Computational modelling of imaging markers to support the diagnosis and monitoring of multiple sclerosis

Multiple sclerosis is a leading cause of neurological disability in young adults which affects more than 2.5 million people worldwide. An important substrate of disability accrual is the loss of neurons and connections between them (neurodegeneration) which can be captured by serial brain imaging, especially in the cerebral grey matter. In this thesis in four separate subprojects, I aimed to assess the strength of imaging-derived grey matter volume as a biomarker in the diagnosis, predicting the evolution of multiple sclerosis, and developing a staging system to stratify patients. In total, I retrospectively studied 1701 subjects, of whom 1548 had longitudinal brain imaging data. I used advanced computational models to investigate cross-sectional and longitudinal datasets. In the cross-sectional study, I demonstrated that grey matter volumes could distinguish multiple sclerosis from another demyelinating disorder (neuromyelitis optica) with an accuracy of 74%. In longitudinal studies, I showed that over time the deep grey matter nuclei had the fastest rate of volume loss (up to 1.66% annual loss) across the brain regions in multiple sclerosis. The volume of the deep grey matter was the strongest predictor of disability progression. I found that multiple sclerosis affects different brain areas with a specific temporal order (or sequence) that starts with the deep grey matter nuclei, posterior cingulate cortex, precuneus, and cerebellum. Finally, with multivariate mechanistic and causal modelling, I showed that brain volume loss causes disability and cognitive worsening which can be delayed with a potential neuroprotective treatment (simvastatin). This work provides conclusive evidence that grey matter volume loss affects some brain regions more severely, can predict future disability progression, can be used as an outcome measure in phase II clinical trials, and causes clinical and cognitive worsening. This thesis also provides a subject staging system based on which patients can be scored during multiple sclerosis

BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes

We present BrainPainter, a software that automatically generates images of highlighted brain structures given a list of numbers corresponding to the output colours of each region. Compared to existing visualisation software (i.e. Freesurfer, SPM, 3D Slicer), BrainPainter has three key advantages: (1) it does not require the input data to be in a specialised format, allowing BrainPainter to be used in combination with any neuroimaging analysis tools, (2) it can visualise both cortical and subcortical structures and (3) it can be used to generate movies showing dynamic processes, e.g. propagation of pathology on the brain. We highlight three use cases where BrainPainter was used in existing neuroimaging studies: (1) visualisation of the degree of atrophy through interpolation along a user-defined gradient of colours, (2) visualisation of the progression of pathology in Alzheimer's disease as well as (3) visualisation of pathology in subcortical regions in Huntington's disease. Moreover, through the design of BrainPainter we demonstrate the possibility of using a powerful 3D computer graphics engine such as Blender to generate brain visualisations for the neuroscience community. Blender's capabilities, e.g. particle simulations, motion graphics, UV unwrapping, raster graphics editing, raytracing and illumination effects, open a wealth of possibilities for brain visualisation not available in current neuroimaging software. BrainPainter is customisable, easy to use, and can run straight from the web browser: https://brainpainter.csail.mit.edu , as well as from source-code packaged in a docker container: https://github.com/mrazvan22/brain-coloring . It can be used to visualise biomarker data from any brain imaging modality, or simply to highlight a particular brain structure for e.g. anatomy courses.Comment: Accepted at the MICCAI Multimodal Brain Imaging Analysis (MBIA) workshop, 201

Serum neurofilament light and MRI predictors of cognitive decline in patients with secondary progressive multiple sclerosis: Analysis from the MS-STAT randomised controlled trial

Magnetic resonance imaging; Neurofilament light; Secondary progressive multiple sclerosisImatge per ressonància magnètica; Llum del neurofilament; Esclerosi múltiple secundària progressivaImagen por resonancia magnética; Luz de neurofilamento; Esclerosis múltiple secundaria progresivaBackground: Cognitive impairment affects 50%–75% of people with secondary progressive multiple sclerosis (PwSPMS). Improving our ability to predict cognitive decline may facilitate earlier intervention. Objective: The main aim of this study was to assess the relationship between longitudinal changes in cognition and baseline serum neurofilament light chain (sNfL) in PwSPMS. In a multi-modal analysis, MRI variables were additionally included to determine if sNfL has predictive utility beyond that already established through MRI. Methods: Participants from the MS-STAT trial underwent a detailed neuropsychological test battery at baseline, 12 and 24 months. Linear mixed models were used to assess the relationships between cognition, sNfL, T2 lesion volume (T2LV) and normalised regional brain volumes. Results: Median age and Expanded Disability Status Score (EDSS) were 51 and 6.0. Each doubling of baseline sNfL was associated with a 0.010 [0.003–0.017] point per month faster decline in WASI Full Scale IQ Z-score (p = 0.008), independent of T2LV and normalised regional volumes. In contrast, lower baseline volume of the transverse temporal gyrus was associated with poorer current cognitive performance (0.362 [0.026–0.698] point reduction per mL, p = 0.035), but not change in cognition. The results were supported by secondary analyses on individual cognitive components. Conclusion: Elevated sNfL is associated with faster cognitive decline, independent of T2LV and regional normalised volumes.The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: No specific funding was received for this research. T.W. is currently funded by the MS-STAT2 trial grant (NCT03387670). This is funded by the NIHR Health Technology Assessment (HTA) Programme, Multiple Sclerosis Society (UK) and the National Multiple Sclerosis Society (US)

DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification

Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks

Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique

Anisotropy; Lesion spatial distribution; Multiple sclerosisAnisotropia; Distribució espacial de la lesió; Esclerosi múltipleAnisotropía; Distribución espacial de la lesión; Esclerosis múltiplePredicting disability in progressive multiple sclerosis (MS) is extremely challenging. Although there is some evidence that the spatial distribution of white matter (WM) lesions may play a role in disability accumulation, the lack of well-established quantitative metrics that characterise these aspects of MS pathology makes it difficult to assess their relevance for clinical progression. This study introduces a novel approach, called SPACE-MS, to quantitatively characterise spatial distributional features of brain MS lesions, so that these can be assessed as predictors of disability accumulation. In SPACE-MS, the covariance matrix of the spatial positions of each patient’s lesional voxels is computed and its eigenvalues extracted. These are combined to derive rotationally-invariant metrics known to be common and robust descriptors of ellipsoid shape such as anisotropy, planarity and sphericity. Additionally, SPACE-MS metrics include a neuraxis caudality index, which we defined for the whole-brain lesion mask as well as for the most caudal brain lesion. These indicate how distant from the supplementary motor cortex (along the neuraxis) the whole-brain mask or the most caudal brain lesions are. We applied SPACE-MS to data from 515 patients involved in three studies: the MS-SMART (NCT01910259) and MS-STAT1 (NCT00647348) secondary progressive MS trials, and an observational study of primary and secondary progressive MS. Patients were assessed on motor and cognitive disability scales and underwent structural brain MRI (1.5/3.0 T), at baseline and after 2 years. The MRI protocol included 3DT1-weighted (1x1x1mm3) and 2DT2-weighted (1x1x3mm3) anatomical imaging. WM lesions were semiautomatically segmented on the T2-weighted scans, deriving whole-brain lesion masks. After co-registering the masks to the T1 images, SPACE-MS metrics were calculated and analysed through a series of multiple linear regression models, which were built to assess the ability of spatial distributional metrics to explain concurrent and future disability after adjusting for confounders. Patients whose WM lesions laid more caudally along the neuraxis or were more isotropically distributed in the brain (i.e. with whole-brain lesion masks displaying a high sphericity index) at baseline had greater motor and/or cognitive disability at baseline and over time, independently of brain lesion load and atrophy measures. In conclusion, here we introduced the SPACE-MS approach, which we showed is able to capture clinically relevant spatial distributional features of MS lesions independently of the sheer amount of lesions and brain tissue loss. Location of lesions in lower parts of the brain, where neurite density is particularly high, such as in the cerebellum and brainstem, and greater spatial spreading of lesions (i.e. more isotropic whole-brain lesion masks), possibly reflecting a higher number of WM tracts involved, are associated with clinical deterioration in progressive MS. The usefulness of the SPACE-MS approach, here demonstrated in MS, may be explored in other conditions also characterised by the presence of brain WM lesions.Carmen Tur is currently being funded by a Junior Leader La Caixa Fellowship. The project that gave rise to these results received the support of a fellowship from ”la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/PI20/11760008. She has also received the 2021 Merck’s Award for the Investigation in MS, awarded by the Merck Foundation. In 2015, she received an ECTRIMS Post-doctoral Research Fellowship and has received funding from the UK MS Society. She has also received honoraria from Roche and Novartis. Francesco Grussu has received funding under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 634541, from the Engineering and Physical Sciences Research Council (EPSRC EP/R006032/1, M020533/1) and Rosetrees Trust (UK), and is now supported by PREdICT (a study funded by AstraZeneca in Spain). Ferran Prados was supported by the Guarantors of Brain and the National Institute for Health Research, University College London Hospitals Biomedical Research Centre. Rosa Cortese is supported by the ECTRIMS-MAGNIMS fellowships programme. Alberto Calvi is supported by ECTRIMS-MAGNIMS fellowship (2018), Guarantors of Brain “Entry” clinical fellowship (2019) and the UK MS Society PhD studentship (2020). Declan Chard is a consultant for Biogen and Hoffmann-La Roche. In the last 3 years, he has received research funding from the International Progressive MS Alliance, the UK MS Society, and the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre. Jeremy Chataway, in the last three years, has received support from the Efficacy and Evaluation (EME) Programme, a Medical Research Council (MRC) and National Institute for Health Research (NIHR) partnership and the Health Technology Assessment (HTA) Programme (NIHR), the UK MS Society, the US National MS Society and the Rosetrees Trust. He is supported in part by the National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK. He has been a local principal investigator for a trial in MS funded by the Canadian MS society. A local principal investigator for commercial trials funded by: Actelion, Biogen, Novartis and Roche; has received an investigator grant from Novartis; and has taken part in advisory boards/consultancy for Azadyne, Biogen, Celgene, Janssen, MedDay, Merck, Novartis and Roche. Alan J Thompson acknowledges grant support from the National Institute for Health Research HTA and BRC, and has received honoraria for consultancy from Eisai and Abbvie (paid to Institution), support for travel for consultancy from the International Progressive MS Alliance and National MS Society (USA), and receives an honorarium from SAGE Publishers as Editor-in-Chief of Multiple Sclerosis Journal. Olga Ciccarelli is supported by the National Institute for Health Research, University College London Hospitals Biomedical Research Centre. OC also receives research grant support from the MS Society of Great Britain and Northern Ireland, and the NIHR UCLH Biomedical Research Centre. She is an Associate Editor for Neurology, for which he receives an honorarium. Claudia A.M. Gandini Wheeler-Kingshott has received research grants (principal investigator and co-applicant) from the UK MS Society (#77), Wings for Life (#169111), BRC (#BRC704/CAP/CGW), UCL Global Challenges Research Fund (GCRF), MRC (#MR/S026088/1), Ataxia UK. CGWK is a shareholder in Queen Square Analytics Ltd

Disease Knowledge Transfer across Neurodegenerative Diseases

We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausible, multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available. For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available. Although validation is challenging due to lack of data in PCA, we validate DKT on synthetic data and two patient datasets (TADPOLE and PCA cohorts), showing it can estimate the ground truth parameters in the simulation and predict unseen biomarkers on the two patient datasets. While we demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other forms of related neurodegenerative diseases. Source code for DKT is available online: https://github.com/mrazvan22/dkt.Comment: accepted at MICCAI 2019, 13 pages, 5 figures, 2 table

Serum neurofilament light and MRI predictors of cognitive decline in patients with secondary progressive multiple sclerosis: Analysis from the MS-STAT randomised controlled trial.

Background: Cognitive impairment affects 50%–75% of people with secondary progressive multiple sclerosis (PwSPMS). Improving our ability to predict cognitive decline may facilitate earlier intervention. Objective: The main aim of this study was to assess the relationship between longitudinal changes in cognition and baseline serum neurofilament light chain (sNfL) in PwSPMS. In a multi-modal analysis, MRI variables were additionally included to determine if sNfL has predictive utility beyond that already established through MRI. Methods: Participants from the MS-STAT trial underwent a detailed neuropsychological test battery at baseline, 12 and 24 months. Linear mixed models were used to assess the relationships between cognition, sNfL, T2 lesion volume (T2LV) and normalised regional brain volumes. Results: Median age and Expanded Disability Status Score (EDSS) were 51 and 6.0. Each doubling of baseline sNfL was associated with a 0.010 [0.003–0.017] point per month faster decline in WASI Full Scale IQ Z-score (p = 0.008), independent of T2LV and normalised regional volumes. In contrast, lower baseline volume of the transverse temporal gyrus was associated with poorer current cognitive performance (0.362 [0.026–0.698] point reduction per mL, p = 0.035), but not change in cognition. The results were supported by secondary analyses on individual cognitive components. Conclusion: Elevated sNfL is associated with faster cognitive decline, independent of T2LV and regional normalised volumes

Influence of nationality on the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS)

OBJECTIVE: In answer to the call for improved accessibility of neuropsychological services to the international community, the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS; MS) was validated in multiple, non-English-speaking countries. It was created to monitor processing speed and learning in MS patients, including abbreviated versions of the Symbol Digit Modalities Test, California Verbal Learning Test, 2nd Edition, and the Brief Visuospatial Memory Test, Revised. The objective of the present study was to examine whether participant nationality impacts performance above and beyond common demographic correlates. METHOD: We combined published data-sets from Argentina, Brazil, Czech Republic, Iran, and the U.S.A. resulting in a database of 1,097 healthy adults, before examining the data via multiple regression. RESULTS: Nationality significantly predicted performance on all three BICAMS tests after controlling for age and years of education. Interactions among the core predictor variables were non-significant. CONCLUSION: We demonstrated that nationality significantly influences BICAMS performance and established the importance of the inclusion of a nationality variable when international norms for the BICAMS are constructed

HLA-DRB1*1501 influences long-term disability progression and tissue damage on MRI in relapse-onset multiple sclerosis

BACKGROUND: Whether genetic factors influence the long-term course of multiple sclerosis (MS) is unresolved. OBJECTIVE: To determine the influence of HLA-DRB1*1501 on long-term disease course in a homogeneous cohort of clinically isolated syndrome (CIS) patients. METHODS: One hundred seven patients underwent clinical and MRI assessment at the time of CIS and after 1, 3, 5 and 15 years. HLA-DRB1*1501 status was determined using Sanger sequencing and tagging of the rs3135388 polymorphism. Linear/Poisson mixed-effects models were used to investigate rates of change in EDSS and MRI measures based on HLA-DRB1*1501 status. RESULTS: HLA-DRB1*1501 -positive (n = 52) patients showed a faster rate of disability worsening compared with the HLA-DRB1*1501 -negative (n = 55) patients (annualised change in EDSS 0.14/year vs. 0.08/year, p < 0.025), and a greater annualised change in T2 lesion volume (adjusted difference 0.45 mL/year, p < 0.025), a higher number of gadolinium-enhancing lesions, and a faster rate of brain (adjusted difference -0.12%/year, p < 0.05) and spinal cord atrophy (adjusted difference -0.22 mm2/year, p < 0.05). INTERPRETATION: These findings provide evidence that the HLA-DRB1*1501 allele plays a role in MS severity, as measured by long-term disability worsening and a greater extent of inflammatory disease activity and tissue loss. HLA-DRB1*1501 may provide useful information when considering prognosis and treatment decisions in early relapse-onset MS

Networks of microstructural damage predict disability in multiple sclerosis

Background: Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods. // Methods: We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures. // Results: We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001). // Conclusions: GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials