The natural history of brain volume loss among patients with multiple sclerosis: A systematic literature review and meta-analysis

Vollmer, Timothy; Signorovitch, James; Huynh, Lynn; Galebach, Philip; Kelley, Caroline; DiBernardo, Allitia; Sasané, Rahul

doi:10.1016/j.jns.2015.07.014

Cited by 66 publications

(46 citation statements)

References 55 publications

(297 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is congruent with emerging data on diffuse tissue loss and brain atrophy, 31 which may be present from the outset of disease course, 6 even at the time of radiologically isolated syndrome, 32 and may be the principal driver of disability during progressive disease. 2,33 The model also incorporates the beneficial effect of large baseline brain volume, which has been shown to exert a protective influence in both cognitive 21 and physical 34 manifestations of the disease.…”

Section: Clinical Phenomena Illuminated By the Topographical Modelsupporting

confidence: 88%

The topographical model of multiple sclerosis

Krieger

Cook

Nino³

et al. 2016

Neurol Neuroimmunol Neuroinflamm

128

View full text Add to dashboard Cite

Relapses and progression contribute to multiple sclerosis (MS) disease course, but neither the relationship between them nor the spectrum of clinical heterogeneity has been fully characterized. A hypothesis-driven, biologically informed model could build on the clinical phenotypes to encompass the dynamic admixture of factors underlying MS disease course. In this medical hypothesis, we put forth a dynamic model of MS disease course that incorporates localization and other drivers of disability to propose a clinical manifestation framework that visualizes MS in a clinically individualized way. The topographical model encapsulates 5 factors (localization of relapses and causative lesions; relapse frequency, severity, and recovery; and progression rate), visualized utilizing dynamic 3-dimensional renderings. The central hypothesis is that, like symptom recrudescence in Uhthoff phenomenon and pseudoexacerbations, progression clinically recapitulates prior relapse symptoms and unmasks previously silent lesions, incrementally revealing underlying lesion topography. The model uses real-time simulation software to depict disease course archetypes and illuminate several well-described but poorly reconciled phenomena including the clinical/MRI paradox and prognostic significance of lesion location and burden on disease outcomes. Utilization of this model could allow for earlier and more clinically precise identification of progressive MS and predictive implications can be empirically tested.

show abstract

Section: Clinical Phenomena Illuminated By the Topographical Modelsupporting

confidence: 88%

The topographical model of multiple sclerosis

Krieger

Cook

Nino³

et al. 2016

Neurol Neuroimmunol Neuroinflamm

128

View full text Add to dashboard Cite

show abstract

“…In general, the white matter regions have a reduction in the MTR (Figure 3) and FA values, and an increase in the diffusion coefficients (MD, PaD and PeD) in the patient group. The above findings are indicative of the well-known myelinic damage in several WM regions (Filippi et al, 2017;Kritas et al, 2014;Papathanasiou et al, 2017;Reuter et al, 2009;Santos, 2007;Vollmer et al, 2015). By other hand, our common finding in gray matter regions of the patients was the increase of the atrophy of cortical and subcortical structures, also in agreement with previous reports (Narayana et al, 2012;Steenwijk et al, 2016).…”

Section: Discussionsupporting

confidence: 92%

Quantitative MRI data in Multiple Sclerosis patients: a pattern recognition study

2018

View full text Add to dashboard Cite

Introduction: Multiple Sclerosis (MS) is a neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Quantitative Magnetic Resonance Imaging (qMRI) enables a detailed characterization of brain tissue, but generates a large number of numerical results. In this study, we elucidated the main qMRI techniques and the brain regions that allow the identification of MS patients from neuroimaging data and pattern recognition techniques. Methods: The data came from the combination of computational tools of image processing and neuroimaging acquired in a 3 Tesla scanner using different techniques: Diffusion, T2 Relaxometry, Magnetization Transfer Ratio (MTR) and Structural Morphometry. Data from 126 brain regions of 203 healthy individuals and 124 MS patients were separated into two groups and processed in a data-mining program using the k-nearest-neighbor (KNN) algorithm. Results: The most relevant anatomical structures in the classification procedure were: corpus callosum, precuneus, left cerebellum and fusiform. Among the quantitative techniques the most relevant was the MTR, being indicated for longitudinal studies of this disease. KNN with 5 neighbors and pre-selected attributes had a better performance with an area under the ROC curve (97.3%) and accuracy (95.7%). A restricted classification considering only brain regions previously reported in the literature as affected by MS brought slightly lower scores, area: 97.1% and accuracy: 93.2%. Conclusion: The use of standard recognition techniques from quantitative neuroimaging techniques has confirmed that the white matter of the brain is the most affected tissue by MS following a global pattern with greater involvement of the left hemisphere.

show abstract

“…Our estimated rates of thalamic volume decline are consistent with what is reported in the literature for whole brain atrophy in both MS and in healthy controls (20.71% and 20.28% per year, respectively). 34,35 Furthermore, at the group level, our data suggest that the rate of thalamic volume decline is consistent across MS clinical subtypes, and perhaps more importantly, throughout the duration of the disease. This consistent rate of decline (ie, sensitivity to change over time), in combination with its sensitivity in early disease phases and reflection of multiple aspects of the disease pathobiology, suggests that thalamic volume may be appropriate for use as a primary MRI endpoint in a neuroprotective trial.…”

Section: Discussionmentioning

confidence: 51%