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Center for Nonlinear Studies |
Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. I will present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. I will discuss an elegant, simple new method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. Although functional connectivity between networks or brain regions of a priori interest (ie, of the so-called functional “connectomeâ€) is of primary interest to many fMRI researchers, there is a more abstract question that such a spatially granular correlation-based viewpoint does not elucidate: are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender. I will discuss a canonical, transparent new approach to characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. This work appears to be the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function. Time permitting I will also discuss recent work on more functionally-resolved directional dynamic domain connectivity, dynamic network coherence, and spatial phase dynamics in fMRI.
Host: Gowri Srinivasan