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Using big data to map the network organization of the brain

Published online by Cambridge University Press:  26 February 2014

James E. Swain ,
Chandra Sripada  and
John D. Swain
James E. Swain
Affiliation:
Department of Psychiatry, University of Michigan School of Medicine, Ann Arbor, MI 48109. jamesswa@med.umich.edu http://www2.med.umich.edu/psychiatry/psy/fac_query4.cfm?link_name=jamesswa Department of Psychology, University of Michigan, Ann Arbor, MI 48105 Child Study Center, Yale University School of Medicine, New Haven, CT 06520
Chandra Sripada
Affiliation:
Department of Psychiatry, University of Michigan School of Medicine, Ann Arbor, MI 48109. jamesswa@med.umich.edu http://www2.med.umich.edu/psychiatry/psy/fac_query4.cfm?link_name=jamesswa Department of Philosophy, University of Michigan, Ann Arbor, MI 48105. sripada@umich.edu http://www.lsa.umich.edu/philosophy/people/faculty/ci.sripadachandra_ci.detail
John D. Swain
Affiliation:
Department of Physics, Northeastern University, Boston, MA 02115. John.swain@cern.ch http://www.physics.neu.edu/Department/Vtwo/faculty/swain.htm
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Abstract

The past few years have shown a major rise in network analysis of “big data” sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension – individuality versus sociality – might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 37 , Issue 1 , February 2014 , pp. 101 - 102
Copyright
Copyright © Cambridge University Press 2014 

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