Neuroimaging clinics of North America
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewCommon Data Elements in Head and Neck Radiology Reporting.
Radiologists must convert the complex information in head and neck imaging into text reports that can be understood and used by clinicians, patients, and fellow radiologists for patient care, research, and quality initiatives. Common data elements in reporting, through use of defined questions with constrained answers and terminology, allow radiologists to incorporate best practice standards and improve communication of information regardless of individual reporting style. Use of common data elements for head and neck reporting has the potential to improve outcomes, reduce errors, and transition data consumption not only for humans but future machine learning systems.
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Neuroimaging Clin. N. Am. · Aug 2020
ReviewArtificial Intelligence in Head and Neck Imaging: A Glimpse into the Future.
Artificial intelligence, specifically machine learning and deep learning, is a rapidly developing field in imaging sciences with the potential to improve the efficiency and effectiveness of radiologists. This review covers common technical terms and basic concepts in imaging artificial intelligence and briefly reviews the application of these techniques to general imaging as well as head and neck imaging. Artificial intelligence has the potential to contribute improvements to all areas of patient care, including image acquisition, processing, segmentation, automated detection of findings, integration of clinical information, quality improvement, and research. Numerous challenges remain, however, before widespread imaging clinical adoption and integration occur.
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Neuroimaging Clin. N. Am. · May 2020
ReviewMagnetoencephalography Research in Pediatric Autism Spectrum Disorder.
Magnetoencephalography (MEG) research indicates differences in neural brain measures in children with autism spectrum disorder (ASD) compared to typically developing (TD) children. As reviewed here, resting-state MEG exams are of interest as well as MEG paradigms that assess neural function across domains (e.g., auditory, resting state). To date, MEG research has primarily focused on group-level differences. Research is needed to explore whether MEG measures can predict, at the individual level, ASD diagnosis, prognosis (future severity), and response to therapy.
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This article provides an overview of research that uses magnetoencephalography to understand the brain basis of human language. The cognitive processes and brain networks that have been implicated in written and spoken language comprehension and production are discussed in relation to different methodologies: we review event-related brain responses, research on the coupling of neural oscillations to speech, oscillatory coupling between brain regions (eg, auditory-motor coupling), and neural decoding approaches in naturalistic language comprehension.
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Neuroimaging Clin. N. Am. · May 2020
ReviewMagnetoencephalography for Mild Traumatic Brain Injury and Posttraumatic Stress Disorder.
Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) are leading causes of sustained physical, cognitive, emotional, and behavioral deficits in the general population, active-duty military personnel, and veterans. However, the underlying pathophysiology of mTBI/PTSD and the mechanisms that support functional recovery for some, but not all individuals is not fully understood. Conventional MR imaging and computed tomography are generally negative in mTBI and PTSD, so there is interest in the development of alternative evaluative strategies. Of particular note are magnetoencephalography (MEG) -based methods, with mounting evidence that MEG can provide sensitive biomarkers for abnormalities in mTBI and PTSD.