HHS Recovery Act Recipient Reporting Readiness Tool

Step 4. Review and Copy the Grant Awards Data

TAGGS provides some – but not all – of the data needed for the Recipient Report. Recipients are responsible for directly collecting and reporting all required data to FederalReporting.gov. Data that HHS does not currently collect are highlighted in yellow. Do not copy this highlighted information. Please enter the appropriate data for your organization in these required fields. For assistance with entering these data please contact FederalReporting.gov.

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Prime Recipient Report

Award Detail for: SINGLE UNIT BASED SEIZURE PREDICTION.
Recipient Name:NEUROSCIENCE RESEARCH INSTITUTE OF NORTH CAROLINA
DUNS Number: 606602295
101 NORTH CHESTNUT
WINSTON SALEM, NC 27101-4046

Reporting Information

Award Type*: Grant

Award Number*: 5R21NS060108-02

Final Report*: Recipient responsible for this data

Award Recipient Information

Recipient DUNS Number*: 606602295

Recipient Account Number: Recipient responsible for this data

Recipient Congressional District*: 5

Award Information

Funding Agency Code*: 7529

Awarding Agency Code*:7529

Award Date*: 03-31-2010

Amount of Award*: $ 206,250

Program Source (TAS)*: 750901

CFDA Number*: 93.701

Sub Account Number for Program Source (TAS)*: Recipient responsible for this data

Total Number of Sub Awards to Individuals*: Recipient responsible for this data

Total Amount of Sub Awards to Individuals*: Recipient responsible for this data

Total Number of Payments to Vendors less than $25,000/award*: Recipient responsible for this data

Total Amount of Payments to Vendors less than $25,000/award*: Recipient responsible for this data

Total Number of Sub Awards less than $25,000/award*: Recipient responsible for this data

Total Amount of Sub Awards less than $25,000/award*: Recipient responsible for this data

Award Description* DESCRIPTION (provided by applicant): Epilepsy is one of the most prevalent neurological disorders, affecting 50 million individuals worldwide, a quarter of which have medically intractable cases. The unpredictability of seizure onset can lead to severe bodily injury or even death. Thus a reliable seizure prediction method is greatly needed to protect epileptic patients from life threading incidents. Furthermore, accurate seizure prediction would allow clinicians to provide well-timed drug delivery or therapeutic brain stimulation. This ability to optimally time interventions could substantially reduce the side effects and toxicity of long term medications for medically responsive patients and provide a novel treatment for patients with intractable epilepsy. In light of this need, the American Epilepsy Society has identified seizure predication as a primary priority for epilepsy research. Neuroscientists, clinicians, mathematicians and bioengineers have developed collaborations devoted to this research field. In spite of great progress in seizure prediction research, reliable seizure prediction remains a challenging task. EEG has been employed for the last 3 decades of seizure prediction research, whilst novel alternative research tools are lacking. EEG data reflect the summation of postsynaptic potentials across a large population of cells in diffuse brain areas and thus lack temporal and spatial specificity. In this proposed study, we will for the first time employ a multiple channel, single unit recording technique to record up to 80 single cells in 5 different brain areas that have been shown to be critically involved in temporal lobe epilepsy (TLE) in rat model of TLE. The major benefits of recording single units are that it provides high temporal and spatial resolution and multiple dimensional data, which allow us to detect subtle changes in brain areas before seizure onset. We will apply this information with a battery of single unit and EEG data analytic tools to try to detect the neural activity changes that portend seizure onset. In this initial study, we will analyze the data off-line to establish a reliable seizure prediction algorithm. Once this algorithm has been established, we will apply real time, prospective seizure perdiction methods to detect seizures in real time and we will combine this prediction tool with deep brain stimulation to prevent seizure development and progression. The long term goal of this research is to translate animal experimental results to the clinic to treat epilepsy patients with this novel approach. PUBLIC HEALTH RELEVANCE: Accurate seizure prediction can prevent bodily injury for epileptic patient and enable clinicians to treat epilepsy with timely drug administration or brain stimulation. This research project will develop a novel multiple channel, single unit recording method to predict spontaneous seizures in rat model of epilepsy. The success of this project will be instrumental in improving seizure prediction in the clinic and benefit millions of patients with epilepsy. PHS 398/2590 (Rev. 09/04) Page Continuation Format Page

Project Information

Project Name or Project/Program Title*: SINGLE UNIT BASED SEIZURE PREDICTION.

Project Status*: Recipient responsible for this data

Total Federal Amount of ARRA Funds Received/Invoiced*: Recipient responsible for this data

Number of Jobs*: Recipient responsible for this data

Description of Jobs Created*: Recipient responsible for this data

Quarterly Activities/Project Description*: Recipient responsible for this data

Activity Code (NAICS or NTEE-NPC)*: Recipient responsible for this data

Total Federal Amount of ARRA Expenditure* (Enter the cumulative total amount of Recovery Funds received that were expended to projects or activities. Refer to the Data Model for details on how to calculate this amount.): Recipient responsible for this data

Total Federal ARRA Infrastructure Expenditure Recipient responsible for this data

Infrastructure Contact Name: Recipient responsible for this data

Infrastructure Contact Email: Recipient responsible for this data

Infrastructure Contact Phone: Recipient responsible for this data

Infrastructure Contact Phone Ext: Recipient responsible for this data

Infrastructure Contact Street Address 1: 101 NORTH CHESTNUT

Infrastructure Contact Street Address 2: Not Available

Infrastructure Contact Street Address 3: Recipient responsible for this data

Infrastructure City: WINSTON SALEM

Infrastructure State: NC

Infrastructure ZIP Code+4: 27101-4046

Infrastructure Purpose and Rationale (If applicable, enter an explanation about how the infrastructure investment will contribute to one or more purposes of the Recovery Act. Refer to the Data Model for details on what to report. 4000 characters or less.): Recipient responsible for this data

Primary Place of Performance

Street Address 1: 101 N CHESTNUT ST, STE 200

Street Address 2: WINSTON-SALEM

City*: WINSTON-SALEM

State*: NC

ZIP Code+4*: 271014046

Congressional District*: Not Available

Country*: US

Recipient Highly Compensated Officers

Prime Recipient Indication of Reporting Applicability*: Recipient responsible for this data

1. Officer Name and Compensation: Recipient responsible for this data
2. Officer Name and Compensation: Recipient responsible for this data
3. Officer Name and Compensation: Recipient responsible for this data
4. Officer Name and Compensation: Recipient responsible for this data
5. Officer Name and Compensation: Recipient responsible for this data

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