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.

中文翻译：

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立体脑电图的高伽马调制语言映射：一种新的分析方法和诊断验证

目标开发了一种新的分析方法，用于立体脑电图 (SEEG) 中与任务相关的高伽马调制 (HGM)，并评估语言映射。方法 对在视觉命名任务期间从耐药性癫痫患者获得的 SEEG 信号进行分析，以在时频域中找到 50-150 Hz 功率调制的集群。开发并验证了用于识别参考神经解剖学和电刺激映射 (ESM) 语音/语言站点内的电极接触的分类器模型。结果 在 21 名患者（9 名女性）中，年龄在 4.8-21.2 岁之间，SEEG HGM 模型以高诊断比值比（DOR 10.9，p < 0.0001）、高特异性 (0.85) 和一般敏感性 (0.66) 预测了 Neurosynth 语言包内的电极位置）。另一个 SEEG HGM 模型将 ESM 语音/语言站点分类为具有显着 DOR (5.0, p < 0.0001)、高特异性 (0.74)，但敏感性不足。达到最大功率变化的时间可靠地定位了 Neurosynth 语言包内的电极，而达到质心功率变化的时间确定了 ESM 位点。结论 SEEG HGM 映射可以准确定位神经解剖学和 ESM 语言位点。意义 结合时间、频率和功率变化幅度的预测建模是与任务相关的 HGM 的有用方法，它提供了对 HGM 语言图与神经解剖学或 ESM 之间差异的见解。重心功率变化的时间确定了 ESM 站点。结论 SEEG HGM 映射可以准确定位神经解剖学和 ESM 语言位点。意义 结合时间、频率和功率变化幅度的预测建模是与任务相关的 HGM 的有用方法，它提供了对 HGM 语言图与神经解剖学或 ESM 之间差异的见解。重心功率变化的时间确定了 ESM 站点。结论 SEEG HGM 映射可以准确定位神经解剖学和 ESM 语言位点。意义 结合时间、频率和功率变化幅度的预测建模是与任务相关的 HGM 的有用方法，它提供了对 HGM 语言图与神经解剖学或 ESM 之间差异的见解。