title: Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients.
abstract: 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. We 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. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject's trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.
authors.affiliation: The Mind Research Network, Albuquerque, New Mexico, United States of America.
authors.forename: Robyn L
authors.initials: RL
authors.lastname: Miller
authors.affiliation: The Mind Research Network, Albuquerque, New Mexico, United States of America.|Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States of America.
authors.forename: Maziar
authors.initials: M
authors.lastname: Yaesoubi
authors.affiliation: Department of Psychology and Neuroscience, Georgia State University, Atlanta, Georgia, United States of America.
authors.forename: Jessica A
authors.initials: JA
authors.lastname: Turner
authors.affiliation: Department of Psychiatry, University of California San Francisco School of Medicine, San Francisco, California, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Mathalon
authors.affiliation: Department of Psychiatry and Human Behavior, University of California Irvine School of Medicine, Irvine, California, United States of America.
authors.forename: Adrian
authors.initials: A
authors.lastname: Preda
authors.affiliation: Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America.|Olin Neuropyschiatry Research Center, New Haven, Connecticut, United States of America.
authors.forename: Godfrey
authors.initials: G
authors.lastname: Pearlson
authors.affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States of America.
authors.forename: Tulay
authors.initials: T
authors.lastname: Adali
authors.affiliation: The Mind Research Network, Albuquerque, New Mexico, United States of America.|Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States of America.|Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America.
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
mesh_terms: D000328:Adult; D001931:Brain Mapping; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D012559:Schizophrenia / Q000473:pathology*