Biology subjects

Kieliba, P.

Publications and source records attributed to Kieliba, P..

5 recordsLinked to original sources

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2020

Malleability of the cortical hand map following a finger nerve block

Individual fingers in the primary somatosensory cortex (S1) are known to be represented separately and adjacently, forming a cortical hand map. Electrophysiological studies in monkeys show that finger amputation triggers increased selectivity to the neighbouring fingers within the deprived S1, causing local reorganisation. Neuroimaging research in humans, however, shows persistent S1 finger representation of the missing hand, even decades after amputation. We aimed to resolve these apparently contrasting evidence by examining finger representation in humans following pharmacological amputation using single-finger nerve block and 7T neuroimaging. We hypothesised that beneath the apparent selectivity of individual fingers in the hand map, peripheral and central processing is distributed across fingers. If each finger contributes to the cortical representation of the others, then localised input loss will weaken finger representation across the hand map. For the same reason, the non-blocked fingers will stabilise the blocked fingers representation, resulting in persistent representation of the blocked finger. Using univariate selectivity profiling, we replicated the electrophysiological findings of local S1 reorganisation. However, more comprehensive analyses confirmed that local blocking reduced representation of all fingers across the entire hand area. Importantly, multivariate analysis demonstrated that despite input loss, representation of the blocked finger remained persistent and distinct from the unblocked fingers. Computational modelling suggested that the observed findings are driven by distributed processing underlying the topographic map, combined with homeostatic mechanisms. Our findings suggest that the long-standing depiction of the somatosensory hand map is misleading. As such, accounts for map reorganisation, e.g. following amputation, need to be reconsidered.

neuroscience↗

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↗

bioRxiv2018

Large-scale intrinsic connectivity is consistent across varying task demands

Measuring whole-brain functional connectivity patterns based on task-free ( restingstate) 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 acquisitions 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. We employed a steadystates 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 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), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. 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.\n\nNew and Noteworthy\n\nDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

neuroscience↗