Functional near infrared spectroscopy (fNIRS) is used to measure cerebral hemodynamics and can be applied to detect seizures but has limited penetration depth and is sensitive to noise caused by scalp hemodynamics. Patients with drug resistant epilepsy are sometimes implanted with intracerebral electrodes to localize their seizure onset zone. These electrodes are limited by a "tunnel vision" effect. We developed an optical anchor bolt (OAB), which integrates near infrared optodes positioned in the skull, as part of an implanted fNIRS (ifNIRS) sensor in order to overcome these limitations. The feasibility and utility of ifNIRS were assessed using a slab model of the head in a Monte-Carlo (MC) simulation and a tissue-mimicking phantom model. Positioning the emitter and detector inside the OAB yielded a signal >30 higher than standard scalp positioned fNIRS at a 3 cm distance, and a number of photons >6 times higher than the limit of detection at a 5 cm separation, with a greatly reduced sensitivity to noise from the scalp. In both the phantom model and the MC simulation, emitter optodes embedded inside a depth electrode, positioned several centimeters deep in the brain, provided a signal detected by an OAB with a maximal depth dependent on the optical properties of white matter. These findings demonstrate the advantages of the OAB and ifNIRS, and potential future directions of development. Further in vivo studies are required to assess the feasibility and safety of the system, and the optical properties of white matter due to their impact on ifNIRS.

Objective: To identify thalamic electrophysiological activity along the trajectory to the subthalamic region using micro-electrode recordings in deep brain stimulation (DBS) surgery by integrating site- and sequence-based approaches, without compromising subthalamic nucleus (STN) detection accuracy. Methods: Electrophysiological data from 29,735 recording sites across 112 patients were used to develop algorithms for automatic detection of the thalamus, STN, and non-cellular brain areas. We combined site-specific features with sequence-based information using two approaches: classical machine learning using a support vector machine and a Gaussian-HMM (SVM-GHMM), and a recurrent neural network (LSTM). The performance of both algorithms was compared to the commercially available HaGuide STN-detection algorithm. We assess algorithm performance on thalamus and STN detection in pseudo-real-time using clinically relevant metrics. Results: Our algorithms achieved 73%-77% sensitivity and 97%-98% specificity for thalamus detection, and 94%-96% sensitivity and 98%-99% specificity for STN detection. The thalamus, with its electrophysiological heterogeneity, is particularly well-suited for sequence-based classification. The SVM-GHMM performed slightly better than the LSTM in clinical metrics for STN detection, though both were significantly better than HaGuide. Both models were also effective in identifying the thalamus and STN in the closely related case of trajectories targeting the nearby posterior subthalamic area. Conclusion: We demonstrated the ability to automatically identify thalamic activity along the trajectory to subthalamic region by leveraging site- and sequence-based algorithms, without compromising on STN detection accuracy. Significance: This study highlights the feasibility of real-time, automated thalamus and STN detection, offering valuable context to neurosurgeons during DBS surgery.

Understanding how epileptic networks may disrupt information processing requires examining the spatial organization of functional connectivity. Here, we present an exploratory case-series analysis of stereotactic EEG (SEEG) recordings from seven hippocampi of epileptic patients, six exhibiting epileptiform activity and one outside the epileptogenic zone. Phase coherence across electrode pairs was evaluated within four-node directed pathways as a function of inter-electrode distance to characterize the spatial-frequency structure of functional interactions. We introduce a novel framework that decomposes phase coherence into two complementary components-propagation and interaction phases-capturing both global coupling strength and its spatial gradient. We show that this framework yields meaningful and consistent results only when applied to electrode pairs embedded within directed pathways, demonstrating that network organization constrains the spatial structure of coherence and that pathway-based selection is essential to reveal this structure. Within this constrained dataset, we observed variability in coupling strength and spatial gradients across hippocampi. Specifically, the hippocampus not involved in the epileptogenic network exhibited lower global coupling strength and steeper spatial gradients, consistent with more modular and spatially differentiated communication. In contrast, hippocampi with epileptiform activity showed higher global coupling and flatter spatial gradients, suggesting reduced differentiation between local and remote interactions. Simulations using a one-dimensional Kuramoto oscillator model reproduced this pattern, indicating that increased coupling strength can lead to a collapse of spatial gradients. These findings illustrate how spatial coherence gradients and pathway-based analysis provide a useful framework for future studies aimed at validating network alterations in epilepsy using adequately powered cohorts.

Background/Objectives: Intracranial macroelectrode implantation is a pivotal clinical tool in the evaluation of drug-resistant epilepsy, allowing further insights into the localization of the epileptogenic zone and the delineation of eloquent cortical regions through cortical stimulation. Additionally, it provides an avenue to study brain functions by analyzing cerebral responses during neuropsychological paradigms. By combining macroelectrodes with microelectrodes, which allow recording the activity of individual neurons or smaller neural clusters, recordings could provide deeper insights into neuronal microcircuits and the brain’s transitions in epilepsy and contribute to a better understanding of neuropsychological functions. In this study, one or two hybrid macro-micro electrodes were implanted in the anterior-inferior insular region in patients with refractory epilepsy. We report our experience and share some preliminary results; we also provide some recommendations regarding the implantation procedure for hybrid electrodes in the insular cortex. Methods: Stereoelectroencephalography was performed in 13 patients, with one or two hybrid macro-microelectrodes positioned in the insular region in each patient. Research neuropsychological paradigms could not be implemented in two patients for clinical reasons. In total, 23 hybrid macro-microelectrodes with eight microcontacts each were implanted, of which 20 were recorded. Spiking activity was detected and assessed using WaveClus3. Results: No spiking neural activity was detected in the microcontacts of the first seven patients. After iterative refinement during this process, successful recordings were obtained from 13 microcontacts in the anterior-inferior insula in the last four patients (13/64, 20.3%). Hybrid electrode implantation was uneventful with no complications. Obstacles included the absence of spiking activity signals, unsuccessful microwire dispersion, and the interference of environmental electrical noise in recordings. Conclusions: Human microelectrode recording presents a complex array of challenges; however, it holds the potential to facilitate a more comprehensive understanding of individual neuronal attributes and their specific stimulus responses.

Background/Objectives: Intracranial macroelectrode implantation is a pivotal clinical tool in the evaluation of drug-resistant epilepsy, allowing further insights into the localization of the epileptogenic zone and the delineation of eloquent cortical regions through cortical stimulation. Additionally, it provides an avenue to study brain functions by analyzing cerebral responses during neuropsychological paradigms. By combining macroelectrodes with microelectrodes, which allow to record the activity of individual neurons or smaller neural clusters, recordings could provide deeper insights into neuronal microcircuits and the brain’s transitions in epilepsy and contribute to a better understanding of neuropsychological functions. In this study, one or two hybrid macro-micro electrodes were implanted in the anterior-inferior insular region in patients with refractory epilepsy. We report our experience and share some preliminary results; we also provide some recommendations regarding the implantation procedure of hybrid electrodes in the insular cortex. Methods: Stereoelectroencephalography was performed in 13 patients, with one or two hybrid macro-microelectrodes positioned in the insular region in each patient. Research neuropsychological paradigms could not be done in two patients for clinical reasons. In total, 23 hybrid macro-microelectrodes with 8 microcontacts each were implanted, of which 20 were recorded. Spiking activity was detected and assessed using WaveClus3. Results: No spiking neural activity was detected in the microcontacts of the first 7 patients. After iterative refinement during this process, successful recordings were obtained from 13 microcontacts in the anterior-inferior insula in the last four patients (13/64, 20.3%). Hybrid electrode implantation was uneventful with no complications. Obstacles included the absence of spiking activity signals, unsuccessful microwire dispersion, and the interference of environmental electrical noise on recordings. Conclusions: Human microelectrode recording presents a complex array of challenges; yet it holds the potential to facilitate a more comprehensive understanding of individual neuronal attributes and their specific stimulus responses.

The data obtained from stereo-elecroencephalography (SEEG) in patients with focal epilepsy are crucial for defining the epileptogenic zone and achieving successful resection, but suboptimal electrode placement impairs SEEG results. We demonstrate an approach for concurrent scalp and depth EEG analysis from one patient with successful intracranial workup and one in whom the seizure onset zone was unsampled by SEEG. Intracranial epileptiform discharges were identified and clustered, their scalp correlates were averaged, and electric source imaging (ESI) was applied to the resulting averaged scalp potential - depth-to-scalp ESI (dsESI). We found temporal differences between intracranial and scalp peaks, as well as variations in averaged scalp spikes morphology and propagation, expressed by their amplitudes and width, and by their jitter across involved electrodes. Put together with the relative degree of focality and location of the averaged scalp spikes' ESI on the cortex, these data could differentiate onset from propagation of interictal activity and identify unexplored nodes in the epileptic network. Our novel analysis highlights the importance of temporal, and not just spatial, spike dynamics within the epileptic network, may be used to validate depth electrode placement and aid in understanding the epileptic network.