Dataset Information

MR g-ratio-weighted connectome analysis in patients with multiple sclerosis.

ABSTRACT: Multiple sclerosis (MS) is a brain network disconnection syndrome. Although the brain network topology in MS has been evaluated using diffusion MRI tractography, the mechanism underlying disconnection in the disorder remains unclear. In this study, we evaluated the brain network topology in MS using connectomes with connectivity strengths based on the ratio of the inner to outer myelinated axon diameter (i.e., g-ratio), thereby providing enhanced sensitivity to demyelination compared with the conventional measures of connectivity. We mapped g-ratio-based connectomes in 14 patients with MS and compared them with those of 14 age- and sex-matched healthy controls. For comparison, probabilistic tractography was also used to map connectomes based on the number of streamlines (NOS). We found that g-ratio- and NOS-based connectomes comprised significant connectivity reductions in patients with MS, predominantly in the motor, somatosensory, visual, and limbic regions. However, only the g-ratio-based connectome enabled detection of significant increases in nodal strength in patients with MS. Finally, we found that the g-ratio-weighted nodal strength in motor, visual, and limbic regions significantly correlated with inter-individual variation in measures of disease severity. The g-ratio-based connectome can serve as a sensitive biomarker for diagnosing and monitoring disease progression.

SUBMITTER: Kamagata K

PROVIDER: S-EPMC6751178 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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MR g-ratio-weighted connectome analysis in patients with multiple sclerosis.

Kamagata Koji K Zalesky Andrew A Yokoyama Kazumasa K Andica Christina C Hagiwara Akifumi A Shimoji Keigo K Kumamaru Kanako K KK Takemura Mariko Y MY Hoshino Yasunobu Y Kamiya Kouhei K Hori Masaaki M Pantelis Christos C Hattori Nobutaka N Aoki Shigeki S

Scientific reports 20190918 1

Multiple sclerosis (MS) is a brain network disconnection syndrome. Although the brain network topology in MS has been evaluated using diffusion MRI tractography, the mechanism underlying disconnection in the disorder remains unclear. In this study, we evaluated the brain network topology in MS using connectomes with connectivity strengths based on the ratio of the inner to outer myelinated axon diameter (i.e., g-ratio), thereby providing enhanced sensitivity to demyelination compared with the co ...[more]

PMID: 31534143

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Project description:Introduction: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory (WM), but the search for neural correlates of WM within circumscribed areas has been inconclusive. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual differences in WM. Materials and Methods: We applied connectome-based predictive modeling to functional magnetic resonance imaging data from WM tasks in two independent samples with relapsing-remitting MS. In the internal sample (ninternal = 36), cross-validation was used to train a model to predict accuracy on the Paced Visual Serial Addition Test from functional connectivity. We hypothesized that this MS-specific model would successfully predict performance on the N-back task in the validation cohort (nvalidation = 36). In addition, we assessed the generalizability of existing WM networks derived in healthy young adults to these samples, and we explored anatomical differences between the healthy and MS networks. Results: We successfully derived an MS-specific predictive model of WM in the internal sample (full: rs = 0.47, permuted p = 0.011), but the predictions were not significant in the validation cohort (rs = -0.047; p = 0.78, mean squared error [MSE] = 0.006, R2 = -2.21%). In contrast, the healthy networks successfully predicted WM in both MS samples (internal: rs = 0.33 p = 0.049, MSE = 0.009, R2 = 13.4%; validation cohort: rs = 0.46, p = 0.005, MSE = 0.005, R2 = 16.9%), demonstrating their translational potential. Discussion: Functional networks identified in a large sample of healthy individuals predicted significant variance in WM in MS. Networks derived in small samples of people with MS may have limited generalizability, potentially due to disease-related heterogeneity. The robustness of models derived in large clinical samples warrants further investigation. ClinicalTrials.gov ID: NCT03244696.

Project description:Purpose To evaluate the ability of high-gradient-diffusion magnetic resonance (MR) imaging by using gradient strengths of up to 300 mT/m to depict axonal disease in lesions and normal-appearing white matter (NAWM) in patients with multiple sclerosis (MS) and to compare high-gradient-diffusion MR findings in these patients with those in healthy control subjects. Materials and Methods In this HIPAA-compliant institutional review board-approved prospective study in which all subjects provided written informed consent, six patients with relapsing-remitting MS and six healthy control subjects underwent diffusion-weighted imaging with a range of diffusion weightings performed with a 3-T human MR imager by using gradient strengths of up to 300 mT/m. A model of intra-axonal, extra-axonal, and free water diffusion was fitted to obtain estimates of axon diameter and density. Differences in axon diameter and density between lesions and NAWM in patients with MS were assessed by using the nonparametric Wilcoxon matched-pairs signed rank test, and differences between NAWM in subjects with MS and white matter in healthy control subjects were assessed by using the Mann-Whitney U test. Results MS lesions showed increased mean axon diameter (10.3 vs 7.9 ?m in the genu, 10.4 vs 9.3 ?m in the body, and 10.6 vs 8.2 ?m in the splenium; P < .05) and decreased axon density ([0.48 vs 1.1] × 10(10)/m(2) in the genu, [0.40 vs 0.70] × 10(10)/m(2) in the body, and [0.35 vs 1.1] × 10(10)/m(2) in the splenium; P < .05) compared with adjacent NAWM. No significant difference in mean axon diameter or axon density was detected between NAWM in subjects with MS and white matter in healthy control subjects. Conclusion High-gradient-diffusion MR imaging using gradient strengths of up to 300 mT/m can be used to characterize axonal disease in patients with MS, with results that agree with known trends from neuropathologic data showing increased axon diameter and decreased axon density in MS lesions when compared with NAWM. (©) RSNA, 2016 Online supplemental material is available for this article.

Project description:The development of accurate prognoses in multiple sclerosis is difficult, as the disease is characterized by heterogeneous patterns of brain abnormalities that relate in an unclear way to future impairments. Here, we use a statistical modeling approach to determine if the baseline pattern of connectome disruption due to T2-FLAIR lesions could predict a patient's future processing speed, as measured using the Symbol Digits Modality Test scores. Imaging data, demographics and Symbol Digits Modality Test scores were collected from 61 early relapsing remitting multiple sclerosis patients. The Network Modification Tool was used to estimate damage to the connectome by quantifying white matter abnormalities' effects on 1) global network properties, 2) regional connectivity and 3) connectivity between pairs of regions. MS subjects showed significant improvement of processing speed between baseline and follow-up (t?=?-2.6, p?=?0.0096); however, both baseline (t?=?-4.01, p?=?0.00012) and follow-up (t?=?-2.10, p?=?0.038) processing speed were significantly lower than age-matched healthy controls. Partial Least Squares Regression was used to create models that predict future processing speed from between baseline imaging metrics and demographics. The model based on region-pair disconnection and gray matter atrophy had the lowest AIC and highest prediction accuracy (R2?=?0.79) compared to models based on global (R2?=?0.41) or regional (R2?=?0.66) disconnection and gray matter atrophy, overlap with an ROI-based atlas and gray matter atrophy (R2?=?0.73) or gray matter atrophy alone (R2?=?0.71). We found that baseline measures of connectivity disruption in various parietal, temporal, occipital and subcortical regions and atrophy in the putamen were important predictors of future processing speed. We conclude that information about disruptions to pairwise brain connections is more informative of future processing speed than regional or global metrics or gray matter atrophy alone. The combination of quantitative disconnectome metrics, gray matter atrophy and statistical modeling approaches could enable clinicians in developing more accurate, individualized prognoses of future cognitive status in multiple sclerosis patients.

Dataset Information

MR g-ratio-weighted connectome analysis in patients with multiple sclerosis.

Publications

MR g-ratio-weighted connectome analysis in patients with multiple sclerosis.

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