Neuroimaging clinics of North America
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Neuroimaging Clin. N. Am. · Nov 2017
ReviewMachine Learning Applications to Resting-State Functional MR Imaging Analysis.
Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances.
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Neuroimaging Clin. N. Am. · Nov 2017
ReviewApplications of Resting State Functional MR Imaging to Traumatic Brain Injury.
Traumatic brain injury (TBI) is an important public health issue. TBI includes a broad spectrum of injury severities and abnormalities. ⋯ Specifically, graph theory is being used to study the change in networks after TBI. Machine learning methods allow researchers to build models capable of predicting injury severity and recovery trajectories.
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Neuroimaging Clin. N. Am. · Nov 2017
ReviewApplications of Resting-State Functional Connectivity to Neurodegenerative Disease.
Neurodegenerative diseases target specific large-scale neuronal networks, leading to distinct behavioral and cognitive dysfunctions. Resting-state functional magnetic resonance imaging (rsfMR imaging)-based functional connectivity method maps symptoms-associated functional network deterioration in vivo. ⋯ Understanding of disease mechanism can further guide early detection and predictions of disease progression and inform development of more effective treatment. With better clinical phenotyping and larger samples across multiple sites, we discuss several possible future directions to further develop rsfMR imaging-based functional connectivity methods into scientifically and clinically useful assays for neurodegenerative disorders.
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Neuroimaging Clin. N. Am. · Nov 2017
ReviewTen Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis.
For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.
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Neuroimaging Clin. N. Am. · Nov 2017
ReviewApplications of Resting State Functional MR Imaging to Neuropsychiatric Diseases.
Resting state studies in neuropsychiatric disorders have already provided much useful information, but the field is regarded as being at a relatively preliminary stage and subject to several design issues that set limits on the overall utility.