Following on from the success of Insights in Brain Imaging Methods: 2021, we would like to further celebrate the exceptional achievements made by scientists, leading to major advancements in the fast-growing field of neuroscience. You can find the 2021 edition here. Frontiers is continuing to organize a series of Research Topics to highlight the latest advancements in research across the field of neuroscience, with articles from the members of our accomplished Editorial Boards. This editorial initiative of particular relevance, led by Prof Vince D Calhoun, Specialty Chief Editor of the Brain Imaging Methods section, together with Professors Federico Giove and Xi-Nian Zuo, is focused on new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives in the field of Brain Imaging Methods.

Brain diseases such as stroke, Alzheimer's disease, and Parkinson's disease cause dysfunction in multiple body systems. Motor dysfunction, cognitive impairment, dysphagia, and emotion disorders are frequently observed in patients with brain diseases. As the dysfunctions are associated with alterations in the brain, brain imaging methods such as functional MRI (fMRI), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and transcranial magnetic stimulation (TMS) are essential for investigating the neural mechanisms underlying the dysfunction caused by brain diseases. Brain imaging methods are also critical for understanding the neural mechanisms for the effectiveness of therapeutic or rehabilitative interventions that promote recovery from brain diseases. The usage of these brain imaging methods would deepen our understanding of brain diseases and potentially translate this knowledge to improve effectiveness of rehabilitative interventions for brain diseases.

Stroke Rehabilitation: Insights from Neuroscience and Imaging informs and challenges neurologists, rehabilitation therapists, imagers, and stroke specialists to adopt more restorative and scientific approaches to stroke rehabilitation based on new evidence from neuroscience and neuroimaging literatures. The fields of cognitive neuroscience and neuroimaging are advancing rapidly and providing new insights into human behavior and learning. Similarly, improved knowledge of how the brain processes information after injury and recovers over time is providing new perspectives on what can be achieved through rehabilitation. Stroke Rehabilitation explores the potential to shape and maximize neural plastic changes in the brain after stroke from a multimodal perspective. Active skill based learning is identified as a central element of a restorative approach to rehabilitation. The evidence behind core learning principles as well as specific learning strategies that have been applied to retrain lost functions of movement, sensation, cognition and language are also discussed. Current interventions are evaluated relative to this knowledge base and examples are given of how active learning principles have been successfully applied in specific interventions. The benefits and evidence behind enriched environments is reviewed with examples of potential application in stroke rehabilitation. The capacity of adjunctive therapies, such as transcranial magnetic stimulation, to modulate receptivity of the damaged brain to benefit from behavioral interventions is also discussed in the context of this multimodal approach. Focusing on new insights from neuroscience and imaging, the book explores the potential to tailor interventions to the individual based on viable brain networks.

Decision Neuroscience addresses fundamental questions about how the brain makes perceptual, value-based, and more complex decisions in non-social and social contexts. This book presents compelling neuroimaging, electrophysiological, lesional, and neurocomputational models in combination with hormonal and genetic approaches, which have led to a clearer understanding of the neural mechanisms behind how the brain makes decisions. The five parts of the book address distinct but inter-related topics and are designed to serve both as classroom introductions to major subareas in decision neuroscience and as advanced syntheses of all that has been accomplished in the last decade. Part I is devoted to anatomical, neurophysiological, pharmacological, and optogenetics animal studies on reinforcement-guided decision making, such as the representation of instructions, expectations, and outcomes; the updating of action values; and the evaluation process guiding choices between prospective rewards. Part II covers the topic of the neural representations of motivation, perceptual decision making, and value-based decision making in humans, combining neurcomputational models and brain imaging studies. Part III focuses on the rapidly developing field of social decision neuroscience, integrating recent mechanistic understanding of social decisions in both non-human primates and humans. Part IV covers clinical aspects involving disorders of decision making that link together basic research areas including systems, cognitive, and clinical neuroscience; this part examines dysfunctions of decision making in neurological and psychiatric disorders, such as Parkinson's disease, schizophrenia, behavioral addictions, and focal brain lesions. Part V focuses on the roles of various hormones (cortisol, oxytocin, ghrelin/leptine) and genes that underlie inter-individual differences observed with stress, food choices, and social decision-making processes. The volume is essential reading for anyone interested in decision making neuroscience. With contributions that are forward-looking assessments of the current and future issues faced by researchers, Decision Neuroscience is essential reading for anyone interested in decision-making neuroscience. - Provides comprehensive coverage of approaches to studying individual and social decision neuroscience, including primate neurophysiology, brain imaging in healthy humans and in various disorders, and genetic and hormonal influences on decision making - Covers multiple levels of analysis, from molecular mechanisms to neural-systems dynamics and computational models of how we make choices - Discusses clinical implications of process dysfunctions, including schizophrenia, Parkinson's disease, eating disorders, drug addiction, and pathological gambling - Features chapters from top international researchers in the field and full-color presentation throughout with numerous illustrations to highlight key concepts

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 coherent mathematical and statistical approach aimed at graduate students incorporates regression and topology as well as graph theory.

Brain imaging has been successfully used to generate image-based biomarkers for various neurological and psychiatric disorders, such as Alzheimer’s and related dementias, Parkinson’s disease, stroke, traumatic brain injury, brain tumors, depression, schizophrenia, etc. However, accurate brain image-based diagnosis at the individual level remains elusive, and this applies to the diagnosis of neuropathological diseases as well as clinical syndromes. In recent years, deep learning techniques, due to their ability to learn complex patterns from large amounts of data, have had remarkable success in various fields, such as computer vision and natural language processing. Applying deep learning methods to brain imaging-assisted diagnosis, while promising, is facing challenges such as insufficiently labeled data, difficulty in interpreting diagnosis results, variations in data acquisition in multi-site projects, integration of multimodal data, clinical heterogeneity, etc. The goal of this research topic is to gather cutting-edge research that showcases the application of deep learning methods in brain imaging for the diagnosis of neurological and psychiatric disorders. We encourage submissions that demonstrate novel approaches to overcome various abovementioned difficulties and achieve more accurate, reliable, generalizable, and interpretable diagnosis of neurological and psychiatric disorders in this field.