Phantom limb pain: Thinking outside the (mirror) box

Despite our best efforts over the past century, our mechanistic understanding of phantom limb pain and our ability to treat it have remained limited. Tamar Makin invites readers to think more critically about some of the most popular approaches to understanding and treating this condition

Soft Embodiment for Engineering Artificial Limbs

We highlight two alternative, yet complementary, solutions for harnessing available neural resources for improving integration of artificial limbs (ALs) through embodiment. ‘Hard’ embodiment exploits neural and cognitive body mechanisms by closely mimicking their original biological functions. ‘Soft’ embodiment exploits these same mechanisms by recycling them to support a different function altogether

Ten common statistical mistakes to watch out for when writing or reviewing a manuscript

Inspired by broader efforts to make the conclusions of scientific research more robust, we have compiled a list of some of the most common statistical mistakes that appear in the scientific literature. The mistakes have their origins in ineffective experimental designs, inappropriate analyses and/or flawed reasoning. We provide advice on how authors, reviewers and readers can identify and resolve these mistakes and, we hope, avoid them in the future

Stability of Sensory Topographies in Adult Cortex

Textbooks teach us that the removal of sensory input to sensory cortex, for example, following arm amputation, results in massive reorganisation in the adult brain. In this opinion article, we critically examine evidence for functional reorganisation of sensory cortical representations, focusing on the sequelae of arm amputation on somatosensory topographies. Based on literature from human and non-human primates, we conclude that the cortical representation of the limb remains remarkably stable despite the loss of its main peripheral input. Furthermore, the purportedly massive reorganisation results primarily from the formation or potentiation of new pathways in subcortical structures and does not produce novel functional sensory representations. We discuss the implications of the stability of sensory representations on the development of upper-limb neuroprostheses

Robotic hand augmentation drives changes in neural body representation

Humans have long been fascinated by the opportunities afforded through augmentation. This vision not only depends on technological innovations but also critically relies on our brain's ability to learn, adapt, and interface with augmentation devices. Here, we investigated whether successful motor augmentation with an extra robotic thumb can be achieved and what its implications are on the neural representation and function of the biological hand. Able-bodied participants were trained to use an extra robotic thumb (called the Third Thumb) over 5 days, including both lab-based and unstructured daily use. We challenged participants to complete normally bimanual tasks using only the augmented hand and examined their ability to develop hand-robot interactions. Participants were tested on a variety of behavioral and brain imaging tests, designed to interrogate the augmented hand's representation before and after the training. Training improved Third Thumb motor control, dexterity, and hand-robot coordination, even when cognitive load was increased or when vision was occluded. It also resulted in increased sense of embodiment over the Third Thumb. Consequently, augmentation influenced key aspects of hand representation and motor control. Third Thumb usage weakened natural kinematic synergies of the biological hand. Furthermore, brain decoding revealed a mild collapse of the augmented hand's motor representation after training, even while the Third Thumb was not worn. Together, our findings demonstrate that motor augmentation can be readily achieved, with potential for flexible use, reduced cognitive reliance, and increased sense of embodiment. Yet, augmentation may incur changes to the biological hand representation. Such neurocognitive consequences are crucial for successful implementation of future augmentation technologies

Expert tool users show increased differentiation between visual representations of hands and tools

The idea that when we use a tool we incorporate it into the neural representation of our body (embodiment) has been a major inspiration for philosophy, science and engineering. While theoretically appealing, there is little direct evidence for tool embodiment at the neural level. Using functional magnetic resonance imaging (fMRI) in male and female human subjects, we investigated whether expert tool users (London litter pickers: n=7) represent their expert tool more like a hand (neural embodiment) or less like a hand (neural differentiation), as compared to a group of tool novices (n=12). During fMRI scans, participants viewed first-person videos depicting grasps performed by either a hand, litter picker or a non-expert grasping tool. Using representational similarity analysis, differences in the representational structure of hands and tools were measured within occipitotemporal (OTC). Contrary to the neural embodiment theory, we find that the experts group represent their own tool less like a hand (not more) relative to novices. Using a case-study approach, we further replicated this effect, independently, in 5 of the 7 individual expert litter pickers, as compared to the novices. An exploratory analysis in left parietal cortex, a region implicated in visuomotor representations of hands and tools, also indicated that experts do not visually represent their tool more similar to hands, compared to novices. Together, our findings suggest that extensive tool use leads to an increased neural differentiation between visual representations of hands and tools. This evidence provides an important alternative framework to the prominent tool embodiment theory

Beyond body maps: Information content of specific body parts is distributed across the somatosensory homunculus

The homunculus in primary somatosensory cortex (S1) is famous for its body part selectivity, but this dominant feature may eclipse other representational features, e.g., information content, also relevant for S1 organization. Using multivariate fMRI analysis, we ask whether body part information content can be identified in S1 beyond its primary region. Throughout S1, we identify significant representational dissimilarities between body parts but also subparts in distant non-primary regions (e.g., between the hand and the lips in the foot region and between different face parts in the foot region). Two movements performed by one body part (e.g., the hand) could also be dissociated well beyond its primary region (e.g., in the foot and face regions), even within Brodmann area 3b. Our results demonstrate that information content is more distributed across S1 than selectivity maps suggest. This finding reveals underlying information contents in S1 that could be harnessed for rehabilitation and brain-machine interfaces

Does Training on Broad Band Tactile Stimulation Promote the Generalization of Perceptual Learning?

Given the clear role of sensory feedback in successful motor control, there is a growing interest in integrating substitutionary tactile feedback into robotic limb devices. To enhance the utility of such feedback, here we investigate how to best improve the limited generalization of tactile learning across body parts and stimulus properties. Specifically, we sought to understand how perceptual learning with different types of tactile stimuli may give rise to different patterns of learning generalization. To address this, we utilized vibro-tactile effectors to present patterns of stimulation in a match-to-sample paradigm. One group of participants trained on narrow-band stimulation consisting of simple sinusoidal vibrations, and the other on broad-band stimulation generated from music. We hypothesized that training on broad-band tactile stimulation would promote greater generalization of learning outcomes. We found training with broad-band stimuli generalized to underlying stimulus features of frequency discrimination but showed weaker generalization to un-trained digits. This study provides a first step towards devising perceptual learning paradigms that will generalize broadly to the untrained perceptual contexts

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

The study of 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 deficiencies: a congenital group - individuals who were born with a partial arm, and an acquired group - who lost their arm following amputation 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 the congenital group started using an artificial arm as toddlers, they produced increased error noise and directional errors when reaching to visual targets, relative to the acquired group who performed similarly to controls. However, the earlier an individual with a congenital limb difference was fitted with an artificial arm, the better their motor control was. Since we found no group differences when reaching without visual feedback, we suggest that the ability to perform efficient visual-based corrective movements is highly dependent on either biological or artificial arm experience at a very young age. Subsequently, opportunities for sensorimotor plasticity become more limited

Large-scale intrinsic connectivity is consistent across varying task demands

Measuring whole-brain functional connectivity patterns based on task-free (‘resting-state’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisition is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns (commonly termed ‘resting state networks’). We employed a ‘steady-states’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing us to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis) using standard resting-state functional connectivity (FC) analysis, we show that the whole-brain network architecture characteristic of the resting-state is comparable across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Changes in functional connectivity were detected locally, within the active networks. But to identify these local changes, the contributions of different FC networks to the global intrinsic connectivity pattern had to be isolated. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state