Lauren Sculthorpe-Petley1, Careesa Liu2, Sujoy Ghosh Hajra2, Hossein Parvar3, Jason Satel4, Thomas P Trappenberg3, Rober Boshra3, Ryan C N D'Arcy5. 1. Biomedical Translational Imaging Centre (BIOTIC), IWK Health Centre, Suite 3900-1796 Summer St., Halifax, Nova Scotia B3H 3A7, Canada. 2. Faculty of Applied Sciences, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada. 3. Faculty of Computer Science, Dalhousie University, 6050 University Ave., P.O. Box 15000, Halifax, Nova Scotia B3H 4R2, Canada. 4. School of Psychology, University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia. 5. Faculty of Applied Sciences, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada. Electronic address: rdarcy@sfu.ca.
Abstract
BACKGROUND: Event-related potentials (ERPs) may provide a non-invasive index of brain function for a range of clinical applications. However, as a lab-based technique, ERPs are limited by technical challenges that prevent full integration into clinical settings. NEW METHOD: To translate ERP capabilities from the lab to clinical applications, we have developed methods like the Halifax Consciousness Scanner (HCS). HCS is essentially a rapid, automated ERP evaluation of brain functional status. The present study describes the ERP components evoked from auditory tones and speech stimuli. ERP results were obtained using a 5-min test in 100 healthy individuals. The HCS sequence was designed to evoke the N100, the mismatch negativity (MMN), P300, the early negative enhancement (ENE), and the N400. These components reflected sensation, perception, attention, memory, and language perception, respectively. Component detection was examined at group and individual levels, and evaluated across both statistical and classification approaches. RESULTS: All ERP components were robustly detected at the group level. At the individual level, nonparametric statistical analyses showed reduced accuracy relative to support vector (SVM) machine classification, particularly for speech-based ERPs. Optimized SVM results were MMN: 95.6%; P300: 99.0%; ENE: 91.8%; and N400: 92.3%. CONCLUSIONS: A spectrum of individual-level ERPs can be obtained in a very short time. Machine learning classification improved detection accuracy across a large healthy control sample. Translating ERPs into clinical applications is increasingly possible at the individual level. Crown
BACKGROUND: Event-related potentials (ERPs) may provide a non-invasive index of brain function for a range of clinical applications. However, as a lab-based technique, ERPs are limited by technical challenges that prevent full integration into clinical settings. NEW METHOD: To translate ERP capabilities from the lab to clinical applications, we have developed methods like the Halifax Consciousness Scanner (HCS). HCS is essentially a rapid, automated ERP evaluation of brain functional status. The present study describes the ERP components evoked from auditory tones and speech stimuli. ERP results were obtained using a 5-min test in 100 healthy individuals. The HCS sequence was designed to evoke the N100, the mismatch negativity (MMN), P300, the early negative enhancement (ENE), and the N400. These components reflected sensation, perception, attention, memory, and language perception, respectively. Component detection was examined at group and individual levels, and evaluated across both statistical and classification approaches. RESULTS: All ERP components were robustly detected at the group level. At the individual level, nonparametric statistical analyses showed reduced accuracy relative to support vector (SVM) machine classification, particularly for speech-based ERPs. Optimized SVM results were MMN: 95.6%; P300: 99.0%; ENE: 91.8%; and N400: 92.3%. CONCLUSIONS: A spectrum of individual-level ERPs can be obtained in a very short time. Machine learning classification improved detection accuracy across a large healthy control sample. Translating ERPs into clinical applications is increasingly possible at the individual level. Crown
Authors: Kristina C Backer; Andrew S Kessler; Laurel A Lawyer; David P Corina; Lee M Miller Journal: J Neurophysiol Date: 2019-07-03 Impact factor: 2.714
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