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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:
Issue 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)
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. 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 = {2015},
month = {8}
}

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Works referenced in this record:

A genetic approach to ARMA filter synthesis for EEG signal simulation
conference, January 2000

  • Janeczko, C.; Lopes, H. S.
  • Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)
  • DOI: 10.1109/CEC.2000.870319

Evaluation of different measures of functional connectivity using a neural mass model
journal, February 2004


Independent Component Analysis of Ictal EEG in Medial Temporal Lobe Epilepsy
journal, February 2002


99mTc-HMPAO SPECT Study of Cerebral Perfusion After Treatment with Medication and Electroconvulsive Therapy in Major Depression
journal, August 2007


A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis
journal, July 2005


Dynamic imaging of coherent sources: Studying neural interactions in the human brain
journal, January 2001

  • Gross, J.; Kujala, J.; Hamalainen, M.
  • Proceedings of the National Academy of Sciences, Vol. 98, Issue 2
  • DOI: 10.1073/pnas.98.2.694

Partial Cross-Correlation Analysis Resolves Ambiguity in the Encoding of Multiple Movement Features
journal, March 2006

  • Stark, Eran; Drori, Rotem; Abeles, Moshe
  • Journal of Neurophysiology, Vol. 95, Issue 3
  • DOI: 10.1152/jn.00981.2005

Time series analysis of magnetoencephalographic data during copying
journal, April 2005

  • Leuthold, Arthur C.; Langheim, Frederick J. P.; Lewis, Scott M.
  • Experimental Brain Research, Vol. 164, Issue 4
  • DOI: 10.1007/s00221-005-2259-0

Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease
journal, September 2006


Synchronous dynamic brain networks revealed by magnetoencephalography
journal, December 2005

  • Langheim, F. J. P.; Leuthold, A. C.; Georgopoulos, A. P.
  • Proceedings of the National Academy of Sciences, Vol. 103, Issue 2, p. 455-459
  • DOI: 10.1073/pnas.0509623102

Machine Learning for Detection and Diagnosis of Disease
journal, August 2006


Entropy analysis of the EEG background activity in Alzheimer's disease patients
journal, January 2006


EEG dynamics in patients with Alzheimer's disease
journal, July 2004


Synchronous neural interactions assessed by magnetoencephalography: a functional biomarker for brain disorders
journal, August 2007

  • Georgopoulos, Apostolos P.; Karageorgiou, Elissaios; Leuthold, Arthur C.
  • Journal of Neural Engineering, Vol. 4, Issue 4
  • DOI: 10.1088/1741-2560/4/4/001

Automatic classification of MR scans in Alzheimer's disease
journal, February 2008


11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment
journal, February 2008

  • Jack, Clifford R.; Lowe, Val J.; Senjem, Matthew L.
  • Brain, Vol. 131, Issue 3
  • DOI: 10.1093/brain/awm336

Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI
journal, March 2004

  • Greicius, M. D.; Srivastava, G.; Reiss, A. L.
  • Proceedings of the National Academy of Sciences, Vol. 101, Issue 13, p. 4637-4642
  • DOI: 10.1073/pnas.0308627101

Selective changes of resting-state networks in individuals at risk for Alzheimer's disease
journal, November 2007

  • Sorg, C.; Riedl, V.; Muhlau, M.
  • Proceedings of the National Academy of Sciences, Vol. 104, Issue 47, p. 18760-18765
  • DOI: 10.1073/pnas.0708803104

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)
journal, April 2007


Small-World Networks and Functional Connectivity in Alzheimer's Disease
journal, February 2006


Quantitative Magnetoencephalography of Spontaneous Brain Activity in Alzheimer Disease: An Exhaustive Frequency Analysis
journal, January 2006


Using Magnetoencephalography to Study Patterns of Brain Magnetic Activity in Alzheimer’s Disease
journal, December 2007

  • Criado, Josè R.; Amo, Carlos; Quint, Patti
  • American Journal of Alzheimer's Disease & Other Dementiasr, Vol. 21, Issue 6
  • DOI: 10.1177/1533317506293502

Resting-State Oscillatory Brain Dynamics in Alzheimer Disease
journal, January 2008

  • de Haan, Willem; Stam, Cornelis J.; Jones, Bethany F.
  • Journal of Clinical Neurophysiology, Vol. 25, Issue 4
  • DOI: 10.1097/WNP.0b013e31817da184

Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology
journal, October 2006