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 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.
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Neuroimaging Clin. N. Am. · Nov 2017
ReviewApplications of Resting-State Functional MR Imaging to Epilepsy.
We discuss the value of resting-state functional MR imaging (rsfMR imaging) as an emerging technique to address questions about memory and language that are central in surgery for temporal-lobe epilepsy, namely the identification and characterization of eloquent cortex to avoid surgical morbidity. The emergence of a robust set of data using rsfMR imaging has opened new avenues for exploring more direct relationships between neural networks and current cognitive function and prediction of postoperative change. These techniques are also being explored for their potential to characterize epilepsy subtypes, identify epileptic foci, and monitor treatment effects.