Understand and Repair Brain Circuits. The Lee Lab uses interdisciplinary approaches from biology and engineering to analyze, debug, and manipulate systems-level brain circuits. We 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.

Jin Hyung Lee, PhD (이진형) 
Associate Professor of Neurology and Neurological Sciences, Stanford University,CA, USA
Principal Investigator of the Lee lab

Professional Education
BS, Seoul National University, Electrical Engineering
MS, Stanford University, Electrical Engineering
PhD, Stanford University, Electrical Engineering (2004)

Academic Appointments 
Associate Professor, Neurology
Associate Professor, Bioengineering
Associate Professor, Neurosurgery
Associate Professor (By courtesy), Electrical Engineering
Member, Bio-X
Member, Wu Tsai Neurosciences Institute

From ofMRI to Mechanogenetic Functional Ultrasound

One of our major research goals is to develop novel neuromodulation, neuroimaging, and recording technologies to enable discovery of circuit mechanisms underlying the brain. Many neuroscience studies have shown that specific cell types within a brain network have unique contributions to behavioral output and that even a single neuron makes connections to large portions of the brain. Therefore, in order to truly get at the problem of uncovering brain function, we need measurements with cellular specificity across the whole brain during behavior. As such, due to technological limitations, our current understanding of global brain circuit mechanisms is extremely limited. Our recent development of optogenetic functional magnetic resonance imaging (ofMRI) technology provides a partial solution. However, challenges still remain: how do you non-invasively deliver cell type specific neuromodulation? How do you image the whole brain function in freely moving subjects?

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Machine Learning and Brain Computational Modeling

Based on the unique data we obtain from novel neuromodulation, imaging, and recording technologies, we can model brain function computationally. Put another way, we can construct a sort of digital circuit map which approximates how information is passed around in the brain. For example, we can use dynamic causal modeling (DCM) of optogenetic fMRI experiments to parameterize the causal relationships among regions of a distributed brain network. Brain circuit models improve our understanding of brain function and will ultimately help us to systematically design novel therapeutics. Brain diseases have traditionally been treated in the context of pathological, molecular changes. However, such changes drive neurological disease symptoms (behavior) through resulting changes in brain circuit function. Therefore, for both diagnosis and therapy of brain disease, understanding circuit mechanisms is critical. 

Circuit Mechanisms underlying Brain Disorders

We combine brain circuit modeling with neuromodulation, imaging, and behavioral experiments in vivo to discover, test, and validate circuit mechanisms underlying brain disorders. In other words, we study brain disease models to determine which brain circuits go “haywire” and malfunction. This has led to the discovery of circuit mechanisms underlying pathological motor behavior, arousal regulation, and protein accumulation. Understanding circuit mechanisms that drive behavioral dysfunction is critical for diagnosis and treatment of neurological diseases.

Novel Circuit-Based Therapeutic Approaches

We combine modeling with neuromodulation, imaging, and behavioral experiments to discover, test, and validate novel circuit based therapeutics. The primary goal of such therapeutics is to normalize brain circuit function. Regardless of the initial cause of disease (environmental toxin, genetic problem, immune system response, gut- brain axis, etc), the reason why patients suffer from symptoms such as tremor or memory loss is because the disease led to a malfunction in the patient’s brain circuitry. Moreover, the unfortunate reality right now is that even experts do not have a way to quantitatively measure and understand the status of those dysfunctional brain circuits. This poses a great problem in diagnosis, treatment selection, and treatment development. You can’t treat what you don’t understand. Fortunately, we envision a near future where we do understand brain circuits and can accurately monitor and treat them just as we do for weight, blood pressure, and glucose level. Through experiments we are conducting in our lab, we are beginning to make inferences about 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 performing (both of which play a critical role in diseases such as Parkinson’s) and where in the circuit a patient might have a dysfunction. This will give doctors much better guidance and enable improved treatments of neurological conditions.

 Recent Publications 

In patients with late-life depression and cognitive decline, adding tDCS to cognitive training does not significantly affect depressive symptoms but shows potential benefits on cognition as measured by fMRI
Journal Papers , 2024

Hemispherically lateralized rhythmic oscillations in the cingulate-amygdala circuit drive affective empathy in mice
Journal Papers , 2023

Whole-brain microscopy reveals distinct temporal and spatial efficacy of anti-Aβ therapies
Journal Papers , 2022

Whole-brain modeling of α-synuclein spreading, aggregation, and decay dynamics as informed by mesoscale connections and gene expression
Conference Proceedings , 2022

Optogenetic neuromodulation modifies α-synuclein spreading dynamics and is predicted by changes in whole-brain function
Conference Proceedings , 2022

Hemispherically lateralized rhythmic oscillations in the cingultate-amygdala circuit drive affective empathy in the mouse
Conference Proceedings , 2022

Investigation of basal ganglia output pathways using optogenetic fMRI
Conference Proceedings , 2022

Mesoscale connections and gene expression empower whole-brain modeling of α-synuclein spread, aggregation, and decay dynamics
Journal Papers , 2022

Solving brain circuit function and dysfunction with computational modeling and optogenetic fMRI
Journal Papers , 2022

지도교수 : 이진형 

Administrative Associate : Clara Choi 

Senior Research Scientist : Greg Cron, PhD 

Homepage : https://llab.stanford.edu/

Contact : gregcron@stanford.edu

Job Opportunities 

We are are a close-knit team of people who help and support each other.  We are looking for individuals who will be invested in the success of the lab.  If you would like to join us, check out the jobs below !  For general job inquiries:  gregcron@stanford.edu

Be part of our team!

Our lab is interested in understanding how the brain works at the systems level.

We are looking for motivated and talented graduate students, post-docs, research scientists, and others to join our group.

Career