Dr. Jin Hyung Lee is an associate professor of neurology and neurological sciences, bioengineering, neurosurgery and electrical engineering (courtesy) at Stanford University. The Lee Lab uses interdisciplinary approaches from biology and engineering to analyze, debug, and manipulate systems-level brain circuits. They seek to understand the connectivity and function of these large-scale networks in order to drive the development of new therapies for neurological diseases. This research finds its basic building blocks in areas ranging from medical imaging and signal processing to genetics and molecular biology.
Dr. Lee is a recipient of the 2008 NIH/NIBIB K99/R00 Pathway to Independence Award, 2010 NIH Director’s New Innovator Award, 2010 Okawa Foundation Research Grant Award, 2011 NSF CAREER Award, 2012 Alfred P. Sloan Research Fellowship, 2012 Epilepsy Therapy Project award, 2013 Alzheimer’s Association New Investigator Award, 2014 IEEE EMBS BRAIN young investigator award, 2017 NIH/NIMH BRAIN grant award, and 2018 Lina 50+ Award Grand Prize.
The following has been paraphrased from an interview with Prof. Jin Hyung Lee on July 18th, 2018
(Click above to listen to the full audio version or click here for a downloadable version)
What have been the biggest breakthroughs in our ability to map brain circuitry in recent years?
The primary goal of neurological disease treatment, including Parkinson’s disease, is normalizing brain circuit function. No matter what the cause of the disease is, whether it is genetic, immune system response, gut- brain axis, or environmental toxins, the reason why patients suffer from symptoms such as tremor, memory loss, etc. is because these events led to malfunction in the patient’s brain circuitry.
Some of the biggest breakthroughs in recent years in our ability to understand these circuits have come from the development of technologies based on genetics, imaging, and computational tools that are now starting to enable us to map brain circuits in a meaningful way. It is also notable that the BRAIN initiative’s primary goal includes developing tools for brain circuit mapping, this support has played a key role in these developments.
What is advanced MRI? What advantages/disadvantages does it have over fMRI and PET scanning?
MRI is a very important, highly versatile imaging technology. It is non-invasive, can flexibly generate different contrasts, and can image very large volumes. However, to solve the problems related to the brain, we still need more advanced capabilities. For example, higher spatio-temporal resolution, the ability to associate measurements with specific cell types, the ability to image stem cell integration with brain circuit, modeling the measurements to give dynamic circuit function measurement beyond what is directly measurable, integrating other measurement tools like electrophysiology, and optical imaging that provide different spatio-temporal scale information. We have brought those techniques together to develop this advanced MRI approach which will enable us to solve neuroscience problems more effectively than previous imaging techniques.
You also work on what you call optogenetic fMRI (ofMRI), could you describe what that means?
It is a technology that we pioneered that enables cell type specific measurements of whole brain function. To this day, we still have very limited ability to describe how the brain circuit drives different behaviors. We often describe specific cells that are related to brain circuit function, or how specific brain areas are related to brain function. These are fascinating discoveries that bring us closer to understanding the brain.
However, to have a level of understanding that circuit engineers have of electronic circuits that enables them to readily fix it, the description needs to be at an algorithmic level. For example, when trying to debug a volume button on a circuit, we cannot stop at just understanding that the volume button has something to do with the sound. We need to know how the volume button interacts with the rest of the circuit to generate and control sound. If we only had the ability to simultaneously press buttons that turned volume up and down, we would have a hard time even knowing that it is a volume button. That is basically what we had with brain circuitry. ofMRI is a technology that enables cell type specific control (similar to separately controlling volume up and down buttons) while monitoring how the signal travels throughout the circuit so that we can have an algorithmic understanding of the brain.
How long do you think it will take until we can accurately map neurodegeneration?
We are very close. For example, we have already created a whole brain circuit map from two primary classes of cell types involved in Parkinson’s disease, the D1 and D2 receptor medium spiny neurons. They drive opposite motor behaviors, and an imbalance in these two circuits is thought to be implicated in Parkinson’s disease. Before, there was no way to simultaneously map how the two different classes of neurons drive the behavior by communicating with the whole brain network. Now we are able to directly measure and build circuit models of how this works. I don’t want to make any promises, but I think these advances will enable us to map neurodegeneration in a living brain within a few years.
How long do you think it will take until we have mapped the entire human brain?
It depends on what we mean by ‘mapped’. If we mean counting every neuron and knowing exactly what each neuron does, that may be quite some time away. However, we do not necessarily need to know every physical equation that describes the building blocks of a building to build skyscrapers. It is very important for us to infer, from the measurements we are starting to make, what the principles of brain organizations are. When we make breakthroughs as to how to approach these problems, we can quickly scale them up and this principle has enabled us to get much closer to treatments for neurological diseases.
What do you see when you envision the future of this field?
We currently can monitor weight, blood pressure, blood glucose level etc. to help us maintain our health. Imagine trying to control your weight by just looking at yourself in the mirror, or monitoring blood pressure by just feeling your blood vessels with your fingers, or guessing your blood glucose level based on how you feel, these would be very difficult to do. The unfortunate reality for neurological diseases right now is that even experts do not have a way to quantitatively measure and understand the status of your brain circuit. This poses a great problem in diagnosis, treatment selection, and new treatment development. You can’t treat what you don’t know.
I envision a future where we know our brain circuits, where we can accurately monitor the status of your motor system functions and treat it the way we do it for weight, blood pressure, and glucose level. I believe we are close to getting there. Through experiments we are conducting in our lab, we are beginning to be able to make inferences as to the organizational principles of important brain circuits. This will allow us to use current brain imaging techniques to output scores of how, for example, your direct and indirect circuits are doing (both circuits play a critical role in symptoms of Parkinson’s disease) and where in the circuit a patient might have dysfunction. This will give doctors much better insight and will enable better treatments for neurological conditions.