Quantifying hubness to predict surgical outcomes in epilepsy: Assessing resection-hub alignment in interictal intracranial EEG networks.

OBJECTIVE: 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)." METHODS: 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. RESULTS: 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). SIGNIFICANCE: 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.

iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices.

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.

Network coupling and surgical treatment response in temporal lobe epilepsy: A proof-of-concept study.

OBJECTIVE: This proof-of-concept study aimed to examine the overlap between structural and functional activity (coupling) related to surgical response. METHODS: We studied intracranial rest and ictal stereoelectroencephalography (sEEG) recordings from 77 seizures in thirteen participants with temporal lobe epilepsy (TLE) who subsequently underwent resective/laser ablation surgery. We used the stereotactic coordinates of electrodes to construct functional (sEEG electrodes) and structural connectomes (diffusion tensor imaging). A Jaccard index was used to assess the similarity (coupling) between structural and functional connectivity at rest and at various intraictal timepoints. RESULTS: We observed that patients who did not become seizure free after surgery had higher connectome coupling recruitment than responders at rest and during early and mid seizure (and visa versa). SIGNIFICANCE: Structural networks provide a backbone for functional activity in TLE. The association between lack of seizure control after surgery and the strength of synchrony between these networks suggests that surgical intervention aimed to disrupt these networks may be ineffective in those that display strong synchrony. Our results, combined with findings of other groups, suggest a potential mechanism that explains why certain patients benefit from epilepsy surgery and why others do not. This insight has the potential to guide surgical planning (e.g., removal of high coupling nodes) following future research.

On the Role of Arkypallidal and Prototypical Neurons for Phase Transitions in the External Pallidum.

The external pallidum (globus pallidus pars externa [GPe]) plays a central role for basal ganglia functions and dynamics and, consequently, has been included in most computational studies of the basal ganglia. These studies considered the GPe as a homogeneous neural population. However, experimental studies have shown that the GPe contains at least two distinct cell types (prototypical and arkypallidal cells). In this work, we provide in silico insight into how pallidal heterogeneity modulates dynamic regimes inside the GPe and how they affect the GPe response to oscillatory input. We derive a mean-field model of the GPe system from a microscopic spiking neural network of recurrently coupled prototypical and arkypallidal neurons. Using bifurcation analysis, we examine the influence of dopamine-dependent changes of intrapallidal connectivity on the GPe dynamics. We find that increased self-inhibition of prototypical cells can induce oscillations, whereas increased inhibition of prototypical cells by arkypallidal cells leads to the emergence of a bistable regime. Furthermore, we show that oscillatory input to the GPe, arriving from striatum, leads to characteristic patterns of cross-frequency coupling observed at the GPe. Based on these findings, we propose two different hypotheses of how dopamine depletion at the GPe may lead to phase-amplitude coupling between the parkinsonian beta rhythm and a GPe-intrinsic γ rhythm. Finally, we show that these findings generalize to realistic spiking neural networks of sparsely coupled Type I excitable GPe neurons.SIGNIFICANCE STATEMENT Our work provides (1) insight into the theoretical implications of a dichotomous globus pallidus pars externa (GPe) organization, and (2) an exact mean-field model that allows for future investigations of the relationship between GPe spiking activity and local field potential fluctuations. We identify the major phase transitions that the GPe can undergo when subject to static or periodic input and link these phase transitions to the emergence of synchronized oscillations and cross-frequency coupling in the basal ganglia. Because of the close links between our model and experimental findings on the structure and dynamics of prototypical and arkypallidal cells, our results can be used to guide both experimental and computational studies on the role of the GPe for basal ganglia dynamics in health and disease.

Cross-frequency phase-amplitude coupling in repetitive movements in patients with Parkinson's disease.

Bradykinesia is a cardinal motor symptom in Parkinson's disease (PD), the pathophysiology of which is not fully understood. We analyzed the role of cross-frequency coupling of oscillatory cortical activity in motor impairment in patients with PD and healthy controls. High-density EEG signals were recorded during various motor activities and at rest. Patients performed a repetitive finger-pressing task normally, but were slower than controls during tapping. Phase-amplitude coupling (PAC) between ß (13-30 Hz) and broadband γ (50-150 Hz) was computed from individual EEG source signals in the premotor, primary motor, and primary somatosensory cortices, and the primary somatosensory complex. In all four regions, averaging the entire movement period resulted in higher PAC in patients than in controls for the resting condition and the pressing task (similar performance between groups). However, this was not the case for the tapping tasks where patients performed slower. This suggests the strength of state-related ß-γ PAC does not determine Parkinsonian bradykinesia. Examination of the dynamics of oscillatory EEG signals during motor transitions revealed a distinctive motif of PAC rise and decay around press onset. This pattern was also present at press offset and slow tapping onset, linking such idiosyncratic PAC changes to transitions between different movement states. The transition-related PAC modulation in patients was similar to controls in the pressing task but flattened during slow tapping, which related to normal and abnormal performance, respectively. These findings suggest that the dysfunctional evolution of neuronal population dynamics during movement execution is an important component of the pathophysiology of Parkinsonian bradykinesia.NEW & NOTEWORTHY Our findings using noninvasive EEG recordings provide evidence that PAC dynamics might play a role in the physiological cortical control of movement execution and may encode transitions between movement states. Results in patients with Parkinson's disease suggest that bradykinesia is related to a deficit of the dynamic regulation of PAC during movement execution rather than its absolute strength. Our findings may contribute to the development of a new concept of the pathophysiology of bradykinesia.

Spatiotemporal features of ß-γ phase-amplitude coupling in Parkinson's disease derived from scalp EEG.

Abnormal phase-amplitude coupling between ß 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. ß 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 ß 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 ß 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.

Sub-regional anterior cingulate cortex functional connectivity revealed default network subsystem dysfunction in patients with major depressive disorder.

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.

In Vivo Neural Recording and Electrochemical Performance of Microelectrode Arrays Modified by Rough-Surfaced AuPt Alloy Nanoparticles with Nanoporosity.

In order to reduce the impedance and improve in vivo neural recording performance of our developed Michigan type silicon electrodes, rough-surfaced AuPt alloy nanoparticles with nanoporosity were deposited on gold microelectrode sites through electro-co-deposition of Au-Pt-Cu alloy nanoparticles, followed by chemical dealloying Cu. The AuPt alloy nanoparticles modified gold microelectrode sites were characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and in vivo neural recording experiment. The SEM images showed that the prepared AuPt alloy nanoparticles exhibited cauliflower-like shapes and possessed very rough surfaces with many different sizes of pores. Average impedance of rough-surfaced AuPt alloy nanoparticles modified sites was 0.23 MΩ at 1 kHz, which was only 4.7% of that of bare gold microelectrode sites (4.9 MΩ), and corresponding in vitro background noise in the range of 1 Hz to 7500 Hz decreased to 7.5 µ V rms from 34.1 µ V rms at bare gold microelectrode sites. Spontaneous spike signal recording was used to evaluate in vivo neural recording performance of modified microelectrode sites, and results showed that rough-surfaced AuPt alloy nanoparticles modified microelectrode sites exhibited higher average spike signal-to-noise ratio (SNR) of 4.8 in lateral globus pallidus (GPe) due to lower background noise compared to control microelectrodes. Electro-co-deposition of Au-Pt-Cu alloy nanoparticles combined with chemical dealloying Cu was a convenient way for increasing the effective surface area of microelectrode sites, which could reduce electrode impedance and improve the quality of in vivo spike signal recording.

Design, Fabrication, Simulation and Characterization of a Novel Dual-Sided Microelectrode Array for Deep Brain Recording and Stimulation.

In this paper, a novel dual-sided microelectrode array is specially designed and fabricated for a rat Parkinson's disease (PD) model to study the mechanisms of deep brain stimulation (DBS). The fabricated microelectrode array can stimulate the subthalamic nucleus and simultaneously record electrophysiological information from multiple nuclei of the basal ganglia system. The fabricated microelectrode array has a long shaft of 9 mm and each planar surface is equipped with three stimulating sites (diameter of 100 µm), seven electrophysiological recording sites (diameter of 20 µm) and four sites with diameter of 50 µm used for neurotransmitter measurements in future work. The performances of the fabricated microelectrode array were characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry. In addition, the stimulating effects of the fabricated microelectrode were evaluated by finite element modeling (FEM). Preliminary animal experiments demonstrated that the designed microelectrode arrays can record spontaneous discharge signals from the striatum, the subthalamic nucleus and the globus pallidus interna. The designed and fabricated microelectrode arrays provide a powerful research tool for studying the mechanisms of DBS in rat PD models.

Optimizing Surgical Planning for Epilepsy Patients With Multimodal Neuroimaging and Neurophysiology Integration: A Case Study.

SUMMARY: Current preoperative evaluation of epilepsy can be challenging because of the lack of a comprehensive view of the network's dysfunctions. To demonstrate the utility of our multimodal neurophysiology and neuroimaging integration approach in the presurgical evaluation, we present a proof-of-concept for using this approach in a patient with nonlesional frontal lobe epilepsy who underwent two resective surgeries to achieve seizure control. We conducted a post-hoc investigation using four neuroimaging and neurophysiology modalities: diffusion tensor imaging, resting-state functional MRI, and stereoelectroencephalography at rest and during seizures. We computed region-of-interest-based connectivity for each modality and applied betweenness centrality to identify key network hubs across modalities. Our results revealed that despite seizure semiology and stereoelectroencephalography indicating dysfunction in the right orbitofrontal region, the maximum overlap on the hubs across modalities extended to right temporal areas. Notably, the right middle temporal lobe region served as an overlap hub across diffusion tensor imaging, resting-state functional MRI, and rest stereoelectroencephalography networks and was only included in the resected area in the second surgery, which led to long-term seizure control of this patient. Our findings demonstrated that transmodal hubs could help identify key areas related to epileptogenic network. Therefore, this case presents a promising perspective of using a multimodal approach to improve the presurgical evaluation of patients with epilepsy.

EEG Ictal Power Dynamics, Function-Structure Associations, and Epilepsy Surgical Outcomes.

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.

iEEG-recon: A Fast and Scalable Pipeline for Accurate Reconstruction of Intracranial Electrodes and Implantable Devices.

Background: Collaboration between epilepsy centers is essential to integrate multimodal data for epilepsy research. Scalable tools for rapid and reproducible data analysis facilitate multicenter data integration and harmonization. Clinicians use intracranial EEG (iEEG) in conjunction with non-invasive 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. These tasks are still performed manually in many epilepsy centers. 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 semi-automatic iEEG annotation, rapid image registration, and electrode assignment on brain MRIs. Its modular architecture includes three modules: 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 (ECoG) and stereoelectroencephalography (SEEG) cases with a 10 minute running time per case, and ~20 min for semi-automatic electrode labeling. 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 post-implant T1-MRI visual inspections. Our use of ANTsPyNet deep learning approach for brain segmentation and electrode classification was consistent with the widely used Freesurfer segmentation. Discussion: iEEG-recon is a valuable tool for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting efficient data analysis, and integration into clinical workflows. The tool's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide. Comprehensive documentation is available at https://ieeg-recon.readthedocs.io/en/latest/.

Insular subdivisions functional connectivity dysfunction within major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a mental disorder characterized by cognitive and affective deficits. Previous studies suggested that insula is a crucial node of the salience network for initiating network switching, and dysfunctional connection to this region may be related to the mechanism of MDD. In this study, we systematically investigated and quantified the altered functional connectivity (FC) of the specific insular subdivisions and its relationship to psychopathology of MDD. METHODS: Resting-state FC of insular subdivisions, including bilateral ventral/dorsal anterior insula and posterior insula, were estimated in 19 MDD patients and 19 healthy controls. Abnormal FC was quantified between groups. Additionally, we investigated the relationships between insular connectivity and depressive symptom severity. RESULTS: MDD patients demonstrated aberrant FC for insular subdivisions to superior temporal sulcus, inferior prefrontal gyrus, amygdala and posterior parietal cortex. Moreover, depression symptoms (Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale scorers) were associated with the FC values of insular subdivisions. LIMITATIONS: First, the sample size of our current study is relatively small, which may affect the statistic power. Second, using standardized insular subdivision seeds for FC analyses may neglect subtle natural differences in size and location of functional area across individuals and may thus affect connectivity maps. CONCLUSIONS: Abnormal FC of insular subdivisions to default network and central executive network may represent impaired intrinsic networks switching which may affect the underlying emotional and sensory disturbances in MDD. And our findings can help to understand the pathophysiology and underlying neural mechanisms of MDD.