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Author Notes:

Corresponding Author: Constantine Dovrolis Email: constantine@gatech.edu

KS and CD designed research

KS, SB, DG, HM, and CD performed research

KS and SB analyzed data

KS, HM, and CD wrote paper

CD and SB are grateful to Prof. Olaf Sporns and his group at Indiana University for hosting SB at the Sporns lab during the summer of 2011 and for discussions about early stages of this work.

The authors would like to thank Dr. Jim Rilling for his contribution of the DTI and structural MRI data that served as basis for the analysis presented in this paper, and Dr. Peter Neher for providing us with the FiberCup phantom ROIs.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Subjects:

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Mathematical & Computational Biology
  • Neurosciences
  • Neurosciences & Neurology
  • connectome
  • diffusion MRI
  • tractography
  • structural network
  • network analysis
  • STATE FUNCTIONAL CONNECTIVITY
  • HUMAN CEREBRAL-CORTEX
  • DEFAULT MODE NETWORK
  • DIFFUSION TRACTOGRAPHY
  • ANATOMICAL PARCELLATION
  • PREFRONTAL CORTEX
  • IN-VIVO
  • MATTER
  • MRI
  • ORGANIZATION

A Symmetry-Based Method to Infer Structural Brain Networks from Probabilistic Tractography Data

Tools:

Journal Title:

Frontiers in Neuroinformatics

Volume:

Volume 10, Number NOV

Publisher:

, Pages 46-46

Type of Work:

Article | Final Publisher PDF

Abstract:

Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP)1 have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs) from probabilistic tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA), does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold. Instead, we exploit a basic limitation of the tractography process: the observed streamlines from a source to a target do not provide any information about the polarity of the underlying white matter, and so if there are some fibers connecting two voxels (or two ROIs) X and Y, tractography should be able in principle to follow this connection in both directions, from X to Y and from Y to X. We leverage this limitation to formulate the network inference process as an optimization problem that minimizes the (appropriately normalized) asymmetry of the observed network. We evaluate the proposed method using both the FiberCup dataset and based on a noise model that randomly corrupts the observed connectivity of synthetic networks. As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects to infer the structural network between 18 corticolimbic ROIs that are associated with various neuropsychiatric conditions including depression, anxiety and addiction.

Copyright information:

© 2016 Shadi, Bakhshi, Gutman, Mayberg and Dovrolis.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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