UCL-CNT Early Career Investigator Award in Neuroimaging Techniques 2020 presented to Yunzhe Liu

by Stefanie Rudrich
Head of Marketing (Brain Products)

UCL-CNT Early Career Investigator Award in Neuroimaging Techniques winner 2020: Yunzhe Liu

UCL-CNT Early Career Investigator Award in Neuroimaging Techniques winner 2020: Yunzhe Liu

On May 6, 2021, the “UCL-CNT Early Career Investigator Award in Neuroimaging Techniques 2020” was presented to Yunzhe Liu. Yunzhe received the award at the University College London’s (UCL) Center for Neuroimaging Techniques (CNT) “Annual Lecture on EEG”. The event had to be held virtually this year, but award presentation as well as the lecture on “EEG responses to cortical perturbations: an exploration across brain states and scales” by Prof. Marcello Massimini (University of Milan, Italy) were well attended.

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 and Brain Products UK are proud to sponsor the award, which includes a trophy, a certificate and a cheque for £1000.

This year’s winner is Yunzhe Liu of the Max Planck UCL Centre for Computational Psychiatry and Ageing Research. He received the award for the outstanding science he has produced over the course of his PhD.

About Yunzhe Liu
Excerpt from the nomination statement by Prof. Raymond J Dolan:

In his paper, “Human replay spontaneously reorganizes experience” [1] Yunzhe showed, for the first time, that fast replay of prior experience could be measured in the human brain. More importantly, he went beyond what is known from animal research to show that replay is not simply a recapitulation of prior experience but represents an inference as to the underlying structure of this experience. This paper has opened many rich avenues for future studies.

Yunzhe’s major methodological contributions include developing a sophisticated method not only to index neural reactivation, but also to determine its sequentially. This tool is equally applicable to human neurophysiological or animal neurophysiological data [2].

Crucially, the aforementioned methodological breakthrough has now enabled him to make novel discoveries in the field of decision-making. For example, in “Experience replay is associated with efficient nonlocal learning” [3], he has discovered a neural signature for experience replay that accounts for non-local learning in a manner that explains credit assignment.

Building on this body of work he has also made a significant breakthrough in determining the functional relevance of activity within the Default Mode Network (DMN). In “Replay bursts in humans coincide with activation of the default mode and parietal alpha networks” [4] he has provided the first demonstration that enhanced activity in this network is coupled to the onset of sequential replay.

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

References

[1] Liu Y, Dolan RJ, Kurth-Nelson Z, Behrens TEJ.
Human replay spontaneously reorganizes experience.
Cell: 2019; 178(3):640-652.e14. DOI: 10.1016/j.cell.2019.06.012. PMID: 31280961.

[2] Liu, Y., Dolan, R.J., Higgins, C., Penagos, H., Woolrich, M.W., Ólafsdóttir, H.F., Barry, C., Kurth-Nelson, Z. and Behrens, T.E., 2021. Temporally delayed linear modelling (TDLM) measures replay in both animals and humans. Elife, 10, p.e66917.

[3] Liu Y, Mattar MG, Behrens TEJ, Daw ND, Dolan RJ.
Experience replay is associated with efficient nonlocal learning.
Science. 2021 May 21;372(6544):eabf1357. doi: 10.1126/science.abf1357. PMID: 34016753; PMCID: PMC7610948.

[4] Higgins C, Liu Y, Vidaurre D, Kurth-Nelson Z, Dolan R, Behrens T, Woolrich M.
Replay bursts in humans coincide with activation of the default mode and parietal alpha networks.
Neuron. 2021 Mar 3;109(5):882-893.e7. doi: 10.1016/j.neuron.2020.12.007. Epub 2020 Dec 23. PMID: 33357412; PMCID: PMC7927915.


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