Reliability of the Dynavision task in virtual reality to explore visuomotor phenotypes

Pratviel, Yvan; Deschodt-Arsac, Véronique; Larrue, Florian; Arsac, Laurent M.

doi:10.1038/s41598-020-79885-9

Cited by 14 publications

(21 citation statements)

References 43 publications

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“…Multiple hierarchies of feed-forward-feedback interactions may be the key to exponentially increase neuronal efficiency. This mode of memory formation and consolidation is consistent with how other parts of the brain function, such as the way the tightly coupled feed-forward feedback networks in the visuomotor integration along the dorsal pathway enable a graceful visually guided movement (Pratviel et al, 2021). Further knowledge of the feed-forward and feedback dynamics in the hippocampus could help design an extremely memoryefficient neuromorphic computer (Abu-Hassan et al, 2019;Lee et al, 2021).…”

Section: Applicationsupporting

confidence: 67%

The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing

Vakilna

Tang

Wheeler

et al. 2021

Front. Neural Circuits

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The tri-synaptic pathway in the mammalian hippocampus enables cognitive learning and memory. Despite decades of reports on anatomy and physiology, the functional architecture of the hippocampal network remains poorly understood in terms of the dynamics of axonal information transfer between subregions. Information inputs largely flow from the entorhinal cortex (EC) to the dentate gyrus (DG), and then are processed further in the CA3 and CA1 before returning to the EC. Here, we reconstructed elements of the rat hippocampus in a novel device over an electrode array that allowed for monitoring the directionality of individual axons between the subregions. The direction of spike propagation was determined by the transmission delay of the axons recorded between two electrodes in microfluidic tunnels. The majority of axons from the EC to the DG operated in the feed-forward direction, with other regions developing unexpectedly large proportions of feedback axons to balance excitation. Spike timing in axons between each region followed single exponential log-log distributions over two orders of magnitude from 0.01 to 1 s, indicating that conventional descriptors of mean firing rates are misleading assumptions. Most of the spiking occurred in bursts that required two exponentials to fit the distribution of inter-burst intervals. This suggested the presence of up-states and down-states in every region, with the least up-states in the DG to CA3 feed-forward axons and the CA3 subregion. The peaks of the log-normal distributions of intra-burst spike rates were similar in axons between regions with modes around 95 Hz distributed over an order of magnitude. Burst durations were also log-normally distributed around a peak of 88 ms over two orders of magnitude. Despite the diversity of these spike distributions, spike rates from individual axons were often linearly correlated to subregions. These linear relationships enabled the generation of structural connectivity graphs, not possible previously without the directional flow of axonal information. The rich axonal spike dynamics between subregions of the hippocampus reveal both constraints and broad emergent dynamics of hippocampal architecture. Knowledge of this network architecture may enable more efficient computational artificial intelligence (AI) networks, neuromorphic hardware, and stimulation and decoding from cognitive implants.

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Section: Applicationsupporting

confidence: 67%

The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing

Vakilna

Tang

Wheeler

et al. 2021

Front. Neural Circuits

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“…Buttons are successively lit up after being reached by the hand-held VR controllers. More details can be found in Pratviel et al (2021) .…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, targets/buttons that are spatially organized on a vertical board must be hit as quick as possible when they light up in a random order. A replication of the task was recently implemented in virtual reality (VR) ( Pratviel et al, 2021 ), with the significant advantage that the VR setup includes tracked VR controllers held in the participant’s hands, which allows hand movements to be captured with sufficiently fine-grained measurements.…”

Section: Introductionmentioning

confidence: 99%

“…The visuomotor task in VR (VMVR) offers a great flexibility to study visuomotor dexterous behavior under varying visual input constraints ( Pratviel et al, 2021 ). For the participant, a successful execution relies on adequate coordination between visual search, the voluntary eye movements that actively scan the environment to capture task-relevant information, and cognitive control in movement system.…”

Section: Introductionmentioning

confidence: 99%

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Fast Hand Movements Unveil Multifractal Roots of Adaptation in the Visuomotor Cognitive System

Pratviel

Deschodt-Arsac

Larrue³

et al. 2021

Front. Physiol.

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Beyond apparent simplicity, visuomotor dexterity actually requires the coordination of multiple interactions across a complex system that links the brain, the body and the environment. Recent research suggests that a better understanding of how perceptive, cognitive and motor activities cohere to form executive control could be gained from multifractal formalisms applied to movement behavior. Rather than a central executive “talking” to encapsuled components, the multifractal intuition suggests that eye-hand coordination arises from multiplicative cascade dynamics across temporal scales of activity within the whole system, which is reflected in movement time series. Here we examined hand movements of sport students performing a visuomotor task in virtual reality (VR). The task involved hitting spatially arranged targets that lit up on a virtual board under critical time pressure. Three conditions were compared where the visual search field changed: whole board (Standard), half-board lower view field (LVF) and upper view field (UVF). Densely sampled (90 Hz) time series of hand motions captured by VR controllers were analyzed by a focus-based multifractal detrended fluctuation analysis (DFA). Multiplicative rather than additive interactions across temporal scales were evidenced by testing comparatively phase-randomized surrogates of experimental series, which confirmed nonlinear processes. As main results, it was demonstrated that: (i) the degree of multifractality in hand motion behavior was minimal in LVF, a familiar visual search field where subjects correlatively reached their best visuomotor response times (RTs); (ii) multifractality increased in the less familiar UVF, but interestingly only for the non-dominant hand; and (iii) multifractality increased further in Standard, for both hands indifferently; in Standard, the maximal expansion of the visual search field imposed the highest demand as evidenced by the worst visuomotor RTs. Our observations advocate for visuomotor dexterity best described by multiplicative cascades dynamics and a system-wide distributed control rather than a central executive. More importantly, multifractal metrics obtained from hand movements behavior, beyond the confines of the brain, offer a window on the fine organization of control architecture, with high sensitivity to hand-related control behavior under specific constraints. Appealing applications may be found in movement learning/rehabilitation, e.g., in hemineglect people, stroke patients, maturing children or athletes.

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“…VR could be used to design motor learning tasks of the requisite intermediate complexity better suited to longitudinal study. That said, despite increasing enthusiasm for VR as a research tool [38,39], tasks have largely remained simple and have cleaved close to their traditional 2-D counterparts (e.g., [40][41][42][43][44][45][46][47][48][49]). Only a handful of studies have attempted to utilize rich virtual environments to motor expertise in real-world complex tasks.…”

Section: Box 1 Differences Between Skill and Expertisementioning

confidence: 99%

Investigating and acquiring motor expertise using virtual reality

Mangalam¹,

Yarossi²,

Furmanek³

et al. 2022

Preprint

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After just months of simulated training, on January 19, 2019, a 23-year-old E-sports pro-gamer, Enzo Bonito, took to the racetrack and beat Lucas di Grassi, a Formula E and ex-Formula 1 driver with decades of real-world racing experience. This event raised the possibility that practicing in virtual reality can be surprisingly effective for acquiring motor expertise in real-world tasks. Here, we evaluate the potential of virtual reality to serve both as a space for training to expert levels in highly complex real-world tasks in compressed time windows at much lower financial cost without the hazards of the real world and as an experimental platform for exploring the science of expertise more generally.

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Reliability of the Dynavision task in virtual reality to explore visuomotor phenotypes

Cited by 14 publications

References 43 publications

The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing

The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing

Fast Hand Movements Unveil Multifractal Roots of Adaptation in the Visuomotor Cognitive System

Investigating and acquiring motor expertise using virtual reality

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