UCL-CNT Early Career Investigator Award in Neuroimaging Techniques 2024 presented to Dr. Johan Medrano

by Stefanie Rudrich
Head of Marketing (Brain Products)

On September 19, 2025, the UCL-CNT (University College London Centre for Neuroimaing Techniques) Early Career Investigator Award in Neuroimaging Techniques 2024 was presented to Dr. Johan Medrano.

Johan is a postdoctoral research fellow in the Methods group at the Functional Imaging Laboratory (FIL) UCL Queen Square Institute of Neurology, University College London, London, UK. He received the award for his outstanding contributions to methods development in the FIL.

Initiated in 2007, this annual award aims to acknowledge an exceptional contribution by a UCL student or staff member in the early stages of their career in the field of Neuroimaging. Brain Products GmbH and Brain Products UK are proud to sponsor the award, which includes a trophy, £1000 and a certificate – signed by the President and Provost of UCL, Professor Michael Spence and Professor Louis Lemieux (chair of the UCL-CNT).

UCL-CNT Early Career Investigator Award in Neuroimaging Techniques 2024 presented to Dr. Johan Medrano

From left to right: Prof. Peter Zeidman, Dr. Johan Medrano (2024 CNT Early Career Award winner), Prof. Louis Lemieux (Chair of the UCL-CNT).

More about Dr. Johan Medrano(Excerpt from the nomination statement by Prof. Peter Zeidman)

Johan’s contributions to methods development in our lab have been extraordinary, both in terms of developing new solutions to imaging analysis problems, and by making those methods freely available to the neuroimaging community via the SPM software package. Johan is creative, self-motivated and enthusiastic about supporting people with their research. […]

Contributions by Johan (examples)

1. Time-Varying Connectivity in Dynamic Causal Modelling: Johan developed a statistical framework that addresses a fundamental challenge in neuroscience: tracking how neural connectivity evolves over time [1]. This extends an established framework called dynamic causal modelling (DCM) by capturing time-varying connectivity, thereby providing an innovative approach to understanding multiscale neural dynamics [2]. Through extensive validation using both synthetic and empirical M/EEG data, Johan demonstrated that this technique robustly tracks slow changes in neural coupling. […]

2. Bayesian Decomposition of Neural Spectra: In parallel, Johan developed a Bayesian framework for decomposing neural power spectra into their periodic and aperiodic components [3]. This method improves existing popular approaches by enabling uncertainty quantification and robust group-level analyses. He validated this method by studying age-related spectral shifts in a large EEG dataset from 204 healthy participants. This revealed systematic shifts in neural oscillations associated with ageing, highlighting the method’s potential for uncovering subtle but important neurophysiological changes. […]

3. A Python Interface to the SPM Software: Johan has also focused on bridging a gap between programming languages that divides our research community, by developing a Python interface for SPM [4]. This interface is more than simply a translation between languages: it preserves the carefully validated software implementation that makes SPM a recognized and trusted neuroimaging software package, while opening these tools to Python users. […]

Johan’s many contributions advance neuroimaging research, improve neuroimaging techniques’ accessibility, and promote open and reproducible science.

On behalf of the whole Brain Products and Brain Products UK team, congratulations, Johan, on winning this award! We will continue to follow your research and wish you all the best for your future.

Most recent articles

References

[1] Johan Medrano, Karl Friston, and Peter Zeidman. Linking fast and slow: The case for generative models.
Network Neuroscience, 8(1):24–43, 2024. https://direct.mit.edu/netn/article/8/1/24/117960.

[2] Johan Medrano, Karl J Friston, and Peter Zeidman.  Dynamic causal models of time-varying connectivity.
arXiv preprint arXiv:2411.16582, 2024. https://arxiv.org/abs/2411.16582.

[3] Johan Medrano, Nicholas A Alexander, Robert A Seymour, and Peter Zeidman. BSD: a Bayesian framework for parametric models of neural spectra.
arXiv preprint arXiv:2410.20896, 2024. https://arxiv.org/abs/2410.20896.

[4] The Python Interface to SPM. https://github.com/spm/spm-python.