Functional connectomics enables researchers to monitor interactions among thousands of units within the whole brain simultaneously by using various vivo imaging technologies. For example, resting-state functional magnetic resonance imaging can image low-frequency fluctuations in the spontaneous brain activities, representing a popular tool for macro-scale functional connectomics to characterize individual differences in normal brain function, mind-brain associations, and the various disorders. Reliability and reproducibility represents the most fundamental and critical aspect for the human brain functional connectomics to both research and clinical practice. Unfortunately, lacking a data platform for researchers to rigorously explore the reliability and reproducibility of the functional connectome indices has been a bottleneck of further development of clinically oriented imaging markers in the field. Recent efforts on open neuroscience, such as Consortium for Reliability and Reproducibility, Human Connectome Project and OpenFMRI, provide the data for the field to refine and evaluate reliability and reproducibility of novel methods as well as those with widespread usage but without sufficient consideration of reliability. This Frontiers Research Topic aims at bringing together contributions from researchers in brain imaging, neuroscience, computer sciences, applied mathematics, psychology and related fields from an interdisciplinary perspective. By focusing on cutting-edge research across these fields, this topic will create new agenda on quantifying the reliability and reproducibility of the myriad connectomics-based measures and informing expectations regarding the potential of biomarker discovery.

Resting-state functional magnetic resonance imaging has the ability to provide information about brain functioning. However, it is difficult to interpret conclusions about rsfMRI data due to questions about the reliability of resting-state functional connectivity (RSFC). This study investigated the test-retest reliability of resting-state networks using a "mini" multiverse approach for individuals who have sustained traumatic brain injuries (TBIs) using back-to-back rsfMRI scans. This is an understudied area that can improve our understanding of RSFC and its potential use in clinical populations. 45 individuals with TBI and 41 healthy controls received back-to-back rsfMRI scans. 25 individuals with TBI and 15 healthy controls received another scanning session approximately 2 years after the first. The data were preprocessed with fMRIPrep. XCP_D was used to create functional connectivity matrices using 8 different brain atlases. Several graph theory metrics were calculated. Intraclass correlation coefficients (ICCs) were utilized to examine the reliability of all graph metrics for each brain atlas across each participant's back-to-back rsfMRI scans for both scanning sessions. Results suggest that within-network connectivity, segregation, and modularity are the most reliable graph metrics, even after significant neurological compromise. The default mode network is one of the most reliable networks, whereas the limbic network is one of the least reliable networks. These results persist across the TBI and HC groups, brain atlases, and over time between the two scanning sessions, though there are some inconsistencies. This study underscores the importance of investigating the variability of ICCs. This will aid in the identification of resting-state biomarkers and will allow us to gain a better understanding of how subject characteristics and fMRI workflows impact RSFC reliability.

This book has brought together leading investigators who work in the new arena of brain connectomics. This includes ‘macro-connectome’ efforts to comprehensively chart long-distance pathways and functional networks; ‘micro-connectome’ efforts to identify every neuron, axon, dendrite, synapse, and glial process within restricted brain regions; and ‘meso-connectome’ efforts to systematically map both local and long-distance connections using anatomical tracers. This book highlights cutting-edge methods that can accelerate progress in elucidating static ‘hard-wired’ circuits of the brain as well as dynamic interactions that are vital for brain function. The power of connectomic approaches in characterizing abnormal circuits in the many brain disorders that afflict humankind is considered. Experts in computational neuroscience and network theory provide perspectives needed for synthesizing across different scales in space and time. Altogether, this book provides an integrated view of the challenges and opportunities in deciphering brain circuits in health and disease.

Connectomics: Applications to Neuroimaging is unique in presenting the frontier of neuro-applications using brain connectomics techniques. The book describes state-of-the-art research that applies brain connectivity analysis techniques to a broad range of neurological and psychiatric disorders (Alzheimer's, epilepsy, stroke, autism, Parkinson's, drug or alcohol addiction, depression, bipolar, and schizophrenia), brain fingerprint applications, speech-language assessments, and cognitive assessment. With this book the reader will learn: - Basic mathematical principles underlying connectomics - How connectomics is applied to a wide range of neuro-applications - What is the future direction of connectomics techniques. This book is an ideal reference for researchers and graduate students in computer science, data science, computational neuroscience, computational physics, or mathematics who need to understand how computational models derived from brain connectivity data are being used in clinical applications, as well as neuroscientists and medical researchers wanting an overview of the technical methods. Features: - Combines connectomics methods with relevant and interesting neuro-applications - Covers most of the hot topics in neuroscience and clinical areas - Appeals to researchers in a wide range of disciplines: computer science, engineering, data science, mathematics, computational physics, computational neuroscience, as well as neuroscience, and medical researchers interested in the technical methods of connectomics - Combines connectomics methods with relevant and interesting neuro-applications - Presents information that will appeal to researchers in a wide range of disciplines, including computer science, engineering, data science, mathematics, computational physics, computational neuroscience, and more - Includes a mathematics primer that formulates connectomics from an applied point-of-view, thus avoiding difficult to understand theoretical perspective - Lists publicly available neuro-imaging datasets that can be used to construct structural and functional connectomes

This issue of Neuroimaging Clinics of North America focuses on Psychoradiology, and is edited by Dr. Qiyong Gong. Articles will include: Clinical Strategies and Technical Challenges in Psychoradiology; Resting State Functional MRI for Psychiatry; Magnetic Resonance Spectroscopy for Psychiatry; Psychoradiology of Major Depression; Psychoradiological Biomarkers for Psychopharmaceutical Effects; Implementing Imaging into Clinical Routine Screening for Psychosis; Imaging of Autism; Individual-specific Analysis for Psychoradiology; Interventional Psychoradiology: Imaging Guided Therapeutic Intervention of Neuropsychiatric Disorders; Imaging-based Subtyping for Psychiatric Syndromes; Imaging of Post-Traumatic Stress Disorder; Imaging of Schizophrenia; and more!

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.

The human brain is incredibly complex, and the more we learn about it, the more we realize how much we need a truly interdisciplinary team to make sense of its intricacies. This eBook presents the latest efforts in collaborative team science from around the world, all aimed at understanding the human brain.