Monday, 24 September 2012

Research: MRI can detect change in MS

Although it is inarguable that conventional MRI (cMRI) has greatly contributed to the diagnosis and assessment of multiple sclerosis (MS), cMRI does not show close correlation with clinical findings or pathologic features, and is unable to predict prognosis or stratify disease severity. To this end, diffusion tensor imaging (DTI) with tractography and neuroconnectivity analysis may assist disease assessment in MS. We, therefore, attempted this pilot study for initial assessment of early relapsing-remitting MS (RRMS). Neuroconnectivity analysis was used for evaluation of 24 early RRMS patients within 2 years of presentation, and compared to the network measures of a group of 30 age-and-gender-matched normal control subjects. To account for the situation that the connections between two adjacent regions may be disrupted by an MS lesion, a new metric, network communicability, was adopted to measure both direct and indirect connections. For each anatomical area, the brain network communicability and average path length were computed and compared to characterize the network changes in efficiencies. Statistically significant (P < 0.05) loss of communicability was revealed in our RRMS cohort, particularly in the frontal and hippocampal/parahippocampal regions as well as the motor strip and occipital lobes. Correlation with the 25-foot Walk test with communicability measures in the left superior frontal (r = -0.71) as well as the left superior temporal gyrus (r = -0.43) and left postcentral gyrus (r = -0.41) were identified. Additionally identified were increased communicability between the deep gray matter structures (left thalamus and putamen) with the major interhemispheric and intrahemispheric white matter tracts, the corpus callosum, and cingulum, respectively. These foci of increased communicability are thought to represent compensatory changes. The proposed DTI-based neuroconnectivity analysis demonstrated quantifiable, structurally relevant alterations of fibre tract connections in early RRMS and paves the way for longitudinal studies in larger patient groups
Conventional MRI (cMRI) has greatly contributed to the diagnosis and assessment of multiple sclerosis (MS), but cMRI does not show close correlation with clinical findings or pathologic features and is unable to predict prognosis or stratify disease severity. This study looks at a different imaging modality-diffusion tensor imaging that aims to detect nerve tracts travelling between one area of the brain to another. It appears that in MS there is significant disruption of these tracts and that it may be a new tool for monitoring trials

However the correlation of DTI with some clinical measures was r= -0.4 or -0.7 in some tisse. We want to see figures close to -1 if we are to see a real correlation as r= -0.4 does not mean much. So whilst there is promise that this can be a new tool and one would image at 7T (high power MRI machine) we have to be caustious if this is going to give closer correlations than with cMRI.

Other groups have a different way of looking at this

Rocca MA, Valsasina P, Martinelli V, Misci P, Falini A, Comi G, Filippi M. Large-scale neuronal network dysfunction in relapsing-remitting multiple sclerosis. Neurology. 2012 [Epub ahead of print] 
OBJECTIVES: Given that multiple sclerosis (MS) hits diffusely the brain hemispheres (halves of the brain), we hypothesized that this should result in a distributed pattern of functional connectivity (FC) abnormalities. To this aim, we assessed, using resting-state fMRI, intrinsic functional connectivity and functional network connectivity of brain large-scale neuronal networks from 85 patients with relapsing-remitting MS (RRMS) and 40 matched controls.

RESULTS: Compared to controls, patients with RRMS experienced a decreased resting-state functional connectivity in regions of the salience (SN), executive control (ECN), working memory (WMN), default mode (DMN), sensorimotor, and visual networks. They also had an increased resting-state functional connectivity in regions of the executive control networks and auditory resting-state networks. An abnormal connectivity between the White Matter Networks and sensory networks was also found. Decreased decreased resting-state functional connectivity was significantly correlated with disability and T2 lesion volumes.

CONCLUSIONS: Functional abnormalities within and between large-scale neuronal networks occur in patients with RRMS and are related to the extent of T2 lesions and the severity of disability. Longitudinal studies should ascertain whether such functional abnormalities confer a systematic vulnerability to disease progression or, conversely, protect against the onset of clinical deficits.

So nerves connect in networks and there are influences on these networks during MS. Time will tell which imaging modalities will best detect change and are being geared to monitor progressive MS.

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