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Title: Analysis of brain patterns using temporal measures

Abstract

A set of brain data representing a time series of neurophysiologic activity acquired by spatially distributed sensors arranged to detect neural signaling of a brain (such as by the use of magnetoencephalography) is obtained. The set of brain data is processed to obtain a dynamic brain model based on a set of statistically-independent temporal measures, such as partial cross correlations, among groupings of different time series within the set of brain data. The dynamic brain model represents interactions between neural populations of the brain occurring close in time, such as with zero lag, for example. The dynamic brain model can be analyzed to obtain the neurophysiologic assessment of the brain. Data processing techniques may be used to assess structural or neurochemical brain pathologies.

Inventors:
Publication Date:
Research Org.:
Regents of the University of Minnesota, Minneapolis, MN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1209483
Patent Number(s):
9,101,276
Application Number:
11/825,509
Assignee:
Regents of the University of Minnesota (Minneapolis, MN) CHO
DOE Contract Number:  
FG02-99ER62764
Resource Type:
Patent
Resource Relation:
Patent File Date: 2007 Jul 06
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE

Citation Formats

Georgopoulos, Apostolos. Analysis of brain patterns using temporal measures. United States: N. p., 2015. Web.
Georgopoulos, Apostolos. Analysis of brain patterns using temporal measures. United States.
Georgopoulos, Apostolos. Tue . "Analysis of brain patterns using temporal measures". United States. doi:. https://www.osti.gov/servlets/purl/1209483.
@article{osti_1209483,
title = {Analysis of brain patterns using temporal measures},
author = {Georgopoulos, Apostolos},
abstractNote = {A set of brain data representing a time series of neurophysiologic activity acquired by spatially distributed sensors arranged to detect neural signaling of a brain (such as by the use of magnetoencephalography) is obtained. The set of brain data is processed to obtain a dynamic brain model based on a set of statistically-independent temporal measures, such as partial cross correlations, among groupings of different time series within the set of brain data. The dynamic brain model represents interactions between neural populations of the brain occurring close in time, such as with zero lag, for example. The dynamic brain model can be analyzed to obtain the neurophysiologic assessment of the brain. Data processing techniques may be used to assess structural or neurochemical brain pathologies.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 11 00:00:00 EDT 2015},
month = {Tue Aug 11 00:00:00 EDT 2015}
}

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