Frontiers in Neuroscience

Purpose: Electrical stimulation of the occipital cortex evokes visual percepts known as phosphenes. To date, most human studies have examined phosphenes elicited by large surface or depth electrodes. These differ considerably from the small penetrating intracortical microelectrodes capable of stimulating highly localized cortical populations that are the basis of current next-generation devices. Here, we provide a detailed characterization of phosphenes evoked by such intracortical microelectrodes. Methods: As part of the CORTIVIS clinical trial, a Utah Electrode Array with 100 penetrating electrodes was implanted in the early visual cortex of four blind volunteers. Participants completed drawing and description tasks in response to single-electrode stimulation. Two participants also performed forced-choice tasks to determine whether phosphenes elicited by different electrodes could be reliably discriminated. Results: Single-electrode stimulation reliably evoked phosphenes in all participants. Most percepts appeared in the contralateral visual field, although two participants reported near-midline ipsilateral percepts. Phosphenes ranged from simple dots to elongated, textured, circular, or otherwise idiosyncratic forms. One participant reported distinguishable phosphene colors, which depended on electrode and amplitude structure and varied across sessions. The two participants tested on forced-choice discrimination could successfully identify electrode-specific phosphenes across multiple sessions. Conclusions: Intracortical microstimulation of the human visual cortex produces phosphenes with diverse, electrode-specific appearances that are recognizable across repeated trials. These findings indicate that penetrating microelectrodes often evoke more structured percepts than simple points or blobs, providing foundational evidence that electrode-specific encoding strategies will be necessary in future visual prosthetic devices.

Despite its high prevalence and socioeconomic costs, the condition of tinnitus shares with related neuropsychiatric disorders the characteristic that, to this day, it cannot be cured. Two contradictory views of the origin of tinnitus (peripheral hyperexcitability and central brain oscillation changes linked to prediction error) are currently discussed without any regard for one another. We now firstly used a compact 64 sensor optically pumped magnetometer (OPM)-MEG system to study a group of tinnitus subjects without co-morbidity of hyperacusis. This new technology provided an unprecedented opportunity for analyzing hemisphere-specific brain activity changes with high spatial resolution in response to pure-tones with a pitch within or outside the tinnitus frequency. We observed in tinnitus smaller ABR amplitudes (reflecting reduced cochlear output synchrony) linked with reduced alpha and enhanced gamma activity at rest (reflecting elevated excitement of intracortical circuits). In tinnitus, reduced alpha and enhanced gamma brain activity at rest were associated with reduced evoked alpha, beta, and gamma in response to pure-tones within tinnitus frequencies (reflecting low signal-to-noise ratios in auditory target regions). Furthermore, elevated gamma activity in key regions in the brain involved in attention control was observed in tinnitus subjects: hypergamma activity was seen in posterior/frontal regions, that -when hyperactive - are predicted to trigger excessive attention to irrelevant stimuli. Weakened cochlear output synchrony, possibly through lowering tonic inhibitory strength in the ascending auditory pathway, can thus reduce alpha activity (default-mode network) and unleash cortical regions that control attention to irrelevant stimuli -- tracing tinnitus to perception.

Tinnitus is one of the most common hearing disorders affecting one-third of Americans and is defined as the buzzing or ringing sound one perceives in one or both ears in the absence of an acoustic stimulus. One objective method for tinnitus screening in rodents is gap prepulse inhibition of the acoustic startle reflex (GPIAS), a reduction in the abrupt motor response elicited by an intense auditory stimulus following a silent gap in noise. Reduced inhibition by gaps embedded in narrow-band noise is hypothesized to reflect the primary tinnitus pitch, as the tinnitus "fills-in" the gap. However, Lobarinas et al. (2013) identified a critical limitation: after acoustic trauma or hearing loss, rodents often exhibit markedly diminished acoustic startle reflexes, creating a "floor effect" where further suppression becomes undetectable even if gap perception remains intact, leading to false-positive tinnitus screening results. To address this limitation, we utilized the CBA/CaJ mouse model to assess tactile airpuff modification efficacy and employed a machine learning algorithm to classify startle responses, achieving 98% accuracy in differentiating startles from non-startles. We induced unilateral conductive hearing loss via ear plugging and found enhanced gap detection ability, contrasting with false-positive tinnitus indicators observed in rats by Lobarinas and colleagues. We also pharmacologically induced tinnitus via sodium salicylate, revealing frequency-specific alterations in gap detection patterns. Our findings suggest that differences in data analysis methodology, specifically using a machine learning algorithm to filter non-startle responses, may explain species-specific differences between mouse and rat models and significantly improve GPIAS validity as a tinnitus screening assessment tool.

Black participants remain underrepresented in electroencephalography (EEG) research, partly because current EEG devices work poorly with dense, coily Type 4 Afro-textured hair. Research-grade EEG typically wires electrodes on a cap, and therefore the electrodes often fail to reach the scalp through dense, Afro-textured hair. Protocols that ask Black participants to unbraid or restyle protective hairstyles add burdens that discourage enrollment in research studies. To address this, we designed and tested 3D-printed attachments that fit onto an EEG electrode and anchored it directly in the hair, using the hairstyle itself as the mounting structure. We fabricated three design configurations for natural Afro-textured hair, cornrows/braids, and twists/microlocs. Seventeen healthy adults with Type 4a-4c hair used electrode attachments that matched their hairstyle. During a single visit, EEG was recorded with 16 conventional cap electrodes defined by the international 10-20 system and with 16 electrodes using novel electrode attachments anchored directly to their hair. Participants completed resting and walking trials with each system. Electrode-to-scalp distance and gel volume were measured during setup. Electrode disconnection, impedance, signal quality (i.e. peak alpha power and aperiodic (1/f) component), and comfort level were measured throughout the data collection. Compared with the conventional cap, the novel attachments reduced electrode-to-scalp distance, gel use, and impedance. Twenty percent of the conventional cap electrodes failed to connect to the scalp compared with 0.1% of novel electrodes. Impedance rose across trials for the conventional cap, whereas the novel attachments showed a smaller increase and maintained lower impedance throughout. Signal quality was significantly better using the novel attachments, while comfort level did not differ between systems. In conclusion, hair-anchored electrode attachment may present a feasible alternative to conventional cap-based EEG for people with Type 4 hair, yielding more usable channels and better signal quality, and supporting more inclusive EEG acquisition.

Background: Understanding fetal brain responses to hypoxia-ischemia is of growing interest, as it remains unclear to what extent these responses differ from those of newborns and whether developmental adaptations during gestation contribute to such differences. Materials and methods: Using a combination of broadband near-infrared spectroscopy (bNIRS) and diffuse correlation spectroscopy (DCS), we measured brain microvascular oxygen saturation (StO2), total hemoglobin concentration (THC), relative cerebral blood flow (rCBF), and the oxidation state of cytochrome c oxidase (oxCCO) during an acute ischemic challenge in the brains of five rabbit fetuses in utero. Simultaneous measurements were acquired from four control fetuses located in the contralateral uterine horn. Results: During the hypoxia-ischemia phase, we observed a rapid reduction of CBF (47% +/- 29%) and StO2, (from 64.7% +/- 1.5% at baseline to 40.6% +/- 2.0%) together with an increase of oxCCO (3.7 uM +/- 0.5 uM). Conclusion: The simultaneous increase in oxCCO and decrease in rCBF and StO2 during hypoxia-ischemia reveals a cerebral response pattern that differs from that commonly reported in the newborn brain. These findings support the hypothesis that unique developmental adaptations influence fetal cerebral metabolism during hypoxic-ischemic stress.

The primary goal of a brain-computer interface (BCI) is to enable interactions with the environment by generating control signals directly from brain activity, independent of physical movement including gaze shifts. Attentional selection of peripheral objects has been used to control a BCI but requires sufficient visual acuity to reliably recognize the target stimulus. Here, we systematically investigated the effect of different stages of visual impairment on event-related potentials (ERPs) and BCI performance while healthy participants directed covert attention to peripheral visual stimuli. We hypothesized that the effect of visual impairments such as blurred or monocular vision might alter attention-related visual ERPs, but has only minor or no impact at all on BCI performance when color is the target feature. Early attention sensitive visual components were altered in monocular vision compared to binocular vision. Simulated vision degradation resulted in reduction of ERPs in the P300 range. The differences in ERPs across vision conditions did not affect the decoding accuracy of the BCI. The study not only revealed differences of visual ERPs in different viewing conditions but primarily demonstrated that a BCI can be reliably controlled by focusing attention on a visual stimulus whose distinguishing features such as color, can be recognized even with visual impairments. This provides a promising approach to enable paralyzed persons to communicate using a non-invasive, gaze-independent BCI.

Auditory hypersensitivity or decreased sound tolerance is a common phenotype of Fragile X Syndrome (FXS). Impairments in adaptation, defined as the reduction in the neuronal responsiveness to repeating sounds, can contribute to this prevalent phenotype. Previous studies on event-related potential observed impairments in mismatch negativity (MMN) in both FXS individuals and in the FMR1-knockout (KO) mouse model of FXS. Therefore, in the present study we characterized stimulus-specific adaptation (SSA), a neural correlate of MMN, at the auditory cortex (AC) of anesthetized (with ketamine/xylazine) female and male postnatal day 20 (P20) wild-type (WT) and FMR1-KO mice, using the oddball paradigm with either 4 Hz or 1 Hz repetition rate. We observed robust SSA at the 4 Hz repetition rate of the oddball paradigm in the AC neurons of all four groups of animals. We also noted that the strength of SSA diminished between 4 to 1 Hz repetition rate. In addition, at both the 4 Hz and 1 Hz repetition rate, reduced SSA was observed particularly in the male FMR1-KO mice compared to their WT counterparts, while female WT and FMR1-KO mice displayed similar SSA. In terms of sex differences, male FMR1-KO mice had lower SSA than female FMR1-KO mice, while SSA was similar in male and female WT mice. Overall, our observation of reduced SSA in the male FMR1-KO mice suggests that impairments in neuronal adaptation potentially contribute to the auditory hypersensitivity phenotype particularly within the male population.

Speech perception ability in a cocktail party environment is highly variable, even for people with clinically healthy hearing. The mechanisms of these differences are poorly understood. A possible reason for these differences could be due to the neural mechanisms of auditory attention. This investigation aims to quantify how well a person is able to neurally entrain to a continuous narrative using a temporal response function (TRF), then evaluate if that TRF measure is correlated with an individual's performance on a selective attention and narrative comprehension task. This was accomplished using a cohort of twenty-five young and healthy listeners who completed speech-in-noise (SIN) perception tasks while their neural activity was recorded with EEG. This EEG data and regressors (speech envelope and gammatone spectrogram) of an auditory stimulus was used to train TRF models. TRF cross validation correlation coefficients were then used in multiple regression models to predict a participant's SIN performance. Task performance had a significant positive relationship with the neural entrainment to an attended stimulus and a negative relationship with the neural entrainment to an unattended stimulus. This was observed with both the selective attention and narrative comprehension measures. The difference of the attended and unattended correlation coefficients were also significantly related to increased task performance. These measures could be a potential biomarker to predict an individual's speech perception in a noisy environment.

Rodent brain matrices that produce coronal or sagittal brain sections for histology confer reproducibility and enable high throughput processing of tissues. However, a stainless steel or acrylic brain matrix that produces tissue sections in a transversal (or horizontal) orientation is currently unavailable as a standard tool. This limits the direct comparison of bilateral brain hemispheres within a single histological section, as freehand trimming to obtain horizontal planes is not easily replicable across samples. To mitigate this challenge, we designed a low-cost (USD 7 per unit), 3D-printed resin-based transverse brain matrix that accommodates mouse brains ranging from 12 to 16 mm in length from the olfactory bulb to the brainstem. Our matrix reproducibly generates horizontal tissue sections with a minimum of 1-mm-thickness without causing visible tissue deformation, which is comparable to the performance of commercial rodent brain matrices. Users may adapt the accompanying CAD code using our video tutorials to customize the transverse brain matrix for their specific needs, including alternative brain size, shape, and tissue thickness.

Purpose. To evaluate whether a Geographic Atrophy Morphology-based Mapping Algorithm (GAMMA) grid, informed by geographic atrophy (GA) lesion morphology, can accelerate functional progression detection compared with a conventional 10-2 grid and a dense grid (129 locations). This work is motivated by emerging regulatory expectations requiring at least five locations to worsen by [&ge;]7 dB from baseline. Methods. Binary atrophy masks from six autofluorescence images were used to simulate GA expansion over 3 years at 3-month intervals using a stochastic perimeter-growth model with a fixed preferential expansion direction (Pdir). For each image, 32 independent growth histories and 32 microperimetric test realisations per history were generated. For each grid (10-2, Dense, and GAMMA), 5-point clusters were selected outside the baseline GA lesion along three directions (0{degrees}, 30{degrees}, 120{degrees}) away from Pdir, simulating full, partial, and no prior knowledge of Pdir. Ground-truth sensitivities were <0 dB inside the GA lesion and normal outside, calculated using a published normative equation. Response variability was simulated following Henson et al. with baseline averaging. Detection time was the first visit at which all five selected locations showed [&ge;]7 dB loss from baseline. Survival curves and median detection times (T50) were used to compare grid performance. Results. The GAMMA grid achieved the earliest progression detection across all scenarios. Under full knowledge of the expansion direction, T50 was 1.0 year for GAMMA versus 1.25 and 1.5 years for Dense and 10-2, respectively. With partial knowledge, GAMMA's T50 was 1.25 years versus 1.5 and 2.0 years for Dense and 10-2. Even under no knowledge, GAMMA detected progression earliest (T50 = 1.5 years), while Dense required 6 months longer and 10-2 nearly double the time (2.75 years). Conclusions. The automatic GAMMA grid accelerates detection of localised functional progression compared with conventional and dense grids. Structure-informed grid optimisation may better align testing with likely expansion paths, potentially reducing follow-up duration and sample sizes in perimetry-based interventional trials.

Individuals with inherited retinal dystrophies (IRDs) undergo a slow, genetically heterogeneous loss of vision, yet how the visual cortex and non-visual sensory, motor, and psychological systems adapt to this deprivation remains poorly characterized. Existing evidence comes mainly from single-modality, cross-sectional studies that rarely account for genetic heterogeneity, making it hard to distinguish adaptive change from a direct, non-retinal mutation effect, since several IRD genes are not retina-specific. To address this gap, we designed an observational, longitudinal, multimodal protocol that combines ophthalmological, genetic, and in silico characterization with electrophysiological (steady-state visual evoked potentials and TMS-EEG), chemosensory, sensorimotor, and psycho-personological assessments. Patients aged 18 to 75 years with rod-cone (retinitis pigmentosa, Usher syndrome) or cone and cone-rod dystrophies will be assessed at baseline (T0) and at an 18-month follow-up (T1); sighted controls, matched for age, sex, and handedness, will complete the same battery once. Importantly, pairing genotypic with phenotypic data allows changes in non-visual domains to be interpreted against, rather than independently of, each patient's molecular background. We expect individuals with IRDs to differ from controls in visual cortical responsiveness and in selected non-visual sensory and sensorimotor measures, with genotype-related differences explored where sample size permits. Given the rarity of IRDs, the design is exploratory and emphasizes effect sizes and individual variability over large-sample inference. The protocol was approved by the Ethics Committee of the University of Verona (CARP 08.R1/2024) and follows the Declaration of Helsinki and the GDPR; findings will be disseminated through peer-reviewed publications and shared with patients and IRD patient associations.

In this study, we investigated the neural and behavioral basis of motivationally objective versus subjective value-based decisions. Using a within-subject fMRI design, healthy participants performed a risky decision-making task that elicited different levels of subjectivity in decision-making in two task conditions. In the Best or objective condition, choices were rewarded only when they were objectively best on a given trial, incentivizing decisions based on externally specified per-trial point maximization. In the Choice or subjective condition, participants received the reward associated with the chosen option, irrespective of how it compared to the unchosen option, allowing greater freedom to exercise subjective preferences in decision policy. Behaviorally, participants relied more on objectively optimal policy in the Best than the Choice condition. There was also a greater consensus across participants in behaviorally displayed and self-reported policies in the Best condition as well as a greater commitment to a single policy by individual participants in this condition, further confirming more objective behavior in the Best condition, compared to Choice. Moreover, behavioral inferences showed a greater agreement with self-report in the Best condition. Our fMRI results showed that the decision-making in Choice, relative to the Best condition, was associated with greater BOLD response in mid-cingulum/posterior cingulate cortex and dorsal anterior cingulate cortex, suggesting their involvement in less externally constrained, or motivationally subjective, decision-making.

Electrocutaneous stimulation provides a means to warn workers of potential hazards. Our previous parameter studies were conducted at rest or under controlled external influences such as vibration, temperature, and humidity, whereas actual work tasks have not yet been addressed. As a step towards greater practical applicability, we conducted two studies in which a circumferential electrocutaneous warning signal was applied to the upper right arm while participants performed a reading task, screw-driving, and polishing. In study 1 (n = 32), the pulse interval was adjusted individually to evoke a vibrating sensation, and the intolerance threshold was determined. In study 2 (n = 29), a fixed pulse interval of 36 ms was applied to all participants, the warning threshold was determined in addition to the intolerance threshold, and muscle twitches were documented both by participant report and by an independent observer. Study 1 demonstrated that electrocutaneous warning during work tasks is feasible, with a median intolerance threshold higher during polishing (16.5 mA) than during reading or screw-driving (14 mA each). In study 2, the fixed parameter setting elicited the intended pulsating or vibrating sensation at rest in 89 % of the participants in both presentations. The median warning threshold was 8 mA and did not differ between the three work tasks, whereas the intolerance threshold was lower during reading (18 mA) than during screw-driving and polishing (20 mA each). Muscle twitches were largely absent at the warning threshold and became frequent towards the intolerance threshold, with a ventral dominance of their location in both studies. An individual parametrization is therefore not required for the majority of users when a binary warning is to be conveyed, and operating the system slightly above the warning threshold provides a usable amplitude range while minimizing muscle twitches. Future work will focus on electrode optimization and on the objective measurement of motor responses.

The pure-tone audiogram remains the cornerstone of hearing assessment, yet it captures only part of the functional consequences of cochlear damage. Most phenotyping schemes rely solely on the audiogram, which cannot detect hidden suprathreshold dysfunction when thresholds are normal. How best to represent audiological data - as discrete phenotypes or as a continuous space - remains unresolved. Using a retrospective database from the Copenhagen Hearing and Balance Centre, Rigshospitalet (Denmark; 1995-2022; 251986 ear-session units from 81883 adults with normal or sensorineural audiograms), we compared linear (PCA) and non-linear (UMAP) embeddings of the audiogram alone and with word recognition in quiet (WRSmax-Q). Here we show that a linear embedding suffices for the audiogram, but a non-linear one becomes advantageous once suprathreshold measures are added. PCA compressed the audiogram onto two interpretable axes, overall level and slope, explaining 91.5% of the variance, with ears forming a continuous cloud that published reference schemes tiled into overlapping regions. For the audiogram alone, UMAP recovered the same organisation. WRSmax-Q varied widely among ears with similar audiograms; adding it left the PCA unchanged but reorganised the UMAP. A synthetic experiment illustrated this: ears with identical audiograms but different WRSmax-Q separated under UMAP, not PCA. Combining thresholds with WRSmax-Q, UMAP yielded a continuous representation - a prototype Hearing Loss Map - where each ear's position reflects threshold and suprathreshold measures. Representing hearing loss as a location in a continuous space rather than as discrete categories may better reflect its graded nature, with clinical relevance for individual patients.

Noise-induced hearing loss (NIHL) degrades auditory sensitivity, but how it affects the learning of sound-guided decisions remains unclear. We trained adult Mongolian gerbils with normal-hearing (NH) or permanent NIHL, induced by a 2-hour exposure to 120 dB SPL broadband noise, to discriminate 4- versus 12-Hz amplitude-modulated (AM) broadband noise presented in a two-alternative forced-choice task. Stimuli were presented at comparable sensation levels across groups. Relative to NH animals, NIHL animals required approximately 2.5 times more sessions and 2.9 times more trials to reach task acquisition criterion. This learning delay was not explained by reduced task engagement, response latency, or auditory brainstem response threshold shift. Video tracking showed that spatial occupancy refined with training in both groups, whereas trial-by-trial movement path trajectories became less variable in NH animals. Per-session and dynamic trial-by-trial logistic regression models revealed that the influence of the task-relevant acoustic stimulus increased with training in both groups but remained consistently lower in NIHL animals, whereas sound-independent choice tendencies were largely unchanged. Together, these results suggest that NIHL delays auditory learning by reducing the influence of acoustic evidence on choice, indicating that hearing loss can alter how sensory experience is used to acquire new behaviors.

ObjectiveElectroencephalography (EEG) is a key tool for studying auditory processing in cochlear implant (CI) users. In particular, EEG recordings obtained during continuous speech are becoming increasingly important for assessing speech and language processing in CI users, and may be utilized for neurofeedback. However, CIs also induce strong stimulation artifacts that are time-locked to the stimulus and mask the neural responses that have smaller magnitudes. Existing artifact reduction methods are typically based on event-related potentials (ERPs) or require manual component selection, making them unsuitable for naturalistic listening conditions or large datasets. ApproachWe develop CORSICA (CORrelation-baSed ICA artifact rejection), a reproducible, parameter-efficient method for CI artifact reduction in EEG responses to continuous speech. CORSICA operates on independent components (ICs) obtained through Infomax ICA and requires no manual component labelling, with performance governed by a single tunable threshold. It exploits the observation that CI artifacts temporally follow the audio signal without delay, whereas neural responses have an inherent lag due to auditory pathway latencies. For each IC, CORSICA computes the cross-correlation with the speech stimulus. Artifacts are identified by a high signal-to-noise ratio (SNR) of the correlation peak near zero lag, and the component is rejected if this SNR exceeds a threshold. To benchmark CORSICA, we evaluate two alternatives: a TRF-based SNR method, in which temporal response functions are fitted to each IC and artifact-driven peaks near zero lag are used for rejection, and a variant replacing ICA with second-order blind identification (SOBI) as the source separation step. Main resultsCORSICA effectively suppressed CI artifacts while preserving neural activity, enabling recovery of physiologically plausible TRFs with only 2% of ICs rejected. Both benchmark methods confirmed the validity of the SNR-based rejection framework, but CORSICA outperformed the TRF-based alternative in artifact suppression quality. Replacing ICA with SOBI as the source separation step required more ICs to be rejected, further supporting ICA as the preferred backbone for CORSICA. SignificanceCORSICA provides a fully objective, label-free approach to identifying CI artifacts in speech-evoked EEG data, with no manual intervention required. By centering artifact rejection on a single interpretable threshold, it offers a reproducible preprocessing standard for future EEG studies on speech processing in CI users. ConclusionOur findings demonstrate that objective CI artifact suppression in speech-evoked EEG data is feasible on the basis of the ICs temporal response patterns.

Electroretinography (ERG) measures the functional response of distinct retinal cells to light, but was largely displaced by structural imaging in the 2000s. Standardization efforts by the International Society for Clinical Electrophysiology of Vision (ISCEV) began in the late 1980s, and collapsed the rich time-series traces into reproducible components and implicit times. Recent improvements in hardware (RETeval) and software (artificial intelligence) may increase the utility of ERG data. However, no ERG-specific foundation models exist, and there are not enough public datasets to train one. We asked whether time-series foundation models (FMs) trained without ERG-specific pre-training could be adapted through transfer learning. Using two public datasets, PERG-IOBA (pattern ERG with ocular diagnoses), and LEOPs (full-field ERG focusing on Autism Spectrum Disorder, ASD), we interrogated how FMs could improve over smaller within-domain models. We measured the binary (healthy/typically developing vs any annotation) and multiclass (specific family/diagnosis) classification performance of both frozen and fine-tuned FMs, alongside custom autoencoder and multiscale models, using patient-aware splits for cross validation. We benchmark the same architectures against PTB-XL, a large 12-lead ECG corpus, as both a control for each approach and to explore scaling behavior. We show that 1) pre-trained FMs can reconstruct masked traces from all three datasets, 2) frozen and fine-tuned embeddings, especially combined with multimodal metadata through masked autoencoders, performed best on classification tasks. Performance on PTB-XL was maintained down to 300 records, comparable in size to the ERG datasets. We could not reproduce published classification performance on the ASD task. Taken together, these results support general purpose foundation models as a practical approach to ERG analysis.

Deep brain stimulation (DBS) is transforming from a static therapy toward adaptive systems that adjust stimulation based on neural biomarkers. However, the detection of reliable biomarkers that capture the multi-dimensional nature of complex symptoms is often challenging. Here we demonstrate volitional DBS (vDBS)--a paradigm in which patients use brain-computer interface (BCI) training to learn self-regulation of a neural signal that then controls closed-loop DBS. Two patients with Parkinson's disease implanted with sensing-enabled neurostimulators completed chronic, at-home BCI training by playing an airplane simulation game. Through training, they were able to effectively down-regulate their cortical beta signal (p's < 1e-10), represented as the real-time position of a plane in the BCI game. Following training, this cortical beta signal served as the input to a closed-loop DBS algorithm. By modulating their beta signal to cross personalized thresholds, patients voluntarily increased or decreased neurostimulation amplitude at will, in the absence of physical movement (p's < 1e-10). This proof-of-principle demonstration establishes that volitional control of intracranial neurostimulation is achievable without the need of an externalized manual controller. BCI-vDBS could potentially be used for a range of neuropsychiatric conditions and brain rehabilitation to support personalized control of neurostimulation.

A new electrical stimulation focused computational model of the visual cortex had been created to aid in the development of cortical visual prosthesis. The model consists of 10,666 biophysical neurons representing 0.13mm3 of a layer 2/3 of the primary visual cortex and was calibrated to match the baseline activity of rat brain recordings. A novel model of electrical stimulation was developed to allow for selective activation of specific neuron types, and matched the single cell stimulation response generated by known stimulation models. The electrode was tuned to match recorded population level change in activity across distances and currents recorded in the rats brain. The model is now ready to explore electrical stimulation effects on the visual cortex for examination of neuron specific stimulation to assist in the development of cortical visual prosthesis.

Carotid endarterectomy carries the risk of intraoperative cerebral ischemia, which is monitored by expert neurophysiologists through continuous electroencephalography (cEEG). Because expert availability is limited, we developed a hybrid novice-artificial intelligence (AI) system that detects ischemia using novice monitors with limited cEEG training. The hybrid system dynamically weights novice and AI inputs to arrive at a final output. Using four novices, we compared hybrid systems against experts alone, novices alone, and AI alone. Hybrid systems were statistically non-inferior to experts in sensitivity and false-positive rate (FPR), whereas novices alone were not. At 80% sensitivity, hybrid systems reduced FPR by half compared with the AI-only system, with similar benefits at 90% sensitivity. Further, the area under the precision-recall curve improved from 0.546 to 0.610-0.726, the area under the receiver operating characteristic curve improved from 0.957 to 0.967-0.971, and calibration improved compared with AI alone. These results highlight the potential of a hybrid system to monitor intraoperative cerebral ischemia.