Analysis of computational models of deep brain stimulation using spherical statistics

Yizi Xiao; Matthew D Johnson

doi:10.1109/NER.2015.7146758

Analysis of computational models of deep brain stimulation using spherical statistics

Yizi Xiao, Matthew D Johnson

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Computational models of deep brain stimulation (DBS) have played a key role in investigating the mechanisms of action of DBS therapies. By estimating a volume of tissue directly modulated by DBS, one can relate the pathways within those volumes to the therapeutic efficacy of a particular DBS setting. With the advent of higher-density DBS electrode arrays, there is a growing need for a systematic method to quantify the morphology of the modulated volumes within the brain. In this study, we applied the tools of spherical statistics to quantify such morphologies through the application of a computational model of a directionally segmented DBS array. The same statistical techniques have broad applications to characterizing distributions of in-vivo electrophysiological recordings and histological labeling of neurons.

Original language	English (US)
Title of host publication	2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
Publisher	IEEE Computer Society
Pages	856-859
Number of pages	4
ISBN (Electronic)	9781467363891
DOIs	https://doi.org/10.1109/NER.2015.7146758
State	Published - Jul 1 2015
Event	7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 - Montpellier, France Duration: Apr 22 2015 → Apr 24 2015

Publication series

Name	International IEEE/EMBS Conference on Neural Engineering, NER
Volume	2015-July
ISSN (Print)	1948-3546
ISSN (Electronic)	1948-3554

Other

Other	7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
Country	France
City	Montpellier
Period	4/22/15 → 4/24/15

Access

10.1109/NER.2015.7146758

Fingerprint Dive into the research topics of 'Analysis of computational models of deep brain stimulation using spherical statistics'. Together they form a unique fingerprint.

Cite this

Xiao, Y., & Johnson, M. D. (2015). Analysis of computational models of deep brain stimulation using spherical statistics. In 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 (pp. 856-859). [7146758] (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/NER.2015.7146758

Analysis of computational models of deep brain stimulation using spherical statistics. / Xiao, Yizi; Johnson, Matthew D.

2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015. IEEE Computer Society, 2015. p. 856-859 7146758 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2015-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xiao, Y & Johnson, MD 2015, Analysis of computational models of deep brain stimulation using spherical statistics. in 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015., 7146758, International IEEE/EMBS Conference on Neural Engineering, NER, vol. 2015-July, IEEE Computer Society, pp. 856-859, 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015, Montpellier, France, 4/22/15. https://doi.org/10.1109/NER.2015.7146758

@inproceedings{1eb1d031006649aea75bc906d9a10fe1,

title = "Analysis of computational models of deep brain stimulation using spherical statistics",

abstract = "Computational models of deep brain stimulation (DBS) have played a key role in investigating the mechanisms of action of DBS therapies. By estimating a volume of tissue directly modulated by DBS, one can relate the pathways within those volumes to the therapeutic efficacy of a particular DBS setting. With the advent of higher-density DBS electrode arrays, there is a growing need for a systematic method to quantify the morphology of the modulated volumes within the brain. In this study, we applied the tools of spherical statistics to quantify such morphologies through the application of a computational model of a directionally segmented DBS array. The same statistical techniques have broad applications to characterizing distributions of in-vivo electrophysiological recordings and histological labeling of neurons.",

author = "Yizi Xiao and Johnson, {Matthew D}",

year = "2015",

month = jul,

day = "1",

doi = "10.1109/NER.2015.7146758",

language = "English (US)",

series = "International IEEE/EMBS Conference on Neural Engineering, NER",

publisher = "IEEE Computer Society",

pages = "856--859",

booktitle = "2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015",

note = "7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 ; Conference date: 22-04-2015 Through 24-04-2015",

}

TY - GEN

T1 - Analysis of computational models of deep brain stimulation using spherical statistics

AU - Xiao, Yizi

AU - Johnson, Matthew D

PY - 2015/7/1

Y1 - 2015/7/1

N2 - Computational models of deep brain stimulation (DBS) have played a key role in investigating the mechanisms of action of DBS therapies. By estimating a volume of tissue directly modulated by DBS, one can relate the pathways within those volumes to the therapeutic efficacy of a particular DBS setting. With the advent of higher-density DBS electrode arrays, there is a growing need for a systematic method to quantify the morphology of the modulated volumes within the brain. In this study, we applied the tools of spherical statistics to quantify such morphologies through the application of a computational model of a directionally segmented DBS array. The same statistical techniques have broad applications to characterizing distributions of in-vivo electrophysiological recordings and histological labeling of neurons.

AB - Computational models of deep brain stimulation (DBS) have played a key role in investigating the mechanisms of action of DBS therapies. By estimating a volume of tissue directly modulated by DBS, one can relate the pathways within those volumes to the therapeutic efficacy of a particular DBS setting. With the advent of higher-density DBS electrode arrays, there is a growing need for a systematic method to quantify the morphology of the modulated volumes within the brain. In this study, we applied the tools of spherical statistics to quantify such morphologies through the application of a computational model of a directionally segmented DBS array. The same statistical techniques have broad applications to characterizing distributions of in-vivo electrophysiological recordings and histological labeling of neurons.

UR - http://www.scopus.com/inward/record.url?scp=84940388104&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84940388104&partnerID=8YFLogxK

U2 - 10.1109/NER.2015.7146758

DO - 10.1109/NER.2015.7146758

M3 - Conference contribution

AN - SCOPUS:84940388104

T3 - International IEEE/EMBS Conference on Neural Engineering, NER

SP - 856

EP - 859

BT - 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015

PB - IEEE Computer Society

T2 - 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015

Y2 - 22 April 2015 through 24 April 2015

ER -