Intracranial EEG fluctuates over months after implanting electrodes in human brain. (1st September 2017)

• Record Type: Journal Article
• Authors: Ung, Hoameng; Baldassano, Steven N; Bink, Hank; Krieger, Abba M; Williams, Shawniqua; Vitale, Flavia; Wu, Chengyuan; Freestone, Dean; Nurse, Ewan; Leyde, Kent; Davis, Kathryn A; Cook, Mark; Litt, Brian
• Is Part Of: Journal of neural engineering. Volume 14:Number 5(2017:Oct.)
• Publication Date: 2017-09-01
• Subjects: electroencephalography -- intracranial -- brain machine interface -- long-term EEG -- epilepsy -- electrodes; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/aa7f40
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. Approach . Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient's recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. Main results . A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14–50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. Significance . These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in patients, depending upon the application, may require extended monitoring.