Dynamic Functional Connectivity In Neuropsychiatric Disorders Methods And Applications
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| Author | : Wenbin Guo |
| Publisher | : Frontiers Media SA |
| Total Pages | : 71 |
| Release | : 2020-12-03 |
| Genre | : Science |
| ISBN | : 2889661954 |
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.
| Author | : Zaicu Cui |
| Publisher | : Frontiers Media SA |
| Total Pages | : 222 |
| Release | : 2023-06-07 |
| Genre | : Science |
| ISBN | : 2832510213 |
Neuropsychiatric disorders have a huge impact on individuals, families and societies. However, the neuropathology underlying cognitive deficits in neuropsychiatric disorders remains unclear. Resting-state functional connectivity provides a powerful way to investigate functional alterations underlying cognitive deficits in neuropsychiatric disorders. Traditional FC analysis measures the correlations of signals with an assumption that functional connectivity remains constant during the observation period. In recent years, several studies have demonstrated the feasibility of dynamic methods in characterization of functional brain changes, such as dynamic functional connectivity investigated by a sliding window method. However, selection of window size, window stepsize and window type are open areas of research and an important parameter to capture the resting-state FC dynamics.
| Author | : Dewen Hu |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2020-11-20 |
| Genre | : Medical |
| ISBN | : 9789813295254 |
This book presents recent advances in pattern analysis of the human connectome. The human connectome, measured by magnetic resonance imaging at the macroscale, provides a comprehensive description of how brain regions are connected. Based on machine learning methods, multiviarate pattern analysis can directly decode psychological or cognitive states from brain connectivity patterns. Although there are a number of works with chapters on conventional human connectome encoding (brain-mapping), there are few resources on human connectome decoding (brain-reading). Focusing mainly on advances made over the past decade in the field of manifold learning, sparse coding, multi-task learning, and deep learning of the human connectome and applications, this book helps students and researchers gain an overall picture of pattern analysis of the human connectome. It also offers valuable insights for clinicians involved in the clinical diagnosis and treatment evaluation of neuropsychiatric disorders.
| Author | : Vaibhav A. Diwadkar |
| Publisher | : Springer Nature |
| Total Pages | : 492 |
| Release | : 2021-05-11 |
| Genre | : Medical |
| ISBN | : 3030597970 |
Brain network function and dysfunction is the dominant model for understanding how the brain gives rise to normal and abnormal behavior. Moreover, neuropsychiatric illnesses continue to resist attempts to reveal an understanding of their bases. Thus, this timely volume provides a synthesis of the uses of multiple analytic methods as they are applied to neuroimaging data, to seek understanding of the neurobiological bases of psychiatric illnesses, understanding that can subsequently aid in their management and treatment. A principle focus is on the analyses and application of methods to functional magnetic resonance imaging (fMRI) data. fMRI remains the most widely used neuroimaging technique for estimating brain network function, and several of the methods covered can estimate brain network dysfunction in resting and task-active states. Additional chapters provide details on how these methods are (and can be) applied in the understanding of several neuropsychiatric disorders, including schizophrenia, mood disorders, autism, borderline personality disorder, and attention deficit hyperactivity disorder (ADHD). A final complement of chapters provides a collective overview of how this framework continues to provoke theoretical advances in our conception of the brain in psychiatry. This unique volume is designed to be a comprehensive resource for imaging researchers interested in psychiatry, and for psychiatrists interested in advanced imaging applications.
| Author | : Takao Yamasaki |
| Publisher | : Frontiers Media SA |
| Total Pages | : 136 |
| Release | : 2024-08-20 |
| Genre | : Science |
| ISBN | : 2832553362 |
This Research Topic aims to highlight the latest experimental techniques and methods used to aid in the classification; diagnosis; visualization; prognosis and treatment of neurological and neuropsychiatric disorders using brain imaging methods. Review articles or opinions on methodologies or applications including the advantages and limitations of each are welcome. This Topic includes technologies and up-to-date methods which help advance the science. The contributions to this collection will undergo peer-review. Novelty and the utility of a method or protocol must be evident. We welcome contributions covering all aspects of novel brain imaging methods that aid clinicians in handling neurological and neuropsychiatric disorders as described above. Submissions will be handled by the team of Topic Editors. Frontiers in Neuroscience supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management and stewardship (Wilkinson et al., Sci. Data 3:160018, 2016). This Research Topic welcomes: • Methods: Describing new methods that are significantly improved or adapted for specific purposes. These manuscripts may include primary (original) data. • Protocols: Detailed descriptions, including pitfalls and troubleshooting, to benefit those who may evaluate or employ the techniques. The protocols must be proven to work. • Perspective or General Commentaries on brain imaging methods and protocols relevant for aiding in the handling of neurological and neuropsychiatric disorders. • Reviews and mini-reviews of topical methods and protocols highlighting the important future directions of the field.
| Author | : Baojuan Li |
| Publisher | : Frontiers Media SA |
| Total Pages | : 142 |
| Release | : 2017-06-22 |
| Genre | : |
| ISBN | : 2889452077 |
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.
| Author | : Jay J. Pillai |
| Publisher | : Elsevier Health Sciences |
| Total Pages | : 209 |
| Release | : 2017-10-11 |
| Genre | : Medical |
| ISBN | : 032354892X |
This issue of Neuroimaging Clinics of North America focuses on Functional Connectivity, and is edited by Dr. Jay Pillai. Articles will include: Applications of rs-fMRI to presurgical mapping: sensorimotor mapping; Dynamic functional connectivity methods; Machine learning applications to rs-fMRI analysis; Frequency domain analysis of rs-fMRI; Applications of rs-fMRI to epilepsy; Data-driven analysis methods for rs-fMRI; Applications of rs-fMRI to presurgical mapping: language mapping; Limitations of rs-fMRI in the setting of focal brain lesions; Applications of rs-fMRI to neuropsychiatric disease; Applications of rs-fMRI to Traumatic Brain Injury; Applications of rs-fMRI to neurodegenerative disease; Graph theoretic analysis of rs-fMRI; and more!
| Author | : Hao Zhang |
| Publisher | : Frontiers Media SA |
| Total Pages | : 151 |
| Release | : 2024-10-14 |
| Genre | : Science |
| ISBN | : 2832555500 |
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.
| Author | : Alan Anticevic |
| Publisher | : Academic Press |
| Total Pages | : 334 |
| Release | : 2017-09-19 |
| Genre | : Medical |
| ISBN | : 0128098260 |
Computational Psychiatry: Mathematical Modeling of Mental Illness is the first systematic effort to bring together leading scholars in the fields of psychiatry and computational neuroscience who have conducted the most impactful research and scholarship in this area. It includes an introduction outlining the challenges and opportunities facing the field of psychiatry that is followed by a detailed treatment of computational methods used in the service of understanding neuropsychiatric symptoms, improving diagnosis and guiding treatments. This book provides a vital resource for the clinical neuroscience community with an in-depth treatment of various computational neuroscience approaches geared towards understanding psychiatric phenomena. Its most valuable feature is a comprehensive survey of work from leaders in this field. - Offers an in-depth overview of the rapidly evolving field of computational psychiatry - Written for academics, researchers, advanced students and clinicians in the fields of computational neuroscience, clinical neuroscience, psychiatry, clinical psychology, neurology and cognitive neuroscience - Provides a comprehensive survey of work from leaders in this field and a presentation of a range of computational psychiatry methods and approaches geared towards a broad array of psychiatric problems
| Author | : Alex Fornito |
| Publisher | : Academic Press |
| Total Pages | : 496 |
| Release | : 2016-03-04 |
| Genre | : Medical |
| ISBN | : 0124081185 |
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. - Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology - Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems - Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience - Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
