Closed-loop parameter optimization for patient-specific phrenic nerve stimulation.

BACKGROUND: Ventilator-induced diaphragm dysfunction occurs rapidly following the onset of mechanical ventilation and has significant clinical consequences. Phrenic nerve stimulation has shown promise in maintaining diaphragm function by inducing diaphragm contractions. Non-invasive stimulation is an attractive option as it minimizes the procedural risks associated with invasive approaches. However, this method is limited by sensitivity to electrode position and inter-individual variability in stimulation thresholds. This makes clinical application challenging due to potentially time-consuming calibration processes to achieve reliable stimulation. METHODS: We applied non-invasive electrical stimulation to the phrenic nerve in the neck in healthy volunteers. A closed-loop system recorded the respiratory flow produced by stimulation and automatically adjusted the electrode position and stimulation amplitude based on the respiratory response. By iterating over electrodes, the optimal electrode was selected. A binary search method over stimulation amplitudes was then employed to determine an individualized stimulation threshold. Pulse trains above this threshold were delivered to produce diaphragm contraction. RESULTS: Nine healthy volunteers were recruited. Mean threshold stimulation amplitude was 36.17 ± 14.34 mA (range 19.38-59.06 mA). The threshold amplitude for reliable nerve capture was moderately correlated with BMI (Pearson's r = 0.66, p = 0.049). Repeating threshold measurements within subjects demonstrated low intra-subject variability of 2.15 ± 1.61 mA between maximum and minimum thresholds on repeated trials. Bilateral stimulation with individually optimized parameters generated reliable diaphragm contraction, resulting in significant inhaled volumes following stimulation. CONCLUSION: We demonstrate the feasibility of a system for automatic optimization of electrode position and stimulation parameters using a closed-loop system. This opens the possibility of easily deployable individualized stimulation in the intensive care setting to reduce ventilator-induced diaphragm dysfunction.

Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics.

The human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement primitives exist, a dimensionality reduction approach designed to exploit these features should outperform existing methods. We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. We show that these temporal features are highly conserved across individuals and can interpolate previously unseen movements, indicating that they capture the intrinsic structure of hand movements. These results indicate that functional hand movements are defined by a low-dimensional basis set of movement primitives with important temporal dynamics and that these features are common across individuals.

Computation of Activating Fields for Approximation of the Orientation-Specific Neural Response to Electrical Stimulation.

Computational methods of determining the response of neural tissue to electrical stimulation have demonstrated value for the development of novel devices and the programming of neuromodulation therapies. Detailed biophysical models are excessively computationally intensive for many applications; simple metrics to approximate activation can speed up progress in this area. The activating function provides such a useful metric. However, this measure, defined for a specific axon orientation, is not immediately applicable to computed electric fields to assess their effects. We demonstrate a method for computation of the activating function generalized to a field in order to allow rapid computation of the effects of stimulation on neural tissue while preserving information on axon orientation. Clinical Relevance- This demonstrates a useful method of approximating the effect of electrical stimulation on nervous tissue for the development of devices and the optimization of parameters for electrical neuromodulation.

Spatial and Temporal Distribution of Information Processing in the Human Dorsal Anterior Cingulate Cortex.

The dorsal anterior cingulate cortex (dACC) is a key node in the human salience network. It has been ascribed motor, pain-processing and affective functions. However, the dynamics of information flow in this complex region and how it responds to inputs remain unclear and are difficult to study using non-invasive electrophysiology. The area is targeted by neurosurgery to treat neuropathic pain. During deep brain stimulation surgery, we recorded local field potentials from this region in humans during a decision-making task requiring motor output. We investigated the spatial and temporal distribution of information flow within the dACC. We demonstrate the existence of a distributed network within the anterior cingulate cortex where discrete nodes demonstrate directed communication following inputs. We show that this network anticipates and responds to the valence of feedback to actions. We further show that these network dynamics adapt following learning. Our results provide evidence for the integration of learning and the response to feedback in a key cognitive region.

Non-invasive phrenic nerve stimulation to avoid ventilator-induced diaphragm dysfunction in critical care.

BACKGROUND: Diaphragm muscle atrophy during mechanical ventilation begins within 24 h and progresses rapidly with significant clinical consequences. Electrical stimulation of the phrenic nerves using invasive electrodes has shown promise in maintaining diaphragm condition by inducing intermittent diaphragm muscle contraction. However, the widespread application of these methods may be limited by their risks as well as the technical and environmental requirements of placement and care. Non-invasive stimulation would offer a valuable alternative method to maintain diaphragm health while overcoming these limitations. METHODS: We applied non-invasive electrical stimulation to the phrenic nerve in the neck in healthy volunteers. Respiratory pressure and flow, diaphragm electromyography and mechanomyography, and ultrasound visualization were used to assess the diaphragmatic response to stimulation. The electrode positions and stimulation parameters were systematically varied in order to investigate the influence of these parameters on the ability to induce diaphragm contraction with non-invasive stimulation. RESULTS: We demonstrate that non-invasive capture of the phrenic nerve is feasible using surface electrodes without the application of pressure, and characterize the stimulation parameters required to achieve therapeutic diaphragm contractions in healthy volunteers. We show that an optimal electrode position for phrenic nerve capture can be identified and that this position does not vary as head orientation is changed. The stimulation parameters required to produce a diaphragm response at this site are characterized and we show that burst stimulation above the activation threshold reliably produces diaphragm contractions sufficient to drive an inspired volume of over 600 ml, indicating the ability to produce significant diaphragmatic work using non-invasive stimulation. CONCLUSION: This opens the possibility of non-invasive systems, requiring minimal specialist skills to set up, for maintaining diaphragm function in the intensive care setting.

Functional Network Mapping Reveals State-Dependent Response to IGF1 Treatment in Rett Syndrome.

Rett Syndrome (RTT) is a neurodevelopmental disorder associated with mutations in the gene MeCP2, which is involved in the development and function of cortical networks. The clinical presentation of RTT is generally severe and includes developmental regression and marked neurologic impairment. Insulin-Like growth factor 1 (IGF1) ameliorates RTT-relevant phenotypes in animal models and improves some clinical manifestations in early human trials. However, it remains unclear whether IGF1 treatment has an impact on cortical electrophysiology in line with MeCP2's role in network formation, and whether these electrophysiological changes are related to clinical response. We performed clinical assessments and resting-state electroencephalogram (EEG) recordings in eighteen patients with classic RTT, nine of whom were treated with IGF1. Among the treated patients, we distinguished those who showed improvements after treatment (responders) from those who did not show any changes (nonresponders). Clinical assessments were carried out for all individuals with RTT at baseline and 12 months after treatment. Network measures were derived using statistical modelling techniques based on interelectrode coherence measures. We found significant interaction between treatment groups and timepoints, indicating an effect of IGF1 on network measures. We also found a significant effect of responder status and timepoint, indicating that these changes in network measures are associated with clinical response to treatment. Further, we found baseline variability in network characteristics, and a machine learning model using these measures applied to pretreatment data predicted treatment response with 100% accuracy (100% sensitivity and 100% specificity) in this small patient group. These results highlight the importance of network pathology in RTT, as well as providing preliminary evidence for the potential of network measures as tools for the characterisation of disease subtypes and as biomarkers for clinical trials.

Optimizing the neuron-electrode interface for chronic bioelectronic interfacing.

Engineering approaches have vast potential to improve the treatment of disease. Brain-machine interfaces have become a well-established means of treating some otherwise medically refractory neurological diseases, and they have shown promise in many more areas. More widespread use of implanted stimulating and recording electrodes for long-term intervention is, however, limited by the difficulty in maintaining a stable interface between implanted electrodes and the local tissue for reliable recording and stimulation.This loss of performance at the neuron-electrode interface is due to a combination of inflammation and glial scar formation in response to the implanted material, as well as electrical factors contributing to a reduction in function over time. An increasing understanding of the factors at play at the neural interface has led to greater focus on the optimization of this neuron-electrode interface in order to maintain long-term implant viability.A wide variety of approaches to improving device interfacing have emerged, targeting the mechanical, electrical, and biological interactions between implanted electrodes and the neural tissue. These approaches are aimed at reducing the initial trauma and long-term tissue reaction through device coatings, optimization of mechanical characteristics for maximal biocompatibility, and implantation techniques. Improved electrode features, optimized stimulation parameters, and novel electrode materials further aim to stabilize the electrical interface, while the integration of biological interventions to reduce inflammation and improve tissue integration has also shown promise.Optimization of the neuron-electrode interface allows the use of long-term, high-resolution stimulation and recording, opening the door to responsive closed-loop systems with highly selective modulation. These new approaches and technologies offer a broad range of options for neural interfacing, representing the possibility of developing specific implant technologies tailor-made to a given task, allowing truly personalized, optimized implant technology for chronic neural interfacing.

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms.

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, and less resource-intensive than imaging. Further, the functional data produced have excellent temporal resolution, which is not achievable with structural imaging. Current applications of electroencephalograms (EEG) are limited by data processing methods. Standard analysis techniques using raw time series data at individual channels are very limited methods of interrogating nervous system activity. More detailed information about cortical function can be achieved by examining relationships between channels and deriving statistical models of how areas are interacting, allowing visualization of connectivity between networks. This manuscript describes a method for deriving statistical models of cortical network activity by recording EEG in a standard manner, then examining the interelectrode coherence measures to assess relationships between the recorded areas. Higher order interactions can be further examined by assessing the covariance between the coherence pairs, producing high-dimensional "maps" of network interactions. These data constructs can be examined to assess cortical network function and its relationship to pathology in ways not achievable with traditional techniques. This approach offers greater sensitivity to network level interactions than is achievable with raw time series analysis. It is, however, limited by the complexity of drawing specific mechanistic conclusions about the underlying neural populations and the high volumes of data generated, requiring more advanced statistical techniques for evaluation, including dimensionality reduction and classifier-based approaches.

Clinical and genetic Rett syndrome variants are defined by stable electrophysiological profiles.

BACKGROUND: Rett Syndrome (RTT) is a complex neurodevelopmental disorder, frequently associated with epilepsy. Despite increasing recognition of the clinical heterogeneity of RTT and its variants (e.g Classical, Hanefeld and PSV(Preserved Speech Variant)), the link between causative mutations and observed clinical phenotypes remains unclear. Quantitative analysis of electroencephalogram (EEG) recordings may further elucidate important differences between the different clinical and genetic forms of RTT. METHODS: Using a large cohort (n = 42) of RTT patients, we analysed the electrophysiological profiles of RTT variants (genetic and clinical) in addition to epilepsy status (no epilepsy/treatment-responsive epilepsy/treatment-resistant epilepsy). The distribution of spectral power and inter-electrode coherence measures were derived from continuous resting-state EEG recordings. RESULTS: RTT genetic variants (MeCP2/CDLK5) were characterised by significant differences in network architecture on comparing first principal components of inter-electrode coherence across all frequency bands (p < 0.0001). Greater coherence in occipital and temporal pairs were seen in MeCP2 vs CDLK5 variants, the main drivers in between group differences. Similarly, clinical phenotypes (Classical RTT/Hanefeld/PSV) demonstrated significant differences in network architecture (p < 0.0001). Right tempero-parietal connectivity was found to differ between groups (p = 0.04), with greatest coherence in the Classical RTT phenotype. PSV demonstrated a significant difference in left-sided parieto-occipital coherence (p = 0.026). Whilst overall power decreased over time, there were no difference in asymmetry and inter-electrode coherence profiles over time. There was a significant difference in asymmetry in the overall power spectra between epilepsy groups (p = 0.04) in addition to occipital asymmetry across all frequency bands. Significant differences in network architecture were also seen across epilepsy groups (p = 0.044). CONCLUSIONS: Genetic and clinical variants of RTT are characterised by discrete patterns of inter-electrode coherence and network architecture which remain stable over time. Further, hemispheric distribution of spectral power and measures of network dysfunction are associated with epilepsy status and treatment responsiveness. These findings support the role of discrete EEG profiles as non-invasive biomarkers in RTT and its genetic/clinical variants.