High-gamma modulation language mapping with stereo-EEG: A novel analytic approach and diagnostic validation. Issue 12 (December 2020)

• Record Type: Journal Article
• Authors: Ervin, Brian; Buroker, Jason; Rozhkov, Leonid; Holloway, Timothy; Horn, Paul S.; Scholle, Craig; Byars, Anna W.; Mangano, Francesco T.; Leach, James L.; Greiner, Hansel M.; Holland, Katherine D.; Arya, Ravindra
• Is Part Of: Clinical neurophysiology. Volume 131:Issue 12(2020:Dec.)
• Page Start: 2851
• Page End: 2860
• Publication Date: 2020-12
• Subjects: Intracranial electrodes -- Epilepsy surgery -- Cortical localization -- High-gamma activation -- Machine learning; AUC Area under the ROC Curve -- AD After-discharge -- CI Confidence Intervals -- CT Computed Tomographic -- CW Cluster Weight -- DOR Diagnostic Odds Ratio -- DRE Drug-Resistant Epilepsy -- ESM Electrical cortical Stimulation Mapping -- FSIQ Full-scale Intelligence Quotient -- GLMM Generalized Linear Mixed Model -- GM Gray Matter -- HGM High-gamma Modulation -- MNI Montreal Neurological Institute -- MRI Magnetic Resonance Imaging -- PCLUSTER Probability that the largest time-frequency cluster during naming response arose from the baseline (inter-trial) distribution -- PPV Positive Predictive Value -- ROC Receiver Operating Characteristic -- SD Standard Deviation -- SEEG Stereo-Electroencephalography -- TCM, FCM, VCM Time, Frequency, and Value (magnitude) of power change at the center-of-mass of the largest cluster of power differential between the baseline and naming conditions -- TLZ, FLZ, VLZ Time, Frequency, and Value (magnitude) of power change at the point of highest z-score within the largest cluster of power differential between the baseline and naming conditions -- TFR Time-Frequency Representation -- WM White Matter; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2020.09.023
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: An approach for analysis of task-related high-gamma modulation in stereo-EEG using distribution of power differential clusters is described. Stereo-EEG high-gamma language mapping effectively localized reference neuroanatomy (Neurosynth). Stereo-EEG high-gamma language mapping also adequately classified electrical stimulation mapping speech/language sites. 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.