Measuring Gray Matter and White Matter Damage in MS: Why This is Not Enough

Enzinger, Christian; Fazekas, Franz

doi:10.3389/fneur.2015.00056

Cited by 18 publications

(12 citation statements)

References 41 publications

(61 reference statements)

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“…We tested possible nonlinear effects of age with quadratic regression analysis, imputing graph parameters as dependent variables. (1) Structural cortical network nodes were defined as regions of interest in the native space cortical gray matter segmentation and corresponded to 3 × 3 × 3 voxel cubes (v j and v m are given as example cubes); (2) The similarity between all N cubes (nodes) in the network was determined with the correlation coefficient resulting in a matrix with 1 to N rows and columns; (3) The similarity matrix was thresholded and binarized with a threshold, ensuring a 5% chance of spurious connections for all subjects; (4-5) Structural cortical networks were extracted, and 20 random matrices for each binarized similarity matrix were generated with the same spatial degree distribution; (6) The network properties, the normalized clustering coefficient (γ), and the normalized path length (λ) were computed. The small-world coefficient (σ) was then obtained as γ/λ.…”

Section: Discussionmentioning

confidence: 99%

“…1 Conventional magnetic resonance imaging (MRI) findings are the best predictors of conversion to MS, although measures such as the white matter (WM) lesion volume only partially correlate with the variable course of the disease. 2 MRI abnormalities in the normal-appearing white and gray matter and connectivity alterations may contribute to clinical outcome of patients with CIS. 3 Functional changes in the brain can lead to related morphological modification in cortical areas 4,5 and, recently, it has become possible to describe these coordinated patterns of cortical morphology with network parameters.…”

Section: Introductionmentioning

confidence: 99%

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Single-subject structural cortical networks in clinically isolated syndrome

et al. 2019

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Background: Structural cortical networks (SCNs) represent patterns of coordinated morphological modifications in cortical areas, and they present the advantage of being extracted from previously acquired clinical magnetic resonance imaging (MRI) scans. SCNs have shown pathophysiological changes in many brain disorders, including multiple sclerosis. Objective: To investigate alterations of SCNs at the individual level in patients with clinically isolated syndrome (CIS), thereby assessing their clinical relevance. Methods: We analyzed baseline data collected in a prospective multicenter (MAGNIMS) study. CIS patients ( n = 60) and healthy controls ( n = 38) underwent high-resolution 3T MRI. Measures of disability and cognitive processing were obtained for patients. Single-subject SCNs were extracted from brain 3D-T1 weighted sequences; global and local network parameters were computed. Results: Compared to healthy controls, CIS patients showed altered small-world topology, an efficient network organization combining dense local clustering with relatively few long-distance connections. These disruptions were worse for patients with higher lesion load and worse cognitive processing speed. Alterations of centrality measures and clustering of connections were observed in specific cortical areas in CIS patients when compared with healthy controls. Conclusion: Our study indicates that SCNs can be used to demonstrate clinically relevant alterations of connectivity in CIS.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single-subject structural cortical networks in clinically isolated syndrome

et al. 2019

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“…For use in clinical practice, there are at present no validated techniques for monitoring atrophy in patients with MS using MR imaging (24,25). Measurement of disease-related changes in MS is hindered by the difficulties imposed by the diseased brain.…”

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confidence: 99%

Measurement of Whole-Brain and Gray Matter Atrophy in Multiple Sclerosis: Assessment with MR Imaging

Storelli¹,

Rocca

Pagani³

et al. 2018

Radiology

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Purpose To compare available methods for whole-brain and gray matter (GM) atrophy estimation in multiple sclerosis (MS) in terms of repeatability (same magnetic resonance [MR] imaging unit) and reproducibility (different system/field strength) for their potential clinical applications. Materials and Methods The softwares ANTs-v1.9, CIVET-v2.1, FSL-SIENAX/SIENA-5.0.1, Icometrix-MSmetrix-1.7, and SPM-v12 were compared. This retrospective study, performed between March 2015 and March 2017, collected data from (a) eight simulated MR images and longitudinal data (2 weeks) from 10 healthy control subjects to assess the cross-sectional and longitudinal accuracy of atrophy measures, (b) test-retest MR images in 29 patients with MS acquired within the same day at different imaging unit field strengths/manufacturers to evaluate precision, and (c) longitudinal data (1 year) in 24 patients with MS for the agreement between methods. Tissue segmentation, image registration, and white matter (WM) lesion filling were also evaluated. Multiple paired t tests were used for comparisons. Results High values of accuracy (0.87-0.97) for whole-brain and GM volumes were found, with the lowest values for MSmetrix. ANTs showed the lowest mean error (0.02%) for whole-brain atrophy in healthy control subjects, with a coefficient of variation of 0.5%. SPM showed the smallest mean error (0.07%) and coefficient of variation (0.08%) for GM atrophy. Globally, good repeatability (P > .05) but poor reproducibility (P < .05) were found for all methods. WM lesion filling technique mainly affected ANTs, MSmetrix, and SPM results (P < .05). Conclusion From this comparison, it would be possible to select a software for atrophy measurement, depending on the requirements of the application (research center, clinical trial) and its goal (accuracy and repeatability or reproducibility). An improved reproducibility is required for clinical application. RSNA, 2018 Online supplemental material is available for this article.

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“…The large heterogeneity in cognitive symptoms, however, cannot be fully explained by GM atrophy alone (6), and the discrepancy could possibly be explained by damage to the WM (ie, WM lesions, lower tract integrity), and/or higher brain reserve and cognitive reserve (5,7). This cognitive reserve is related to an individual's level of education and intellectual enrichment and has been hypothesized to postpone or reduce the rate of cognitive decline in MS and Alzheimer disease (7)(8)(9).…”

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confidence: 99%

Determinants of Cognitive Impairment in Patients with Multiple Sclerosis with and without Atrophy

et al. 2018

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Purpose To investigate the discrepancy between patients with multiple sclerosis (MS) without atrophy who have already developed cognitive impairment and patients with MS with atrophy who have preserved cognitive function. Materials and Methods This retrospective imaging study, with imaging acquired between 2008 and 2012, included 332 patients with MS (106 men and 226 women; mean age, 48.1 years; range, 23.0-72.5 years) and 96 healthy control participants. Cognitive impairment was defined as cognitive performance of z less than -1.5 compared with that in control participants in greater than or equal to two cognitive domains. Atrophy was defined as cortical and deep gray matter volumes of z less than -1.5 compared with that in control participants. White matter lesions were assessed with T2-imaging, tract fractional anisotropy (ie, integrity) with diffusion MRI, and regional centrality (ie, importance within network) with functional MRI. Within each atrophy group, patients with cognitive impairment and preserved cognitive function were compared and regression analyses were performed to predict cognitive impairment. Results A total of 132 of 328 patients with MS had no atrophy; of these, 42 of 132 (32%) had cognitive impairment. Cognitive impairment in patients without atrophy was predicted by level of education (Wald test, 11.63; P < .01) and posterior cingulate centrality (Wald test, 6.82; P < .01). A total of 65 of 328 patients with MS had atrophy; of these, 49 of 65 (75%) had cognitive impairment. Cognitive impairment in patients with atrophy was predicted by white matter tract fractional anisotropy (Wald test, 4.89; P = .03) and posterior cingulate centrality (Wald test, 7.19; P < .01). Conclusion Cognitive impairment was related to white matter damage, but only in patients with MS with atrophy. In patients without atrophy, a lower level of education was most important for cognitive impairment. Posterior cingulate cortex showed functional abnormalities in all MS groups with cognitive impairment, regardless of atrophy.

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Measuring Gray Matter and White Matter Damage in MS: Why This is Not Enough

Cited by 18 publications

References 41 publications

Single-subject structural cortical networks in clinically isolated syndrome

Single-subject structural cortical networks in clinically isolated syndrome

Measurement of Whole-Brain and Gray Matter Atrophy in Multiple Sclerosis: Assessment with MR Imaging

Determinants of Cognitive Impairment in Patients with Multiple Sclerosis with and without Atrophy

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