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Abstract Details

Activity Number: 541
Type: Invited
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #303817
Title: Modeling Complex Functional Brain Networks: A Brief Overview of Challenges and Potential Solutions
Author(s): Sean Lorenzo Simpson*+
Companies: Wake Forest School of Medicine
Address: Medical Center Blvd., Winston-Salem, NC, 27157,

Modeling and comparing binary and weighted functional brain networks remains a fertile area for methodological development. As noted in Rubinov and Sporns (2010), "between-subject comparisons in studies of brain networks will require the development of accurate statistical comparison tools." However, to date, most models have been fairly simplistic and based on a specific feature of brain networks (local communication, connectivity distribution, etc.). For the few more complex models, little work has been done on model assessment or robustness to model misspecification. Additionally, none account for network estimation error. Here we give a brief overview of the challenges faced in attempting to statistically model whole-brain networks and discuss a couple of potential solutions.

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