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.

Parametrizing the Conditionally Gaussian Prior Model for Source Localization with Reference to the P20/N20 Component of Median Nerve SEP/SEF

In this article, we focused on developing the conditionally Gaussian hierarchical Bayesian model (CG-HBM), which forms a superclass of several inversion methods for source localization of brain activity using somatosensory evoked potential (SEP) and field (SEF) measurements. The goal of this proof-of-concept study was to improve the applicability of the CG-HBM as a superclass by proposing a robust approach for the parametrization of focal source scenarios. We aimed at a parametrization that is invariant with respect to altering the noise level and the source space size. The posterior difference between the gamma and inverse gamma hyperprior was minimized by optimizing the shape parameter, while a suitable range for the scale parameter can be obtained via the prior-over-measurement signal-to-noise ratio, which we introduce as a new concept in this study. In the source localization experiments, the primary generator of the P20/N20 component was detected in the Brodmann area 3b using the CG-HBM approach and a parameter range derived from the existing knowledge of the Tikhonov-regularized minimum norm estimate, i.e., the classical Gaussian prior model. Moreover, it seems that the detection of deep thalamic activity simultaneously with the P20/N20 component with the gamma hyperprior can be enhanced while using a close-to-optimal shape parameter value.

Stability analysis of a neural field self-organizing map

We provide theoretical conditions guaranteeing that a self-organizing map efficiently develops representations of the input space. The study relies on a neural field model of spatiotemporal activity in area 3b of the primary somatosensory cortex. We rely on Lyapunov’s theory for neural fields to derive theoretical conditions for stability. We verify the theoretical conditions by numerical experiments. The analysis highlights the key role played by the balance between excitation and inhibition of lateral synaptic coupling and the strength of synaptic gains in the formation and maintenance of self-organizing maps.

Somatosensory Cortex of Macaque Monkeys is Designed for Opposable Thumb

The evolution of opposable thumb has enabled fine grasping ability and precision grip, which led to the capacity for fine manipulation of objects and refined tool use. Since tactile inputs to an opposable thumb are often spatially and temporally out of synch with inputs from the fingers, we hypothesized that inputs from the opposable thumb would be processed in an independent module in the primary somatosensory cortex (area 3b). Here we show that in area 3b of macaque monkeys, most neurons in the thumb representation do not respond to tactile stimulation of other digits and receive few intrinsic cortical inputs from other digits. However, neurons in the representations of other digits respond to touch on any of the four digits and are significantly more interconnected in the cortex. The thumb inputs are thus processed in an independent module, whereas there is significantly more inter-digital information exchange between the other digits. This cortical organization reflects behavioral use of the hand with an opposable thumb.

A neural surveyor in somatosensory cortex

SummaryPerhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Though it seems straightforward, this simple representation belies the complex link between an activation in somatosensory Area 3b and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, though how this is computed by neural networks is unknown. We propose that somatosensory cortex implements multilateration, a common computation used by surveying and GPS systems to localize objects. Specifically, to decode touch location on the body, the somatosensory system estimates the relative distance between the afferent input and the body’s joints. We show that a simple feedforward neural network which captures the receptive field properties of somatosensory cortex implements a Bayes-optimal multilateral decoder via a combination of bell-shaped (Area 3b) and sigmoidal (Areas 1/2) tuning curves. Simulations demonstrated that this decoder produced a unique pattern of localization variability between two joints that was not produced by other known neural decoders. Finally, we identify this neural signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization.

Differential effects of the mode of touch, active and passive, on experience-driven plasticity in the S1 cutaneous digit representation of adult macaque monkeys

This study compared the receptive field (RF) properties and firing rates of neurons in the cutaneous hand representation of primary somatosensory cortex (areas 3b, 1, and 2) of 9 awake, adult macaques that were intensively trained in a texture discrimination task using active touch (fingertips scanned over the surfaces using a single voluntary movement), passive touch (surfaces displaced under the immobile fingertips), or both active and passive touch. Two control monkeys received passive exposure to the same textures in the context of a visual discrimination task. Training and recording extended over 1–2 yr per animal. All neurons had a cutaneous receptive field (RF) that included the tips of the stimulated digits (D3 and/or D4). In area 3b, RFs were largest in monkeys trained with active touch, smallest in those trained with passive touch, and intermediate in those trained with both; i.e., the mode of touch differentially modified the cortical representation of the stimulated fingers. The same trends were seen in areas 1 and 2, but the changes were not significant, possibly because a second experience-driven influence was seen in areas 1 and 2, but not in area 3b: smaller RFs with passive exposure to irrelevant tactile inputs compared with recordings from one naive hemisphere. We suggest that added feedback during active touch and higher cortical firing rates were responsible for the larger RFs with behavioral training; this influence was tempered by periods of more restricted sensory feedback during passive touch training in the active + passive monkeys. NEW & NOTEWORTHY We studied experience-dependent sensory cortical plasticity in relation to tactile discrimination of texture using active and/or passive touch. We showed that neuronal receptive fields in primary somatosensory cortex, especially area 3b, are largest in monkeys trained with active touch, smallest in those trained with passive touch, and intermediate in those trained using both modes of touch. Prolonged, irrelevant tactile input had the opposite influence in areas 1 and 2, favoring smaller receptive fields.

Resting-State Functional Networks of Different Topographic Representations in the Somatosensory Cortex of Macaque Monkeys and Humans

Information processing in the brain is mediated through a complex functional network architecture whose comprising nodes integrate and segregate themselves on different timescales. To gain an understanding of the network function it is imperative to identify and understand the network structure with respect to the underlying anatomical connectivity and the topographic organization. Here we show that the previously described resting-state network for the somatosensory area 3b comprises of distinct networks that are characteristic for different topographic representations. Seed-based resting-state functional connectivity analysis in macaque monkeys and humans using BOLD-fMRI signals from the face, the hand and rest of the medial somatosensory representations of area 3b revealed different correlation patterns. Both monkeys and humans have many similarities in the connectivity networks, although the networks are more complex in humans with many more nodes. In both the species face area network has the highest ipsilateral and contralateral connectivity, which included areas 3b and 4, and ventral premotor area. The area 3b hand network included ipsilateral hand representation in area 4.The emergent functional network structures largely reflect the known anatomical connectivity. Our results show that different body part representations in area 3b have independent functional networks perhaps reflecting differences in the behavioral use of different body parts. The results also show that large cortical areas if considered together, do not give a complete and accurate picture of the network architecture.HighlightsSomatosensory resting-state functional network is not uniform across the entire area 3b. Different body part representations have different connectivity networks.These functional connectivity networks have many similarities in the two primate species, i.e. macaque monkeys and humans, although the human network is more complex.In both the species network of the face representation is most extensive, which includes ipsilateral face motor cortex and PMv in both hemispheres.The hand representation in area 3b has connectivity with ipsilateral hand motor cortex.Bilateral connectivity with homologous and nonhomologous area 3b representations was observed only in humans.The functional connectivity networks largely reflect the underlying anatomical connectivity.

Reorganization of Higher-Order Somatosensory Cortex After Sensory Loss from Hand in Squirrel Monkeys

Unilateral dorsal column lesions (DCL) at the cervical spinal cord deprive the hand regions of somatosensory cortex of tactile activation. However, considerable cortical reactivation occurs over weeks to months of recovery. While most studies focused on the reactivation of primary somatosensory area 3b, here, for the first time, we address how the higher-order somatosensory cortex reactivates in the same monkeys after DCL that vary across cases in completeness, post-lesion recovery times, and types of treatments. We recorded neural responses to tactile stimulation in areas 3a, 3b, 1, secondary somatosensory cortex (S2), parietal ventral (PV), and occasionally areas 2/5. Our analysis emphasized comparisons of the responsiveness, somatotopy, and receptive field size between areas 3b, 1, and S2/PV across DCL conditions and recovery times. The results indicate that the extents of the reactivation in higher-order somatosensory areas 1 and S2/PV closely reflect the reactivation in primary somatosensory cortex. Responses in higher-order areas S2 and PV can be stronger than those in area 3b, thus suggesting converging or alternative sources of inputs. The results also provide evidence that both primary and higher-order fields are effectively activated after long recovery times as well as after behavioral and electrocutaneous stimulation interventions.