An important aspect of neuroscience is to characterize the underlying connectivity patterns of the human brain (i.e., human connectomics). Over the past few years, researchers have demonstrated that by combining a variety of different neuroimaging technologies (e.g., structural MRI, diffusion MRI and functional MRI) with sophisticated analytic strategies such as graph theory, it is possible to noninvasively map the patterns of structural and functional connectivity of human whole-brain networks. With these novel approaches, many studies have shown that human brain networks have nonrandom properties such as modularity, small-worldness and highly connected hubs. Importantly, these quantifiable network properties change with age, learning and disease. Moreover, there is growing evidence for behavioral and genetic correlates. Network analysis of neuroimaging data is opening up a new avenue of research into the understanding of the organizational principles of the brain that will be of interest for all basic scientists and clinical researchers. Such approaches are powerful but there are a number of challenging issues when extracting reliable brain networks from various imaging modalities and analyzing the topological properties, e.g., definitions of network nodes and edges and reproducibility of network analysis. We assembled contributions related to the state-of-the-art methodologies of brain connectivity and the applications involving development, aging and neuropsychiatric disorders such as Alzheimer’s disease, schizophrenia, attention deficit hyperactivity disorder and mood and anxiety disorders. It is anticipated that the articles in this Research Topic will provide a greater range and depth of provision for the field of imaging connectomics.

The widespread application of brain imaging to the study of psychiatric disorders has led to a revolution in our understanding of the neural basis of psychiatric illness. In particular, the advent of magnetic resonance imaging (MRI) has provided an unprecedented capacity for quantifying diverse aspects of brain structure and function in vivo, and has been used to identify brain changes associated with the full spectrum of psychopathology. With respect to major psychiatric disorders, it is now abundantly clear from this literature that focal brain dysfunction is rare. Rather, most disorders are associated with abnormalities in large-scale networks of spatially distributed and interconnected brain regions; i.e., they are disorders of brain connectivity. Such considerations highlight the need to understand brain dysfunction in psychiatric illness from a network-based perspective. This goal is starting to be realized through recent advances in the use of MRI to map the brain’s complex connectivity architecture. In this special edition, we invite contributions that address brain network dysfunction in psychiatric illnesses. Specifically, the work must be concerned with understanding interactions between brain regions, and how their alterations are affected by psychiatric disease. These interactions can be studied at the level of anatomy using diffusion-MRI or function using functional MRI (fMRI), with the full range of methods available (e.g., tractography, seed-based correlations, independent component analysis, graph analysis, dynamic causal modeling, etc.). Contributions can be either reviews of recent, relevant literature addressing brain network dysfunction in psychiatric disease, or experimental papers describing novel insights into brain network disturbances in such illnesses. Contributions will be invited covering a broad spectrum of psychiatric disease, including mood and anxiety disorders, schizophrenia, autism, attention-deficit hyperactivity disorder and neurodegenerative conditions. It is intended that this volume will provide important insights into how brain networks are perturbed by psychiatric disease, and allow identification of commonalities and differences across diagnostic categories.

Previous studies showed that both healthy and pathological aging are associated with changes in brain structure and function of the mature human brain. The most prominent anatomical alteration are changes in prefrontal cortex morphology, volume loss and reduced white-matter integrity and hippocampal atrophy. Cognitive decline affects mainly the performance of episodic memory, speed of sensory information processing, working memory, inhibitory function and long-term memory. It has been also proposed that due to the aforementioned changes the aging brain engages in compensatory brain mechanism such as a broader activation of cortical regions (mainly frontal) rather than specialized activation. Evidence suggests that similar changes occur with pathological aging but to a greater extent. In this case information flow is disrupted due to neurodegeneration, functional activation of posterior (occipito-temporal) regions is decreased and as a consequence the brain fails to process sensorial input in the ventral pathway and cognitive deficits appear. In the last years, functional alterations associated with aging have been studied using the mathematical notion of graph theory that offers an integrative approach since it examines different properties of the brain network: 1) Organization level 2) amount of local information processing, 3) information flow 4) cortical community structure and 5) identification of functional / anatomical hubs. So, graph theory offers an attractive way to model brain networks organization and to quantify their pathological deviations. Previous studies have already employed this mathematical notion and demonstrated that age-related neurodegeneration is often accompanied by loss of optimal network organization either due to diminished local information processing or due to progressive isolation of distant brain regions. They have also found that changes in network properties may be present even in the preclinical phase, which could be taken as a biological marker of disease.

It is a real pleasure to introduce the excellent papers contributed by leading experts to our Research Topic proposing various network models of dementia spread. The focus is strongly on disease-specific mathematical modeling rather than general graph theory. The emerging field of network neuroscience visualizes the brain as a graph consisting of nodes representing regions and edges as connections between them. This complex network supports efficient communication along neural projections, but also, unfortunately, the transmission and progression of Alzheimer’s and other neurodegenerative disorders. If we could know the brain’s network organization, could we then predict how degenerative processes might develop on this network? As these papers demonstrate, the answer is, increasingly, yes.

The brain is the most complex computational device we know, consisting of highly interacting and redundant networks of areas, supporting specific brain functions. The rules by which these areas organize themselves to perform specific computations have only now started to be uncovered. Advances in non-invasive neuroimaging technologies have revolutionized our understanding of the functional anatomy of cortical circuits in health and disease states, which is the focus of this book. The first section of this book focuses on methodological issues, such as combining functional MRI technology with other brain imaging modalities. The second section examines the application of brain neuroimaging to understand cognitive, visual, auditory, motor and decision-making networks, as well as neurological diseases. The use of non-invasive neuroimaging technologies will continue to stimulate an exponential growth in understanding basic brain processes, largely as a result of sustained advances in neuroimaging methods and applications.

This issue of Neuroimaging Clinics of North America focuses on fMRI for the Diagnosis, Treatment and Management of Brain Tumors and is edited by Dr. Andrei I. Holodny. Articles will include: Introduction to fMRI; How to start your clinical fMRI program; Diffusion tractography; The problem of neurovascular uncoupling; Dynamic Resting State fMRI; Resting State fMRI of language function; Methods of fMRI analysis; Patient preparation and fMRI paradigm design; Functional brain anatomy; Neurosurgical Applications of fMRI; and more!

This book describes the roles magnetic resonance imaging (MRI) can play in deep brain stimulation (DBS). DBS therapeutically modulates aberrant neural circuits implicated in a broad range of neurological disorders. Following surgical insertion, an electrode placed into the desired brain target generates constant electricity, analogous to a cardiac pacemaker. Most commonly employed in movement disorders such as Parkinson’s disease, dystonia, and tremor, DBS is also being investigated for use in psychiatric and cognitive disorders, including depression and Alzheimer’s disease. It is estimated that more than 230,000 patients have undergone DBS surgery worldwide. Imaging techniques, specifically MRI, have played key roles in the preoperative and postoperative aspects of DBS. This book focuses on the established as well as the innovative roles of MRI in DBS. MRI and DBS are first introduced from an historical perspective and a review of the clinical aspects of DBS is performed. Then, the preoperative and postoperative applications of MRI in DBS are covered. The crucial aspect of MRI safety in these patients is also discussed. Finally, possible upcoming MRI applications for patients with DBS are discussed in a future directions chapter. Chapters are written by experts from the University of Toronto, a world leader in the field of DBS, alongside international co-authors to ensure a thorough review of the topics. This is an ideal guide for both clinicians (neurosurgeons, neurologists, psychiatrists, and neuroradiologists) and researchers as well as trainees interested in neuroimaging for DBS.

The parasympathetic and sympathetic autonomic systems are involved in several body functions. Cardiovascular risk factors can be estimated through autonomic nervous system (ANS) activity. Previous studies have already supported the relevance of improved parasympathetic function to maintain a good health status. Currently, it is well documented that people with high physical activity status present better ANS function, as well as non-smokers subjects. Many factors are related to impaired ANS function, including, sedentary habits, smoking, high fat intake, genetic changes, etc.