NEUROMODEL · Statistical Modelling for relating multimodal neuroimaging to clinical outcomes in order to predict patient response to depression therapy.

Statistical Modelling for relating multimodal neuroimaging to clinical outcomes in order to predict patient response to depression therapy.

Understanding brain mechanisms and how they are affected by pharmacological interventions is essential to improve prevention and treatment of brain disorders. For instance, in major depressive disorders (MDD), less than half of the patients respond to first-line antidepressant treatment and previous attempts to identify a single biomarker that can critically predict individualized treatment response have so far failed. It has been hypothesized that the serotonin system is a key factor in MDD and most antidepressants attempt to act on this system. Recent advances in medical imaging allow to simultaneously visualize the brain structure (using Magnetic Resonance Imaging - MRI) and the serotonin system (using Positron Emission Tomography - PET) in order to characterize the role of serotonin in MDD and antidepressant therapy, e.g. to determine whether an abnormal serotonin levels make the patient more likely to respond to antidepressive treatment. This is being investigating in research project called Neuropharm (https://np.nru.dk/) conducted in Copenhagen, Denmark. The aim of this project is to provide the statistical tools to successfully analyze complex data mixing brain images, clinical, psychological, and genetic data. Latent variable models (LVMs), a multivariate technique using latent variables to model the relationship between indirect measurements of quantities of interest (e.g., of the brain serotonin level and the depression status of the patient), are of particular interest but need to be adapted to the context of neuroscience. Because PET and MRI measurement are expensive, studies involve a limited number of individuals (typically less than 100) and are intended to investigate several hypotheses. This is a challenge for the validity of the statistical analysis since traditional results holds in large samples and for a single hypothesis. Moreover due to the complexity of the brain system, it is difficult to specify a priori a valid statistical model. Efficient model-selection procedures need to be developed for LVMs. To ensure the diffusion of the methods to neuroscientists, the project also aimed at making the proposed statistical tools publicly available, with appropriate documentation, and assisting neuroscientists in their use.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз