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ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.

ABSTRACT: Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically "mass univariate" and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary choice of the smoothing parameter. Here we present a Bayesian spatial model to accurately model binary lesion maps and to determine if there is spatial dependence between lesion location and subject specific covariates such as MS subtype, age, gender, disease duration and disease severity measures. We apply our model to binary lesion maps derived from T2-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.

SUBMITTER: Ge T

PROVIDER: S-EPMC4243942 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.

Ge Tian T Müller-Lenke Nicole N Bendfeldt Kerstin K Nichols Thomas E TE Johnson Timothy D TD

The annals of applied statistics 20140101 2

Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically "mass univariate" and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary c ...[more]

PMID: 25431633

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Project description:Multiple sclerosis (MS) is a demyelinating disease of the central nervous system characterized by increased inflammation and immune responses, oxidative injury, mitochondrial dysfunction, and iron dyshomeostasis leading to demyelination and axonal damage. In MS, incomplete remyelination results in chronically demyelinated axons and degeneration coinciding with disability. This suggests a failure in the ability to remyelinate in MS, however, the precise underlying mechanisms remain unclear. We aimed to identify proteins whose expression was altered in chronic inactive white matter lesions and periplaque white matter in MS tissue to reveal potential pathogenic mechanisms. Laser capture microdissection coupled to proteomics was used to interrogate spatially preserved changes in formalin-fixed paraffin-embedded brain tissue from chronic MS individuals and controls with no apparent neurological complications. Histopathological maps guided the capture of inactive lesions, periplaque white matter, and cortex from chronic MS individuals along with corresponding white matter and cortex from control tissue. Label free quantitation by liquid chromatography tandem mass spectrometry was used to discover differentially expressed proteins between the various brain regions. In addition to confirming loss of several myelin-associated proteins known to be affected in MS, proteomics analysis of chronic inactive MS lesions revealed alterations in myelin assembly, metabolism, and cytoskeletal organization. Notably, a subset of proteins that were altered in MS white matter indicate altered lipid metabolism. Our findings highlight proteome changes in chronic inactive MS white matter lesions and periplaque white matter, which may be crucial for proper myelinogenesis, bioenergetics, focal adhesions, and cellular function. These findings highlight the importance and feasibility of spatial approaches such as laser capture microdissection-based proteomics analysis of pathologically distinct regions of MS brain tissue. Identification of spatially resolved changes in the proteome of MS brain tissue should aid in the understanding of pathophysiological mechanisms and the development of novel therapies.

Dataset Information

ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.

Publications

ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.

Similar Datasets

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