Neuroimaging clinics of North America
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Neuroimaging Clin. N. Am. · May 2020
ReviewPediatric Magnetoencephalography in Clinical Practice and Research.
Magnetoencephalography (MEG) is a noninvasive neuroimaging technique that measures the electromagnetic fields generated by the human brain. This article highlights the benefits that pediatric MEG has to offer to clinical practice and pediatric research, particularly for infants and young children; reviews the existing literature on adult MEG systems for pediatric use; briefly describes the few pediatric MEG systems currently extant; and draws attention to future directions of research, with focus on the clinical use of MEG for patients with drug-resistant epilepsy.
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Neuroimaging Clin. N. Am. · May 2020
ReviewRole of Magnetoencephalography in the Early Stages of Alzheimer Disease.
As synaptic dysfunction is an early manifestation of Alzheimer disease (AD) pathology, magnetoencephalography (MEG) is capable of detecting disruptions by assessing the synchronized oscillatory activity of thousands of neurons that rely on the integrity of neural connections. MEG findings include slowness of the oscillatory activity, accompanied by a reduction of the alpha band power, and dysfunction of the functional networks. ⋯ These neurophysiological biomarkers predict which patients with mild cognitive impairment will develop dementia. MEG has demonstrated its utility as a noninvasive biomarker for early detection of AD.
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Neuroimaging Clin. N. Am. · Feb 2020
ReviewResting-State Functional Connectivity: Signal Origins and Analytic Methods.
Resting state functional connectivity (RSFC) has been widely studied in functional magnetic resonance imaging (fMRI) and is observed by a significant temporal correlation of spontaneous low-frequency signal fluctuations (SLFs) both within and across hemispheres during rest. Different hypotheses of RSFC include the biophysical origin hypothesis and cognitive origin hypothesis, which show that the role of SLFs and RSFC is still not completely understood. ⋯ RSFC data analysis methods include time domain analysis, seed-based correlation, regional homogeneity, and principal and independent component analyses. Despite advances in RSFC, the authors also discuss challenges and limitations, ranging from head motion to methodological limitations.