Researchers are analyzing brain connectomes to understand how normal and abnormal interactions between functional brain networks affect healthy brain development and contribute to disorders such as epilepsy. The Brain Connectome is the focus of a two-part Special Issue of Brain Connectivity
, a peer-reviewed journal from Mary Ann Liebert, Inc., publishers
. Click here
to read the full-text articles free on the Brain Connectivity
website through April 7, 2019.
The Special Issues were led by Guest Editors Brent Munsell, PhD, College of Charleston (SC), Guofong Wu, PhD, University of North Carolina at Chapel Hill, Leonardo Bonilha, MD, PhD, Medical University of South Carolina (Charleston), and Paul Laurienti, MD, PhD, Wake Forest School of Medicine (Winston-Salem, NC)
Featured in Part 1 is the article entitled “Asymmetric Insular Connectomics Revealed by Diffusion Magnetic Resonance Imaging Analysis of Healthy Brain Development.” In this article, Jacob Levman and coauthors from Boston Children’s Hospital (MA), Massachusetts General Hospital (Charlestown), Harvard Medical School (Boston, MA), and St. Francis Xavier University (Antigonish, Canada) focus on a part of the brain called the insula. The researchers present an analysis of structural connectivity between the insula and the rest of the brain across 642 examinations. This research demonstrates the feasibility of performing connectomics (mapping out the many connections in the human brain) with real world clinical data in a pediatric population, supported by high quality data acquisition fromBoston Children’s Hospital. The study demonstrates the ability to clinically detect neural fiber pathway lateralization in the brain, mapping out hemispheric asymmetries that may help in better understanding how brain structure supports functions such as language tasks and awareness. This study also advances the field of connectomics by demonstrating the remarkable potential from its inclusion in large-scale routine clinical magnetic resonance imaging (MRI).
Sourabh Palande and a team of researchers from University of Utah (Salt Lake City) analyze an approach to visualize brain networks based on howsimilar the gray matter properties in brain regions are acrosssubjects in the article entitled “Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference.” This approach provides a way to visualize brain networks based on howsimilar the gray matter properties in brain regions are acrosssubjects. Using methods from topological data analysis, the researchers identify abnormalities in a specific network in subjects with autism, the salience network. This network is responsible for processing new or unexpected information in the brain.These results add to growing evidence of statistically significant abnormalities in gray matter structure underlying the salience networkin autism.
Research reported in this publication was supported by the National Institutes of Health under Award Numbers R21MH118739, R03NS091587, R01HD078561, R03NS091587, U01NS093650, R01EB022876, K08MH100609, and R01MH080826. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.