Nanoscale Connectomics Annotation Standards Framework.

The promise of large-scale, high-resolution datasets from Electron Microscopy (EM) and X-ray Microtomography (XRM) lies in their ability to reveal neural structures and synaptic connectivity, which is critical for understanding the brain. Effectively managing these complex and rapidly increasing datasets will enable new scientific insights, facilitate querying, and support secondary use across the neuroscience community. However, without effective neurodata standards that permit use of these data across multiple systems and workflows, these valuable and costly datasets risk being underutilized especially as they surpass petascale levels.

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling.

Background: Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability.

Methods: We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.

Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months.

Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex.

Characteristics and stability of sensorimotor activity driven by isolated-muscle group activation in a human with tetraplegia.

Understanding the cortical representations of movements and their stability can shed light on improved brain-machine interface (BMI) approaches to decode these representations without frequent recalibration. Here, we characterize the spatial organization (somatotopy) and stability of the bilateral sensorimotor map of forearm muscles in an incomplete-high spinal-cord injury study participant implanted bilaterally in the primary motor and sensory cortices with Utah microelectrode arrays (MEAs). We built representation maps by recording bilateral multiunit activity (MUA) and surface electromyography (EMG) as the participant executed voluntary contractions of the extensor carpi radialis (ECR), and attempted motions in the flexor carpi radialis (FCR), which was paralytic.

Benchmarking Human Performance for Visual Search of Aerial Images.

Aerial images are frequently used in geospatial analysis to inform responses to crises and disasters but can pose unique challenges for visual search when they contain low resolution, degraded information about color, and small object sizes. Aerial image analysis is often performed by humans, but machine learning approaches are being developed to complement manual analysis. To date, however, relatively little work has explored how humans perform visual search on these tasks, and understanding this could ultimately help enable human-machine teaming.

Intracortical microstimulation of somatosensory cortex enables object identification through perceived sensations.

Advances in brain-machine interfaces have helped restore function and independence for individuals with sensorimotor deficits; however, providing efficient and effective sensory feedback remains challenging. Intracortical microstimulation (ICMS) of sensorimotor brain regions is a promising technique for providing bioinspired sensory feedback. In a human participant with chronically-implanted microelectrode arrays, we provided ICMS to the primary somatosensory cortex to generate tactile percepts in his hand.

An Integrated Toolkit for Extensible and Reproducible Neuroscience.

As neuroimagery datasets continue to grow in size, the complexity of data analyses can require a detailed understanding and implementation of systems computer science for storage, access, processing, and sharing. Currently, several general data standards (e.g.

Intracortical Somatosensory Stimulation to Elicit Fingertip Sensations in an Individual With Spinal Cord Injury.

Background And Objectives: The restoration of touch to fingers and fingertips is critical to achieving dexterous neuroprosthetic control for individuals with sensorimotor dysfunction. However, localized fingertip sensations have not been evoked via intracortical microstimulation (ICMS).

Methods: Using a novel intraoperative mapping approach, we implanted electrode arrays in the finger areas of left and right somatosensory cortex and delivered ICMS over a 2-year period in a human participant with spinal cord injury.

DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries.

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses.

Novel intraoperative online functional mapping of somatosensory finger representations for targeted stimulating electrode placement: technical note.

Defining eloquent cortex intraoperatively, traditionally performed by neurosurgeons to preserve patient function, can now help target electrode implantation for restoring function. Brain-machine interfaces (BMIs) have the potential to restore upper-limb motor control to paralyzed patients but require accurate placement of recording and stimulating electrodes to enable functional control of a prosthetic limb. Beyond motor decoding from recording arrays, precise placement of stimulating electrodes in cortical areas associated with finger and fingertip sensations allows for the delivery of sensory feedback that could improve dexterous control of prosthetic hands.

Extended home use of an advanced osseointegrated prosthetic arm improves function, performance, and control efficiency.

Full restoration of arm function using a prosthesis remains a grand challenge; however, advances in robotic hardware, surgical interventions, and machine learning are bringing seamless human-machine interfacing closer to reality.Through extensive data logging over 1 year, we monitored at-home use of the dexterous Modular Prosthetic Limb controlled through pattern recognition of electromyography (EMG) by an individual with a transhumeral amputation, targeted muscle reinnervation, and osseointegration (OI).Throughout the study, continuous prosthesis usage increased (1% per week,< 0.

Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets.

Background: Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, or X-ray microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods.

Neural Reconstruction Integrity: A Metric for Assessing the Connectivity Accuracy of Reconstructed Neural Networks.

Neuroscientists are actively pursuing high-precision maps, or consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we developed a metric for measuring the performance of such methods by comparing their output with those generated by human annotators ("ground truth" data).

High Precision Neural Decoding of Complex Movement Trajectories using Recursive Bayesian Estimation with Dynamic Movement Primitives.

Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedom of robotic end-effectors. While BMIs have yet to achieve the level of reliability desired for widespread clinical use, environmental sensors (e.

Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject.

Objective: We used native sensorimotor representations of fingers in a brain-machine interface (BMI) to achieve immediate online control of individual prosthetic fingers.

Approach: Using high gamma responses recorded with a high-density electrocorticography (ECoG) array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: (1) if any finger was moving, and, if so, (2) which digit was moving.

Neuroinflammation in primary blast neurotrauma: Time course and prevention by torso shielding.

Mechanisms of primary blast injury caused by overpressure are not fully understood. In particular, the presence and time course of neuroinflammation are unknown and so are the signatures of reactive inflammatory cells, especially the neuroprotective versus injurious roles of microglia. In general, chronic microglial activation in the injured brain suggests a pro-degenerative role for these reactive cells.

The effects of chronic intracortical microstimulation on neural tissue and fine motor behavior.

Objective: One approach to conveying sensory feedback in neuroprostheses is to electrically stimulate sensory neurons in the cortex. For this approach to be viable, it is critical that intracortical microstimulation (ICMS) causes minimal damage to the brain. Here, we investigate the effects of chronic ICMS on the neuronal tissue across a variety of stimulation regimes in non-human primates.

Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas.

Simultaneous neural control of simple reaching and grasping with the modular prosthetic limb using intracranial EEG.

Intracranial electroencephalographic (iEEG) signals from two human subjects were used to achieve simultaneous neural control of reaching and grasping movements with the Johns Hopkins University Applied Physics Lab (JHU/APL) Modular Prosthetic Limb (MPL), a dexterous robotic prosthetic arm. We performed functional mapping of high gamma activity while the subject made reaching and grasping movements to identify task-selective electrodes. Independent, online control of reaching and grasping was then achieved using high gamma activity from a small subset of electrodes with a model trained on short blocks of reaching and grasping with no further adaptation.

Hollow polymer microneedle array fabricated by photolithography process combined with micromolding technique.

Transdermal drug delivery through microneedles is a minimally invasive procedure causing little or no pain, and is a potentially attractive alternative to intramuscular and subdermal drug delivery methods. This paper demonstrates the fabrication of a hollow microneedle array using a polymer-based process combining UV photolithography and replica molding techniques. The key characteristic of the proposed fabrication process is to define a hollow lumen for microfluidic access via photopatterning, allowing a batch process as well as high throughput.

Packaging and characterization of mechanically actuated microtweezers for biomedical applications.

This paper presents the successful design, fabrication, and packaging of a mechanically actuated micro-electro-mechanical-systems (MEMS) microtweezer, and its use in a variety of biological environments. This complete and low cost MEMS system has minimal manufacturing complexity and it can be augmented to any standard micromanipulator or positioning system. Characterization of the system shows that precise and controlled tool actuation is achieved with maximal tip closing forces of 367 mN.