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Transcranial electric stimulation (TES) is an increasingly popular method for non-invasive modulation of brain activity and a potential treatment for neuropsychiatric disorders. However, there are concerns about the reliability of its application because of variability in TES-induced intracranial electric fields across individuals. While realistic computational models offer can help to alleviate these concerns, their direct empirical validation is sparse, and their practical implications are not always clear. In this study, we combine direct intracranial measurements of electric fields generated by TES in surgical epilepsy patients with computational modeling. First, we directly validate the computational models and identify key parameters needed for accurate model predictions. Second, we derive practical guidelines for a reliable application of TES in terms of the precision of electrode placement needed to achieve a desired electric field distribution. Based on our results, we recommend electrode placement accuracy to be < 1 cm for a reliable application of TES across sessions. •Validation of computational models for TES•Investigation of electrode placement accuracy in 25 head models•Recommendation for electrode placements guidelines for TES

Abstract BACKGROUND Both stereoelectroencephalography (SEEG) and subdural strip electrodes (SSE) are used for intracranial electroencephalographic recordings in the invasive investigation of patients with drug-resistant epilepsy. OBJECTIVE To compare SEEG and SSE with respect to feasibility, complications, and outcome in this single-center study. METHODS Patient characteristics, periprocedural parameters, complications, and outcome were acquired from a pro- and retrospectively managed databank to compare SEEG and SSE cases. RESULTS A total of 500 intracranial electroencephalographic monitoring cases in 450 patients were analyzed (145 SEEG and 355 SSE). Both groups were of similar age, gender distribution, and duration of epilepsy. Implantation of each SEEG electrode took 13.9 ± 7.6 min (20 ± 12 min for each SSE; P < .01). Radiation exposure to the patient was 4.3 ± 7.7 s to a dose area product of 14.6 ± 27.9 rad*cm2 for SEEG and 9.4 ± 8.9 s with 21 ± 22.4 rad*cm2 for SSE (P < .01). There was no difference in the length of stay (12.2 ± 7.2 and 12 ± 6.3 d). The complication rate was low in both groups. No infections were seen in SEEG cases (2.3% after SSE). The rate of hemorrhage was 2.8% for SEEG and 1.4% for SSE. Surgical outcome was similar. CONCLUSION SEEG allows targeting deeply situated foci with a non-inferior safety profile to SSE and seizure outcome comparable to SSE. Graphical Abstract Graphical Abstract

Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance’s identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate. Speech neuroprosthetic devices should be capable of restoring a patient’s ability to participate in interactive dialogue. Here, the authors demonstrate that the context of a verbal exchange can be used to enhance neural decoder performance in real time.

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.

The zebra finch is a well-studied animal model for investigating the neural mechanisms of vocal learning, and electrophysiology is the primary technique for understanding their song system. Most of the studies on zebra finches have focused on intracerebral recordings. However, these methods are only affordable for limited laboratories. Recently, different open-source hardware for acquiring electroencephalography (EEG) signals has been developed. It’s unclear whether these solutions suit zebra finch studies as they have not been evaluated. Electrocorticography signals can provide a preliminary guide for more in-depth inquiries and also aid in understanding the global behavior of the bird’s brain, as opposed to the more common localized approach. We present a detailed protocol for acquiring intracranial electroencephalography (iEEG) data from zebra finches using the OpenBCI Cyton board, an open-source device. We implemented stainless steel electrodes on the brain’s surface and recorded the brain signals from two recording sites above two auditory-responsive nuclei. To validate our method, we ran two different experiments. In the first experiment, we recorded neural activity under various concentrations of isoflurane and extracted the suppression duration to measure anesthesia depth. In the second experiment, we head-fixed the birds and, under light anesthesia, presented them with various auditory stimuli to evaluate event-related potentials (ERP) and generate spectrograms. The results showed a significant increase in suppression duration with deeper anesthesia, and the ERP and spectrogram responses to auditory stimuli differed accordingly. These findings indicate that using our methodology, one can successfully collect iEEG signals from zebra finches. These findings pave the way for future studies to use iEEG to investigate bird cognition in a more affordable way.

The long‐term performance of microscale subdural arrays is often compromised by adverse tissue responses, leading to increased electrochemical impedance and signal deterioration during chronic applications. Here, the study presents an adhesive hydrogel‐integrated micro‐electrocorticography (aGel‐µECoG) array that improves mechanical compatibility and adhesion to brain tissue, effectively mitigating tissue responses and enabling stable, high‐performance electrical communication over days to months. The hydrogel consists of a hydrophilic polyvinyl alcohol and hydrophobic poly3‐(trimethoxysilyl) propyl methacrylate heteronetwork, serving as a biological and mechanical bridging to enable seamless, anti‐fibrotic, long‐term integration with brain tissue. The aGel‐µECoG achieves robust tissue adhesion (25.2 ± 3.8 kPa) with reversible and safe removal, enabling sutureless implantation. With an ultrathin 10‐µm ionic conductive hydrogel coating, the array exhibits high electrical fidelity, effectively preserving fine‐grained sub‐millimeter spatial resolution and maintaining unattenuated signal strength for functional cortical mapping. Compared to conventional µECoG arrays, aGel‐µECoG exhibits a 20‐fold reduction in acute‐phase impedance increases, attributed to significantly reduced neuroinflammation and fibrotic tissue formation, as confirmed by histological analyses. Long‐term recordings further reveal that aGel‐µECoG maintained 94.8% of the signal‐to‐noise ratio for steady‐state visually evoked potentials over 16 weeks, whereas uncoated µECoG arrays declined to 69.5%. These findings establish aGel‐µECoG as a durable and high‐performance neural interface. An adhesive hydrogel‐integrated micro‐electrocorticography (aGel‐µECoG) array is developed to ensure stable neural recordings by minimizing neuroinflammation and fibrosis during implantation. The hydrogel serves as a biological and mechanical bridge, enabling bioadhesive, reversible, anti‐fibrotic integration with brain tissue. The platform demonstrates high electrical fidelity, negligible adverse tissue responses, and stable recordings over four months, offering a promising solution for long‐term neural interfacing applications.

Abstract BACKGROUND Intraoperative research during deep brain stimulation (DBS) surgery has enabled major advances in understanding movement disorders pathophysiology and potential mechanisms for therapeutic benefit. In particular, over the last decade, recording electrocorticography (ECoG) from the cortical surface, simultaneously with subcortical recordings, has become an important research tool for assessing basal ganglia-thalamocortical circuit physiology. OBJECTIVE To provide confirmation of the safety of performing ECoG during DBS surgery, using data from centers involved in 2 BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative-funded basic human neuroscience projects. METHODS Data were collected separately at 4 centers. The primary endpoint was complication rate, defined as any intraoperative event, infection, or postoperative magnetic resonance imaging abnormality requiring clinical follow-up. Complication rates for explanatory variables were compared using point biserial correlations and Fisher exact tests. RESULTS A total of 367 DBS surgeries involving ECoG were reviewed. No cortical hemorrhages were observed. Seven complications occurred: 4 intraparenchymal hemorrhages and 3 infections (complication rate of 1.91%; CI = 0.77%-3.89%). The placement of 2 separate ECoG research electrodes through a single burr hole (84 cases) did not result in a significantly different rate of complications, compared to placement of a single electrode (3.6% vs 1.5%; P = .4). Research data were obtained successfully in 350 surgeries (95.4%). CONCLUSION Combined with the single report previously available, which described no ECoG-related complications in a single-center cohort of 200 cases, these findings suggest that research ECOG during DBS surgery did not significantly alter complication rates.

The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies. A major challenge across a variety of fields is how to process the vast quantities of data produced by sensors without large computation resources. Here, the authors present a neuromorphic chip which can detect a relevant signature of epileptogenic tissue from intracranial recordings in patients.

Brain‐computer interfaces (BCIs) enable communication between individuals and computers or other assistive devices by decoding brain activity, thereby reconstructing speech and motor functions for patients with neurological disorders. This study presents a high‐resolution micro‐electrocorticography (µECoG) BCI based on a flexible, high‐density µECoG electrode array, capable of chronically stable and real‐time motor decoding. Leveraging micro‐nano manufacturing technology, the µECoG BCI achieves a 64‐fold increase in electrode density compared to conventional clinical electrode arrays, enhancing spatial resolution while featuring scalability. Over a 203‐day in vivo experiment, high‐resolution µECoG carrying fine spatial specificity information demonstrated the potential to improve decoding performance while reduce implanted devices size. These advancements provide a pathway to overcome the limitations of conventional ECoG BCIs. During awake surgery, the µECoG BCI enabled game control after 7 min of model training. Furthermore, during practice of 19.87 h, the participant achieved cursor control with a bit rate of 1.13 bits per second (BPS) under full volitional control, and the bit rate reached up to 4.15 BPS with enhanced user interface. These results show that the µECoG BCI achieves comparable performance to intracortical electroencephalographic (iEEG) BCIs without intracortical invasiveness, marking a breakthrough in the clinical feasibility of flexible BCIs. A high‐resolution micro‐electrocorticographic (µECoG) brain‐computer interface (BCI) for real‐time motor decoding is reported. The application of flexible, scalable µECoG electrode arrays overcomes the insufficient spatial resolution in conventional ECoG BCIs. The real‐time motor and motor imagery decoding achieved in the long‐term in vivo experiment and clinical practices demonstrates the practical value of flexible µECoG BCIs.

A brain–computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current state-of-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail.