Biology subjects

Faisal, A. A.

Publications and source records attributed to Faisal, A. A..

14 recordsLinked to original sources

bioRxiv2024

Motor Learning Mechanisms are not modified by Feedback Manipulations in a Real-World Task

Error-based and reward-based mechanisms of motor learning co-occur in real-world scenarios but are traditionally isolated in laboratory tasks via feedback manipulations. This study examines the distinctiveness of these mechanisms by applying a lab-based feedback manipulation to a real-world task. Using Embodied Virtual Reality (EVR) of pool billiards - allowing for full proprioception via interaction with the physical pool table, cue stick, and balls - we introduced visual perturbations to a real-world task. 32 participants (12 F) underwent two sessions learning a visuomotor rotation, once with error and once with reward feedback. While naive participants corrected the entire rotation with error feedback, only partial correction was observed with reward feedback, highlighting the influence of the feedback regime on learning. However, the reward-dependent motor variability, lag-1 autocorrelation decay, and inter-trial variability decay - all indicators of reward-based and skill learning - were higher in the error feedback session, suggesting that the provided visual feedback did not exclusively engage specific learning mechanisms. Analysis of post-movement beta rebound (PMBR), a brain activity marker of learning mechanisms, revealed a decrease in PMBR with reward feedback but no consistent trend during error feedback sessions. These findings support the behavioural results, suggesting that while reward feedback was absent in error conditions, participants still engaged in reward-based learning. This study underscores the complexity of motor learning processes and highlights that visual feedback by itself can not elucidate the interplay between error-based and reward-based mechanisms in real-world contexts.

neuroscience↗

bioRxiv2021

Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders

Proprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry of PDs. 2. Few neurons will encode just a single joint. Our model provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with applications to the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots. Author SummaryIt is well established that proprioception is essential for effective for motor control, yet our understanding of proprioceptive coding in somatosensory cortex is far behind that of more established sensory modalities such as vision and touch. Here, we use unsupervised learning of a deep neural network imposed with biological constraints to reproduce coding properties of proprioceptive neurons in area 2 of primary somatosensory cortex. With this model, we demonstrate that the tendency for area 2 neurons in close physical proximity to share similar directional tuning can be explained by local topographic organisation driven by Mexican hat lateral effects, a phenomenon that is otherwise unobservable with the spatial resolution of available electrode microarray recordings. This provides, to the best of our knowledge, the first evidence of local topographic organisation in area 2 proprioceptive neurons. We also predict that the structure of proprioceptive receptive fields are typically multi-joint rather than single-joint in nature and provide, to the best of our knowledge, and find that our model best reproduces the tuning properties of neural data when training on natural kinematic recordings, rather than those from a stereotyped task, underlining the importance of training on data that reflect the natural distribution of sensory stimuli.

neuroscience↗

bioRxiv2021

Reproducing Human Motor Adaptation in Spiking Neural Simulation and known Synaptic Learning Rules

Sensorimotor adaptation enables us to adjust our goal-oriented movements in response to external perturbations. These phenomena have been studied experimentally and computationally at the level of human and animals reaching movements, and have clear links to the cerebellum as evidenced by cerebellar lesions and neurodegeneration. Yet, despite our macroscopic understanding of the high-level computational mechanisms it is unclear how these are mapped and are implemented in the neural substrates of the cerebellum at a cellular-computational level. We present here a novel spiking neural circuit model of the sensorimotor system including a cerebellum which control physiological muscle models to reproduce behaviour experiments. Our cerebellar model is composed of spiking neuron populations reflecting cells in the cerebellar cortex and deep cerebellar nuclei, which generate motor correction to change behaviour in response to perturbations. The model proposes two learning mechanisms for adaptation: predictive learning and memory formation, which are implemented with synaptic updating rules. Our model is tested in a force-field sensorimotor adaptation task and successfully reproduce several phenomena arising from human adaptation, including well-known learning curves, aftereffects, savings and other multi-rate learning effects. This reveals the capability of our model to learn from perturbations and generate motor corrections while providing a bottom-up view for the neural basis of adaptation. Thus, it also shows the potential to predict how patients with specific types of cerebellar damage will perform in behavioural experiments. We explore this by in silico experiments where we selectively incapacitate selected cerebellar circuits of the model which generate and reproduce defined motor learning deficits. Author summaryA rich body of work in human motor neuroscience developed high-level computational theories of sensorimotor control, learning and adaptation. But there is a gap in understanding how this may be implemented and learn on the level of neurons, synapses and spikes. Conversely, studies of patients with cerebellar lesions or neurological disease highlight the essential role the cerebellum plays in our ability to perform motor learning. Yet, how these anatomical and molecular defects play out in terms of human movement have to date not been linked to a model that spans multiple level of biological organisation from neural circuits to reproducing human motor experiments. To address this gap, we present a spiking neuron of the sensorimotor system focused on the cerebellum, with which we can on the one side reproduce the high-level behaviour learning phenomena observed in healthy subjects, as well as quantitatively predicting the putative effects on human movement trajectories of cerebellar lesions implemented at the cellular level.

neuroscience↗

bioRxiv2021

Early life experience sets hard limits on motor learning as evidenced from artificial arm use

The study or artificial arms provides a unique opportunity to address long-standing questions on sensorimotor plasticity and development. Learning to use an artificial arm arguably depends on fundamental building blocks of body representation and would therefore be impacted by early-life experience. We tested artificial arm motor-control in two adult populations with upper-limb deficiency: congenital one-handers - who were born with a partial arm, and amputees - who lost their biological arm in adulthood. Brain plasticity research teaches us that the earlier we train to acquire new skills (or use a new technology) the better we benefit from this practice as adults. Instead, we found that although one-hander started using an artificial arm as toddlers, they produced increased error noise and directional errors when reaching to visual targets, relative to amputees who performed similarly to controls. However, the earlier a one-hander was fitted with an artificial arm the better their motor control was. We suggest that visuomotor integration, underlying the observed deficits, is highly dependent on either biological or artificial arm experience at a very young age. Subsequently, opportunities for sensorimotor plasticity become more limited.

neuroscience↗

bioRxiv2020

Dense movement with embedded sparse action-type representations in the output layer of motor cortex

Motor cortex generates descending output necessary for executing a wide range of limb movements. Although movement-related activity has been described throughout motor cortex, the spatiotemporal organization of movement-specific signaling in deep layers remains largely unknown. Here, we recorded layer 5B population dynamics in the caudal forelimb area of motor cortex while mice performed a forelimb push/pull task and found that most neurons show movement-invariant responses, with a minority displaying movement specificity. Cell-type-specific imaging identified that movement-invariant responses dominated pyramidal tract (PT) neuron activity, with a small subpopulation representing movement type, whereas a larger proportion of intratelencephalic (IT) neurons displayed movement-specific signaling. The proportion of IT neurons decoding movement-type peaked prior to movement initiation, while for PT neurons this occurred during movement execution. Our data suggest that layer 5B population dynamics largely reflect movement-invariant signaling, with information related to movement-type being differentially routed through relatively small, distributed subpopulations of projection neurons.

neuroscience↗

bioRxiv2020

Learning to play the piano with the Supernumerary Robotic 3rd Thumb

Contemporary robotics gives us mechatronic capabilities for augmenting human bodies with extra limbs. However, how our motor control capabilities pose limits on such augmentation is an open question. We developed a Supernumerary Robotic 3rd Thumbs (SR3T) with two degrees-of-freedom controlled by the users body to endow them with an extra contralateral thumb on the hand. We demonstrate that a pianist can learn to play the piano with 11 fingers within an hour. We then evaluate 6 naive and 6 experienced piano players in their prior motor coordination and their capability in piano playing with the robotic augmentation. We show that individuals augmented performance could be predicted by our new custom motor coordination assessment, the Human Augmentation Motor Coordination Assessment (HAMCA) performed pre-augmentation. Our work demonstrates how supernumerary robotics can augment humans in skilled tasks and that individual differences in their augmentation capability are predictable by their individual motor coordination abilities.

neuroscience↗

bioRxiv2020

Embodied virtual reality for the study of real-world motor learning

Motor-learning literature focuses on simple laboratory-tasks due to their controlled manner and the ease to apply manipulations to induce learning and adaptation. Recently, we introduced a billiards paradigm and demonstrated the feasibility of real-world-neuroscience using wearables for naturalistic full-body motion-tracking and mobile-brain-imaging. Here we developed an embodied virtual-reality (VR) environment to our real-world billiards paradigm, which allows to control the visual feedback for this complex real-world task, while maintaining sense of embodiment. The setup was validated by comparing real-world ball trajectories with the trajectories of the virtual balls, calculated by the physics engine. We then ran our learning protocol in the embodied VR. Subjects played billiard shots when they held the physical cue and hit a physical ball on the table while seeing it all in VR. We found comparable learning trends in the embodied VR to those we previously reported in the physical real-world task. Embodied VR can be used for learning real-world tasks in a highly controlled environment which enables applying visual manipulations, common in laboratory-tasks and rehabilitation, to a real-world full-body task. Embodied VR enables to manipulate feedback and apply perturbations to isolate and assess interactions between specific motor-learning components, thus enabling addressing the current questions of motor-learning in real-world tasks. Such a setup can be used for rehabilitation, where VR is gaining popularity but the transfer to the real-world is currently limited, presumably, due to the lack of embodiment.

neuroscience↗

bioRxiv2020

Neural biomarkers of multiple motor-learning mechanisms in a real-world task

Many recent studies found signatures of motor learning in neural Beta oscillations (13- 30Hz), and specifically in the post-movement Beta rebound (PMBR). All these studies were in controlled laboratory-tasks in which the task designed to induce the studied learning mechanism. Interestingly, these studies reported opposing dynamics of the PMBR magnitude over learning for the error-based and reward-based tasks (increase versus decrease, respectively). Here we explored the PMBR dynamics during real-world motor-skill-learning in a billiards task using mobile-brain-imaging. Our EEG recordings highlight the opposing dynamics of PMBR magnitudes (increase versus decrease) between different subjects performing the same task. The groups of subjects, defined by their neural dynamics, also showed behavioural differences expected for different learning mechanisms. Our results suggest that when faced with the complexity of the real-world different subjects might use different learning mechanisms for the same complex task. We speculate that all subjects combine multi-modal mechanisms of learning, but different subjects have different predominant learning mechanisms.

neuroscience↗

bioRxiv2020

Linear Embodied Saliency: a Model of Full-Body Kinematics-based Visual Attention

Gaze behaviour and motor actions are fundamentally interlinked in both a spatial and temporal manner. However, the vast majority of gaze behaviour research has focused to date on reductionist head-fixed screen viewing experiments and ignored the motor aspect of visuomotor behaviour, thereby neglecting a critical component of the perception-action loop. We address this with an experimental design to capture, rather than constrain, the full range of simultaneous gaze and motor behaviour in a range of natural daily life tasks. Through building autoregressive models and applying these to our novel datasets we find that beyond simple static regions of interest, we can predict visual attention shifts from freely-moving first person body kinematics, through explaining gaze dynamics in the context of body dynamics, on the timescale of freely moving interactive behaviour in individuals, expanding our understanding of natural visuomotor behaviour.

neuroscience↗

bioRxiv2019

Neurobehavioural Signatures in Race Car Driving

Recent technological developments in mobile brain and body imaging are enabling new frontiers of real-world neuroscience. Simultaneous recordings of body movement and brain activity from highly skillful individuals as they demonstrate their exceptional skills in real-world settings, can shed new light on neurobehavioural structure of human expertise. Driving is a real-world skill which many of us acquire on different levels of expertise. Here we ran a case-study on a subject with the highest level of driving expertise - a Formula E Champion. We studied the expert drivers neural and motor patterns while he drove a sports car in the "Top Gear" race track under extreme conditions (high speed, low visibility, low temperature, wet track). His brain activity, eye movements and hand/foot movements were recorded. Brain activity in the delta, alpha, and beta frequency bands showed causal relation to hand movements. We demonstrate, here in summary, that even in extreme situations (race track driving) a method for conducting human ethomic (Ethology + Omics) data that encompasses information on the sensory inputs and motor outputs outputs of the brain as well as brain state to characterise complex human skills.

neuroscience↗

bioRxiv2019

Long term outcomes of Everolimus therapy in de novo liver transplantation: a systematic review and meta-analysis of randomized controlled trials

BackgroundRisk of nephrotoxicity in liver transplant patients on calcineurin inhibitors (CnIs) is a concern. Several controlled trials reported benefit of Everolimus (EVR) in minimizing this risk when combined with a reduced CnIs dose.\n\nObjectiveTo systematically review the efficacy and safety of EVR, alone or with reduced CnI dose, as compared to CnI alone post liver transplantation.\n\nMethodsWe searched MEDLINE, Scopus and the Cochrane Library for randomized controlled trials (RCTs) comparing EVR and CnI based regimens post liver transplanation. Assessment of studies and data extraction was undertaken independently.\n\nResultsEight studies were selected describing 769 patients. Cockcroft-Gault GFR (CG-GFR) was significantly higher at one (p=0.05), 3 & 5 years (p=0.030) in patients receiving EVR as compared to those receiving CnI therapy. The composite end point of efficacy failure was similar between the two arms after 1, 3 & 5 years of study. Higher number of patients discontinued EVR due to adverse effects in one year, however no difference was noted after 3 & 5 years. A higher rates of proteinuria, peripheral edema and incisional hernia were noted in patients on EVR.\n\nConclusionThe analysis confirms non-inferiority of EVR and reduced CnI combination. Patients on the combination regimen had better renal function compared to standard CnI therapy.

immunology↗

bioRxiv2019

Neurobehavioural signatures of learning that emerge in a real-world motor skill task

The neurobehavioral mechanisms of human motor-control and learning evolved in free behaving, real-life settings, yet this is studied mostly in reductionistic lab-based experiments. Here we take a step towards a more real-world motor neuroscience using wearables for naturalistic full-body motion-tracking and the sports of pool billiards to frame a real-world skill learning experiment. First, we asked if well-known features of motor learning in lab-based experiments generalize to a real-world task. We found similarities in many features such as multiple learning rates, and the relationship between task-related variability and motor learning. Our data-driven approach reveals the structure and complexity of movement, variability, and motor-learning, enabling an in-depth understanding of the structure of motor learning in three ways: First, while expecting most of the movement learning is done by the cue-wielding arm, we find that motor-learning affects the whole body, changing motor-control from head to toe. Second, during learning, all subjects decreased their movement variability and their variability in the outcome. Subjects who were initially more variable were also more variable after learning. Lastly, when screening the link across subjects between initial variability in individual joints and learning, we found that only the initial variability in the right forearm supination shows a significant correlation to the subjects learning rates. This is in-line with the relationship between learning and variability: while learning leads to an overall reduction in movement variability, only initial variability in specific task-relevant dimensions can facilitate faster learning.

neuroscience↗

bioRxiv2018

Does internal metabolic state determine human motor coordination strategy?

Motor coordination requires the orchestration of multiple degrees of freedom in order to perform actions. Humans display characteristic and predictable reaching trajectories even though multiple trajectories are possible. Computational theories of motor control can explain these reaching trajectories by assuming that subjects orchestrate their movements to minimise a cost function, such as end-point variability or movement effort. However, how internal metabolic states influence decision making and sensorimotor control is not well understood. Here we measure human behaviour during a centre out reaching task in two distinct metabolic conditions in the morning - after having had breakfast and not. We find that humans alter their patterns of motor coordination according to their internal metabolic state and that this change in behaviour results in a 20% lower task-related energy expenditure when fasted. We suggest that movements are orchestrated according to different criteria in different metabolic states so that metabolic costs are reduced in low metabolic states. We also predict that motor coordination strategies take the metabolic costs of specific muscle groups into account when planning and executing movements. Thus, metabolic state may alter the computational strategies of decision making between animal-based experiments (in typically low metabolic conditions) and human psychophysics experiments (in typically high metabolic conditions).

neuroscience↗

bioRxiv2018

Grammars of action in human behavior and evolution

Distinctive human behaviors from tool-making to language are thought to rely on a uniquely evolved capacity for hierarchical action sequencing. Unfortunately, testing of this idea has been hampered by a lack of objective, generalizable methods for measuring the structural complexity of real-world behaviors. Here we present a data-driven approach for quantifying hierarchical structure by extracting action grammars from basic ethograms. We apply this method to the evolutionarily-relevant behavior of stone tool-making by comparing sequences from the experimental replication of {small tilde}2.5 Mya Oldowan vs. more recent {small tilde}0.5 Mya Achuelean tools. Results show that, while using the same \"alphabet\" of elementary actions, Acheulean sequences are structurally more complex. Beyond its specific evolutionary implications, this finding illustrates the broader applicability of our method to investigate the structure of naturalistic human behaviors and cognition. We demonstrate one application by using our complexity measures to re-analyze data from an fMRI study of tool-making action observation.

neuroscience↗