Deep learning for cancer survival prediction

Cancer claimed 18.1 millions deaths worldwide in 2018 and $87.8 billion for health-care in 2014 in USA. The tremendous impact this disease supposes worldwide, combined with the increasingly availability of genomic and transcriptomic data, have aroused the interest on incorporating cutting edge technologies, such as Deep Learning (DL), in the fight against cancer. DL has stand out in the last years, particularly because of the performance of the Convolutional Neural Networks (ConvNets) models in image recognition. The problem for which all models in this project have been trained is the prediction of cancer survival in a discrete set of time intervals, from RNA-Seq data, because of the importance survival analysis have in the study of cancer treatment and its improvement. The very nature of biological data brings some inconvenients when using it for training a ConvNet model. These data are usually composed by a much bigger number of features (M) than observations (N). This is known as the Curse of Dimensionality (M>>N). Other inconvenient is the lack, a priori, of spatial information among biological features. ConvNet is a DL model which is specially designed for image processing, in which the pixels composing them are related to its neighbour. This relation is used by ConvNets to extract more knowledge from observations and have, in consequence, a better performance. This project proposes some strategies to try to solve these two inconvenients. In order to equip gene-expression-profiles with structure, five strategies have been proposed, applied and compared. Similarly, the transfer learning technique known as fine-tuning have been applied to try to solve the inconvenient which we refer to as the Curse of Dimensionality. The comparison of these models, all trained with the same set of features and observations, has been made by calculating the Concordance Index (C-index) metric for each of them.El cáncer se cobró 18,1 millones de muertes a nivel mundial en 2018 y$87,8 billones para cuidados de salud durante el año 2014 en EEUU. El tremendo impacto que esta enfermedad supone a nivel mundial, junto con la disponibilidad cada vez mayor de datos genómicos y transcriptómicos, han potenciado el interés en incorporar tecnologías de vanguardia, como es el Aprendizaje Profundo (AI), a la lucha contra el cáncer. AI ha destacado en los últimos años, particularmente por el rendimiento de los modelos de Redes Neuronales Convolucionales (RNC) en reconocimiento de imágenes. El problema para el cual todos los modelos de este proyecto han sido entrenados es la predicción de supervivencia en cáncer en un conjunto discreto de intervalos de tiempo a partir de datos de RNA-Seq, debido a la importancia que el análisis de la supervivencia tiene en cuanto al estudio de los tratamientos contra el cáncer y su mejora. La propia naturaleza de los datos biológicos trae consigo algunos inconvenientes cuando se usan para entrenar modelos de RNC. Estos datos normalmente est´an formados por un número mucho mayor de variables (M) que de observaciones (N). Esto se conoce como la maldición de la dimensionalidad (en inglés, the Curse of Dimensionality) (M>>N). Otro inconveniente es la falta, a priori, de información espacial entre las variables biológicas. RNC son un tipo de modelo concreto de Aprendizaje Profundo que está especialmente pensado para el procesado de imágenes, en las cuales los píxeles que las componen se relacionan con sus píxeles vecinos. Esta relación se usa en las RNC para extraer más conocimientos de las observaciones y tener, en consecuencia, un mejor rendimiento. En este proyecto se proponen algunas estrategias para tratar de resolver estos dos inconvenientes. Con el objetivo de equipar a los perfiles de expresión génica con estructura, cinco estrategias han sido propuestas, aplicadas y comparadas. ..

COVID-19 after two years : trajectories of different components of mental health in the Spanish population

Our study aimed to (1) identify trajectories on different mental health components during a two-year follow-up of the COVID-19 pandemic and contextualise them according to pandemic periods; (2) investigate the associations between mental health trajectories and several exposures, and determine whether there were differences among the different mental health outcomes regarding these associations. We included 5535 healthy individuals, aged 40-65 years old, from the Barcelona Brain Health Initiative (BBHI). Growth mixture models (GMM) were fitted to classify individuals into different trajectories for three mental health-related outcomes (psychological distress, personal growth and loneliness). Moreover, we fitted a multinomial regression model for each outcome considering class membership as the independent variable to assess the association with the predictors. For the outcomes studied we identified three latent trajectories, differentiating two major trends, a large proportion of participants was classified into 'resilient' trajectories, and a smaller proportion into 'chronic-worsening' trajectories. For the former, we observed a lower susceptibility to the changes, whereas, for the latter, we noticed greater heterogeneity and susceptibility to different periods of the pandemic. From the multinomial regression models, we found global and cognitive health, and coping strategies as common protective factors among the studied mental health components. Nevertheless, some differences were found regarding the risk factors. Living alone was only significant for those classified into 'chronic' trajectories of loneliness, but not for the other outcomes. Similarly, secondary or higher education was only a risk factor for the 'worsening' trajectory of personal growth. Finally, smoking and sleeping problems were risk factors which were associated with the 'chronic' trajectory of psychological distress. Our results support heterogeneity in reactions to the pandemic and the need to study different mental health-related components over a longer follow-up period, as each one evolves differently depending on the pandemic period. In addition, the understanding of modifiable protective and risk factors associated with these trajectories would allow the characterisation of these segments of the population to create targeted interventions

Brain system segregation and pain catastrophizing in chronic pain progression

Pain processing involves emotional and cognitive factors that can modify pain perception. Increasing evidence suggests that pain catastrophizing (PC) is implicated, through pain-related self-thoughts, in the maladaptive plastic changes related to the maintenance of chronic pain (CP). Functional magnetic resonance imaging (fMRI) studies have shown an association between CP and two main networks: default mode (DMN) and dorsoattentional (DAN). Brain system segregation degree (SyS), an fMRI framework used to quantify the extent to which functional networks are segregated from each other, is associated with cognitive abilities in both healthy individuals and neurological patients. We hypothesized that individuals suffering from CP would show worst health-related status compared to healthy individuals and that, within CP individuals, longitudinal changes in pain experience (pain intensity and affective interference), could be predicted by SyS and PC subdomains (rumination, magnification, and helplessness). To assess the longitudinal progression of CP, two pain surveys were taken before and after an in-person assessment (physical evaluation and fMRI). We first compared the sociodemographic, health-related, and SyS data in the whole sample (no pain and pain groups). Secondly, we ran linear regression and a moderation model only in the pain group, to see the predictive and moderator values of PC and SyS in pain progression. From our sample of 347 individuals (mean age = 53.84, 55.2% women), 133 responded to having CP, and 214 denied having CP. When comparing groups, results showed significant differences in health-related questionnaires, but no differences in SyS. Within the pain group, helplessness (β = 0.325; p = 0.003), higher DMN (β = 0.193; p = 0.037), and lower DAN segregation (β = 0.215; p = 0.014) were strongly associated with a worsening in pain experience over time. Moreover, helplessness moderated the association between DMN segregation and pain experience progression (p = 0.003). Our findings indicate that the efficient functioning of these networks and catastrophizing could be used as predictors of pain progression, bringing new light to the influence of the interplay between psychological aspects and brain networks. Consequently, approaches focusing on these factors could minimize the impact on daily life activities