Citations

Abstract Objective: Adaptive neuromodulation systems and implantable brain-computer interfaces (BCIs) are promising therapies for neurological and psychiatric disorders. However, their broader translation into research and clinical practice remains limited by technological complexity, restricted access to implantable research platforms, and the lack of standardized, reproducible experimental workflows. We therefore aimed to develop and validate an open, general-purpose translational ecosystem that enables rapid development, evaluation, and dissemination of novel neuromodulation and implantable BCI paradigms.
Approach: The CorTec Brain Interchange (BIC) implantable neural sensing and stimulation device was integrated with the open-source BCI2000 platform to create a modular, extensible neuromodulation ecosystem. We established a standardized battery of quantitative assessments to characterize implantable neuromodulation systems to comprehensively evaluate the CorTec BIC device through benchtop characterization, long-term preclinical in vivo validation, and a human feasibility demonstration using the benchtop BIC system connected to externalized leads.
Results: Comprehensive benchtop characterization of the BIC device independently validated previously reported performance while extending its evaluation through quantification of recording noise, acquisition and stimulation latencies, and validation of impedance measurements. Long-term in vivo validation in five canines, with the longest implantation exceeding three years, demonstrated stable chronic recordings while also capturing progressive deterioration of individual channels through longitudinal changes in signal quality and impedance. The ecosystem enabled active functional decoding more than two years after implantation, closed-loop stimulation using arbitrary spectral biomarkers, capturing brain stimulation evoked potentials, detection of epilepsy-associated biomarkers and observational assessment of their response to stimulation. In addition, we translated an established one-dimensional BCI cursor-control paradigm to the BIC benchtop system, demonstrating ecosystem portability and feasibility with a human participant. Finally, we share surgical protocols, imaging and analysis pipelines, datasets, and software to facilitate reproducible neuromodulation research.
Significance: We present a versatile, open-source translational ecosystem that supports a wide range of neuromodulation and implantable BCI applications with minimal modification. By combining comprehensive hardware characterization with standardized software tools and experimental workflows, this work provides a practical reference for researchers adopting the BIC platform while establishing a broadly applicable framework for systematic characterization of implantable neuromodulation systems. The ecosystem lowers technical barriers to implantable neurotechnology research, promotes reproducibility, and provides a foundation for accelerating the development and clinical translation of next-generation adaptive neuromodulation and implantable BCI therapies for patients with neurological and psychiatric disorders.

Abstract Studying mammalian brain function aids our understanding of human brain evolution. We implanted a beagle with a prototype human neuromodulation platform that measures activity from the brain surface. One year later, a set of simple sensory tasks was performed, finding visual and somatosensory representation in the canine homologs of the expected areas in humans. Surprisingly, the canine anterior ectosylvian gyrus, which is anatomically homologous to human receptive speech areas, was selectively active during independent social reinforcement tasks. This suggests that human speech understanding may have evolved from more general mammalian brain structures that are specialized for social reinforcement.

Abstract Several studies have shown that mouth movements related to the pronunciation of individual phonemes are represented in the sensorimotor cortex. This would theoretically allow for brain computer interfaces that are capable of decoding continuous speech by training classifiers based on the activity in the sensorimotor cortex related to the production of individual phonemes. To address this, we investigated the decodability of trials with individual and paired phonemes (pronounced consecutively with one second interval) using activity in the sensorimotor cortex. Fifteen participants pronounced 3 different phonemes and 3 combinations of two of the same phonemes in a 7T functional MRI experiment. We confirmed that support vector machine (SVM) classification of single and paired phonemes was possible. Importantly, by combining classifiers trained on single phonemes, we were able to classify paired phonemes with an accuracy of 53% (33% chance level), demonstrating that activity of isolated phonemes is present and distinguishable in combined phonemes. A SVM searchlight analysis showed that the phoneme representations are widely distributed in the ventral sensorimotor cortex. These findings provide insights about the neural representations of single and paired phonemes. Furthermore, it supports the notion that speech BCI may be feasible based on machine learning algorithms trained on individual phonemes using intracranial electrode grids.

AbstractFunctional magnetic resonance imaging (fMRI) studies have often recorded robust univariate group effects in the amygdala of subjects exposed to emotional stimuli. Yet it is unclear to what extent this effect also holds true when multi‐voxel pattern analysis (MVPA) is applied at the level of the individual participant. Here we sought to answer this question. To this end, we combined fMRI data from two prior studies (N = 112). For each participant, a linear support vector machine was trained to decode the valence of emotional pictures (negative, neutral, positive) based on brain activity patterns in either the amygdala (primary region‐of‐interest analysis) or the whole‐brain (secondary exploratory analysis). The accuracy score of the amygdala‐based pattern classifications was statistically significant for only a handful of participants (4.5%) with a mean and standard deviation of 37% ± 5% across all subjects (range: 28–58%; chance‐level: 33%). In contrast, the accuracy score of the whole‐brain pattern classifications was statistically significant in roughly half of the participants (50.9%), and had an across‐subjects mean and standard deviation of 49% ± 6% (range: 33–62%). The current results suggest that the information conveyed by the emotional pictures was encoded by spatially distributed parts of the brain, rather than by the amygdala alone, and may be of particular relevance to studies that seek to target the amygdala in the treatment of emotion regulation problems, for example via real‐time fMRI neurofeedback training.