High-gamma modulation language mapping with stereo-EEG: A novel analytic approach and diagnostic validation

Area of Science:

• Neuroscience
• Neurology
• Medical Imaging

Background:

• Accurate language mapping is crucial for neurosurgical planning.
• Stereo-electroencephalography (SEEG) offers high spatial resolution for brain activity.
• Task-related high-gamma modulation (HGM) reflects neural activity during cognitive tasks.

Purpose of the Study:

• To develop and evaluate a novel analytic approach for task-related HGM in SEEG for language mapping.
• To assess the accuracy of SEEG HGM in localizing neuroanatomic and electrical stimulation mapping (ESM) language sites.

Main Methods:

• SEEG signals from epilepsy patients performing a visual naming task were analyzed.
• Time-frequency analysis identified clusters of 50-150 Hz power modulations.
• Classifier models were developed to predict electrode contacts within neuroanatomy and ESM speech/language sites.

Main Results:

• The SEEG HGM model accurately predicted electrode locations within Neurosynth language parcels (DOR 10.9, p < 0.0001).
• The model showed high specificity (0.85) and fair sensitivity (0.66) for neuroanatomic mapping.
• Another model classified ESM speech/language sites with significant DOR (5.0, p < 0.0001) and high specificity (0.74).

Conclusions:

• SEEG HGM mapping provides an accurate method for localizing both neuroanatomic and ESM language sites.
• Predictive modeling of HGM offers insights into discrepancies between language maps and established localization methods.