Analysis of brain patterns using temporal measures
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.
- Issue Date:
- OSTI Identifier:
- Regents of the University of Minnesota (Minneapolis, MN) CHO
- Patent Number(s):
- Application Number:
- Contract Number:
- Resource Relation:
- Patent File Date: 2007 Jul 06
- Research Org:
- Regents of the University of Minnesota, Minneapolis, MN (United States)
- Sponsoring Org:
- Country of Publication:
- United States
- 62 RADIOLOGY AND NUCLEAR MEDICINE
Other works cited in this record:Evaluation of different measures of functional connectivity using a neural mass model
journal, February 2004
- David, Olivier; Cosmelli, Diego; Friston, Karl J.
- NeuroImage, Vol. 21, Issue 2, p. 659-673
Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease
journal, September 2006
- Stam, C. J.; Jones, B. F.; Manshanden, I.
- NeuroImage, Vol. 32, Issue 3, p. 1335-1344
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
EEG dynamics in patients with Alzheimer's disease
journal, July 2004
- Jeong, Jaeseung
- Clinical Neurophysiology, Vol. 115, Issue 7, p. 1490-1505
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
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
Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)
journal, April 2007
- Lehmann, Christoph; Koenig, Thomas; Jelic, Vesna
- Journal of Neuroscience Methods, Vol. 161, Issue 2, p. 342-350
Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology
journal, October 2006
- Uhlhaas, Peter J.; Singer, Wolf
- Neuron, Vol. 52, Issue 1, p. 155-168
Similar records in DOepatents and OSTI.GOV collections: