Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Statistical inference of body representation in the macaque brain.

ABSTRACT: The sense of one's own body is a pillar of self-consciousness and could be investigated by inducing human illusions of artificial objects as part of the self. Here, we present a nonhuman primate version of a rubber-hand illusion that allowed us to determine its computational and neuronal mechanisms. We implemented a video-based system in a reaching task in monkeys and combined a casual inference model to establish an objective and quantitative signature for the monkey's body representation. Similar to humans, monkeys were more likely to perceive an external object as part of the self when the dynamics (spatial disparity) and the features (shape and structure) of visual (V) input was closer to proprioceptive (P) signals. Neural signals in the monkey's premotor cortex reflected the strength of illusion and the likelihood of misattributing the illusory hand to oneself, thus, revealing a cortical representation of body ownership.

SUBMITTER: Fang W

PROVIDER: S-EPMC6778219 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Statistical inference of body representation in the macaque brain.

Fang Wen W Li Junru J Qi Guangyao G Li Shenghao S Sigman Mariano M Wang Liping L

Proceedings of the National Academy of Sciences of the United States of America 20190903 40

The sense of one's own body is a pillar of self-consciousness and could be investigated by inducing human illusions of artificial objects as part of the self. Here, we present a nonhuman primate version of a rubber-hand illusion that allowed us to determine its computational and neuronal mechanisms. We implemented a video-based system in a reaching task in monkeys and combined a casual inference model to establish an objective and quantitative signature for the monkey's body representation. Simi ...[more]

PMID: 31481617

Similar Datasets

Auditory representation of learned sound sequences in motor regions of the macaque brain.

Project description:Human speech production requires the ability to couple motor actions with their auditory consequences. Nonhuman primates might not have speech because they lack this ability. To address this question, we trained macaques to perform an auditory-motor task producing sound sequences via hand presses on a newly designed device ("monkey piano"). Catch trials were interspersed to ascertain the monkeys were listening to the sounds they produced. Functional MRI was then used to map brain activity while the animals listened attentively to the sound sequences they had learned to produce and to two control sequences, which were either completely unfamiliar or familiar through passive exposure only. All sounds activated auditory midbrain and cortex, but listening to the sequences that were learned by self-production additionally activated the putamen and the hand and arm regions of motor cortex. These results indicate that, in principle, monkeys are capable of forming internal models linking sound perception and production in motor regions of the brain, so this ability is not special to speech in humans. However, the coupling of sounds and actions in nonhuman primates (and the availability of an internal model supporting it) seems not to extend to the upper vocal tract, that is, the supralaryngeal articulators, which are key for the production of speech sounds in humans. The origin of speech may have required the evolution of a "command apparatus" similar to the control of the hand, which was crucial for the evolution of tool use.

| S-EPMC7334521 | biostudies-literature

Statistical inference in brain graphs using threshold-free network-based statistics.

Project description:The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge-wise group-level statistical inference in brain graphs while controlling for multiple-testing associated false-positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold-free network-based statistics (TFNBS). The TFNBS combines threshold-free cluster enhancement, a method commonly used in voxel-wise statistical inference, and network-based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge-wise significance values and does not require the a priori definition of a hard cluster-defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false-positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.

| S-EPMC6619254 | biostudies-literature

How does the body representation system develop in the human brain?

Project description:Exploration of the body representation system (BRS) from kinaesthetic illusions in fMRI has revealed a complex network composed of sensorimotor and frontoparietal components. Here, we evaluated the degree of maturity of this network in children aged 7-11 years, and the extent to which structural factors account for network differences with adults. Brain activation following tendon vibration at 100Hz ('illusion') and 30Hz ('no illusion') were analysed using the two-stage random effects model, with or without white and grey matter covariates. The BRS was already well established in children as revealed by the contrast 'illusion' vs 'no illusion', although still immature in some aspects. This included a lower level of activation in primary somatosensory and posterior parietal regions, and the exclusive activation of the frontopolar cortex (FPC) in children compared to adults. The former differences were related to structure, while the latter difference reflected a functional strategy where the FPC may serve as the 'top' in top-down modulation of the activity of the other BRS regions to facilitate the establishment of body representations. Hence, the development of the BRS not only relies on structural maturation, but also involves the disengagement of an executive region not classically involved in body processing.

| S-EPMC6987789 | biostudies-literature

A broadly tuned network for affective body language in the macaque brain.

Project description:Body language is a powerful tool that we use to communicate how we feel, but it is unclear whether other primates also communicate in this way. Here, we use functional magnetic resonance imaging to show that the body-selective patches in macaques are activated by affective body language. Unexpectedly, we found these regions to be tolerant of naturalistic variation in posture as well as species; the bodies of macaques, humans, and domestic cats all evoked a stronger response when they conveyed fear than when they conveyed no affect. Multivariate analyses confirmed that the neural representation of fear-related body expressions was species-invariant. Collectively, these findings demonstrate that, like humans, macaques have body-selective brain regions in the ventral visual pathway for processing affective body language. These data also indicate that representations of body stimuli in these regions are built on the basis of emergent properties, such as socio-affective meaning, and not just putative image properties.

| S-EPMC9699662 | biostudies-literature

The representation of colored objects in macaque color patches.

Project description:An important question about color vision is how does the brain represent the color of an object? The recent discovery of "color patches" in macaque inferotemporal (IT) cortex, the part of the brain responsible for object recognition, makes this problem experimentally tractable. Here we recorded neurons in three color patches, middle color patch CLC (central lateral color patch), and two anterior color patches ALC (anterior lateral color patch) and AMC (anterior medial color patch), while presenting images of objects systematically varied in hue. We found that all three patches contain high concentrations of hue-selective cells, and that the three patches use distinct computational strategies to represent colored objects: while all three patches multiplex hue and shape information, shape-invariant hue information is much stronger in anterior color patches ALC/AMC than CLC. Furthermore, hue and object shape specifically for primate faces/bodies are over-represented in AMC, but not in the other two patches.

| S-EPMC5727180 | biostudies-literature

Representation of multiple objects in macaque category-selective areas.

Project description:Object recognition in the natural world usually occurs in the presence of multiple surrounding objects, but responses of neurons in inferotemporal (IT) cortex, the large brain area responsible for object recognition, have mostly been studied only to isolated objects. We study rules governing responses to multiple objects by cells in two category-selective regions of macaque IT cortex, the middle lateral face patch (ML) and the middle body patch (MB). We find that responses of single ML and MB cells to pairs of objects can be explained by the widely accepted framework of normalization, with one added ingredient: homogeneous category selectivity of neighboring neurons forming the normalization pool. This rule leads to winner-take-all, contralateral-take-all, or weighted averaging behavior in single cells, depending on the category, spatial configuration, and relative contrast of the two objects. The winner-take-all behavior suggests a potential mechanism for clutter-invariant representation of face and bodies under certain conditions.

| S-EPMC5932008 | biostudies-literature

Neural Activity during Voluntary Movements in Each Body Representation of the Intracortical Microstimulation-Derived Map in the Macaque Motor Cortex.

Project description:In order to accurately interpret experimental data using the topographic body map identified by conventional intracortical microstimulation (ICMS), it is important to know how neurons in each division of the map respond during voluntary movements. Here we systematically investigated neuronal responses in each body representation of the ICMS map during a reach-grasp-retrieval task that involves the movements of multiple body parts. The topographic body map in the primary motor cortex (M1) generally corresponds to functional divisions of voluntary movements; neurons at the recording sites in each body representation with movement thresholds of 10 μA or less were differentially activated during the task, and the timing of responses was consistent with the movements of the body part represented. Moreover, neurons in the digit representation responded differently for the different types of grasping. In addition, the present study showed that neural activity depends on the ICMS current threshold required to elicit body movements and the location of the recording on the cortical surface. In the ventral premotor cortex (PMv), no correlation was found between the response properties of neurons and the body representation in the ICMS map. Neural responses specific to forelimb movements were often observed in the rostral part of PMv, including the lateral bank of the lower arcuate limb, in which ICMS up to 100 μA evoked no detectable movement. These results indicate that the physiological significance of the ICMS-derived maps is different between, and even within, areas M1 and PMv.

| S-EPMC4975470 | biostudies-literature

Copula directional dependence for inference and statistical analysis of whole-brain connectivity from fMRI data.

Project description:INTRODUCTION:Inferring connectivity between brain regions has been raising a lot of attention in recent decades. Copula directional dependence (CDD) is a statistical measure of directed connectivity, which does not require strict assumptions on probability distributions and linearity. METHODS:In this work, CDDs between pairs of local brain areas were estimated based on the fMRI responses of human participants watching a Pixar animation movie. A directed connectivity map of fourteen predefined local areas was obtained for each participant, where the network structure was determined by the strengths of the CDDs. A resampling technique was further applied to determine the statistical significance of the connectivity directions in the networks. In order to demonstrate the effectiveness of the suggested method using CDDs, statistical group analysis was conducted based on graph theoretic measures of the inferred directed networks and CDD intensities. When the 129 fMRI participants were grouped by their age (3-5 year-old, 7-12 year-old, adult) and gender (F, M), nonparametric two-way analysis of variance (ANOVA) results could identify which cortical regions and connectivity structures correlated with the two physiological factors. RESULTS:Especially, we could identify that (a) graph centrality measures of the frontal eye fields (FEF), the inferior temporal gyrus (ITG), and the temporopolar area (TP) were significantly affected by aging, (b) CDD intensities between FEF and the primary motor cortex (M1) and between ITG and TP were highly significantly affected by aging, and (c) CDDs between M1 and the anterior prefrontal cortex (aPFC) were highly significantly affected by gender. SOFTWARE:The R source code for fMRI data preprocessing, estimation of directional dependences, network visualization, and statistical analyses are available at https://github.com/namgillee/CDDforFMRI.

| S-EPMC6346668 | biostudies-literature

Multiply robust inference for statistical interactions.

Project description:A primary focus of an increasing number of scientific studies is to determine whether two exposures interact in the effect that they produce on an outcome of interest. Interaction is commonly assessed by fitting regression models in which the linear predictor includes the product between those exposures. When the main interest lies in the interaction, this approach is not entirely satisfactory because it is prone to (possibly severe) bias when the main exposure effects or the association between outcome and extraneous factors are misspecified. In this article, we therefore consider conditional mean models with identity or log link which postulate the statistical interaction in terms of a finite-dimensional parameter, but which are otherwise unspecified. We show that estimation of the interaction parameter is often not feasible in this model because it would require nonparametric estimation of auxiliary conditional expectations given high-dimensional variables. We thus consider 'multiply robust estimation' under a union model that assumes at least one of several working submodels holds. Our approach is novel in that it makes use of information on the joint distribution of the exposures conditional on the extraneous factors in making inferences about the interaction parameter of interest. In the special case of a randomized trial or a family-based genetic study in which the joint exposure distribution is known by design or by Mendelian inheritance, the resulting multiply robust procedure leads to asymptotically distribution-free tests of the null hypothesis of no interaction on an additive scale. We illustrate the methods via simulation and the analysis of a randomized follow-up study.

| S-EPMC3097121 | biostudies-literature

Statistical inference for multi-pathogen systems.

Project description:There is growing interest in understanding the nature and consequences of interactions among infectious agents. Pathogen interactions can be operational at different scales, either within a co-infected host or in host populations where they co-circulate, and can be either cooperative or competitive. The detection of interactions among pathogens has typically involved the study of synchrony in the oscillations of the protagonists, but as we show here, phase association provides an unreliable dynamical fingerprint for this task. We assess the capacity of a likelihood-based inference framework to accurately detect and quantify the presence and nature of pathogen interactions on the basis of realistic amounts and kinds of simulated data. We show that when epidemiological and demographic processes are well understood, noisy time series data can contain sufficient information to allow correct inference of interactions in multi-pathogen systems. The inference power is dependent on the strength and time-course of the underlying mechanism: stronger and longer-lasting interactions are more easily and more precisely quantified. We examine the limitations of our approach to stochastic temporal variation, under-reporting, and over-aggregation of data. We propose that likelihood shows promise as a basis for detection and quantification of the effects of pathogen interactions and the determination of their (competitive or cooperative) nature on the basis of population-level time-series data.

| S-EPMC3158042 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data