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

Autor: Zachary F. Jessen, R. Mark Richardson, Ashley C. Whiteman, Michael J. Ward, Avniel Singh Ghuman, Matthew J. Boring, Thomas A. Wozny
Rok vydání: 2019
Předmět:
Adult
Male
Visual perception
Movement disorders
Deep brain stimulation
Parkinson's disease
Computer science
Cognitive Neuroscience
medicine.medical_treatment
Deep Brain Stimulation
Globus Pallidus
behavioral disciplines and activities
050105 experimental psychology
Article
Machine Learning
03 medical and health sciences
Epilepsy
Young Adult
0302 clinical medicine
Spatio-Temporal Analysis
Neuroimaging
Obsessive compulsive
Subthalamic Nucleus
medicine
Humans
0501 psychology and cognitive sciences
Aged
Cerebral Cortex
Artifact (error)
Modality (human–computer interaction)
medicine.diagnostic_test
05 social sciences
Chronic pain
Magnetoencephalography
Parkinson Disease
Middle Aged
medicine.disease
nervous system diseases
surgical procedures
operative
Neurology
nervous system
Visual Perception
Female
medicine.symptom
Artifacts
Neuroscience
therapeutics
030217 neurology & neurosurgery
Zdroj: Neuroimage
ISSN: 1095-9572
Popis: Deep 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, in 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 relatively unaffected by DBS, to quantify how well neural data can be salvaged from artifact contamination introduced by DBS and how comparable DBS-on and DBS-off data are after artifact removal. Machine learning also allowed us to determine whether the spatiotemporal pattern of neural activity recorded during stimulation are comparable to those recorded when stimulation is off. The spatiotemporal patterns of visually evoked neural fields could be accurately classified in all 8 patients with DBS implants during both DBS-on and DBS-off conditions and performed comparably across those two conditions. Further, the classification accuracy for classifiers trained on the spatiotemporal patterns evoked during DBS-on trials and applied to DBS-off trials, and vice versa, were similar to that of the classifiers trained and tested on either trial type, demonstrating the comparability of these patterns across conditions. Together, these results demonstrate the ability of MEG preprocessing techniques, like temporal signal space separation, to salvage neural data from recordings contaminated with DBS artifacts and validate MEG as a powerful tool to study the cortical consequences of DBS.
Databáze: OpenAIRE
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