Complex Patterns Of Activity Research Articles

Recent Insights into the Rhythmogenic Core of the Locomotor CPG.

In order for locomotion to occur, a complex pattern of muscle activation is required. For more than a century, it has been known that the timing and pattern of stepping movements in mammals are generated by neural networks known as central pattern generators (CPGs), which comprise multiple interneuron cell types located entirely within the spinal cord. A genetic approach has recently been successful in identifying several populations of spinal neurons that make up this neural network, as well as the specific role they play during stepping. In spite of this progress, the identity of the neurons responsible for generating the locomotor rhythm and the manner in which they are interconnected have yet to be deciphered. In this review, we summarize key features considered to be expressed by locomotor rhythm-generating neurons and describe the different genetically defined classes of interneurons which have been proposed to be involved.

Device-Free Human Activity Recognition Based on GMM-HMM Using Channel State Information

This paper presents a machine learning method, Gaussian Mixture Hidden Markov Model (GMM-HMM), for device-free activity recognition using WiFi channel state information (CSI). The basic concept of CSI is introduced and signal changes caused by human activity are described, which demonstrates that human activity can be identified using a unique mapping between action and signal variations. The phase difference expanded matrix is built by the mean and standard deviation of phase difference as feature matrix after linear correction and Savitzky-Golay filter is performed on the CSI raw phase information. The GMM-HMM is used for classification as the human activity can be modeled as the Markov process and the complex activity patterns can be fitted by multiple Gaussian density functions, respectively. The proposed system is verified on the self-collected datasets and several factors affecting the recognition accuracy are analyzed. Furthermore, the system has compared with the previous work. High accuracy and robustness in universal scenarios are realized. Experimental results show that the average recognition accuracy of the proposed system is over 97%.

A Systematic Review of Studies Comparing Actigraphy Indices in Patients with Depression and Schizophrenia

Introduction: Abnormalities in sleep-wake activities are frequently observed among patients with depression and schizophrenia. These abnormalities are variable and may have discriminative, clinical, diagnostic, and therapeutic significance if measured objectively with actigraphy. Aim: To systematically review the published literature on actigraphy in depression and schizophrenia, particularly to identify areas of research that need to be addressed before their clinical application in practice. Materials and Methods: The electronic databases (PubMed and Google Scholar) were searched for studies using the the key terms ‘actigraphy’ OR ‘actigraphic recording’ OR ‘wrist actigraphy’ OR ‘actometer’ OR ‘actimeter’ OR ‘actical’ OR ‘actiwatch’ OR ‘sleep- watch’ AND ‘schizoaffective’ OR ‘schizophr*’ OR ‘psychosis’ AND ‘depression’ OR ‘depressive’ in title and abstract. The literature search was limited to articles published in English and until 31st December, 2020. Data were abstracted by two reviewers and presented as a narrative summary of the findings. A qualitative synthesis of the study designs, populations, and outcomes was conducted. The quantitative synthesis of the results was not possible to conduct due to the heterogeneity and scarcity of the included studies. Results: Out of 33 searched articles, a total of four studies (three observational and one case report) were included for the review. The included studies were heterogeneous, small in sample size, divergent in methods, inclusive of clinical population, having more than two groups (depression, schizophrenia, mania, or bipolar disorders), and with a low degree of evidence. Overall, the patients with schizophrenia have more structured and less complex activity pattern than those with depression. Furthermore, the patients with schizophrenia showed more irregular patterns in the morning period and increased fluctuations inactivity in the evening period than depression patients. Conclusion: Currently, available information is insufficient to draw firm conclusions on use of actigraphy indices (e.g., duration of active periods, a pattern of activity, and sleep) for diagnosis and discrimination among the patients with schizophrenia and depression. Large, prospective, and comparative studies are required to identify role of actigraphy among these patients.

Computational Study on Spatially Distributed Sequential Stimulation for Fatigue Resistant Neuromuscular Electrical Stimulation.

Neuromuscular electrical stimulation (NMES) is used to artificially induce muscle contractions of paralyzed limbs in individuals with stroke or spinal cord injury, however, the therapeutic efficacy can be significantly limited by rapid fatiguing of the targeted muscle. A unique stimulation method, called spatially distributed sequential stimulation (SDSS), has been shown clinically to reduce fatiguing during FES, but further improvement is needed. The purpose of this study was to gain a better understanding of SDSS-induced neural activation in the human lower leg using a computational approach. We developed a realistic finite element model of the lower leg to investigate SDSS, by solving the electric field generated by SDSS and predicting neural activation. SDSS applied at 10 Hz was further compared with conventional transcutaneous stimulation that delivered electrical pulses at 40 Hz through a single electrode. We found that SDSS electrically activated multiple sub-populations of motor neurons within the TA muscle that fired at frequencies ranging between 10 Hz and 40 Hz. This complex nerve activation pattern depicts the mechanism of action of SDSS for reducing muscle fatigue during NMES.

Enterovirus Infection Induces Massive Recruitment of All Isoforms of Small Cellular Arf GTPases to the Replication Organelles.

Enterovirus replication requires the cellular protein GBF1, a guanine nucleotide exchange factor for small Arf GTPases. When activated, Arfs associate with membranes, where they regulate numerous steps of membrane homeostasis. The requirement for GBF1 implies that Arfs are important for replication, but which of the different Arfs function(s) during replication remains poorly understood. Here, we established cell lines expressing each of the human Arfs fused to a fluorescent tag and investigated their behavior during enterovirus infection. Arf1 was the first to be recruited to the replication organelles, where it strongly colocalized with the viral antigen 2B and mature virions but not double-stranded RNA. By the end of the infectious cycle, Arf3, Arf4, Arf5, and Arf6 were also concentrated on the replication organelles. Once on the replication membranes, all Arfs except Arf3 were no longer sensitive to inhibition of GBF1, suggesting that in infected cells they do not actively cycle between GTP- and GDP-bound states. Only the depletion of Arf1, but not other class 1 and 2 Arfs, significantly increased the sensitivity of replication to GBF1 inhibition. Surprisingly, depletion of Arf6, a class 3 Arf, normally implicated in plasma membrane events, also increased the sensitivity to GBF1 inhibition. Together, our results suggest that GBF1-dependent Arf1 activation directly supports the development and/or functioning of the replication complexes and that Arf6 plays a previously unappreciated role in viral replication. Our data reveal a complex pattern of Arf activation in enterovirus-infected cells that may contribute to the resilience of viral replication in different cellular environments.IMPORTANCE Enteroviruses include many known and emerging pathogens, such as poliovirus, enteroviruses 71 and D68, and others. However, licensed vaccines are available only against poliovirus and enterovirus 71, and specific anti-enterovirus therapeutics are lacking. Enterovirus infection induces the massive remodeling of intracellular membranes and the development of specialized domains harboring viral replication complexes, replication organelles. Here, we investigated the roles of small Arf GTPases during enterovirus infection. Arfs control distinct steps in intracellular membrane traffic, and one of the Arf-activating proteins, GBF1, is a cellular factor required for enterovirus replication. We found that all Arfs expressed in human cells, including Arf6, normally associated with the plasma membrane, are recruited to the replication organelles and that Arf1 appears to be the most important Arf for enterovirus replication. These results document the rewiring of the cellular membrane pathways in infected cells and may provide new ways of controlling enterovirus infections.

Simple models including energy and spike constraints reproduce complex activity patterns and metabolic disruptions.

In this work, we introduce new phenomenological neuronal models (eLIF and mAdExp) that account for energy supply and demand in the cell as well as the inactivation of spike generation how these interact with subthreshold and spiking dynamics. Including these constraints, the new models reproduce a broad range of biologically-relevant behaviors that are identified to be crucial in many neurological disorders, but were not captured by commonly used phenomenological models. Because of their low dimensionality eLIF and mAdExp open the possibility of future large-scale simulations for more realistic studies of brain circuits involved in neuronal disorders. The new models enable both more accurate modeling and the possibility to study energy-associated disorders over the whole time-course of disease progression instead of only comparing the initially healthy status with the final diseased state. These models, therefore, provide new theoretical and computational methods to assess the opportunities of early diagnostics and the potential of energy-centered approaches to improve therapies.

Lower complexity of motor primitives ensures robust control of high-speed human locomotion

Walking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we challenged human locomotion by having our participants walk and run at a very broad spectrum of submaximal and maximal speeds. The synergistic activations of lower limb locomotor muscles were obtained through decomposition of electromyographic data via non-negative matrix factorization. We analyzed the duration and complexity (via fractal analysis) over time of motor primitives, the temporal components of muscle synergies. We found that the motor control of high-speed locomotion was so challenging that the neuromotor system was forced to produce wider and less complex muscle activation patterns. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that humans cope with the challenges of high-speed locomotion by adapting the neuromotor dynamics through a set of strategies that allow for efficient creation and control of locomotion.

The motor cortex uses active suppression to sculpt movement.

Even the simplest movements are generated by a remarkably complex pattern of muscle activity. Fast, accurate movements at a single joint are produced by a stereotyped pattern that includes a decrease in any preexisting activity in antagonist muscles. This premovement suppression is necessary to prevent the antagonist muscle from opposing movement generated by the agonist muscle. Here, we provide evidence that the primary motor cortex (M1) sends a command signal that generates this premovement suppression. Thus, output neurons in M1 sculpt complex spatiotemporal patterns of motor output not only by actively turning on muscles but also by actively turning them off.

Spatially Localized Sono-Photoacoustic Activation of Phase-Change Contrast Agents

Sono-photoacoustic (SPA) activation lowers the threshold of phase-change contrast agents by timing a laser shot to coincide with the arrival of an acoustic wave at a region of interest. The combination of photothermal heating from optical absorption and negative pressure from the acoustic wave greatly reduces the droplet’s combined vaporization threshold compared to using laser energy or acoustic energy alone. In previous studies, SPA imaging used a broadly illuminated optical pulse combined with plane wave acoustic pulses transmitted from a linear ultrasound array. Acoustic plane waves cover a wide lateral field of view, enabling direct visualization of the contrast agent distribution. In contrast, we demonstrate here that localized SPA activation is possible using electronically steered/focused ultrasound pulses. The focused SPA activation region is defined axially by the number of cycles in the acoustic pulse and laterally by the acoustic beam width. By reducing the spot size and enabling rapid electronic steering, complex activation patterns are possible, which may be particularly useful in therapeutic applications.

Spatiotemporal mapping of bacterial membrane potential responses to extracellular electron transfer

Extracellular electron transfer (EET) allows microorganisms to gain energy by linking intracellular reactions to external surfaces ranging from natural minerals to the electrodes of bioelectrochemical renewable energy technologies. In the past two decades, electrochemical techniques have been used to investigate EET in a wide range of microbes, with emphasis on dissimilatory metal-reducing bacteria, such as Shewanella oneidensis MR-1, as model organisms. However, due to the typically bulk nature of these techniques, they are unable to reveal the subpopulation variation in EET or link the observed electrochemical currents to energy gain by individual cells, thus overlooking the potentially complex spatial patterns of activity in bioelectrochemical systems. Here, to address these limitations, we use the cell membrane potential as a bioenergetic indicator of EET by S. oneidensis MR-1 cells. Using a fluorescent membrane potential indicator during in vivo single-cell-level fluorescence microscopy in a bioelectrochemical reactor, we demonstrate that membrane potential strongly correlates with EET. Increasing electrode potential and associated EET current leads to more negative membrane potential. This EET-induced membrane hyperpolarization is spatially limited to cells in contact with the electrode and within a near-electrode zone (<30 μm) where the hyperpolarization decays with increasing cell-electrode distance. The high spatial and temporal resolution of the reported technique can be used to study the single-cell-level dynamics of EET not only on electrode surfaces, but also during respiration of other solid-phase electron acceptors.

Subthalamic nucleus oscillations during vocal emotion processing are dependent of the motor asymmetry of Parkinson's disease

The subthalamic nucleus (STN) is involved in different aspects of emotional processes and more specifically in emotional prosody recognition. Recent studies on the behavioral effects of deep brain stimulation (DBS) in patients with Parkinson's disease (PD) have uncovered an asymmetry in vocal emotion decoding in PD, with left-onset PD patients showing deficits for the processing of happy voices. Whether and how PD asymmetry affects STN electrophysiological responses to emotional prosody, however, remains unknown.In the current study, local field potential activity was recorded from eight left- and six right-lateralized motor-onset PD patients (LOPD/ROPD) undergoing DBS electrodes implantation, while they listened to angry, happy and neutral voices.Time-frequency decomposition revealed that theta (2–6 Hz), alpha (6–12 Hz) and gamma (60–150 Hz) band responses to emotion were mostly bilateral with a differential pattern of response according to patient's sides-of onset. Conversely, beta-band (12–20 Hz and 20–30 Hz) emotional responses were mostly lateralized in the left STN for both patient groups. Furthermore, STN theta, alpha and gamma band responses to happiness were either absent (theta band) or reduced (alpha and gamma band) in the most affected STN hemisphere (contralateral to the side-of onset), while a late low-beta band left STN happiness-specific response was present in ROPD patients and did not occur in LOPD patients.Altogether, in this study, we demonstrate a complex pattern of oscillatory activity in the human STN in response to emotional voices and reveal a crucial influence of disease laterality on STN low-frequency oscillatory activity.

Serotonergic psychedelics LSD & psilocybin increase the fractal dimension of cortical brain activity in spatial and temporal domains

Psychedelic drugs, such as psilocybin and LSD, represent unique tools for researchers investigating the neural origins of consciousness. Currently, the most compelling theories of how psychedelics exert their effects is by increasing the complexity of brain activity and moving the system towards a critical point between order and disorder, creating more dynamic and complex patterns of neural activity. While the concept of criticality is of central importance to this theory, few of the published studies on psychedelics investigate it directly, testing instead related measures such as algorithmic complexity or Shannon entropy. We propose using the fractal dimension of functional activity in the brain as a measure of complexity since findings from physics suggest that as a system organizes towards criticality, it tends to take on a fractal structure. We tested two different measures of fractal dimension, one spatial and one temporal, using fMRI data from volunteers under the influence of both LSD and psilocybin. The first was the fractal dimension of cortical functional connectivity networks and the second was the fractal dimension of BOLD time-series. In addition to the fractal measures, we used a well-established, non-fractal measure of signal complexity and show that they behave similarly. We were able to show that both psychedelic drugs significantly increased the fractal dimension of functional connectivity networks, and that LSD significantly increased the fractal dimension of BOLD signals, with psilocybin showing a non-significant trend in the same direction. With both LSD and psilocybin, we were able to localize changes in the fractal dimension of BOLD signals to brain areas assigned to the dorsal-attenion network. These results show that psychedelic drugs increase the fractal dimension of activity in the brain and we see this as an indicator that the changes in consciousness triggered by psychedelics are associated with evolution towards a critical zone.

Transcriptomic characterization of microglia activation in a rat model of ischemic stroke

Microglia are key regulators of inflammatory response after stroke and brain injury. To better understand activation of microglia as well as their phenotypic diversity after ischemic stroke, we profiled the transcriptome of microglia after 75 min transient focal cerebral ischemia in 3-month- and 12-month-old male spontaneously hypertensive rats. Microglia were isolated from the brains by FACS sorting on days 3 and 14 after cerebral ischemia. GeneChip Rat 1.0ST microarray was used to profile the whole transcriptome of sorted microglia. We identified an evolving and complex pattern of activation from 3 to 14 days after stroke onset. M2-like patterns were extensively and persistently upregulated over time. M1-like patterns were only mildly upregulated, mostly at day 14. Younger 3-month-old brains showed a larger microglial response in both pro- and anti-inflammatory pathways, compared to older 12-month-old brains. Importantly, our data revealed that after stroke, most microglia are activated towards a wide spectrum of novel polarization states beyond the standard M1/M2 dichotomy, especially in pathways related to TLR2 and dietary fatty acid signaling. Finally, classes of transcription factors that might potentially regulate microglial activation were identified. These findings should provide a comprehensive database for dissecting microglial mechanisms and pursuing neuroinflammation targets for acute ischemic stroke.

Correction: Representation of abstract semantic knowledge in populations of human single neurons in the medial temporal lobe

[This corrects the article DOI: 10.1371/journal.pbio.3000290.].

Interstitial ions: A key regulator of state-dependent neural activity?

Throughout the nervous system, ion gradients drive fundamental processes. Yet, the roles of interstitial ions in brain functioning is largely forgotten. Emerging literature is now revitalizing this area of neuroscience by showing that interstitial cations (K+, Ca2+ and Mg2+) are not static quantities but change dynamically across states such as sleep and locomotion. In turn, these state-dependent changes are capable of sculpting neuronal activity; for example, changing the local interstitial ion composition in the cortex is sufficient for modulating the prevalence of slow-frequency neuronal oscillations, or potentiating the gain of visually evoked responses. Disturbances in interstitial ionic homeostasis may also play a central role in the pathogenesis of central nervous system diseases. For example, impairments in K+ buffering occur in a number of neurodegenerative diseases, and abnormalities in neuronal activity in disease models disappear when interstitial K+ is normalized. Here we provide an overview of the roles of interstitial ions in physiology and pathology. We propose the brain uses interstitial ion signaling as a global mechanism to coordinate its complex activity patterns, and ion homeostasis failure contributes to central nervous system diseases affecting cognitive functions and behavior.

Network dilution and asymmetry in an efficient brain

ABSTRACT The ultimate goal of neuroscience is to ultimately understand how the brain functions. The advancement of brain imaging shows us how the brain continuously alternates complex activity patterns and experimentally reveals how these patterns are responsible for memory, association, reasoning, and countless other tasks. Two fundamental parameters, dilution (the number of connections per node), and symmetry (the number of bidirectional connections of the same weight) characterise two fundamental features underlying the networks that connect the single neurons in the brain and generate these patterns. Mammalian brains show large variations of dilution, and mostly asymmetric connectivity, unfortunately the advantages which drove evolution to these state of network dilution and asymmetry are still unknown. Here, we studied the effects of symmetry and dilution on a discrete-time recurrent neural network with McCulloch–Pitts neurons. We use an exhaustive approach, in which we probe all possible inputs for several randomly connected neuron networks with different degrees of dilution and symmetry. We find an optimum value for the synaptic dilution and symmetry, which turns out to be in striking quantitative agreement with what previous researchers have found in the brain cortex, neocortex and hippocampus. The diluted asymmetric brain shows high memory capacity and pattern recognition speed, but most of all it is the less energy-consumptive with respect to fully connected and symmetric network topologies.

Optimality of sparse olfactory representations is not affected by network plasticity.

The neural representation of a stimulus is repeatedly transformed as it moves from the sensory periphery to deeper layers of the nervous system. Sparsening transformations are thought to increase the separation between similar representations, encode stimuli with great specificity, maximize storage capacity of associative memories, and provide an energy efficient instantiation of information in neural circuits. In the insect olfactory system, odors are initially represented in the periphery as a combinatorial code with relatively simple temporal dynamics. Subsequently, in the antennal lobe this representation is transformed into a dense and complex spatiotemporal activity pattern. Next, in the mushroom body Kenyon cells (KCs), the representation is dramatically sparsened. Finally, in mushroom body output neurons (MBONs), the representation takes on a new dense spatiotemporal format. Here, we develop a computational model to simulate this chain of olfactory processing from the receptor neurons to MBONs. We demonstrate that representations of similar odorants are maximally separated, measured by the distance between the corresponding MBON activity vectors, when KC responses are sparse. Sparseness is maintained across variations in odor concentration by adjusting the feedback inhibition that KCs receive from an inhibitory neuron, the Giant GABAergic neuron. Different odor concentrations require different strength and timing of feedback inhibition for optimal processing. Importantly, as observed in vivo, the KC–MBON synapse is highly plastic, and, therefore, changes in synaptic strength after learning can change the balance of excitation and inhibition, potentially leading to changes in the distance between MBON activity vectors of two odorants for the same level of KC population sparseness. Thus, what is an optimal degree of sparseness before odor learning, could be rendered sub–optimal post learning. Here, we show, however, that synaptic weight changes caused by spike timing dependent plasticity increase the distance between the odor representations from the perspective of MBONs. A level of sparseness that was optimal before learning remains optimal post-learning.

A Deep Learning Pipeline for Identification of Motor Units in Musculoskeletal Ultrasound

Ultrasound imaging provides information from a large part of the muscle. It has recently been shown that ultrafast ultrasound imaging can be used to record and analyze the mechanical response of individual MUs using blind source separation. In this work, we present an alternative method - a deep learning pipeline - to identify active MUs in ultrasound image sequences, including segmentation of their territories and signal estimation of their mechanical responses (twitch train). We train and evaluate the model using simulated data mimicking the complex activation pattern of tens of activated MUs with overlapping territories and partially synchronized activation patterns. Using a slow fusion approach (based on 3D CNNs), we transform the spatiotemporal image sequence data to 2D representations and apply a deep neural network architecture for segmentation. Next, we employ a second deep neural network architecture for signal estimation. The results show that the proposed pipeline can effectively identify individual MUs, estimate their territories, and estimate their twitch train signal at low contraction forces. The framework can retain spatio-temporal consistencies and information of the mechanical response of MU activity even when the ultrasound image sequences are transformed into a 2D representation for compatibility with more traditional computer vision and image processing techniques. The proposed pipeline is potentially useful to identify simultaneously active MUs in whole muscles in ultrasound image sequences of voluntary skeletal muscle contractions at low force levels.

Studying complex brain dynamics using Drosophila

The field has successfully used Drosophila genetic tools to identify neurons and sub-circuits important for specific functions. However, for an organism with complex and changing internal states to succeed in a complex and changing natural environment, many neurons and circuits need to interact dynamically. Drosophila’s many advantages, combined with new imaging tools, offer unique opportunities to study how the brain functions as a complex dynamical system. We give an overview of complex activity patterns and how they can be observed, as well as modeling strategies, adding proof of principle in some cases.

Approach to catheter ablation of left atrial flutters.

Left atrial tachycardias (ATs) most commonly occur after catheter or surgical ablation of atrial fibrillation and in patients with atrial myopathies. Pre-existing scar in the left atrium (LA) can result in complex circuits, sometimes with narrow channels that can be detected with high-resolution mapping. The most common forms of macroreentrant AT from the LA are variants of peri-mitral and roof-dependent reentry. Localized reentrant rhythms occur in the setting of fibrosis that gives rise to slow conduction and may occur adjacent to areas of prior ablation. The approach to treating these ATs involves first identifying the left atrial origin, defining the tachycardia circuit - which can be facilitated by ultrahigh density mapping and entrainment - and selecting a suitable isthmus to target for ablation. An important endpoint in ablating left atrial flutters is to establish and confirm bidirectional line of the block. Challenges in ablating these ATs include the presence of multiple tachycardias, defining circuits with complex activation patterns and achieving durable lines of block, particularly in the lateral mitral isthmus. Progress in treating these arrhythmias has come from new mapping technologies and the recognition of epicardial connections that allow for persistent conduction across ablation lesions. Also, advances in delivering energy to obtain complete transmural lesions promise to improve the long-term success of ablating ATs from the LA.