Valentin Riedl

Valentin Riedl is a Full Professor of Neuroimaging at Friedrich-Alexander University (FAU) in Erlangen and holds a dual research leadership at the Technical University of Munich (TUM), where he heads the Neuroenergetics Lab. He is affiliated with the Department of Neuroradiology at the University Hospital Erlangen and conducts cutting-edge research using hybrid PET/MRI and ultra-high field MRI (7T) to study brain energy metabolism and its relationship to neuronal signaling and brain networks.

• University: Friedrich-Alexander University (FAU), Technical University of Munich (TUM)
• School: University Hospital Erlangen
• Department: Department of Neuroradiology
• Academic Rank: Professor

His research spans neuroenergetics, brain metabolism, functional and effective connectivity, neuromodulators, and neuroimaging methods. He investigates how energy consumption supports cognitive functions and how it is disrupted in neuropsychiatric diseases such as Alzheimer’s. His work integrates multimodal imaging (PET, MRI, MRS, TMS) with network modeling and computational approaches.

The recent publications highlight a strong trend in understanding the metabolic basis of brain connectivity, neurotransmitter dynamics, and the differential effects of brain stimulation across cortical regions. His studies frequently appear in top journals such as Science Advances, PNAS, and Journal of Neuroscience, reflecting a consistent focus on quantitative neuroenergetics and brain network modeling.

• ERC Starting Grant 759659
• DFG Research Grant RI2519
• VolkswagenStiftung 'Change of Course' Grant
• Alzheimer Forschungsinitiative Funding
• EBRAINS 2.0 EU Infrastructure Funding

Valentin Riedl actively mentors PhD and postdoctoral researchers and leads a multidisciplinary team integrating neuroscience, engineering, and computational modeling. His lab fosters collaboration across FAU and TUM, with strong ties to graduate programs such as the Munich Graduate School of Systemic Neurosciences (GSN) and MLST. He also promotes open science through code and data sharing on GitHub.