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 proceedingConference contribution

19 Citations (Scopus)

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 languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
PublisherSpringer Verlag
Pages196-203
Number of pages8
Volume7511 LNCS
ISBN (Print)9783642334177
StatePublished - 2012
Event2nd MICCAI Workshop on Medical Computer Vision, MICCAI-MCV 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France
Duration: Oct 5 2012Oct 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7511 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd MICCAI Workshop on Medical Computer Vision, MICCAI-MCV 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period10/5/1210/5/12

Fingerprint

Binary trees
Network Connectivity
Community Structure
Brain
Metric
Vertex of a graph
Binary Tree
Path Length
Modularity
Community
Framework

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Gadelkarim, J. J., Schonfeld, D., Ajilore, O., Zhan, L., Zhang, A. F., Feusner, J. D., ... Leow, A. D. (2012). A framework for quantifying node-level community structure group differences in brain connectivity networks. In Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings (Vol. 7511 LNCS, 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.

A framework for quantifying node-level community structure group differences in brain connectivity networks. / Gadelkarim, Johnson J.; Schonfeld, Dan; Ajilore, Olusola; Zhan, Liang; Zhang, Aifeng F.; Feusner, Jamie D.; Thompson, Paul M.; Simon, Tony J; Kumar, Anand; Leow, Alex D.

Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Vol. 7511 LNCS 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 proceedingConference contribution

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 Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. vol. 7511 LNCS, 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, 2nd MICCAI Workshop on Medical Computer Vision, MICCAI-MCV 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 10/5/12.
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 Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Vol. 7511 LNCS. Springer Verlag. 2012. p. 196-203. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Gadelkarim, Johnson J. ; Schonfeld, Dan ; Ajilore, Olusola ; Zhan, Liang ; Zhang, Aifeng F. ; Feusner, Jamie D. ; Thompson, Paul M. ; Simon, Tony J ; Kumar, Anand ; Leow, Alex D. / 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. Vol. 7511 LNCS Springer Verlag, 2012. pp. 196-203 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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