Biosignal and medical imaging processing
Over the last few decades translational neuroscience has transitioned from qualitative case reports to quantitative, longitudinal and multivariate population studies in the quest for defining patterns of disease pathogenesis, prognostic indicators and treatment response. Massive data analysis and computational processing has revealed previously unknown interactions, providing potential biomarkers for diagnosis and prognosis in clinical practice.
The field of biosignal and medical imaging processing consists on using computational and mathematical approaches based on the statistical learning theory with the final objective of developing computer-aided diagnosis systems in neuroscience. In the sense we aim to provide tools for clinicians that support the study and diagnosis of neuropathological diseases, such as Alzheimer or Parkinson diseases; tools that will largely influence treatment and patient management. On the other hand, advanced signal processing methods developed for neurophysiological signals allowed the measurement of the response to specific stimulus and provide the arena for the construction of complex functional connectivity models.
From the perspective of data analysis, a variety of statistical signal processing strategies are proposed, including the statistical comparison of individual’s data with reference images of control subjects for the in-vivo assessment of brain functional/structural parameters, the joint modelling of brain images to understand the different sources of variability in brain function and structure, and many other approaches that may support an informed decision in neurodegenerative diseases. Morever, the use of deep learning and bayesian deep learning based models for image and time series data constitute a powerful alternative to classical analysis methods, not only for discriminative analysis but also for exploratory analysis.
Responsible: Juan Manuel Górriz Sáez