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

• Authors: Kamal Shadi, Georgia Institute of Technology; Saideh Bakhshi, Georgia Institute of Technology; David Gutman, Emory University; Helen Mayberg, Emory University; Constantine Dovrolis, Georgia Institute of Technology
• Journal Title: Frontiers in Neuroinformatics
• Volume: Volume 10, Number NOV
• Publisher: Frontiers Media | 2016-11-04, Pages 46-46
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3389/fninf.2016.00046
• Subjects: Biology, Neuroscience; Computer Science
• 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

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.