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Schizophrenia in the Era of Brain Complexity.

W e are entering a new era of schizophrenia research. Technological advances are providing mappings of the brain at an unprecedented level of detail, and collaborative efforts of psychiatric consortia allow for mega-analyses of genomic data with ever-increasing sample size and power. Integrating the resulting data with novel insights into brain development including recent findings on critical window biology may finally uncover how schizophrenia develops. This issue of The Journal of Nervous and Mental Disease focuses exclusively on the multifaceted syndrome that is known as schizophrenia. As illustrated by several of the papers comprising this issue, the disorder’s clinical picture encompasses more than just psychosis and cognitive dysfunction, including apathy (Bull et al., 2016), suicidality (Jahn et al., 2016), posttraumatic symptoms (Ng et al., 2016), and substance abuse (Maremmani et al., 2016). This vast clinical heterogeneity relates to the complexity of the organ that gives rise to cognitive (dys-) function and to the intricacy of its structure, function, and development. In this introduction, we will argue that viewing the brain as a complex system offers a conceptual framework to account for the clinical, neural, and genetic complexity that characterizes schizophrenia. If the brain is understood as a complex system, mental states are thought to emerge from the interaction among its physical elements across multiple scales of space and time (Bassett and Gazzaniga, 2011). In this view, cognitive, affective, and behavioral functions are interpreted as the emergent consequences of a large number of simpler, noncognitive (e.g., neurochemical or electrophysiological) processes (McClelland, 2010). One straightforward method to study the brain as a complex network is the use of the mathematical framework of graph theory. Applications of graph theory to neural connectivity data from electron microscopy, tract tracing, and neuroimaging studies have shown that the organization of the brain’s network has many nontrivial features, including a high degree of clustering and modularity, short communication relays, and the presence of highly connected brain hubs (Bullmore and Sporns, 2009). These characteristics are by no means exclusive to the brain; they are common macroscopic behaviors of complex systems in general. For example, the Internet and social networks show remarkable similarities in network architecture despite profound differences in the microscopic details of the network’s elements or their mechanisms of interaction (Bullmore and Sporns, 2009). Moreover, many of the aforementioned features of brain network organization are evident across spatial scales of neural connectivity. Hubs, for instance, can be identified in the macroscopic network of interconnected cortical brain areas (van den Heuvel and Sporns, 2013), as well as in microscopic networks of hippocampal neurons (Schroeter et al., 2015). As a whole, the brain’s network spanning multiple scales of spatial resolution is thought to provide the physical basis for the complex neural dynamics underlying cognitive processes (Sporns, 2013). Perturbations of this network, by disturbances in anatomical connectivity or alterations in network architecture, are thought to invoke disruptions in neural information processing and integration and thereby to give rise to symptoms such as psychosis and cognitive dysfunction (Kahn and Keefe, 2013). The notion that “mental states” or cognitive functions require interaction among disparate areas of the brain can be traced back to late 19th century scholars such as neuropathologist Carl Wernicke (Collin et al., 2016). Attributing cognitive function to neural communication and integration, Wernicke proposedmental disorders to arise from disruptions in association fibers and resulting disturbances in the cooperation between brain regions (Wernicke, 1906). This early version of the disconnectivity theory of schizophrenia is easily overlooked, but it fits well with current empirical evidence, including neuroimaging findings of structural and functional connectivity deficits in schizophrenia patients (Ellison-Wright and Bullmore, 2009; Fornito et al., 2012; Pettersson-Yeo et al., 2011). Another early and commonly overlooked conceptualization of the pathophysiology of psychiatric disorders dates from 1948, when mathematician and computer scientist Norbert Wiener published a transdisciplinary work on control and communication in computing machines and the mammalian nervous system (Wiener, 1948). His account of neural traffic jams due to excessive occupation of “neuron chains” and the associated rerouting of neural information may be the first modern conceptualization of disruptions in information flow and system dynamics as the underlying substrate for mental disorders (Sporns, 2010). In recent years, effective connectivity approaches (e.g., Deserno et al., 2012; Dima et al., 2009; Lefebre et al., 2016; Schmidt et al., 2014; Wagner et al., 2015) and studies using computational models to probe the consequences of altered connectome wiring on functional brain dynamics (e.g., Breakspear et al., 2010; Deco et al., 2012; Vertes et al., 2012; 2014) have started to explore how disturbances in