| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 7, 2014 |
| Latest Amendment Date: | May 1, 2017 |
| Award Number: | 1406447 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jie Yang
jyang@nsf.gov (703)292-4768 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2014 |
| End Date: | July 31, 2018 (Estimated) |
| Total Intended Award Amount: | $300,000.00 |
| Total Awarded Amount to Date: | $348,000.00 |
| Funds Obligated to Date: |
FY 2015 = $16,000.00 FY 2016 = $16,000.00 FY 2017 = $16,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
6100 MAIN ST Houston TX US 77005-1827 (713)348-4820 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
6100 Main St Houston TX US 77005-1827 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
CRCNS-Computation Neuroscience, Smart and Connected Health |
| Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT 01001617DB NSF RESEARCH & RELATED ACTIVIT 01001718DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Understanding the relationship between brain activity and human behavior is not only one of the most important scientific challenges of our generation but also one of the most important challenges in medicine and public health. This project develops new technology that can address the minute size of the neurons, and the vast amount of data generated by neural activity. This project leverages the collaborative environment between Rice and Texas Medical Center to develop novel electrical stimulation approaches to modulate the seizure network, adaptively and selectively. If successful, the end result would be a reparative therapy that leverages inherent brain plasticity mechanisms and may one day be independent of chronically implanted electronics.
This project develops algorithms that capture the dynamic, frequency dependent connectivity of the brain from real-time monitoring of the brain using ECoG (Electrocorticography) and then identifying the "optimal" parameters of the LFS (low-frequency electrical stimulation) to modulate the connectivity of the epilepsy network with temporal and spatial precision. The complexity of modeling such connectivity in real-time is managed by first segmenting neural activity into different epochs and spectral bands and then deriving the sparse connectivity in each of the segments. Effective connectivity in each spectral-temporal segment is estimated using Granger causality. LFS is applied after detecting interictal epileptiform discharges (IEDs) at spatial locations identified from the model. These critical steps lead to the development of a prototype system of real-time stimulation with a natural trade-off of complexity versus accuracy prompting a compromise between battery life and efficacy. The efficacy of spatially-optimized, activity-triggered LFS is evaluated by measuring the irritability of the seizure network and comparing the rate of IEDs detected during pre- and post-treatment periods. These experiments would point the way to treatment of pharmacologically refractory epilepsy without surgical resection of brain tissue and lead to reparative therapies leveraging inherent brain plasticity. The proposed methodology presents the first of its kind reparative, real-time, and selective network modulation to treat a debilitating disease.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Patients with epilepsy suffer from seizures - periods in which hypersynchronous neural activity spreads from one or more small diseased circuits in the brain to malignantly entrain activity more broadly. For about 1 million Americans with epilepsy, seizures cannot be controlled pharmacologically resulting in a pervasive disability. Fortunately, a substantial proportion of these patients have focal epilepsies - their seizure networks are spatially localized to a small portion of tissue. In these cases, surgical resection of the epileptogenic focus can potentially cure their seizures. However, a major constraint on these surgeries is that the seizure focus is frequently not clearly identifiable by imaging or non-invasive electrophysiologic techniques.
Spatiotemporally structured stimulation has been shown to induce long-term changes in the strength of connections between individual neurons in vitro and in vivo. A general principle is that structured co-activation at higher frequencies tends to strengthen connections (long-term potentiation, LTP), whereas slower more-randomly structured activation tends to weaken connections (long-term depression, LTD). In particular low-frequency (~1 Hz) trains of paired stimulation pulses have been shown to produce long-lasting decreases in connection strength. Paired pulses are thought to activate LTD by activating synapses a second time during their absolute refractory periods. This suggests three key approaches that might result in LTD of the hyperexcitable seizure network.
We developed a set of information-theoretic tools to identify the seizure onset zone and then to measure coherency among different population of neurons in the zone in epileptic patients. These tools were then used to develop a machine learning pipeline to predict the onset of seizures.
Last Modified: 11/29/2018
Modified by: Behnaam Aazhang
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