Electrical (voltage) signal is the primary substrate of information processing in the brain. Detecting and recording voltage changes from neurons in living animals remains the ultimate goal of experimental neuroscience to the present day. Standard glass and metal electrodes are hugely invasive and their use suffers from poor spatial resolution, limited coverage, and blindness to cellular identity. The popular calcium-sensitive indicators generate signals contaminated with changes in intracellular calcium that are unrelated to neuronal electrical signals, and only indirectly report the electrical signals with distorted tiing and highly distorted waveform. A conceptually ideal principle to achieve monitoring of neuronal electrical activity is provided by optical voltage imaging using genetically-encoded voltage indicators (GEVIs). However, even the best performing GEVIs currently available are suitable only for in vivo monitoring of compound synaptic potentials (the summated voltage signal from unidentified number of neurons), but fail to resolve simultaneously signals from many individual cells in intact nervous tissue. While better performing GEVIs with higher sensitivity are expected to emerge from the on-going BRAIN Initiative-funded activities, this alone does not resolve the single-cell resolution voltage imaging problem: signals from individual cells are not spatially segregated. The solution to this issue requires other technologies including refined genetic targeting and data analysis methods. We plan to develop protocols for sparse GEVI targeting of central nervous system neurons. Sparse cellular targeting will allow imaging of neuronal activity with little spatial overlap. We also plan to develop data analysis routines for isolating single cel responses from data obtained in sparsely labelled tissue and, building on this, in densely targeted tissue. In summary, we propose to develop a novel genetically-directed voltage imaging tool that is qualitatively different than those currently available, along with data analyss methods to facilitate the use of electrical signals from large number of neurons embedded in functioning neuronal networks. The novel GEVI labelling protocols and data acquisition and analysis algorithms developed here should be immediately useful and impactful for studying brain function in health and disease.
Massive networks of neurons communicate via electrical signals. Very mild disruptions in network activity are believed to be responsible for a number of mental disorders. Understanding the molecular and cellular events underlying brain function would allow us to pinpoint the best time periods and techniques to preempt the onset of symptoms or to halt the progression of mental illness.
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