A framework for quantifying node-level community structure group differences in brain connectivity networks

Johnson J. Gadelkarim; Dan Schonfeld; Olusola Ajilore; Liang Zhan; Aifeng F. Zhang; Jamie D. Feusner; Paul M. Thompson; Tony J. Simon; Anand Kumar; Alex D. Leow

doi:10.1007/978-3-642-33418-4_25

A framework for quantifying node-level community structure group differences in brain connectivity networks

Johnson J. Gadelkarim, Dan Schonfeld, Olusola Ajilore, Liang Zhan, Aifeng F. Zhang, Jamie D. Feusner, Paul M. Thompson, Tony J. Simon, Anand Kumar, Alex D. Leow

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

22 Scopus citations

Abstract

We propose a framework for quantifying node-level community structures between groups using anatomical brain networks derived from DTI-tractography. To construct communities, we computed hierarchical binary trees by maximizing two metrics: the well-known modularity metric (Q), and a novel metric that measures the difference between inter-community and intra-community path lengths. Changes in community structures on the nodal level were assessed between generated trees and a statistical framework was developed to detect local differences between two groups of community structures.We applied this framework to a sample of 42 subjects with major depression and 47 healthy controls. Results showed that several nodes (including the bilateral precuneus, which have been linked to self-awareness) within the default mode network exhibited significant differences between groups. These findings are consistent with those reported in previous literature, suggesting a higher degree of ruminative self-reflections in depression.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
Editors	Bjoern H. Menze, Zhuowen Tu, Georg Langs, Albert Montillo, Georg Langs, Nicholas Ayache, Hervé Delingette, Bjoern H. Menze, Antonio Criminisi, Le Lu, Polina Golland, Kensaku Mori
Publisher	Springer Verlag
Pages	196-203
Number of pages	8
ISBN (Print)	9783642334177
DOIs	https://doi.org/10.1007/978-3-642-33418-4_25
State	Published - 2012
Externally published	Yes
Event	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France Duration: 5 Oct 2012 → 5 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7511 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	5/10/12 → 5/10/12

Access to Document

10.1007/978-3-642-33418-4_25

Cite this

Gadelkarim, J. J., Schonfeld, D., Ajilore, O., Zhan, L., Zhang, A. F., Feusner, J. D., Thompson, P. M., Simon, T. J., Kumar, A., & Leow, A. D. (2012). A framework for quantifying node-level community structure group differences in brain connectivity networks. In B. H. Menze, Z. Tu, G. Langs, A. Montillo, G. Langs, N. Ayache, H. Delingette, B. H. Menze, A. Criminisi, L. Lu, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings (pp. 196-203). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33418-4_25

Gadelkarim, Johnson J. ; Schonfeld, Dan ; Ajilore, Olusola et al. / A framework for quantifying node-level community structure group differences in brain connectivity networks. Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. editor / Bjoern H. Menze ; Zhuowen Tu ; Georg Langs ; Albert Montillo ; Georg Langs ; Nicholas Ayache ; Hervé Delingette ; Bjoern H. Menze ; Antonio Criminisi ; Le Lu ; Polina Golland ; Kensaku Mori. Springer Verlag, 2012. pp. 196-203 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{fa461780a30c48d7a097302bb3893958,

title = "A framework for quantifying node-level community structure group differences in brain connectivity networks",

abstract = "We propose a framework for quantifying node-level community structures between groups using anatomical brain networks derived from DTI-tractography. To construct communities, we computed hierarchical binary trees by maximizing two metrics: the well-known modularity metric (Q), and a novel metric that measures the difference between inter-community and intra-community path lengths. Changes in community structures on the nodal level were assessed between generated trees and a statistical framework was developed to detect local differences between two groups of community structures.We applied this framework to a sample of 42 subjects with major depression and 47 healthy controls. Results showed that several nodes (including the bilateral precuneus, which have been linked to self-awareness) within the default mode network exhibited significant differences between groups. These findings are consistent with those reported in previous literature, suggesting a higher degree of ruminative self-reflections in depression.",

author = "Gadelkarim, {Johnson J.} and Dan Schonfeld and Olusola Ajilore and Liang Zhan and Zhang, {Aifeng F.} and Feusner, {Jamie D.} and Thompson, {Paul M.} and Simon, {Tony J.} and Anand Kumar and Leow, {Alex D.}",

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doi = "10.1007/978-3-642-33418-4_25",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Menze, {Bjoern H.} and Zhuowen Tu and Georg Langs and Albert Montillo and Georg Langs and Nicholas Ayache and Herv{\'e} Delingette and Menze, {Bjoern H.} and Antonio Criminisi and Le Lu and Polina Golland and Kensaku Mori",

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Gadelkarim, JJ, Schonfeld, D, Ajilore, O, Zhan, L, Zhang, AF, Feusner, JD, Thompson, PM, Simon, TJ, Kumar, A & Leow, AD 2012, A framework for quantifying node-level community structure group differences in brain connectivity networks. in BH Menze, Z Tu, G Langs, A Montillo, G Langs, N Ayache, H Delingette, BH Menze, A Criminisi, L Lu, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7511 LNCS, Springer Verlag, pp. 196-203, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 5/10/12. https://doi.org/10.1007/978-3-642-33418-4_25

A framework for quantifying node-level community structure group differences in brain connectivity networks. / Gadelkarim, Johnson J.; Schonfeld, Dan; Ajilore, Olusola et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. ed. / Bjoern H. Menze; Zhuowen Tu; Georg Langs; Albert Montillo; Georg Langs; Nicholas Ayache; Hervé Delingette; Bjoern H. Menze; Antonio Criminisi; Le Lu; Polina Golland; Kensaku Mori. Springer Verlag, 2012. p. 196-203 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS).

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

TY - GEN

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AU - Schonfeld, Dan

AU - Ajilore, Olusola

AU - Zhan, Liang

AU - Zhang, Aifeng F.

AU - Feusner, Jamie D.

AU - Thompson, Paul M.

AU - Simon, Tony J.

AU - Kumar, Anand

AU - Leow, Alex D.

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AB - We propose a framework for quantifying node-level community structures between groups using anatomical brain networks derived from DTI-tractography. To construct communities, we computed hierarchical binary trees by maximizing two metrics: the well-known modularity metric (Q), and a novel metric that measures the difference between inter-community and intra-community path lengths. Changes in community structures on the nodal level were assessed between generated trees and a statistical framework was developed to detect local differences between two groups of community structures.We applied this framework to a sample of 42 subjects with major depression and 47 healthy controls. Results showed that several nodes (including the bilateral precuneus, which have been linked to self-awareness) within the default mode network exhibited significant differences between groups. These findings are consistent with those reported in previous literature, suggesting a higher degree of ruminative self-reflections in depression.

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SN - 9783642334177

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Tu, Zhuowen

A2 - Langs, Georg

A2 - Montillo, Albert

A2 - Langs, Georg

A2 - Ayache, Nicholas

A2 - Delingette, Hervé

A2 - Menze, Bjoern H.

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Gadelkarim JJ, Schonfeld D, Ajilore O, Zhan L, Zhang AF, Feusner JD et al. A framework for quantifying node-level community structure group differences in brain connectivity networks. In Menze BH, Tu Z, Langs G, Montillo A, Langs G, Ayache N, Delingette H, Menze BH, Criminisi A, Lu L, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. p. 196-203. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33418-4_25

A framework for quantifying node-level community structure group differences in brain connectivity networks

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