Assessment and optimisation of MRI measures of atrophy as potential markers of disease progression in multiple sclerosis

There is a need for sensitive measures of disease progression in multiple sclerosis (MS) to monitor treatment effects and understand disease evolution. MRI measures of brain atrophy have been proposed for this purpose. This thesis investigates a number of measurement techniques to assess their relative ability to monitor disease progression in clinically isolated syndromes (CIS) and early relapsing remitting MS (RRMS). Presented, is work demonstrating that measurement techniques and MR acquisitions can be optimised to give small but significant improvements in measurement sensitivity and precision, which provided greater statistical power. Direct comparison of numerous techniques demonstrated significant differences between them. Atrophy measurements from SIENA and the BBSI (registration-based techniques) were significantly more precise than segmentation and subtraction of brain volumes, although larger percentage losses were observed in grey matter fraction. Ventricular enlargement (VE) gave similar statistical power and these techniques were robust and reliable; scan-rescan measurement error was <0.01% of brain volume for BBSI and SIENA and <0.04ml for VE. Annual atrophy rates (using SIENA) were -0.78% in RRMS and -0.52% in CIS patients who progressed to MS, which were significantly greater than the rate observed in controls (-0.07%). Sample size calculations for future trials of disease-modifying treatments in RRMS, using brain atrophy as an outcome measure, are described. For SIENA, the BBSI and VE respectively, an estimated 123, 157 and 140 patients per treatment arm respectively would be required to show a 30% slowing of atrophy rate over two years. In CIS subjects brain atrophy rate was a significant prognostic factor, independent of T2 MRI lesions at baseline, for development of MS by five year follow-up. It was also the most significant MR predictor of disability in RRMS subjects. Cognitive assessment of RRMS patients at five year follow-up is described, and brain atrophy rate was a significant predictor of overall cognitive performance, and more specifically, of performance in tests of memory. The work in this thesis has identified methods for sensitively measuring progressive brain atrophy in MS. It has shown that brain atrophy changes in early MS are related to early clinical evolution, providing complementary information to clinical assessment that could be utilised to monitor disease progression

Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.

OBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD

Biomarker for tracking progression of Alzheimer's disease in clinical trials

Currently, there are no treatments available for mitigating the neurological effects of Alzheimer's disease. All clinical trials of disease-modifying treatments, which showed promise in animal models, have failed to show a significant treatment effect in human trials. The lack of a sensitive outcome measure and the focus on the dementia stage for investigating treatments are believed to be the primary reasons behind the failure of all clinical trials till date. The currently used outcome measure, the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog), suffers from low sensitivity in tracking progression of cognitive impairment in clinical trials. A shift in the focus to the prodromal mild cognitive impairment (MCI) stage may help improve the efficiency of clinical trials. However, even lower sensitivity of the ADAS-Cog and an inability to specifically select progressive MCI patients limit the efficiency of clinical trials in the MCI stage. Cerebral atrophy measured on structural magnetic resonance (MR) imaging is highly promising for tracking disease progression in clinical trials. However, cerebral atrophy has not been yet approved as a valid biomarker due to the lack of an understanding behind its relationship with cognitive impairment. The focus of this dissertation spans across the two research areas of (i) developing automatic algorithms for analysis of patients' brain MR volumes, and (ii) improving the efficiency of clinical trials of disease-modifying treatments. This dissertation presents a novel knowledge-driven decision theory approach for automatic tissue segmentation of brain MR volumes, which shows better segmentation performance than the existing approaches. The remaining dissertation contributions focus at improving the efficiency of clinical trials of disease-modifying treatments. An improved scoring methodology is presented for the ADAS-Cog outcome measure, which measures cognitive impairment with better accuracy and significantly improves the sensitivity of the ADAS-Cog in the mild-to-moderate Alzheimer's disease stage. However, the ADAS-Cog continues to suffers from low sensitivity in the MCI stage due to inherent limitations of its items. For improving the efficiency of clinical trials in the MCI stage, a biomarker has been developed that combines the ADAS-Cog with cerebral atrophy for more accurate tracking of Alzheimer's progression and facilitating selection of MCI patients in clinical trials.Biomedical Engineerin

Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefa- - cts is also presented. Cross-sectional and

Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks

Segmentation of both white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. Typically these tasks are performed separately: in this paper we present a single segmentation solution based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of multimodal magnetic resonance images into lesion classes and normal-appearing grey- and white-matter structures. We show substantial, statistically significant improvements in both Dice coefficient and in lesion-wise specificity and sensitivity, compared to previous approaches, and agreement with individual human raters in the range of human inter-rater variability. The method is trained on data gathered from a single centre: nonetheless, it performs well on data from centres, scanners and field-strengths not represented in the training dataset. A retrospective study found that the classifier successfully identified lesions missed by the human raters. Lesion labels were provided by human raters, while weak labels for other brain structures (including CSF, cortical grey matter, cortical white matter, cerebellum, amygdala, hippocampus, subcortical GM structures and choroid plexus) were provided by Freesurfer 5.3. The segmentations of these structures compared well, not only with Freesurfer 5.3, but also with FSL-First and Freesurfer 6.0

Improving longitudinal spinal cord atrophy measurements for clinical trials in multiple sclerosis by using the generalised boundary shift integral (GBSI)

Spinal cord atrophy is a common and clinically relevant feature of multiple sclerosis (MS), and can be used to monitor disease progression and as an outcome measure in clinical trials. Spinal cord atrophy is conventionally estimated with segmentation-based methods (e.g., cross-sectional spinal cord area (CSA)), where spinal cord change is calculated indirectly by numerical difference between timepoints. In this thesis, I validated the generalised boundary shift integral (GBSI), as the first registration-based method for longitudinal spinal cord atrophy measurement. The GBSI registers the baseline and follow-up spinal cord scans in a common half-way space, to directly determine atrophy on the cord edges. First, on a test dataset (9 MS patients and 9 controls), I have found that GBSI presented with lower random measurement error, than CSA, reflected by lower standard deviation, coefficient of variation and median absolute deviation. Then, on multi-centre, multi-manufacturer, and multi–field‐strength scans (282 MS patients and 82 controls), I confirmed that GBSI provided lower measurement variability in all MS subtypes and controls, than CSA, resulting into better separation between MS patients and controls, improved statistical power, and reduced sample size estimates. Finally, on a phase 2 clinical trial (220 primary-progressive MS patients), I demonstrated that spinal cord atrophy measurements on GBSI could be obtained from brain scans, considering their quality and association with corresponding spinal cord MRI-derived measurements. Not least, 1-year spinal cord atrophy measurements on GBSI, but not CSA, were associated with upper and lower limb motor function. In conclusion, spinal cord atrophy on the GBSI had higher measurement precision and stronger clinical correlates, than the segmentation method, and could be derived from high-quality brain acquisitions. Longitudinal spinal cord atrophy on GBSI could become a gold standard for clinical trials including spinal cord atrophy as an outcome measure, but should remain a secondary outcome measure, until further advancements increase the ease of acquisition and processing

Evaluation of Cerebral Lateral Ventricular Enlargement Derived from Magnetic Resonance Imaging: A Candidate Biomarker of Alzheimer Disease Progression in Vivo

Alzheimer disease (AD) is the most common form of dementia and has grievous mortality rates. Measuring brain volumes from structural magnetic resonance images (MRI) may be useful for illuminating disease progression. The goal of this thesis was to (1) help refine a novel technique used to segment the lateral cerebral ventricles from MRI, (2) validate this tool, and determine group-wise differences between normal elderly controls (NEC) and subjects with mild cognitive impairment (MCI) and AD and (3) determine the number of subjects necessary to detect a 20 percent change from the natural history of ventricular enlargement with respect to genotype. Three dimensional Ti-weighted MRI and cognitive measures were acquired from 504 subjects (NEC n = 152, MCI n = 247 and AD n = 105) participating in the multi-centre Alzheimer\u27s Disease Neuroimaging Initiative. Cerebral ventricular volume was quantified at baseline and after six months. For secondary analyses, all groups were dichotomized for Apolipoprotein E genotype based on the presence of an e4 polymorphism. The AD group had greater ventricular enlargement compared to both subjects with MCI (P = 0.0004) and NEC (P \u3c 0.0001), and subjects with MCI had a greater rate of ventricular enlargement compared to NEC (P =0.0001). MCI subjects that progressed to clinical AD after six months had greater ventricular enlargement than stable MCI subjects (P = 0.0270). Ventricular enlargement was different between apolipoprotein E genotypes within the AD group (P = 0.010). The number of subjects required to demonstrate a 20% change in ventricular enlargement (AD: N=342, MCI: N=1180) was substantially lower than that required to demonstrate a 20% change in cognitive scores (MMSE) (AD: N=7056, MCI: N=7712). Therefore, ventricular enlargement represents a feasible short-term marker of disease progression in subjects with MCI and subjects with AD for multi-centre studie

Visual ratings of atrophy in MCI: prediction of conversion and relationship with CSF biomarkers.

Medial temporal lobe atrophy (MTA) and cerebrospinal fluid (CSF) markers of Alzheimer's disease (AD) pathology may aid the early detection of AD in mild cognitive impairment (MCI). However, the relationship between structural and pathological markers is not well understood. Furthermore, while posterior atrophy (PA) is well recognized in AD, its value in predicting conversion from late-onset amnestic MCI to AD is unclear. In this study we used visual ratings of MTA and PA to assess their value in predicting conversion to AD in 394 MCI patients. The relationship of atrophy patterns with CSF Aβ1-42, tau, and p-tau(181) was further investigated in 114 controls, 192 MCI, and 99 AD patients. There was a strong association of MTA ratings with conversion to AD (p < 0.001), with a weaker association for PA ratings (p = 0.047). Specific associations between visual ratings and CSF biomarkers were found; MTA was associated with lower levels of Aβ1-42 in MCI, while PA was associated with elevated levels of tau in MCI and AD, which may reflect widespread neuronal loss including posterior regions. These findings suggest both that posterior atrophy may predict conversion to AD in late-onset MCI, and that there may be differential relationships between CSF biomarkers and regional atrophy patterns