DocumentCode :
636654
Title :
An active method for tracking connectivity in temporally changing brain networks
Author :
Lepage, Kyle Q. ; Kramer, Mark A. ; ShiNung Ching
Author_Institution :
Dept. of Math. & Stat., Boston Univ., Boston, MA, USA
fYear :
2013
fDate :
3-7 July 2013
Firstpage :
4374
Lastpage :
4377
Abstract :
The inference of connectivity in brain networks has typically been performed using passive measurements of ongoing activity across recording sites. Passive measures of connectivity are harder to interpret, however, in terms of causality - how evoked activity in one region might induce activity in another. To obviate this issue, recent work has proposed the use of active stimulation in conjunction with network estimation. By actively stimulating the network, more accurate information can be gleaned regarding evoked connectivity. The assumption in these previous works, however, was that the underlying networks were static and do not change in time. Such an assumption may be limiting in situations of clinical relevance, where the introduction of a drug or of brain pathology, might change the underlying networks structure. Here, an extension of the evoked connectivity paradigm is introduced that enables tracking networks that change in time.
Keywords :
bioelectric phenomena; biomedical measurement; brain; diseases; drugs; medical disorders; neurophysiology; active stimulation; brain networks; brain pathology; drug; evoked activity; evoked connectivity; evoked connectivity paradigm; network estimation; network structure; passive measurements; tracking connectivity; tracking networks; Brain stimulation; Detectors; Electrodes; Image edge detection; Indexes; Oscillators; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
ISSN :
1557-170X
Type :
conf
DOI :
10.1109/EMBC.2013.6610515
Filename :
6610515
Link To Document :
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