Wednesday 20th December 2023
This was another blog I wrote for an early attempt at a personal website 'naturallyneuroscientific.wordpress.com', which I don't think exists any longer.
Collaborations between scientists from different disciplines is usually described in one of three ways: ‘crossdisciplinary’, ‘multidisciplinary’ and ‘interdisciplinary’. The difference between these is the nature of the collaboration, with ‘crossdisciplinary’ meaning ‘borrowing’ temporarily from another discipline and ‘multidisciplinary’ meaning multiple groups working on the same question but within the boundaries of their science. ‘Interdisciplinary’ science involves integrating different perspectives together to approach the problem in a more holistic way. Here I provide three examples of interdisciplinary research at the University of York. I provide examples that are primarily centred around neuroscience since I’m most interested in the brain.
I cannot describe interdisciplinary research at York without mentioning my own final year project. The approach of analysing steady-state visually evoked potentials (SSVEPs) recorded from Drosophila was born out of a collaboration between Prof Alex Wade from the Department of Psychology and Dr Chris Elliott from the Department of Biology in the mid 2010s to study Parkinson’s disease (Afsari et al., 2014) but has since been used in a variety of contexts such as autism (Vilidaite et al., 2018) and circadian rhythms (Nippe et al., 2017) (and in my case, microtubule disorganisation). SSVEPs offer an advantage over flash electroretinograms due to their high signal-to-noise ratio and the ability to separate responses from different retinal cells (Afsari et al., 2014). This interdisciplinary approach means that visual responses between different organisms can be compared directly and in a resource-effective way.
Another topic close to home is what underlies our impressive capacities for visual memory (Brady et al., 2008). A collaboration between Dr Karla Evans from the Department of Psychology and Dr Adrian Bors from the Department of Computer Science led to the development of ‘Visual Memory Schemas’ as a way to conceptualise how certains images can be remembered consistently between humans (Akagunduz et al., 2019). In the past, machine learning has been used to predict which images are memorable (e.g., Khosla et al., 2015), but this study conceptualises which parts of an image drives this memorability.
A quick honourable mention goes to the collaboration between Prof Tim Andrews from the Department of Psychology and Dr William Smith and Prof Edwin Hancock from the Department of Computer Science to study face perception using MEG (Ewbank et al., 2007) by generating synthetic faces (Smith & Hancock, 2006).
The Centre for Hyperpolarisation in Magnetic Resonance (CHyM) is a collaboration between the Departments of Psychology and Chemistry that aim to revolutionise clinical imaging by investigating the fundamental process of hyperpolarisation. A popular neuroimaging method, magnetic resonance imaging (MRI), is itself based on extracting signals emitted from altering the magnetisation of water but this has limited sensitivity which prevents observations of the complex underlying biochemistry. Here, researchers come together to advance both the theoretical and practical applications of this technique.
Another quick honourable mention goes to this paper ‘Dietary modulation of cortical excitation and inhibition‘ showing consuming Marmite may affect brain function.
A question I have asked myself since my interest in the brain began was “How can we connect biology to cognition?”. Those that study the biology of learning and memory might offer long-term potentiation as the cellular basis of memory (Bliss & Collingridge, 1993), but it doesn’t entirely explain the richness of episodic memories as events embedded in a spatiotemporal context (Sugar & Moser, 2019). In vision science, biophysically-constrained artificial neural networks try to mimic the behaviour of our visual systems (Doerig et al., 2023). I find these types of research most exciting because they incorporate so much of the knowledge we have already acquired. I am still far from getting involved in these types of research, but I am in the process of building the skill set and knowledge base to do so.