madineh sarvestani | Penn State University (original) (raw)
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Papers by madineh sarvestani
Neuromatch Academy (https://neuromatch.io/academy) was designed as an online summer school to cov... more Neuromatch Academy (https://neuromatch.io/academy) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.
Trends in Cognitive Sciences, 2021
Visual Neuroscience, 2022
The purpose of this brief communication is to make publicly available three unpublished manuscrip... more The purpose of this brief communication is to make publicly available three unpublished manuscripts on the organization of retinal ganglion cells in the tree shrew. The manuscripts were authored in 1986 by Dr. Edward DeBruyn, a PhD student in the laboratory of the late Dr. Vivien Casagrande at Vanderbilt University. As diurnal animals closely related to primates, tree shrews are ideally suited for comparative analyses of visual structures including the retina. We hope that providing this basic information in a citable form inspires other groups to pursue further characterization of the tree shrew retina using modern techniques.
Journal of Neuroscience Methods, 2009
The organophosphorous compound soman is an acetylcholinesterase inhibitor that causes damage to t... more The organophosphorous compound soman is an acetylcholinesterase inhibitor that causes damage to the brain. Exposure to soman causes neuropathology as a result of prolonged and recurrent seizures. In the present study, long-term recordings of cortical EEG were used to develop an unbiased means to quantify measures of seizure activity in a large data set while excluding other signal types. Rats were implanted with telemetry transmitters and exposed to soman followed by treatment with therapeutics similar to those administered in the field after nerve agent exposure. EEG, activity and temperature were recorded continuously for a minimum of 2 days pre-exposure and 15 days post-exposure. A set of automatic MATLAB algorithms have been developed to remove artifacts and measure the characteristics of long-term EEG recordings. The algorithms use short-time Fourier transforms to compute the power spectrum of the signal for 2-s intervals. The spectrum is then divided into the delta, theta, alpha, and beta frequency bands. A linear fit to the power spectrum is used to distinguish normal EEG activity from artifacts and high amplitude spike wave activity. Changes in time spent in seizure over a prolonged period are a powerful indicator of the effects of novel therapeutics against seizures. A graphical user interface has been created that simultaneously plots the raw EEG in the time domain, the power spectrum, and the wavelet transform. Motor activity and temperature are associated with EEG changes. The accuracy of this algorithm is also verified against visual inspection of video recordings up to 3 days after exposure.
Neuromatch Academy (https://neuromatch.io/academy) was designed as an online summer school to cov... more Neuromatch Academy (https://neuromatch.io/academy) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function.
Trends in Cognitive Sciences, 2021
Visual Neuroscience, 2022
The purpose of this brief communication is to make publicly available three unpublished manuscrip... more The purpose of this brief communication is to make publicly available three unpublished manuscripts on the organization of retinal ganglion cells in the tree shrew. The manuscripts were authored in 1986 by Dr. Edward DeBruyn, a PhD student in the laboratory of the late Dr. Vivien Casagrande at Vanderbilt University. As diurnal animals closely related to primates, tree shrews are ideally suited for comparative analyses of visual structures including the retina. We hope that providing this basic information in a citable form inspires other groups to pursue further characterization of the tree shrew retina using modern techniques.
Journal of Neuroscience Methods, 2009
The organophosphorous compound soman is an acetylcholinesterase inhibitor that causes damage to t... more The organophosphorous compound soman is an acetylcholinesterase inhibitor that causes damage to the brain. Exposure to soman causes neuropathology as a result of prolonged and recurrent seizures. In the present study, long-term recordings of cortical EEG were used to develop an unbiased means to quantify measures of seizure activity in a large data set while excluding other signal types. Rats were implanted with telemetry transmitters and exposed to soman followed by treatment with therapeutics similar to those administered in the field after nerve agent exposure. EEG, activity and temperature were recorded continuously for a minimum of 2 days pre-exposure and 15 days post-exposure. A set of automatic MATLAB algorithms have been developed to remove artifacts and measure the characteristics of long-term EEG recordings. The algorithms use short-time Fourier transforms to compute the power spectrum of the signal for 2-s intervals. The spectrum is then divided into the delta, theta, alpha, and beta frequency bands. A linear fit to the power spectrum is used to distinguish normal EEG activity from artifacts and high amplitude spike wave activity. Changes in time spent in seizure over a prolonged period are a powerful indicator of the effects of novel therapeutics against seizures. A graphical user interface has been created that simultaneously plots the raw EEG in the time domain, the power spectrum, and the wavelet transform. Motor activity and temperature are associated with EEG changes. The accuracy of this algorithm is also verified against visual inspection of video recordings up to 3 days after exposure.