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There is a growing appreciation that many psychiatric (and neurological) conditions can be understood as functional disconnection syndromes – as reflected in aberrant functional integration and synaptic connectivity. This Research Topic considers recent advances in understanding psychopathology in terms of aberrant effective connectivity – as measured noninvasively using functional magnetic resonance imaging (fMRI). Recently, there has been increasing interest in inferring directed connectivity (effective connectivity) from fMRI data. Effective connectivity refers to the influence that one neural system exerts over another and quantifies the directed coupling among brain regions – and how they change with pathophysiology. Compared to functional connectivity, effective connectivity allows one to understand how brain regions interact with each other in terms of context sensitive changes and directed coupling – and therefore may provide mechanistic insights into the neural basis of psychopathology. Established models of effective connectivity include psychophysiological interaction (PPI), structural equation modeling (SEM) and dynamic causal modelling (DCM). DCM is unique because it explicitly models the interaction among brain regions in terms of latent neuronal activity. Moreover, recent advances in DCM such as stochastic and spectral DCM, make it possible to characterize the interaction between different brain regions both at rest and during a cognitive task.

brain connectivity --- Granger causality analysis --- fMRI --- effective connectivity --- psychophysiological interaction --- dynamic causal modeling --- brain connectivity --- Granger causality analysis --- fMRI --- effective connectivity --- psychophysiological interaction --- dynamic causal modeling

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There is a growing appreciation that many psychiatric (and neurological) conditions can be understood as functional disconnection syndromes – as reflected in aberrant functional integration and synaptic connectivity. This Research Topic considers recent advances in understanding psychopathology in terms of aberrant effective connectivity – as measured noninvasively using functional magnetic resonance imaging (fMRI). Recently, there has been increasing interest in inferring directed connectivity (effective connectivity) from fMRI data. Effective connectivity refers to the influence that one neural system exerts over another and quantifies the directed coupling among brain regions – and how they change with pathophysiology. Compared to functional connectivity, effective connectivity allows one to understand how brain regions interact with each other in terms of context sensitive changes and directed coupling – and therefore may provide mechanistic insights into the neural basis of psychopathology. Established models of effective connectivity include psychophysiological interaction (PPI), structural equation modeling (SEM) and dynamic causal modelling (DCM). DCM is unique because it explicitly models the interaction among brain regions in terms of latent neuronal activity. Moreover, recent advances in DCM such as stochastic and spectral DCM, make it possible to characterize the interaction between different brain regions both at rest and during a cognitive task.

brain connectivity --- Granger causality analysis --- fMRI --- effective connectivity --- psychophysiological interaction --- dynamic causal modeling

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

neural circuits --- neurobiology --- neuroscience --- developing and aging brain disorders --- structural and functional brain connectivity

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Cerebral cortex is probably the most complex biological network. Here many millions of individual neurons, the functional units of cortex, are interconnected through a massive yet highly organized pattern of axonal and dendritic wiring. This wiring enables both near and distant cells to coordinate their responses and generate a rich variety of cognitions and behaviours. When the wiring is damaged through disease or trauma it may reorganize but this may lead to characteristic pathological behaviours. While there have been significant advances in mapping cortical connectivity, the organizing principles and function of this connectivity are not well understood. On the one hand, there appears to be general design constraints governing cortical wiring, as first recognised by Rámon y Cajal's in his laws of conduction, material, and volume conservation. Yet on the other hand, particular patterns of cortical wiring exist to serve specific functions. There is a wide gap in understanding how the response and connectivity properties of a single neuron contribute to emergent network functions such as in detecting perceptually relevant features. Unravelling this intimate causal relationship represents one of the major challenges in neuroscience. This Research Topic will examine progress in understanding cortical wiring principles. This Research Topic aims to draw together recent advances in methods and understanding as well as recent challenges to existing ideas about how cerebral cortex is wired. This is particularly timely because new automated techniques may soon yield huge datasets in need of explanation. Recent studies have, for instance, empirically evaluated Rámon y Cajal's conservation laws for cerebral cortex, while others have shown some unexpected connectivity features that may refine the traditional view of how corticocortical connections are organised with regard to functional representations of auditory, somatosensory and visual cortices. Understanding these data will help improve the fidelity of neural models of cerebral cortical function and take into account the diversity of connections at both micro- and mesoscopic scales not seen at such a depth before.

brain connectivity --- morphology --- Axon --- Cerebral Cortex --- connectome --- sensory systems --- Grey Matter --- Brain wiring --- networks --- Dendrite --- brain connectivity --- morphology --- Axon --- Cerebral Cortex --- connectome --- sensory systems --- Grey Matter --- Brain wiring --- networks --- Dendrite

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Construction of comprehensive and detailed brain regions neuroanatomical connections matrices (macro-connectomes) is necessary to understand how the nervous system is organized and to elucidate how its different parts interact. Macro-connectomes also are the structural foundation of any finer granularity approaches at the neuron classes and types (meso-connectomes) or individual neuron (micro-connectomes) levels. The advent of novel neuroanatomical methods, as well as combinations of classic techniques, form the basis of several large scale projects with the ultimate goal of producing publicly available connectomes at different levels. A parallel approach, that of systematic and comprehensive collation of connectivity data from the published literature and from publicly accessible neuroinformatics platforms, has produced macro-connectomes of different parts of the central nervous system (CNS) in several mammalian species. The emergence of these public platforms that allow for the manipulation of rich connectivity data sets and enable the construction of CNS macro-connectomes in different species may have significant and long lasting implications. Moreover, when these efforts are leveraged by novel statistical methods, they may influence our way of thinking about the brain. Hence, the present brain region-centric paradigm may be challenged by a network-centric one. Ultimately, these projects will provide the information and knowledge for understanding how different neuronal parts communicate and function, developing novel approaches to diseases and disorders, and facilitating translational efforts in neurosciences. With this Research Topic we bring together the current state of macro-connectome related projects including the large scale production of thousands of publicly available neuronatomical experiments, databases with tens of thousands of connectivity records collated from the published literature, and the newest methods for displaying and analyzing this information. This topic also includes a wide range of challenges and how they are addressed - from platforms designed to integrate connectivity data across different sources, species and CNS levels of organization, to languages specifically designed to use these data in models at different scales of resolution, to efforts of 3D reconstruction and data integration, and to approaches for extraction and representation of this knowledge. Finally, we address the present state of different efforts of meso-connectomes construction, and of computational modeling in the context of the

brain connectivity --- brain map --- neuroinformatics --- functional connectivity --- Brain region --- macro-connectome --- Computational Neuroanatomy --- structural networks --- neuroanatomy data --- brain connectivity --- brain map --- neuroinformatics --- functional connectivity --- Brain region --- macro-connectome --- Computational Neuroanatomy --- structural networks --- neuroanatomy data