Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients

• Published in: PloS one Vol. 11; no. 3; p. e0149849
• Main Authors: Miller, Robyn L., Yaesoubi, Maziar, Turner, Jessica A., Mathalon, Daniel, Preda, Adrian, Pearlson, Godfrey, Adali, Tulay, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.03.2016; Public Library of Science (PLoS)
• Subjects: Adult; Biology and Life Sciences; Brain; Brain Mapping; Brain research; Computer and Information Sciences; Computer engineering; Data processing; Dimensional analysis; Female; Functional magnetic resonance imaging; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medicine; Medicine and Health Sciences; Mental disorders; Methods; Microprocessors; Multidimensional data; Neural circuitry; Neural networks; Neuroimaging; Neurological diseases; Observations; Patients; People and Places; Physical Sciences; Physiological aspects; Population characteristics; Properties (attributes); Psychiatry; Research and Analysis Methods; Schizophrenia; Schizophrenia - pathology; Social Sciences; San Francisco California; Georgia; California; New Mexico; United States > US; Connecticut
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0149849

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.