Sensory Reweighting System Differences on Vestibular Feedback With Increased Task Constraints in Individuals With and Without Chronic Ankle Instability

CONTEXT

Chronic ankle instability (CAI) is associated with a less flexible and adaptable sensorimotor system. Thus, individuals with CAI may present an inadequate sensory reweighting system, inhibiting their ability to place more emphasis (upweight) on reliable sensory feedback to control posture. However, how individuals with CAI reweight sensory feedback to maintain postural control in bilateral and unilateral stances has not been established.

OBJECTIVES

To examine (1) group differences in how the sensory reweighting system changes to control posture in a simple double-limb stance and a more complex single-limb stance (uninjured limb and injured limb) under increased environmental constraints manipulating somatosensory and visual information for individuals with and without CAI and (2) the effect of environmental and task constraints on postural control.

DESIGN

Case-control study.

SETTING

Laboratory.

PATIENTS OR OTHER PARTICIPANTS

A total of 21 individuals with CAI (age = 26.4 ± 5.7 years, height = 171.2 ± 9.8 cm, mass = 76.6 ± 15.17 kg) and 21 individuals without CAI (control group; age = 25.8 ± 5.7 years, height = 169.5 ± 9.5 cm, mass = 72.4 ± 15.0 kg) participated.

MAIN OUTCOME MEASURE(S)

We examined the equilibrium scores based on the first 10 seconds of trials in which participants completed 6 environmental conditions of the Sensory Organization Test during 3 tasks (double-limb and single-limb [uninjured and injured] stances). Sensory reweighting ratios for sensory systems (somatosensory, vision, and vestibular) were computed from paired equilibrium scores based on the first 10 seconds of the trials.

RESULTS

We observed 3-factor interactions between groups, sensory systems, and tasks (F4,160 = 3.754, P = .006) and for group, task, and environment (F10,400 = 2.455, P = .007). The CAI group did not downweight vestibular feedback compared with the control group while maintaining posture on the injured limb (P = .03). The CAI group demonstrated better postural stability than the control group while standing with absent vision (ie, eyes closed), fixed surroundings, and a moving platform on the injured limb (P = .03).

CONCLUSIONS

The CAI group relied on vestibular feedback while maintaining better postural stability than the control group in injured-limb stance. Group differences in postural control depended on both environmental (absent vision and moving platform) and task (injured limb) constraints.

Hierarchical VAEs provide a normative account of motion processing in the primate brain

The relationship between perception and inference, as postulated by Helmholtz in the 19th century, is paralleled in modern machine learning by generative models like Variational Autoencoders (VAEs) and their hierarchical variants. Here, we evaluate the role of hierarchical inference and its alignment with brain function in the domain of motion perception. We first introduce a novel synthetic data framework, Retinal Optic Flow Learning (ROFL), which enables control over motion statistics and their causes. We then present a new hierarchical VAE and test it against alternative models on two downstream tasks: (i) predicting ground truth causes of retinal optic flow (e.g., self-motion); and (ii) predicting the responses of neurons in the motion processing pathway of primates. We manipulate the model architectures (hierarchical versus non-hierarchical), loss functions, and the causal structure of the motion stimuli. We find that hierarchical latent structure in the model leads to several improvements. First, it improves the linear decodability of ground truth factors and does so in a sparse and disentangled manner. Second, our hierarchical VAE outperforms previous state-of-the-art models in predicting neuronal responses and exhibits sparse latent-to-neuron relationships. These results depend on the causal structure of the world, indicating that alignment between brains and artificial neural networks depends not only on architecture but also on matching ecologically relevant stimulus statistics. Taken together, our results suggest that hierarchical Bayesian inference underlines the brain's understanding of the world, and hierarchical VAEs can effectively model this understanding.

Quantifying virtual self-motion sensations induced by galvanic vestibular stimulation

BACKGROUND

The vestibular system provides a comprehensive estimate of self-motion in 3D space. Widely used to artificially stimulate the vestibular system, binaural-bipolar square-wave Galvanic Vestibular Stimulation (GVS) elicits a virtual sensation of roll rotation. Postural responses to GVS have been clearly delineated, however quantifying the perceived virtual rotation vector has not been fully realised.

OBJECTIVE

We aimed to quantify the perceived virtual roll rotation vector elicited by GVS using a psychophysical approach on a 3D turntable.

METHODS

Participants were placed supine on the 3D turntable and rotated around the naso-occipital axis while supine and received square-wave binaural-bipolar GVS or sham stimulation. GVS amplitudes and intensities were systematically manipulated. The turntable motion profile consisted of a velocity step of 20°/s2 until the trial velocity between 0-20°/s was reached, followed by a 1°/s ramp until the end of the trial. In a psychophysical adaptive staircase procedure, we systematically varied the roll velocity to identify the exact velocity that cancelled the perceived roll sensation induced by GVS.

RESULTS

Participants perceived a virtual roll rotation towards the cathode of approximately 2°/s velocity for 1 mA GVS and 6°/s velocity for 2.5 mA GVS. The observed values were stable across repetitions.

CONCLUSIONS

Our results quantify for the first time the perceived virtual roll rotations induced by binaural-bipolar square-wave GVS. Importantly, estimates were based on perceptual judgements, in the absence of motor or postural responses and in a head orientation where the GVS-induced roll sensation did not interact with the perceived direction of gravity. This is an important step towards applications of GVS in different settings, including sensory substitution or Virtual Reality.

Following during physically-coupled joint action engages motion area MT+/V5

Interpersonal coordination during joint action depends on the perception of the partner's movements. In many such situations - for example, while moving furniture together or dancing a tango - there are kinesthetic interactions between the partners due to the forces shared between them that allow them to directly perceive one another's movements. Joint action of this type often involves a contrast between the roles of leader and follower, where the leader imparts forces onto the follower, and the follower has to be responsive to these force-cues during movement. We carried out a novel 2-person functional MRI study with trained couple dancers engaged in bimanual contact with an experimenter standing next to the bore of the magnet, where the two alternated between being the leader and follower of joint improvised movements, all with the eyes closed. One brain area that was unexpectedly more active during following than leading was the region of MT+/V5. While classically described as an area for processing visual motion, it has more recently been shown to be responsive to tactile motion as well. We suggest that MT+/V5 responds to motion based on force-cues during joint haptic interaction, most especially when a follower responds to force-cues coming from a leader's movements.

Coordinates of Human Visual and Inertial Heading Perception

Heading estimation involves both inertial and visual cues. Inertial motion is sensed by the labyrinth, somatic sensation by the body, and optic flow by the retina. Because the eye and head are mobile these stimuli are sensed relative to different reference frames and it remains unclear if a perception occurs in a common reference frame. Recent neurophysiologic evidence has suggested the reference frames remain separate even at higher levels of processing but has not addressed the resulting perception. Seven human subjects experienced a 2s, 16 cm/s translation and/or a visual stimulus corresponding with this translation. For each condition 72 stimuli (360° in 5° increments) were delivered in random order. After each stimulus the subject identified the perceived heading using a mechanical dial. Some trial blocks included interleaved conditions in which the influence of ±28° of gaze and/or head position were examined. The observations were fit using a two degree-of-freedom population vector decoder (PVD) model which considered the relative sensitivity to lateral motion and coordinate system offset. For visual stimuli gaze shifts caused shifts in perceived head estimates in the direction opposite the gaze shift in all subjects. These perceptual shifts averaged 13 ± 2° for eye only gaze shifts and 17 ± 2° for eye-head gaze shifts. This finding indicates visual headings are biased towards retina coordinates. Similar gaze and head direction shifts prior to inertial headings had no significant influence on heading direction. Thus inertial headings are perceived in body-centered coordinates. Combined visual and inertial stimuli yielded intermediate results.

Integration of visual motion and locomotion in mouse visual cortex

Successful navigation through the world requires accurate estimation of one's own speed. To derive this estimate, animals integrate visual speed gauged from optic flow and run speed gauged from proprioceptive and locomotor systems. The primary visual cortex (V1) carries signals related to visual speed, and its responses are also affected by run speed. To study how V1 combines these signals during navigation, we recorded from mice that traversed a virtual environment. Nearly half of the V1 neurons were reliably driven by combinations of visual speed and run speed. These neurons performed a weighted sum of the two speeds. The weights were diverse across neurons, and typically positive. As a population, V1 neurons predicted a linear combination of visual and run speeds better than either visual or run speeds alone. These data indicate that V1 in the mouse participates in a multimodal processing system that integrates visual motion and locomotion during navigation.