By Sean O’Malley
To understand how much humility is required to work in neuroscience, consider this: there are about 86 billion neurons in the human brain and we still don’t know exactly what goes on in any one of them, or how they interact with each other. The rise of neuroinformatics has allowed us to build the most sophisticated simulations ever of human neurons and the networks of the brain, but we have not been able to study experimentally how the neurons interact in a living brain. Therefore, the leading theories about depression in the brain have been essentially based on generalized observation and educated guesses about how the living human brain actually works and responds to medication.
Thanks to a unique partnership between the Krembil Centre for Neuroinformatics at CAMH and the Krembil Brain Institute at Toronto Western University, CAMH scientists have created the world’s first model of human brain microcircuitry that incorporates the properties of live human brain cells. The living brain cells come from patients with epilepsy who underwent a rare form of brain surgery that involved removing a tiny portion of the brain to reduce certain types of seizures.
Among the first results from that partnership is a CAMH-led study entitled, “Reduced inhibition in depression impairs stimulus processing in human cortical microcircuits,” recently published in the journal Cell Reports. In the paper, the authors have demonstrated how a type of treatment-resistant depression manifests itself in the brain, specifically linking the role of a particular type of neuron known as a Somatostatin (SST) to impaired cognitive function.
The authors believe this new simulation of the human brain integrating data from living brain cells may have enormous implications for future research at CAMH and around the world, and could pave the way for a new generation of drugs for treatment-resistant depression.
We caught up with lead author Dr. Etay Hay, Independent Scientist at the Krembil Centre for Neuroinformatics at CAMH to discuss his groundbreaking research.
What was the main purpose of this particular research study?
Is the main significance of this study that with live human cells and computational models you have validating one of the leading theories of depression in the brain?
Our goal was to use computer models based on live human brain tissue to confirm a hypothesis about how depression looks in the brain. The result was that, yes, we were able to confirm our hypothesis, and provide even more information about what happens in our brains during depression.
What is the value of incorporating real human brain cells into your computer model?
This paper is part of a project that is producing a very realistic model of human brain cell networks that enables us to study accurately and ethically what happens inside the human brain. Using this model, we’re able to study human disorders, especially mental health conditions, with much more accuracy than we ever have before.
How does your new human brain model address one of the biggest challenges in neuroscience in terms of being able to study living brain cells rather than post-mortem brain tissue?
Incorporating what we’ve learned from living brain tissue, our new model allows us to better predict how different types of conditions will affect real functioning brain cell networks. This is brand new and the potential for what we can learn using this model is very exciting.
Do you agree with the premise that we still don’t really know what really goes on in a single brain cell?
To an extent, yes. Rodent brain cells have been studied for decades now and there are similarities to the human brain cell, so we are not in uncharted waters. But there are some key differences and they make it important to develop models for human cells and simulate human microcircuits to study them. So I would say we know quite a bit about the single neuron, but there is still more to learn, especially in the human neurons, and also how they function within a network, which is how our whole brain functions.
How important is it to understand what happens in our physical brains during depression, rather than just looking to help the symptoms?
In order to develop drugs to treat mental health conditions, we need to really understand how these conditions physically affect the brain. This new model of our brain connections enables us to test our theories about depression in a realistic environment which can help play an important role in the development of future drugs.
Do you believe this research has fairly large implications for practical research as well as patient care?
Absolutely. For example, we are already using this model to study how to better diagnose depression using clinically-relevant brain signals. In one study we are using our computer model to simulate the effect of the SST neuron on EEG brain signals, so that we can eventually diagnose this in patients. Another one is testing new compounds like the ones Senior Scientist and Campbell Family Chair in Clinical Neuroscience Dr. Etienne Sibille has developed (in his work on reversing memory loss), to demonstrate their real-world impact on the brain. Testing these treatments on our realistic computer model is the next step towards eventual human trials.
Why did you decide to come to Canada from your native Israel?
I really like the country. I like the mentality here. I like that people respect each other. I love nature, and Canada is really beautiful so it really suits me. When I came here to do my undergrad I didn’t know much about it but very soon I fell in love with Canada and I knew that eventually I want to settle here.
I do like the neuroscientific community in the GTA, especially with Toronto becoming a greater hub for different kinds of neuroscientific research, including artificial intelligence. So it’s a pretty exciting place to be. It is also really attractive to have applications to our research, where our computational simulation results can actually affect the diagnosis and care of patients. I’ve done many years of theoretical and basic science research that serve the research community. My previous models have been used in labs across the world, but it’s very satisfying to actually know that my research has some more real-world relevance.