The group “Systems Medicine” in INM7, co-led by Dr. Felix Hoffstaedter and Dr. Masoud Tahmasian applies a multi-level approach utilizing computational tools and big data to gain overall insight into neurological and psychiatric disorders to pave the way for personalized medicine. By applying state-of-art methodologies such as machine-learning to big clinical and population-based databases using high performance computing, we aim to refine pathophysiology of neuropsychiatric disorders like Alzheimer’s and Parkinson’s diseases, Major Depressive Disorder, as well as Sleep Disorders to identify the risk factors and health consequences, and also to improve current diagnostic approaches.
Our main topics are:
- Multivariate modeling of typical brain aging in comparison to the development of age-related disorders. The development, application and evaluation of reproducible (f)MRI preprocessing workflows for automated preparation of big multimodal imaging dataset using comprehensive provenance tracking. The adaptation of established neuroimaging methods in humans to structural and functional MRI data in primates, in particular in chimpanzees, baboons and macaques to bridge the gap between human and animal models considering primate brain evolution.
- The interplay between sleep and medical and mental conditions using genetics, phenotypic, and multimodal neuroimaging data (e.g., sMRI, fMRI, PET). In addition, using the ENIGMA-Sleep framework, we are performing large-scale mega- & meta-analyses for better understanding the neurobiology of sleep disorders and their neuropsychiatric consequences, to assess the predictive role of poor sleep on mood and cognitive performances, and the neurobiological effects of sleep deprivation.
Methods and infrastructures
MRI & PET, Multivariate Big Data Analysis, Meta-Analysis, Machine learning, High Performance Computing
Prof. Dr. Simon Eickhoff
Building 14.6y / Room 2042
Ahmadi R, Rahimi-Jafari S, et al. Insomnia and post-traumatic stress disorder: A meta-analysis on interrelated association (n = 57,618) and prevalence (n = 573,665), Neuroscience & Biobehavioral Reviews, 2022 Oct; 141:104850