Background: Information transmission into the human nervous system is the basis for a variety of prosthetic applications. Spinal cord stimulation (SCS) systems are widely available, have a well documented safety record, can be implanted minimally invasively, and are known to stimulate afferent pathways. Nonetheless, SCS devices are not yet used for computer-brain-interfacing applications. Objective: Here we aimed to establish computer-to-brain communication via medical SCS implants in a group of 20 individuals who had been operated for the treatment of chronic neuropathic pain. Methods: In the initial phase, we conducted interface calibration with the aim of determining personalized stimulation settings that yielded distinct and reproducible sensations. These settings were subsequently utilized to generate inputs for a range of behavioral tasks. We evaluated the required calibration time, task training duration, and the subsequent performance in each task. Results: We could establish a stable spinal computer-brain interface in 18 of the 20 participants. Each of the 18 then performed one or more of the following tasks: A rhythm-discrimination task (n = 13), a Morse-decoding task (n = 3), and/or two different balance/body-posture tasks (n = 18; n = 5). The median calibration time was 79 min. The median training time for learning to use the interface in a subsequent task was 1:40 min. In each task, every participant demonstrated successful performance, surpassing chance levels. Conclusion: The results constitute the first proof-of-concept of a general purpose computer-brain interface paradigm that could be deployed on present-day medical SCS platforms.

IntroductionAlthough stimulation-induced sensations are typically considered undesirable side effects in clinical DBS therapy, there are emerging scenarios, such as computer-brain interface applications, where these sensations may be intentionally created. The selection of stimulation parameters, whether to avoid or induce sensations, is a challenging task due to the vast parameter space involved. This study aims to streamline DBS parameter selection by employing a machine learning model to predict the occurrence and somatic location of paresthesias in response to thalamic DBS.MethodsWe used a dataset comprising 3,359 paresthetic sensations collected from 18 thalamic DBS leads from 10 individuals in two clinical centers. For each stimulation, we modeled the Volume of Tissue Activation (VTA). We then used the stimulation parameters and the VTA information to train a machine learning model to predict the occurrence of sensations and their corresponding somatic areas.ResultsOur results show fair to substantial agreement with ground truth in predicting the presence and somatic location of DBS-evoked paresthesias, with Kappa values ranging from 0.31 to 0.72. We observed comparable performance in predicting the presence of paresthesias for both seen and unseen cases (Kappa 0.72 vs. 0.60). However, Kappa agreement for predicting specific somatic locations was significantly lower for unseen cases (0.53 vs. 0.31).ConclusionThe results suggest that machine learning can potentially be used to optimize DBS parameter selection, leading to faster and more efficient postoperative management. Outcome predictions may be used to guide clinical DBS programming or tuning of DBS based computer-brain interfaces.

Sensory substitution offers a promising approach to restore lost sensory functions. Here we show that spinal cord stimulation (SCS), typically used for chronic pain management, can potentially serve as a novel auditory sensory substitution device. We recruited 13 patients undergoing SCS implantation and translated everyday sound samples into personalized SCS patterns during their trial phase. In a sound identification task-where chance-level performance was 33.3%-participants ( n = 8 ) achieved a mean accuracy of 72.8% using only SCS input. We observed a weak positive correlation between stimulation bitrate and identification accuracy. A follow-up discrimination task ( n = 5 ) confirmed that reduced bitrates significantly impaired participants' ability to distinguish between consecutive SCS patterns, indicating effective processing of additional information at higher bitrates. These findings demonstrate the feasibility of using existing SCS technology to create a novel neural interface for a sound prosthesis. Our results pave the way for future research to enhance stimulation fidelity, assess long-term training effects, and explore integration with other auditory aids for comprehensive hearing rehabilitation.

Various common neurological disorders such as e.g., Parkinson's Disease are characterized by balance and gait difficulties. Spinal-Cord-Stimulation (SCS) utilizes stimulation to sensory pathways to suppress pain, but could potentially also provide a readily available method of information transmission into the CNS to aid subjects in their mobility via supportive proprioceptive, balance or gait structuring inputs.

Deep brain stimulation (DBS) is a complex surgical procedure that requires detailed anatomical knowledge. In many fields of neurosurgery navigation systems are used to display anatomical structures during an operation to aid performing these surgeries. In frame-based DBS, the advantage of visualization has not yet been evaluated during the procedure itself. In this study, we added live visualization to a frame-based DBS system, using a standard navigation system and investigated its accuracy and potential use in DBS surgery. As a first step, a phantom study was conducted to investigate the accuracy of the navigation system in conjunction with a frame-based approach. As a second step, 5 DBS surgeries were performed with this combined approach. Afterwards, 3 neurosurgeons and 2 neurologists with different levels of experience evaluated the potential use of the system with a questionnaire. Moreover, the additional personnel, costs and required set up time were noted and compared to 5 consecutive standard procedures. In the phantom study, the navigation system showed an inaccuracy of 2.1 mm (mean SD 0.69 mm). In the questionnaire, a mean of 9.4/10 points was awarded for the use of the combined approach as a teaching tool, a mean of 8.4/10 for its advantage in creating a 3-dimensional (3-D) map and a mean of 8/10 points for facilitating group discussions. Especially neurosurgeons and neurologists in training found it useful to better interpret clinical results and side effects (mean 9/10 points) and neurosurgeons appreciated its use to better interpret microelectrode recordings (mean 9/10 points). A mean of 6/10 points was awarded when asked if the benefits were worth the additional efforts. Initially 2 persons, then one additional person was required to set up the system with no relevant added time or costs. Using a navigation system for live visualization during frame-based DBS surgery can improve the understanding of the complex 3-D anatomy and many aspects of the procedure itself. For now, we would regard it as an excellent teaching tool rather than a necessity to perform DBS surgeries.

BACKGROUND:Automatic segmentation is gaining relevancy in image-based targeting of neural structures. OBJECTIVE:To evaluate its feasibility, we retrospectively analyzed the concordance of magnetic resonance imaging (MRI)-based automatic segmentation of the subthalamic nucleus (STN) and intraoperative microelectrode recordings (MERs). METHODS:Electrodes (n = 60) for deep brain stimulation were implanted in the STN of patients (n = 30; median age 57 yr) with Parkinson disease (n = 29) or rapid-onset dystonia parkinsonism (n = 1). Elements (Brainlab, Munich, Germany) were used to segment the STN, using 2 volumetric T1 (±contrast) and volumetric T2 images as input. The stereotactic computed tomography was coregistered with the imaging, and the original stereotactic coordinates were imported. MERs (0.5-1 mm steps) along the anterior, central, and lateral trajectories were used to determine differences between the image-segmented STN boundary and MER-based STN entry and exit. RESULTS:Of 175 trajectories, 105 penetrated or touched (≤0.7 mm) the STN. The overall median deviation between the segmented STN boundary and electrophysiological recordings was 1.1 mm for MER-based STN entry and 2.0 mm for STN exit. Regarding the entry point of the STN, there was no statistically significant difference between MRI-based automatic segmentation and the electrophysiological trajectories analyzed with intraoperative MER. The exit point was significantly different between both methods in the central and lateral trajectories. CONCLUSION:MRI-based automatic segmentation of the STN is a viable, patient-specific targeting approach that can be used alongside traditional targeting methods in deep brain stimulation to support preoperative planning and visualization of target structures and aid postoperative optimization of programming.

We read with interest the manuscript by Li et al. "Toward a unified connectomic target for deep brain stimulation in obsessive-compulsive disorder", published on the preprint server bioRxiv (13 april 2019) [18]. We would like to comment on the far-reaching conclusions drawn from the use of aggregated normative and connectomic data for their impact on decision making for surgical targeting in deep brain stimulation (DBS). Furthermore, we would like to discuss the emerging use of publication of unreviewed scientific data in various open formats.

OBJECTIVE:Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary.METHODS:We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance.RESULTS:Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error).CONCLUSIONS:Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance.SIGNIFICANCE:This confirms that structural brain traits contribute to individual performance in BCI use.

Background. Robot-assisted training may improve motor function in some hemiparetic patients after stroke, but no physiological predictor of rehabilitation progress is reliable. Resting state functional magnetic resonance imaging (RS-fMRI) may serve as a method to assess and predict changes in the motor network. Objective. The authors examined the effects of upper-extremity robot-assisted rehabilitation (MANUS) versus an electroencephalography-based brain computer interface setup with motor imagery (MI EEG-BCI) and compared pretreatment and posttreatment RS-fMRI. Methods. In all, 9 adults with upper-extremity paresis were trained for 4 weeks with a MANUS shoulder-elbow robotic rehabilitation paradigm. In 3 participants, robot-assisted movement began if no voluntary movement was initiated within 2 s. In 6 participants, MI-BCI–based movement was initiated if motor imagery was detected. RS-fMRI and Fugl-Meyer (FM) upper-extremity motor score were assessed before and after training. Results. The individual gain in FM scores over 12 weeks could be predicted from functional connectivity changes (FCCs) based on the pre-post differences in RS-fMRI measurements. Both the FM gain and FCC were numerically higher in the MI-BCI group. Increases in FC of the supplementary motor area, the contralesional and ipsilesional motor cortex, and parts of the visuospatial system with mostly association cortex regions and the cerebellum correlated with individual upper-extremity function improvement. Conclusion. FCC may predict the steepness of individual motor gains. Future training could therefore focus on directly inducing these beneficial increases in FC. Evaluation of the treatment groups suggests that MI is a potential facilitator of such neuroplasticity.

Anterior brain asymmetry, assessed through the alpha and beta band in resting-state electroencephalogram (EEG) is associated with approach-related behavioral dispositions, particularly with aggression in the general population. To date, the association between frontal asymmetry and aggression has not been examined in highly aggressive groups. We examined the topographic characteristics of alpha and beta activity, the relation of both asymmetry metrics to trait aggression, and whether alpha asymmetry was extreme in anterior regions according to clinical standards in a group of imprisoned violent offenders. As expected, these individuals were characterized by stronger right than left-hemispheric alpha activity, which was putatively extreme in anterior regions in one third of the cases. We also report that in line with observations made in the general population, aggression was associated with stronger right-frontal alpha activity in these violent individuals. This suggests that frontal alpha asymmetry, as a correlate of trait aggression, might be utilizable as an outcome measure in studies which assess the effects of anti-aggressiveness training in violent offenders.

Background. Robot-assisted training may improve motor function in some hemiparetic patients after stroke, but no physiological predictor of rehabilitation progress is reliable. Resting state functional magnetic resonance imaging (RS-fMRI) may serve as a method to assess and predict changes in the motor network. Objective. The authors examined the effects of upper-extremity robot-assisted rehabilitation (MANUS) versus an electroencephalography-based brain computer interface setup with motor imagery (MI EEG-BCI) and compared pretreatment and posttreatment RS-fMRI. Methods. In all, 9 adults with upper-extremity paresis were trained for 4 weeks with a MANUS shoulder-elbow robotic rehabilitation paradigm. In 3 participants, robot-assisted movement began if no voluntary movement was initiated within 2 s. In 6 participants, MI-BCI– based movement was initiated if motor imagery was detected. RS-fMRI and Fugl-Meyer (FM) upper-extremity motor score were assessed before and after training. Results. The individual gain in FM scores over 12 weeks could be predicted from functional connectivity changes (FCCs) based on the pre-post differences in RS-fMRI measurements. Both the FM gain and FCC were numerically higher in the MI-BCI group. Increases in FC of the supplementary motor area, the contralesional and ipsilesional motor cortex, and parts of the visuospatial system with mostly association cortex regions and the cerebellum correlated with individual upper-extremity function improvement. Conclusion. FCC may predict the steepness of individual motor gains. Future training could therefore focus on directly inducing these beneficial increases in FC. Evaluation of the treatment groups suggests that MI is a potential facilitator of such neuroplasticity.

Brain-computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram (EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a second session these participants performed a motor imagery, motor observation and motor execution task in a functional magnetic resonance imaging (fMRI) scanner.Group difference analysis between high and low aptitude BCI users revealed significantly higher activation of the supplementary motor areas (SMA) for the motor imagery and the motor observation tasks in high aptitude users. Low aptitude users showed no activation when observing movement. The number of activated voxels during motor observation was significantly correlated with accuracy in the EEG-BCI task (r = 0.53). Furthermore, the number of activated voxels in the right middle frontal gyrus, an area responsible for processing of movement observation, correlated (r = 0.72) with BCI-performance. This strong correlation highlights the importance of these areas for task monitoring and working memory as task goals have to be activated throughout the BCI session.The ability to regulate behavior and the brain through learning mechanisms involving imagery such as required to control a BCI constitutes the consequence of ideo-motor co-activation of motor brain systems during observation of movements. The results demonstrate that acquisition of a sensorimotor program reflected in SMR-BCI-control is tightly related to the recall of such sensorimotor programs during observation of movements and unrelated to the actual execution of these movement sequences. (C) 2011 Elsevier Inc. All rights reserved.

Background In the graph theoretical analysis of anatomical brain connectivity, the white matter connections between regions of the brain are identified and serve as basis for the assessment of regional connectivity profiles, for example, to locate the hubs of the brain. But regions of the brain can be characterised further with respect to their gray matter volume or resting state perfusion. Local anatomical connectivity, gray matter volume and perfusion are traits of each brain region that are likely to be interdependent, however, particular patterns of systematic covariation have not yet been identified. Methodology/Principal Findings We quantified the covariation of these traits by conducting an integrative MRI study on 23 subjects, utilising a combination of Diffusion Tensor Imaging, Arterial Spin Labeling and anatomical imaging. Based on our hypothesis that local connectivity, gray matter volume and perfusion are linked, we correlated these measures and particularly isolated the covariation of connectivity and perfusion by statistically controlling for gray matter volume. We found significant levels of covariation on the group- and regionwise level, particularly in regions of the Default Brain Mode Network. Conclusions/Significance Connectivity and perfusion are systematically linked throughout a number of brain regions, thus we discuss these results as a starting point for further research on the role of homology in the formation of functional connectivity networks and on how structure/function relationships can manifest in the form of such trait interdependency.