Scholar] [CrossRef]
  20. Cover, G.; Pereira, M.E.C.; Bento, M.P.; Appenzeler, S.; Rittner, L. Data-Driven Corpus Callosum Parcellation Method through Diffusion Tensor Imaging. IEEE Access 2017, 5, 22421–22432. [Google Scholar] [CrossRef]
  21. Tan, E.M.; Cohen, A.S.; Fries, J.F.; Masi, A.T.; Mcshane, D.J.; Rothfield, N.F.; Schaller, J.G.; Talal, N.; Winchester, R.J. The 1982 revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 1982, 25, 1271–1277. [Google Scholar] [CrossRef]
  22. Borba, F.E.; Latorre, C.L.; Brenol, T.C.J.; Kayser, C.; Silva, A.N.; Zimmermann, F.A.; de Pádua, P.M.; Costallat, L.T.L.; Bonfá, E.; Sato, E.I.; et al. Consenso de Lúpus Eritematoso Sistêmico. Revista Brasileira Reumatologia 2008, 48, 196–207. [Google Scholar] [CrossRef] [Green Version]
  23. Aringer, M.; Costenbader, K.; Daikh, D.; Brinks, R.; Mosca, M.; Ramsey-Goldman, R.; Smolen, J.S.; Wofsy, D.; Boumpas, D.T.; Kamen, D.L.; et al. 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus. Ann. Rheum. Dis. 2019, 78, 1151–1159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Harris, E.N.; Gharavi, A.E.; Patel, S.P.; Hughes, G.R. Evaluation of the anticardiolipin antibody test: Report of an international workshop held 4 April 1986. Clin. Exp. Immunol. 1987, 68, 215–222. [Google Scholar] [PubMed]
  25. Brandt, J.T.; Triplett, D.A.; Alving, B.; Scharrer, I. On behalf of the Subcommittee on Lupus Anticoagulant/Antiphospholipid Antibody of the Scientific and Standardisation Committee of the ISTH. Criteria for the diagnosis of lupus anticoagulants: An update. Thromb. Haemost. 1995, 74, 1185–1190. [Google Scholar] [CrossRef] [PubMed]
  26. Bombardier, C.; Gladman, D.D.; Urowitz, M.B.; Caron, D.; Chang, C.H. The Committee on Prognosis Studies in SLE. Derivation of the SLEDAI: A disease activity index for lupus patients. Arthritis Rheum. 1992, 35, 630–640. [Google Scholar] [CrossRef]
  27. Yee, C.-S.; Farewell, V.; Isenberg, D.; Griffiths, B.; Teh, L.-S.; Bruce, I.N.; Ahmad, Y.; Rahman, A.; Prabu, A.; Akil, M.; et al. The use of Systemic Lupus Erythematosus Disease Activity Index-2000 to define active disease and minimal clinically meaningful change based on data from a large cohort of systemic lupus erythematosus patients. Rheumatology 2011, 50, 982–988. [Google Scholar] [CrossRef] [Green Version]
  28. Gladman, D.D.; Urowitz, M.B.; Goldsmith, C.H.; Fortin, P.; Ginzler, E.; Gordon, C.; Hanly, J.G.; Isenberg, D.A.; Kalunian, K.; Nived, O.; et al. The reliability of the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index in patients with systemic lupus erythematosus. Arthritis Rheum. 1997, 40, 809–813. [Google Scholar] [CrossRef]
  29. ACR Ad Hoc Committee on Neuropsychiatric Lupus Nomenclature. The American College of Rheumatology nomenclature and case definitions for neuropsychiatric lupus syndromes. Arthritis Rheum. 1999, 42, 599–608. [Google Scholar] [CrossRef]
  30. Ross, G.S.; Zelko, F.; Klein-Gitelman, M.; Levy, D.M.; Muscal, E.; Schanberg, L.E.; Anthony, K.; Brunner, H.I. A proposed framework to standardize the neurocognitive assessment of patients with pediatric systemic lupus erythematosus. Arthritis Care Res (Hoboken) 2010, 62, 1029–1033. [Google Scholar] [CrossRef] [Green Version]
  31. Memória, C.M.; Yassuda, M.S.; Nakano, E.Y.; Forlenza, O.V. Brief screening for mild cognitive impairment: Validation of the Brazilian version of the Montreal cognitive assessment. Int. J. Geriatr. Psychiatry 2013, 28, 34–40. [Google Scholar] [CrossRef]
  32. Beck, A.T.; Ward, C.H.; Mendelson, M.; Mock, J.; Erbaugh, J. An inventory for measuring depression. Arch. Gen. Psychiatry 1961, 4, 561–571. [Google Scholar] [CrossRef] [Green Version]
  33. Gomes-Oliveira, M.H.; Gorenstein, C.; Lotufo Neto, F.; Andrade, L.H.; Wang, Y.P. Validation of the Brazilian Portuguese version of the Beck Depression Inventory-II in a community sample. Braz. J. Psychiatry. 2012, 34, 389–394. [Google Scholar] [CrossRef]
  34. Quintão, S.; Delgado, A.R.; Prieto, G. Validity study of the Beck Anxiety Inventory (Portuguese version) by the Rasch Rating Scale model. Psicol. Reflex Crit. 2013, 26, 305–310. [Google Scholar] [CrossRef] [Green Version]
  35. Aryanto, K.Y.E.; Oudkerk, M.; van Ooijen, P.M.A. Free DICOM de-identification tools in clinical research: Functioning and safety of patient privacy. Eur. Radiol. 2015, 25, 3685–3695. [Google Scholar] [CrossRef] [Green Version]
  36. Jenkinson, M.; Beckmann, C.F.; Behrens, T.E.; Woolrich, M.W.; Smith, S.M. FSL. Neuroimage 2012, 62, 782–790. [Google Scholar] [CrossRef] [Green Version]
  37. Rittner, L.; Freitas, P.; Appenzeller, A.; Lotufo, R. Automatic DTI-based parcellation of the corpus callosum through the watershed transform. Braz. J. Biomed. Eng. 2014, 30, 132–143. [Google Scholar] [CrossRef] [Green Version]
  38. Caldeira, T.; Julio, P.R.; Appenzeller, S.; Rittner, L. inCCsight: A software for exploration and visualization of DT-MRI data of the Corpus Callosum. Comput. Graph. 2021, 99, 259–271. [Google Scholar] [CrossRef]
  39. Herrera, W.G.; Pereira, M.; Bento, M.; Lapa, A.T.; Appenzeller, S.; Rittner, L. A framework for quality control of corpus callosum segmentation in large-scale studies. J. Neurosci. Methods 2020, 334, 108593. [Google Scholar] [CrossRef]
  40. Jones, J.T.; DiFrancesco, M.; Zaal, A.I.; Klein-Gitelman, M.S.; Gitelman, D.; Ying, J.; Brunner, H.I. Childhood-onset lupus with clinical neurocognitive dysfunction shows lower streamline density and pairwise connectivity on diffusion tensor imaging. Lupus 2015, 24, 1081–1086. [Google Scholar] [CrossRef] [Green Version]
  41. Zhao, L.; Tan, X.; Wang, J.; Han, K.; Niu, M.; Xu, J.; Liu, X.; Zhao, X.; Zhong, M.; Huang, Q.; et al. Brain white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus. Brain Imaging Behav. 2018, 12, 142–155. [Google Scholar] [CrossRef]
  42. Wiseman, S.J.; Bastin, M.E.; Hamilton, I.F.; Hunt, D.; Ritchie, S.J.; Amft, E.N.; Thomson, S.; Belch, J.F.; Ralston, S.H.; Wardlaw, J.M. Fatigue and cognitive function in systemic lupus erythematosus: Associations with white matter microstructural damage. A diffusion tensor MRI study and meta-analysis. Lupus 2017, 26, 588–597. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Cesar, B.; Dwyer, M.; Shucard, J.; Polak, P.; Bergsland, N.; Benedict, R.; Weinstock-Guttman, B.; Shucard, D.; Zivadinov, R. Cognitive and White Matter Tract Differences in MS and Diffuse Neuropsychiatric Systemic Lupus Erythematosus. AJNR Am. J. Neuroradiol. 2015, 36, 1874–1883. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Ercan, E.; Ingo, C.; Tritanon, O.; Magro-Checa, C.; Smith, A.; Smith, S.; Huizinga, T.; van Buchem, M.A.; Ronen, I. A multimodal MRI approach to identify and characterize microstructural brain changes in neuropsychiatric systemic lupus erythematosus. Neuroimage Clin. 2015, 8, 337–344. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Lee, S.-P.; Wu, C.-S.; Cheng, J.-Z.; Chen, C.-M.; Chen, Y.-C.; Chou, M.-C. Automatic Segmentation of the Corpus Callosum Using a Cell-Competition Algorithm: Diffusion Tensor Imaging-Based Evaluation of Callosal Atrophy and Tissue Alterations in Patients With Systemic Lupus Erythematosus. J. Comput. Assist. Tomogr. 2015, 39, 781–786. [Google Scholar] [CrossRef] [PubMed]
  46. Schmidt-Wilcke, T.; Cagnoli, P.; Wang, P.; Schultz, T.; Lotz, A.; Mccune, W.J.; Sundgren, P.C. Diminished white matter integrity in patients with systemic lupus erythematosus. Neuroimage Clin. 2014, 5, 291–297. [Google Scholar] [CrossRef]
  47. Zimny, A.; Szmyrka-Kaczmarek, M.; Szewczyk, P.; Bladowska, J.; Pokryszko-Dragan, A.; Gruszka, E.; Wiland, P.; Sasiadek, M. In vivo evaluation of brain damage in the course of systemic lupus erythematosus using magnetic resonance spectroscopy, perfusion-weighted and diffusiontensor imaging. Lupus 2014, 23, 10–19. [Google Scholar] [CrossRef]
  48. Hughes, M.; Sundgren, P.C.; Fan, X.; Foerster, B.; Nan, B.; Welsh, R.C.; Williamson, J.A.; Attwood, J.; Maly, P.V.; Chenevert, T.L.; et al. Diffusion tensor imaging in patients with acute onset of neuropsychiatric systemic lupus erythematosus: A prospective study of apparent diffusion coefficient, fractional anisotropy values, and eigenvalues in different regions of the brain. Acta Radiol. 2007, 48, 213–222. [Google Scholar] [CrossRef]
  49. Emmer, B.J.; Veer, I.M.; Steup-Beekman, G.M.; Huizinga, T.W.J.; van der Grond, J.; van Buchem, M.A. Tract-based spatial statistics on diffusion tensor imaging in systemic lupus erythematosus reveals localized involvement of white matter tracts. Arthritis Rheum. 2010, 62, 3716–3721. [Google Scholar] [CrossRef]
  50. Ercan, E.; Checa, C.M.; Valabregue, R.; Branzoli, F.; Wood, E.; Steup-Beekman, G.M.; Webb, A.G.; Huizinga, T.W.J.; Van Buchem, M.A.; Ronen, I. Glial and axonal changes in systemic lupus erythematosus measured with diffusion of intracellular metabolites. Brain 2016, 139, 1447–1457. [Google Scholar] [CrossRef] [Green Version]
  51. Shapira-Lichter, I.; Weinstein, M.; Lustgarten, N.; Ash, E.; Litinsky, I.; Aloush, V.; Anouk, M.; Caspi, D.; Hendler, T.; Paran, D. Impaired diffusion tensor imaging findings in the corpus callosum and cingulum may underlie impaired learning and memory abilities in systemic lupus erythematosus. Lupus 2016, 25, 1200–1208. [Google Scholar] [CrossRef]
  52. Shastri, R.; Shah, G.; Wang, P.; Cagnoli, P.; Schmidt-Wilcke, T.; McCune, J.; Harris, R.; Sundgren, P. MR Diffusion Tractography to Identify and Characterize Microstructural White Matter Tract Changes in Systemic Lupus Erythematosus Patients. Acad. Radiol. 2016, 23, 1431–1440. [Google Scholar] [CrossRef]
  53. Appenzeller, S.; Carnevalle, A.D.; Li, L.M.; Costallat, L.T.; Cendes, F. Hippocampal atrophy in systemic lupus erythematosus. Ann. Rheum. Dis. 2006, 65, 1585–1589. [Google Scholar] [CrossRef] [Green Version]
  54. Appenzeller, S.; Rondina, J.M.; Li, L.M.; Costallat, L.T.; Cendes, F. Cerebral and corpus callosum atrophy in systemic lupus erythematosus. Arthritis Rheum. 2005, 52, 2783–2789. [Google Scholar] [CrossRef]
  55. Jeltsch-David, H.; Muller, S. Neuropsychiatric systemic lupus erythematosus and cognitive dysfunction: The MRL-lpr mouse strain as a model. Autoimmun. Rev. 2014, 13, 963–973. [Google Scholar] [CrossRef] [Green Version]
  56. Luyendijk, J.; Steens, S.C.A.; Ouwendijk, W.J.N.; Steup-Beekman, G.M.; Bollen, E.L.E.M.; van der Grond, J.; Huizinga, T.W.J.; Emmer, B.J.; van Buchem, M.A. Neuropsychiatric systemic lupus erythematosus: Lessons learned from magnetic resonance imaging. Arthritis Rheum. 2011, 63, 722–732. [Google Scholar] [CrossRef]
  57. Barraclough, M.; Elliott, R.; McKie, S.; Parker, B.; Bruce, I.N. Cognitive dysfunction and functional magnetic resonance imaging in systemic lupus erythematosus. Lupus 2015, 24, 1239–1247. [Google Scholar] [CrossRef]
Cells 12 00355 g001 550
Figure 1. Automatic segmentation of the corpus callosum and parcellation. (A) Area of the corpus callosum analyzed in the study. (B) Description of the parcels: (A) Rostrum; (B) Genu; (C) Body; (D) Isthmus; (E) Splenium.
Figure 1. Automatic segmentation of the corpus callosum and parcellation. (A) Area of the corpus callosum analyzed in the study. (B) Description of the parcels: (A) Rostrum; (B) Genu; (C) Body; (D) Isthmus; (E) Splenium.
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Figure 2. Comparison of mid-sagittal area of corpus callosum between the groups, showing a reduced area in the posterior region of the corpus callosum when comparing the controls and cSLE (C), the controls and aSLE (B), and similar areas in cSLE and aSLE (A).
Figure 2. Comparison of mid-sagittal area of corpus callosum between the groups, showing a reduced area in the posterior region of the corpus callosum when comparing the controls and cSLE (C), the controls and aSLE (B), and similar areas in cSLE and aSLE (A).
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Table
Table 1. Demographic data, laboratory findings, neuropsychiatric manifestations, and treatment in cSLE, aSLE, and the controls.
Table 1. Demographic data, laboratory findings, neuropsychiatric manifestations, and treatment in cSLE, aSLE, and the controls.
Demographic DatacSLE
N = 71		aSLE
N = 49		Controls
N = 58
Sex - female64 (90.1%)44 (89.8%)42 (72.4%) *
Current age (years)24.7 (SD ± 4.6)33.2 (SD ± 3.7) *29.9 (SD ± 4.1)
Disease duration (years)11.8 (SD ± 4.8)11.3 (SD ± 4.05)
Clinical Data
Malar rash38 (53.5%)22 (44.9%)
Discoid injury03 (4.2%)05 (7.0%)
Photosensitivity24 (33.8%) *25 (51%)
Oral injury15 (21.1%) *04 (8.2%)
Arthritis54 (76.1%)35 (71.4%)
Serositis17 (23.9%)17 (34.7%)
Nephritis35 (49.3%)27 (55.1%)
Hematological alteration50 (70.4%)35 (71.4%)
Laboratorial Data
↓C322 (31.4%)22 (45.8%)
↓C418 (25.3%)16 (32.6%)
ANA69 (97.2%)47 (96%)
dsDNA15 (21.4%)19 (38.8%)
Anti-Ro16 (23.5%)19 (42.2%)
Anti-Sm23 (33.8%)13 (28.9%)
Anti-La2 (2.8%)8 (16.3%) *
APS12 (16.9%)9 (18.8%)
Lupus anticoagulant15 (21.1%)8 (16.3%)
Anticardiolipin antibodies22 (31%)9 (18.8%)
Leukopenia36 (50.7%)29 (59.2%)
Hemolytic anemia9 (12.7%)12 (24.5%)
Thrombocytopenia26 (36.6%)14 (28.6%)
NPSLE
Overt NPSLE manifestations53 (74.6%)36 (73.5%)
Anxiety22 (31%)26 (53.1%) *
Depression18 (25.4%)14 (28.6%)
Cognitive impairment31 (43.7%)16 (32.7%)
Headache15 (21.1%)13 (26.5%)
Seizure12 (16.9%)9 (18.4%)
Treatment
Corticosteroids55 (77.5%)42 (85.7%)
Immunosuppressive49 (69%)26 (53.1%)
Azathioprine25 (35.2%)16 (32.7%)
Mycophenolate19 (26.8%)6 (12.2%)
Cyclosporine6 (8.5%)0
Methotrexate05 (10.2%)
Hydroxychloroquine50 (70.4%)36 (73.4%)
* p value < 0.05 after Bonferroni correction for multiple corrections.
Table
Table 2. DTI-based scalar maps in the entire corpus callosum and different parcels.
Table 2. DTI-based scalar maps in the entire corpus callosum and different parcels.
cSLE *aSLE *p Valor *cSLE *Controls *p Valor *aSLE *Controls *p Valor *
FA total0.63603 (±0.0665)0.67624 (±0.08920)0.0020.63603 (±0.0665)0.64599 (±0.0319)0.6470.67624 (±0.08920)0.64599 (±0.0319)0.038
MD total0.00096 (±0.00014)0.00087 (±0.00015)0.0020.00096 (±0.00014)0.00093 (±0.00008)0.5640.00087 (±0.00015)0.00093 (±0.00008)0.042
RD total0.00057 (±0.00015)0.00048 (±0.00019)0.0020.00057 (±0.00015)0.00055 (±0.0001)0.7120.00048 (±0.00019)0.00055 (±0.0001)0.026
AD total0.0017 (±0.00014)0.0016 (±0.00011)0.0240.0017 (±0.00014)0.0016 (±0.0001)0.3870.0016 (±0.00011)0.0016 (±0.0001)0.383
Parcel A
FA—A0.66445 (±0.0744)0.68242 (±0.1104)0.4380.66445 (±0.0744)0.66942 (±0.4413)0.9320.68242 (±0.1104)0.66942 (±0.4413)0.672
MD—A0.00089 (±0.0001)0.00087 (±0.00027)0.8190.00089 (±0.0001)0.00088 (±0.00007)0.9180.00087 (±0.00027)0.00088 (±0.00007)0.974
RD—A0.00050 (±0.00013)0.00047 (±0.00031)0.7370.00050 (±0.00013)0.00049 (±0.00007)0.9420.00047 (±0.00031)0.00049 (±0.00007)0.910
AD—A0.00168 (±0.00015)0.00168 (±0.00022)0.9930.00168 (±0.00015)0.00167 (±0.00016)0.8990.00168 (±0.00022)0.00167 (±0.00016)0.955
Parcel B
FA—B0.59203 (±0.10095)0.64464 (±0.10792)0.0050.59203 (±0.10095)0.60636 (±0.04704)0.6370.64464 (±0.10792)0.60636 (±0.04704)0.073
MD—B0.00093 (±0.00025)0.00081 (±0.00019)0.0050.00093 (±0.00025)0.00090 (±0.00013)0.7370.00081 (±0.00019)0.00090 (±0.00013)0.054
RD—B0.00060 (±0.00027)0.00047 (±0.00023)0.0040.00060 (±0.00027)0.00056 (±0.00022)0.7790.00047 (±0.00023)0.00056 (±0.00022)0.039
AD—B0.00158 (±0.00025)0.00148 (±0.00016)0.0350.00158 (±0.00025)0.00155 (±0.00016)0.7180.00148 (±0.00016)0.00155 (±0.00016)0.212
Parcel C
FA—C0.58574 (±0.09617)0.65456 (±0.09985)<0.0010.58574 (±0.09617)0.60965 (±0.04811)0.2510.65456 (±0.09985)0.60965 (±0.04811)0.019
MD—C0.00095 (±0.00019)0.00083 (±0.00021)0.0010.00095 (±0.00019)0.00091 (±0.00013)0.3760.00083 (±0.00021)0.00091 (±0.00013)0.078
RD—C0.00062 (±0.00020)0.00048 (±0.00023)<0.0010.00062 (±0.00020)0.00058 (±0.00012)0.4020.00048 (±0.00023)0.00058 (±0.00012)0.027
AD—C0.00161 (±0.00019)0.00153 (±0.00019)0.1260.00161 (±0.00019)0.00157 (±0.00018)0.4930.00153 (±0.00019)0.00157 (±0.00018)0.681
Parcel D
FA—D0.58105 (±0.09756)0.63433 (±0.10684)0.0060.58105 (±0.09756)0.59873 (±0.06512)0.5180.63433 (±0.10684)0.59873 (±0.06512)0.112
MD—D0.00108 (±0.00030)0.00090 (±0.00016)<0.0010.00108 (±0.00030)0.00102 (±0.00014)0.3400.00090 (±0.00016)0.00102 (±0.00014)0.020
RD—D0.00072 (±0.00032)0.00054 (±0.00020)<0.0010.00072 (±0.00032)0.00067 (±0.00015)0.4630.00054 (±0.00020)0.00067 (±0.00015)0.020
AD—D0.00179 (±0.00028)0.00164 (±0.00015)<0.0010.00179 (±0.00028)0.00173 (±0.00015)0.2210.00164 (±0.00015)0.00173 (±0.00015)0.068
Parcel E
FA—E0.68225 (±0.06640)0.72460 (±0.04514)<0.0010.68225 (±0.06640)0.69112 (±0.04791)0.6380.72460 (±0.04514)0.69112 (±0.04791)0.006
MD—E0.00099 (±0.00013)0.00090 (±0.00007)<0.0010.00099 (±0.00013)0.00096 (±0.00010)0.3410.00090 (±0.00007)0.00096 (±0.00010)0.005
RD—E0.00055 (±0.00013)0.00044 (±0.00008)<0.0010.00055 (±0.00013)0.00053 (±0.00010)0.6000.00044 (±0.00008)0.00053 (±0.00010)<0.001
AD—E0.00188 (±0.00019)0.00180 (±0.00014)0.0420.00188 (±0.00019)0.00184 (±0.00016)03090.00180 (±0.00014)0.00184 (±0.00016)0.582
* Values expressed in mm2/s. Values expressed as mean and ± standard deviation. Statistical test: ANOVA.
Table
Table 3. DTI scalar maps and association with reduced C4, the presence of anticardiolipin antibodies, and antiphospholipid syndrome. Statistical test: ANOVA.
Table 3. DTI scalar maps and association with reduced C4, the presence of anticardiolipin antibodies, and antiphospholipid syndrome. Statistical test: ANOVA.
DTI Scalar MapsReduced C4Positive Anticardiolipin
Antibodies		Antiphospholipid
Syndrome
FA total0.0160.0030.065
MD total0.0030.0190.102
RD total0.0030.0080.093
AD total0.0350.3340.259
FA—A0.0590.0000.001
MD—A0.0040.0050.007
RD—A0.0050.0010.002
AD—A0.0250.2900.196
FA—B0.0260.0170.058
MD—B0.1610.2420.283
RD—B0.0860.1220.196
AD—B0.5000.8930.681
FA—C0.0370.2220.519
MD—C0.0060.0330.208
RD—C0.0070.0510.292
AD—C0.0370.0390.146
FA—D0.0060.0360.073
MD—D0.0070.0390.123
RD—D0.0220.0280.081
AD—D0.0020.1460.393
FA—E0.5780.0580.958
MD—E0.1590.0480.461
RD—E0.1590.0340.841
AD—E0.1590.3050.213
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Julio, P.R.; Caldeira, T.; Pinheiro, G.R.; Capello, C.H.; Fritolli, R.B.; Marini, R.; Cendes, F.; Fernandes, P.T.; Costallat, L.T.L.; Rittner, L.; et al. Microstructural Changes in the Corpus Callosum in Systemic Lupus Erythematous. Cells 2023, 12, 355. https://doi.org/10.3390/cells12030355

AMA Style

Julio PR, Caldeira T, Pinheiro GR, Capello CH, Fritolli RB, Marini R, Cendes F, Fernandes PT, Costallat LTL, Rittner L, et al. Microstructural Changes in the Corpus Callosum in Systemic Lupus Erythematous. Cells. 2023; 12(3):355. https://doi.org/10.3390/cells12030355

Chicago/Turabian Style

Julio, Paulo Rogério, Thais Caldeira, Gustavo Retuci Pinheiro, Carla Helena Capello, Renan Bazuco Fritolli, Roberto Marini, Fernando Cendes, Paula Teixeira Fernandes, Lilian T. L. Costallat, Leticia Rittner, and et al. 2023. "Microstructural Changes in the Corpus Callosum in Systemic Lupus Erythematous" Cells 12, no. 3: 355. https://doi.org/10.3390/cells12030355

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