Modern neuroscience is in the middle of a transformation, driven by the development of novel high-resolution brain mapping and recording technologies that deliver increasingly large and detailed “big neuroscience data”. Network science has emerged as one of the principal approaches to model and analyze neural systems, from individual neurons to circuits spanning the whole brain. Research in brain networks has taken center stage at national and international conferences, in special issues of leading scientific journals, and as part of large-scale initiatives directed at understanding brain function such as the U.S. Human Connectome Project, the BRAIN Initiative and the European Brain Project.
A core theme of network neuroscience is the mapping of patterns of anatomical and functional brain connectivity, often called Connectomics. This field is inherently interdisciplinary in nature and engages empirical and computational neuroscientists working across multiple scales, from synapses to large-scale systems. Several interdisciplinary projects involving over twenty distinct labs associated with IUNI currently focus on mapping patterns of brain connectivity and their implications for cognitive, social and health functioning. Many of these labs are engaged in collaborative projects that provide integrative training and educational opportunities for graduate trainees. For example, in a collaborative project involving several institutions, graduate students have recorded spontaneously active cortical microcircuits, extracted patterns of directed dynamic interactions, and revealed networks of information flow among up to 700 individually resolved neurons (Beggs lab).
In another project, graduate students are using sophisticated statistical models to infer the trajectory and shape of cortico-cortical tracts that span the white matter of the human brain and underpin human cognitive function (Pestilli lab). The dynamics of human brain networks across time, at fast time scales of seconds to minutes as well as long time scales of months and years, have been a core theme in graduate research that combines brain mapping and computational network modeling (Sporns lab). Clinical applications of brain network approaches are carried forward by students who are probing for significant disturbances of network organization in the brains of people with schizophrenia and addiction (Hetrick and O’Donnell labs).
The CNS-NRT program allows collaborative projects with a deeper computational and complex systems modeling emphasis in a unique team interaction between neuroscience and CNS. CNS-NRT students work on large-scale computer simulations to explore dynamic brain networks in the healthy and diseased brain (Sporns, Rocha, Saykin, Fortunato); develop new technologies for mapping individual anatomical connectivity to enable “precision connectomics” (Pestilli, Sporns, Rocha, Ahn); model the patterns and dynamics of neuronal signaling, control, and information flow that unfold within brain networks (Sporns, Pestilli, Beggs, Rocha, Saykin, Ahn); develop new tools for recording and quantifying neural network connectivity and collective activity (Beggs, E. Newman); Identify potential network biomarkers in clinical disorders (Hetrick, O’Donnell, S. Newman, Pestilli); link network mechanisms across multiple scales, from genes to brains to social systems (Pescosolido, Saykin, Shen, Sporns).