Brian Ervin1, Jason Buroker2, Leonid Rozhkov2, Timothy Holloway3, Paul S Horn4, Craig Scholle2, Anna W Byars4, Francesco T Mangano5, James L Leach6, Hansel M Greiner4, Katherine D Holland4, Ravindra Arya7. 1. Comprehensive Epilepsy Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA. 2. Comprehensive Epilepsy Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. 3. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. 4. Comprehensive Epilepsy Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. 5. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Division of Pediatric Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. 6. Division of Pediatric Neuro-radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. 7. Comprehensive Epilepsy Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. Electronic address: Ravindra.Arya@cchmc.org.
Abstract
OBJECTIVE: A novel analytic approach for task-related high-gamma modulation (HGM) in stereo-electroencephalography (SEEG) was developed and evaluated for language mapping. METHODS: SEEG signals, acquired from drug-resistant epilepsy patients during a visual naming task, were analyzed to find clusters of 50-150 Hz power modulations in time-frequency domain. Classifier models to identify electrode contacts within the reference neuroanatomy and electrical stimulation mapping (ESM) speech/language sites were developed and validated. RESULTS: In 21 patients (9 females), aged 4.8-21.2 years, SEEG HGM model predicted electrode locations within Neurosynth language parcels with high diagnostic odds ratio (DOR 10.9, p < 0.0001), high specificity (0.85), and fair sensitivity (0.66). Another SEEG HGM model classified ESM speech/language sites with significant DOR (5.0, p < 0.0001), high specificity (0.74), but insufficient sensitivity. Time to largest power change reliably localized electrodes within Neurosynth language parcels, while, time to center-of-mass power change identified ESM sites. CONCLUSIONS: SEEG HGM mapping can accurately localize neuroanatomic and ESM language sites. SIGNIFICANCE: Predictive modelling incorporating time, frequency, and magnitude of power change is a useful methodology for task-related HGM, which offers insights into discrepancies between HGM language maps and neuroanatomy or ESM.
OBJECTIVE: A novel analytic approach for task-related high-gamma modulation (HGM) in stereo-electroencephalography (SEEG) was developed and evaluated for language mapping. METHODS: SEEG signals, acquired from drug-resistant epilepsypatients during a visual naming task, were analyzed to find clusters of 50-150 Hz power modulations in time-frequency domain. Classifier models to identify electrode contacts within the reference neuroanatomy and electrical stimulation mapping (ESM) speech/language sites were developed and validated. RESULTS: In 21 patients (9 females), aged 4.8-21.2 years, SEEG HGM model predicted electrode locations within Neurosynth language parcels with high diagnostic odds ratio (DOR 10.9, p < 0.0001), high specificity (0.85), and fair sensitivity (0.66). Another SEEG HGM model classified ESM speech/language sites with significant DOR (5.0, p < 0.0001), high specificity (0.74), but insufficient sensitivity. Time to largest power change reliably localized electrodes within Neurosynth language parcels, while, time to center-of-mass power change identified ESM sites. CONCLUSIONS: SEEG HGM mapping can accurately localize neuroanatomic and ESM language sites. SIGNIFICANCE: Predictive modelling incorporating time, frequency, and magnitude of power change is a useful methodology for task-related HGM, which offers insights into discrepancies between HGM language maps and neuroanatomy or ESM.
Authors: Brian Ervin; Jason Buroker; Anna W Byars; Leonid Rozhkov; James L Leach; Paul S Horn; Craig Scholle; Francesco T Mangano; Hansel M Greiner; Katherine D Holland; Tracy A Glauser; Ravindra Arya Journal: Clin Neurophysiol Date: 2021-09-30 Impact factor: 3.708
Authors: Ravindra Arya; Brian Ervin; Jason Buroker; Hansel M Greiner; Anna W Byars; Leonid Rozhkov; Jesse Skoch; Paul S Horn; Clayton Frink; Craig Scholle; James L Leach; Francesco T Mangano; Tracy A Glauser; Katherine D Holland Journal: Front Neurosci Date: 2022-05-19 Impact factor: 5.152