Magnetic resonance imaging in relapsing-remitting multiple sclerosis.

The work presented in this thesis employed magnetic resonance imaging (MRI) techniques to determine the volume and metabolite profile of brain grey matter (GM) and white matter (WM) in people with clinically early relapsing-remitting multiple sclerosis (MS). Cross-sectional MRI and clinical data was obtained from 27 subjects with relapsing-remitting MS within 3 years of first symptom onset, and compared with MRI data from 29 normal control subjects. Subsets of these groups also provided longitudinal data over 18 months for volumetric analysis. The principal observations were that: GM and WM atrophy may be observed early in the clinical course of the disease: WM atrophy was more apparent at baseline, but over the period of follow-up GM atrophy occurred more rapidly than that of WM: changes in metabolite concentrations were found in GM and WM suggesting neuronal and axonal damage, and WM glial activation and or proliferation: WM lesion loads explained a fraction of GM and WM atrophy and metabolite variability: clinical outcome related more closely to tissue metabolite changes (GM glutamate and glutamine, and normal-appearing WM inositol) than atrophy at this stage of the disease

Accurate GM atrophy quantification in MS using lesion-filling with co-registered 2D lesion masks.

BACKGROUND: In multiple sclerosis (MS), brain atrophy quantification is affected by white matter lesions. LEAP and FSL-lesion_filling, replace lesion voxels with white matter intensities; however, they require precise lesion identification on 3DT1-images. AIM: To determine whether 2DT2 lesion masks co-registered to 3DT1 images, yield grey and white matter volumes comparable to precise lesion masks. METHODS: 2DT2 lesion masks were linearly co-registered to 20 3DT1-images of MS patients, with nearest-neighbor (NNI), and tri-linear interpolation. As gold-standard, lesion masks were manually outlined on 3DT1-images. LEAP and FSL-lesion_filling were applied with each lesion mask. Grey (GM) and white matter (WM) volumes were quantified with FSL-FAST, and deep gray matter (DGM) volumes using FSL-FIRST. Volumes were compared between lesion mask types using paired Wilcoxon tests. RESULTS: Lesion-filling with gold-standard lesion masks compared to native images reduced GM overestimation by 1.93 mL (p < .001) for LEAP, and 1.21 mL (p = .002) for FSL-lesion_filling. Similar effects were achieved with NNI lesion masks from 2DT2. Global WM underestimation was not significantly influenced. GM and WM volumes from NNI, did not differ significantly from gold-standard. GM segmentation differed between lesion masks in the lesion area, and also elsewhere. Using the gold-standard, FSL-FAST quantified as GM on average 0.4% of the lesion area with LEAP and 24.5% with FSL-lesion_filling. Lesion-filling did not influence DGM volumes from FSL-FIRST. DISCUSSION: These results demonstrate that for global GM volumetry, precise lesion masks on 3DT1 images can be replaced by co-registered 2DT2 lesion masks. This makes lesion-filling a feasible method for GM atrophy measurements in MS

T1 histograms of normal-appearing brain tissue are abnormal in early relapsing-remitting Multiple Sclerosis

Objective: To use both whole-brain and normal-appearing brain tissue (NABT) T1 relaxation time histograms to investigate abnormalities in early relapsing-remitting (RR) multiple sclerosis (MS). Background. In patients with established MS, both lesions and NABT exhibit an increase in T1 relaxation time. By using T1 histogram analysis, it is hoped that such changes in early disease can be detected. Method. Twenty-seven patients and 14 age- and sex-matched controls underwent magnetic resonance imaging (MRI) of the brain, which included the following sequences: 1) proton density (PD)- and T2-weighted fast spin echo (FSE) to measure T2 lesion load, 2) PD- and T1-weighted gradient echos from which T1 relaxation was calculated, and 3) T1-weighted SE imaging pre- and post-triple dose (0.3 mmol/kg) gadolinium (Gd-DTPA) to measure T1 hypointense and gadolinium-enhancing lesion loads, respectively. All patients had RR MS with disease duration <3 years (median 1.7 years). Statistical parametric mapping (SPM) 99 was used to segment brain from cerebrospinal fluid (CSF), and lesions were segmented using a local thresholding technique. Results: Both whole-brain and NABT histograms were abnormal for all six T1 histogram parameters that were measured. For NABT, the mean T1 was 102 7 (+/- 74) ms in patients and 969 ( 4 1) ms in controls (p = 0.003). There was little difference between the global and NABT histograms, which indicates that most of the whole-brain histogram abnormality derives from normal-appearing tissues. There was a correlation between the Nine-Hole Peg Test and NABT T1 measures. Conclusion: There are widespread abnormalities of NABT in early RR MS, which were sensitively detected by T1 relaxation time histogram analysis. As such, T1 histogram analysis appears promising for studying the natural history of early RR MS, and in the monitoring of response to treatment

Normal-appearing brain tissue MTR histograms in clinically isolated syndromes suggestive of MS

Segmented normal-appearing brain tissue (NABT) was investigated in 40 patients with a recent onset and 13 patients with a remote onset of a clinically isolated syndrome (CIS) using magnetization transfer ratio (MTR) histograms. Abnormalities were present in patients with a high risk for MS (recent onset and T2-weighted lesions present) and in those with a low risk for relapse (recent onset without T2-weighted lesions). Similar mild NABT abnormality was present with CIS and no further disease activity 14 years later. NABT MTR abnormality in CIS may indicate susceptibility to demyelination but not to disease progression

The normal appearing grey matter in primary progressive multiple sclerosis. A magnetisation transfer imaging study

Background In 10-15 % of patients with multiple sclerosis (MS), the clinical course is characterized by slow progression in disability without relapses (primary progressive (PP) MS). The mechanism of disability in this form of MS is poorly understood. Using magnetization transfer ratio (MTR) imaging, we investigated normal appearing white matter (NAWM) and normal appearing grey matter (NAGM) in PPMS and explored the relationship of MTR measures with disability. Methods Thirty patients with PPMS and 30 age matched controls had spin echo based MTR imaging to study lesions and normal appearing tissues. The brain was segmented into NAWM and NAGM using SPM99 with lesions segmented using a semiautomated local thresholding technique. A 75% probability threshold for classification of NAWM and NAGM was used to diminish partial volume effects. From normalized histograms of MTR intensity values, six MTR parameters were measured. Mean lesion MTR and T2 lesion volume were also measured. Disability was assessed using Kurtzke's expanded disability status scale (EDSS). Results Compared with controls, patients exhibited a significant reduction in mean NAWM (p = 0.001) and NAGM (p = 0.004) MTR. Spearman's rank correlation of EDSS with the six MTR parameters in NAWM and NAGM, mean lesion MTR, and T2 lesion volume, was only significant with mean NAGM MTR (r = -0.41, p = 0.02), the 25(th) percentile of NAGM MTR intensity (r = -0.37, p = 0.05), and T2 lesion volume (r = 0.39, p = 0.04). Multiple regression analysis of the relationship between EDSS and 4 MR parameters representing each tissue type (mean NAWM MTR, mean NAGM MTR, mean lesion MTR, T2 lesion volume) showed that the association of EDSS with mean NAGM MTR remained significant. Conclusions There appear to be significant abnormalities in the NAGM in PP MS. Further investigation of the pathological basis and functional significance of grey matter abnormality in PPMS is warranted

Predicting outcome in clinically isolated syndrome using machine learning

We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clinically isolated syndrome (CIS), on the basis of single patient's lesion features and clinical/demographic characteristics. Seventy-four patients at onset of CIS were scanned and clinically reviewed after one and three years. CDMS was used as the gold standard against which SVM classification accuracy was tested. Radiological features related to lesional characteristics on conventional MRI were defined a priori and used in combination with clinical/demographic features in an SVM. Forward recursive feature elimination with 100 bootstraps and a leave-one-out cross-validation was used to find the most predictive feature combinations. 30 % and 44 % of patients developed CDMS within one and three years, respectively. The SVMs correctly predicted the presence (or the absence) of CDMS in 71.4 % of patients (sensitivity/specificity: 77 %/66 %) at 1 year, and in 68 % (60 %/76 %) at 3 years on average over all bootstraps. Combinations of features consistently gave a higher accuracy in predicting outcome than any single feature. Machine-learning-based classifications can be used to provide an “individualised” prediction of conversion to MS from subjects' baseline scans and clinical characteristics, with potential to be incorporated into routine clinical practice

Advances in imaging to support the development of novel therapies for multiple sclerosis.

Multiple sclerosis (MS) is a common neurological disease in North America and Europe. Although most patients develop major locomotor disability over the course of 15-20 years, in approximately one-third of patients the long-term course is favorable, with minimal disability. Although current disease-modifying treatments reduce the relapse rate, their long-term effects are uncertain. MS treatment trials are challenging because of the variable clinical course and typically slow evolution of the disease. Magnetic resonance imaging (MRI) is sensitive in monitoring MS pathology and facilitates evaluation of potential new treatments. MRI measurements of lesion activity have identified new immunomodulatory treatments for preventing relapse. Quantitative measurements of tissue volume and structural integrity, capable of detecting neuroprotection and repair, should facilitate new treatments designed to prevent irreversible disability. Higher-field MR scanners and new positron emission tomography (PET) radioligands are providing new insights into cellular and pathophysiological abnormalities, and should be valuable in future therapeutic trials. Retinal axonal loss measured using optical coherence tomography (OCT) can assess acute neuroprotection in optic neuritis