Frequency-Domain Analysis of Intrinsic Neuronal Properties using High-Resistant Electrodes

Intrinsic cellular properties of neurons in culture or slices are usually studied by the whole cell clamp method using low-resistant patch pipettes. These electrodes allow detailed analyses with standard electrophysiological methods such as current- or voltage-clamp. However, in these preparations large parts of the network and dendritic structures may be removed, thus preventing an adequate study of synaptic signal processing. Therefore, intact in vivo preparations or isolated in vitro whole brains have been used in which intracellular recordings are usually made with sharp, high-resistant electrodes to optimize the impalement of neurons. The general non-linear resistance properties of these electrodes, however, severely limit accurate quantitative studies of membrane dynamics especially needed for precise modelling. Therefore, we have developed a frequency-domain analysis of membrane properties that uses a Piece-wise Non-linear Electrode Compensation (PNEC) method. The technique was tested in second-order vestibular neurons and abducens motoneurons of isolated frog whole brain preparations using sharp potassium chloride- or potassium acetate-filled electrodes. All recordings were performed without online electrode compensation. The properties of each electrode were determined separately after the neuronal recordings and were used in the frequency-domain analysis of the combined measurement of electrode and cell. This allowed detailed analysis of membrane properties in the frequency-domain with high-resistant electrodes and provided quantitative data that can be further used to model channel kinetics. Thus, sharp electrodes can be used for the characterization of intrinsic properties and synaptic inputs of neurons in intact brains

Quantification of Head Movement Predictability and Implications for Suppression of Vestibular Input during Locomotion

Achieved motor movement can be estimated using both sensory and motor signals. The value of motor signals for estimating movement should depend critically on the stereotypy or predictability of the resulting actions. As predictability increases, motor signals become more reliable indicators of achieved movement, so weight attributed to sensory signals should decrease accordingly. Here we describe a method to quantify this predictability for head movement during human locomotion by measuring head motion with an inertial measurement unit (IMU), and calculating the variance explained by the mean movement over one stride, i.e., a metric similar to the coefficient of determination. Predictability exhibits differences across activities, being most predictable during running, and changes over the course of a stride, being least predictable around the time of heel-strike and toe-off. In addition to quantifying predictability, we relate this metric to sensory-motor weighting via a statistically optimal model based on two key assumptions: (1) average head movement provides a conservative estimate of the efference copy prediction, and (2) noise on sensory signals scales with signal magnitude. The model suggests that differences in predictability should lead to changes in the weight attributed to vestibular sensory signals for estimating head movement. In agreement with the model, prior research reports that vestibular perturbations have greatest impact at the time points and during activities where high vestibular weight is predicted. Thus, we propose a unified explanation for time-and activity-dependent modulation of vestibular effects that was lacking previously. Furthermore, the proposed predictability metric constitutes a convenient general method for quantifying any kind of kinematic variability. The probabilistic model is also general; it applies to any situation in which achieved movement is estimated from both motor signals and zero-mean sensory signals with signal-dependent noise

The Healthcare Benefits and Impact of Artificial Intelligence Applications on Behaviour of Healthcare Users: A Structured Review of Primary Literature

Introduction: Artificial intelligence (AI) is one of the most considered topics of the current time. AI has the power to bring revolutionary improvements to the world of technology not only in the field of computer science but also in other fields like medical sciences. Objectives: This paper assumes the adoption of appropriate AI engineering principals in previous studies, and focusses on providing a structured review of the impact of AI on human society and the individual human being as a technology user. Additionally, it opens a window on how the future will look like in terms of AI and personalised medicine. Methods: The paper employed a qualitative research approach and data were collected through a structured literature review. Twenty-three peer reviewed papers were identified and analysed in relation to their relevance to the study. Results: Previous studies show a positive impact on users' behaviour is expected in supporting their healthcare needs especially in decision-making, personalised treatment and future diseases prediction, and that integrating users in studying AI impact is essential to test possible implications of the technology. Conclusion: Results indicate that without a clear understanding of why patients need AI, or how AI can support individuals with their healthcare needs, it is difficult to visualise the kinds of AI applications that have a meaningful and sustainable impact the daily lives of individuals. Therefore, there is an emerging need to understand the impact of AI technology on users' behaviour to maximise the potential benefits of AI technology

A model-based theory on the origin of downbeat nystagmus

The pathomechanism of downbeat nystagmus (DBN), an ocular motor sign typical for vestibulo-cerebellar lesions, remains unclear. Previous hypotheses conjectured various deficits such as an imbalance of central vertical vestibular or smooth pursuit pathways to be causative for the generation of spontaneous upward drift. However, none of the previous theories explains the full range of ocular motor deficits associated with DBN, i.e., impaired vertical smooth pursuit (SP), gaze evoked nystagmus, and gravity dependence of the upward drift. We propose a new hypothesis, which explains the ocular motor signs of DBN by damage of the inhibitory vertical gaze-velocity sensitive Purkinje cells (PCs) in the cerebellar flocculus (FL). These PCs show spontaneous activity and a physiological asymmetry in that most of them exhibit downward on-directions. Accordingly, a loss of vertical floccular PCs will lead to disinhibition of their brainstem target neurons and, consequently, to spontaneous upward drift, i.e., DBN. Since the FL is involved in generation and control of SP and gaze holding, a single lesion, e.g., damage to vertical floccular PCs, may also explain the associated ocular motor deficits. To test our hypothesis, we developed a computational model of vertical eye movements based on known ocular motor anatomy and physiology, which illustrates how cortical, cerebellar, and brainstem regions interact to generate the range of vertical eye movements seen in healthy subjects. Model simulation of the effect of extensive loss of floccular PCs resulted in ocular motor features typically associated with cerebellar DBN: (1) spontaneous upward drift due to decreased spontaneous PC activity, (2) gaze evoked nystagmus corresponding to failure of the cerebellar loop supporting neural integrator function, (3) asymmetric vertical SP deficit due to low gain and asymmetric attenuation of PC firing, and (4) gravity-dependence of DBN caused by an interaction of otolith-ocular pathways with impaired neural integrator functio

An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models

Human motor control is highly efficient in generating accurate and appropriate motor behavior for a multitude of tasks. This paper examines how kinematic and dynamic properties of the musculoskeletal system are controlled to achieve such efficiency. Even though recent studies have shown that the human motor control relies on multiple models, how the central nervous system (CNS) controls this combination is not fully addressed. In this study, we utilize an Inverse Optimal Control (IOC) framework in order to find the combination of those internal models and how this combination changes for different reaching tasks. We conducted an experiment where participants executed a comprehensive set of free-space reaching motions. The results show that there is a trade-off between kinematics and dynamics based controllers depending on the reaching task. In addition, this trade-off depends on the initial and final arm configurations, which in turn affect the musculoskeletal load to be controlled. Given this insight, we further provide a discomfort metric to demonstrate its influence on the contribution of different inverse internal models. This formulation together with our analysis not only support the multiple internal models (MIMs) hypothesis but also suggest a hierarchical framework for the control of human reaching motions by the CNS

Somatosensory Influence on Platform-Induced Translational Vestibulo-Ocular Reflex in Vertical Direction in Humans

The vestibulo-ocular reflex (VOR) consists of two components, the rotational VOR (rVOR) elicited by semicircular canal signals and the translational VOR (tVOR) elicited by otolith signals. Given the relevant role of the vertical tVOR in human walking, this study aimed at measuring the time delay of eye movements in relation to whole-body vertical translations in natural standing position. Twenty (13 females and 7 males) healthy, young subjects (mean 25 years) stood upright on a motor-driven platform and were exposed to sinusoidal movements while fixating a LED, positioned at a distance of 50 cm in front of the eyes. The platform motion induced a vertical translation of 2.6 cm that provoked counteracting eye movements similar to self-paced walking. The time differences between platform and eye movements indicated that the subject's timing of the extraocular motor reaction depended on stimulus frequency and number of repetitions. At low stimulus frequencies (<0.8 Hz) and small numbers of repetitions (<3), eye movements were phase advanced or in synchrony with platform movements. At higher stimulus frequencies or continuous stimulation, eye movements were phase lagged by ~40 ms. Interestingly, the timing of eye movements depended on the initial platform inclination. Starting with both feet in dorsiflexion, eye movements preceded platform movements by 137 ms, whereas starting with both feet in plantar flexion eye movement precession was only 19 ms. This suggests a remarkable influence of foot proprioceptive signals on the timing of eye movements, indicating that the dynamics of the vertical tVOR is controlled by somatosensory signals

Quantification of Head Movement Predictability and Implications for Suppression of Vestibular Input during Locomotion

Achieved motor movement can be estimated using both sensory and motor signals. The value of motor signals for estimating movement should depend critically on the stereotypy or predictability of the resulting actions. As predictability increases, motor signals become more reliable indicators of achieved movement, so weight attributed to sensory signals should decrease accordingly. Here we describe a method to quantify this predictability for head movement during human locomotion by measuring head motion with an inertial measurement unit (IMU), and calculating the variance explained by the mean movement over one stride, i.e., a metric similar to the coefficient of determination. Predictability exhibits differences across activities, being most predictable during running, and changes over the course of a stride, being least predictable around the time of heel-strike and toe-off. In addition to quantifying predictability, we relate this metric to sensory-motor weighting via a statistically optimal model based on two key assumptions: (1) average head movement provides a conservative estimate of the efference copy prediction, and (2) noise on sensory signals scales with signal magnitude. The model suggests that differences in predictability should lead to changes in the weight attributed to vestibular sensory signals for estimating head movement. In agreement with the model, prior research reports that vestibular perturbations have greatest impact at the time points and during activities where high vestibular weight is predicted. Thus, we propose a unified explanation for time-and activity-dependent modulation of vestibular effects that was lacking previously. Furthermore, the proposed predictability metric constitutes a convenient general method for quantifying any kind of kinematic variability. The probabilistic model is also general;it applies to any situation in which achieved movement is estimated from both motor signals and zero-mean sensory signals with signal-dependent noise

Neural signatures of reinforcement learning correlate with strategy adoption during spatial navigation

Human navigation is generally believed to rely on two types of strategy adoption, route-based and map-based strategies. Both types of navigation require making spatial decisions along the traversed way although formal computational and neural links between navigational strategies and mechanisms of value-based decision making have so far been underexplored in humans. Here we employed functional magnetic resonance imaging (fMRI) while subjects located different objects in a virtual environment. We then modelled their paths using reinforcement learning (RL) algorithms, which successfully explained decision behavior and its neural correlates. Our results show that subjects used a mixture of route and map-based navigation and their paths could be well explained by the model-free and model-based RL algorithms. Furthermore, the value signals of model-free choices during routebased navigation modulated the BOLD signals in the ventro-medial prefrontal cortex (vmPFC), whereas the BOLD signals in parahippocampal and hippocampal regions pertained to model-based value signals during map-based navigation. Our findings suggest that the brain might share computational mechanisms and neural substrates for navigation and value-based decisions such that model-free choice guides route-based navigation and model-based choice directs map-based navigation. These findings open new avenues for computational modelling of wayfinding by directing attention to value-based decision, differing from common direction and distances approaches

An Information-Theoretic Approach for Evaluating Probabilistic Tuning Functions of Single Neurons

Neuronal tuning functions can be expressed by the conditional probability of observing a spike given any combination of explanatory variables. However, accurately determining such probabilistic tuning functions from experimental data poses several challenges such as finding the right combination of explanatory variables and determining their proper neuronal latencies. Here we present a novel approach of estimating and evaluating such probabilistic tuning functions, which offers a solution for these problems. By maximizing the mutual information between the probability distributions of spike occurrence and the variables, their neuronal latency can be estimated, and the dependence of neuronal activity on different combinations of variables can be measured. This method was used to analyze neuronal activity in cortical area MSTd in terms of dependence on signals related to eye and retinal image movement. Comparison with conventional feature detection and regression analysis techniques shows that our method offers distinct advantages, if the dependence does not match the regression model

Duration reproduction under memory pressure: Modeling the roles of visual memory load in duration encoding and reproduction

Duration estimates are often biased by the sampled statistical context, yielding the classical central-tendency effect, i.e., short durations are over- and long duration underestimated. Most studies of the central-tendency bias have primarily focused on the integration of the sensory measure and the prior information, without considering any cognitive limits. Here, we investigated the impact of cognitive (visual working-memory) load on duration estimation in the duration encoding and reproduction stages. In four experiments, observers had to perform a dual, attention-sharing task: reproducing a given duration (primary) and memorizing a variable set of color patches (secondary). We found an increase in memory load (i.e., set size) during the duration-encoding stage to increase the central-tendency bias, while shortening the reproduced duration in general; in contrast, increasing the load during the reproduction stage prolonged the reproduced duration, without influencing the central tendency. By integrating an attentional-sharing account into a hierarchical Bayesian model, we were able to predict both the general over- and underestimation and the central-tendency effects observed in all four experiments. The model suggests that memory pressure during the encoding stage increases the sensory noise, which elevates the central-tendency effect. In contrast, memory pressure during the reproduction stage only influences the monitoring of elapsed time, leading to a general duration over-reproduction without impacting the central tendency.Competing Interest StatementThe authors have declared no competing interest