Integrated Biophysical and Neural Model of Electrical Stimulation Effects

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Project Abstract Electrical stimulation is widely used to activate and/or disrupt neuronal activity. Despite its critical importance in experimental and clinical neuroscience, at present, there is no validated method to predict which neural elements of the brain will be activated by a given stimulation regime. Based on our pilot studies, we propose here to develop a novel computational approach for predicting the specific neurons which will be activated by a given stimulation protocol, based on neuron shape, location, type and connectivity. We will use biophysical modeling to calculate the spatial distribution of activating currents, and then convolve this distribution with the spatial distribution and orientation of the axons and dendrites of the major pyramidal and interneuron cell types to determine their probability of firing. We will then propagate this activity through the cortical circuitry. We will model different species (rats, mice, humans) and cortical areas (primary sensory and associative). We will examine the effects of sleep stage and background activity, including characteristic sleep rhythms, on the evoked thalamocortical network activity. The predictions of the model will be validated with extensive empirical measurements, primarily calcium imaging in mice using advanced microscopy methods that allow the entire relevant cortical volume to be characterized at high resolution. Cell type specific labeling and anatomical reconstructions will permit identification of different neuronal populations and measurement of their activation probability. This will be supplemented by voltage-sensitive dye imaging and laminar electrophysiological recordings to provide temporal resolution. The laminar recordings will be repeated in humans, in both acute intraoperative and semi-chronic settings. The models will be modified in light of the validation studies. The integrated biophysical and neural model with documentation and tutorials will be made available on the web.