Constantinos Eleftheriou

Centre for Discovery Brain Sciences

Hugh Robson Building

George Square

Edinburgh, United Kingdom

I received a BSc in Neuroscience at the University of Edinburgh in 2018 with First Class honours. My undergraduate research project was supervised by Dr Cyril Pernet at the Centre for Clinical Brain Sciences, where I investigated language processing in stroke patients with Wernicke’s aphasia using fMRI and computational modelling.

In 2018, I joined the Duguid Lab as a PhD student to study visuomotor learning deficits in a mouse model of Rett syndrome, a devastating neurodevelopmental disorder caused by mutations in the Mecp2 gene. Using behavioural assays in tandem with calcium imaging of the dorsal cortex, I established that associative learning deficits precede loss of motor control in mouse models of Rett Syndrome, driven by specific circuit dysfunction in higher-order decision-making cortical areas. My PhD was funded by a joint studentship from the Simons Initiative for the Developing Brain and the University of Edinburgh Principal’s Career Development Scholarship. I graduated in 2023; my thesis titled “Visuomotor learning in Rett syndrome” won that year’s Alison Douglas Prize for best PhD thesis in the Centre for Discovery Brain Sciences.

My current research aims to establish whether associative learning deficits in Rett Syndrome are amendable to gene therapy. This work is done in close collaboration with Prof Stuart Cobb and is funded by the Medical Research Council and the Simons Initiative for the Developing Brain.

I am also interested in software sustainability and the management of large datasets in research, leveraging my past experience in systems administration and software development. Since 2025, I co-lead the SIDB Data Engineering Core facility, which provides research software and data management services to the SIDB community.

latest posts

Feb 3, 2026	a phd is more than just the research

selected publications

Better Statistical Reporting Does Not Lead to Statistical Rigour: Lessons from Two Decades of Pseudoreplication in Mouse-Model Studies of Neurological Disorders

Constantinos Eleftheriou, Sarah Giachetti, Raven Hickson, Laura Kamnioti-Dumont, Robert Templaar, Alina Aaltonen, Eleni Tsoukala, Nawon Kim, Lysandra Fryer-Petridis, Chloe Henley, Ceren Erdem, Emma Wilson, Beatriz Maio, Jingjing Ye, Jessica C. Pierce, Kath Mazur, Lucia Landa-Navarro, Nina G. Petrović, Sarah Bendova, Hanan Woods, Manuela Rizzi, Vanesa Salazar-Sanchez, Natasha Anstey, Antonios Asiminas, Shinjini Basu, Sam A. Booker, Anjanette Harris, Sam Heyes, Adam Jackson, Alex Crocker-Buque, Aoife C. McMahon, Sally M. Till, Lasani S. Wijetunge, David JA Wyllie, Catherine M. Abbott, Timothy O’Leary, and Peter C. Kind

Molecular Autism, May 2025

Abs

Accurately determining the sample size (“N”) of a dataset is a key consideration for experimental design. Misidentification of sample size can lead to pseudoreplication, a process of artificially inflating the number of experimental replicates which systematically underestimates variability, overestimates effect sizes and invalidates statistical tests performed on the data. While many journals have adopted stringent requirements with regard to statistical reporting over the last decade, it remains unknown whether such efforts have had a meaningful impact on statistical rigour.
Visiomode: An open-source platform for building rodent touchscreen-based behavioral assays

Constantinos Eleftheriou, Thomas Clarke, V. Poon, Marie Zechner, and Ian Duguid

Journal of Neuroscience Methods, May 2023

Abs PDF

Touchscreen-based behavioral assays provide a robust method for assessing cognitive behavior in rodents, offering great flexibility and translational potential. The development of touchscreen assays presents a significant programming and mechanical engineering challenge, where commercial solutions can be prohibitively expensive and open-source solutions are underdeveloped, with limited adaptability. Here, we present Visiomode (www.visiomode.org), an open-source platform for building rodent touchscreen-based behavioral tasks. Visiomode leverages the inherent flexibility of touchscreens to offer a simple yet adaptable software and hardware platform. The platform is built on the Raspberry Pi computer combining a web-based interface and powerful plug-in system with an operant chamber that can be adapted to generate a wide range of behavioral tasks. As a proof of concept, we use Visiomode to build both simple stimulus-response and more complex visual discrimination tasks, showing that mice display rapid sensorimotor learning including switching between different motor responses (i.e., nose poke versus reaching). Commercial solutions are the go-to for rodent touchscreen behaviors, but the associated costs can be prohibitive, limiting their uptake by the wider neuroscience community. While several open-source solutions have been developed, efforts so far have focused on reducing the cost, rather than promoting ease of use and adaptability. Visiomode addresses these unmet needs providing a low-cost, extensible platform for creating touchscreen tasks. Developing an open-source, rapidly scalable and low-cost platform for building touchscreen-based behavioral assays should increase uptake across the science community and accelerate the investigation of cognition, decision-making and sensorimotor behaviors both in health and disease.