Title: Exploring a multimodal serotonin atlas with the help of machine learning methods

Postdoc Fellow: Melanie Ganz-Benjaminsen, NRU

Abstract:

In recent years there has been an increasing interest towards applying multivariate machine learning techniques to the analysis of neuroimaging data. Determining disease-related variations of the anatomy and its function is an important step in better understanding diseases and developing early diagnostic systems. In particular, image-based multivariate prediction models and the “relevant features” they produce are attracting attention from the community. Therefore, image-based prediction models hold great promise for improving clinical practice. The ability to understand biological systems as well as predict the state of a disease based on its anatomical and functional signatures opens up new avenues for early diagnostic systems. Comparing images of healthy controls and patients can highlight such variations on a macroscale that would be difficult to identify using histopathology. This is essential for improving our understanding of disease and refining the predictive power of machine learning methods.

In this project, we want to explore how machine learning techniques can be applied to robustly identify variations in the human brain as determined by molecular and structural brain imaging obtained with functional Positron Emission Tomography (PET) and structural Magnetic Resonance Imaging (MRI). Specifically, we want to utilize a unique multi-modal data set of the serotonin neurotransmitter system that has been collected at the Center for Integrated Molecular Brain Imaging (Cimbi). It consists of approx. 200 high-resolution brain PET scans of healthy volunteers that target 4 different serotonin neuroreceptors (5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4) as well as the serotonin transporter (SERT), paired with the corresponding MR scans. This high dimensional neuroimaging data will be examined by machine learning tools such as clustering methods or multivariate regression and utilized to build a population-based atlas of the serotonin system that can be used as a reference and for constructing biomarkers of brain disease.

We believe that this project has the potential to lead to an enduring collaboration across disciplines with great social and economic significance.