Biology subjects

Clode, D.

Publications and source records attributed to Clode, D..

9 recordsLinked to original sources

bioRxiv2026

Spatial localisation of touch on a robotic limb in the absence of direct haptic feedback

Artificial limbs typically lack somatosensory receptors, limiting users access to haptic feedback about movement outcomes. However, indirect tactile signals generated during object interaction are transmitted to the body-limb interface and may serve as a haptic feedback source. This study investigates whether such indirect tactile signals convey distinct information that can support localisation of touch on an artificial body part. Using an additional robotic thumb (The Third Thumb, Dani Clode Design), we measured tactile signals at the interface between the Third Thumb and the hand following stimulation from four vibration motors positioned along the Thumb. We then assessed whether participants could discriminate these signals and integrate them with different Thumb postures to spatially localise touch. Measurements showed that each stimulation site produced distinct tactile signatures at the interface. Behaviourally, participants localised touch at above-chance levels. Representational similarity analyses further revealed that performance was best explained by the spatial positions of stimuli, rather than by limb-based coordinates or indirect signal patterns alone, indicating flexible mapping of indirect touch signals onto the Third Thumb posture. Together, these findings demonstrate that indirect tactile signals transmitted through an artificial limb can be discriminated and flexibly remapped to support spatial localisation of touch.

neuroscience↗

bioRxiv2025

Motor Learning Outside the Body: Broad Skill Generalisation with an Extra Robotic Limb

Our ability to transfer motor skills across tools and contexts is what makes modern technology usable. The success of motor augmentation devices, such as supernumerary robotic limbs, hinges on users capacity for generalised motor performance. We trained participants over seven days to use an extra robotic thumb (Third Thumb, Dani Clode Design), worn on the right hand and controlled via the toes. We tested whether motor learning was confined to the specific tasks and body parts involved in controlling and interacting with the Third Thumb, or whether it could generalise beyond them. Participants showed broad skill generalisation across tasks, body postures, and even when either the Third Thumb or the controller was reassigned to a different body part, suggesting the development of abstract, body-independent motor representations. Training also reduced cognitive demands and increased the sense of agency over the device. However, participants still preferred using their biological hand over the Third Thumb when given the option, suggesting that factors beyond motor skill generalisation, cognitive effort, and embodiment must be addressed to support the real-world adoption of such technologies.

neuroscience↗

bioRxiv2025

Muscle control of an extra robotic digit

Controlling an extra robotic finger requires the brain to adapt existing motor signals. While most current strategies exploit physical movement, there is growing interest in harnessing muscle activity directly via surface electromyography (EMG) as a more seamless interface. We systematically compared muscle- (EMG) and movement-based (force sensor) control of a Third Thumb. Using identical instructions and a counterbalanced within-participants design, we assessed initial skill, learning, and cognitive load across a variety of tasks, enabling a blinded comparison across control modalities. Both control modalities afforded successful Third Thumb control and learning, although force control consistently delivered better performance. Despite execution differences, learning rates and cognitive loads were comparable, with a similar evoked sense of agency. Signal analyses showed performance was predicted by real-time force sensor parameters but not by EMG, reflecting distinct control dynamics. Nonetheless, EMG training led to greater skill transfer to force control, suggesting it may better support generalisable learning. These findings challenge the assumption that proximity to neural signals ensures better control. Although EMG underperformed in execution, it showed unique advantages, including enhanced generalisation and access to richer signals, highlighting the need for improved real-time decoding to fully exploit its potential.

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bioRxiv2025

Developing a Sensory Representation of an Artificial Body Part

Somatosensory feedback is essential for motor control, yet artificial limbs are thought to lack such feedback. We investigated how the body and brain gather informative sensory signals from a wearable augmentation interface (a robotic digit for motor augmentation), and whether naturally-mediated feedback can support technological embodiment. Participants intuitively interpreted natural feedback across perceptual tasks, performing comparably to state-of-the-art artificial feedback systems. fMRI revealed an immediate and distinct, topographically-organised representation of the robotic digit. After longitudinal training, this representation was further refined, becoming more similar to the biological digits, which correlated with increased subjective somatosensory embodiment. Our findings demonstrate that wearable devices naturally provide a powerful source of feedback which is immediately integrated with our body. Long-term use can then promote device-embodiment across the sensorimotor hierarchy.

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bioRxiv2024

Shaping the developing homunculus: the roles of deprivation and compensatory behaviour in sensory remapping

Some of the most dramatic examples of neuroplasticity in the human brain follow congenital sensory deprivation, though we have limited understanding of the plasticity mechanisms driving such large-scale remapping. Hand loss due to congenital limb differences (CLD) offers a unique temporal dissociation of developmental neuroplasticity mechanisms: While sensory deprivation is congenital, compensatory motor behaviours develop progressively across childhood. Using paediatric neuroimaging and semi-ecological behavioural analysis in children (5-7 years old) and adults (>25 years old) with unilateral upper-limb CLD, we studied deprivation- and use-dependent plasticity in the deprived primary somatosensory cortex and beyond. We reveal that global remapping, encompassing the entire sensory homunculus, is established early and maintained in adulthood. We demonstrate that deprivation-driven homeostatic plasticity can drive this global remapping, with Hebbian-based compensatory learning further contributing to inter-individual differences both in childhood and adulthood. Our findings emphasise the early establishment and stability of cortical maps, despite extensive daily-life behavioural adaptation.

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bioRxiv2022

Single-cell Mendelian randomisation identifies cell-type specific genetic effects on human brain disease and behaviour

Translating genome-wide association loci to therapies requires knowledge of the causal genes, their directionality of effect and the cell-types in which they act. To infer these relationships in the human brain, we implemented Mendelian randomisation using single cell-type expression quantitative trait loci (eQTLs) as genetic anchors. Expression QTLs were mapped across 8 major cell-types in brain tissue exclusively ascertained from donors with no history of brain disease. We report evidence for a causal association between the change in expression of 118 genes and one or more of 16 brain phenotypes, revealing candidate targets for risk mitigation and opportunities for shared preventative therapeutic strategies. We highlight key causal genes for neurodegenerative and neuropsychiatric disease and for each, we report its cellular context and the therapeutic directionality required for risk mitigation. Our use of control samples establishes a new resource for the causal interpretation of GWAS risk alleles for human brain phenotypes.

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bioRxiv2021

Beyond body maps: information content of specific body parts is distributed across the somatosensory Homunculus

The somatosensory homunculus in primary somatosensory cortex (S1) is topographically organised, with relatively high selectivity to each body part in its primary area. This dominant feature may eclipse other organising principles in S1. Recent multivariate methodologies allow us to identify representational features beyond selectivity, e.g., information content, providing new opportunities to characterise the homunculus. Using Representational Similarity Analysis, we asked whether body part information content can be identified in S1 beyond the primary area of a given body part. Representational dissimilarities in fMRI activity patterns were compared between different body parts (face, hand and feet) and subparts (e.g., fingers), and between different actions performed with the same body part. Throughout the S1 homunculus, we identified significant dissimilarities between non-primary body parts (e.g., between the hand and the lips in the foot area). We also observed significant dissimilarities between body subparts in distant non-primary areas (e.g., different face parts in the foot area). Finally, we could significantly dissociate between two movements performed by one body part (e.g., the hand) well beyond its primary area (e.g., in the foot and face areas), even when focusing the analysis along the most topographically organised sub-region of S1, Brodmann area 3b. Altogether, our results demonstrate that body part and action-related information content is more distributed across S1 homunculus than previously considered. While this finding does not revoke the general topographic organising principle of S1, it reveals yet unexplored underlying information contents that could be harnessed for rehabilitation, as well as novel brain-machine interfaces.

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bioRxiv2021

Somatosensory signals from the controllers of an extra robotic finger support motor learning

Considerable resources are being invested to provide bidirectional control of substitutive and augmentative motor interfaces through artificial somatosensory feedback. Here, we investigated whether intrinsic somatosensory information, from body part(s) proportionally controlling an augmentation device, can be utilised to infer the devices state and position, to better support motor control and learning. In a placebo-controlled design, we used local anaesthetic to attenuate somatosensory inputs to the big toes while participants learned to operate a toe-controlled robotic extra finger (Third Thumb) using pressure sensors. Motor learning outcomes were compared against a control group who received sham anaesthetic. The availability of somatosensory cues about the amount of exerted pressure generally facilitated acquisition, retention and transfer of motor skills, and performance under cognitive load. Motor performance was not impaired by anaesthesia when tasks involved close collaboration with the biological fingers, indicating that the brain could close the gap of the missing pressure signals by alternative means, including feedback from other body parts involved in the motor task. Together, our findings demonstrate that there are intrinsic natural avenues to provide surrogate position information to support motor control of an artificial body part, beyond artificial extrinsic signalling.

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bioRxiv2020

Neurocognitive consequences of hand augmentation

From hand tools to cyborgs, humans have long been fascinated by the opportunities afforded by augmenting ourselves. Here, we studied how motor augmentation with an extra robotic thumb (the Third Thumb) impacts the biological hand representation in the brains of able-bodied people. Participants were tested on a variety of behavioural and neuroimaging tests designed to interrogate the augmented hands representation before and after 5-days of semi-intensive training. Training improved the Thumbs motor control, dexterity and hand-robot coordination, even when cognitive load was increased or when vision was occluded, and resulted in increased sense of embodiment over the robotic Thumb. Thumb usage also weakened natural kinematic hand synergies. Importantly, brain decoding of the augmented hands motor representation demonstrated mild collapsing of the canonical hand structure following training, suggesting that motor augmentation may disrupt the biological hand representation. Together, our findings unveil critical neurocognitive considerations for designing human body augmentation.

neuroscience↗