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

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

An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training

© 2023 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Large medical imaging data sets are becoming increasingly available. A common challenge in these data sets is to ensure that each sample meets minimum quality requirements devoid of significant artefacts. Despite a wide range of existing automatic methods having been developed to identify imperfections and artefacts in medical imaging, they mostly rely on data-hungry methods. In particular, the scarcity of artefact-containing scans available for training has been a major obstacle in the development and implementation of machine learning in clinical research. To tackle this problem, we propose a novel framework having four main components: (1) a set of artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, (2) a set of abstract and engineered features to represent images compactly, (3) a feature selection process that depends on the class of artefact to improve classification performance, and (4) a set of Support Vector Machine (SVM) classifiers trained to identify artefacts. Our novel contributions are threefold: first, we use the novel physics-based artefact generators to generate synthetic brain MRI scans with controlled artefacts as a data augmentation technique. This will avoid the labour-intensive collection and labelling process of scans with rare artefacts. Second, we propose a large pool of abstract and engineered image features developed to identify 9 different artefacts for structural MRI. Finally, we use an artefact-based feature selection block that, for each class of artefacts, finds the set of features that provide the best classification performance. We performed validation experiments on a large data set of scans with artificially-generated artefacts, and in a multiple sclerosis clinical trial where real artefacts were identified by experts, showing that the proposed pipeline outperforms traditional methods. In particular, our data augmentation increases performance by up to 12.5 percentage points on the accuracy, F1, F2, precision and recall. At the same time, the computation cost of our pipeline remains low – less than a second to process a single scan – with the potential for real-time deployment. Our artefact simulators obtained using adversarial learning enable the training of a quality control system for brain MRI that otherwise would have required a much larger number of scans in both supervised and unsupervised settings. We believe that systems for quality control will enable a wide range of high-throughput clinical applications based on the use of automatic image-processing pipelines.Peer reviewe

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

The temporal event-based model: Learning event timelines in progressive diseases

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80% with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions

Early imaging predictors of long-term outcomes in relapse-onset multiple sclerosis.

The clinical course of relapse-onset multiple sclerosis is highly variable. Demographic factors, clinical features and global brain T2 lesion load have limited value in counselling individual patients. We investigated early MRI predictors of key long-term outcomes including secondary progressive multiple sclerosis, physical disability and cognitive performance, 15 years after a clinically isolated syndrome. A cohort of patients with clinically isolated syndrome (n = 178) was prospectively recruited within 3 months of clinical disease onset and studied with MRI scans of the brain and spinal cord at study entry (baseline) and after 1 and 3 years. MRI measures at each time point included: supratentorial, infratentorial, spinal cord and gadolinium-enhancing lesion number, brain and spinal cord volumetric measures. The patients were followed-up clinically after ∼15 years to determine disease course, and disability was assessed using the Expanded Disability Status Scale, Paced Auditory Serial Addition Test and Symbol Digit Modalities Test. Multivariable logistic regression and multivariable linear regression models identified independent MRI predictors of secondary progressive multiple sclerosis and Expanded Disability Status Scale, Paced Auditory Serial Addition Test and Symbol Digit Modalities Test, respectively. After 15 years, 166 (93%) patients were assessed clinically: 119 (72%) had multiple sclerosis [94 (57%) relapsing-remitting, 25 (15%) secondary progressive], 45 (27%) remained clinically isolated syndrome and two (1%) developed other disorders. Physical disability was overall low in the multiple sclerosis patients (median Expanded Disability Status Scale 2, range 0-10); 71% were untreated. Baseline gadolinium-enhancing (odds ratio 3.16, P < 0.01) and spinal cord lesions (odds ratio 4.71, P < 0.01) were independently associated with secondary progressive multiple sclerosis at 15 years. When considering 1- and 3-year MRI variables, baseline gadolinium-enhancing lesions remained significant and new spinal cord lesions over time were associated with secondary progressive multiple sclerosis. Baseline gadolinium-enhancing (β = 1.32, P < 0.01) and spinal cord lesions (β = 1.53, P < 0.01) showed a consistent association with Expanded Disability Status Scale at 15 years. Baseline gadolinium-enhancing lesions was also associated with performance on the Paced Auditory Serial Addition Test (β = - 0.79, P < 0.01) and Symbol Digit Modalities Test (β = -0.70, P = 0.02) at 15 years. Our findings suggest that early focal inflammatory disease activity and spinal cord lesions are predictors of very long-term disease outcomes in relapse-onset multiple sclerosis. Established MRI measures, available in routine clinical practice, may be useful in counselling patients with early multiple sclerosis about long-term prognosis, and personalizing treatment plans

Assessing Neurofilaments as Biomarkers of Neuroprotection in Progressive Multiple Sclerosis: From the MS-STAT Randomized Controlled Trial.

BACKGROUND AND OBJECTIVES: Improved biomarkers of neuroprotective treatment are needed in progressive multiple sclerosis (PMS) to facilitate more efficient phase 2 trial design. The MS-STAT randomized controlled trial supported the neuroprotective potential of high-dose simvastatin in secondary progressive MS (SPMS). Here, we analyze serum from the MS-STAT trial to assess the extent to which neurofilament light (NfL) and neurofilament heavy (NfH), both promising biomarkers of neuroaxonal injury, may act as biomarkers of simvastatin treatment in SPMS. METHODS: The MS-STAT trial randomized patients to 80 mg simvastatin or placebo. Serum was analyzed for NfL and NfH using Simoa technology. We used linear mixed models to investigate the treatment effects of simvastatin compared with placebo on NfL and NfH. Additional models examined the relationships between neurofilaments and MRI and clinical measures of disease severity. RESULTS: A total of 140 patients with SPMS were included. There was no evidence for a simvastatin treatment effect on NfL or NfH: compared with placebo, NfL was 1.2% lower (95% CI 10.6% lower to 9.2% higher; p = 0.820) and NfH was 0.4% lower (95% CI 18.4% lower to 21.6% higher; p = 0.969) in the simvastatin treatment group. Secondary analyses suggested that higher NfL was associated with greater subsequent whole brain atrophy, higher T2 lesion volume, and more new/enlarging T2 lesions in the previous 12 months, as well as greater physical disability. There were no significant associations between NfH and MRI or clinical variables. DISCUSSION: We found no evidence of a simvastatin treatment effect on serum neurofilaments. While confirmation of the neuroprotective benefits of simvastatin is awaited from the ongoing phase 3 study (NCT03387670), our results suggest that treatments capable of slowing the rate of whole brain atrophy in SPMS, such as simvastatin, may act via mechanisms largely independent of neuroaxonal injury, as quantified by NfL. This has important implications for the design of future phase 2 clinical trials in PMS. TRIAL REGISTRATION INFORMATION: MS-STAT: NCT00647348. CLASSIFICATION OF EVIDENCE: This study provides class I evidence that simvastatin treatment does not have a large impact on either serum NfL or NfH, as quantified in this study, in SPMS