White Matter Volume and Microstructural Integrity Are Associated with Fatigue in Relapsing Multiple Sclerosis

White Matter Volume and Microstructural Integrity Are Associated with Fatigue in Relapsing Multiple Sclerosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article White Matter Volume and Microstructural Integrity Are Associated with Fatigue in Relapsing Multiple Sclerosis Alejandra Figueroa-Vargas, Sebastián Navarrete, Claudia Cárcamo, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5419035/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 May, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Multiple sclerosis (MS) is a prevalent neurological disorder marked by inflammation and demyelination, with fatigue being one of the most reported and debilitating symptoms. While fatigue occurs across various neurological conditions and even in healthy individuals, the specific mechanisms contributing to fatigue in each context remain unclear. In this study, we conducted a cross-sectional analysis involving 33 people with relapsing MS (PwRMS) and 29 healthy controls who also reported fatigue. Participants underwent MRI scans, including T1-weighted and diffusion-weighted imaging, to evaluate brain structure. Additionally, the Modified Fatigue Impact Scale was utilized. To investigate the hypothesis that fatigue correlates differently with brain structures in PwRMS, we employed Bayesian LASSO and Spike-and-Slab LASSO regression models. Our findings indicated that lower white matter volume and compromised microstructural integrity in specific brain regions—such as the caudate part of cingulate gyrus, inferior frontal gyrus, and the banks of the superior temporal sulcus—were significantly associated with fatigue scores in PwRMS. These results suggest that alterations in specific brain regions may play a critical role in the clinical manifestation of fatigue in MS. Understanding these insights could help differentiate general mechanisms of fatigue from those affecting people with relapsing MS, which may guide future therapeutic strategies. Biological sciences/Neuroscience/Cognitive ageing Biological sciences/Neuroscience/Neuroimmunology Health sciences/Neurology/Neurological disorders/Multiple sclerosis Biological sciences/Biological techniques/Imaging/Diffusion tensor imaging Biological sciences/Biological techniques/Imaging/Magnetic resonance imaging People with Relapsing Multiple Sclerosis (PwRMS) MRI DTI fatigue inferior frontal gyrus Figures Figure 1 Figure 2 Figure 3 Introduction Multiple sclerosis is the most common neurological non traumatic condition that generates disability in young adults 1 ,2 . Relapsing multiple sclerosis (RMS) represents the most prevalent subtype of MS, accounting for approximately 85% of cases at diagnosis. It is characterized by episodes of neurological dysfunction, or relapses, followed by periods of remission, during which there is either partial or full recovery 3 . Fatigue is one of the most frequently reported and disabling symptoms in people with multiple sclerosis (PwMS), affecting an estimated 60–80% of this population 4,5 . The presence of fatigue negatively impacts the quality of life, employment, psychological state, and daily functioning 6,7 . Fatigue is difficult to define and measure objectively, especially in PwMS. Currently, most fatigue assessments for pwMS rely on self-reported measures, with several widely recognized instruments available, including the Modified Fatigue Impact Scale (MFIS) 8,9 . The pathophysiological mechanisms underlying fatigue may involve central factors, such as disruptions in neuronal energetics, function, or signal conduction; peripheral mechanisms, including muscular dysfunction; or systemic factors, such as immune system dysregulation. Despite great efforts to understand the mechanisms underlying fatigue, these have not yet been elucidated 4,10,11 . Beyond pwMS, fatigue is a symptom present in diverse medical conditions, and the prevalence of fatigue increases significantly in a number of diseases that involve dysregulation of the immune system, such as cancer, chronic infection, autoimmune diseases, and neurological diseases, such as rheumatoid arthritis 12 , lupus 13 , Long-Covid 14 , cancer 15 . Fatigue has also been reported in healthy individuals, and its prevalence of general fatigue is 20.4% in healthy adults 16 . In clinical practice, fatigue is among the top five most frequently presented health complaints in primary care 17 . This highlights the relevance and complexity of fatigue as a symptom, suggesting that its underlying pathophysiological mechanisms are multifactorial, which makes it difficult to differentiate general mechanics from pathology-specific and patient-specific causes 18 . This fact limits the therapeutic approach that efficiently resolves each patient's condition. MRI is a widely utilized non-invasive imaging technique in clinical practice that enables the in vivo detection of central nervous system (CNS) damage associated with MS 19–21 . Structural MRI has been extensively employed to investigate brain abnormalities in PwMS, offering valuable insights into the location and severity of structural damage, including gray matter, white matter lesion (WML) burden, and brain atrophy 22 . Although conventional MRI provides valuable insights through qualitative evaluations or volumetric analyses, relying solely on these techniques to explain clinical symptomatology in MS is limited and often inconsistent 4 . To overcome these limitations, incorporating quantitative analyses and advanced imaging methods like diffusion MRI (dMRI) is crucial 23 . dMRI enables the exploration of subtle brain abnormalities by assessing structural connectivity, revealing how different brain regions are interconnected to form networks 24 . In MS, microstructural damage to tissues, including myelin and axons, is a defining feature, even in the early stages of the disease 3 . Such damage disrupts structural connectivity impairing functional connectivity 25,26 . These disruptions in brain networks likely play a pivotal role in the clinical manifestation of MS symptoms 27 including fatigue 28,29 . Previous studies have identified abnormalities in the cortico-striato-thalamo-cortical loop as key contributors to fatigue in various subtypes of MS 30–32 . However, brain connectivity changes specific to fatigue in PwMS have yet to be thoroughly investigated 3 . Given that the pathophysiological mechanisms and clinical characteristics of relapsing MS differ significantly from those of progressive MS subtypes 33 , it is crucial to examine the underlying brain alterations associated with fatigue, specifically within this group. In this study, we aimed to assess the relationship between brain structural MRI measures, including volume and connectivity and reported fatigue in people with relapsing multiple sclerosis (PwRMS) and control participants with subjective fatigue but no neurological conditions. This approach may offer new insights into distinguishing general mechanisms underlying fatigue from those specifically impacting PwRMS. Results Descriptive of the participant characteristics. The characteristics of the pwRMS and HC experiencing fatigue are shown in Table 1. For PwRMS, the mean age of 33 participants was 37.63 years (SD: 7.88 years); 59.3% were female, and schooling was 18.56 years (SD: 1.85). The mean total MFIS score was 44.96 (SD: 15.59). Four PwRMS had depression diagnosis under treatment. The median PASAT was 0.45 (IQR: 0.95), and the median SDMT Z score was 0.5 (IQR: 0.86). For healthy controls experiencing fatigue, the mean age of 29 participants was 39.02 years (SD: 7.03 years) (no differences between groups, t(59) = − 0.72, p = 0.46); 58.6% were female (no differences between groups, x 2 = 0, df = 1, p = 1), and schooling was 19 years (SD: 1.09) (no differences between groups, t(59) = − 0.55, p = 0.58). The mean total MFIS score was 42.41 (SD: 11.74) (no differences between groups, t(59) = 0.72, p = 0.47). The median SDMT Z score was 0.09 (IQR: 0.88) (with a significantly smaller difference compared to PwRMS, t(59) = 2.05, p = 0.044). Three fatigued healthy controls had depression diagnoses under treatment (no differences between groups, x 2 = 1e-31, df = 1, p = 1). Association between structural MRI measures and fatigue score. First, we calculated the volumes of cortical and subcortical gray matter, as well as white matter for PwRMS and fatigued HC, using the first two steps of the Human Connectome Project (HCP) pipeline mentioned previously. Second, we calculated the Principal Components Analysis (PCA) for cortical and subcortical gray matter and white matter for each group and selected the first twenty PCA that had a main contribution. Third, we implemented Bayesian LASSO and Bayesian Spike-and-Slab LASSO (SSL) linear regression models to examine relationships between compositional predictors—including the PCA of cortical, subcortical gray matter, and white matter volumes—and total fatigue scores. This analysis was conducted within and between groups (PwRMS and healthy individuals experiencing fatigue) to identify specific volumetric associations with self-reported fatigue scores. We found that only PC #15 of the white matter volumes showed a significant relation with fatigue in PwRMS (LASSO: posterior distribution mean: 11.5, 95%HDI [2.4 21.2], p MCMC =0.01; SSL mean: 8.5, 95%HDI [0.01 17.7], p MCMC =0.04), lead a significant difference in its relation between the PwRMS and fatigued healthy controls (LASSO: posterior distribution mean: 7.7, 95%HDI [1.6 13.6], p MCMC =0.0072; SSL means 5.8, 95%HDI = [0.01 11.5], p MCMC =0.03 (Fig. 1 ). Then, we identified the first twenty white matter regions that contributed to PC # 15 (Table 2) and ordered them according to the value absolute loading of the major to minor in PC # 15. The main white matter regions of PC # 15 are shown in Fig. 2 , and the first twenty principal white matter regions included were left hemisphere caudal anterior cingulate, left hemisphere pars triangularis of the inferior frontal gyrus, right hemisphere banks of the superior temporal sulcus, left hemisphere transverse temporal region, right hemisphere insula, left hemisphere cerebellum, left hemisphere entorhinal region, right hemisphere lateral occipital region, right hemisphere rostral anterior cingulate gyrus, and left hemisphere paracentral gyrus. No significant relationships were found when analyzing subcortical gray matter segmentations and cortical gray matte segmentations. Association between connectivity MRI measures and fatigue score. Regarding the analysis of FA, we calculate this measure in all the bungles that connected the area of PC15, and that can be determined in all participants. Thus, four bungles were tested using a LASSO and SSL models, and a significant increase in FA was found for the streamlines that connect within the region Pars Triangularis (LASSO posterior distribution mean = 86.2, 95%HDI = [29 143], p MCMC =0.0019; SSL mean = 98.3, 95%HDI = [48 146], p MCMC =0.0001; Fig. 2 ). Discussion This study provides novel insights into the relationship between structural abnormalities and connectivity changes associated with fatigue in PwRMS compared to HC experiencing fatigue. Our findings reveal distinct white matter volumetric patterns and structural connectivity contributing to fatigue, particularly in regions involved in cognitive, language, sensorial integration, emotional, and social processing. The most significant findings of our study include, on the one hand, the identification of specific white matter volume that correlated with fatigue differently between individuals with PwRMS and HC experiencing fatigue. Thus, the fatigue experienced by the patients seems to be a neurobiological mechanism dependent on white matter changes, in contrast to that experienced by HC. Notably, both samples are comparable in all other variables measured and were characterized by preserved cognitive capacity, minimal or no disability, and a comparable rate of mood symptoms. This main finding associated with matter volume identifies brain regions related to fatigue in PwRMS. These areas involve the left caudal anterior cingulate, left pars triangularis, right banks of the superior temporal sulcus, left transverse temporal areas, right insula, left cerebellum, left entorhinal cortex, right lateral occipital, right rostral anterior cingulate, and left paracentral regions. These areas are crucial to networks involved in language 34 , cognitive control 35–37 , social cognition 38–40 , sensorimotor integration 41 , and emotional and pain processing 42 . On the other hand, in the case of PwRMS, changes in the specific superficial white matter fiber are identified through DTI 43–46 , specifically in FA as reported by other studies 47,48 . Interestingly, the increase of FA in SWM related to fatigue was observed in PwRMS, in bundles associated with the left pars triangularis, one the most relevant white matter regions that contribute more to the difference in the relation between white matter volumes and fatigue. This supports the claim that fatigue in PwRMS may have a distinct neuroanatomical signature compared to fatigue in healthy individuals 49 . Our findings extend prior research on fatigue in PwMS, with notable involvement in different white matter regions, such as, the anterior cingulate cortex, a region consistently implicated in fatigue across neurological disorders 47,50 . Altered white matter volume in this area in PwRMS may reflect compromised functions such as error detection, and performance monitoring 51,52 , as well as effort-based decision-making 53,54 , and pain processing 55,56 , in a similar way than that observed in other pathologies like schizophrenia 57,58 . Observed alterations in the banks of the superior temporal sulcus (bSTS) contribute to understanding the disruptions in multimodal sensory integration 59 and social cognition 60,61 , presented in PwRMS. Further, differences in the transverse temporal regions, involved in basic auditory processing 62 , highlight alterations in speech comprehension and auditory attention 63 even at early stages of RMS. Differences in the insula’s white matter volume indicate possible early effects on interoception, emotional processing, feedback monitoring processing 64 , and cognitive integration in PwRMS 65 . One region we highlight, both for its structural characteristics and connectivity, is the white matter in the pars triangularis and the SWM bundles traversing this area. This suggests that fatigue affects cognitive networks beyond motor function, aligning with recent findings that MS-related fatigue impacts language processing, sustained communication, social cognition, and executive functions, including inhibitory control and decision-making 66–68 . Nonetheless, the elevated fractional anisotropy (FA) associated with fatigue in SWM bundles connecting to the left pars triangularis may indicate early compensatory mechanisms aimed at offsetting changes in white matter regions associated with fatigue in PwRMS. This finding aligns with the report by Buyukturkoglu et al. 43 , which demonstrated that patients in the early stages of MS exhibit significant changes in SWM bundles, including those connected to the insula, inferior frontal, orbitofrontal, superior and medial temporal, and pre-and post-central cortices, even prior pronounced structural and functional alterations emerge. Our findings have several important clinical implications. Overall, the observed white matter volume differences reflect impairments in networks associated with sensory integration, language, executive functions essential for cognitive control, and social cognition. Together, these processes are crucial for daily functioning 69 , and they are associated with decreased quality of life in people with MS 70 . Early disruptions in white matter integrity and regional connectivity hold significant clinical implications, underscoring the need for close monitoring. There is a need for multimodal interventions that address various dimensions of fatigue. This includes cognitive rehabilitation strategies to enhance cognitive function 71 , emotional support to manage mood disturbances 72 , and therapies to improve sensory integration 73 . Because the current treatments are limited, there is some controversy regarding their efficacy 74,75 . For example, a randomized, placebo-controlled, crossover, double-blind trial led by Nourbakhsh suggests that Amantadine and Modafinil are not better than placebo in improving MS fatigue 76 . In this context, our results provide insights that could guide future interventions to slow disease progression and reduce both the long-term effects of the disease and those related to aging. In recent years, non-invasive brain stimulation, informed by precise knowledge of functional and structural brain alterations, has shown the potential to alleviate various symptoms in brain pathologies 77–79 . These techniques have demonstrated their ability to produce changes in brain activity dependent on structural brain connectivity, leading to modulations in cognitive processing 36,80,81 . For example, studies found that interventions using transcranial direct current stimulation of the dorsolateral prefrontal cortex improved fatigue in PwRMS 82,83 . Comprehensive assessments for evaluating fatigue in PwMS are essential. This should encompass not only physical measures but also cognitive evaluations and emotional assessments to provide an integral view of the patient’s experience with fatigue. Additionally, incorporating neuroinflammatory biomarkers specific to MS-related fatigue, such as cytokine levels, glial activation markers, and other indicators of neuroimmune activity, could enhance the precision of fatigue assessments and clarify the disease-related and non-disease-related contributions to fatigue in MS 84 . Notably, while the scale used in this study to assess subjective fatigue is validated for PwMS 85 , it lacks specificity in differentiating PwRMS experiencing fatigue from healthy controls with fatigue. In contrast, structural white matter measures effectively distinguished the two groups, underscoring the potential of multimodal assessments to capture fatigue-related differences. This highlights the need for comprehensive, multimodal evaluations integrating relevant information to manage fatigue in pwMS efficiently. Several limitations should be considered when interpreting our results. First, the cross-sectional design of this study limits our ability to establish causal relationships between white matter changes and the development of fatigue. Second, while our sample size was adequate for detecting group differences in the relation between white matter and fatigue, larger studies are needed to validate these findings and explore potential phenotypic variations. Furthermore, despite our samples being well matched in several clinical factors, the small sample size restricts our ability to control for possible confound factors, including common comorbidities such as depression and anxiety 86 . Third, focusing solely on structural changes may not fully capture the complexity of fatigue mechanisms, highlighting the need for multimodal imaging studies that include functional connectivity analyses 26 to investigate additional phenotypes, as well as comparisons with healthy controls with and without fatigue. Future research should investigate longitudinal changes in these brain regions as fatigue develops and fluctuates, explore the relationship between structural changes and functional connectivity 87 , examine how these structural differences relate to treatment response, and consider the impact of disease-modifying therapies on these white matter patterns. In conclusion, this study provides evidence of distinct white matter volumetric patterns associated with fatigue in pwRMS compared to healthy individuals experiencing fatigue. The involvement of regions essential for sensory integration, language, executive functions crucial for cognitive control, and social cognition suggests a unique neuroanatomical basis for fatigue in PwRMS. These findings advance our understanding of fatigue pathophysiology in RMS and underscore the importance of integrating additional measures, such as, neuroinflammatory and stress-related measures to enhance the specificity and sensitivity of fatigue assessments in both clinical and research settings, ultimately contributing to developing more targeted therapeutic approaches. Materials and methods Study design and inclusion criteria. We conducted a cross-sectional study involving 33 participants with relapsing multiple sclerosis (PwRMS), with a mean age of 37.63 years (SD: 7.88 years), 59.3% were female, and schooling was 18.56 years (SD: 1.85); and 29 healthy controls (HC) experiencing fatigue, with a mean age of 39.02 years (SD: 7.03 years), 58.6% were female, and schooling was 19 years (SD: 1.09). The characteristics of the PwRMS and HC experiencing fatigue are shown in Table 1. All PwRMS were enrolled in the Multiple Sclerosis Program at Pontificia Universidad Católica de Chile. In the study, we collected clinical and demographic data on PwRMS, including standardized patient history, MRI outcome measures, and tests of neurological and cognitive functions, using an electronic medical record. Individuals with a clinically confirmed diagnosis of relapsing multiple sclerosis (RMS) were eligible for participation in the study. PwMS inclusion criteria were a requirement to have a brain MRI within 6 months before the survey, to complete a fatigue assessment Modified Fatigue Impact Scale (MFIS), no relapses or progression in the last 6 months, no history of other major neurological and psychiatric disorders, preserved cognitive capacity evaluated with Paced Auditory Serial Addition Test (PASAT) < -1.5 Z-score, and Symbol Digit Modalities Test (SDMT) < -1.5 Z-score, null o mild level disability evaluated by the Expanded Disability Status Scale 88 (EDSS ≤ 3), maintenance of usual medication for at least 6 months, and no uncorrected visual deficits. All healthy controls (HC) experiencing fatigue were enrolled in Laboratorio de Neurociencia Social y Neuromodulación (NeuroCICS) at Centro de Investigación en Complejidad Social at Universidad del Desarrollo, Chile. For HC, we collected clinical and demographic data and MRI outcomes. Inclusion criteria for HC included having a brain MRI within 6 months before the study, completing the fatigue assessment Modified Fatigue Impact Scale (MFIS), no history of major neurological or psychiatric disorders, and no uncorrected visual deficits. Ethics declarations All participants gave informed consent, and all experimental procedures were approved by the Ethical Committee for Health Sciences at Pontificia Universidad Católica de Chile, Chile (ID: 220711001). These consent processes and all procedures complied with Chilean national legislation, institutional guidelines, and the Declaration of Helsinki. MRI outcome measures. The MRI protocol for PwMS was conducted as part of the Multiple Sclerosis Program within the Faculty of Medicine at the Pontifical Catholic University of Chile, according to the policies of the Chilean Ministry of Health. These were performed on Philips 3T scanners using standardized three-dimensional fluid-attenuated inversion recovery (3D FLAIR) and 3D T1 (MPRAGE [magnetization-prepared rapid gradient-echo imaging]) acquisition sequences. Both structural and functional images were acquired on a Philips Ingenia 3 T MRI scanner. T1 weighted 3D images (TR 7.8 ms, TE 3.6 ms, FOV 240 × 240 × 164, flip angle 8°, SENSE factor 2.5, acquisition time 4 min 8 s) and a FLAIR (TR 4800 ms, TE 290 ms, FOV 240 × 240 × 164, acquisition time 4 min 33 s). The MRI protocol for HC was conducted using Siemens Skyra 3T scanners and included (i) a sagittal 3D anatomical MPRAGE T1-weighted imaging (repetition time [TR]/ echo time [TE] = 2530/2.19 ms, inversion time [TI] = 1100 ms, flip angle = 7°; 1x1x1 mm3 voxels), (ii) a sagital 3D anatomical SPC T2-weighted (TR/TE = 3200/412 ms, flip angle = 120°; echo train length [ETL] = 258; 1x1x1 mm3 voxels), (iii) a sagittal 3D fluid attenuation inversion recovery (FLAIR) imaging (TR/TE = 5000/388 ms, TI = 1800 ms, ETL = 251; flip angle = 120º, 1-mm slice thickness, 1x1x1 mm3 voxels), (iv) an axial 3D echo-planar imaging (EPI) (TR/TE = 8600/95 ms, 2x2x2 mm3 voxels, flip angle = 90°) with diffusion gradients applied in 60 non-collinear directions and two optimized b factors (b1 = 0 and b2 = 1000 s/mm2) with two repetitions. Patient-reported outcomes Fatigue was evaluated using the Modified Fatigue Impact Scale (MFIS) 89,90 , a 21-item self-reported measure of fatigue that assesses impact on psychosocial, physical and cognitive function. The total score ranges from 0 to 84 (the higher the score, the worse the fatigue). MFIS items can be aggregated into 3 subscales: physical (score range 0–36); cognitive (score range 0–40), and psychosocial (score range 0–8). The MFIS is a valid and reliable tool for assessing fatigue, has a low floor and ceiling effect, and captures both physical and cognitive aspects of fatigue 90 . It is the recommended tool for assessing fatigue in clinical practice and research in PwMS 91 . The MFIS was administered for the PwRMS and healthy controls enrolled in the study. Descriptive analysis Descriptive statistics for demographic and clinical study participants were presented as mean (SD) or percentages. For fatigue analysis, the global fatigue of MFIS was obtained by the sum of points from three subscales: cognitive (10 items), physical (9 items), and psychosocial fatigue (2 items). The values of global fatigue ranged from 0 to 84 points, with higher values indicating greater fatigue. For age, education level, SDMT, and MFIS (total score and the cognitive, physical, and psychosocial categories), a t-test was performed to assess differences between the PwRMS and HC groups in terms of fatigue. For variables such as sex and depression, chi-square statistical analysis was used Brain Segmentation Volumes and Cortical Thickness Measurements In the first step, we segmented brain areas and consulted volumes. Structural Image processing was conducted using the first two steps of the Human Connectome Project (HCP) pipeline, as described in our prior works 92,93 , and detailed elsewhere 94 . Briefly, the "PreFreeSurfer" phase generated an undistorted native structural volume space, aligned T1-weighted (T1w) and T2-weighted (T2w) images, corrected bias fields derived from both images, and registered the native space to the common MNI coordinate space using a rigid affine registration, and them, a non-linear registration to standard space. Utilizing FreeSurfer version 6, the "FreeSurfer" stage conducted volume segmentation and cortical surface reconstructions, including delineation of the "white" (gray/white matter boundary) and "pial" (gray/cerebrospinal fluid [CSF] boundary) surfaces, followed by registration to a common template (fsaverage). In the second step, to study the relationship between cortical and subcortical gray matter volumes, white matter volumes, and self-reported global fatigue from the Modified Fatigue Impact Scale (MFIS) in both people with relapsing multiple sclerosis (PwRMS) and healthy controls, we conducted a Principal Component Analysis (PCA). This analysis was performed on three regions: subcortical gray matter, white matter, and cortical gray matter. The latter was segmented using the Desikan–Killiany atlas for cortical regions. PCA allowed us to reduce the complexity of volumetric data while retaining the most variance, facilitating an exploration of the associations between brain structures and fatigue scores across the study groups. This approach has been used for complex data from MRI and integrated modalities by other groups 95 . In the third step, we applied a Bayesian Least Absolute Shrinkage and Selection Operator (LASSO) and Bayesian Spike-and-Slab Lasso (SSL) linear regression models using R (A language and environment for statistical computing) in conjunction with the Just Another Gibbs Sampler (JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling). LASSO and Spike-and-Slab LASSO offer robust frameworks for addressing the challenges associated with multiple comparisons in regression analysis. Their ability to perform automatic feature selection while controlling for overfitting makes them tools for high-dimensional data analysis. This method enabled the analysis of compositional data, improving the accuracy of parameter estimation and offering deeper insights into the relationships between the compositional predictors—namely, the PCA of cortical and subcortical gray matter and white matter volumes—and the outcome variable of fatigue. Since compositional data inherently reflect the relative nature of its components, analyzing them allows for better differentiation of ratings and reduces response biases. This approach is effective whether all components contributing to the total are fully quantified or only a subset is included 96 . Thus, these methods mitigate the type I error rate through its inherent feature selection process, as it tends to include only a subset of statistically significant predictors, thus reducing the number of tests conducted on non-informative variables 97 . For these analyses, we used the first 10 components for subcortical segmentation and the first 20 for white and cortical gray matter. Inferences for significant regressors were assessed using the 95% highest density interval (HDI) of the posterior distribution. A p-value equivalent (pMCMC) was calculated by comparing Markov Chain Monte Carlo (MCMC) samples against a reference value of zero. P-values below 0.05 were considered statistically significant, and all comparisons were two-tailed. Statistical analyses were conducted using R (version 4.2.1). Connectivity MRI Analysis The diffusion imaging was processed using MRtrix3 software 98 . The first stage of the process was to apply a pre-processing pipeline, which involved a number of steps designed to enhance image quality and reduce the impact of artifacts. These steps included image denoising, correction of Gibbs ringing, motion and eddy current distortion correction, and N4 bias field correction. The pre-processed diffusion images were used to calculate the Diffusion Tensor Imaging (DTI) model and extract the fractional anisotropy (FA) mean diffusivity. A deterministic tractography algorithm ( Tensor_det ) with Anatomically Constrained Tractography (ACT) was then applied using the following parameters: angular threshold = 60°, step size = 0.2 mm, minimum length = 25 mm, maximum length = 250 mm, and FA threshold = 0.06 (adjusted to 0.03 when ACT is used). The T1w image was coregistered to the diffusion-weighted imaging (DWI) space using FSL's FLIRT tool, with 6 degrees of freedom (DOF) rigid-body transformation. With the T1w image aligned to the diffusion space, subject parcellation (Desikan atlas) was performed using FreeSurfer MRI_SynthSeg. Since the diffusion data for PwRMS and healthy controls experiencing fatigue were acquired in different centers using different scanners, direct comparison between the centers was not possible; any significant results could potentially be attributed to scanner differences rather than biological factors. Therefore, 2 different analyses were conducted solely among PwRMS, focusing on the relationship between fatigue scores and FA values within these streamlined bundles. Mean FA measures of ROI tracts Using the tractogram (DWI space) and the cortical parcellation, tractography-based connectome was generated using MRtrix3 (tck2connectome command) and then used to select the streamlines that connect, at both ends, the regions that show a difference in white matter (WM) thickness that were previously calculated (connectome2tck). These segmented tracts were then used to conduct a statistical analysis based on the mean FA measures in these regions (Fig. 3 ). Declarations Competing interests The authors report no competing interests. Funding This work was supported by Agencia Nacional de Investigación y Desarrollo de Chile (ANID), FONDECYT (1211227 to PB, AF-V; 1190513 to FZ and PB), FONDEQUIP EQM150076, ANID-Basal Project FB0008 (AC3E) to PG. Author Contribution AF-V, FA, RH-C, PG, CC, EC, and PB conceptualized the study. MV, BS, CM, MI-C, MPM-M, PC-P, MA-O, XS, CM, VM-R, PF-T, MD-D, JH, and FZ collected the data. AF-V, SN, PG and PB analyzed the data. AF-V, SN, FA, and PB wrote the main manuscripts and prepared the figures. All authors reviewed the manuscript. Data Availability All data are available in the OpenNeuro repository once accepted. All codes are available in the GitHub repository once accepted. For any further inquiries, please contact Pablo Billeke at [email protected] , Alejandra Figueroa at [email protected] . References Walton, C. et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult. Scler. J. 26, 1816–1821 (2020). Thompson, A. J., Baranzini, S. E., Geurts, J., Hemmer, B. & Ciccarelli, O. Multiple sclerosis. Lancet 391, 1622–1636 (2018). Kampaite, A. et al. Brain connectivity changes underlying depression and fatigue in relapsing-remitting multiple sclerosis: A systematic review. PLOS ONE 19, e0299634 (2024). Meijboom, R. et al. Fatigue in early multiple sclerosis: MRI metrics of neuroinflammation, relapse and neurodegeneration. Brain Commun. 6, fcae278 (2024). Ahvenjärvi, H. et al. Fatigue and health-related quality of life depend on the disability status and clinical course in RRMS. Mult. Scler. Relat. Disord. 77, 104861 (2023). Ramirez, A. O. et al. Prevalence and burden of multiple sclerosis-related fatigue: a systematic literature review. BMC Neurol. 21, 468 (2021). Bass, A. D. et al. Multiple Sclerosis Impact on Daily Activities, Emotional Well-Being, and Relationships: The Global vsMSTM Survey. Int. J. MS Care 22, 158–164 (2019). Sandry, J., Genova, H. M., Dobryakova, E., DeLuca, J. & Wylie, G. Subjective Cognitive Fatigue in Multiple Sclerosis Depends on Task Length. Front. Neurol. 5, 214 (2014). Larson, R. D. Psychometric Properties of the Modified Fatigue Impact Scale. Int. J. MS Care 15, 15–20 (2013). Adibi, I. et al. Multiple sclerosis-related fatigue lacks a unified definition: A narrative review. J. Res. Méd. Sci. : Off. J. Isfahan Univ. Méd. Sci. 27, 24 (2022). Patejdl, R. & Zettl, U. K. The pathophysiology of motor fatigue and fatigability in multiple sclerosis. Front. Neurol. 13, 891415 (2022). Hammam, N. et al. Fatigue in Rheumatoid Arthritis Patients: Association With Sleep Quality, Mood Status, and Disease Activity. Reum. Clínica 16, 339–344 (2020). Tarazi, M., Gaffney, R. G., Pearson, D., Kushner, C. J. & Werth, V. P. Fatigue in systemic lupus erythematosus and other autoimmune skin diseases. Br. J. Dermatol. 180, 1468–1472 (2019). Raveendran, A. V., Jayadevan, R. & Sashidharan, S. Long COVID: An overview. Diabetes Metab. Syndr.: Clin. Res. Rev. 15, 869–875 (2021). Maqbali, M. A., Sinani, M. A., Naamani, Z. A., Badi, K. A. & Tanash, M. I. Prevalence of Fatigue in Patients With Cancer: A Systematic Review and Meta-Analysis. J. Pain Symptom Manag. 61, 167-189.e14 (2021). Yoon, J.-H. et al. The demographic features of fatigue in the general population worldwide: a systematic review and meta-analysis. Front. Public Heal. 11, 1192121 (2023). Nicholson, K., Stewart, M. & Thind, A. Examining the symptom of fatigue in primary care: a comparative study using electronic medical records. J. Innov. Heal. Inform. 22, 235–243 (2015). Manjaly, Z.-M. et al. Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis. J. Neurol., Neurosurg. Psychiatry 90, 642 (2019). Jakimovski, D. et al. Multiple sclerosis. Lancet 403, 183–202 (2024). Filippi, M. et al. Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol. 18, 198–210 (2019). Wattjes, M. P. et al. 2021 MAGNIMS–CMSC–NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol. 20, 653–670 (2021). Kutzelnigg, A. et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain 128, 2705–2712 (2005). York, E. N. et al. Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: Magnetisation transfer, water diffusion and g-ratio. NeuroImage: Clin. 36, 103228 (2022). Galazzo, I. B. et al. Unraveling the MRI‐Based Microstructural Signatures Behind Primary Progressive and Relapsing–Remitting Multiple Sclerosis Phenotypes. J. Magn. Reson. Imaging 55, 154–163 (2022). Shi, Z. et al. Microstructural alterations in different types of lesions and their perilesional white matter in relapsing-remitting multiple sclerosis based on diffusion kurtosis imaging. Mult. Scler. Relat. Disord. 71, 104572 (2023). Figueroa-Vargas, A. et al. Frontoparietal connectivity correlates with working memory performance in multiple sclerosis. Sci Rep-uk 10, 9310 (2020). Wenger, A. L. et al. An investigation of the association between focal damage and global network properties in cognitively impaired and cognitively preserved patients with multiple sclerosis. Front. Neurosci. 17, 1007580 (2023). Høgestøl, E. A. et al. Symptoms of fatigue and depression is reflected in altered default mode network connectivity in multiple sclerosis. PLoS ONE 14, e0210375 (2019). Danciut, I. et al. Understanding the mechanisms of fatigue in multiple sclerosis: linking interoception, metacognition and white matter dysconnectivity. Brain Commun. 6, fcae292 (2024). Baran, T. M., Zhang, Z., Anderson, A. J., McDermott, K. & Lin, F. Brain structural connectomes indicate shared neural circuitry involved in subjective experience of cognitive and physical fatigue in older adults. Brain Imaging Behav. 14, 2488–2499 (2020). Román, C. A. F., Wylie, G. R., DeLuca, J. & Yao, B. Associations of White Matter and Basal Ganglia Microstructure to Cognitive Fatigue Rate in Multiple Sclerosis. Front. Neurol. 13, 911012 (2022). Margoni, M. et al. Resting state functional connectivity modifications in monoaminergic circuits underpin fatigue development in patients with multiple sclerosis. Mol. Psychiatry 29, 2647–2656 (2024). Herring, T. E. et al. Differences in correlates of fatigue between relapsing and progressive forms of multiple sclerosis. Mult. Scler. Relat. Disord. 54, 103109 (2021). Bulut, T. Domain‐general and domain‐specific functional networks of Broca’s area underlying language processing. Brain Behav. 13, e3046 (2023). Orr, C. & Hester, R. Error-related anterior cingulate cortex activity and the prediction of conscious error awareness. Front. Hum. Neurosci. 6, 177 (2012). Martínez-Molina, M. P. et al. Lateral Prefrontal Theta Oscillations Causally Drive a Computational Mechanism Underlying Conflict Expectation and Adaptation. (2024). Zamorano, F. et al. Lateral Prefrontal Theta Oscillations Reflect Proactive Cognitive Control Impairment in Males With Attention Deficit Hyperactivity Disorder. Front. Syst. Neurosci. 14, 37 (2020). Deen, B., Koldewyn, K., Kanwisher, N. & Saxe, R. Functional Organization of Social Perception and Cognition in the Superior Temporal Sulcus. Cereb. Cortex 25, 4596–4609 (2015). Kausel, L., Michon, M., Soto-Icaza, P. & Aboitiz, F. A multimodal interface for speech perception: the role of the left superior temporal sulcus in social cognition and autism. Cereb. Cortex 34, 84–93 (2024). Lavín, C., Soto-Icaza, P., López, V. & Billeke, P. Another in need enhances prosociality and modulates frontal theta oscillations in young adults. Front. Psychiatry 14, 1160209 (2023). Venezia, J. H. et al. Auditory, Visual and Audiovisual Speech Processing Streams in Superior Temporal Sulcus. Front. Hum. Neurosci. 11, 174 (2017). Coffeen, U. et al. The Role of the Insular Cortex and Serotonergic System in the Modulation of Long-Lasting Nociception. Cells 13, 1718 (2024). Buyukturkoglu, K. et al. Machine learning to investigate superficial white matter integrity in early multiple sclerosis. J. Neuroimaging 32, 36–47 (2022). Cavallari, M. et al. Fatigue predicts disease worsening in relapsing-remitting multiple sclerosis patients. Mult. Scler. J. 22, 1841–1849 (2016). Zhou, F. et al. Altered Inter-Subregion Connectivity of the Default Mode Network in Relapsing Remitting Multiple Sclerosis: A Functional and Structural Connectivity Study. PLoS ONE 9, e101198 (2014). Zhou, F. et al. Intrinsic Functional Plasticity of the Thalamocortical System in Minimally Disabled Patients with Relapsing-Remitting Multiple Sclerosis. Front. Hum. Neurosci. 10, 2 (2016). Andreasen, A. K. et al. Regional brain atrophy in primary fatigued patients with multiple sclerosis. NeuroImage 50, 608–615 (2010). Pardini, M., Bonzano, L., Mancardi, G. L. & Roccatagliata, L. Frontal Networks Play a Role in Fatigue Perception in Multiple Sclerosis. Behav. Neurosci. 124, 329–336 (2010). Nakagawa, S. et al. Basal ganglia correlates of fatigue in young adults. Sci. Rep. 6, 21386 (2016). Pezzetta, R. et al. Combined EEG and immersive virtual reality unveil dopaminergic modulation of error monitoring in Parkinson’s Disease. npj Park.’s Dis. 9, 3 (2023). López-Góngora, M. et al. Neurophysiological Evidence of Compensatory Brain Mechanisms in Early-Stage Multiple Sclerosis. PLoS ONE 10, e0136786 (2015). Cercignani, M. et al. Cognitive fatigue in multiple sclerosis is associated with alterations in the functional connectivity of monoamine circuits. Brain Commun. 3, fcab023 (2021). Muhlert, N. et al. The grey matter correlates of impaired decision-making in multiple sclerosis. J. Neurol., Neurosurg. Psychiatry 86, 530 (2015). Brassard, S. L., Liu, H., Dosanjh, J., MacKillop, J. & Balodis, I. Neurobiological foundations and clinical relevance of effort-based decision-making. Brain Imaging Behav. 1–30 (2024) doi:10.1007/s11682-024-00890-x. Heitmann, H. et al. Fatigue, depression, and pain in multiple sclerosis: How neuroinflammation translates into dysfunctional reward processing and anhedonic symptoms. Mult. Scler. J. 28, 1020–1027 (2020). Brown, F. S. et al. Pain and cognitive performance in adults with multiple sclerosis: A systematic review. Mult. Scler. Relat. Disord. 71, 104584 (2023). Polli, F. E. et al. Reduced error-related activation in two anterior cingulate circuits is related to impaired performance in schizophrenia. Brain 131, 971–986 (2008). Billeke, P. et al. Paradoxical Expectation: Oscillatory Brain Activity Reveals Social Interaction Impairment in Schizophrenia. Biol. Psychiatry 78, 421–431 (2015). Achiron, A. et al. Superior temporal gyrus thickness correlates with cognitive performance in multiple sclerosis. Brain Struct. Funct. 218, 943–950 (2013). Yokote, H., Okano, K. & Toru, S. Theory of mind and its neuroanatomical correlates in people with multiple sclerosis. Mult. Scler. Relat. Disord. 55, 103156 (2021). Billeke, P., Zamorano, F., Cosmelli, D. & Aboitiz, F. Oscillatory Brain Activity Correlates with Risk Perception and Predicts Social Decisions. Cereb. Cortex 23, 2872–2883 (2013). Nourski, K. V. et al. Processing of auditory novelty in human cortex during a semantic categorization task. Hear. Res. 444, 108972 (2024). Carter, S. L. et al. Differences in resting state functional connectivity relative to multiple sclerosis and impaired information processing speed. Front. Neurol. 14, 1250894 (2023). Billeke, P. et al. Human Anterior Insula Encodes Performance Feedback and Relays Prediction Error to the Medial Prefrontal Cortex. Cereb. Cortex 30, 4011–4025 (2020). Salamone, P. C. et al. Altered neural signatures of interoception in multiple sclerosis. Hum. Brain Mapp. 39, 4743–4754 (2018). Roheger, M., Grothe, L., Hasselberg, L., Grothe, M. & Meinzer, M. A systematic review and meta-analysis of socio-cognitive impairments in multiple sclerosis. Sci. Rep. 14, 7096 (2024). Chen, M. H., DeLuca, J., Genova, H. M., Yao, B. & Wylie, G. R. Cognitive Fatigue Is Associated with Altered Functional Connectivity in Interoceptive and Reward Pathways in Multiple Sclerosis. Diagnostics 10, 930 (2020). Ratzan, A. S. et al. Characterizing the Extended Language Network in Individuals with Multiple Sclerosis. medRxiv 2023.08.30.23294843 (2023) doi:10.1101/2023.08.30.23294843. Shah, A. Fatigue in Multiple Sclerosis. Phys. Med. Rehabilitation Clin. North Am. 20, 363–372 (2009). Ziemssen, T. Multiple sclerosis beyond EDSS: depression and fatigue. J. Neurol. Sci. 277, S37–S41 (2009). Chen, M. H., Chiaravalloti, N. D. & DeLuca, J. Neurological update: cognitive rehabilitation in multiple sclerosis. J. Neurol. 268, 4908–4914 (2021). Mohr, D. C., Hart, S. L. & Goldberg, A. Effects of Treatment for Depression on Fatigue in Multiple Sclerosis. Psychosom. Med. 65, 542–547 (2003). Gandolfi, M. et al. Sensory integration balance training in patients with multiple sclerosis: A randomized, controlled trial. Mult. Scler. J. 21, 1453–1462 (2014). NG220, guideline & NICE. Multiple Sclerosis in Adults: Management. (2022). Braley, T. J. & Chervin, R. D. Fatigue in Multiple Sclerosis: Mechanisms, Evaluation, and Treatment. Sleep 33, 1061–1067 (2010). Nourbakhsh, B. et al. Safety and efficacy of amantadine, modafinil, and methylphenidate for fatigue in multiple sclerosis: a randomised, placebo-controlled, crossover, double-blind trial. Lancet Neurol. 20, 38–48 (2021). Figueroa-Vargas, A. et al. The effect of a cognitive training therapy based on stimulation of brain oscillations in patients with mild cognitive impairment in a Chilean sample: study protocol for a phase IIb, 2 × 3 mixed factorial, double-blind randomised controlled trial. Trials 25, 144 (2024). Soto-Icaza, P. et al. Oscillatory activity underlying cognitive performance in children and adolescents with autism: a systematic review. Front. Hum. Neurosci. 18, 1320761 (2024). Burton, C. Z. et al. Combined Cognitive Training and Transcranial Direct Current Stimulation in Neuropsychiatric Disorders: A Systematic Review and Meta-analysis. Biol. Psychiatry: Cogn. Neurosci. Neuroimaging 8, 151–161 (2023). Valdebenito-Oyarzo, G. et al. The parietal cortex has a causal role in ambiguity computations in humans. PLOS Biol. 22, e3002452 (2024). Momi, D. et al. Perturbation of resting-state network nodes preferentially propagates to structurally rather than functionally connected regions. Sci. Rep. 11, 12458 (2021). Mortezanejad, M., Ehsani, F., Masoudian, N., Zoghi, M. & Jaberzadeh, S. Comparing the effects of multi-session anodal trans-cranial direct current stimulation of primary motor and dorsolateral prefrontal cortices on fatigue and quality of life in patients with multiple sclerosis: a double-blind, randomized, sham-controlled trial. Clin. Rehabilitation 34, 1103–1111 (2020). Chalah, M. A. et al. Fatigue in Multiple Sclerosis: Neural Correlates and the Role of Non-Invasive Brain Stimulation. Front. Cell. Neurosci. 9, 460 (2015). Filippo, M. D. et al. Fluid biomarkers in multiple sclerosis: from current to future applications. Lancet Reg. Heal. - Eur. 44, 101009 (2024). Amtmann, D. et al. Comparison of the Psychometric Properties of Two Fatigue Scales in Multiple Sclerosis. Rehabilitation Psychol. 57, 159–166 (2012). Boeschoten, R. E. et al. Prevalence of depression and anxiety in Multiple Sclerosis: A systematic review and meta-analysis. J. Neurol. Sci. 372, 331–341 (2017). Simpson, A. C. et al. Structural MRI measures are associated with fatigue severity and persistence in a large, real-world cohort of people with multiple sclerosis. Mult. Scler. J. 30, 738–746 (2024). Kurtzke, J. F. Rating neurologic impairment in multiple sclerosis An expanded disability status scale (EDSS). Neurology 33, 1444–1444 (1983). Rooney, S., McFadyen, D. A., Wood, D. L., Moffat, D. F. & Paul, P. L. Minimally important difference of the fatigue severity scale and modified fatigue impact scale in people with multiple sclerosis. Mult. Scler. Relat. Disord. 35, 158–163 (2019). Nourbakhsh, B. et al. Longitudinal associations between brain structural changes and fatigue in early MS. Mult. Scler. Relat. Disord. 5, 29–33 (2016). Rottoli, M., Gioia, S. L., Frigeni, B. & Barcella, V. Pathophysiology, assessment and management of multiple sclerosis fatigue: an update. Expert Rev. Neurother. 17, 373–379 (2017). Kausel, L. et al. Patients recovering from COVID-19 who presented with anosmia during their acute episode have behavioral, functional, and structural brain alterations. Sci. Rep. 14, 19049 (2024). Ivanovic, D. et al. Brain structural parameters correlate with University Selection Test outcomes in Chilean high school graduates. Sci. Rep. 12, 20562 (2022). Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013). Kucukboyaci, N. E. et al. Integration of multimodal MRI data via PCA to explain language performance. NeuroImage: Clin. 5, 197–207 (2014). Liu, Y. & Tong, X. A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS. J. Behav. Data Sci. 4, 81–104 (2024). Brink-Jensen, K. & Ekstrøm, C. T. Inference for feature selection using the Lasso with high-dimensional data. arXiv (2014) doi:10.48550/arxiv.1403.4296. Tournier, J.-D. et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202, 116137 (2019). Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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significant interaction between PwRMS and healthy controls experiencing fatigue (HC).\u003cstrong\u003e B. \u003c/strong\u003ePrincipal Component 1 (PC1) showed no significant association with fatigue measures. \u003cstrong\u003eA-B: \u003c/strong\u003eThe black dot indicates the median, the line shows the 95% high-density interval of the posterior distribution, and the shaded area represents the entire posterior distribution. \u003cstrong\u003e\u0026nbsp;C. \u003c/strong\u003eLoading for PC15\u003cstrong\u003e. \u003c/strong\u003eThe main white matter volumes are shown according to absolute values of loading of PC15. In the \u003cem\u003e\u003cstrong\u003eright hemisphere\u003c/strong\u003e\u003c/em\u003e: banks of the superior temporal sulcus (0.26), insula (0.20), lateral occipital (0.19), rostral anterior cingulate (0.19). In the \u003cem\u003e\u003cstrong\u003eleft hemisphere\u003c/strong\u003e\u003c/em\u003e: caudal anterior cingulate (0.27), pars triangularis (0.26), transverse temporal (0.24), cerebellum (0.20) (it is not visible in the figure), cortex entorhinal (0.20), and paracentral (0.18). HC: Health control experience fatigue, MC i: Multiple sclerosis interaction, that means the difference in the correlation between HC and patients with multiple sclerosis, \u003cem\u003erh: right hemisphere, lh: left hemisphere. Statistical significance levels: n.s., no significant difference; *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5419035/v1/41b742bec9435e7ee893417b.png"},{"id":71481321,"identity":"68c27de5-efc3-4dbc-8a74-a661c5061da4","added_by":"auto","created_at":"2024-12-16 06:02:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":530399,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Visualization of white matter volumes in the left hemisphere’s Inferior Frontal Gyrus (Pars Triangularis) shown in brown, with connecting fibers highlighted in green in patients with Multiple Sclerosis. \u003cstrong\u003eB. \u003c/strong\u003ePosterior distribution of the regressor for Fractional Anisotropy in fibers connecting within the Inferior Frontal Gyrus (Pars Triangularis) for the fatigue SSL model. IFG, Inferior Frontal Gyrus; FA, Fractional Anisotropy. Statistical significance levels: n.s., no significant difference; *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5419035/v1/76f41abdd95b8136d297647f.png"},{"id":71481325,"identity":"e93a5c2e-937a-4e1f-86a8-c3b5d3c18bc2","added_by":"auto","created_at":"2024-12-16 06:02:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":215298,"visible":true,"origin":"","legend":"\u003cp\u003eConnectivity analysis methodology. a) Raw diffusion-weighted images (DWI) undergo various preprocessing steps to enhance image quality. Following pre-processing, DTI, FA, and DWI mask images are generated. These outputs are used to create a deterministic whole-brain tractography. b) The T1w image is co-registered to DWI space. After co-registration, cortical parcellation is generated using the Freesurfer mri_synthseg algorithm. c) FA maps from all subjects are analyzed using FSL’s Tract-Based Spatial Statistics (TBSS) framework. d) Using the cortical parcellation and the tractogram, a connectome is generated to identify and segment the streamlines connecting the specific ROIs. The mean FA values of these ROI tracts are then calculated and used for statistical analysis.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5419035/v1/5559250dd2b8ecfa55df6f14.png"},{"id":83068064,"identity":"6b8847ee-29b3-4974-93fd-da8daf641522","added_by":"auto","created_at":"2025-05-19 16:09:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1939915,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5419035/v1/b97f9204-e81b-41bd-b17c-e3e7cb91d443.pdf"},{"id":71482789,"identity":"10166dbb-eb1a-48b1-b1f0-89f8b29bf4ce","added_by":"auto","created_at":"2024-12-16 06:10:44","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":181442,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5419035/v1/6869441827f3ba8e21095f85.png"},{"id":71481323,"identity":"46df025b-3d55-476d-bb91-41ffc15a2751","added_by":"auto","created_at":"2024-12-16 06:02:44","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":161184,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5419035/v1/01e3c2f0cdc692219f858228.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"White Matter Volume and Microstructural Integrity Are Associated with Fatigue in Relapsing Multiple Sclerosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis is the most common neurological non traumatic condition that generates disability in young adults \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,2\u003c/sup\u003e. Relapsing multiple sclerosis (RMS) represents the most prevalent subtype of MS, accounting for approximately 85% of cases at diagnosis. It is characterized by episodes of neurological dysfunction, or relapses, followed by periods of remission, during which there is either partial or full recovery \u003csup\u003e3\u003c/sup\u003e. Fatigue is one of the most frequently reported and disabling symptoms in people with multiple sclerosis (PwMS), affecting an estimated 60\u0026ndash;80% of this population \u003csup\u003e4,5\u003c/sup\u003e. The presence of fatigue negatively impacts the quality of life, employment, psychological state, and daily functioning \u003csup\u003e6,7\u003c/sup\u003e. Fatigue is difficult to define and measure objectively, especially in PwMS. Currently, most fatigue assessments for pwMS rely on self-reported measures, with several widely recognized instruments available, including the Modified Fatigue Impact Scale (MFIS) \u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe pathophysiological mechanisms underlying fatigue may involve central factors, such as disruptions in neuronal energetics, function, or signal conduction; peripheral mechanisms, including muscular dysfunction; or systemic factors, such as immune system dysregulation. Despite great efforts to understand the mechanisms underlying fatigue, these have not yet been elucidated \u003csup\u003e4,10,11\u003c/sup\u003e. Beyond pwMS, fatigue is a symptom present in diverse medical conditions, and the prevalence of fatigue increases significantly in a number of diseases that involve dysregulation of the immune system, such as cancer, chronic infection, autoimmune diseases, and neurological diseases, such as rheumatoid arthritis \u003csup\u003e12\u003c/sup\u003e, lupus \u003csup\u003e13\u003c/sup\u003e, Long-Covid \u003csup\u003e14\u003c/sup\u003e, cancer \u003csup\u003e15\u003c/sup\u003e. Fatigue has also been reported in healthy individuals, and its prevalence of general fatigue is 20.4% in healthy adults \u003csup\u003e16\u003c/sup\u003e. In clinical practice, fatigue is among the top five most frequently presented health complaints in primary care \u003csup\u003e17\u003c/sup\u003e. This highlights the relevance and complexity of fatigue as a symptom, suggesting that its underlying pathophysiological mechanisms are multifactorial, which makes it difficult to differentiate general mechanics from pathology-specific and patient-specific causes \u003csup\u003e18\u003c/sup\u003e. This fact limits the therapeutic approach that efficiently resolves each patient's condition.\u003c/p\u003e \u003cp\u003eMRI is a widely utilized non-invasive imaging technique in clinical practice that enables the in vivo detection of central nervous system (CNS) damage associated with MS \u003csup\u003e19\u0026ndash;21\u003c/sup\u003e. Structural MRI has been extensively employed to investigate brain abnormalities in PwMS, offering valuable insights into the location and severity of structural damage, including gray matter, white matter lesion (WML) burden, and brain atrophy \u003csup\u003e22\u003c/sup\u003e. Although conventional MRI provides valuable insights through qualitative evaluations or volumetric analyses, relying solely on these techniques to explain clinical symptomatology in MS is limited and often inconsistent \u003csup\u003e4\u003c/sup\u003e. To overcome these limitations, incorporating quantitative analyses and advanced imaging methods like diffusion MRI (dMRI) is crucial \u003csup\u003e23\u003c/sup\u003e. dMRI enables the exploration of subtle brain abnormalities by assessing structural connectivity, revealing how different brain regions are interconnected to form networks \u003csup\u003e24\u003c/sup\u003e. In MS, microstructural damage to tissues, including myelin and axons, is a defining feature, even in the early stages of the disease \u003csup\u003e3\u003c/sup\u003e. Such damage disrupts structural connectivity impairing functional connectivity \u003csup\u003e25,26\u003c/sup\u003e. These disruptions in brain networks likely play a pivotal role in the clinical manifestation of MS symptoms \u003csup\u003e27\u003c/sup\u003e including fatigue \u003csup\u003e28,29\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have identified abnormalities in the cortico-striato-thalamo-cortical loop as key contributors to fatigue in various subtypes of MS \u003csup\u003e30\u0026ndash;32\u003c/sup\u003e. However, brain connectivity changes specific to fatigue in PwMS have yet to be thoroughly investigated \u003csup\u003e3\u003c/sup\u003e. Given that the pathophysiological mechanisms and clinical characteristics of relapsing MS differ significantly from those of progressive MS subtypes \u003csup\u003e33\u003c/sup\u003e, it is crucial to examine the underlying brain alterations associated with fatigue, specifically within this group. In this study, we aimed to assess the relationship between brain structural MRI measures, including volume and connectivity and reported fatigue in people with relapsing multiple sclerosis (PwRMS) and control participants with subjective fatigue but no neurological conditions. This approach may offer new insights into distinguishing general mechanisms underlying fatigue from those specifically impacting PwRMS.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDescriptive of the participant characteristics.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe characteristics of the pwRMS and HC experiencing fatigue are shown in Table\u0026nbsp;1. For PwRMS, the mean age of 33 participants was 37.63 years (SD: 7.88 years); 59.3% were female, and schooling was 18.56 years (SD: 1.85). The mean total MFIS score was 44.96 (SD: 15.59). Four PwRMS had depression diagnosis under treatment. The median PASAT was 0.45 (IQR: 0.95), and the median SDMT Z score was 0.5 (IQR: 0.86). For healthy controls experiencing fatigue, the mean age of 29 participants was 39.02 years (SD: 7.03 years) (no differences between groups, t(59)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.72, p\u0026thinsp;=\u0026thinsp;0.46); 58.6% were female (no differences between groups, x\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;1), and schooling was 19 years (SD: 1.09) (no differences between groups, t(59)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.55, p\u0026thinsp;=\u0026thinsp;0.58). The mean total MFIS score was 42.41 (SD: 11.74) (no differences between groups, t(59)\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;=\u0026thinsp;0.47). The median SDMT Z score was 0.09 (IQR: 0.88) (with a significantly smaller difference compared to PwRMS, t(59)\u0026thinsp;=\u0026thinsp;2.05, p\u0026thinsp;=\u0026thinsp;0.044). Three fatigued healthy controls had depression diagnoses under treatment (no differences between groups, x\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1e-31, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation between structural MRI measures and fatigue score.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst, we calculated the volumes of cortical and subcortical gray matter, as well as white matter for PwRMS and fatigued HC, using the first two steps of the Human Connectome Project (HCP) pipeline mentioned previously. Second, we calculated the Principal Components Analysis (PCA) for cortical and subcortical gray matter and white matter for each group and selected the first twenty PCA that had a main contribution. Third, we implemented Bayesian LASSO and Bayesian Spike-and-Slab LASSO (SSL) linear regression models to examine relationships between compositional predictors\u0026mdash;including the PCA of cortical, subcortical gray matter, and white matter volumes\u0026mdash;and total fatigue scores. This analysis was conducted within and between groups (PwRMS and healthy individuals experiencing fatigue) to identify specific volumetric associations with self-reported fatigue scores.\u003c/p\u003e \u003cp\u003eWe found that only PC #15 of the white matter volumes showed a significant relation with fatigue in PwRMS (LASSO: posterior distribution mean: 11.5, 95%HDI [2.4 21.2], p\u003csub\u003eMCMC\u003c/sub\u003e=0.01; SSL mean: 8.5, 95%HDI [0.01 17.7], p\u003csub\u003eMCMC\u003c/sub\u003e=0.04), lead a significant difference in its relation between the PwRMS and fatigued healthy controls (LASSO: posterior distribution mean: 7.7, 95%HDI [1.6 13.6], p\u003csub\u003eMCMC\u003c/sub\u003e=0.0072; SSL means 5.8, 95%HDI = [0.01 11.5], p\u003csub\u003eMCMC\u003c/sub\u003e=0.03 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Then, we identified the first twenty white matter regions that contributed to PC # 15 (Table\u0026nbsp;2) and ordered them according to the value absolute loading of the major to minor in PC # 15. The main white matter regions of PC # 15 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and the first twenty principal white matter regions included were left hemisphere caudal anterior cingulate, left hemisphere pars triangularis of the inferior frontal gyrus, right hemisphere banks of the superior temporal sulcus, left hemisphere transverse temporal region, right hemisphere insula, left hemisphere cerebellum, left hemisphere entorhinal region, right hemisphere lateral occipital region, right hemisphere rostral anterior cingulate gyrus, and left hemisphere paracentral gyrus.\u003c/p\u003e \u003cp\u003eNo significant relationships were found when analyzing subcortical gray matter segmentations and cortical gray matte segmentations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation between connectivity MRI measures and fatigue score.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRegarding the analysis of FA, we calculate this measure in all the bungles that connected the area of PC15, and that can be determined in all participants. Thus, four bungles were tested using a LASSO and SSL models, and a significant increase in FA was found for the streamlines that connect within the region Pars Triangularis (LASSO posterior distribution mean\u0026thinsp;=\u0026thinsp;86.2, 95%HDI = [29 143], p\u003csub\u003eMCMC\u003c/sub\u003e =0.0019; SSL mean\u0026thinsp;=\u0026thinsp;98.3, 95%HDI = [48 146], p\u003csub\u003eMCMC\u003c/sub\u003e =0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides novel insights into the relationship between structural abnormalities and connectivity changes associated with fatigue in PwRMS compared to HC experiencing fatigue. Our findings reveal distinct white matter volumetric patterns and structural connectivity contributing to fatigue, particularly in regions involved in cognitive, language, sensorial integration, emotional, and social processing.\u003c/p\u003e \u003cp\u003eThe most significant findings of our study include, on the one hand, the identification of specific white matter volume that correlated with fatigue differently between individuals with PwRMS and HC experiencing fatigue. Thus, the fatigue experienced by the patients seems to be a neurobiological mechanism dependent on white matter changes, in contrast to that experienced by HC. Notably, both samples are comparable in all other variables measured and were characterized by preserved cognitive capacity, minimal or no disability, and a comparable rate of mood symptoms. This main finding associated with matter volume identifies brain regions related to fatigue in PwRMS. These areas involve the left caudal anterior cingulate, left pars triangularis, right banks of the superior temporal sulcus, left transverse temporal areas, right insula, left cerebellum, left entorhinal cortex, right lateral occipital, right rostral anterior cingulate, and left paracentral regions. These areas are crucial to networks involved in language \u003csup\u003e34\u003c/sup\u003e, cognitive control \u003csup\u003e35\u0026ndash;37\u003c/sup\u003e, social cognition \u003csup\u003e38\u0026ndash;40\u003c/sup\u003e, sensorimotor integration \u003csup\u003e41\u003c/sup\u003e, and emotional and pain processing \u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the other hand, in the case of PwRMS, changes in the specific superficial white matter fiber are identified through DTI \u003csup\u003e43\u0026ndash;46\u003c/sup\u003e, specifically in FA as reported by other studies \u003csup\u003e47,48\u003c/sup\u003e. Interestingly, the increase of FA in SWM related to fatigue was observed in PwRMS, in bundles associated with the left pars triangularis, one the most relevant white matter regions that contribute more to the difference in the relation between white matter volumes and fatigue. This supports the claim that fatigue in PwRMS may have a distinct neuroanatomical signature compared to fatigue in healthy individuals \u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings extend prior research on fatigue in PwMS, with notable involvement in different white matter regions, such as, the anterior cingulate cortex, a region consistently implicated in fatigue across neurological disorders \u003csup\u003e47,50\u003c/sup\u003e. Altered white matter volume in this area in PwRMS may reflect compromised functions such as error detection, and performance monitoring \u003csup\u003e51,52\u003c/sup\u003e, as well as effort-based decision-making \u003csup\u003e53,54\u003c/sup\u003e, and pain processing \u003csup\u003e55,56\u003c/sup\u003e, in a similar way than that observed in other pathologies like schizophrenia \u003csup\u003e57,58\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eObserved alterations in the banks of the superior temporal sulcus (bSTS) contribute to understanding the disruptions in multimodal sensory integration \u003csup\u003e59\u003c/sup\u003e and social cognition \u003csup\u003e60,61\u003c/sup\u003e, presented in PwRMS. Further, differences in the transverse temporal regions, involved in basic auditory processing \u003csup\u003e62\u003c/sup\u003e, highlight alterations in speech comprehension and auditory attention \u003csup\u003e63\u003c/sup\u003e even at early stages of RMS. Differences in the insula\u0026rsquo;s white matter volume indicate possible early effects on interoception, emotional processing, feedback monitoring processing \u003csup\u003e64\u003c/sup\u003e, and cognitive integration in PwRMS \u003csup\u003e65\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne region we highlight, both for its structural characteristics and connectivity, is the white matter in the pars triangularis and the SWM bundles traversing this area. This suggests that fatigue affects cognitive networks beyond motor function, aligning with recent findings that MS-related fatigue impacts language processing, sustained communication, social cognition, and executive functions, including inhibitory control and decision-making \u003csup\u003e66\u0026ndash;68\u003c/sup\u003e. Nonetheless, the elevated fractional anisotropy (FA) associated with fatigue in SWM bundles connecting to the left pars triangularis may indicate early compensatory mechanisms aimed at offsetting changes in white matter regions associated with fatigue in PwRMS. This finding aligns with the report by Buyukturkoglu et al. \u003csup\u003e43\u003c/sup\u003e, which demonstrated that patients in the early stages of MS exhibit significant changes in SWM bundles, including those connected to the insula, inferior frontal, orbitofrontal, superior and medial temporal, and pre-and post-central cortices, even prior pronounced structural and functional alterations emerge.\u003c/p\u003e \u003cp\u003eOur findings have several important clinical implications. Overall, the observed white matter volume differences reflect impairments in networks associated with sensory integration, language, executive functions essential for cognitive control, and social cognition. Together, these processes are crucial for daily functioning \u003csup\u003e69\u003c/sup\u003e, and they are associated with decreased quality of life in people with MS \u003csup\u003e70\u003c/sup\u003e. Early disruptions in white matter integrity and regional connectivity hold significant clinical implications, underscoring the need for close monitoring. There is a need for multimodal interventions that address various dimensions of fatigue. This includes cognitive rehabilitation strategies to enhance cognitive function \u003csup\u003e71\u003c/sup\u003e, emotional support to manage mood disturbances \u003csup\u003e72\u003c/sup\u003e, and therapies to improve sensory integration \u003csup\u003e73\u003c/sup\u003e. Because the current treatments are limited, there is some controversy regarding their efficacy \u003csup\u003e74,75\u003c/sup\u003e. For example, a randomized, placebo-controlled, crossover, double-blind trial led by Nourbakhsh suggests that Amantadine and Modafinil are not better than placebo in improving MS fatigue \u003csup\u003e76\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this context, our results provide insights that could guide future interventions to slow disease progression and reduce both the long-term effects of the disease and those related to aging. In recent years, non-invasive brain stimulation, informed by precise knowledge of functional and structural brain alterations, has shown the potential to alleviate various symptoms in brain pathologies \u003csup\u003e77\u0026ndash;79\u003c/sup\u003e. These techniques have demonstrated their ability to produce changes in brain activity dependent on structural brain connectivity, leading to modulations in cognitive processing \u003csup\u003e36,80,81\u003c/sup\u003e. For example, studies found that interventions using transcranial direct current stimulation of the dorsolateral prefrontal cortex improved fatigue in PwRMS \u003csup\u003e82,83\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eComprehensive assessments for evaluating fatigue in PwMS are essential. This should encompass not only physical measures but also cognitive evaluations and emotional assessments to provide an integral view of the patient\u0026rsquo;s experience with fatigue. Additionally, incorporating neuroinflammatory biomarkers specific to MS-related fatigue, such as cytokine levels, glial activation markers, and other indicators of neuroimmune activity, could enhance the precision of fatigue assessments and clarify the disease-related and non-disease-related contributions to fatigue in MS \u003csup\u003e84\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, while the scale used in this study to assess subjective fatigue is validated for PwMS \u003csup\u003e85\u003c/sup\u003e, it lacks specificity in differentiating PwRMS experiencing fatigue from healthy controls with fatigue. In contrast, structural white matter measures effectively distinguished the two groups, underscoring the potential of multimodal assessments to capture fatigue-related differences. This highlights the need for comprehensive, multimodal evaluations integrating relevant information to manage fatigue in pwMS efficiently.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting our results. First, the cross-sectional design of this study limits our ability to establish causal relationships between white matter changes and the development of fatigue. Second, while our sample size was adequate for detecting group differences in the relation between white matter and fatigue, larger studies are needed to validate these findings and explore potential phenotypic variations. Furthermore, despite our samples being well matched in several clinical factors, the small sample size restricts our ability to control for possible confound factors, including common comorbidities such as depression and anxiety \u003csup\u003e86\u003c/sup\u003e. Third, focusing solely on structural changes may not fully capture the complexity of fatigue mechanisms, highlighting the need for multimodal imaging studies that include functional connectivity analyses \u003csup\u003e26\u003c/sup\u003e to investigate additional phenotypes, as well as comparisons with healthy controls with and without fatigue. Future research should investigate longitudinal changes in these brain regions as fatigue develops and fluctuates, explore the relationship between structural changes and functional connectivity \u003csup\u003e87\u003c/sup\u003e, examine how these structural differences relate to treatment response, and consider the impact of disease-modifying therapies on these white matter patterns.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides evidence of distinct white matter volumetric patterns associated with fatigue in pwRMS compared to healthy individuals experiencing fatigue. The involvement of regions essential for sensory integration, language, executive functions crucial for cognitive control, and social cognition suggests a unique neuroanatomical basis for fatigue in PwRMS. These findings advance our understanding of fatigue pathophysiology in RMS and underscore the importance of integrating additional measures, such as, neuroinflammatory and stress-related measures to enhance the specificity and sensitivity of fatigue assessments in both clinical and research settings, ultimately contributing to developing more targeted therapeutic approaches.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eStudy design and inclusion criteria.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe conducted a cross-sectional study involving 33 participants with relapsing multiple sclerosis (PwRMS), with a mean age of 37.63 years (SD: 7.88 years), 59.3% were female, and schooling was 18.56 years (SD: 1.85); and 29 healthy controls (HC) experiencing fatigue, with a mean age of 39.02 years (SD: 7.03 years), 58.6% were female, and schooling was 19 years (SD: 1.09). The characteristics of the PwRMS and HC experiencing fatigue are shown in Table\u0026nbsp;1. All PwRMS were enrolled in the Multiple Sclerosis Program at Pontificia Universidad Cat\u0026oacute;lica de Chile. In the study, we collected clinical and demographic data on PwRMS, including standardized patient history, MRI outcome measures, and tests of neurological and cognitive functions, using an electronic medical record. Individuals with a clinically confirmed diagnosis of relapsing multiple sclerosis (RMS) were eligible for participation in the study. PwMS inclusion criteria were a requirement to have a brain MRI within 6 months before the survey, to complete a fatigue assessment Modified Fatigue Impact Scale (MFIS), no relapses or progression in the last 6 months, no history of other major neurological and psychiatric disorders, preserved cognitive capacity evaluated with Paced Auditory Serial Addition Test (PASAT) \u0026lt; -1.5 Z-score, and Symbol Digit Modalities Test (SDMT) \u0026lt; -1.5 Z-score, null o mild level disability evaluated by the Expanded Disability Status Scale \u003csup\u003e88\u003c/sup\u003e (EDSS\u0026thinsp;\u0026le;\u0026thinsp;3), maintenance of usual medication for at least 6 months, and no uncorrected visual deficits. All healthy controls (HC) experiencing fatigue were enrolled in Laboratorio de Neurociencia Social y Neuromodulaci\u0026oacute;n (NeuroCICS) at Centro de Investigaci\u0026oacute;n en Complejidad Social at Universidad del Desarrollo, Chile. For HC, we collected clinical and demographic data and MRI outcomes. Inclusion criteria for HC included having a brain MRI within 6 months before the study, completing the fatigue assessment Modified Fatigue Impact Scale (MFIS), no history of major neurological or psychiatric disorders, and no uncorrected visual deficits.\u003c/p\u003e\n\u003ch3\u003eEthics declarations\u003c/h3\u003e\n\u003cp\u003e All participants gave informed consent, and all experimental procedures were approved by the Ethical Committee for Health Sciences at Pontificia Universidad Cat\u0026oacute;lica de Chile, Chile (ID: 220711001). These consent processes and all procedures complied with Chilean national legislation, institutional guidelines, and the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMRI outcome measures.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e The MRI protocol for PwMS was conducted as part of the Multiple Sclerosis Program within the Faculty of Medicine at the Pontifical Catholic University of Chile, according to the policies of the Chilean Ministry of Health. These were performed on Philips 3T scanners using standardized three-dimensional fluid-attenuated inversion recovery (3D FLAIR) and 3D T1 (MPRAGE [magnetization-prepared rapid gradient-echo imaging]) acquisition sequences. Both structural and functional images were acquired on a Philips Ingenia 3 T MRI scanner. T1 weighted 3D images (TR 7.8 ms, TE 3.6 ms, FOV 240 \u0026times; 240 \u0026times; 164, flip angle 8\u0026deg;, SENSE factor 2.5, acquisition time 4 min 8 s) and a FLAIR (TR 4800 ms, TE 290 ms, FOV 240 \u0026times; 240 \u0026times; 164, acquisition time 4 min 33 s).\u003c/p\u003e \u003cp\u003eThe MRI protocol for HC was conducted using Siemens Skyra 3T scanners and included (i) a sagittal 3D anatomical MPRAGE T1-weighted imaging (repetition time [TR]/ echo time [TE]\u0026thinsp;=\u0026thinsp;2530/2.19 ms, inversion time [TI]\u0026thinsp;=\u0026thinsp;1100 ms, flip angle\u0026thinsp;=\u0026thinsp;7\u0026deg;; 1x1x1 mm3 voxels), (ii) a sagital 3D anatomical SPC T2-weighted (TR/TE\u0026thinsp;=\u0026thinsp;3200/412 ms, flip angle\u0026thinsp;=\u0026thinsp;120\u0026deg;; echo train length [ETL]\u0026thinsp;=\u0026thinsp;258; 1x1x1 mm3 voxels), (iii) a sagittal 3D fluid attenuation inversion recovery (FLAIR) imaging (TR/TE\u0026thinsp;=\u0026thinsp;5000/388 ms, TI\u0026thinsp;=\u0026thinsp;1800 ms, ETL\u0026thinsp;=\u0026thinsp;251; flip angle\u0026thinsp;=\u0026thinsp;120\u0026ordm;, 1-mm slice thickness, 1x1x1 mm3 voxels), (iv) an axial 3D echo-planar imaging (EPI) (TR/TE\u0026thinsp;=\u0026thinsp;8600/95 ms, 2x2x2 mm3 voxels, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;) with diffusion gradients applied in 60 non-collinear directions and two optimized b factors (b1\u0026thinsp;=\u0026thinsp;0 and b2\u0026thinsp;=\u0026thinsp;1000 s/mm2) with two repetitions.\u003c/p\u003e\n\u003ch3\u003ePatient-reported outcomes\u003c/h3\u003e\n\u003cp\u003eFatigue was evaluated using the Modified Fatigue Impact Scale (MFIS) \u003csup\u003e89,90\u003c/sup\u003e, a 21-item self-reported measure of fatigue that assesses impact on psychosocial, physical and cognitive function. The total score ranges from 0 to 84 (the higher the score, the worse the fatigue). MFIS items can be aggregated into 3 subscales: physical (score range 0\u0026ndash;36); cognitive (score range 0\u0026ndash;40), and psychosocial (score range 0\u0026ndash;8). The MFIS is a valid and reliable tool for assessing fatigue, has a low floor and ceiling effect, and captures both physical and cognitive aspects of fatigue \u003csup\u003e90\u003c/sup\u003e. It is the recommended tool for assessing fatigue in clinical practice and research in PwMS \u003csup\u003e91\u003c/sup\u003e. The MFIS was administered for the PwRMS and healthy controls enrolled in the study.\u003c/p\u003e\n\u003ch3\u003eDescriptive analysis\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics for demographic and clinical study participants were presented as mean (SD) or percentages. For fatigue analysis, the global fatigue of MFIS was obtained by the sum of points from three subscales: cognitive (10 items), physical (9 items), and psychosocial fatigue (2 items). The values of global fatigue ranged from 0 to 84 points, with higher values indicating greater fatigue.\u003c/p\u003e \u003cp\u003eFor age, education level, SDMT, and MFIS (total score and the cognitive, physical, and psychosocial categories), a t-test was performed to assess differences between the PwRMS and HC groups in terms of fatigue. For variables such as sex and depression, chi-square statistical analysis was used\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBrain Segmentation Volumes and Cortical Thickness Measurements\u003c/h2\u003e \u003cp\u003eIn the first step, we segmented brain areas and consulted volumes. Structural Image processing was conducted using the first two steps of the Human Connectome Project (HCP) pipeline, as described in our prior works \u003csup\u003e92,93\u003c/sup\u003e, and detailed elsewhere \u003csup\u003e94\u003c/sup\u003e. Briefly, the \"PreFreeSurfer\" phase generated an undistorted native structural volume space, aligned T1-weighted (T1w) and T2-weighted (T2w) images, corrected bias fields derived from both images, and registered the native space to the common MNI coordinate space using a rigid affine registration, and them, a non-linear registration to standard space. Utilizing FreeSurfer version 6, the \"FreeSurfer\" stage conducted volume segmentation and cortical surface reconstructions, including delineation of the \"white\" (gray/white matter boundary) and \"pial\" (gray/cerebrospinal fluid [CSF] boundary) surfaces, followed by registration to a common template (fsaverage).\u003c/p\u003e \u003cp\u003eIn the second step, to study the relationship between cortical and subcortical gray matter volumes, white matter volumes, and self-reported global fatigue from the Modified Fatigue Impact Scale (MFIS) in both people with relapsing multiple sclerosis (PwRMS) and healthy controls, we conducted a Principal Component Analysis (PCA). This analysis was performed on three regions: subcortical gray matter, white matter, and cortical gray matter. The latter was segmented using the Desikan\u0026ndash;Killiany atlas for cortical regions. PCA allowed us to reduce the complexity of volumetric data while retaining the most variance, facilitating an exploration of the associations between brain structures and fatigue scores across the study groups. This approach has been used for complex data from MRI and integrated modalities by other groups \u003csup\u003e95\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the third step, we applied a Bayesian Least Absolute Shrinkage and Selection Operator (LASSO) and Bayesian Spike-and-Slab Lasso (SSL) linear regression models using R (A language and environment for statistical computing) in conjunction with the Just Another Gibbs Sampler (JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling). LASSO and Spike-and-Slab LASSO offer robust frameworks for addressing the challenges associated with multiple comparisons in regression analysis. Their ability to perform automatic feature selection while controlling for overfitting makes them tools for high-dimensional data analysis. This method enabled the analysis of compositional data, improving the accuracy of parameter estimation and offering deeper insights into the relationships between the compositional predictors\u0026mdash;namely, the PCA of cortical and subcortical gray matter and white matter volumes\u0026mdash;and the outcome variable of fatigue. Since compositional data inherently reflect the relative nature of its components, analyzing them allows for better differentiation of ratings and reduces response biases. This approach is effective whether all components contributing to the total are fully quantified or only a subset is included \u003csup\u003e96\u003c/sup\u003e. Thus, these methods mitigate the type I error rate through its inherent feature selection process, as it tends to include only a subset of statistically significant predictors, thus reducing the number of tests conducted on non-informative variables \u003csup\u003e97\u003c/sup\u003e. For these analyses, we used the first 10 components for subcortical segmentation and the first 20 for white and cortical gray matter.\u003c/p\u003e \u003cp\u003eInferences for significant regressors were assessed using the 95% highest density interval (HDI) of the posterior distribution. A p-value equivalent (pMCMC) was calculated by comparing Markov Chain Monte Carlo (MCMC) samples against a reference value of zero. P-values below 0.05 were considered statistically significant, and all comparisons were two-tailed. Statistical analyses were conducted using R (version 4.2.1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConnectivity MRI Analysis\u003c/h3\u003e\n\u003cp\u003eThe diffusion imaging was processed using MRtrix3 software \u003csup\u003e98\u003c/sup\u003e. The first stage of the process was to apply a pre-processing pipeline, which involved a number of steps designed to enhance image quality and reduce the impact of artifacts. These steps included image denoising, correction of Gibbs ringing, motion and eddy current distortion correction, and N4 bias field correction. The pre-processed diffusion images were used to calculate the Diffusion Tensor Imaging (DTI) model and extract the fractional anisotropy (FA) mean diffusivity. A deterministic tractography algorithm (\u003cb\u003eTensor_det\u003c/b\u003e) with Anatomically Constrained Tractography (ACT) was then applied using the following parameters: angular threshold\u0026thinsp;=\u0026thinsp;60\u0026deg;, step size\u0026thinsp;=\u0026thinsp;0.2 mm, minimum length\u0026thinsp;=\u0026thinsp;25 mm, maximum length\u0026thinsp;=\u0026thinsp;250 mm, and FA threshold\u0026thinsp;=\u0026thinsp;0.06 (adjusted to 0.03 when ACT is used). The T1w image was coregistered to the diffusion-weighted imaging (DWI) space using FSL's FLIRT tool, with 6 degrees of freedom (DOF) rigid-body transformation. With the T1w image aligned to the diffusion space, subject parcellation (Desikan atlas) was performed using FreeSurfer MRI_SynthSeg.\u003c/p\u003e \u003cp\u003eSince the diffusion data for PwRMS and healthy controls experiencing fatigue were acquired in different centers using different scanners, direct comparison between the centers was not possible; any significant results could potentially be attributed to scanner differences rather than biological factors. Therefore, 2 different analyses were conducted solely among PwRMS, focusing on the relationship between fatigue scores and FA values within these streamlined bundles.\u003c/p\u003e\n\u003ch3\u003eMean FA measures of ROI tracts\u003c/h3\u003e\n\u003cp\u003eUsing the tractogram (DWI space) and the cortical parcellation, tractography-based connectome was generated using MRtrix3 (tck2connectome command) and then used to select the streamlines that connect, at both ends, the regions that show a difference in white matter (WM) thickness that were previously calculated (connectome2tck). These segmented tracts were then used to conduct a statistical analysis based on the mean FA measures in these regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors report no competing interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Agencia Nacional de Investigaci\u0026oacute;n y Desarrollo de Chile (ANID), FONDECYT (1211227 to PB, AF-V; 1190513 to FZ and PB), FONDEQUIP EQM150076, ANID-Basal Project FB0008 (AC3E) to PG.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAF-V, FA, RH-C, PG, CC, EC, and PB conceptualized the study. MV, BS, CM, MI-C, MPM-M, PC-P, MA-O, XS, CM, VM-R, PF-T, MD-D, JH, and FZ collected the data. AF-V, SN, PG and PB analyzed the data. AF-V, SN, FA, and PB wrote the main manuscripts and prepared the figures. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data are available in the OpenNeuro repository once accepted. All codes are available in the GitHub repository once accepted. For any further inquiries, please contact Pablo Billeke at [email protected] , Alejandra Figueroa at [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWalton, C. et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult. Scler. J. 26, 1816\u0026ndash;1821 (2020).\u003c/li\u003e\n\u003cli\u003eThompson, A. J., Baranzini, S. E., Geurts, J., Hemmer, B. \u0026amp; Ciccarelli, O. Multiple sclerosis. Lancet 391, 1622\u0026ndash;1636 (2018).\u003c/li\u003e\n\u003cli\u003eKampaite, A. et al. Brain connectivity changes underlying depression and fatigue in relapsing-remitting multiple sclerosis: A systematic review. PLOS ONE 19, e0299634 (2024).\u003c/li\u003e\n\u003cli\u003eMeijboom, R. et al. Fatigue in early multiple sclerosis: MRI metrics of neuroinflammation, relapse and neurodegeneration. Brain Commun. 6, fcae278 (2024).\u003c/li\u003e\n\u003cli\u003eAhvenj\u0026auml;rvi, H. et al. Fatigue and health-related quality of life depend on the disability status and clinical course in RRMS. Mult. Scler. Relat. Disord. 77, 104861 (2023).\u003c/li\u003e\n\u003cli\u003eRamirez, A. O. et al. Prevalence and burden of multiple sclerosis-related fatigue: a systematic literature review. BMC Neurol. 21, 468 (2021).\u003c/li\u003e\n\u003cli\u003eBass, A. D. et al. Multiple Sclerosis Impact on Daily Activities, Emotional Well-Being, and Relationships: The Global vsMSTM Survey. Int. J. MS Care 22, 158\u0026ndash;164 (2019).\u003c/li\u003e\n\u003cli\u003eSandry, J., Genova, H. M., Dobryakova, E., DeLuca, J. \u0026amp; Wylie, G. Subjective Cognitive Fatigue in Multiple Sclerosis Depends on Task Length. Front. Neurol. 5, 214 (2014).\u003c/li\u003e\n\u003cli\u003eLarson, R. D. Psychometric Properties of the Modified Fatigue Impact Scale. Int. J. MS Care 15, 15\u0026ndash;20 (2013).\u003c/li\u003e\n\u003cli\u003eAdibi, I. et al. Multiple sclerosis-related fatigue lacks a unified definition: A narrative review. J. Res. Méd. Sci. : Off. J. Isfahan Univ. Méd. Sci. 27, 24 (2022).\u003c/li\u003e\n\u003cli\u003ePatejdl, R. \u0026amp; Zettl, U. K. The pathophysiology of motor fatigue and fatigability in multiple sclerosis. Front. Neurol. 13, 891415 (2022).\u003c/li\u003e\n\u003cli\u003eHammam, N. et al. Fatigue in Rheumatoid Arthritis Patients: Association With Sleep Quality, Mood Status, and Disease Activity. Reum. Cl\u0026iacute;nica 16, 339\u0026ndash;344 (2020).\u003c/li\u003e\n\u003cli\u003eTarazi, M., Gaffney, R. G., Pearson, D., Kushner, C. J. \u0026amp; Werth, V. P. Fatigue in systemic lupus erythematosus and other autoimmune skin diseases. Br. J. Dermatol. 180, 1468\u0026ndash;1472 (2019).\u003c/li\u003e\n\u003cli\u003eRaveendran, A. V., Jayadevan, R. \u0026amp; Sashidharan, S. Long COVID: An overview. Diabetes Metab. Syndr.: Clin. Res. Rev. 15, 869\u0026ndash;875 (2021).\u003c/li\u003e\n\u003cli\u003eMaqbali, M. A., Sinani, M. A., Naamani, Z. A., Badi, K. A. \u0026amp; Tanash, M. I. Prevalence of Fatigue in Patients With Cancer: A Systematic Review and Meta-Analysis. J. Pain Symptom Manag. 61, 167-189.e14 (2021).\u003c/li\u003e\n\u003cli\u003eYoon, J.-H. et al. The demographic features of fatigue in the general population worldwide: a systematic review and meta-analysis. Front. Public Heal. 11, 1192121 (2023).\u003c/li\u003e\n\u003cli\u003eNicholson, K., Stewart, M. \u0026amp; Thind, A. Examining the symptom of fatigue in primary care: a comparative study using electronic medical records. J. Innov. Heal. Inform. 22, 235\u0026ndash;243 (2015).\u003c/li\u003e\n\u003cli\u003eManjaly, Z.-M. et al. Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis. J. Neurol., Neurosurg. Psychiatry 90, 642 (2019).\u003c/li\u003e\n\u003cli\u003eJakimovski, D. et al. Multiple sclerosis. Lancet 403, 183\u0026ndash;202 (2024).\u003c/li\u003e\n\u003cli\u003eFilippi, M. et al. Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol. 18, 198\u0026ndash;210 (2019).\u003c/li\u003e\n\u003cli\u003eWattjes, M. P. et al. 2021 MAGNIMS\u0026ndash;CMSC\u0026ndash;NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol. 20, 653\u0026ndash;670 (2021).\u003c/li\u003e\n\u003cli\u003eKutzelnigg, A. et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain 128, 2705\u0026ndash;2712 (2005).\u003c/li\u003e\n\u003cli\u003eYork, E. N. et al. Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: Magnetisation transfer, water diffusion and g-ratio. NeuroImage: Clin. 36, 103228 (2022).\u003c/li\u003e\n\u003cli\u003eGalazzo, I. B. et al. Unraveling the MRI‐Based Microstructural Signatures Behind Primary Progressive and Relapsing\u0026ndash;Remitting Multiple Sclerosis Phenotypes. J. Magn. Reson. Imaging 55, 154\u0026ndash;163 (2022).\u003c/li\u003e\n\u003cli\u003eShi, Z. et al. Microstructural alterations in different types of lesions and their perilesional white matter in relapsing-remitting multiple sclerosis based on diffusion kurtosis imaging. Mult. Scler. Relat. Disord. 71, 104572 (2023).\u003c/li\u003e\n\u003cli\u003eFigueroa-Vargas, A. et al. Frontoparietal connectivity correlates with working memory performance in multiple sclerosis. Sci Rep-uk 10, 9310 (2020).\u003c/li\u003e\n\u003cli\u003eWenger, A. L. et al. An investigation of the association between focal damage and global network properties in cognitively impaired and cognitively preserved patients with multiple sclerosis. Front. Neurosci. 17, 1007580 (2023).\u003c/li\u003e\n\u003cli\u003eH\u0026oslash;gest\u0026oslash;l, E. A. et al. Symptoms of fatigue and depression is reflected in altered default mode network connectivity in multiple sclerosis. PLoS ONE 14, e0210375 (2019).\u003c/li\u003e\n\u003cli\u003eDanciut, I. et al. Understanding the mechanisms of fatigue in multiple sclerosis: linking interoception, metacognition and white matter dysconnectivity. Brain Commun. 6, fcae292 (2024).\u003c/li\u003e\n\u003cli\u003eBaran, T. M., Zhang, Z., Anderson, A. J., McDermott, K. \u0026amp; Lin, F. Brain structural connectomes indicate shared neural circuitry involved in subjective experience of cognitive and physical fatigue in older adults. Brain Imaging Behav. 14, 2488\u0026ndash;2499 (2020).\u003c/li\u003e\n\u003cli\u003eRom\u0026aacute;n, C. A. F., Wylie, G. R., DeLuca, J. \u0026amp; Yao, B. Associations of White Matter and Basal Ganglia Microstructure to Cognitive Fatigue Rate in Multiple Sclerosis. Front. Neurol. 13, 911012 (2022).\u003c/li\u003e\n\u003cli\u003eMargoni, M. et al. Resting state functional connectivity modifications in monoaminergic circuits underpin fatigue development in patients with multiple sclerosis. Mol. Psychiatry 29, 2647\u0026ndash;2656 (2024).\u003c/li\u003e\n\u003cli\u003eHerring, T. E. et al. Differences in correlates of fatigue between relapsing and progressive forms of multiple sclerosis. Mult. Scler. Relat. Disord. 54, 103109 (2021).\u003c/li\u003e\n\u003cli\u003eBulut, T. Domain‐general and domain‐specific functional networks of Broca\u0026rsquo;s area underlying language processing. Brain Behav. 13, e3046 (2023).\u003c/li\u003e\n\u003cli\u003eOrr, C. \u0026amp; Hester, R. Error-related anterior cingulate cortex activity and the prediction of conscious error awareness. Front. Hum. Neurosci. 6, 177 (2012).\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Molina, M. P. et al. Lateral Prefrontal Theta Oscillations Causally Drive a Computational Mechanism Underlying Conflict Expectation and Adaptation. (2024).\u003c/li\u003e\n\u003cli\u003eZamorano, F. et al. Lateral Prefrontal Theta Oscillations Reflect Proactive Cognitive Control Impairment in Males With Attention Deficit Hyperactivity Disorder. Front. Syst. Neurosci. 14, 37 (2020).\u003c/li\u003e\n\u003cli\u003eDeen, B., Koldewyn, K., Kanwisher, N. \u0026amp; Saxe, R. Functional Organization of Social Perception and Cognition in the Superior Temporal Sulcus. Cereb. Cortex 25, 4596\u0026ndash;4609 (2015).\u003c/li\u003e\n\u003cli\u003eKausel, L., Michon, M., Soto-Icaza, P. \u0026amp; Aboitiz, F. A multimodal interface for speech perception: the role of the left superior temporal sulcus in social cognition and autism. Cereb. Cortex 34, 84\u0026ndash;93 (2024).\u003c/li\u003e\n\u003cli\u003eLav\u0026iacute;n, C., Soto-Icaza, P., L\u0026oacute;pez, V. \u0026amp; Billeke, P. Another in need enhances prosociality and modulates frontal theta oscillations in young adults. Front. Psychiatry 14, 1160209 (2023).\u003c/li\u003e\n\u003cli\u003eVenezia, J. H. et al. Auditory, Visual and Audiovisual Speech Processing Streams in Superior Temporal Sulcus. Front. Hum. Neurosci. 11, 174 (2017).\u003c/li\u003e\n\u003cli\u003eCoffeen, U. et al. The Role of the Insular Cortex and Serotonergic System in the Modulation of Long-Lasting Nociception. Cells 13, 1718 (2024).\u003c/li\u003e\n\u003cli\u003eBuyukturkoglu, K. et al. Machine learning to investigate superficial white matter integrity in early multiple sclerosis. J. Neuroimaging 32, 36\u0026ndash;47 (2022).\u003c/li\u003e\n\u003cli\u003eCavallari, M. et al. Fatigue predicts disease worsening in relapsing-remitting multiple sclerosis patients. Mult. Scler. J. 22, 1841\u0026ndash;1849 (2016).\u003c/li\u003e\n\u003cli\u003eZhou, F. et al. Altered Inter-Subregion Connectivity of the Default Mode Network in Relapsing Remitting Multiple Sclerosis: A Functional and Structural Connectivity Study. PLoS ONE 9, e101198 (2014).\u003c/li\u003e\n\u003cli\u003eZhou, F. et al. Intrinsic Functional Plasticity of the Thalamocortical System in Minimally Disabled Patients with Relapsing-Remitting Multiple Sclerosis. Front. Hum. Neurosci. 10, 2 (2016).\u003c/li\u003e\n\u003cli\u003eAndreasen, A. K. et al. Regional brain atrophy in primary fatigued patients with multiple sclerosis. NeuroImage 50, 608\u0026ndash;615 (2010).\u003c/li\u003e\n\u003cli\u003ePardini, M., Bonzano, L., Mancardi, G. L. \u0026amp; Roccatagliata, L. Frontal Networks Play a Role in Fatigue Perception in Multiple Sclerosis. Behav. Neurosci. 124, 329\u0026ndash;336 (2010).\u003c/li\u003e\n\u003cli\u003eNakagawa, S. et al. Basal ganglia correlates of fatigue in young adults. Sci. Rep. 6, 21386 (2016).\u003c/li\u003e\n\u003cli\u003ePezzetta, R. et al. Combined EEG and immersive virtual reality unveil dopaminergic modulation of error monitoring in Parkinson\u0026rsquo;s Disease. npj Park.\u0026rsquo;s Dis. 9, 3 (2023).\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-G\u0026oacute;ngora, M. et al. Neurophysiological Evidence of Compensatory Brain Mechanisms in Early-Stage Multiple Sclerosis. PLoS ONE 10, e0136786 (2015).\u003c/li\u003e\n\u003cli\u003eCercignani, M. et al. Cognitive fatigue in multiple sclerosis is associated with alterations in the functional connectivity of monoamine circuits. Brain Commun. 3, fcab023 (2021).\u003c/li\u003e\n\u003cli\u003eMuhlert, N. et al. The grey matter correlates of impaired decision-making in multiple sclerosis. J. Neurol., Neurosurg. Psychiatry 86, 530 (2015).\u003c/li\u003e\n\u003cli\u003eBrassard, S. L., Liu, H., Dosanjh, J., MacKillop, J. \u0026amp; Balodis, I. Neurobiological foundations and clinical relevance of effort-based decision-making. Brain Imaging Behav. 1\u0026ndash;30 (2024) doi:10.1007/s11682-024-00890-x.\u003c/li\u003e\n\u003cli\u003eHeitmann, H. et al. Fatigue, depression, and pain in multiple sclerosis: How neuroinflammation translates into dysfunctional reward processing and anhedonic symptoms. Mult. Scler. J. 28, 1020\u0026ndash;1027 (2020).\u003c/li\u003e\n\u003cli\u003eBrown, F. S. et al. Pain and cognitive performance in adults with multiple sclerosis: A systematic review. Mult. Scler. Relat. Disord. 71, 104584 (2023).\u003c/li\u003e\n\u003cli\u003ePolli, F. E. et al. Reduced error-related activation in two anterior cingulate circuits is related to impaired performance in schizophrenia. Brain 131, 971\u0026ndash;986 (2008).\u003c/li\u003e\n\u003cli\u003eBilleke, P. et al. Paradoxical Expectation: Oscillatory Brain Activity Reveals Social Interaction Impairment in Schizophrenia. Biol. Psychiatry 78, 421\u0026ndash;431 (2015).\u003c/li\u003e\n\u003cli\u003eAchiron, A. et al. Superior temporal gyrus thickness correlates with cognitive performance in multiple sclerosis. Brain Struct. Funct. 218, 943\u0026ndash;950 (2013).\u003c/li\u003e\n\u003cli\u003eYokote, H., Okano, K. \u0026amp; Toru, S. Theory of mind and its neuroanatomical correlates in people with multiple sclerosis. Mult. Scler. Relat. Disord. 55, 103156 (2021).\u003c/li\u003e\n\u003cli\u003eBilleke, P., Zamorano, F., Cosmelli, D. \u0026amp; Aboitiz, F. Oscillatory Brain Activity Correlates with Risk Perception and Predicts Social Decisions. Cereb. Cortex 23, 2872\u0026ndash;2883 (2013).\u003c/li\u003e\n\u003cli\u003eNourski, K. V. et al. Processing of auditory novelty in human cortex during a semantic categorization task. Hear. Res. 444, 108972 (2024).\u003c/li\u003e\n\u003cli\u003eCarter, S. L. et al. Differences in resting state functional connectivity relative to multiple sclerosis and impaired information processing speed. Front. Neurol. 14, 1250894 (2023).\u003c/li\u003e\n\u003cli\u003eBilleke, P. et al. Human Anterior Insula Encodes Performance Feedback and Relays Prediction Error to the Medial Prefrontal Cortex. Cereb. Cortex 30, 4011\u0026ndash;4025 (2020).\u003c/li\u003e\n\u003cli\u003eSalamone, P. C. et al. Altered neural signatures of interoception in multiple sclerosis. Hum. Brain Mapp. 39, 4743\u0026ndash;4754 (2018).\u003c/li\u003e\n\u003cli\u003eRoheger, M., Grothe, L., Hasselberg, L., Grothe, M. \u0026amp; Meinzer, M. A systematic review and meta-analysis of socio-cognitive impairments in multiple sclerosis. Sci. Rep. 14, 7096 (2024).\u003c/li\u003e\n\u003cli\u003eChen, M. H., DeLuca, J., Genova, H. M., Yao, B. \u0026amp; Wylie, G. R. Cognitive Fatigue Is Associated with Altered Functional Connectivity in Interoceptive and Reward Pathways in Multiple Sclerosis. Diagnostics 10, 930 (2020).\u003c/li\u003e\n\u003cli\u003eRatzan, A. S. et al. Characterizing the Extended Language Network in Individuals with Multiple Sclerosis. medRxiv 2023.08.30.23294843 (2023) doi:10.1101/2023.08.30.23294843.\u003c/li\u003e\n\u003cli\u003eShah, A. Fatigue in Multiple Sclerosis. Phys. Med. Rehabilitation Clin. North Am. 20, 363\u0026ndash;372 (2009).\u003c/li\u003e\n\u003cli\u003eZiemssen, T. Multiple sclerosis beyond EDSS: depression and fatigue. J. Neurol. Sci. 277, S37\u0026ndash;S41 (2009).\u003c/li\u003e\n\u003cli\u003eChen, M. H., Chiaravalloti, N. D. \u0026amp; DeLuca, J. Neurological update: cognitive rehabilitation in multiple sclerosis. J. Neurol. 268, 4908\u0026ndash;4914 (2021).\u003c/li\u003e\n\u003cli\u003eMohr, D. C., Hart, S. L. \u0026amp; Goldberg, A. Effects of Treatment for Depression on Fatigue in Multiple Sclerosis. Psychosom. Med. 65, 542\u0026ndash;547 (2003).\u003c/li\u003e\n\u003cli\u003eGandolfi, M. et al. Sensory integration balance training in patients with multiple sclerosis: A randomized, controlled trial. Mult. Scler. J. 21, 1453\u0026ndash;1462 (2014).\u003c/li\u003e\n\u003cli\u003eNG220, guideline \u0026amp; NICE. Multiple Sclerosis in Adults: Management. (2022).\u003c/li\u003e\n\u003cli\u003eBraley, T. J. \u0026amp; Chervin, R. D. Fatigue in Multiple Sclerosis: Mechanisms, Evaluation, and Treatment. Sleep 33, 1061\u0026ndash;1067 (2010).\u003c/li\u003e\n\u003cli\u003eNourbakhsh, B. et al. Safety and efficacy of amantadine, modafinil, and methylphenidate for fatigue in multiple sclerosis: a randomised, placebo-controlled, crossover, double-blind trial. Lancet Neurol. 20, 38\u0026ndash;48 (2021).\u003c/li\u003e\n\u003cli\u003eFigueroa-Vargas, A. et al. The effect of a cognitive training therapy based on stimulation of brain oscillations in patients with mild cognitive impairment in a Chilean sample: study protocol for a phase IIb, 2 \u0026times; 3 mixed factorial, double-blind randomised controlled trial. Trials 25, 144 (2024).\u003c/li\u003e\n\u003cli\u003eSoto-Icaza, P. et al. Oscillatory activity underlying cognitive performance in children and adolescents with autism: a systematic review. Front. Hum. Neurosci. 18, 1320761 (2024).\u003c/li\u003e\n\u003cli\u003eBurton, C. Z. et al. Combined Cognitive Training and Transcranial Direct Current Stimulation in Neuropsychiatric Disorders: A Systematic Review and Meta-analysis. Biol. Psychiatry: Cogn. Neurosci. Neuroimaging 8, 151\u0026ndash;161 (2023).\u003c/li\u003e\n\u003cli\u003eValdebenito-Oyarzo, G. et al. The parietal cortex has a causal role in ambiguity computations in humans. PLOS Biol. 22, e3002452 (2024).\u003c/li\u003e\n\u003cli\u003eMomi, D. et al. Perturbation of resting-state network nodes preferentially propagates to structurally rather than functionally connected regions. Sci. Rep. 11, 12458 (2021).\u003c/li\u003e\n\u003cli\u003eMortezanejad, M., Ehsani, F., Masoudian, N., Zoghi, M. \u0026amp; Jaberzadeh, S. Comparing the effects of multi-session anodal trans-cranial direct current stimulation of primary motor and dorsolateral prefrontal cortices on fatigue and quality of life in patients with multiple sclerosis: a double-blind, randomized, sham-controlled trial. Clin. Rehabilitation 34, 1103\u0026ndash;1111 (2020).\u003c/li\u003e\n\u003cli\u003eChalah, M. A. et al. Fatigue in Multiple Sclerosis: Neural Correlates and the Role of Non-Invasive Brain Stimulation. Front. Cell. Neurosci. 9, 460 (2015).\u003c/li\u003e\n\u003cli\u003eFilippo, M. D. et al. Fluid biomarkers in multiple sclerosis: from current to future applications. Lancet Reg. Heal. - Eur. 44, 101009 (2024).\u003c/li\u003e\n\u003cli\u003eAmtmann, D. et al. Comparison of the Psychometric Properties of Two Fatigue Scales in Multiple Sclerosis. Rehabilitation Psychol. 57, 159\u0026ndash;166 (2012).\u003c/li\u003e\n\u003cli\u003eBoeschoten, R. E. et al. Prevalence of depression and anxiety in Multiple Sclerosis: A systematic review and meta-analysis. J. Neurol. Sci. 372, 331\u0026ndash;341 (2017).\u003c/li\u003e\n\u003cli\u003eSimpson, A. C. et al. Structural MRI measures are associated with fatigue severity and persistence in a large, real-world cohort of people with multiple sclerosis. Mult. Scler. J. 30, 738\u0026ndash;746 (2024).\u003c/li\u003e\n\u003cli\u003eKurtzke, J. F. Rating neurologic impairment in multiple sclerosis An expanded disability status scale (EDSS). Neurology 33, 1444\u0026ndash;1444 (1983).\u003c/li\u003e\n\u003cli\u003eRooney, S., McFadyen, D. A., Wood, D. L., Moffat, D. F. \u0026amp; Paul, P. L. Minimally important difference of the fatigue severity scale and modified fatigue impact scale in people with multiple sclerosis. Mult. Scler. Relat. Disord. 35, 158\u0026ndash;163 (2019).\u003c/li\u003e\n\u003cli\u003eNourbakhsh, B. et al. Longitudinal associations between brain structural changes and fatigue in early MS. Mult. Scler. Relat. Disord. 5, 29\u0026ndash;33 (2016).\u003c/li\u003e\n\u003cli\u003eRottoli, M., Gioia, S. L., Frigeni, B. \u0026amp; Barcella, V. Pathophysiology, assessment and management of multiple sclerosis fatigue: an update. Expert Rev. Neurother. 17, 373\u0026ndash;379 (2017).\u003c/li\u003e\n\u003cli\u003eKausel, L. et al. Patients recovering from COVID-19 who presented with anosmia during their acute episode have behavioral, functional, and structural brain alterations. Sci. Rep. 14, 19049 (2024).\u003c/li\u003e\n\u003cli\u003eIvanovic, D. et al. Brain structural parameters correlate with University Selection Test outcomes in Chilean high school graduates. Sci. Rep. 12, 20562 (2022).\u003c/li\u003e\n\u003cli\u003eGlasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105\u0026ndash;124 (2013).\u003c/li\u003e\n\u003cli\u003eKucukboyaci, N. E. et al. Integration of multimodal MRI data via PCA to explain language performance. NeuroImage: Clin. 5, 197\u0026ndash;207 (2014).\u003c/li\u003e\n\u003cli\u003eLiu, Y. \u0026amp; Tong, X. A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS. J. Behav. Data Sci. 4, 81\u0026ndash;104 (2024).\u003c/li\u003e\n\u003cli\u003eBrink-Jensen, K. \u0026amp; Ekstr\u0026oslash;m, C. T. Inference for feature selection using the Lasso with high-dimensional data. arXiv (2014) doi:10.48550/arxiv.1403.4296.\u003c/li\u003e\n\u003cli\u003eTournier, J.-D. et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202, 116137 (2019).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"People with Relapsing Multiple Sclerosis (PwRMS), MRI, DTI, fatigue, inferior frontal gyrus","lastPublishedDoi":"10.21203/rs.3.rs-5419035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5419035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultiple sclerosis (MS) is a prevalent neurological disorder marked by inflammation and demyelination, with fatigue being one of the most reported and debilitating symptoms. While fatigue occurs across various neurological conditions and even in healthy individuals, the specific mechanisms contributing to fatigue in each context remain unclear. In this study, we conducted a cross-sectional analysis involving 33 people with relapsing MS (PwRMS) and 29 healthy controls who also reported fatigue. Participants underwent MRI scans, including T1-weighted and diffusion-weighted imaging, to evaluate brain structure. Additionally, the Modified Fatigue Impact Scale was utilized. To investigate the hypothesis that fatigue correlates differently with brain structures in PwRMS, we employed Bayesian LASSO and Spike-and-Slab LASSO regression models. Our findings indicated that lower white matter volume and compromised microstructural integrity in specific brain regions\u0026mdash;such as the caudate part of cingulate gyrus, inferior frontal gyrus, and the banks of the superior temporal sulcus\u0026mdash;were significantly associated with fatigue scores in PwRMS. These results suggest that alterations in specific brain regions may play a critical role in the clinical manifestation of fatigue in MS. Understanding these insights could help differentiate general mechanisms of fatigue from those affecting people with relapsing MS, which may guide future therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"White Matter Volume and Microstructural Integrity Are Associated with Fatigue in Relapsing Multiple Sclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 06:02:39","doi":"10.21203/rs.3.rs-5419035/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-29T10:00:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-25T18:40:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-21T01:01:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218990449449563985482715947411775956405","date":"2024-11-15T08:52:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295198086348655227385860981912528127068","date":"2024-11-15T06:56:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-15T01:35:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-14T23:47:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-12T08:45:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-09T04:08:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-11-08T23:14:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"973f0479-71a4-4e88-8710-4fdffd21843c","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40739589,"name":"Biological sciences/Neuroscience/Cognitive ageing"},{"id":40739590,"name":"Biological sciences/Neuroscience/Neuroimmunology"},{"id":40739591,"name":"Health sciences/Neurology/Neurological disorders/Multiple sclerosis"},{"id":40739592,"name":"Biological sciences/Biological techniques/Imaging/Diffusion tensor imaging"},{"id":40739593,"name":"Biological sciences/Biological techniques/Imaging/Magnetic resonance imaging"}],"tags":[],"updatedAt":"2025-05-19T16:07:52+00:00","versionOfRecord":{"articleIdentity":"rs-5419035","link":"https://doi.org/10.1038/s41598-025-01465-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-12 15:57:27","publishedOnDateReadable":"May 12th, 2025"},"versionCreatedAt":"2024-12-16 06:02:39","video":"","vorDoi":"10.1038/s41598-025-01465-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-01465-6","workflowStages":[]},"version":"v1","identity":"rs-5419035","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5419035","identity":"rs-5419035","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}