OBJECTIVE: Minimally-invasive approaches are needed for long-term reliable Electroencephalography (EEG) recordings to assist with epilepsy diagnosis, investigation and more naturalistic monitoring. This study compared three methods for long-term implantation of sub-scalp EEG electrodes. METHODS: Three types of electrodes (disk, ring, and peg) were fabricated from biocompatible materials and implanted under the scalp in five ambulatory ewes for 3months. Disk electrodes were inserted into sub-pericranial pockets. Ring electrodes were tunneled under the scalp. Peg electrodes were inserted into the skull, close to the dura. EEG was continuously monitored wirelessly. High resolution CT imaging, histopathology, and impedance measurements were used to assess the status of the electrodes at the end of the study. RESULTS: EEG amplitude was larger in the peg compared with the disk and ring electrodes (p<0.05). Similarly, chewing artifacts were lower in the peg electrodes (p<0.05). Electrode impedance increased after long-term implantation particularly for those within the bone (p<0.01). Micro-CT scans indicated that all electrodes stayed within the sub-scalp layers. All pegs remained within the burr holes as implanted with no evidence of extrusion. Eight of 10 disks partially eroded into the bone by 1.0mm from the surface of the skull. The ring arrays remained within the sub-scalp layers close to implantation site. Histology revealed that the electrodes were encapsulated in a thin fibrous tissue adjacent to the pericranium. Overlying this was a loose connective layer and scalp. Erosion into the bone occurred under the rim of the sub-pericranial disk electrodes. CONCLUSIONS: The results indicate that the peg electrodes provided high quality EEG, mechanical stability, and lower chewing artifact. Whereas, ring electrode arrays tunneled under the scalp enable minimal surgical techniques to be used for implantation and removal.

Nano-engineered particles have the potential to enhance therapeutic success and reduce toxicity-based treatment side effects via the targeted delivery of drugs to cells. This delivery relies on complex interactions between numerous biological, chemical and physical processes. The intertwined nature of these processes has thus far hindered attempts to understand their individual impact. Variation in experimental data, such as the number of particles inside each cell, further inhibits understanding. Here, we present a mathematical framework that is capable of examining the impact of individual processes during particle delivery. We demonstrate that variation in experimental particle uptake data can be explained by three factors: random particle motion; variation in particle-cell interactions; and variation in the maximum particle uptake per cell. Without all three factors, the experimental data cannot be explained. This work provides insight into biological mechanisms that cause heterogeneous responses to treatment, and enables precise identification of treatment-resistant cell subpopulations.

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1‐year follow‐up was assessed in 30 individuals with a schizophrenia‐spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting‐state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1‐year follow‐up varied markedly among individuals (interquartile range: 55%). Dynamic resting‐state connectivity measured within the default‐mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow‐up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1‐year follow‐up were predicted by hyper‐connectivity and hypo‐dynamism within the default‐mode network at baseline assessment, while hypo‐connectivity and hyper‐dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.

Surface acoustic waves can induce force gradients on the length scales of micro- and nanoparticles, allowing precise manipulation for particle capture, alignment and sorting activities. These waves typically occupy a spatial region much larger than a single particle, resulting in batch manipulation. Circular arc transducers can focus a SAW into a narrow beam on the order of the particle diameter for highly localised, single-particle manipulation by exciting wavelets which propagate to a common focal point. The anisotropic nature of SAW substrates, however, elongates and shifts the focal region. Acousto-microfluidic applications are highly dependent on the morphology of the underlying substrate displacement and, thus, become dependent on the microchannel position relative to the circular arc transducer. This requires either direct measurement or computational modelling of the SAW displacement field. We show that the directly measured elongation and shift in the focal region are recapitulated by an analytical model of beam steering, derived from a simulated slowness curve for 128° Y-cut lithium niobate. We show how the negative effects of beam steering can be negated by adjusting the curvature of arced transducers according to the slowness curve of the substrate, for which we present a simple function for convenient implementation in computational design software. Slowness-curve adjusted transducers do not require direct measurement of the SAW displacement field for microchannel placement and can capture smaller particles within the streaming vortices than can circular arc IDTs.

Background: Joint space assessment for rheumatoid arthritis (RA) by ordinal conventional radiographic scales is susceptible to floor and ceiling effects. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides superior resolution, and may detect earlier changes. The goal of this work was to compare existing 3D methods to calculate joint space width (JSW) metrics in human metacarpophalangeal (MCP) joints with HR-pQCT and reach consensus for future studies. Using the consensus method, we established reproducibility with repositioning as well as feasibility for use in second-generation HR-pQCT scanners. Methods: Three published JSW methods were compared using datasets from individuals with RA from three research centers. A SPECTRA consensus method was developed to take advantage of strengths of the individual methods. Using the SPECTRA method, reproducibility after repositioning was tested and agreement between scanner generations was also established. Results: When comparing existing JSW methods, excellent agreement was shown for JSW minimum and mean (ICC 0.987–0.996) but not maximum and volume (ICC 0.000–0.897). Differences were identified as variations in volume definitions and algorithmic differences that generated high sensitivity to boundary conditions. The SPECTRA consensus method reduced this sensitivity, demonstrating good scan-rescan reliability (ICC >0.911) except for minimum JSW (ICC 0.656). There was strong agreement between results from first- and second-generation HR-pQCT (ICC >0.833). Conclusions: The SPECTRA consensus method combines unique strengths of three independently-developed algorithms and leverages underlying software updates to provide a mature analysis to measure 3D JSW. This method is robust with respect to repositioning and scanner generations, suggesting its suitability for detecting change.

There is a crucial need to identify biomarkers of epileptogenesis that will help predict later development of seizures. This work identifies two novel electrophysiological biomarkers that quantify epilepsy progression in a rat model of epileptogenesis. The long-term tetanus toxin rat model was used to show the development and remission of epilepsy over several weeks. We measured the response to periodic electrical stimulation and features of spontaneous seizure dynamics over several weeks. Both biomarkers showed dramatic changes during epileptogenesis. Electrically induced responses began to change several days before seizures began and continued to change until seizures resolved. These changes were consistent across animals and allowed development of an algorithm that could differentiate which animals would later develop epilepsy. Once seizures began, there was a progression of seizure dynamics that closely follows recent theoretical predictions, suggesting that the underlying brain state was changing over time. This research demonstrates that induced electrical responses and seizure onset dynamics are useful biomarkers to quantify dynamical changes in epileptogenesis. These tools hold promise for robust quantification of the underlying epileptogenicity and prediction of later development of seizures.

BACKGROUND: Dual orthogonal plating of clavicle fractures may provide greater stiffness and strength than unilateral plate constructs and allow the use of lower-profile plates. We aim to biomechanically compare three clavicle plating constructs in a comminuted clavicle fracture model. METHOD: Fifteen clavicle sawbones were osteotomised, simulating a comminuted midshaft fracture and allocated to either: group 1, single superior plate (3.5 mm superior plate); group 2, combination plating (3.5 mm superior plate, 2.8 mm anterior plate) and group 3, dual mini-plates (two 2.8-mm orthogonal mini-plates). Specimens were biomechanically tested under torsion and cantilever bending. Construct stiffness (Nm/degree) and load to failure (Nm) were measured. RESULTS: Group 2 had higher torsional (0.70 vs. 0.60 Nm/deg, p = 0.017) and cantilever bending stiffness (0.61 vs. 0.51 Nm/deg, p = 0.025) than group 1. Group 3 had lower cantilever bending stiffness (0.39 vs. 0.51 Nm/deg, p < 0.004) and load to failure (40.87 vs. 54.84 Nm, p < 0.01) than group 1. All dual plate constructs that catastrophically failed did so from fracture at the lateral ends of the plates. Single plate constructs failed due to plate bending. CONCLUSION: Dual orthogonal fixation with mini-plates demonstrated lower stiffness and strength than traditional superior plating. The addition of an anterior mini-plate to a traditional superior plating improved construct stiffness and may have a role in patients seeking early return to activity. LEVEL OF EVIDENCE: Basic science biomechanical study.

There is evidence to suggest that motor execution and motor imagery both involve planning and execution of the same motor plan, however, in the latter the output is inhibited. Currently, little is known about the underlying neural mechanisms of motor output inhibition during motor imagery. Uncovering the distinctive characteristics of motor imagery may help us better understand how we abstract complex thoughts and acquire new motor skills. The current study aimed to dissociate the cognitive processes involved in two distinct inhibitory mechanisms of motor inhibition and motor imagery restraint. Eleven healthy participants engaged in an imagined GO/NO-GO task during a 7 Tesla fMRI experiment. Participants planned a specific type of motor imagery, then, imagined the movements during the GO condition and restrained from making a response during the NO-GO condition. The results revealed that specific sub-regions of the supplementary motor cortex (SMC) and the primary motor cortex (M1) were recruited during the imagination of specific movements and information flowed from the SMC to the M1. Such condition-specific recruitment was not observed when motor imagery was restrained. Instead, general recruitment of the posterior parietal cortex (PPC) was observed, while the BOLD activity in the SMC and the M1 decreased below the baseline at the same time. Information flowed from the PPC to the SMC, and recurrently between the M1 and the SMC, and the M1 and the PPC. These results suggest that motor imagery involves task-specific motor output inhibition partly imposed by the SMC to the M1, while the PPC globally inhibits motor plans before they are passed on for execution during the restraint of responses.

Implantable medical devices are now in regular use to treat or ameliorate medical conditions, including movement disorders, chronic pain, cardiac arrhythmias, and hearing or vision loss. Aside from offering alternatives to pharmaceuticals, one major advantage of device therapy is the potential to monitor treatment efficacy, disease progression, and perhaps begin to uncover elusive mechanisms of diseases pathology. In an ideal system, neural stimulation, neural recording, and electrochemical sensing would be conducted by the same electrode in the same anatomical region. Carbon fiber (CF) microelectrodes are the appropriate size to achieve this goal and have shown excellent performance, in vivo. Their electrochemical properties, however, are not suitable for neural stimulation and electrochemical sensing. Here, we present a method to deposit high surface area conducting diamond on CF microelectrodes. This unique hybrid microelectrode is capable of recording single-neuron action potentials, delivering effective electrical stimulation pulses, and exhibits excellent electrochemical dopamine detection. Such electrodes are needed for the next generation of miniaturized, closed-loop implants that can self-tune therapies by monitoring both electrophysiological and biochemical biomarkers.

Acoustic waves can be used to accurately position cells and particles and are appropriate for this activity owing to their biocompatibility and ability to generate microscale force gradients. Such fields, however, typically take the form of only periodic one or two-dimensional grids, limiting the scope of patterning activities that can be performed. Recent work has demonstrated that the interaction between microfluidic channel walls and travelling surface acoustic waves can generate spatially variable acoustic fields, opening the possibility that the channel geometry can be used to control the pressure field that develops. In this work we utilize this approach to create novel acoustic fields. Designing the channel that results in a desired acoustic field, however, is a non-trivial task. To rapidly generate designed acoustic fields from microchannel elements we utilize a deep learning approach based on a deep neural network (DNN) that is trained on images of pre-solved acoustic fields. We use then this trained DNN to create novel microchannel architectures for designed microparticle patterning.

Several studies have illustrated that transcutaneous vagus nerve stimulation (tVNS) can elicit therapeutic effects that are similar to those produced by its invasive counterpart, vagus nerve stimulation (VNS). VNS is an FDA-approved therapy for the treatment of both depression and epilepsy, but it is limited to the management of more severe, intervention-resistant cases as a second or third-line treatment option due to perioperative risks involved with device implantation. In contrast, tVNS is a non-invasive technique that involves the application of electrical currents through surface electrodes at select locations, most commonly targeting the auricular branch of the vagus nerve (ABVN) and the cervical branch of the vagus nerve in the neck. Although it has been shown that tVNS elicits hypo- and hyperactivation in various regions of the brain associated with anxiety and mood regulation, the mechanism of action and influence of stimulation parameters on clinical outcomes remains predominantly hypothetical. Suppositions are largely based on correlations between the neurobiology of the vagus nerve and its effects on neural activity. However, tVNS has also been investigated for several other disorders, including tinnitus, migraine and pain, by targeting the vagus nerve at sites in both the ear and the neck. As most of the described methods differ in the parameters and protocols applied, there is currently no firm evidence on the optimal location for tVNS or the stimulation parameters that provide the greatest therapeutic effects for a specific condition. This review presents the current status of tVNS with a focus on stimulation parameters, stimulation sites, and available devices. For tVNS to reach its full potential as a non-invasive and clinically relevant therapy, it is imperative that systematic studies be undertaken to reveal the mechanism of action and optimal stimulation modalities.

The cytoskeletal mechanics and cell mechanical properties play an important role in cellular behaviors. In this study, in order to provide comprehensive insights into the relationship between different cytoskeletal components and cellular elastic moduli, we built a phase-modulated surface acoustic wave microfluidic device to measure cellular compressibility and a microfluidic micropipette-aspiration device to measure cellular Young's modulus. The microfluidic devices were validated based on experimental data and computational simulations. The contributions of structural cytoskeletal actin filament and microtubule to cellular compressibility and Young's modulus were examined in MCF-7 cells. The compressibility of MCF-7 cells was increased after microtubule disruption, whereas actin disruption had no effect. In contrast, Young's modulus of MCF-7 cells was reduced after actin disruption but unaffected by microtubule disruption. The actin filaments and microtubules were stained to confirm the structural alteration in cytoskeleton. Our findings suggest the dissimilarity in the structural roles of actin filaments and microtubules in terms of cellular compressibility and Young's modulus. Based on the differences in location and structure, actin filaments mainly contribute to tensile Young's modulus and microtubules mainly contribute to compressibility. In addition, different responses to cytoskeletal alterations between acoustophoresis and micropipette aspiration demonstrated that micropipette aspiration was better at detecting the change from actin cortex, while the response to acoustophoresis was governed by microtubule networks.