Correction: Decentralized control of insect walking: A simple neural network explains a wide range of behavioral and neurophysiological results.
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ABSTRACT:
SUBMITTER: PLOS Computational Biology Staff
PROVIDER: S-EPMC8412292 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Controlling the six legs of an insect walking in an unpredictable environment is a challenging task, as many degrees of freedom have to be coordinated. Solutions proposed to deal with this task are usually based on the highly influential concept that (sensory-modulated) central pattern generators (CPG) are required to control the rhythmic movements of walking legs. Here, we investigate a different view. To this end, we introduce a sensor based controller operating on artificial neurons, being applied to a (simulated) insectoid robot required to exploit the "loop through the world" allowing for simplification of neural computation. We show that such a decentralized solution leads to adaptive behavior when facing uncertain environments which we demonstrate for a broad range of behaviors never dealt with in a single system by earlier approaches. This includes the ability to produce footfall patterns such as velocity dependent "tripod", "tetrapod", "pentapod" as well as various stable intermediate patterns as observed in stick insects and in Drosophila. These patterns are found to be stable against disturbances and when starting from various leg configurations. Our neuronal architecture easily allows for starting or interrupting a walk, all being difficult for CPG controlled solutions. Furthermore, negotiation of curves and walking on a treadmill with various treatments of individual legs is possible as well as backward walking and performing short steps. This approach can as well account for the neurophysiological results usually interpreted to support the idea that CPGs form the basis of walking, although our approach is not relying on explicit CPG-like structures. Application of CPGs may however be required for very fast walking. Our neuronal structure allows to pinpoint specific neurons known from various insect studies. Interestingly, specific common properties observed in both insects and crustaceans suggest a significance of our controller beyond the realm of insects.
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Project description:Down syndrome (DS) is the most common genetic cause of intellectual disability and a major biomedical concern. Despite a general consensus that protein homeostasis is essential for normal brain function, little is known about its role in DS. Here, we show that the integrated stress response (ISR)—a conserved signaling network that maintains proteostasis by controlling protein synthesis—is activated in the brains of DS mice and individuals with DS, leading to reduced translation. Genetic and pharmacological suppression of the ISR, either by inhibiting the ISR-initiating double-stranded RNA-activated protein kinase (PKR) or boosting the function of the eIF2•eIF2B complex, which becomes limiting upon ISR activation, de-repressed translation, reversed enhanced inhibitory synaptic transmission, and rescued the deficits in synaptic plasticity and memory in DS mice. Our findings unveil a crucial role for the ISR in DS pathophysiology and suggest tuning of the ISR as a promising therapeutic intervention for DS.
2019-11-02 | GSE138371 | GEO
Project description:Simple motor tics are sudden, nonrhythmic jerk like movements that are often preceded by premonitory urge and can be voluntarily suppressed. Some clinical characteristic of tics such as variability of movement, distractibility and fluctuating course can mimic functional movement disorders. Here we report a case of motor tics where the physiological approach in addition to careful clinical assessments is helpful to support the diagnosis of tic.
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Project description:In many animals the ability to navigate over long distances is an important prerequisite for foraging. For example, it is widely accepted that desert ants and honey bees, but also mammals, use path integration for finding the way back to their home site. It is however a matter of a long standing debate whether animals in addition are able to acquire and use so called cognitive maps. Such a 'map', a global spatial representation of the foraging area, is generally assumed to allow the animal to find shortcuts between two sites although the direct connection has never been travelled before. Using the artificial neural network approach, here we develop an artificial memory system which is based on path integration and various landmark guidance mechanisms (a bank of individual and independent landmark-defined memory elements). Activation of the individual memory elements depends on a separate motivation network and an, in part, asymmetrical lateral inhibition network. The information concerning the absolute position of the agent is present, but resides in a separate memory that can only be used by the path integration subsystem to control the behaviour, but cannot be used for computational purposes with other memory elements of the system. Thus, in this simulation there is no neural basis of a cognitive map. Nevertheless, an agent controlled by this network is able to accomplish various navigational tasks known from ants and bees and often discussed as being dependent on a cognitive map. For example, map-like behaviour as observed in honey bees arises as an emergent property from a decentralized system. This behaviour thus can be explained without referring to the assumption that a cognitive map, a coherent representation of foraging space, must exist. We hypothesize that the proposed network essentially resides in the mushroom bodies of the insect brain.
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Project description:Animal, and insect walking (locomotion) in particular, have attracted much attention from scientists over many years up to now. The investigations included behavioral, electrophysiological experiments, as well as modeling studies. Despite the large amount of material collected, there are left many unanswered questions as to how walking and related activities are generated, maintained, and controlled. It is obvious that for them to take place, precise coordination within muscle groups of one leg and between the legs is required: intra- and interleg coordination. The nature, the details, and the interactions of these coordination mechanisms are not entirely clear. To help uncover them, we made use of modeling techniques, and succeeded in developing a six-leg model of stick-insect walking. Our main goal was to prove that the same model can mimic a variety of walking-related behavioral modes, as well as the most common coordination patterns of walking just by changing the values of a few input or internal variables. As a result, the model can reproduce the basic coordination patterns of walking: tetrapod and tripod and the transition between them. It can also mimic stop and restart, change from forward-to-backward walking and back. Finally, it can exhibit so-called search movements of the front legs both while walking or standing still. The mechanisms of the model that enable it to produce the aforementioned behavioral modes can hint at and prove helpful in uncovering further details of the biological mechanisms underlying walking.
| S-EPMC6487367 | biostudies-literature
Project description:Quadrupeds exhibit versatile and adaptive running by exploiting the flying phase during the stride cycle. Various interlimb coordination mechanisms focusing on mechanical loads during the stance phase have been proposed to understand the underlying control mechanism, and various gait patterns have been reproduced. However, the essential control mechanism required to achieve both steady running patterns and non-steady behaviours, such as jumping and landing, remains unclear. Therefore, we focus on the vertical motions of the body parts and propose a new decentralized interlimb coordination mechanism. The simulation results demonstrate that the robot can generate efficient and various running patterns in response to the morphology of the body. Furthermore, the proposed model allows the robot to smoothly change its behaviour between steady running and non-steady landing depending on the situation. These results suggest that the steady and non-steady behaviours in quadruped adaptive running may share a common simple control mechanism based on the mechanical loads and vertical velocities of the body parts.
| S-EPMC8564617 | biostudies-literature
Project description:PurposeAttenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images.MethodsNon-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center).ResultsIn terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21-14.81%) and FL-PL (CI:11.82-13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32-12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34-26.10%). Furthermore, the Mann-Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed.ConclusionDeep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.
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Project description:The neuroscience of flavor perception is hence becoming increasingly important to understand food flavor perception that guides food selection, ingestion and appreciation. We recently provided evidence that rats can use the retronasal mode of olfaction, an essential element of human flavor perception. We showed that in rats, like humans, odors can acquire a taste. We and others also defined how the input of the olfactory bulb (OB) -not functionally imageable in humans- codes retronasal smell in anesthetized rat. The powerful awake transgenic mouse, however, would be a valuable additional model in the study of flavor neuroscience. We used a go/no-go behavioral task to test the mouse's ability to detect and discriminate the retronasal odor amyl acetate. In this paradigm a tasteless aqueous odor solution was licked by water-restricted head-fixed mice from a lick spout. Orthonasal contamination was avoided. The retronasal odor was successfully discriminated by mice against pure distilled water in a concentration-dependent manner. Bulbectomy removed the mice's ability to discriminate the retronasal odor but not tastants. The OB showed robust optical calcium responses to retronasal odorants in these awake mice. These results suggest that mice, like rats, are capable of smelling retronasally. This direct neuro-behavioral evidence establishes the mouse as a useful additional animal model for flavor research.
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Project description:Effective therapies for the so-called atypical parkinsonian syndrome (APS) such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), or corticobasal syndrome (CBS) are not available. Dopamine agonists (DA) are not often used in APS because of inefficacy and in a minority of case, their side effects, like dyskinesias, impairment of extrapyramidal symptoms or the appearance of psychosis, and REM sleep behavioral disorders (RBD). Transdermal rotigotine (RTG) is a non-ergot dopamine agonist indicated for use in early and advanced Parkinson's disease with a good tolerability and safety. Moreover, its action on a wide range of dopamine receptors, D1, D2, D3, unlike other DA, could make it a good option in APS, where a massive dopamine cell loss is documented. In this pilot, observational open-label study we evaluate the efficacy and tolerability of RTG in patients affected by APS. Thirty-two subjects with diagnosis of APS were treated with transdermal RTG. APS diagnosis was: MSA parkinsonian type (MSA-P), MSA cerebellar type (MSA-C), PSP, and CBS. Patients were evaluated by UPDRS-III, neuropsychiatric inventory, mini mental state examination at baseline, and after 6, 12, and 18 months. The titration schedule was maintained very flexible, searching the major clinical effect and the minor possible adverse events (AEs) at each visit. AEs were recorded. APS patients treated with RTG show an overall decrease of UPDRS-III scores without increasing behavioral disturbances. Only three patients were dropped out of the study. Main AEs were hypotension, nausea, vomiting, drowsiness, and tachycardia. The electroencephalographic recording power spectra analysis shows a decrease of theta and an increase of low alpha power. In conclusion, transdermal RTG seems to be effective and well tolerated in APS patients.
| S-EPMC4046164 | biostudies-literature
Project description:Seeing an action activates neurons in the premotor, motor, and somatosensory cortex. Since a significant fraction of these pyramidal neurons project to the spinal motor circuits, a central question is why we do not automatically perform the actions that we see. Indeed, seeing an action increases both cortical and spinal excitability of consistent motor patterns that correspond to the observed ones. Thus, it is believed that such imitative motor patterns are either suppressed or remain at a sub-threshold level. This would predict, however, that seeing someone make a corrective movement while one is actively involved in the same action should either suppress evoked responses or suppress or modulate the action itself. Here we tested this prediction, and found that seeing someone occasionally stepping over an obstacle while walking on a treadmill did not affect the normal walking pattern at all. However, cutaneously evoked reflexes in the anterior tibial and soleus muscles were modulated as if the subject was stepping over an obstacle. This result thus indicates that spinal activation was not suppressed and was neither at sub-threshold motor resonance. Rather, the spinal modulation from observed stepping reflects an adaptive mechanism for regulating predictive control mechanisms. We conclude that spinal excitability during action observation is not an adverse side-effect of action understanding but reflects adaptive and predictive motor control.
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