Central stress pathways in the development of cardiovascular disease.

PURPOSE: Mental stress is of essential consideration when assessing cardiovascular pathophysiology in all patient populations. Substantial evidence indicates associations among stress, cardiovascular disease and aberrant brain-body communication. However, our understanding of the flow of stress information in humans, is limited, despite the crucial insights this area may offer into future therapeutic targets for clinical intervention. METHODS: Key terms including mental stress, cardiovascular disease and central control, were searched in PubMed, ScienceDirect and Scopus databases. Articles indicative of heart rate and blood pressure regulation, or central control of cardiovascular disease through direct neural innervation of the cardiac, splanchnic and vascular regions were included. Focus on human neuroimaging research and the flow of stress information is described, before brain-body connectivity, via pre-motor brainstem intermediates is discussed. Lastly, we review current understandings of pathophysiological stress and cardiovascular disease aetiology. RESULTS: Structural and functional changes to corticolimbic circuitry encode stress information, integrated by the hypothalamus and amygdala. Pre-autonomic brain-body relays to brainstem and spinal cord nuclei establish dysautonomia and lead to alterations in baroreflex functioning, firing of the sympathetic fibres, cellular reuptake of norepinephrine and withdrawal of the parasympathetic reflex. The combined result is profoundly adrenergic and increases the likelihood of cardiac myopathy, arrhythmogenesis, coronary ischaemia, hypertension and the overall risk of future sudden stress-induced heart failure. CONCLUSIONS: There is undeniable support that mental stress contributes to the development of cardiovascular disease. The emerging accumulation of large-scale multimodal neuroimaging data analytics to assess this relationship promises exciting novel therapeutic targets for future cardiovascular disease detection and prevention.

Central mechanisms in sympathetic nervous dysregulation in obesity.

Cardiovascular and metabolic complications associated with excess adiposity are linked to chronic activation of the sympathetic nervous system, resulting in a high risk of mortality among obese individuals. Obesity-related positive energy balance underlies the progression of hypertension, end-organ damage, and insulin resistance, driven by increased sympathetic tone throughout the body. It is, therefore, important to understand the central network that drives and maintains sustained activation of the sympathetic nervous system in the obese state. Experimental and clinical studies have identified structural changes and altered dynamics in both grey and white matter regions in obesity. Aberrant activation in certain brain regions has been associated with altered reward circuitry and metabolic pathways including leptin and insulin signaling along with adiposity-driven systemic and central inflammation. The impact of these pathways on the brain via overactivity of the sympathetic nervous system has gained interest in the past decade. Primarily, the brainstem, hypothalamus, amygdala, hippocampus, and cortical structures including the insular, orbitofrontal, temporal, cingulate, and prefrontal cortices have been identified in this context. Although the central network involving these structures is much more intricate, this review highlights recent evidence identifying these regions in sympathetic overactivity in obesity.

Learning receptive field properties of complex cells in V1.

There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.

Analysis of extracellular spike waveforms and associated receptive fields of neurons in cat primary visual cortex.

KEY POINTS: Extracellular spikes recorded in the visual cortex (Area 17/18, V1) are commonly classified into either regular-spiking (RS) or fast-spiking (FS). Using multi-electrode arrays positioned in cat V1 and a broadband stimulus, we show that there is also a distinct class with positive-spiking (PS) waveforms. PS units were associated mainly with non-oriented receptive fields while RS and FS units had orientation-selective receptive fields. We suggest that PS units are recordings of axons originating from the thalamus. This conclusion was reinforced by our finding that we could record PS units after cortical silencing, but not record RS and FS units. The importance of our findings is that we were able to correlate spike shapes with receptive field characteristics with high precision using multi-electrode extracellular recording techniques. This allows considerable increases in the amount of information that can be extracted from future cortical experiments. ABSTRACT: Extracellular spike waveforms from recordings in the visual cortex have been classified into either regular-spiking (RS) or fast-spiking (FS) units. While both these types of spike waveforms are negative-dominant, we show that there are also distinct classes of spike waveforms in visual Area 17/18 (V1) of anaesthetised cats with positive-dominant waveforms, which are not regularly reported. The spatial receptive fields (RFs) of these different spike waveform types were estimated, which objectively revealed the existence of oriented and non-oriented RFs. We found that units with positive-dominant spikes, which have been associated with recordings from axons in the literature, had mostly non-oriented RFs (84%), which are similar to the centre-surround RFs observed in the dorsal lateral geniculate nucleus (dLGN). Thus, we hypothesise that these positive-dominant waveforms may be recordings from dLGN afferents. We recorded from V1 before and after the application of muscimol (a cortical silencer) and found that the positive-dominant spikes (PS) remained while the RS and FS cells did not. We also noted that the PS units had spiking characteristics normally associated with dLGN units (i.e. higher response spike rates, lower response latencies and higher proportion of burst spikes). Our findings show quantitatively that it is possible to correlate the RF properties of cortical neurons with particular spike waveforms. This has implications for how extracellular recordings should be interpreted and complex experiments can now be contemplated that would have been very challenging previously, such as assessing the feedforward connectivity between brain areas in the same location of cortical tissue.

Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons.

Implantable retinal stimulators activate surviving neurons to restore a sense of vision in people who have lost their photoreceptors through degenerative diseases. Complex spatial and temporal interactions occur in the retina during multi-electrode stimulation. Due to these complexities, most existing implants activate only a few electrodes at a time, limiting the repertoire of available stimulation patterns. Measuring the spatiotemporal interactions between electrodes and retinal cells, and incorporating them into a model may lead to improved stimulation algorithms that exploit the interactions. Here, we present a computational model that accurately predicts both the spatial and temporal nonlinear interactions of multi-electrode stimulation of rat retinal ganglion cells (RGCs). The model was verified using in vitro recordings of ON, OFF, and ON-OFF RGCs in response to subretinal multi-electrode stimulation with biphasic pulses at three stimulation frequencies (10, 20, 30 Hz). The model gives an estimate of each cell's spatiotemporal electrical receptive fields (ERFs); i.e., the pattern of stimulation leading to excitation or suppression in the neuron. All cells had excitatory ERFs and many also had suppressive sub-regions of their ERFs. We show that the nonlinearities in observed responses arise largely from activation of presynaptic interneurons. When synaptic transmission was blocked, the number of sub-regions of the ERF was reduced, usually to a single excitatory ERF. This suggests that direct cell activation can be modeled accurately by a one-dimensional model with linear interactions between electrodes, whereas indirect stimulation due to summated presynaptic responses is nonlinear.

Neural mass models as a tool to investigate neural dynamics during seizures.

Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value. After establishing the critical value for seizure spread, we explored how to correct the effect by altering particular synaptic gains. The spreading of seizures is quantified using numerical methods for seizure detection. The results from this study provide a new avenue of exploration for seizure control.

A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina.

Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron's electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy.

Single-compartment models of retinal ganglion cells with different electrophysiologies.

There are more than 15 different types of retinal ganglion cells (RGCs) in the mammalian retina. To model responses of RGCs to electrical stimulation, we use single-compartment Hodgkin-Huxley-type models and run simulations in the Neuron environment. We use our recently published in vitro data of different morphological cell types to constrain the model, and study the effects of electrophysiology on the cell responses separately from the effects of morphology. We find simple models that can match the spike patterns of different types of RGCs. These models, with different input-output properties, may be used in a network to study retinal network dynamics and interactions.

Spike history neural response model.

There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.

The effect of morphology upon electrophysiological responses of retinal ganglion cells: simulation results.

Retinal ganglion cells (RGCs) display differences in their morphology and intrinsic electrophysiology. The goal of this study is to characterize the ionic currents that explain the behavior of ON and OFF RGCs and to explore if all morphological types of RGCs exhibit the phenomena described in electrophysiological data. We extend our previous single compartment cell models of ON and OFF RGCs to more biophysically realistic multicompartment cell models and investigate the effect of cell morphology on intrinsic electrophysiological properties. The membrane dynamics are described using the Hodgkin - Huxley type formalism. A subset of published patch-clamp data from isolated intact mouse retina is used to constrain the model and another subset is used to validate the model. Two hundred morphologically distinct ON and OFF RGCs are simulated with various densities of ionic currents in different morphological neuron compartments. Our model predicts that the differences between ON and OFF cells are explained by the presence of the low voltage activated calcium current in OFF cells and absence of such in ON cells. Our study shows through simulation that particular morphological types of RGCs are capable of exhibiting the full range of phenomena described in recent experiments. Comparisons of outputs from different cells indicate that the RGC morphologies that best describe recent experimental results are ones that have a larger ratio of soma to total surface area.

Membrane depolarization mediates both the inhibition of neural activity and cell-type-differences in response to high-frequency stimulation.

Neuromodulation using high frequency (>1 kHz) electric stimulation (HFS) enables preferential activation or inhibition of individual neural types, offering the possibility of more effective treatments across a broad spectrum of neurological diseases. To improve effectiveness, it is important to better understand the mechanisms governing activation and inhibition with HFS so that selectivity can be optimized. In this study, we measure the membrane potential (Vm) and spiking responses of ON and OFF α-sustained retinal ganglion cells (RGCs) to a wide range of stimulus frequencies (100-2500 Hz) and amplitudes (10-100 µA). Our findings indicate that HFS induces shifts in Vm, with both the strength and polarity of the shifts dependent on the stimulus conditions. Spiking responses in each cell directly correlate with the shifts in Vm, where strong depolarization leads to spiking suppression. Comparisons between the two cell types reveal that ON cells are more depolarized by a given amplitude of HFS than OFF cells-this sensitivity difference enables the selective targeting. Computational modeling indicates that ion-channel dynamics largely account for the shifts in Vm, suggesting that a better understanding of the differences in ion-channel properties across cell types may improve the selectivity and ultimately, enhance HFS-based neurostimulation strategies.

Understanding visual processing of motion: completing the picture using experimentally driven computational models of MT.

Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural computation. Computational modeling of the neuronal pathways of the visual cortex has been successful in developing theories of biological motion processing. This review describes a range of computational models that have been inspired by neurophysiological experiments. Theories of local motion integration and pattern motion processing are presented, together with suggested neurophysiological experiments designed to test those hypotheses.

Nanoparticle-based optical interfaces for retinal neuromodulation: a review.

Degeneration of photoreceptors in the retina is a leading cause of blindness, but commonly leaves the retinal ganglion cells (RGCs) and/or bipolar cells extant. Consequently, these cells are an attractive target for the invasive electrical implants colloquially known as "bionic eyes." However, after more than two decades of concerted effort, interfaces based on conventional electrical stimulation approaches have delivered limited efficacy, primarily due to the current spread in retinal tissue, which precludes high-acuity vision. The ideal prosthetic solution would be less invasive, provide single-cell resolution and an ability to differentiate between different cell types. Nanoparticle-mediated approaches can address some of these requirements, with particular attention being directed at light-sensitive nanoparticles that can be accessed via the intrinsic optics of the eye. Here we survey the available known nanoparticle-based optical transduction mechanisms that can be exploited for neuromodulation. We review the rapid progress in the field, together with outstanding challenges that must be addressed to translate these techniques to clinical practice. In particular, successful translation will likely require efficient delivery of nanoparticles to stable and precisely defined locations in the retinal tissues. Therefore, we also emphasize the current literature relating to the pharmacokinetics of nanoparticles in the eye. While considerable challenges remain to be overcome, progress to date shows great potential for nanoparticle-based interfaces to revolutionize the field of visual prostheses.

Functional Connectivity Analysis of Transcutaneous Vagus Nerve Stimulation (tVNS) Using Magnetoencephalography (MEG).

Altered brain functional connectivity has been observed in conditions such as schizophrenia, dementia and depression and may represent a target for treatment. Transcutaneous vagus nerve stimulation (tVNS) is a form of non-invasive brain stimulation that is increasingly used in the treatment of a variety of health conditions. We previously combined tVNS with magnetoencephalography (MEG) and observed that various stimulation frequencies affected different brain areas in healthy individuals. We further investigated whether tVNS had an effect on functional connectivity with a focus on brain regions associated with mood. We compared functional connectivity (whole-head and region of interest) in response to four stimulation frequencies of tVNS using data collected from concurrent MEG and tVNS in 17 healthy participants using Weighted Phase Lag Index (WPLI) to calculate correlation between brain areas. Different frequencies of stimulation lead to changes in functional connectivity across multiple regions, notably areas linked to the default mode network (DMN), salience network (SN) and the central executive network (CEN). It was observed that tVNS delivered at a frequency of 24 Hz was the most effective in increasing functional connectivity between these areas and sub-networks in healthy participants. Our results indicate that tVNS can alter functional connectivity in regions that have been associated with mood and memory disorders. Varying the stimulation frequency led to alterations in different brain areas, which may suggest that personalized stimulation protocols can be developed for the targeted treatment of different medical conditions using tVNS.

Video See-Through Pipelines for Virtual Reality Headsets and their Impact on Gait.

This study investigated the impact of different video see-through pipelines in virtual reality on gait. A mobility task was conducted with healthy participants to evaluate the gait adaptions using different video see-through pipelines. The gait parameters observed for this study were stride length, maximum toe clearance and walking speed. The results showed an impact on gait where the gait parameters were reduced when participants used a high latency and restricted field of view pipeline. However, when participants used a pipeline with low latency and a field of view closer to normal vision, less impact on gait was achieved. As virtual reality poses a promising future for gait rehabilitation in patients with Parkinson's disease, this result highlights the need to carefully consider the video see-through pipeline and display characteristics when considering its use for gait rehabilitation or mobility studies in general.Clinical relevance- This study demonstrates the impact of virtual reality systems on gait using different video see- through pipelines during a mobility task. This may be useful for clinicians who use virtual reality in gait rehabilitation and aid them in choosing the most suitable virtual reality system for therapy.

Effect of Stimulation Current in Transcutaneous Vagus Nerve Stimulation (tVNS): A Study Using Concurrent Magnetoencephalography (MEG).

Transcutaneous vagus nerve stimulation (tVNS) is a non-invasive method of brain stimulation that has been investigated for its use in the clinical treatment of a number of different conditions. There has been little investigation into the stimulation current that is delivered and the effect on individual variability in response to tVNS.Seventeen participants underwent tVNS, and stimulation current was determined based on individual pain threshold. To investigate individual variability, brain dynamics were measured concurrently using magnetoencephalography (MEG) in response to two different stimulation protocols of tVNS. The first protocol consisted of a sequence of equally spaced short (1ms) stimulation pulses applied 24 times per second (24 Hz), and the second consisted of a sequence of 24 pulses per second spaced according to a 6 Hz pulse frequency modulation (PFM). Both stimulation sequences were delivered to the cymba concha in the left ear.The difference in brain responses to the two sequences was initially calculated using a one-sample t-test at the group level, based on z-scoring of the data at the individual level, and no statistically significant differences were observed. Further investigation of individual variability suggested that participants fell into two groups; one that responded more strongly to 24 Hz and one that responded more strongly to the irregular spacing of pulses in the PFM protocol.We tested whether the stimulation current that the participant received could predict how they would respond to the stimulation, but we did not observe any correlation. This supports the literature that suggests that selecting stimulation current based on individual pain threshold is a suitable procedure for tVNS, and higher stimulation intensities does not correspond to stronger brain response. Further investigation into individual variability in response to different frequencies and pulse spacing of tVNS should also be investigated further and may lead to the development of personalised stimulation protocols.Clinical relevance- The stimulation current at which tVNS is delivered does not appear to influence brain response to stimulation, and the value of stimulation current should be selected based on individual participant comfort.

Self-Attention Based Vision Processing for Prosthetic Vision.

We investigate Self-Attention (SA) networks for directly learning visual representations for prosthetic vision. Specifically, we explore how the SA mechanism can be leveraged to produce task-specific scene representations for prosthetic vision, overcoming the need for explicit hand-selection of learnt features and post-processing. Further, we demonstrate how the mapping of importance to image regions can serve as an explainability tool to analyse the learnt vision processing behaviour, providing enhanced validation and interpretation capability than current learning-based methods for prosthetic vision. We investigate our approach in the context of an orientation and mobility (OM) task, and demonstrate its feasibility for learning vision processing pipelines for prosthetic vision.

Quantifying visual acuity for pre-clinical testing of visual prostheses.

Objective.Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity.Approach.This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provides a quantified level of visual perception in the clinical Snellen and MAR scales. The magnitude of the resulting value was demonstrated using scaled standardized 'C' and 'E' optotypes.Main results.The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different electrode pitch configurations and differently sized spreads of neural activity. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in both circumstances.Significance.Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.

Classification of Epileptic and Psychogenic Non-Epileptic Seizures Using Electroencephalography and Electrocardiography.

Patients with psychogenic non-epileptic seizures (PNES) may exhibit similar clinical features to patients with epileptic seizures (ES). Misdiagnosis of PNES and ES can lead to inappropriate treatment and significant morbidity. This study investigates the use of machine learning techniques for classification of PNES and ES based on electroencephalography (EEG) and electrocardiography (ECG) data. Video-EEG-ECG of 150 ES events from 16 patients and 96 PNES from 10 patients were analysed. Four preictal periods (time before event onset) in EEG and ECG data were selected for each PNES and ES event (60-45 min, 45-30 min, 30-15 min, 15-0 min). Time-domain features were extracted from each preictal data segment in 17 EEG channels and 1 ECG channel. The classification performance using k-nearest neighbour, decision tree, random forest, naive Bayes, and support vector machine classifiers were evaluated. The results showed the highest classification accuracy was 87.83% using the random forest on 15-0 min preictal period of EEG and ECG data. The performance was significantly higher using 15-0 min preictal period data than 30-15 min, 45-30 min, and 60-45 min preictal periods ( [Formula: see text]). The classification accuracy was improved from 86.37% to 87.83% by combining ECG data with EEG data ( [Formula: see text]). The study provided an automated classification algorithm for PNES and ES events using machine learning techniques on preictal EEG and ECG data.

Activity of Retinal Neurons Can Be Modulated by Tunable Near-Infrared Nanoparticle Sensors.

The vision of patients rendered blind by photoreceptor degeneration can be partially restored by exogenous stimulation of surviving retinal ganglion cells (RGCs). Whereas conventional electrical stimulation techniques have failed to produce naturalistic visual percepts, nanoparticle-based optical sensors have recently received increasing attention as a means to artificially stimulate the RGCs. In particular, nanoparticle-enhanced infrared neural modulation (NINM) is a plasmonically mediated photothermal neuromodulation technique that has a demonstrated capacity for both stimulation and inhibition, which is essential for the differential modulation of ON-type and OFF-type RGCs. Gold nanorods provide tunable absorption through the near-infrared wavelength window, which reduces interference with any residual vision. Therefore, NINM may be uniquely well-suited to retinal prosthesis applications but, to our knowledge, has not previously been demonstrated in RGCs. In the present study, NINM laser pulses of 100 µs, 500 µs and 200 ms were applied to RGCs in explanted rat retinae, with single-cell responses recorded via patch-clamping. The shorter laser pulses evoked robust RGC stimulation by capacitive current generation, while the long laser pulses are capable of inhibiting spontaneous action potentials by thermal block. Importantly, an implicit bias toward OFF-type inhibition is observed, which may have important implications for the feasibility of future high-acuity retinal prosthesis design based on nanoparticle sensors.