This study aims to evaluate two distinct approaches for fiber radius estimation using diffusion‐relaxation MRI data acquired in biomimetic microfiber phantoms that mimic hollow axons. The methods considered are the spherical mean power‐law approach and a T2‐based pore size estimation technique.

Purpose To characterize the orientation dependence of magnetization transfer (MT) measures in white matter (WM) and propose a first correction method for such measures. Methods A characterization method was developed using the fiber orientation obtained from diffusion MRI (dMRI) with diffusion tensor imaging (DTI) and constrained spherical deconvolution (CSD). This allowed for characterization of the orientation dependence of measures in all of WM, regardless of the number of fiber orientation in a voxel. Furthermore, a first correction method was proposed from the results of characterization, aiming at removing said orientation dependence. Both methods were tested on a 20-subject dataset and effects on tractometry results were also evaluated. Results Previous results for single-fiber voxels were reproduced and a novel characterization was produced in voxels of crossing fibers, which seems to follow trends consistent with single-fiber results. Unwanted effects of the orientation dependence on MT measures were highlighted, for which the correction method was able to produce improved results. Conclusion Encouraging results of corrected MT measures showed the importance of such correction, opening the door for future research on the topic.

Background: Optical coherence tomography (OCT) is a biomarker of neuroaxonal loss in multiple sclerosis (MS). Objective: The objective was to assess the relative role of OCT, next to magnetic resonance imaging (MRI) and serum markers of disability in MS. Methods: A total of 100 patients and 52 controls underwent OCT to determine peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell-inner plexiform layers (GCIPL). Serum neurofilament light chain (sNfL), total lesion volume (TLV), and brain parenchymal fraction (BPF) were also assessed. The associations of OCT with disability were examined in linear regression models with correction for age, vision, and education. Results: In patients, pRNFL was associated with the Symbol Digit Modalities Test (SDMT; p = 0.030). In the multivariate analysis including sNfL and MRI measures, pRNFL (β = 0.19, p = 0.044) and TLV (β = −0.24, p = 0.023) were the only markers associated with the SDMT. pRNFL (p < 0.001) and GCIPL (p < 0.001) showed associations with the Expanded Disability Status Scale (EDSS). In the multivariate analysis, GCIPL showed the strongest association with the EDSS (β = −0.32, p < 0.001) followed by sNfL (β = 0.18, p = 0.024). Conclusion: The associations of OCT measures with cognitive and physical disability were independent of serum and brain MRI markers of neuroaxonal loss. OCT can be an important tool for stratification in MS, while longitudinal studies using combinations of biomarkers are warranted.

Objective: Identifying disability-related brain changes is important for multiple sclerosis (MS) patients. Currently, there is no clear understanding about which pathological features drive disability in single MS patients. In this work, we propose a novel comprehensive approach, GAMER-MRIL, leveraging whole-brain quantitative MRI (qMRI), convolutional neural network (CNN), and an interpretability method from classifying MS patients with severe disability to investigating relevant pathological brain changes. Methods: One-hundred-sixty-six MS patients underwent 3T MRI acquisitions. qMRI informative of microstructural brain properties was reconstructed, including quantitative T1 (qT1), myelin water fraction (MWF), and neurite density index (NDI). To fully utilize the qMRI, GAMER-MRIL extended a gated-attention-based CNN (GAMER-MRI), which was developed to select patch-based qMRI important for a given task/question, to the whole-brain image. To find out disability-related brain regions, GAMER-MRIL modified a structure-aware interpretability method, Layer-wise Relevance Propagation (LRP), to incorporate qMRI. Results: The test performance was AUC=0.885. qT1 was the most sensitive measure related to disability, followed by NDI. The proposed LRP approach obtained more specifically relevant regions than other interpretability methods, including the saliency map, the integrated gradients, and the original LRP. The relevant regions included the corticospinal tract, where average qT1 and NDI significantly correlated with patients' disability scores ($\rho$=-0.37 and 0.44). Conclusion: These results demonstrated that GAMER-MRIL can classify patients with severe disability using qMRI and subsequently identify brain regions potentially important to the integrity of the mobile function. Significance: GAMER-MRIL holds promise for developing biomarkers and increasing clinicians' trust in NN.

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.

Assessing the consistency of quantitative MRI measurements is critical for inclusion in longitudinal studies and clinical trials. Intraclass coefficient correlation and coefficient of variation were used to evaluate the different consistency aspects of diffusion‐ and myelin‐based MRI measures. Multi‐shell diffusion and inhomogeneous magnetization transfer data sets were collected from 20 healthy adults at a high‐frequency of five MRI sessions. The consistency was evaluated across whole bundles and the track‐profile along the bundles. The impact of the fiber populations on the consistency was also evaluated using the number of fiber orientations map. For whole and profile bundles, moderate to high reliability of diffusion and myelin measures were observed. We report higher reliability of measures for multiple fiber populations than single. The overall portrait of the most consistent measurements and bundles drawn from a wide range of MRI techniques presented here will be particularly useful for identifying reliable biomarkers capable of detecting, monitoring and predicting white matter changes in clinical applications and has the potential to inform patient‐specific treatment strategies.

Introduction The presence of focal cortical and white matter damage in patients with multiple sclerosis (pwMS) might lead to specific alterations in brain networks that are associated with cognitive impairment. We applied microstructure-weighted connectomes to investigate (i) the relationship between global network metrics and information processing speed in pwMS, and (ii) whether the disruption provoked by focal lesions on global network metrics is associated to patients’ information processing speed. Materials and methods Sixty-eight pwMS and 92 healthy controls (HC) underwent neuropsychological examination and 3T brain MRI including multishell diffusion (dMRI), 3D FLAIR, and MP2RAGE. Whole-brain deterministic tractography and connectometry were performed on dMRI. Connectomes were obtained using the Spherical Mean Technique and were weighted for the intracellular fraction. We identified white matter lesions and cortical lesions on 3D FLAIR and MP2RAGE images, respectively. PwMS were subdivided into cognitively preserved (CPMS) and cognitively impaired (CIMS) using the Symbol Digit Modalities Test (SDMT) z-score at cut-off value of −1.5 standard deviations. Statistical analyses were performed using robust linear models with age, gender, and years of education as covariates, followed by correction for multiple testing. Results Out of 68 pwMS, 18 were CIMS and 50 were CPMS. We found significant changes in all global network metrics in pwMS vs HC (p < 0.05), except for modularity. All global network metrics were positively correlated with SDMT, except for modularity which showed an inverse correlation. Cortical, leukocortical, and periventricular lesion volumes significantly influenced the relationship between (i) network density and information processing speed and (ii) modularity and information processing speed in pwMS. Interestingly, this was not the case, when an exploratory analysis was performed in the subgroup of CIMS patients. Discussion Our study showed that cortical (especially leukocortical) and periventricular lesions affect the relationship between global network metrics and information processing speed in pwMS. Our data also suggest that in CIMS patients increased focal cortical and periventricular damage does not linearly affect the relationship between network properties and SDMT, suggesting that other mechanisms (e.g. disruption of local networks, loss of compensatory processes) might be responsible for the development of processing speed deficits.

Importance There is a lack of validated biomarkers for disability progression independent of relapse activity (PIRA) in multiple sclerosis (MS). Objective To determine how serum glial fibrillary acidic protein (sGFAP) and serum neurofilament light chain (sNfL) correlate with features of disease progression vs acute focal inflammation in MS and how they can prognosticate disease progression. Design, Setting, and Participants Data were acquired in the longitudinal Swiss MS cohort (SMSC; a consortium of tertiary referral hospitals) from January 1, 2012, to October 20, 2022. The SMSC is a prospective, multicenter study performed in 8 centers in Switzerland. For this nested study, participants had to meet the following inclusion criteria: cohort 1, patients with MS and either stable or worsening disability and similar baseline Expanded Disability Status Scale scores with no relapses during the entire follow-up; and cohort 2, all SMSC study patients who had initiated and continued B-cell-depleting treatment (ie, ocrelizumab or rituximab). Exposures Patients received standard immunotherapies or were untreated. Main Outcomes and Measures In cohort 1, sGFAP and sNfL levels were measured longitudinally using Simoa assays. Healthy control samples served as the reference. In cohort 2, sGFAP and sNfL levels were determined cross-sectionally. Results This study included a total of 355 patients (103 [29.0%] in cohort 1: median [IQR] age, 42.1 [33.2-47.6] years; 73 female patients [70.9%]; and 252 [71.0%] in cohort 2: median [IQR] age, 44.3 [33.3-54.7] years; 156 female patients [61.9%]) and 259 healthy controls with a median [IQR] age of 44.3 [36.3-52.3] years and 177 female individuals (68.3%). sGFAP levels in controls increased as a function of age (1.5% per year; P < .001), were inversely correlated with BMI (-1.1% per BMI unit; P = .01), and were 14.9% higher in women than in men (P = .004). In cohort 1, patients with worsening progressive MS showed 50.9% higher sGFAP levels compared with those with stable MS after additional sNfL adjustment, whereas the 25% increase of sNfL disappeared after additional sGFAP adjustment. Higher sGFAP at baseline was associated with accelerated gray matter brain volume loss (per doubling: 0.24% per year; P < .001) but not white matter loss. sGFAP levels remained unchanged during disease exacerbations vs remission phases. In cohort 2, median (IQR) sGFAP z scores were higher in patients developing future confirmed disability worsening compared with those with stable disability (1.94 [0.36-2.23] vs 0.71 [-0.13 to 1.73]; P = .002); this was not significant for sNfL. However, the combined elevation of z scores of both biomarkers resulted in a 4- to 5-fold increased risk of confirmed disability worsening (hazard ratio [HR], 4.09; 95% CI, 2.04-8.18; P < .001) and PIRA (HR, 4.71; 95% CI, 2.05-9.77; P < .001). Conclusions and Relevance Results of this cohort study suggest that sGFAP is a prognostic biomarker for future PIRA and revealed its complementary potential next to sNfL. sGFAP may serve as a useful biomarker for disease progression in MS in individual patient management and drug development.

Detecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan‐PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow‐up of MS patients; however, multicenter validation studies are lacking.