Visualizing adipocyte-brain crosstalk: joint independent component analysis on neuroimaging and hormone data, Project 7

Principal Investigator:PD Dr. Kamila Jauch-Chara

Prof. Dr. Thomas Münte
Projektmitarbeiter:M.S. Arkan Al-Zubaidi
Janis Nolde
Dr. rer. nat. Uwe H. Melchert


Endocrine signals play a major role in energy homeostasis of the body. Various hormones are secreted by different peripheral sides of the body depending on the body’s energy status. For instance, leptin and adiponectin are released by the fat tissue in proportional amounts to a total fat volume. Insulin and ghrelin are hormones reacting to acute changes of energy levels. All of these hormones have been shown to have a certain impact on the central nervous system. In this field of research several studies focused on the function of the hypothalamus, a brain site well known for the integration of endocrine signals and the regulation of basal homeostatic processes. Although there is an increasing amount of evidence, that many other brain sides are involved in these processes as well, little is known about the relationship between the temporal fluctuation of hormone signal patterns and brain activity in general. Therefore, it is important that this relationship be investigated in a more comprehensive, integrated fashion.

Aim of the project

This project has two aims. The first aim is to investigate the dynamics in the secretion of adipocyte-dependent hormones in different metabolic conditions related to diverse brain network activation. The second one is to investigate the effect of the interaction between adipocyte-dependent and gut-dependent signals on resting state fMRI and task related fMRI. In this project, we suggest the use of independent component analysis (ICA) as a framework to achieve these goals.


Specifically, several different variants of independent component analysis (ICA) of multimodal data-sets such as joint ICA and parallel ICA have been proposed. ICA is increasingly utilized as a tool for evaluating the hidden spatiotemporal structure contained within brain imaging data. Firstly, we will apply the parallel ICA onto the two modalities described earlier with the goal of identifying functional brain networks, hormone associations as well as their relationship. Secondly, we will use the joint ICA to investigate the time courses of adipocyte-dependent and gut-dependent hormone levels. Finally, the two sets of data will be analyzed to figure out if these different hormones will result in changes of distinct brain networks.