https://orcid.org/0000-0001-9655-0450
Figures
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely utilized to investigate plasticity mechanisms and functional reorganization in multiple sclerosis (MS). Among many resting state (RS) networks, a significant role is played by the salience network (SN, ventral attention network). Previous reports have demonstrated the involvement of osteopontin (OPN) in the pathogenesis of MS, which acts as a proinflammatory cytokine ultimately leading to neurodegeneration. Concentration of serum OPN was related to MRI findings 10.22±2.84 years later in 44 patients with MS. Local and interhemispheric correlations (LCOR, IHC), ROI-to-ROI and seed-based connectivity analyses were performed using serum OPN levels as independent variable along with age and gender as nuisance variables. We found significant associations between OPN levels and local correlation in right and left clusters encompassing the central opercular- and insular cortices (p-FDR = 0.0018 and p-FDR = 0.0205, respectively). Moreover, a significant association was identified between OPN concentration and interhemispheric correlation between central opercular- and insular cortices (p-FDR = 0.00015). Significant positive associations were found between OPN concentration and functional connectivity (FC) within the SN (FC strength between the anterior insula ventral division and 3 other insular regions, F(2,13) = 7.84, p-FDR = 0.0117). Seed-based connectivity analysis using the seven nodes of the SN resulted in several positive and inverse associations with OPN level. Serum OPN level may predict FC alterations within the SN in 10 years.
Citation: Kakucs Z, Illes Z, Hayden Z, Berki T, Orsi G (2024) Osteopontin predicts late-time salience network-related functional connectivity in multiple sclerosis. PLoS ONE 19(8): e0309563. https://doi.org/10.1371/journal.pone.0309563
Editor: Tanja Grubić Kezele, University of Rijeka Faculty of Medicine: Sveuciliste u Rijeci Medicinski fakultet, CROATIA
Received: February 27, 2024; Accepted: August 13, 2024; Published: August 29, 2024
Copyright: © 2024 Kakucs et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data related to this study cannot be made publicly accessible due to ethical and privacy concerns. To request access to the MR data and patients’ medical data, please contact Dr. Erzsébet Románcz at the Regional Research Ethics Committee, University of Pécs (email: romancz.erzsebet@pte.hu). The third-party data recipient should be named explicitly in the data request and apply for a separate ethical approval to get access to the MR dataset and related sensitive medical data.
Funding: This work was supported by the Translational Neuroscience National Laboratory (RRF-2.3.1-21-2022-00011). Our research was partly financed by the National Research, Development and Innovation Fund of Hungary’s Thematic Excellence Programmes [TKP-2021-EGA-13 and TKP2021-EGA-16]. This work was supported by the Collegium Talentum Programme of Hungary (ZK) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease of the central nervous system (CNS), the leading cause of nontraumatic neurological disability in young adults [1].
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely utilized in recent years to advance our understanding of the pathophysiology of MS, allowing us to investigate plasticity mechanisms and functional reorganization in the MS population. Identification of these structural and functional network changes continues to be crucial in order to decipher the source of complex conditions like cognitive impairment, depression and physical disability [2].
While the default-mode network (DMN) is a key focus in MS research, clinically relevant functional connectivity (FC) changes extend beyond the DMN [3–5]. Previous studies have highlighted the involvement of many resting-state (RS) networks, with a significant role played by the salience network (SN, ventral attention network) [6]. The main components of the SN are the anterior insula and the dorsal anterior cingulate cortex, which are involved in directing the information flow and modulating the balance between DMN and the central executive network (CEN) [7]. The SN is responsible for the recognition of salient environmental stimuli, ensuring normal network efficiency during different tasks [8].
Osteopontin (OPN), also known as early T cell-activation gene 1 or secreted phosphoprotein 1 (SPP1), was originally identified as an immobilized extracellular matrix molecule in mineralized tissue [9]. Recent studies have demonstrated the role of OPN in the pathogenesis of MS, where it functions as a proinflammatory cytokine, contributing to the amplification of the immunological cascade and ultimately leading to neurodegeneration [10].
In our previous study, we found a strong correlation between OPN concentration measured from the cerebrospinal fluid (CSF) and late time atrophy markers (increased CSF and ventricular volumes along with reduced regional brain volumes in both gray- and white matter) in MS patients [11].
Moreover, we observed a significant association between CSF OPN levels and microstructural alterations in the normal appearing white matter (NAWM) and total white matter lesion volume in the brain a decade later (10.22±2.84 years). Furthermore, serum OPN levels showed numerous associations with FC changes between the nodes of DMN [4].
In this current study, building upon previous findings in MS [6], we aimed to assess the association between OPN levels and FC alterations within the SN.
Materials and methods
Subjects and samples
Fourty-four patients (32 females, mean age 42.8 years [range 20–68]) were prospectively recruited for the study. Inclusion criteria were definitive MS diagnosis according to the 2017 modified McDonald diagnostic criteria [12], available serum sample stored in the Institutional Serum and Liquor Bank collected at least 5 years earlier. The median EDSS of the included patients was 2, the disease course distribution was as follows: relapsing-remitting MS (RRMS) 68% of which 12 patients were classified as benign RRMS, primary progressive MS (PPMS) 11%, secondary progressive MS (SPMS) 21% (Table 1). RRMS-SPMS conversion occurred between the initial serum collection and the MRI measurement in seven patients, while in case of an additional patient, the conversion was observed around the initial sample collection.
The study was conducted according to the World Medical Association Declaration of Helsinki and approved by the Regional Ethical Committee of the University of Pecs (7068-PTE 2018). All patients signed written informed consent.
Serum samples were collected 10.22±2.84 years before MRI, and aliquots were kept at −80°C until processing. A commercially available enzyme-linked immunosorbent assayhttps://www.sigmaaldrich.com/US/en/products/protein-biology/immunoassay-platform-solutions/elisa-kits (ELISA) kit (Human Osteopontin DuoSet ELISA, R&D Systems, Minneapolis, MN) was used, according to the manufacturer’s instructions. All samples were run in duplicates. Optical density was detected at 450 nm using an iEMS MF microphotometer (Thermo Labsystem, Beverly MA, USA).
Magnetic resonance imaging
Subjects were scanned using a 3T MRI scanner (MAGNETOM PrismaFit, Siemens-Healthineers, Erlangen, Germany). The MRI study protocol included the following sequences: 3D T1 magnetization-prepared rapid acquisition with gradient echo (MPRAGE), 3D fluid-attenuated inversion recovery (FLAIR), and rs-fMRI with field mapping to reduce image distortions due to B0 inhomogeneities. Imaging parameters are disclosed in the Supplementary materials.
rs-fMRI evaluation
Evaluation was carried out by using CONN (21.a) [13] and SPM (12.7771) [14]. All preprocessing and denoising steps were done using the recommended default settings, except for the following steps: voxel displacement maps were created for distortion correction, EPI-normalization was switched to indirect method, and final Gaussian smoothing was reduced to 6 mm full-width at half-maximum (FWHM).
First-level analyses included local correlation map (LCOR) calculations estimated as the weighted average of all short-range connections between a voxel and a 25 mm FWHM Gaussian neighbourhood area, interhemispheric correlation map (IHC) calculations characterizing the strength of homotopic FC between the two hemispheres, ROI-to-ROI connectivity analysis characterizing the FC between each pair of regions, and seed-based connectivity maps estimation characterizing the spatial pattern of FC with a seed area.
ROIs of the SN were defined by the MIST122 atlas [15]. Details of pre-processing, denoising, and group analyses are elaborated in the Supplementary materials.
Statistical analysis
For each individual voxel (or connection in case of ROI-to-ROI analysis) a separate GLM was estimated, with first-level connectivity measures at this voxel (or connection) as dependent variable and osteopontin level, age, and gender as independent variables. Storage time was also included as covariate of no interest, but was eventually removed from all models, due to the lack of significant contribution. Voxel-level or connection level (in case of ROI-to-ROI) hypotheses were evaluated using multivariate parametric statistics with random-effects across subjects and sample covariance estimation across multiple measurements. Inferences were performed at the level of individual clusters (groups of contiguous voxels or in case of ROI-to- analysis: groups of similar connections). Cluster-level inferences were based on parametric statistics from Gaussian Random Field theory. Results were thresholded using a combination of a cluster-forming p < 0.001 voxel-level threshold, and a familywise corrected p-FDR < 0.05 cluster-size threshold, or in case of ROI-to-ROI analysis: a combination of a p < 0.05 connection-level threshold and a familywise corrected p-FDR < 0.05 cluster-level threshold. Difference in median OPN concentrations between the two timepoints was assessed by Wilcoxon Signed Rank test, correlation between storage time and measured OPN concentration was assessed by Pearson product-moment correlation.
Results
Clinical characteristics and measured OPN concentrations are presented in Table 1. Median OPN did not differed significantly between the two timepoints (Z = -0.966, p = 0.352).
LCOR analysis using serum OPN levels as independent variable along with age and gender as nuisance variables resulted in significant association between OPN levels and local correlation in right and left clusters encompassing the central opercular- and insular cortices (p-FDR = 0.0018 and p-FDR = 0.0205, respectively), see Fig 1A.
A: Local correlation analysis using serum osteopontin levels as independent variable along with age and gender as nuisance variables. Significant clusters are shown bilaterally in the central opercular and insular cortices. B: Interhemispheric correlation analysis using serum osteopontin levels as independent variable along with age and gender as nuisance variables. Significant associations are shown bilaterally in the central opercular and insular cortices. C: ROI-to-ROI analysis in the salience network. Significant associations were observed between OPN concentration and FC strength between the AINS_v and 3 other insular regions (AINS_pd, PINS_d, and PINS_v). Abbreviations: ACcor_d: Anterior Cingulate Cortex dorsal division; AINS_ad: Anterior Insula anterodorsal division; AINS_pd: Anterior Insula posterodorsal division; AINS_v: Anterior Insula ventral division; PINS_d: Posterior Insula dorsal division; PINS_v: Posterior Insula ventral division; PI_sul: Periinsular sulcus. Part A and B: Random field theory parametric statistics (voxel threshold p < 0.001, cluster threshold p < 0.05, cluster-size p-FDR corrected). Images are shown in radiologic convention. Part C: Parametric multivariate statistics. Cluster threshold p < 0.05, cluster-level p-FDR corrected (MVPA omnibus test) connection threshold p < 0.05.
IHC analysis using serum osteopontin levels as independent variable along with age and gender as nuisance variables resulted in significant association between OPN levels and interhemispheric correlation between central opercular- and insular cortices (p-FDR = 0.00015), see Fig 1B.
Analysis was conducted between the nodes of the salience network defined using the MIST122 atlas. The following seven labels were used for the analysis: anterior cingulum dorsal part (ACcor_d); posterior insular cortex ventral and dorsal divisions (PINS_v, PINS_d); anterior insular cortex anterodorsal, posterodorsal, and ventral divisions (AINS_ad, AINS_pd, AINS_v); and periinsular sulcus (PIsul). Significant positive associations were found between OPN concentration and FC strength between the AINS_v and 3 other insular regions (AINS_pd, PINS_d, and PINS_v, F(2,13) = 7.84, p-FDR = 0.0117), see Fig 1C.
Seed-based connectivity analysis using the seven nodes of the SN, defined in the previous section, resulted in several positive and inverse associations with OPN measured from samples taken 10.22 years before the MRI. Results are summarized based on the seed region. Significant regions for ACcor_d seed were right temporal pole and lingual gyrus, while insular seed regions mainly resulted in significant regions in other parts of the insular- and opercular cortices, temporal pole, and postcentral gyrus. Detailed results are listed in Table 2.
Discussion
The results of the current study indicate that the functional alterations within the SN are associated with the serum OPN concentration measured approximately 10 years earlier.
OPN is involved in a variety of physiologic functions and pathological conditions such as bone remodeling, wound healing, cancer development, vascular disorders [16]. Previous studies have also pointed out the important role of OPN in the pathogenesis of various autoimmune diseases, such as Sjögren disease [17], inflammatory bowel disease [18], neuromyelitis optica spectrum disease [19] and MS [20]. This pleiotropic cytokine widely expressed by various immune cells, including T cells, B cells, macrophages, dendritic cells, and natural killer cells. OPN promotes the production of pro-inflammatory cytokine by Th1 cells (IL-12, IL-17, IFNγ) and inhibits IL-10 expression by macrophages [21]. OPN also regulates Th17 cell responses and inhibits the TH2 responses [22]. All these pathways are suggested to be involved in the pathogenesis of MS [23].
OPN is expressed in active lesions, especially in acute active MS plaques. Moreover, enhanced OPN expression was found in macrovascular endothelial cells and macrophages in these plaques, as well as in the white matter surrounding the plaques [24]. OPN also interacts with microglial cells, modulating inflammatory responses and attenuating secondary neurodegeneration [25].
According to a meta-analysis including 22 studies, higher levels of OPN were shown to present in peripheral blood and CSF of MS patients compared to the healthy controls (p<0.05) [16]. Moreover, in all types of MS patients, except for those with clinically isolated syndrome (CIS), increased blood levels of OPN were found compared to controls. The concentration of OPN in the CSF was significantly elevated in patients with RRMS compared to those with CIS and SPMS (p<0.05). Additionally, individuals with active MS displayed significantly higher OPN levels in their CSF compared to those with stable disease (p = 0.007).
Similarly, in a recent study, serum OPN levels were significantly higher in RRMS patients (29.48±27.71 ng/ml among patients in remission phase and 23.72±27.6 ng/ml in patients during relapse before receiving treatment) compared to healthy controls (4.1±1.79) [26].
The SN, specifically the right fronto-insular cortex, plays a critical role in switching between internally directed cognition of the DMN and the externally directed cognition of the CEN [27]. The normal functioning of both the DMN and CEN is known to be crucial for fulfilling various cognitive tasks [28]. The compromised connectivity between SN and these two regions can have detrimental effects on cognitive processing. Prior studies have documented varying global connectivity levels of the DMN and the CEN in relation to cognitive impairment in MS. This discrepancy raises questions about how these FC changes can influence cognitive performance. Beyond the directionality of these changes, it remains unclear whether these cognitive disturbances are primarily attributed to modifications in within-network connectivity, connectivity with the broader brain network, or alterations in between-network connectivity.
Compared to healthy controls, MS patients with relapsing-remitting multiple sclerosis (RRSM) presented a decreased RS FC in regions of SN, CEN and DMN. Moreover, increased FC was found between SN and CEN [2]. Recent observations showed disease-stage specific course, even though longitudinal studies evaluating connectivity changes are still rare. A recent study indicates that the centrality of SN increases in the early stages in association with increased FC between the SN and DMN, and between SN and CEN respectively. Conversion from intact cognition to cognitive impairment was related to an initial abnormal function of SN, and this network disturbance was subsequently transferred to DMN. These findings suggest that SN changes may precede DMN dysfunction [6].
The insula comprises anterior and posterior sections: the anterior insula is specifically sensitive to salient environmental events [8]. The involvement of the insular cortex in the pathogenesis of MS has already been demonstrated by several studies. Significant decreases in cortical gray matter (GM) volume were found bilaterally in the insular region of RRMS patients compared to normal controls [29]. The GM volume of the insular lobes are in association with the cognitive scores in MS patients [30]. Moreover, the degree of a key long-range FC of the anterior insula to was related to the measured cognitive reserve index in MS patients [31], while local activation differences within the left insula were also demonstrated in MS patients compared to healthy controls [8]. All the above studies emphasize the importance of insular function in MS pathogenesis. In line with these findings, the results of the current study show that RS insular function within the insula and between bilateral insular regions showed highly significant associations with OPN, which was shown to be involved in MS pathogenesis, typically in the observed long-term structural and functional disturbances [11].
Moreover, the insula and cingulate cortex are predilection locations for cortical demyelinated lesions in MS patients [32]. Thus, any structural and functional change in these sites may result in aberrant SN connectivity.
In summary, the results of the current ROI-to-ROI analysis within the SN shows that part of the functional alterations observed within the SN may be predicted by OPN, suggesting that the functional disturbance of the SN is closely related to MS pathogenesis. Given the main function of the SN, we may hypothesize that the disturbed function of SN could lead to an imbalance between DMN and CEN, associated with altered diversity of structural and functional connections for information flow. This is hypothesis is strengthened by the observations of Huiskapm et al., who concluded that the functional changes within the SN may precede DMN dysfunction [6].
Limitations
This study has inherent limitations related to its design. The main limitation is the lack of initial MRI measurements. Moreover, this study was conducted at a single center and involved a relatively limited cohort of participants. Previous reports suggest that patients with RRMS during relapse presented higher serum OPN levels than patients with RRMS during clinical remission [10]. In this study, serum samples were obtained in all MS patients regardless of the clinical presentation or MS subtype. Even though there was no correlation between storage time and measured OPN concentrations a decade later (Pearson’s r = 0.026, p = 0.913), neither did the inclusion of storage time changed the statistical results presented in this paper, we cannot rule out that storage time had a direct impact on the measured concentrations.
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