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Optogenetic Functional MRI
Published on: April 19, 2016
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Solving brain circuit function and dysfunction with computational modeling and optogenetic fMRI
Jin Hyung Lee1,2,3,4, Qin Liu1, Ehsan Dadgar-Kiani1,2
1Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA.
Science (New York, N.Y.)
|November 3, 2022
Summary
This review explores brain circuit models to understand neurological diseases and predict therapeutic outcomes. Combining optogenetic functional magnetic resonance imaging (fMRI) with computational modeling offers a path toward restoring brain function.
Area of Science:
- Neuroscience
- Computational Biology
- Systems Engineering
Background:
- Neurological diseases involve complex whole-brain circuit dysfunctions.
- Understanding spatial spreading mechanisms of pathologies is crucial.
- Current models lack the precision to predict therapeutic outcomes.
Purpose of the Study:
- To review current approaches for modeling brain function in neurological disease.
- To explore future directions for understanding circuit mechanisms.
- To enable prediction of therapeutic intervention outcomes.
Main Methods:
- Combining optogenetic functional magnetic resonance imaging (fMRI) with computational modeling.
- Developing cell type-specific, large-scale brain circuit models.
- Quantitatively parameterizing brain function and dysfunction.
Main Results:
- Emerging methods allow quantitative parameterization of large-scale brain circuits.
- Optogenetic fMRI and computational modeling are key tools.
- Progress is being made in understanding cell type-specific circuit function.
Conclusions:
- A systems engineering approach can guide the development of targeted therapeutics.
- Restoring brain function is a viable goal for future treatments.
- Integrated modeling and imaging techniques are essential for advancing neurological disease research.
