Neuroimaging refers to the utilization of quantitative methods for the study of the structure and function of the central nervous system. It was developed as a non-invasive method to scientifically investigate the healthy human brain. It is widely being used in quantitative investigations for psychiatric illnesses and brain disorders. Various techniques that are used in neuroimaging include computed axial tomography and magnetic resonance imaging, diffuse optical imaging, positron emission tomography, cranial ultrasound. The human brain is a massive, interconnected and complex system. Advanced neuroimaging techniques such as diffusion tensor imaging and functional magnetic resonance imaging allow us to investigate the underlying principles of brain structural and functional architectures. It also enables us to study the pathological changes in a variety of brain disorders. Multi-modal neuroimaging techniques could provide a more thorough understanding of the pathomechanism of brain disorders, which facilitates early diagnosis and helps in assessing prognosis and therapeutic effect. This book elucidates the concepts and innovative models around prospective developments with respect to the characterization of brain disorders. The topics covered herein offer the readers new insights in the field of neuroimaging.

There has been increasing interests in exploring biomarkers from brain images, aiming to have a better understanding and a more effective diagnosis of brain disorders such as schizophrenia, bipolar disorder, schizoaffective disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder, Alzheimer’s disease and so on. Therefore, it is important to identify disease-specific changes for distinguishing healthy controls and patients with brain disorders as well as for differentiating patients with different disorders showing similar clinical symptoms. Biomarkers can be identified from different types of brain Imaging techniques including functional magnetic resonance imaging (fMRI), structural MRI, positron emission tomography (PET), electroencephalography (EEG), and magnetoencephalography (MEG) by using statistical analysis methods. Furthermore, based on measures from brain imaging techniques, machine learning techniques can help to classify or predict disease for individual subjects. In fact, fusion of features from multiple modalities may benefit the understanding of disease mechanism and improve the classification performance. This Research Topic further explores the functional or structural alterations in brain disorders.

Notable experts in the field of neuroimaging provide comprehensive overviews of advances in functional and structural aspects of both common and uncommon brain disorders. Functional imaging is evolving quickly but researchers and clinicians do not always have a strong understanding of the fundamental basis of the imaging techniques that they use. By focusing on both structure and function this book will provide a strong foundation for emerging developments in the field.

This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.

In the last few years, advances in human structural and functional neuroimaging (fMRI, PET, EEG/MEG) have resulted in an explosion of studies investigating the anatomical and functional connectivity between different regions of the brain. More and more studies have employed resting and task-related connectivity analyses to assess functional interactions, and diffusion-weighted tractography to study white matter organization. Many of these studies have addressed normal human function, but recently, a number of investigators have turned their attention to examining brain disorders. The study of brain disorders is a complex endeavor; not only does it require understanding the normal brain, and the regions involved in a particular function, but also it needs a deeper understanding of brain networks and their dynamics. This Research Topic will provide the scientific community with an overview of how to apply connectivity methods to study brain disease, and with perspectives on what are the strength and limitations of each modality. For this Research Topic, we solicit both reviews and original research articles on the use of brain connectivity analysis, with non-human or human models, to explore neurological, psychiatric, developmental and neurodegenerative disorders from a system perspective. Connectivity studies that have focused on one or more of the following will be of particular interest: (1) detection of abnormal functional/structural connectivity; (2) neural plasticity, assessed by changes in connectivity, in patients with brain disorders; (3) assessment of therapy using connectivity measures; (4) relation of connectivity changes to behavioral changes.