Background: The scratch collapse test (SCT) has gained popularity as a physical examination technique for diagnosing compression neuropathy. This systematic review aims to assess the reliability of the SCT as a diagnostic tool for compression neuropathy, as well as to propose the underlying physiological mechanisms involved. Specific criteria was developed to broaden the potential anatomical applications of the SCT. Methods: A literature search was conducted using PubMed, Embase, Scopus, and Google Scholar. Eleven articles meeting predefined inclusion/exclusion criteria were selected for numerical analysis, which yielded sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy values. Results: In total, 890 patients with carpal tunnel syndrome were reported in 10 studies. The mean (+/- SD) sensitivity, specificity, PPV, NPV, and accuracy were 0.442 +/- 0.272, 0.788 +/- 0.163, 0.834 +/- 0.143, 0.433 +/- 0.297, and 48.8% (range, 31%-82%), respectively. Of the studies that provided interrater reliability (kappa), the mean was 0.544 +/- 0.441, indicating moderate agreement. A total of 121 patients with cubital tunnel syndrome were reported in three studies, with a mean (+/- SD) sensitivity and specificity of 0.635 +/- 0.367 and 0.945 +/- 0.06, respectively. Twenty-four patients with peroneal nerve compression, reported in one study, had sensitivity, specificity, PPV, NPV, and accuracy of 0.77, 0.99, 0.95, 0.92, and 93%, respectively. Conclusions: Current literature indicates that the SCT can serve as a provocative test to assist in diagnosing compression neuropathy. Nevertheless, the heterogeneity of reported values underscores the necessity for further investigation aimed at enhancing the objectivity of SCT, thus improving interrater reliability and minimizing potential bias.

Neurofibromatosis (NF) type I is a neuroectodermal and mesodermal dysplasia caused by a mutation of the neurofibromin tumor suppressor gene. Phenotypic features of NF1 vary, and patients develop benign peripheral nerve sheath tumors and malignant neoplasms, such as malignant peripheral nerve sheath tumor, malignant melanoma, and astrocytoma. Multiparametric whole-body MR imaging (WBMRI) plays a critical role in disease surveillance. Multiparametric MRI, typically used in prostate imaging, is a general term for a technique that includes multiple sequences, i.e. anatomic, diffusion, and Dixon-based pre- and post-contrast imaging. This article discusses the value of multiparametric WBMRI and illustrates the spectrum of whole-body lesions of NF1 in a single imaging setting. Examples of lesions include those in the skin (tumors and axillary freckling), soft tissues (benign and malignant peripheral nerve sheath tumors, visceral plexiform, and diffuse lesions), bone and joints (nutrient nerve lesions, non-ossifying fibromas, intra-articular neurofibroma, etc.), spine (acute-angled scoliosis, dural ectasia, intraspinal tumors, etc.), and brain/skull (optic nerve glioma, choroid plexus xanthogranuloma, sphenoid wing dysplasia, cerebral hamartomas, etc.). After reading this article, the reader will gain knowledge of the variety of lesions encountered with NF1 and their WBMRI appearances. Timely identification of such lesions can aid in accurate diagnosis and appropriate patient management.

Objective: Neural prostheses generate an extensive volume of data during neural recording and decoding, presenting challenges in data processing and storage. A strategy to address this issue is compressing the data before transmission. Methods: This paper shows the possibility of efficiently compressing nerve data while achieving improved performance for an upper-limb neural prosthetic device. The new method, named Compression-Based Feature Reduction (CBFR), is divided into two stages: (1) applying a lightweight compression method to the original signal, and (2) reducing the number of features after feature extraction, thereby enhancing the classification accuracy in task-based experiments. Results: In an in vivo experiment involving patients with amputated hands, CBFR improved the classification performance of each finger movement up to 1.6% and 6.9% using Symmlet4 Discrete Wavelet Transform (DWT) and Walsh-Hadamard Transform (WHT), respectively, over the original signal. Both methods achieved up to 3x compression ratio from the original signal. Significane: The proposed data compression technique could help advance the field of neural prosthetics by addressing data transmission challenges and enhancing the performance of upper-limb neural prosthetic devices.

Background:. Sporadic inclusion body myositis (sIBM) is a rare and slowly progressive skeletal muscle disease that can cause hand dysfunction, which is a major source of disability. Tendon transfers have been reliably used to improve function in other neuromuscular settings. Given that sIBM patients often present with flexion impairments and mostly functioning extensors, we investigated the potential opportunity for tendon transfer surgery to improve hand dysfunction in sIBM patients. Methods:. We conducted a scoping review for studies of sIBM and tendon transfers, extracted descriptions of hand function and surgical technique, and recorded results in terms of hand function. We also conducted an institutional review board–approved survey with 470 participants to determine baseline patient-reported function and to determine participant perceptions and expectations for tendon transfer surgery to improve hand function in sIBM. Results:. We identified three published case reports on tendon transfers in sIBM patients with subjectively improved grip and pinch strength, but standardized measures of hand function or quality-of-life were not reported. Within the surveyed cohort, half of participants reported that they would consider surgery, yet only 8% had been referred to a hand surgeon. Fifty four percent of participants reported that they would consider surgery if there would be 1–2 years of benefit after surgery. All participants who would consider surgery also had significant upper extremity disability. Discussion:. Tendon transfer surgery has the potential to improve quality-of-life for sIBM patients, and there is significant patient interest in this approach. To objectively assess its efficacy, we propose conducting a surgical trial.

During contact, phasic and tonic responses provide feedback that is used for task performance and perceptual processes. These disparate temporal dynamics are carried in peripheral nerves, and produce overlapping signals in cortex. Using longitudinal intrafascicular electrodes inserted into the median nerve of a nonhuman primate, we delivered composite stimulation consisting of onset and release bursts to capture rapidly adapting responses and sustained stochastic stimulation to capture the ongoing response of slowly adapting receptors. To measure the stimulation’s effectiveness in producing natural responses, we monitored the local field potential in somatosensory cortex. We compared the cortical responses to peripheral nerve stimulation and vibrotactile/punctate stimulation of the fingertip, with particular focus on gamma band (30–65 Hz) responses. We found that vibrotactile stimulation produces consistently phase locked gamma throughout the duration of the stimulation. By contrast, punctate stimulation responses were phase locked at the onset and release of stimulation, but activity maintained through the stimulation was not phase locked. Using these responses as guideposts for assessing the response to the peripheral nerve stimulation, we found that constant frequency stimulation produced continual phase locking, whereas composite stimulation produced gamma enhancement throughout the stimulus, phase locked only at the onset and release of the stimulus. We describe this response as an “Appropriate Response in the gamma band” (ARγ), a trend seen in other sensory systems. Our demonstration is the first shown for intracortical somatosensory local field potentials. We argue that this stimulation paradigm produces a more biomimetic response in somatosensory cortex and is more likely to produce naturalistic sensations for readily usable neuroprosthetic feedback.

Fascicular targeting of longitudinal intrafascicular electrode (FAST-LIFE) interface enables hand dexterity with exogenous electrical microstimulation for sensory restoration, custom neural recording hardware, and deep learning–based artificial intelligence for motor intent decoding. The purpose of this technical report from a prospective pilot study was to illustrate magnetic resonance neurography (MRN) mapping of hand and nerve anatomy in amputees and incremental value of MRN over electrophysiology findings in pre-surgical planning of FAST-LIFE interface (robotic hand) patients. After obtaining informed consent, patients with upper extremity amputations underwent pre-operative 3-T MRN, X-rays, and electrophysiology. MRN findings were correlated with electrophysiology reports. Descriptive statistics were performed. Five patients of ages 21–59 years exhibited 3/5 partial hand amputations, and 2/5 transradial amputations on X-rays. The median and ulnar nerve end bulb neuromas measured 10.1 ± 3.04 mm (range: 5.5–14 mm, median: 10.5 mm) and 10.9 ± 7.64 mm (2–22 mm, 9.75 mm), respectively. The ADC of median and ulnar nerves were increased at 1.64 ± 0.1 × 10−3 mm2/s (range: 1.5–1.8, median: 1.64 × 10−3 mm2/s) and 1.70 ± 0.17 × 10−3 mm2/s (1.49–1.98 × 10−3 mm2/s, 1.65 × 10−3 mm2/s), respectively. Other identified lesions were neuromas of superficial branch of the radial nerve and anterior interosseous nerve. On electrophysiology, 2/5 reports were unremarkable, 2/5 showed mixed motor-sensory neuropathies of median and ulnar nerves along with radial sensory neuropathy, and 1/5 showed sensory neuropathy of lateral cutaneous nerve of the forearm. All patients regained naturalistic sensations and motor control of digits. 3-T MRN allows excellent demonstration of forearm and hand nerve anatomy, altered diffusion characteristics, and their neuromas despite unremarkable electrophysiology for pre-surgical planning of the FAST-LIFE (robotic hand) interfaces.

Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. Methods: Here we present a neuroprosthetic system to demonstrate that principle by employing an artificial intelligence (AI) agent to translate the amputee's movement intent through a peripheral nerve interface. The AI agent is designed based on the recurrent neural network (RNN) and could simultaneously decode six degree-of-freedom (DOF) from multichannel nerve data in real-time. The decoder's performance is characterized in motor decoding experiments with three human amputees. Results: First, we show the AI agent enables amputees to intuitively control a prosthetic hand with individual finger and wrist movements up to 97-98% accuracy. Second, we demonstrate the AI agent's real-time performance by measuring the reaction time and information throughput in a hand gesture matching task. Third, we investigate the AI agent's long-term uses and show the decoder's robust predictive performance over a 16-month implant duration. Conclusion & significance: Our study demonstrates the potential of AI-enabled nerve technology, underling the next generation of dexterous and intuitive prosthetic hands.

PURPOSE: Despite advances in artificial sensory feedback and EMG-based motor control, robotic hand “dexter-ity” remains elusive due to the inability to provide reliable individual digit motor control. We previously demonstrated functionally-relevant tactile and proprioceptive sensory feedback using fascicle-specific targeting of longitudinal intrafascicular electrodes (FAST-LIFE interfaces) implanted in the residual nerves of the amputated upper limb. Here we describe advances in motor control obtained by combin-ing FAST-LIFE interfacing with peripheral nerve-specific motor control technology developed by our group. METHODS: Oversight by the UTSW IRB. 6 patient trials were performed, duration 3 - 15 months. Amputation level: 3 partial hand (FAST-LIFE interfaces in motor/sensory fascicles of ulnar nerve); 3 transradial (FAST-LIFE interfaces in motor/sensory fascicles of median + ulnar nerves). RESULTS: We demonstrated individual digit control of a robotic hand prosthesis using: 1) FAST-LIFE interfacing, 2) hardware for recording peripheral nerve signals, 3) machine learning decoding of motor intent, and 4) portable control-lers for individual digit actuation. Details of decoding accu-racy, task matching trials, and individual digit control will be discussed. The study culminated in a “take-home” trial. CONCLUSION: Individual digit control of a robotic hand prosthesis can be accomplished using only signals derived from FAST-LIFE implants placed in the residual nerves of the amputated upper limb. Our novel motor control strat-egy holds promise for robotic hand control in higher level amputations, where conventional EMG ineffec-tive total the forearm PURPOSE: This study examines various techniques to determine how to provide the best long-term outcome for preservation of superior pole projection and inferior pole support in challenging mastopexy patients. METHODS: This was a prospective cohort study of all augmentation mastopexy patients by one surgeon utilizing the same superiomedial pedicle mastopexy technique. A total of 655 patients (Age range: 21-68; average follow-up: 7.7 year) were enrolled between 2002 and 2021 of which 418 were followed for more than one year. An algorithm was developed to assign the patients to 3 groups:1) Pri-mary correction of overstretched skin with simultaneous superior auto-augmentation and autologous inferior pole dermal flap support (N=207) 2) Secondary correction of augmentation mastopexy with autologous internal dermal flap (N=109) 3) Secondary Correction with ADM (N=102) Objective parameters utilizing 3-D Vectra analysis comparison of pre- and post-op photos were made to provide objective comparison of the breast projection with up to 18 year follow ups. RESULTS: By 3 years after surgery, there was a statis-tically significant difference (p>0.1) in the position of the NAC, the superior pole projection, and the nipple inframammary distance in patients who received simultaneous auto-augmentation with inferior dermal flap support. CONCLUSION: We describe the detailed anatomic technique for the consistent execution of simultaneous auto-augmentation and inferior pole support and provide an algorithm for categorization of these challenging patients. We believe that these techniques will allow a similar expectation of long term results like rhinoplasty and will ultimately lead to better outcomes in all mastopexy patients.

Background: Intraneural ganglion cysts are nonneoplastic mucinous cysts that form by the accumulation of thick mucinous fluid in the epineurium of peripheral nerves. Symptoms arise from mechanical compression of adjacent nerve fascicles from the intraneural ganglion cyst, and include local or radiating pain, paresthesias, weakness, and muscle atrophy. Methods: Retrospective review of three cases of symptomatic intraneural ganglion cysts affecting the upper and lower extremity. Results: In our cases, the intraneural ganglion cysts were completely decompressed with resection of the articular branches, leading to improvement in the patient's symptoms. Conclusions: Treatment of intraneural ganglion cysts requires an understanding of the underlying anatomy and pathophysiology; accurate early diagnosis is important and can lead to timely treatment and better outcomes.

Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a “pseudo-online” dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity, making it feasible for translation to a real-time paradigm. Next, we investigate two different strategies for deploying deep learning models: a one-step (1S) approach when big input data are available and a two-step (2S) when input data are limited. This research predicts five individual finger movements and four combinations of the fingers. The 1S approach using a recurrent neural network (RNN) to concurrently predict all fingers' trajectories generally gives better prediction results than all the machine learning algorithms that do the same task. This result reaffirms that deep learning is more advantageous than classic machine learning methods for handling a large dataset. However, when training on a smaller input data set in the 2S approach, which includes a classification stage to identify active fingers before predicting their trajectories, machine learning techniques offer a simpler implementation while ensuring comparably good decoding outcomes to the deep learning ones. In the classification step, either machine learning or deep learning models achieve the accuracy and F1 score of 0.99. Thanks to the classification step, in the regression step, both types of models result in a comparable mean squared error (MSE) and variance accounted for (VAF) scores as those of the 1S approach. Our study outlines the trade-offs to inform the future implementation of real-time, low-latency, and high accuracy deep learning-based motor decoder for clinical applications.

A high-resolution neurostimulator is the essential component of many bidirectional neural interfaces. In practice, the effective resolution of fully integrated neurostimulator designs is often hindered by the transistor mismatch, especially in submicrometer CMOS processes. In this article, we present a new circuit technique called redundant crossfire (RXF) to address this challenge. It is derived from our redundant sensing (RS) framework, which aims at engineering information redundancy into the system architecture to enhance its effective resolution. RXF involves combining (i.e., crossfiring) the outputs of two or more current drivers to form a redundant structure that, when properly configured, can produce accurate current pulses with an effective super-resolution beyond the limitation commonly permitted by the physical constraints. Unlike any previous works, the proposed technique achieves high-accuracy by directly exploiting the random transistor mismatch with an excessively large mismatch ratio of 10%–20%. The effectiveness of RXF is verified through both Monte Carlo simulations and measurement results of a fully integrated neurostimulator chip. Equipped with a 5-bit current digital-to-analog converter (IDAC) and two 4-bit current multipliers, the stimulator achieves an effective resolution of 9.75 bits in a 1.1-mA full range. An application of the fabricated chip is to deliver neuro-feedback to a human amputee through peripheral nerves where the amplitude of stimulation pulses is accurately controlled to encode the tactile response’s intensity.

Objective: Deep learning-based neural decoders have emerged as the prominent approach to enable dexterous and intuitive control of neuroprosthetic hands. Yet few studies have materialized the use of deep learning in clinical settings due to its high computational requirements. Methods: Recent advancements of edge computing devices bring the potential to alleviate this problem. Here we present the implementation of a neuroprosthetic hand with embedded deep learning-based control. The neural decoder is designed based on the recurrent neural network (RNN) architecture and deployed on the NVIDIA Jetson Nano - a compacted yet powerful edge computing platform for deep learning inference. This enables the implementation of the neuroprosthetic hand as a portable and self-contained unit with real-time control of individual finger movements. Results: The proposed system is evaluated on a transradial amputee using peripheral nerve signals (ENG) with implanted intrafascicular microelectrodes. The experiment results demonstrate the system's capabilities of providing robust, high-accuracy (95-99%) and low-latency (50-120 msec) control of individual finger movements in various laboratory and real-world environments. Conclusion: Modern edge computing platforms enable the effective use of deep learning-based neural decoders for neuroprosthesis control as an autonomous system. Significance: This work helps pioneer the deployment of deep neural networks in clinical applications underlying a new class of wearable biomedical devices with embedded artificial intelligence.

Multichannel longitudinal intrafascicular electrode (LIFE) interfaces provide optimized balance of invasiveness and stability for chronic sensory stimulation and motor recording/decoding of peripheral nerve signals. Using a fascicle-specific targeting (FAST)-LIFE approach, where electrodes are individually placed within discrete sensory- and motor-related fascicular subdivisions of the residual ulnar and/or median nerves in an amputated upper limb, FAST-LIFE interfacing can provide discernment of motor intent for individual digit control of a robotic hand, and restoration of touch- and movement-related sensory feedback. The authors describe their findings from clinical studies performed with 6 human amputee trials using FAST-LIFE interfacing of the residual upper limb.

INTRODUCTION:Proximal phalanx neck fractures occur almost exclusively in children. Fractures of the proximal phalanx neck can be difficult to treat nonoperatively given the anatomic location and associated extrinsic forces. A divergent or crossed pin configuration is often utilized for the stabilization of these fractures.PURPOSE:We present a surgical technique with a single Kirschner (K-wire) placed axially along the affected finger, with a hyperextension reduction maneuver, to reduce and fixate proximal phalanx neck fractures in children and adolescents.METHODS:We performed a retrospective review of all pediatric proximal phalanx neck fractures treated by a single surgeon. Demographic data, as well as clinical and radiographic outcomes were recorded. We then directly compared axial pinning [14 patients; average age 6.63 y (9 mo to 17 y)] with nonoperative treatment [28 patients; average age 8.03 y (9 mo to 16 y)], and open treatment [8 patients; average age 8.13 y (1 to 14 y)].RESULTS:Patients who underwent closed reduction and axial pinning had significantly improved final coronal alignment compared with nonoperative treatment. Compared with fractures which required open reduction, closed reduction with axial pinning resulted in significantly shorter surgical duration and time to radiographic healing. The final range of motion showed no difference between intervention types, as all patients regained full range of motion.CONCLUSIONS:We find this axial pinning technique to be simpler and faster than divergent pin fixation, with no significant differences in time to radiographic healing, time to full activity, final proximal interphalangeal active flexion or extension, loss of reduction, or radiographic parameters.LEVEL OF EVIDENCE:Level III-Therapeutic.

Background: Migraine surgery is an increasingly popular treatment option for migraine patients. The lesser occipital nerve is a common trigger point for headache abnormalities, but there is a paucity of research regarding the lesser occipital nerve and its intimate association with the spinal accessory nerve. Methods: Six cadaver necks were dissected. The lesser occipital, great auricular, and spinal accessory nerves were identified and systematically measured and recorded. These landmarks included the longitudinal axis (vertical line drawn in the posterior), the horizontal axis (defined as a line between the most anterosuperior points of the external auditory canals) and the earlobe. Mean distances and standard deviations were calculated to delineate the relationship between the spinal accessory, lesser occipital, and great auricular nerves. Results: The point of emergence of the spinal accessory nerve was determined to be 7.17 ± 1.15 cm lateral to the y axis and 7.77 ± 1.10 caudal to the x axis. The lesser occipital nerve emerges 7.5 ± 1.31 cm lateral to the y axis and 8.47 ± 1.11 cm caudal to the x axis. The great auricular nerve emerges 8.33 ± 1.31 cm lateral to the y axis and 9.4 ±1.07 cm caudal to the x axis. The decussation of the spinal accessory and the lesser occipital nerves was found to be 7.70 ± 1.16 cm caudal to the x axis and 7.17 ± 1.15 lateral to the y axis. Conclusion: Understanding the close relationship between the lesser occipital nerve and spinal accessory nerve in the posterior, lateral neck area is crucial for a safer approach to occipital migraine headaches, occipital neuralgia, and new daily persistent headaches and other reconstructive or cosmetic operations.

Introduction: MR neurography (MRN) of the brachial plexus has emerged in recent years as a safe and accurate modality for the identification of brachial plexopathies in pediatric and adult populations. While clinical differentiation of brachial plexopathy from cervical spine-related radiculopathy or nerve injury has long relied upon nonspecific physical exam and electrodiagnostic testing modalities, MRN now permits detailed interrogation of peripheral nerve anatomy and pathology, as well as assessment of surrounding soft tissues and musculature, thereby facilitating accurate diagnosis. The reader will learn about the current state of brachial plexus MRN, including recent advances and future directions, and gain knowledge about the adult and pediatric brachial plexopathies that can be characterized using these techniques.Areas Covered: The review details recent developments in brachial plexus MRN, including increasing availability of 3.0-T MR scanners at both private and academic diagnostic imaging centers, as well as the advent of multiple new vascular and fat signal suppression techniques. A literature search of PubMed and SCOPUS was used as the principal source of information gathered for this review.Expert Opinion: Refinement of fat-suppression, 3D techniques and diffusion MR imaging modalities has improved the accuracy of MRN, rendering it as a useful adjunct to clinical findings during the evaluation of suspected brachial plexus lesions.

Objective. While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful.Approach. Here we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject's motor intention.Main results. A pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient's true intention.Significance. Our study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways.Clinical trial: DExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.

PURPOSE: 18-25,000 upper limb amputations occur in the US annually. We have developed fascicular targeting (FAST) as a surgical approach for implanting electrode interfaces within the individual component fascicular groups of the nerves in the residual limb of upper extremity amputees. Our hypothesis is that FAST, when combined with custom-developed technologies including longitudinal intrafascicular electrode (LIFE) interfaces, nerve stimulation and recording electronics, sensory stimulation patterns, and artificial intelligence (AI) motor decoding algorithms, will enable the restoration of naturalistic hand function in robotic hand protheses used by upper extremity amputees. METHODS: 5 upper extremity amputees were implanted with LIFE + cuff interfaces for durations ranging from 3 months up to 1 year. 3 subjects were partial-hand amputees with 2 FAST interfaces (2 ulnar nerve fascicles). 2 subjects were transradial amputees with 4 FAST interfaces (2 ulnar nerve and 2 median nerve fascicles). Weekly experimental sessions were performed: to assess stimulation parameters for sensory feedback, to record motor signals for robotic hand control, and to train and measure functional performance following restoration of naturalistic sensory feedback and motor control. Implants were removed at the completion of the trial. RESULTS: All implants were well-tolerated by subjects, with little to no functional morbidity caused by their participation in the trial. Sensory stimulation thresholds remained within a usable range for the duration of the trial. Stimulation patterns were used to elicit sensations perceived as either natural or non-natural, according to subjects’ preferences. Sensory discrimination and anatomic localization were also assessed. Motor signals were recorded using a novel microchip design with built-in artifact rejection circuitry. Acquisition of single-unit motor data permitted the use of AI-based signal decoding algorithms to establish independent, free-will control of all 5 digits of the robotic hand. Single-session decodes continued to work for over 3–4 weeks post-training. Functional performance using closed-loop sensorimotor control, with anatomically relevant sensory feedback and user controlled activation of individual digits of the robotic hand, will be discussed in detail. CONCLUSION: Fascicular targeting, when combined with specialized sensory stimulation and motor recording strategies, can enable true dexterity for upper extremity amputees using robotic hands. This holds promise for the development of full clinical systems to restore the hands of upper extremity amputees.

Peripheral nerve sheath tumors (PNSTs) account for ~ 5% of soft tissue neoplasms and are responsible for a wide spectrum of morbidities ranging from localized neuropathy to fulminant metastatic spread and death. MR imaging represents the gold standard for identification of these neoplasms, however, current anatomic MR imaging markers do not reliably detect or differentiate benign and malignant lesions, and therefore, biopsy or excision is required for definitive diagnosis. Diffusion-weighted MR imaging (DWI) serves as a useful tool in the evaluation and management of PNSTs by providing functional information regarding the degree of diffusion, while diffusion tensor imaging (DTI) aids in determining the directional information of predominant diffusion and has been shown to be particularly useful for pre-operative planning of these tumors by delineating healthy and pathologic fascicles. The article focuses on these important neurogenic lesions, highlighting the current utility of diffusion MR imaging and future directions including computerized radiomic analysis. KEY POINTS: • Anatomic MRI is moderately accurate in differentiating benign from malignant PNST. • Diffusion tensor imaging facilitates pre-operative planning of PNSTs by depicting neuropathy and tractography. • Radiomics will likely augment current observer-based diagnostic criteria for PNSTs.