Abstract
Statistical parametric mapping (SPM) is a technique with which one can delineate brain activity statistically deviated from the normative mean, and has been commonly employed in noninvasive neuroimaging and EEG studies. Using the concept of SPM, we developed a novel technique for quantification of the statistical deviation of an intracranial electrocorticography (ECoG) measure from the nonepileptic mean. We validated this technique using data previously collected from 123 patients with drug-resistant epilepsy who underwent resective epilepsy surgery. We determined how the measurement of statistical deviation of modulation index (MI) from the non-epileptic mean (rated by z-score) improved the performance of seizure outcome classification model solely based on conventional clinical, seizure onset zone (SOZ), and neuroimaging variables. Here, MI is a summary measure quantifying the strength of in-situ coupling between high-frequency activity at >150 Hz and slow wave at 3–4 Hz. We initially generated a normative MI atlas showing the mean and standard deviation of slow-wave sleep MI of neighboring non-epileptic channels of 47 patients, whose ECoG sampling involved all four lobes. We then calculated ‘MI z-score’ at each electrode site. SOZ had a greater ‘MI z-score’ compared to non-SOZ in the remaining 76 patients. Subsequent multivariate logistic regression analysis and receiver operating characteristic analysis to the combined data of all patients revealed that the full regression model incorporating all predictor variables, including SOZ and ‘MI z-score’, best classified the seizure outcome with sensitivity/specificity of 0.86/0.76. The model excluding ‘MI z-score’ worsened its sensitivity/specificity to 0.86/0.48. Furthermore, the leave-one-out analysis successfully cross-validated the full regression model. Measurement of statistical deviation of MI from the non-epileptic mean on invasive recording is technically feasible. Our analytical technique can be used to evaluate the utility of ECoG biomarkers in epilepsy presurgical evaluation.
Highlights
Statistical parametric mapping (SPM) is a technique with which one can delineate brain activity statistically deviated from the normative mean, and has been commonly employed in noninvasive neuroimaging and EEG studies
Based on the observations that interictal spike-and-wave discharges are accompanied by HFA > 150 Hz coupled with local slow wave in a stereotypical manner, we suggest that the modulation index (MI), quantifying the phase-amplitude coupling between interictal HFA > 150 Hz and phase of slow wave at 3–4 Hz, would be an excellent surrogate marker of the irritative zone[22,23]
We excluded patients if (a) the epileptogenic zone was determined to be present independently in both hemispheres based on the non-invasive evaluation, (b) they needed hemispherectomy or hemispherotomy, (c) extensive brain malformations distorting major anatomical landmarks prevented analysis on the FreeSurfer average brain[31], (d) postoperative follow-up was shorter than 12 months, (e) prior resective epilepsy surgery was done, or (f) age was
Summary
Statistical parametric mapping (SPM) is a technique with which one can delineate brain activity statistically deviated from the normative mean, and has been commonly employed in noninvasive neuroimaging and EEG studies. We determined how the measurement of statistical deviation of modulation index (MI) from the non-epileptic mean (rated by z-score) improved the performance of seizure outcome classification model solely based on conventional clinical, seizure onset zone (SOZ), and neuroimaging variables. Suboptimal outcome prediction by HFA rate measures was partly attributed to the notion that HFA is generated by non-epileptic recording sites, defined as those not involved in the SOZ, interictal spike discharges, or epileptogenic lesions[19,20]. We determined whether the statistical deviation of MI from the non-epileptic mean (rated by z-score of MI [‘MI z-score’]) would accurately classify the SOZ responsible for the generation of habitual seizures in a different patient cohort. We expected that measurement of statistical deviation of MI from the non-epileptic mean (i.e., normative mean) would be technically feasible, partly because MI is a continuous variable whereas the occurrence rate of HFA or interictal epileptiform activity is a discrete one
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