Quantitatively validating the efficacy of artifact suppression techniques to study the cortical consequences of deep brain stimulation with magnetoencephalography

Publication date: Available online 31 May 2019Source: NeuroImageAuthor(s): Matthew J. Boring, Zachary F. Jessen, Thomas A. Wozny, Michael J. Ward, Ashley C. Whiteman, R. Mark Richardson, Avniel Singh GhumanAbstractDeep brain stimulation (DBS) is an established and effective treatment for several movement disorders and is being developed to treat a host of neuropsychiatric disorders including epilepsy, chronic pain, obsessive compulsive disorder, and depression. However, the neural mechanisms through which DBS produces therapeutic benefits, and in some cases unwanted side effects, for each of these disorders are only partially understood. Non-invasive neuroimaging techniques that can assess the neural effects of active stimulation are important for advancing our understanding of the neural basis of DBS therapy. Magnetoencephalography (MEG) is a safe, passive imaging modality with relatively high spatiotemporal resolution, which makes it a potentially powerful method for examining the cortical network effects of DBS. However, the degree to which magnetic artifacts produced by stimulation and the associated hardware can be suppressed from MEG data, and the comparability between signals measured during DBS-on and DBS-off conditions, have not been fully quantified. The present study used machine learning methods in conjunction with a visual perception task, which should be unaffected by DBS, to quantify how well neural data can be salvaged from artifact contamination introduced by...
Source: NeuroImage - Category: Neuroscience Source Type: research