Imaging biomarkers extraction and classification for Prion disease

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by inheritance of prion protein gene mutations or exposure to prions. To date, there are no accurate imaging biomarkers that can be used to predict the future diagnosis of a subject or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the large heterogeneity of phenotypes of prion disease and the lack of a consistent spatial pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of the human form of prion disease. Using a tailored framework, I extracted quantitative imaging biomarkers for characterisation of patients with Prion diseases. Following the extraction of patient-specific imaging biomarkers from multiple images, I implemented a Gaussian Process approach to correlated symptoms with disease types and stages. The model was used on three different tasks: diagnosis, differential diagnosis and stratification, addressing an unmet need to automatically identify patients with or at risk of developing Prion disease. The work presented in this thesis has been extensively validated in a unique Prion disease cohort, comprising both the inherited and sporadic forms of the disease. The model has shown to be effective in the prediction of this illness. Furthermore, this approach may have used in other disorders with heterogeneous imaging features, being an added value for the understanding of neurodegenerative diseases. Lastly, given the rarity of this disease, I also addressed the issue of missing data and the limitations raised by it. Overall, this work presents progress towards modelling of Prion diseases and which computational methodologies are potentially suitable for its characterisation

Machine Learning for Alzheimer’s Disease and Related Dementias

Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing

Illness Characteristics of COVID-19 in Children Infected with the SARS-CoV-2 Delta Variant.

BACKGROUND: The Delta (B.1.617.2) SARS-CoV-2 variant was the predominant UK circulating strain between May and November 2021. We investigated whether COVID-19 from Delta infection differed from infection with previous variants in children. METHODS: Through the prospective COVID Symptom Study, 109,626 UK school-aged children were proxy-reported between 28 December 2020 and 8 July 2021. We selected all symptomatic children who tested positive for SARS-CoV-2 and were proxy-reported at least weekly, within two timeframes: 28 December 2020 to 6 May 2021 (Alpha (B.1.1.7), the main UK circulating variant) and 26 May to 8 July 2021 (Delta, the main UK circulating variant), with all children unvaccinated (as per national policy at the time). We assessed illness profiles (symptom prevalence, duration, and burden), hospital presentation, and presence of long (≥28 day) illness, and calculated odds ratios for symptoms presenting within the first 28 days of illness. RESULTS: 694 (276 younger (5-11 years), 418 older (12-17 years)) symptomatic children tested positive for SARS-CoV-2 with Alpha infection and 706 (227 younger and 479 older) children with Delta infection. Median illness duration was short with either variant (overall cohort: 5 days (IQR 2-9.75) with Alpha, 5 days (IQR 2-9) with Delta). The seven most prevalent symptoms were common to both variants. Symptom burden over the first 28 days was slightly greater with Delta compared with Alpha infection (in younger children, 3 (IQR 2-5) symptoms with Alpha, 4 (IQR 2-7) with Delta; in older children, 5 (IQR 3-8) symptoms with Alpha, 6 (IQR 3-9) with Delta infection ). The odds of presenting several symptoms were higher with Delta than Alpha infection, including headache and fever. Few children presented to hospital, and long illness duration was uncommon, with either variant. CONCLUSIONS: COVID-19 in UK school-aged children due to SARS-CoV-2 Delta strain B.1.617.2 resembles illness due to the Alpha variant B.1.1.7., with short duration and similar symptom burden

Quantitative comparison of multi-centre MRI data for mild to severe traumatic brain injury

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica)Universidade de Lisboa, Faculdade de Ciências, 2015O trauma cerebral (frequentemente denominado de TBI – Traumatic Brain Injury) é uma das principais causas de morte e incapacidade em jovens adultos, afectando cerca de 2,5 milhões de sujeitos, por ano, só na Europa, sendo que 75 000 acabam por morrer em consequência do mesmo. Actualmente, TBI é classificada pela Organização Mundial de Saúde como uma epidemia silenciosa e um grave problema de saúde pública. O diagnóstico do trauma cerebral assenta em parâmetros como a escala de coma de Glasgow ou o nível de perda de consciência, o que condiciona um diagnóstico exacto, já que por vezes os sintomas associados ao trauma cerebral não se manifestam de imediato. Assim, as técnicas de imagem médicas derivadas da Ressonância Magnética, como é o caso das imagens obtidas usando o tensor de difusão - DTI (Diffusion Tensor Imaging) – e imagens de Ressonância Magnética funcional – fMRI (Functional Magnetic Resonance Imaging) - têm-se mostrado bastante relevantes para um diagnóstico mais eficaz do TBI. TBI trata-se de um conjunto de reacções a um agressão externa, que dependem de diversos factores e que por esse motivo tornam difícil definir TBI, assim como a melhor abordagem para o seu tratamento. Por conseguinte, uma das formas mais eficazes de estudar TBI, tentando definir um tratamento adequado, é através de estudos longitudinais, que permitam, abrangendo um número alargado de pacientes, melhor caracterizar esta patologia. É neste sentido que surge o projecto CENTER-TBI. O CENTER-TBI é um estudo multicentro que visa, por meio da aquisição de dados em 60 centros e abrangendo um total de 5400 indivíduos, uma caracterização mais eficaz do trauma cerebral, assim como a identificação da intervenção mais eficaz para o tratamento de TBI. Considerando as mais-valias que um estudo multicentro oferece, entre as quais se destaca o aumento da população em estudo o que permite um aumento de poder estatístico dos testes, assim como a garantia de que a população é o mais heterógena possível permitindo a análise de diferentes características e sintomas associados ao TBI, será possível definir uma abordagem que esclareça a comunidade médica sobre como proceder perante um paciente com trauma cerebral. No entanto, a viabilidade deste tipo de estudos, e do projecto CENTER-TBI em particular, está fortemente dependente da reprodutibilidade dos dados. Para tal, foi definido qual o procedimento a adoptar para a aquisição de imagens médicas, estabelecendo-se protocolos que definem qual a sequência de aquisição de MRI. Porém, seja por incapacidade de implementação da sequência tal como está definida, seja por características intrínsecas ao scanner utilizado, existe uma fracção de variabilidade em cada imagem adquirida que é inerente ao scanner. Tal facto introduz um viés nos dados que impossibilita que os mesmos sejam exactamente reprodutíveis e comparáveis entre scanners. Este projecto tem como objectivo principal a redução da variabilidade entre dados provenientes de scanners diferentes, assegurando a reprodutibilidade dos mesmos. Assim, numa primeira fase, após uma extensa pesquisa sobre o estado da arte relativo a estudos multicentro, procedeu-se ao desenvolvimento de algoritmos que permitam a quantificação da variabilidade que é originada pelas singularidades do hardware. De seguida, procedeu-se à comparação não só dados provenientes de sujeitos saudáveis, definidos como grupo de controlo, mas também dados provenientes de pacientes com TBI. Desse modo, foi possível estimar o quanto a variabilidade introduzida pelo scanner afecta o diagnóstico de pacientes. Posteriormente, após terminada a fase de quantificação, procedeu-se à aplicação de dois métodos distintos de correcção de variabilidade pelo hardware, sendo que no segundo caso, ao método testado foram introduzidas várias variações nas quais se tentou obter uma melhor performance do algoritmo em análise. Esperou-se que com a aplicação de ambos os métodos de correcção, as diferenças encontradas entre os vários scans, adquiridos em diferentes centros, se ficam a dever exclusivamente a diferenças anatómicas e/ou fisiológicas entre pacientes, permitindo desse modo a comparação dos diferentes indivíduos em análise. Assim, as conclusões e pressupostos assumidos tendo por base estudos e análises de dados multicentro terão a sua fiabilidade assegurada. Em suma, este projecto teve como objectivo a quantificação e correcção da variabilidade entre scanners, dado que esta se pode tornar um factor de erro com ênfase suficiente para colocar em causa a fiabilidade de estudos multicentro.Introduction: Multicentre studies have proven themselves very useful to collect data from subjects with interesting characteristics for Traumatic Brain Injury (TBI) research, such as heterogeneous approaches for TBI treatment. Multicentre projects have been contributing to increase the statistical power of the TBI studies and to improve the reliability of the assumptions held about this illness. It is necessary to ensure the data reproducibility in order to guarantee the studies reliability. In this way, it is crucial to remove any source of error in these projects, such as variability and bias present in the data, introduced by the hardware. The present dissertation presents a project whose main goal is to quantify and correct for the variability introduced by the hardware. Therefore, the first step to guarantee the viability of the results is to measure the variability present in the data. Second, the data needed to be corrected so as to eliminate the sources of variability, considering the several approaches suggested in the literature. It was also important to determine the best approach to achieve the lowest level of variability in the data, without removing relevant features regarding pathology. Materials and Methods: In an initial phase of the project, a quantification of the variability across scanners and within centre was performed. For that, the Coefficient of Variation (COV) was calculated for each type of maps in analysis – Fractional Anisotropy (FA), Mean Diffusivity (MD), Grey Matter (GM) and White Matter (WM). A voxel based analysis and Regions of Interest (ROI) analysis was performed in order to characterize the variability present in the data and to confirm the need for the use of a correction model to remove the variability introduced by the hardware in multicentre studies. After that, two methodologies to correct the variability were tested. In the case of the second methodology applied, variations of the initial model were also tested in order to improve the performance of this model. Finally, a comparison of the effectiveness of the methods tested was performed. For that, a Support Vector Machine (SVM) algorithm was applied to obtain an indirect measure of the accuracy of the methods tested. Results: The results obtained in this initial phase were as expected, in agreement with previous studies described in the literature. The models tested were effective in the correction of the variability introduced by the scanner. The Regression Models showed the best performance in the correction of the variability. However, the Spatial Filtering models were simpler and quicker to apply, and the effectiveness of their performance suggested that these kind of models could be applied in the context of CENTER-TBI and the variability would be corrected. Conclusions: As expected, the scanners introduced a significant level of variability in the data. The variability introduced by the hardware can be quantified within scanners, analysing the data from the same device, or across scanners and comparing the data from different devices. In both cases, the results suggest that the correction and elimination of variability introduced by the hardware are needed, before proceeding with further analysis using data from multicentre studies, in order to ensure the reliability of the results. The methods tested in this dissertation showed to be effective in the elimination of the variability introduced by the scanner

Volumetric MRI is a promising outcome measure of muscle reinnervation

Abstract The development of outcome measures that can track the recovery of reinnervated muscle would benefit the clinical investigation of new therapies which hope to enhance peripheral nerve repair. The primary objective of this study was to assess the validity of volumetric Magnetic Resonance Imaging (MRI) as an outcome measure of muscle reinnervation by testing its reproducibility, responsiveness and relationship with clinical indices of muscular function. Over a 3-year period 25 patients who underwent nerve transfer to reinnervate elbow flexor muscles were assessed using intramuscular electromyography (EMG) and MRI (median post-operative assessment time of 258 days, ranging from 86 days pre-operatively to 1698 days post- operatively). Muscle power (Medical Research Council (MRC) grade) and Stanmore Percentage of Normal Elbow Assessment (SPONEA) assessment was also recorded for all patients. Sub-analysis of peak volitional force (PVF), muscular fatigue and co-contraction was performed in those patients with MRC > 3. The responsiveness of each parameter was compared using Pearson or Spearman correlation. A Hierarchical Gaussian Process (HGP) was implemented to determine the ability of volumetric MRI measurements to predict the recovery of muscular function. Reinnervated muscle volume per unit Body Mass Index (BMI) demonstrated good responsiveness (R2 = 0.73, p < 0.001). Using the temporal and muscle volume per unit BMI data, a HGP model was able to predict MRC grade and SPONEA with a mean absolute error (MAE) of 0.73 and 1.7 respectively. Muscle volume per unit BMI demonstrated moderate to good positive correlations with patient reported impairments of reinnervated muscle; co- contraction (R2 = 0.63, p = 0.02) and muscle fatigue (R2 = 0.64, p = 0.04). In summary, volumetric MRI analysis of reinnervated muscle is highly reproducible, responsive to post-operative time and demonstrates correlation with clinical indices of muscle function. This encourages the view that volumetric MRI is a promising outcome measure for muscle reinnervation which will drive advancements in motor recovery therapy

Putaminal diffusion tensor imaging measures predict disease severity across human prion diseases

Therapeutic trials of disease-modifying agents in neurodegenerative disease typically require several hundred participants and long durations for clinical endpoints. Trials of this size are not feasible for prion diseases, rare dementia disorders associated with misfolding of prion protein. In this situation, biomarkers are particularly helpful. On diagnostic imaging, prion diseases demonstrate characteristic brain signal abnormalities on diffusion-weighted MRI. The aim of this study was to determine whether cerebral water diffusivity could be a quantitative imaging biomarker of disease severity. We hypothesized that the basal ganglia were most likely to demonstrate functionally relevant changes in diffusivity. Seventy-one subjects (37 patients and 34 controls) of whom 47 underwent serial scanning (23 patients and 24 controls) were recruited as part of the UK National Prion Monitoring Cohort. All patients underwent neurological assessment with the Medical Research Council Scale, a functionally orientated measure of prion disease severity, and diffusion tensor imaging. Voxel-based morphometry, voxel-based analysis of diffusion tensor imaging and regions of interest analyses were performed. A significant voxel-wise correlation of decreased Medical Research Council Scale score and decreased mean, radial and axial diffusivities in the putamen bilaterally was observed (P < 0.01). Significant decrease in putamen mean, radial and axial diffusivities over time was observed for patients compared with controls (P = 0.01), and there was a significant correlation between monthly decrease in putamen mean, radial and axial diffusivities and monthly decrease in Medical Research Council Scale (P < 0.001). Step-wise linear regression analysis, with dependent variable decline in Medical Research Council Scale, and covariates age and disease duration, showed the rate of decrease in putamen radial diffusivity to be the strongest predictor of rate of decrease in Medical Research Council Scale (P < 0.001). Sample size calculations estimated that, for an intervention study, 83 randomized patients would be required to provide 80% power to detect a 75% amelioration of decline in putamen radial diffusivity. Putamen radial diffusivity has potential as a secondary outcome measure biomarker in future therapeutic trials in human prion diseases

Metabolomic and gut microbiome profiles across the spectrum of community-based COVID and non-COVID disease

Abstract Whilst most individuals with SARS-CoV-2 infection have relatively mild disease, managed in the community, it was noted early in the pandemic that individuals with cardiovascular risk factors were more likely to experience severe acute disease, requiring hospitalisation. As the pandemic has progressed, increasing concern has also developed over long symptom duration in many individuals after SARS-CoV-2 infection, including among the majority who are managed acutely in the community. Risk factors for long symptom duration, including biological variables, are still poorly defined. Here, we examine post-illness metabolomic profiles, using nuclear magnetic resonance (Nightingale Health Oyj), and gut-microbiome profiles, using shotgun metagenomic sequencing (Illumina Inc), in 2561 community-dwelling participants with SARS-CoV-2. Illness duration ranged from asymptomatic (n = 307) to Post-COVID Syndrome (n = 180), and included participants with prolonged non-COVID-19 illnesses (n = 287). We also assess a pre-established metabolomic biomarker score, previously associated with hospitalisation for both acute pneumonia and severe acute COVID-19 illness, for its association with illness duration. We found an atherogenic-dyslipidaemic metabolic profile, including biomarkers such as fatty acids and cholesterol, was associated with longer duration of illness, both in individuals with and without SARS-CoV-2 infection. Greater values of a pre-existing metabolomic biomarker score also associated with longer duration of illness, regardless of SARS-CoV-2 infection. We found no association between illness duration and gut microbiome profiles in convalescence. This highlights the potential role of cardiometabolic dysfunction in relation to the experience of long duration symptoms after symptoms of acute infection, both COVID-19 as well as other illnesses

COVID-19 due to the B.1.617.2 (Delta) variant compared to B.1.1.7 (Alpha) variant of SARS-CoV-2:a prospective observational cohort study

The Delta (B.1.617.2) variant was the predominant UK circulating SARS-CoV-2 strain between May and December 2021. How Delta infection compares with previous variants is unknown. This prospective observational cohort study assessed symptomatic adults participating in the app-based COVID Symptom Study who tested positive for SARS-CoV-2 from May 26 to July 1, 2021 (Delta overwhelmingly the predominant circulating UK variant), compared (1:1, age- and sex-matched) with individuals presenting from December 28, 2020 to May 6, 2021 (Alpha (B.1.1.7) the predominant variant). We assessed illness (symptoms, duration, presentation to hospital) during Alpha- and Delta-predominant timeframes; and transmission, reinfection, and vaccine effectiveness during the Delta-predominant period. 3581 individuals (aged 18 to 100 years) from each timeframe were assessed. The seven most frequent symptoms were common to both variants. Within the first 28 days of illness, some symptoms were more common with Delta versus Alpha infection (including fever, sore throat, and headache) and some vice versa (dyspnoea). Symptom burden in the first week was higher with Delta versus Alpha infection; however, the odds of any given symptom lasting ≥ 7 days was either lower or unchanged. Illness duration ≥ 28 days was lower with Delta versus Alpha infection, though unchanged in unvaccinated individuals. Hospitalisation for COVID-19 was unchanged. The Delta variant appeared more (1.49) transmissible than Alpha. Re-infections were low in all UK regions. Vaccination markedly reduced the risk of Delta infection (by 69-84%). We conclude that COVID-19 from Delta or Alpha infections is similar. The Delta variant is more transmissible than Alpha; however, current vaccines showed good efficacy against disease. This research framework can be useful for future comparisons with new emerging variants