Chair: Robin W. Wilkins,
Venue: lecture room 29
Network-based approaches to the study of complex systems have become ubiquitous in a variety of research areas. Steeped in the mathematical foundation of graph theory, network science has led to a greater understanding of the interactions between components in systems as disparate as social networks, biological systems, communication arrays, and transportation networks. More recently, the fields of neuroscience more broadly and neuroimaging more specifically have greatly benefited from network science methods. Here, the brain is subdivided into regions (represented as network nodes) and inter-regional interactions (represented as network edges) estimated from structural or functional imaging modalities, including diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), or magnetoencephalography (MEG).
This symposium aims is to explore how network science methods can be successfully applied to neuroimaging data for connectivity analyses. Vital to these studies is the development of methods and algorithms for understanding the distinct properties of brain-based connectivity graphs. This workshop includes two components. First, we will provide an overview of network neuroscience, including a description of (i) the application of graph theory methods to neuroimaging data, (ii) how seminal network neuroscience studies have extended our understanding of brain structure and functional connectivity in health and disease, and (iii) methodological limitations. Second, we will provide a panel discussion of methods developed to analyze brain networks, including community detection techniques, group analyses, and dynamical analyses in time-dependent systems. Endemic to each presentation will be the use of network neuroscience techniques to uncover information underlying one of the most complex systems–the human brain.
Speakers will provide a review of the current network-based techniques used in network neuroimaging studies, and a description of emerging network analysis methods designed to understand underlying structures and connectivity relationships in the brain. Attendees of this workshop will gain an understanding of the newer field of network neuroscience.
Specific content areas include:
- How network science methods are applied to data derived from the human brain and what research questions are relevant to the field.
- Analysis methods developed for analyzing brain networks and how these tools provide insight into the development, organization and/or function of the human brain.
- Implications of study results and future directions in the emerging field of network neuroscience.
Following the presentations, this symposium will conclude with a special interactive panel discussion for conference attendees.
Richard F. Betzel
Department of Bioengineering, University of Pennsylvania USA
Detection and Characterization of Modules in Multi-scale, Multi-layer Brain Networks
Martijn van den Heuvel
Dutch Connectome Lab, Brain Center Rudolf Mangus Department of Psychiatry UMC Utrech
Head of the CONNplexity Lab. School of Industrial Engineering and Weldon School of Biomedical Engineering. Purdue University. West-Lafayette, IN, USA.
ConnICA: Uses and Interpretations
Complex Systems Group, University of Pennsylvania USA
Understanding Community Structure: Dynamics in Brain Networks
Robin W. Wilkins
Director of Human Neuroimaging, Gateway MRI Center, Network Neuroimaging Laboratory for Complex Systems, Joint School for Nanoscience and Nanoengineering, University of North Carolina, Greensboro USA
Network Neuroscience Methods for Brain Connectivity Research