- Email: [email protected]
Functional Connectivity
Functional Connectivity
P re f a c e Functional Connectivity
Jay J. Pillai, MD Editor
Neuroimag Clin N Am 27 (2017) xvii http://dx.doi.org/10.1016/j.nic.2017.08.001 1052-5149/17/Ó 2017 Published by Elsevier Inc.
article describing potential limitations of this method in some settings such as brain tumors. Many high-quality color illustrations are included throughout this issue to highlight important details, and an extensive list of references is included for each article in this issue, as well as a listing of key “take-home” points and a synopsis for each article. We hope that this compilation of salient articles will serve to both introduce cognitive neuroscientists, neurologists, psychiatrists, neuroradiologists, and neurosurgeons to this very rapidly evolving field that is sure to greatly enhance our understanding of brain function, as well as bring veterans in this field up-to-date via insights provided by current experts in the field. As we enter the era of big data, radiogenomics, and personalized medicine, the hope is that such new powerful methods and applications may eventually provide us with new functional “fingerprints” of normal and diseased brain to advance neuroscience and promote future physiologic imaging-based therapies based on functional as well as genotypic stratification of clinical populations and development of novel therapeutic biomarkers. Jay J. Pillai, MD Division of Neuroradiology The Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine Johns Hopkins Hospital, 1800 Orleans Street Baltimore, MD 21287, USA E-mail address: [email protected]
neuroimaging.theclinics.com
This issue of Neuroimaging Clinics addresses the current state-of-the-art in human brain functional connectivity as assessed through resting-state blood oxygen level–dependent fMRI. Although other functional imaging modalities such as magnetoencephalography and task-based fMRI have been included at least in brief description, as in the case of applications to traumatic brain injury for the former, most of this issue concentrates on resting state fMRI (rs-fMRI). The issue is divided into an initial section that includes articles that focus on methodological aspects, since at this point there is little consensus in the functional imaging community as to which approaches are best for analysis of such data. This first section includes discussion of datadriven methods such as independent component analysis, frequency domain analysis (eg, ALFF), and graph theoretic analysis. Specific inclusion of cutting-edge topics in rs-fMRI such as dynamic functional connectivity and machine learning applications (including specific applications of the latter to neuropsychiatric disease) to rs-fMRI analysis is a key feature of this section. The second section attempts to provide a broad overview of clinical applications of rs-fMRI that are prominent at the time of publication, although such applications will continue to increase exponentially in the very near future as the power harnessed via big data and deep learning initiatives continue to be realized in the era of human connectomics. Such applications include applications to sensorimotor mapping, language mapping, neurodegenerative diseases, traumatic brain injury, epilepsy, and neuropsychiatric disease, with an additional
P re f a c e Functional Connectivity
Jay J. Pillai, MD Editor
Neuroimag Clin N Am 27 (2017) xvii http://dx.doi.org/10.1016/j.nic.2017.08.001 1052-5149/17/Ó 2017 Published by Elsevier Inc.
article describing potential limitations of this method in some settings such as brain tumors. Many high-quality color illustrations are included throughout this issue to highlight important details, and an extensive list of references is included for each article in this issue, as well as a listing of key “take-home” points and a synopsis for each article. We hope that this compilation of salient articles will serve to both introduce cognitive neuroscientists, neurologists, psychiatrists, neuroradiologists, and neurosurgeons to this very rapidly evolving field that is sure to greatly enhance our understanding of brain function, as well as bring veterans in this field up-to-date via insights provided by current experts in the field. As we enter the era of big data, radiogenomics, and personalized medicine, the hope is that such new powerful methods and applications may eventually provide us with new functional “fingerprints” of normal and diseased brain to advance neuroscience and promote future physiologic imaging-based therapies based on functional as well as genotypic stratification of clinical populations and development of novel therapeutic biomarkers. Jay J. Pillai, MD Division of Neuroradiology The Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine Johns Hopkins Hospital, 1800 Orleans Street Baltimore, MD 21287, USA E-mail address: [email protected]
neuroimaging.theclinics.com
This issue of Neuroimaging Clinics addresses the current state-of-the-art in human brain functional connectivity as assessed through resting-state blood oxygen level–dependent fMRI. Although other functional imaging modalities such as magnetoencephalography and task-based fMRI have been included at least in brief description, as in the case of applications to traumatic brain injury for the former, most of this issue concentrates on resting state fMRI (rs-fMRI). The issue is divided into an initial section that includes articles that focus on methodological aspects, since at this point there is little consensus in the functional imaging community as to which approaches are best for analysis of such data. This first section includes discussion of datadriven methods such as independent component analysis, frequency domain analysis (eg, ALFF), and graph theoretic analysis. Specific inclusion of cutting-edge topics in rs-fMRI such as dynamic functional connectivity and machine learning applications (including specific applications of the latter to neuropsychiatric disease) to rs-fMRI analysis is a key feature of this section. The second section attempts to provide a broad overview of clinical applications of rs-fMRI that are prominent at the time of publication, although such applications will continue to increase exponentially in the very near future as the power harnessed via big data and deep learning initiatives continue to be realized in the era of human connectomics. Such applications include applications to sensorimotor mapping, language mapping, neurodegenerative diseases, traumatic brain injury, epilepsy, and neuropsychiatric disease, with an additional