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Objective Methods Results Significance Intracranial EEG can identify epilepsy‐related networks in patients with focal epilepsy; however, the association between network organization and post‐surgical seizure outcomes remains unclear. Hubness serves as a critical metric to assess network organization by identifying brain regions that are highly influential to other regions. In this study, we tested the hypothesis that favorable post‐operative seizure outcomes are associated with the surgical removal of interictal network hubs, measured by the novel metric “Resection‐Hub Alignment Degree (RHAD).”We analyzed Phase II interictal intracranial EEG from 69 patients with epilepsy who were seizure‐free (n = 45) and non–seizure‐free (n = 24) 1 year post‐operatively. Connectivity matrices were constructed from intracranial EEG recordings using imaginary coherence in various frequency bands, and centrality metrics were applied to identify network hubs. The RHAD metric quantified the congruence between hubs and resected/ablated areas. We used a logistic regression model, incorporating other clinical factors, and evaluated the association of this alignment regarding post‐surgical seizure outcomes.There was a significant difference in RHAD in fast gamma (80–200 Hz) interictal network between patients with favorable and unfavorable surgical outcomes (p = .025). This finding remained similar across network definitions (i.e., channel‐based or region‐based network) and centrality measurements (Eigenvector, Closeness, and PageRank). The alignment between surgically removed areas and other commonly used clinical quantitative measures (seizure‐onset zone, irritative zone, high‐frequency oscillations zone) did not reveal significant differences in post‐operative outcomes. This finding suggests that the hubness measurement may offer better predictive performance and finer‐grained network analysis. In addition, the RHAD metric showed explanatory validity both alone (area under the curve [AUC] = .66) and in combination with surgical therapy type (resection vs ablation, AUC = .71).Our findings underscore the role of network hub surgical removal, measured through the RHAD metric of interictal intracranial EEG high gamma networks, in enhancing our understanding of seizure outcomes in epilepsy surgery. [ABSTRACT FROM AUTHOR]

Objective: Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug‐resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms. Methods: We created iEEG‐recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG‐recon in a containerized format that allows integration into clinical workflows. We propose a cloud‐based implementation of iEEG‐recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts. Results: We used iEEG‐recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30‐min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG‐recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre‐ and postimplant T1‐MRI visual inspections. We also found that our use of ANTsPyNet deep learning‐based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations. Significance: iEEG‐recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG‐recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide. [ABSTRACT FROM AUTHOR]

Background: Major depressive disorder (MDD) is a prevalent mental disorder characterized by impairments in affect, behaviour and cognition. Previous studies have indicated that the anterior cingulate cortex (ACC) may play an essential role in the pathophysiology of depression. In this study, we systematically identified changes in functional connectivity (FC) for ACC subdivisions that manifest in MDD and further investigated the relationship between these changes and the clinical symptoms of depression. Methods: Sub-regional ACC FC was estimated in 41 first-episode medication-naïve MDD patients compared to 43 healthy controls. The relationships between depressive symptom severity and aberrant FC of ACC subdivisions were investigated. In addition, we conducted a meta-analysis to generate the distributions of MDD-related abnormal regions from previously reported results and compared them to FC deficits revealed in this study. Results: In MDD patients, the subgenual and perigenual ACC demonstrated decreased FC with the posterior regions of the default network (DN), including the posterior inferior parietal lobule and posterior cingulate cortex. FC of these regions was negatively associated with the Automatic Thoughts Questionnaire scores and largely overlapped with previously reported abnormal regions. In addition, reduced FC between the caudal ACC and precuneus was negatively correlated with the Hamilton Anxiety Scale scores. We also found increased FC between the rostral ACC and dorsal medial prefrontal cortex. Conclusions: Our findings confirmed that functional interaction changes in different ACC sub-regions are specific and associated with distinct symptoms of depression. Our findings provide new insights into the role of ACC sub-regions and DN in the pathophysiology of MDD. [ABSTRACT FROM AUTHOR]

Abnormal phase-amplitude coupling between β and broadband-γ activities has been identified in recordings from the cortex or scalp of patients with Parkinson's disease. While enhanced phase-amplitude coupling has been proposed as a biomarker of Parkinson's disease, the neuronal mechanisms underlying the abnormal coupling and its relationship to motor impairments in Parkinson's disease remain unclear. To address these issues, we performed an in-depth analysis of high-density EEG recordings at rest in 19 patients with Parkinson's disease and 20 age- and sex-matched healthy control subjects. EEG signals were projected onto the individual cortical surfaces using source reconstruction techniques and separated into spatiotemporal components using independent component analysis. Compared to healthy controls, phase-amplitude coupling of Parkinson's disease patients was enhanced in dorsolateral prefrontal cortex, premotor cortex, primary motor cortex and somatosensory cortex, the difference being statistically significant in the hemisphere contralateral to the clinically more affected side. β and γ signals involved in generating abnormal phase-amplitude coupling were not strictly phase-phase coupled, ruling out that phase-amplitude coupling merely reflects the abnormal activity of a single oscillator in a recurrent network. We found important differences for couplings between the β and γ signals from identical components as opposed to those from different components (originating from distinct spatial locations). While both couplings were abnormally enhanced in patients, only the latter were correlated with clinical motor severity as indexed by part III of the Movement Disorder Society Unified Parkinson's Disease Rating Scale. Correlations with parkinsonian motor symptoms of such inter-component couplings were found in premotor, primary motor and somatosensory cortex, but not in dorsolateral prefrontal cortex, suggesting motor domain specificity. The topography of phase-amplitude coupling demonstrated profound differences in patients compared to controls. These findings suggest, first, that enhanced phase-amplitude coupling in Parkinson's disease patients originates from the coupling between distinct neural networks in several brain regions involved in motor control. Because these regions included the somatosensory cortex, abnormal phase-amplitude coupling is not exclusively tied to the hyperdirect tract connecting cortical regions monosynaptically with the subthalamic nucleus. Second, only the coupling between β and γ signals from different components appears to have pathophysiological significance, suggesting that therapeutic approaches breaking the abnormal lateral coupling between neuronal circuits may be more promising than targeting phase-amplitude coupling per se. [ABSTRACT FROM AUTHOR]

Background and Objectives: Postoperative seizure control in drug-resistant temporal lobe epilepsy (TLE) remains variable, and the causes for this variability are not well understood. One contributing factor could be the extensive spread of synchronized ictal activity across networks. Our study used novel quantifiable assessments from intracranial EEG (iEEG) to test this hypothesis and investigated how the spread of seizures is determined by underlying structural network topological properties., Methods: We evaluated iEEG data from 157 seizures in 27 patients with TLE: 100 seizures from 17 patients with postoperative seizure control (Engel score I) vs 57 seizures from 10 patients with unfavorable surgical outcomes (Engel score II-IV). We introduced a quantifiable method to measure seizure power dynamics within anatomical regions, refining existing seizure imaging frameworks and minimizing reliance on subjective human decision-making. Time-frequency power representations were obtained in 6 frequency bands ranging from theta to gamma. Ictal power spectrums were normalized against a baseline clip taken at least 6 hours away from ictal events. Electrodes' time-frequency power spectrums were then mapped onto individual T1-weighted MRIs and grouped based on a standard brain atlas. We compared spatiotemporal dynamics for seizures between groups with favorable and unfavorable surgical outcomes. This comparison included examining the range of activated brain regions and the spreading rate of ictal activities. We then evaluated whether regional iEEG power values were a function of fractional anisotropy (FA) from diffusion tensor imaging across regions over time., Results: Seizures from patients with unfavorable outcomes exhibited significantly higher maximum activation sizes in various frequency bands. Notably, we provided quantifiable evidence that in seizures associated with unfavorable surgical outcomes, the spread of beta-band power across brain regions is significantly faster, detectable as early as the first second after seizure onset. There was a significant correlation between beta power during seizures and FA in the corresponding areas, particularly in the unfavorable outcome group. Our findings further suggest that integrating structural and functional features could improve the prediction of epilepsy surgical outcomes., Discussion: Our findings suggest that ictal iEEG power dynamics and the structural-functional relationship are mechanistic factors associated with surgical outcomes in TLE.