title: The challenge of non-ergodicity in network neuroscience.
abstract: Ergodicity can be assumed when the structure of data is consistent across individuals and time. Neural network approaches do not frequently test for ergodicity in data which holds important consequences for data integration and intepretation. To demonstrate this problem, we present several network models in healthy and clinical samples where there exists considerable heterogeneity across individuals. We offer suggestions for the analysis, interpretation, and reporting of neural network data. The goal is to arrive at an understanding of the sources of non-ergodicity and approaches for valid network modeling in neuroscience.
authors.affiliation: Department of Psychology, Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: Medaglia
authors.forename: Deepa M
authors.initials: DM
authors.lastname: Ramanathan
authors.forename: Umesh M
authors.initials: UM
authors.lastname: Venkatesan
authors.forename: Frank G
authors.initials: FG
authors.lastname: Hillary
mesh_terms: D001930:Brain Injuries / Q000503:physiopathology; D059907:Functional Neuroimaging / Q000379:methods / Q000639:trends; D006801:Humans; D016571:Neural Networks, Computer; D009488:Neurosciences / Q000379:methods / Q000639:trends*