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AbstractThe study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Many of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs and an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models to the neuroscience setting. Stochastic Actor-Oriented Models are designed to capture network dynamics representing a variety of influences on network change in a continuous-time Markov chain framework. Network dynamics are characterized through both endogenous (i.e. network related) and exogenous effects, where the latter include mechanisms conjectured in the literature. We outline an application to the resting-state functional magnetic resonance imaging setting with data from the Alzheimer's Disease Neuroimaging Initiative study. We draw illustrative conclusions at the subject level and make a comparison between elderly controls and individuals with Alzheimer's disease.

This book provides an organizational model of the common ground needed to improve patient care and adapt to today's healthcare environment. The relationship between a hospital CEO and a general surgeon and how they aligned, motivated and partnered with physicians to change the culture of a hospital and implement patient oriented systems is the essential element of this text. Sustainable transformation processes will also be presented for the various roles and contributions of the healthcare team. Written from a team perspective, Transforming the Patient Experience is a practical guide for healthcare team members and leaders to follow.