High-gamma modulation language mapping with stereo-EEG: A novel analytic approach and diagnostic validation |
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| Affiliation: | 1. Department of Neurosurgery, Stanford University School of Medicine, United States of America;2. Department of Neurology, Stanford University School of Medicine, United States of America;3. Division of Pediatric Neurology, Lucile Packard Children''s Hospital Stanford, United States of America;4. Division of Pediatric Neurosurgery, Lucile Packard Children''s Hospital Stanford, United States of America;1. Comprehensive Epilepsy Center, Division of Neurology, Cincinnati Children''s Hospital Medical Center, Cincinnati, OH, United States of America;2. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America;3. Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States of America;4. Neuroscience Institute, Le Bonheur Children''s Hospital, Memphis, TN, United States of America;5. Department of Anatomy and Neurobiology, The University of Tennessee Health Science Center, Memphis, TN, United States of America;6. Pediatric Neuroimaging Research Consortium, Cincinnati Children''s Hospital Medical Center, Cincinnati, OH, United States of America;7. Division of Pediatric Neurosurgery, Cincinnati Children''s Hospital Medical Center, Cincinnati, OH, United States of America |
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| Abstract: | ObjectiveA novel analytic approach for task-related high-gamma modulation (HGM) in stereo-electroencephalography (SEEG) was developed and evaluated for language mapping.MethodsSEEG 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.ResultsIn 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.ConclusionsSEEG HGM mapping can accurately localize neuroanatomic and ESM language sites.SignificancePredictive 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. |
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| Keywords: | Intracranial electrodes Epilepsy surgery Cortical localization High-gamma activation Machine learning AUC" },{" #name" :" keyword" ," $" :{" id" :" k0035" }," $$" :[{" #name" :" text" ," _" :" Area under the ROC Curve AD" },{" #name" :" keyword" ," $" :{" id" :" k0045" }," $$" :[{" #name" :" text" ," _" :" After-discharge CI" },{" #name" :" keyword" ," $" :{" id" :" k0055" }," $$" :[{" #name" :" text" ," _" :" Confidence Intervals CT" },{" #name" :" keyword" ," $" :{" id" :" k0065" }," $$" :[{" #name" :" text" ," _" :" Computed Tomographic CW" },{" #name" :" keyword" ," $" :{" id" :" k0075" }," $$" :[{" #name" :" text" ," _" :" Cluster Weight DOR" },{" #name" :" keyword" ," $" :{" id" :" k0085" }," $$" :[{" #name" :" text" ," _" :" Diagnostic Odds Ratio DRE" },{" #name" :" keyword" ," $" :{" id" :" k0095" }," $$" :[{" #name" :" text" ," _" :" Drug-Resistant Epilepsy ESM" },{" #name" :" keyword" ," $" :{" id" :" k0105" }," $$" :[{" #name" :" text" ," _" :" Electrical cortical Stimulation Mapping FSIQ" },{" #name" :" keyword" ," $" :{" id" :" k0115" }," $$" :[{" #name" :" text" ," _" :" Full-scale Intelligence Quotient GLMM" },{" #name" :" keyword" ," $" :{" id" :" k0125" }," $$" :[{" #name" :" text" ," _" :" Generalized Linear Mixed Model GM" },{" #name" :" keyword" ," $" :{" id" :" k0135" }," $$" :[{" #name" :" text" ," _" :" Gray Matter HGM" },{" #name" :" keyword" ," $" :{" id" :" k0145" }," $$" :[{" #name" :" text" ," _" :" High-gamma Modulation MNI" },{" #name" :" keyword" ," $" :{" id" :" k0155" }," $$" :[{" #name" :" text" ," _" :" Montreal Neurological Institute MRI" },{" #name" :" keyword" ," $" :{" id" :" k0165" }," $$" :[{" #name" :" text" ," _" :" Magnetic Resonance Imaging Probability that the largest time-frequency cluster during naming response arose from the baseline (inter-trial) distribution PPV" },{" #name" :" keyword" ," $" :{" id" :" k0185" }," $$" :[{" #name" :" text" ," _" :" Positive Predictive Value ROC" },{" #name" :" keyword" ," $" :{" id" :" k0195" }," $$" :[{" #name" :" text" ," _" :" Receiver Operating Characteristic SD" },{" #name" :" keyword" ," $" :{" id" :" k0205" }," $$" :[{" #name" :" text" ," _" :" Standard Deviation SEEG" },{" #name" :" keyword" ," $" :{" id" :" k0215" }," $$" :[{" #name" :" text" ," _" :" Stereo-Electroencephalography 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 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" },{" #name" :" keyword" ," $" :{" id" :" k0245" }," $$" :[{" #name" :" text" ," _" :" Time-Frequency Representation WM" },{" #name" :" keyword" ," $" :{" id" :" k0255" }," $$" :[{" #name" :" text" ," _" :" White Matter |
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