Two stages of speech envelope tracking in human auditory cortex modulated by speech intelligibility.

The envelope is essential for speech perception. Recent studies have shown that cortical activity can track the acoustic envelope. However, whether the tracking strength reflects the extent of speech intelligibility processing remains controversial. Here, using stereo-electroencephalogram technology, we directly recorded the activity in human auditory cortex while subjects listened to either natural or noise-vocoded speech. These 2 stimuli have approximately identical envelopes, but the noise-vocoded speech does not have speech intelligibility. According to the tracking lags, we revealed 2 stages of envelope tracking: an early high-γ (60-140 Hz) power stage that preferred the noise-vocoded speech and a late θ (4-8 Hz) phase stage that preferred the natural speech. Furthermore, the decoding performance of high-γ power was better in primary auditory cortex than in nonprimary auditory cortex, consistent with its short tracking delay, while θ phase showed better decoding performance in right auditory cortex. In addition, high-γ responses with sustained temporal profiles in nonprimary auditory cortex were dominant in both envelope tracking and decoding. In sum, we suggested a functional dissociation between high-γ power and θ phase: the former reflects fast and automatic processing of brief acoustic features, while the latter correlates to slow build-up processing facilitated by speech intelligibility.

Temporal order of signal propagation within and across intrinsic brain networks.

We studied the temporal dynamics of activity within and across functional MRI (fMRI)-derived nodes of intrinsic resting-state networks of the human brain using intracranial electroencephalography (iEEG) and repeated single-pulse electrical stimulation (SPES) in neurosurgical subjects implanted with intracranial electrodes. We stimulated and recorded from 2,133 and 2,372 sites, respectively, in 29 subjects. We found that N1 and N2 segments of the evoked responses are associated with intra- and internetwork communications, respectively. In a separate cognitive experiment, evoked electrophysiological responses to visual target stimuli occurred with less temporal separation across pairs of electrodes that were located within the same fMRI-defined resting-state networks compared with those located across different resting-state networks. Our results suggest intranetwork prior to internetwork information processing at the subsecond timescale.

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown. OBJECTIVE: We aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome. We also expected that the model could provide interpretable factors and estimate the uncertainty of the model outputs at a customized confidence level. METHODS: In this retrospective study, 9135 patients with sepsis requiring vasopressor treatment within 24 hours after sepsis onset were enrolled from Beth Israel Deaconess Medical Center. This cohort was used for model development, and 10-fold cross-validation with 50 repeats was used for internal validation. In total, 3743 patients with sepsis from the eICU Collaborative Research Database were used as the external validation cohort. All included patients with sepsis were stratified based on disease progression trajectories: rapid death, recovery, and persistent ill. A total of 148 variables were selected for predicting the 3 trajectories. Four machine learning algorithms with 3 different setups were used. We estimated the uncertainty of the model outputs using conformal prediction (CP). The Shapley Additive Explanations method was used to explain the model. RESULTS: The multiclass gradient boosting machine was identified as the best-performing model with good discrimination and calibration performance in both validation cohorts. The mean area under the receiver operating characteristic curve with SD was 0.906 (0.018) for rapid death, 0.843 (0.008) for recovery, and 0.807 (0.010) for persistent ill in the internal validation cohort. In the external validation cohort, the mean area under the receiver operating characteristic curve (SD) was 0.878 (0.003) for rapid death, 0.764 (0.008) for recovery, and 0.696 (0.007) for persistent ill. The maximum norepinephrine equivalence, total urine output, Acute Physiology Score III, mean systolic blood pressure, and the coefficient of variation of oxygen saturation contributed the most. Compared to the model without CP, using the model with CP at a mixed confidence approach reduced overall prediction errors by 27.6% (n=62) and 30.7% (n=412) in the internal and external validation cohorts, respectively, as well as enabled the identification of more potentially persistent ill patients. CONCLUSIONS: The implementation of our model has the potential to reduce heterogeneity and enroll more homogeneous patients in sepsis clinical trials. The use of CP for estimating the uncertainty of the model outputs allows for a more comprehensive understanding of the model's reliability and assists in making informed decisions based on the predicted outcomes.

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. OBJECTIVE: This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. METHODS: We retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. RESULTS: A total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. CONCLUSIONS: By combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making.

Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Does seizure propagate within or across intrinsic brain networks? An intracranial EEG study.

BACKGROUND: Understanding the spatiotemporal propagation profiles of seizures is crucial for the preoperative assessment of epilepsy patients. The present study aimed to investigate whether seizures exhibit propagation patterns that align with intrinsic networks (INs). METHODS: A quantitative analysis was conducted to examine ictal fast activity (IFA). The Epileptogenicity Index (EI) was employed to assess the epileptogenicity, spectral features, and temporal characteristics of IFA. Intra-network and inter-network comparisons were made regarding the IFA-related metrics. Additionally, the metrics were correlated with Euclidean distance. Network connection maps were generated to visualize seizures originating from different INs, allowing for comparisons between distinct groups. RESULTS: Data for 81 seizures in 43 subjects were captured using stereoelectroencephalography implantation. Three metrics were compared: EI, time involvement (TI), and energy ratio index (ERI). Intra-network channels exhibited higher EI, earlier involvement of IFA, and stronger high-frequency energy. These findings were further validated through subgroup analyses stratified by neuropathology, seizure type, and seizure origination lobe. Correlation analyses revealed a negative association between distance and both EI and ERI, while distance exhibited a positive correlation with TI. Seizures originating from different INs exhibited varying propagation characteristics. CONCLUSIONS: The study findings highlight the dominant role of intra-network dynamics over inter-network during seizure propagation. These results contribute to our understanding of seizure dynamics and their relationship with INs.

Interictal HFO and FDG-PET correlation predicts surgical outcome following SEEG.

OBJECTIVE: This study aimed to investigate the quantitative relationship between interictal 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and interictal high-frequency oscillations (HFOs) from stereo-electroencephalography (SEEG) recordings in patients with refractory epilepsy. METHODS: We retrospectively included 32 patients. FDG-PET data were quantified through statistical parametric mapping (SPM) t test modeling with normal controls. Interictal SEEG segments with four, 10-min segments were selected randomly. HFO detection and classification procedures were automatically performed. Channel-based HFOs separating ripple (80-250 Hz) and fast ripple (FR; 250-500 Hz) counts were correlated with the surrounding metabolism T score at the individual and group level, respectively. The association was further validated across anatomic seizure origins and sleep vs wake states. We built a joint feature FR × T reflecting the FR and hypometabolism concordance to predict surgical outcomes in 28 patients who underwent surgery. RESULTS: We found a negative correlation between interictal FDG-PET and HFOs through the linear mixed-effects model (R2  = .346 and .457 for ripples and FRs, respectively, p < .001); these correlations were generalizable to different epileptogenic-zone lobar localizations and vigilance states. The FR × T inside the resection volume could be used as a predictor for surgical outcomes with an area under the curve of 0.81. SIGNIFICANCE: The degree of hypometabolism is associated with HFO generation rate, especially for FRs. This relationship would be meaningful for selection of SEEG candidates and for optimizing SEEG scheme planning. The concordance between FRs and hypometabolism inside the resection volume could provide prognostic information regarding surgical outcome.

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Identification of indications for albumin administration in septic patients with liver cirrhosis.

BACKGROUND: Albumin infusion is the primary therapeutic strategy for septic patients with liver cirrhosis. Although recent studies have investigated the efficacy of albumin in the resuscitation stage of septic patients with liver cirrhosis, it remains unclear whether daily albumin administration can improve outcomes. Furthermore, the indications for initiating albumin therapy are not well defined. METHODS: Septic patients with liver cirrhosis were obtained from the Medical Information Mart for Intensive Care (MIMIC-IV 2.0) database. Marginal structural Cox models were employed to investigate the association between daily albumin infusion and 28-day mortality. We also aimed to explore under what circumstances enrolled patients could benefit most from albumin administration, based on the clinical parameters collected on the day of albumin infusion, including serum albumin concentration, serum lactate concentration, mean arterial pressure (MAP), and vasopressor dosage. RESULTS: A total of 2265 patients were included in the final analysis, of whom 1093 (48.3%) had received albumin treatment at least once. The overall 28-day mortality was 29.6%. After marginal structural modeling, daily albumin infusion was associated with a reduced risk of 28-day death (hazard ratio, 0.76; 95% CI 0.61-0.94). We found that patients benefit most from albumin infusion when initiated on the day of serum albumin concentration between 2.5 and 3.0 g/dL, serum lactate concentration greater than or equal to 2 mmol/L, MAP less than 60 mmHg, or vasopressor dosage between 0.2 and 0.3 mcg/kg/min (norepinephrine equivalent, NEE). CONCLUSIONS: Albumin infusion is associated with a reduction in mortality in septic patients with liver cirrhosis under specific circumstances. Serum albumin concentration, serum lactate, MAP, and vasopressor dosage were found to be modifiers of treatment effectiveness and should be considered when deciding to initial albumin infusion.

Intracranial Electroencephalography Reveals Selective Responses to Cognitive Stimuli in the Periventricular Heterotopias.

Our recent work suggests that non-lesional epileptic brain tissue is capable of generating normal neurophysiological responses during cognitive tasks, which are then seized by ongoing pathologic epileptic activity. Here, we aim to extend the scope of our work to epileptic periventricular heterotopias (PVH) and examine whether the PVH tissue also exhibits normal neurophysiological responses and network-level integration with other non-lesional cortical regions. As part of routine clinical assessment, three adult patients with PVH underwent implantation of intracranial electrodes and participated in experimental cognitive tasks. We obtained simultaneous recordings from PVH and remote cortical sites during rest as well as controlled experimental conditions. In all three subjects (two females), cognitive experimental conditions evoked significant electrophysiological responses in discrete locations within the PVH tissue that were correlated with responses seen in non-epileptic cortical sites. Moreover, the responsive PVH sites exhibited correlated electrophysiological activity with responsive, non-lesional cortical sites during rest conditions. Taken together, our work clearly demonstrates that the PVH tissue may be functionally organized and it may be functionally integrated within cognitively engaged cortical networks despite its anatomic displacement during neurodevelopment.SIGNIFICANCE STATEMENT Periventricular heterotopias (PVH) are developmentally abnormal brain tissues that frequently cause epileptic seizures. In a rare opportunity to obtain direct electrophysiological recordings from PVH, we were able to show that, contrary to common assumptions, PVH functional activity is similar to healthy cortical sites during a well-established cognitive task and exhibits clear resting state connectivity with the responsive cortical regions.

Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study.

OBJECTIVE: Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. METHODS: The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. RESULTS: FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. SIGNIFICANCE: FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy.

Whole-brain metabolic pattern analysis in patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis.

BACKGROUND AND PURPOSE: Faciobrachial dystonic seizures (FBDS) and hyponatremia are the distinct clinical features of autoimmune encephalitis (AE) caused by antibodies against leucine-rich glioma-inactivated 1 (LGI1). The present study aims to explore the pathophysiological patterns and neural mechanisms underlying these symptoms. METHODS: We included 30 patients with anti-LGI1 AE and 30 controls from a retrospective observational cohort. Whole-brain metabolic pattern analysis was performed to assess the pathological network of anti-LGI1 AE, as well as the symptom networks associated with FBDS. Logistic regression was applied to explore independent predictors of FBDS. Finally, we used a multiple regression model to investigate the hyponatremia-associated brain network and its effect on serum sodium levels. RESULTS: The pathological network of anti-LGI1 AE involved hypermetabolism in the cerebellum, subcortical structures and Rolandic area, as well as hypometabolism in the medial prefrontal cortex. The symptom network of FBDS included hypometabolism in the cerebellum and Rolandic area (pFDR <0.05). Hypometabolism in the cerebellum was an independent predictor of FBDS (p < 0.001). Hyponatremia-associated network highlighted a negative effect on the caudate nucleus, frontal and temporal white matter. The metabolism of the hypothalamus was negatively associated with (Pearson's R = -0.180, p = 0.342), while not the independent predictor for serum sodium level (path c' = -7.238, 95% confidence interval = -30.947 to 16.472). CONCLUSIONS: Our results provide insights into the whole-brain metabolic patterns of patients with anti-LGI1 AE, including the symptom network associated with FBDS and the hyponatremia-associated brain network. The findings help us to understand the neural mechanisms underlying anti-LGI1 AE and to evaluate the progress of this disease.

Neural underpinnings of default mode network on empathy revealed by intracranial stereoelectroencephalography.

BACKGROUND: Empathy is the ability to understand and share the feelings of others. It is fundamental to emotional intelligence and social iterations. Neuroimaging studies have demonstrated that empathy activates brain regions associated with the social cognition network. AIM: To explore the neural underpinnings of empathy revealed by stereoelectroencephalography utilizing recurrence quantification analysis (RQA). METHODS: This retrospective cohort included 38 epilepsy patients with stereoelectroencephalography implantation. RQA metrics were applied to parameterize the network organization of default mode network (DMN) brain regions. The relationships between DMN, seizure burden activity, and empathy, as measured using the Interpersonal Reactivity Index, were examined using partial least-square regression and mediation analysis. RESULTS: RQA metrics with DMN (R2  = 0.75, PBonferroni  < 0.001) and its subsystems (medial temporal subsystem: R2  = 0.53, PBonferroni  < 0.001; core subsystem: R2  = 0.70, PBonferroni  < 0.001; dorsal medial subsystem: R2  = 0.48, PBonferroni  < 0.001) were positively correlated with empathy scores. Of 13 RQA metrics, the mean diagonal line length, entropy of the diagonal line lengths, trapping time, maximal vertical line length, and recurrence time of second type were found to be statistically higher in patient cohorts with reportedly high empathy. Furthermore, DMN characteristics (b path: F = 3.69, P = 0.04), rather than seizure burdens (direct effect: t = 0.33, P = 0.74, c' = - 0.007), mediated empathy status. CONCLUSION: The present study used various RQA metrics to parameterize the network organization of DMN and determine the neural underpinning of DMN for empathy modulation.

Automatic analysis of integrated magnetic resonance and positron emission tomography images improves the accuracy of detection of focal cortical dysplasia type IIb lesions.

We aimed to develop an efficient and objective pre-evaluation method to identify the precise location of a focal cortical dysplasia lesion before surgical resection to reduce medication use and decrease the post-operative frequency of seizure attacks. We developed a novel machine learning-based approach using cortical surface-based features by integrating MRI and metabolic PET to identify focal cortical dysplasia lesions. Significant surface-based features of 22 patients with histopathologically proven FCD IIb lesions were extracted from PET and MRI images using FreeSurfer. We modified significant parameters, trained and tested the XGBoost model using these surface-based features, and made predictions. We detected lesions in all 20 patients using the XGBoost model, with an accuracy of 91%. We used one-way chi-squared test to test the null hypothesis that the population proportion was 50% (p = 0.0001), indicating that our classification of the algorithm was statistically significant. The sensitivity, specificity, and false-positive rates were 93%, 91%, and 9%, respectively. We developed an objective, quantitative XGBoost classifier that combined MRI and PET imaging features to locate focal cortical dysplasia. This automated method yielded better outcomes than conventional visual analysis and single modality quantitative analysis for surgical pre-evaluation, especially in subtle or visually unidentifiable FCD lesions. This time-efficient method would also help doctors identify otherwise overlooked details.

Neuroimaging Phenotyping and Assessment of Structural-Metabolic-Electrophysiological Alterations in the Temporal Neocortex of Focal Cortical Dysplasia IIIa.

BACKGROUND: Focal cortical dysplasia IIIa (FCD IIIa) is a common histopathological finding in temporal lobe epilepsy. However, subtle alterations in the temporal neocortex of FCD IIIa renders presurgical diagnosis and definition of the resective range challenging. PURPOSE: To explore neuroimaging phenotyping and structural-metabolic-electrophysiological alterations in FCD IIIa. STUDY TYPE: Retrospective. SUBJECTS: One hundred and sixty-seven subjects aged 4-39 years, including 64 FCD IIIa patients, 89 healthy controls and 14 FCD I patients as disease controls. FIELD STRENGTH/SEQUENCE: 3 T, fast-spin-echo T2 -weighted fluid-attenuated inversion recovery (FLAIR), synthetic T1 -weighted magnetization prepared rapid acquisition gradient echo (MPRAGE). ASSESSMENT: Surface-based linear model was applied to reveal neuroimaging phenotyping in FCD IIIa and assess its relationship with clinical variables. Logistic regression was implemented to identify FCD IIIa patients. Epileptogenicity mapping (EM) was conducted to explore the structural-metabolic-electrophysiological alterations in temporal neocortex of FCD IIIa. STATISTICAL TESTS: Student's t-test was applied to determine the significance of paired differences. Calibration curves were plotted to assess the goodness-of-fit (GOF) of the models, combined with the Hosmer-Lemeshow test. RESULTS: FCD IIIa exhibited widespread hyperintensities in temporal neocortex, and these alterations correlated with disease duration (Puncorrected < 0.01). Machine learning model accurately identified 84.4% of FCD IIIa patients, 92.1% of healthy controls and 92.9% of FCD I patients. Cross-modality analysis showed a significant negative correlation between FLAIR hyperintensity and positron emission tomography hypometabolism P < 0.01). Furthermore, epileptogenic cortices were located predominantly in brain regions with FLAIR hyperintensity and hypometabolism. DATA CONCLUSION: FCD IIIa exhibited widespread temporal neocortex FLAIR hyperintensity. Automated machine learning of neuroimaging patterns is conducive for accurate identification of FCD IIIa. The degree and distribution of morphological alterations related to the extent of metabolic and epileptogenic abnormalities, lending support to its potential value for reduction of the radiative and invasive approaches during presurgical workup. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

The Rolandic operculum generates different semiologies in insulo-opercular and temporal lobe epilepsies.

PURPOSE: The role of the Rolandic operculum in in mesial temporal lobe epilepsy (MTLE) is to produce oroalimentary automatisms (OAAs). In insulo-opercular epilepsy (IOE), the Rolandic operculum may produce perioral muscle clonic or tonic movements or contractions. This paper aims to confirm the symptomatogenic zone of facial symptoms in IOE and to explain this phenomenon. METHODS: A total of 45 IOE patients and 15 MTLE patients were analyzed. The patients with IOE were divided into facial (+) and (-) groups according to the facial symptoms. The interictal positron emission tomography (PET) data were compared among groups. Furthermore, electroclinical correlation, functional connectivity and energy ratio (ER) were analyzed with stereo-electroencephalography (SEEG). RESULTS: Intergroup PET differences were observed mainly in the Rolandic operculum. Electroclinical correlation showed that the Rolandic operculum was the only brain area showing any correlations. Compared with the facial (-) group, the facial (+) group showed stronger functional connectivity and a higher ER in the alpha 1, alpha 2 and beta sub-bands. In the Rolandic operculum, compared with those of the MTLE group, the h2 and ER of the facial (+) group were higher in the high frequency sub-bands. Intergroup comparison of the ER in the seizure onset zones (SOZ) showed no significant difference. SIGNIFICANCE: The symptomatogenic zone of facial symptoms in IOE is the Rolandic operculum. Seizure propagation to the Rolandic operculum generates different semiologies because of the different synchronization frequencies and energies of the sub-bands depending on the site of seizure origin. This may be due to the complex spreading pathway from the SOZ to the symptomatogenic zone.

Stereoelectroencephalography-guided radiofrequency thermocoagulation for hypothalamic hamartoma: Electroclinical patterns and the relationship with surgical prognosis.

BACKGROUND: Radiofrequency thermocoagulation (RFTC) guided by stereo-electroencephalography (SEEG) has been proved to be a safe method to reduce seizure frequency in patients with drug-resistant epilepsy. However, there are few reports addressing the value and safety of this procedure in hypothalamic hamartoma (HH). OBJECTIVE: To present the results of our experience using SEEG-guided RFTC in HH patients with drug-resistant epilepsy, and identify outcome predictors. METHODS: We retrospectively reviewed the clinical and surgical characteristics of 27 HH-related patients with epilepsy in our center between 2015 and 2019. All patients underwent invasive recordings with SEEG before RFTC was performed. We reported surgical outcome predictors and postoperative follow-up concerning safety and efficacy (mean follow-up, 27.3 months; range, 12-63). Surgical strategy was also analyzed. RESULTS: Nineteen patients (70.4%) achieved Engel's class I outcome, while 4 patients (14.8%) did not show significant improvement. Of all observed seizures, two different onset patterns of intracranial electrophysiology recorded by SEEG were observed. Patients presented with focal low-voltage fast activity were more likely to obtain seizure freedom (p = 0.045), while classification (p = 0.478), volume (p = 0.546), history of resection (p = 0.713), seizure types (p = 0.859), or seizure duration (p = 0.415) showed no significant effect on the outcome. Weight gain was the most common long-term complication (18.5%). CONCLUSION: The SEEG can guide the ablation of HH and serve as an important factor to predict favorable seizure outcomes. Radiofrequency thermocoagulation guided by SEEG can offer a minimally invasive and low-risk surgical approach with excellent outcomes. Disconnecting the attachment of HH should be the appropriate strategy to obtain the best seizure outcome.

Neural networks underlying hyperkinetic seizures: A quantitative PET and SEEG study.

OBJECTIVE: Hyperkinetic seizures (HKS) are characterized by complex movements that commonly occur during seizures arising from diverse cortical structures. A common semiology network may exist and analyzing the anatomo-electrical mechanisms would facilitate presurgical evaluation. Here, quantitative positron emission tomography (PET) and stereoelectroencephalography (SEEG) analysis was used to explore the underlying mechanism of HKS. METHODS: We retrospectively collected patients with epilepsy with HKS between 2014 and 2019. The interictal PET data of patients with epilepsy with HKS were compared with those of 25 healthy subjects using statistical parametric mapping to identify regions with significant hypometabolism. Then, regions of interest (ROI) for SEEG analysis were identified based on the results of PET analysis. Patients in which the ROIs were covered by intracerebral electrodes were selected for further analysis. Stereoelectroencephalography -clinical correlations with latency measurements were analyzed, and we also performed coherence analysis among ROIs both before and during HKS. RESULTS: Based on the inclusion criteria, 27 patients were analyzed. In the PET analysis, significant hypometabolism was observed in the ipsilateral dorsoanterior insular lobe, bilateral mesial frontal lobes (supplementary motor area/middle cingulate cortex, SMA/MCC), and the bilateral heads of the caudate nuclei in patients with HKS compared with the control group (p < 0.001). We selected dorsoanterior insula and SMA/MCC as ROIs for SEEG analysis. Eight patients with 23 HKS events were selected for further analysis. There was a linear correlation between the ictal involvement of both the dorsoanterior insula and SMA/MCC with the onset of HKS. Stereoelectroencephalography analysis indicated alpha range activity seemed more often associated with dorsoanterior insula and SMA/MCC involvement during HKS. CONCLUSIONS: The dorsoanterior insular lobe, mesial frontal lobes (SMA/MCC), and the bilateral heads of the caudate nuclei were probably involved in the generation of HKS. The SEEG analysis further indicated that the occurrence of HKS might be partly associated with synchronized rhythmical alpha activity between dorsoanterior insula and SMA/MCC.

The electroclinical features and surgical outcomes of inferior perisylvian epilepsy.

OBJECTIVE: To summarize the clinical and electrophysiological observations of epilepsy originating from the inferior perisylvian cortex, and analyze the potential epileptic networks underlying the semiological manifestations. METHODS: We retrospectively analyzed patients with refractory inferior perisylvian epilepsy (IPE) who had undergone resective surgery, and then reviewed the demographic, clinical, neuroelectrophysiological, neuroimaging, surgical, histopathological, and follow-up data of the patients from the respective medical records. The selected patients were then categorized in accordance with the results of semiological analysis. Quantitative 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) analysis was performed to investigate the underlying neural network. RESULTS: Of the 18 IPE patients assessed in this study, ipsilateral frontotemporal epileptic discharges or its onsets were the dominant interictal or ictal scalp EEG observations. In addition, oroalimentary or manual automatism was the most frequently documented manifestation, followed by facial tonic or clonic movements. Moreover, the semiological analysis identified and classified the patients into 2 patterns, and the PET statistical analyses conducted on these 2 groups revealed differences in the neural network between them. CONCLUSION: Inferior perisylvian epilepsy possesses semiological manifestations similar to those of mesial temporal lobe epilepsy or rolandic opercular epilepsy, hence these conditions should be carefully differentiated. Performing lesionectomy or cortectomy, sparing the mesial temporal structures, was found to be an effective and safe treatment modality for IPE.

Effective connectivity among the hippocampus, amygdala, and temporal neocortex in epilepsy patients: A cortico-cortical evoked potential study.

OBJECTIVE: Mesial temporal lobe epilepsy (MTLE) is one of the most common types of intractable epilepsy. The hippocampus and amygdala are two crucial structures of the mesial temporal lobe and play important roles in the epileptogenic network of MTLE. This study aimed to explore the effective connectivity among the hippocampus, amygdala, and temporal neocortex and to determine whether differences in effective connectivity exist between MTLE patients and non-MTLE patients. METHODS: This study recruited 20 patients from a large cohort of drug-resistant epilepsy patients, of whom 14 were MTLE patients. Single-pulse electrical stimulation (SPES) was performed to acquire cortico-cortical evoked potentials (CCEPs). The root mean square (RMS) was used as the metric of the magnitude of CCEP to represent the effective connectivity. We then conducted paired and independent sample t-tests to assess the directionality of the effective connectivity. RESULTS: In both MTLE patients and non-MTLE patients, the directional connectivity from the amygdala to the hippocampus was stronger than that from the hippocampus to the amygdala (P < 0.01); the outward connectivity from the amygdala to the cortex was stronger than the inward connectivity from the cortex to the amygdala (P < 0.01); the amygdala had stronger connectivity to the neocortex than the hippocampus (P < 0.01). In MTLE patients, the neocortex had stronger connectivity to the hippocampus than to the amygdala (P < 0.01). No significant differences in directional connectivity were noted between the two groups. CONCLUSIONS: A unique effective connectivity pattern among the hippocampus, amygdala, and temporal neocortex was identified through CCEPs analysis. This study may aid in our understanding of physiological and pathological networks in the brain and inspire neurostimulation protocols for neurological and psychiatric disorders.