Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

This paper presents “Craniux,” an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development

MRI-guided laser interstitial thermal therapy using the Visualase system and Navigus frameless stereotaxy in an infant: Technical case report

Laser interstitial thermal therapy (LITT) is increasingly used as a surgical option for the treatment of epilepsy. Placement of the laser fibers relies on stereotactic navigation with cranial fixation pins. In addition, the laser fiber is stabilized in the cranium during the ablation using a cranial bolt that assumes maturity of the skull. Therefore, younger infants (\u3c 2 years of age) have traditionally not been considered as candidates for LITT. However, LITT is an appealing option for patients with familial epilepsy syndromes, such as tuberous sclerosis complex (TSC), due to the multiplicity of lesions and the likely need for multiple procedures. A 4-month-old infant with TSC presented with refractory focal seizures despite receiving escalating doses of 5 antiepileptic medications. Electrographic and clinical seizures occurred numerous times daily. Noninvasive evaluations, including MRI, magnetoencephalography, scalp EEG, and SPECT, localized the ictal onset to a left frontal cortical tuber in the premotor area. In this paper, the authors report a novel technique to overcome the challenges of performing LITT in an infant with an immature skull by repurposing the Navigus biopsy skull mount for stereotactic placement of a laser fiber using electromagnetic-based navigation. The patient underwent successful ablation of the tuber and remained seizure free 4 months postoperatively. To the authors\u27 knowledge, this is the youngest reported patient to undergo LITT. A safe method is described to perform LITT in an infant using commonly available tools without dedicated instrumentation beyond standard stereotactic navigation, a biopsy platform, and the Visualase system

Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces

Prior studies have used graph analysis of resting-state magnetoencephalography (MEG) to characterize abnormal brain networks in neurological disorders. However, a present challenge for researchers is the lack of guidance on which network construction strategies to employ. The reproducibility of graph measures is important for their use as clinical biomarkers. Furthermore, global graph measures should ideally not depend on whether the analysis was performed in the sensor or source space. Therefore, MEG data of the 89 healthy subjects of the Human Connectome Project were used to investigate test–retest reliability and sensor versus source association of global graph measures. Atlas-based beamforming was used for source reconstruction, and functional connectivity (FC) was estimated for both sensor and source signals in six frequency bands using the debiased weighted phase lag index (dwPLI), amplitude envelope correlation (AEC), and leakage-corrected AEC. Reliability was examined over multiple network density levels achieved with proportional weight and orthogonal minimum spanning tree thresholding. At a 100% density, graph measures for most FC metrics and frequency bands had fair to excellent reliability and significant sensor versus source association. The greatest reliability and sensor versus source association was obtained when using amplitude metrics. Reliability was similar between sensor and source spaces when using amplitude metrics but greater for the source than the sensor space in higher frequency bands when using the dwPLI. These results suggest that graph measures are useful biomarkers, particularly for investigating functional networks based on amplitude synchrony

Neoadjuvant Chemotherapy with Laser Interstitial Thermal Therapy in Central Nervous System Neuroblastoma: Illustrative Case and Literature Review

Primitive neuroectodermal tumors of the central nervous system, or CNS neuroblastoma, are rare neoplasms in children. Recently, methylation profiling enabled the discovery of four distinct entities of these tumors. The current treatment paradigm involves surgical resection followed by chemotherapy and radiation. However, upfront surgical resection carries high surgical morbidity in this patient population due to their young age, tumor vascularity, and often deep location in the brain. We report a case of CNS neuroblastoma that can be successfully treated with neoadjuvant chemotherapy followed by minimally invasive laser interstitial thermal therapy and radiation. The patient has complete treatment with no evidence of recurrence at one year follow-up. This case illustrates a potential paradigm shift in the treatment of these rare tumors can be treated using minimally invasive surgical approach to achieve a favorable outcome

Brain Computer Interface Learning for Systems Based on Electrocorticography and Intracortical Microelectrode Arrays

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning

A Pediatric Tumor Found Frequently in the Adult Population: A Case of Anaplastic Astroblastoma in an Elderly Patient and Review of the Literature

Astroblastomas are rare, potentially curable primary brain tumors which can be difficult to diagnose. We present the case of astroblastoma in a 73-year-old male, an atypical age for this tumor, more classically found in pediatric and young adult populations. Through our case and review of the literature, we note that this tumor is frequently reported in adult populations and the presentation of this tumor in the elderly is well described. This tumor is an important consideration in the differential diagnosis when managing both pediatric and adult patients of any age who present with the imaging findings characteristic of this rare tumor

Remapping cortical modulation for electrocorticographic brain-computer interfaces: a somatotopy-based approach in individuals with upper-limb paralysis

OBJECTIVE: Brain-computer interface (BCI) technology aims to provide individuals with paralysis a means to restore function. Electrocorticography (ECoG) uses disc electrodes placed on either the surface of the dura or the cortex to record field potential activity. ECoG has been proposed as a viable neural recording modality for BCI systems, potentially providing stable, long-term recordings of cortical activity with high spatial and temporal resolution. Previously we have demonstrated that a subject with spinal cord injury (SCI) could control an ECoG-based BCI system with up to three degrees of freedom (Wang et al 2013 PLoS One). Here, we expand upon these findings by including brain-control results from two additional subjects with upper-limb paralysis due to amyotrophic lateral sclerosis and brachial plexus injury, and investigate the potential of motor and somatosensory cortical areas to enable BCI control. APPROACH: Individuals were implanted with high-density ECoG electrode grids over sensorimotor cortical areas for less than 30 d. Subjects were trained to control a BCI by employing a somatotopic control strategy where high-gamma activity from attempted arm and hand movements drove the velocity of a cursor. MAIN RESULTS: Participants were capable of generating robust cortical modulation that was differentiable across attempted arm and hand movements of their paralyzed limb. Furthermore, all subjects were capable of voluntarily modulating this activity to control movement of a computer cursor with up to three degrees of freedom using the somatotopic control strategy. Additionally, for those subjects with electrode coverage of somatosensory cortex, we found that somatosensory cortex was capable of supporting ECoG-based BCI control. SIGNIFICANCE: These results demonstrate the feasibility of ECoG-based BCI systems for individuals with paralysis as well as highlight some of the key challenges that must be overcome before such systems are translated to the clinical realm. ClinicalTrials.gov Identifier: NCT01393444

Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning