One useful tool in trying to understand the vast parameter space of variables influencing epilepsy is computational modeling. Computer modeling allows the user to quickly change descriptive variables such as ion channels and synaptic properties, connections, and the temporal characteristics of synaptic messaging. A variety of cell geometries and numbers of neurons have been implemented in an effort to describe various features of the activities of linked cellular networks all in an attempt to reproduce various experimental findings. We propose in this current effort to compare and calibrate the activity derived from a single compartment Hodgkin-Huxley based model with a more sophisticated multicompartmental cellular model where in both cases the cells are arranged and connected in an architecturally realistic fashion to mimic neocortex. Additionally, the simpler single compartment model will be used as the surrounding boundary region for the more precise multicompartment model in an effort to expand the modeled region and still provide meaningful electrophysiologic correlates. We will examine specifically bursting activity derived from a region of the model which acts as an epileptic focus. This comparative work will be used to pursue the following aims:
Aim 1) To measure the rate of ictal spread within a calibrated architecturally realistic neuronal network using a hybrid compartment organization, and to perform a sensitivity analysis of this spread as a function of connectivities in the Layer II/III and Layer V pyramidal cell systems.
Aim 2) To measure the underlying activity threshold for ictal onset through graded, laminar specific pharmacologic blockade in the context of this model for comparison with experiment.
Aim 3) To compare the results obtained in our simulation studies with recordings obtained from a new subdural grid design incorporating standard electrode elements and microwires for local field potential measurements. The goal of these studies is to provide information in a quantitative manner on seizure spatial spread and temporal evolution as a function of neighboring or intrinsic pathology. We plan to model relatively large regions of cortex (6.4 mm X 6.4 mm), while examining the activity of a very high precision smaller (250

Public Health Relevance

This grant describes the development of a computer model that accurately portrays seizure evolution in a realistic cortical architecture. This would provide an additional tool in understanding seizure dynamics and the application of new drug or stimulation therapies. Estimates of seizure origin, rate of spread and the volume of tissue involved in pathologic behavior will aid in understanding the disruptive effects of epilepsy.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Clinical Investigator Award (CIA) (K08)
Project #
5K08NS066099-05
Application #
8450862
Study Section
NST-2 Subcommittee (NST)
Program Officer
Stewart, Randall R
Project Start
2010-05-01
Project End
2015-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
5
Fiscal Year
2013
Total Cost
$193,023
Indirect Cost
$14,298
Name
Johns Hopkins University
Department
Neurology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Kudela, Pawel; Anderson, William S (2015) Computational Modeling of Subdural Cortical Stimulation: A Quantitative Spatiotemporal Analysis of Action Potential Initiation in a High-Density Multicompartment Model. Neuromodulation 18:552-64 ; discussion 564-5
Basu, Ishita; Kudela, Pawel; Korzeniewska, Anna et al. (2015) A study of the dynamics of seizure propagation across micro domains in the vicinity of the seizure onset zone. J Neural Eng 12:046016
Fifer, Matthew S; Hotson, Guy; Wester, Brock A et al. (2014) Simultaneous neural control of simple reaching and grasping with the modular prosthetic limb using intracranial EEG. IEEE Trans Neural Syst Rehabil Eng 22:695-705
Chen, L Leon; Madhavan, Radhika; Rapoport, Benjamin I et al. (2013) Real-time brain oscillation detection and phase-locked stimulation using autoregressive spectral estimation and time-series forward prediction. IEEE Trans Biomed Eng 60:753-62
Lewis, Laura D; Weiner, Veronica S; Mukamel, Eran A et al. (2012) Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness. Proc Natl Acad Sci U S A 109:E3377-86
Peyrache, Adrien; Dehghani, Nima; Eskandar, Emad N et al. (2012) Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proc Natl Acad Sci U S A 109:1731-6
Anderson, William S; Azhar, Feraz; Kudela, Pawel et al. (2012) Epileptic seizures from abnormal networks: why some seizures defy predictability. Epilepsy Res 99:202-13
Azhar, Feraz; Anderson, William S (2012) Predicting single-neuron activity in locally connected networks. Neural Comput 24:2655-77
Truccolo, Wilson; Donoghue, Jacob A; Hochberg, Leigh R et al. (2011) Single-neuron dynamics in human focal epilepsy. Nat Neurosci 14:635-41
Anderson, William S; Lenz, Frederick A (2011) Review of motor and phantom-related imagery. Neuroreport 22:939-42

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