Naming-related spectral responses predict neuropsychological outcome after epilepsy surgery.

This prospective study determined the use of intracranially recorded spectral responses during naming tasks in predicting neuropsychological performance following epilepsy surgery. We recruited 65 patients with drug-resistant focal epilepsy who underwent preoperative neuropsychological assessment and intracranial EEG recording. The Clinical Evaluation of Language Fundamentals evaluated the baseline and postoperative language function. During extra-operative intracranial EEG recording, we assigned patients to undergo auditory and picture naming tasks. Time-frequency analysis determined the spatiotemporal characteristics of naming-related amplitude modulations, including high gamma augmentation at 70-110 Hz. We surgically removed the presumed epileptogenic zone based on the intracranial EEG and MRI abnormalities while maximally preserving the eloquent areas defined by electrical stimulation mapping. The multivariate regression model incorporating auditory naming-related high gamma augmentation predicted the postoperative changes in Core Language Score with r2 of 0.37 and in Expressive Language Index with r2 of 0.32. Independently of the effects of epilepsy and neuroimaging profiles, higher high gamma augmentation at the resected language-dominant hemispheric area predicted a more severe postoperative decline in Core Language Score and Expressive Language Index. Conversely, the model incorporating picture naming-related high gamma augmentation predicted the change in Receptive Language Index with an r2 of 0.50. Higher high gamma augmentation independently predicted a more severe postoperative decline in Receptive Language Index. Ancillary regression analysis indicated that naming-related low gamma augmentation and alpha/beta attenuation likewise independently predicted a more severe Core Language Score decline. The machine learning-based prediction model suggested that naming-related high gamma augmentation, among all spectral responses used as predictors, most strongly contributed to the improved prediction of patients showing a >5-point Core Language Score decline (reflecting the lower 25th percentile among patients). We generated the model-based atlas visualizing sites, which, if resected, would lead to such a language decline. With a 5-fold cross-validation procedure, the auditory naming-based model predicted patients who had such a postoperative language decline with an accuracy of 0.80. The model indicated that virtual resection of an electrical stimulation mapping-defined language site would have increased the relative risk of the Core Language Score decline by 5.28 (95% confidence interval: 3.47-8.02). Especially, that of an electrical stimulation mapping-defined receptive language site would have maximized it to 15.90 (95% confidence interval: 9.59-26.33). In summary, naming-related spectral responses predict neuropsychological outcomes after epilepsy surgery. We have provided our prediction model as an open-source material, which will indicate the postoperative language function of future patients and facilitate external validation at tertiary epilepsy centres.

Long-term satisfaction after extraoperative invasive EEG recording.

This retrospective cohort study investigated 53 patients with drug-resistant focal epilepsy and identified factors predictive of long-term satisfaction of patients and families following extraoperative intracranial EEG (iEEG) recording. The mixed model analysis assessed the utility of intracranial EEG (iEEG) predictor variables, including the seizure-onset zone (SOZ), modulation index (MI), and naming-related high-gamma activity. Modulation index, quantifying the coupling between high-frequency activity at >80 Hz and local slow wave at 3-4 Hz, effectively functions as a surrogate marker of the burden of interictal spike-and-slow-wave discharges. The mixed model specifically incorporated 'subtraction-MI', defined as the subtraction of mean z-score normalized MI across all preserved sites from that across all resected sites. Auditory naming-related high-gamma activity at 70-110 Hz is a biomarker to characterize the underlying language and speech function. The model incorporated 'maximum resected high-gamma', defined as the high-gamma percent change largest among sites included in the resected language-dominant hemispheric region. The model also incorporated the clinical and imaging profiles of given patients. The analysis revealed that complete removal of SOZ (p = 0.003) and younger patient age (p = 0.040) were independently associated with greater satisfaction. Neither 'subtraction-MI' nor 'maximum naming-related high-gamma' showed a significant and independent association with long-term satisfaction in our patient cohort. The observed impact of complete resection of SOZ and early surgery can be considered when counseling candidates for epilepsy surgery.

Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-term Postoperative Language Improvement in Children with Drug-resistant Epilepsy.

OBJECTIVE: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modular networks (LMNs) within the preoperative whole-brain diffusion-weighted imaging connectome (wDWIC). METHODS: We employed a three-step approach. First, our previous DCNN-based tract classification to detect true-positive eloquent tracts was extended using an open-source database of high-quality wDWIC to facilitate the accurate classification of truepositive tracts within the preoperative backbone wDWIC of individual patients. Next, we applied psychometry-driven DWIC analysis to the resulting DCNN-based backbone wDWIC in order to create core, expressive, and receptive LMNs. Finally, graph and circuit theory-based connectivity markers were assessed within the three LMNs and compared using a series of machine learning algorithms to predict the presence of postoperative language improvement from a given LMN. RESULTS: The results showed that the extended DCNN tract classification significantly improved the reproducibility of connectivity markers by up to 35.5% of F-statistics across different LMNs. The prediction accuracy increased by up to 40% across different machine learning algorithms. Notably, the best algorithm achieved the accuracy of 96%/94%/96% to predict the presence of language improvement about two months after surgery in core/expressive/receptive domain of an independent validation cohort. CONCLUSION: These domains hold great potential to assist physicians in identifying candidates whose language skills stand to benefit from early surgery. SIGNIFICANCE: DCNN tract classification may be an effective tool to improve predicting short-term postoperative language improvement in pediatric epilepsy surgery.

Clinical significance and developmental changes of auditory-language-related gamma activity.

OBJECTIVE: We determined the clinical impact and developmental changes of auditory-language-related augmentation of gamma activity at 50-120 Hz recorded on electrocorticography (ECoG). METHODS: We analyzed data from 77 epileptic patients ranging 4-56 years in age. We determined the effects of seizure-onset zone, electrode location, and patient-age upon gamma-augmentation elicited by an auditory-naming task. RESULTS: Gamma-augmentation was less frequently elicited within seizure-onset sites compared to other sites. Regardless of age, gamma-augmentation most often involved the 80-100 Hz frequency band. Gamma-augmentation initially involved bilateral superior-temporal regions, followed by left-side dominant involvement in the middle-temporal, medial-temporal, inferior-frontal, dorsolateral-premotor, and medial-frontal regions and concluded with bilateral inferior-Rolandic involvement. Compared to younger patients, those older than 10 years had a larger proportion of left dorsolateral-premotor and right inferior-frontal sites showing gamma-augmentation. The incidence of a post-operative language deficit requiring speech therapy was predicted by the number of resected sites with gamma-augmentation in the superior-temporal, inferior-frontal, dorsolateral-premotor, and inferior-Rolandic regions of the left hemisphere assumed to contain essential language function (r(2) = 0.59; p = 0.001; odds ratio = 6.04 [95% confidence-interval: 2.26-16.15]). CONCLUSIONS: Auditory-language-related gamma-augmentation can provide additional information useful to localize the primary language areas. SIGNIFICANCE: These results derived from a large sample of patients support the utility of auditory-language-related gamma-augmentation in presurgical evaluation.

Multimodality language mapping in patients with left-hemispheric language dominance on Wada test.

OBJECTIVE: We determined the utility of electrocorticography (ECoG) and stimulation for detecting language-related sites in patients with left-hemispheric language-dominance on Wada test. METHODS: We studied 13 epileptic patients who underwent language mapping using event-related gamma-oscillations on ECoG and stimulation via subdural electrodes. Sites showing significant gamma-augmentation during an auditory-naming task were defined as language-related ECoG sites. Sites at which stimulation resulted in auditory perceptual changes, failure to verbalize a correct answer, or sensorimotor symptoms involving the mouth were defined as language-related stimulation sites. We determined how frequently these methods revealed language-related sites in the superior-temporal, inferior-frontal, dorsolateral-premotor, and inferior-Rolandic regions. RESULTS: Language-related sites in the superior-temporal and inferior-frontal gyri were detected by ECoG more frequently than stimulation (p < 0.05), while those in the dorsolateral-premotor and inferior-Rolandic regions were detected by both methods equally. Stimulation of language-related ECoG sites, compared to the others, more frequently elicited language symptoms (p < 0.00001). One patient developed dysphasia requiring in-patient speech therapy following resection of the dorsolateral-premotor and inferior-Rolandic regions containing language-related ECoG sites not otherwise detected by stimulation. CONCLUSIONS: Language-related gamma-oscillations may serve as an alternative biomarker of underlying language function in patients with left-hemispheric language-dominance. SIGNIFICANCE: Measurement of language-related gamma-oscillations is warranted in presurgical evaluation of epileptic patients.