scholarly journals Stomach-brain synchrony binds neural representations of the body in a novel, delayed-connectivity resting-state network

2017 ◽  
Author(s):  
Ignacio Rebollo ◽  
Anne-Dominique Devauchelle ◽  
Benoît Béranger ◽  
Catherine Tallon-Baudry
Keyword(s):  
Resting State ◽  
Temporal Dynamics ◽  
The Body ◽  
Resting State Networks ◽  
Body Area ◽  
Functional Architecture ◽  
Resting State Network ◽  
Neural Representations ◽  
Extrastriate Body Area ◽  
The Brain

AbstractResting-state networks offer a unique window into the brain’s functional architecture, but their characterization remains limited to instantaneous connectivity thus far. Here, we describe a novel resting-state network based on the delayed connectivity between the brain and the slow electrical rhythm (0.05 Hz) generated in the stomach. The gastric network cuts across classical resting-state networks with little overlap with autonomic regulation areas. This network is composed of regions with convergent functional properties involved in mapping bodily space through touch, action or vision, as well as mapping external space in bodily coordinates. The network is characterized by a precise temporal sequence of activations within a gastric cycle, beginning with somato-motor cortices and ending with the extrastriate body area and dorsal precuneus. Our results demonstrate that canonical resting-state networks based on instantaneous connectivity represent only one of the possible partitions of the brain into coherent networks based on temporal dynamics.

scholarly journals Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ignacio Rebollo ◽  
Anne-Dominique Devauchelle ◽  
Benoît Béranger ◽  
Catherine Tallon-Baudry
Keyword(s):  
Functional Properties ◽  
Resting State ◽  
Temporal Dynamics ◽  
Resting State Networks ◽  
Body Area ◽  
Functional Architecture ◽  
Resting State Network ◽  
Extrastriate Body Area ◽  
Partial Overlap ◽  
The Brain

Resting-state networks offer a unique window into the brain’s functional architecture, but their characterization remains limited to instantaneous connectivity thus far. Here, we describe a novel resting-state network based on the delayed connectivity between the brain and the slow electrical rhythm (0.05 Hz) generated in the stomach. The gastric network cuts across classical resting-state networks with partial overlap with autonomic regulation areas. This network is composed of regions with convergent functional properties involved in mapping bodily space through touch, action or vision, as well as mapping external space in bodily coordinates. The network is characterized by a precise temporal sequence of activations within a gastric cycle, beginning with somato-motor cortices and ending with the extrastriate body area and dorsal precuneus. Our results demonstrate that canonical resting-state networks based on instantaneous connectivity represent only one of the possible partitions of the brain into coherent networks based on temporal dynamics.

scholarly journals Disrutpted resting-state functional architecture of the brain after 45-day simulated microgravity

2014 ◽  
Vol 8 ◽  
Author(s):  
Yuan Zhou ◽  
Yun Wang ◽  
Li-Lin Rao ◽  
Zhu-Yuan Liang ◽  
Xiao-Ping Chen ◽  
...  
Keyword(s):  
Resting State ◽  
Simulated Microgravity ◽  
Functional Architecture ◽  
The Brain

scholarly journals Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI

2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Lan Yang ◽  
Jing Wei ◽  
Ying Li ◽  
Bin Wang ◽  
Hao Guo ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Resting State Networks ◽  
Functional Magnetic Resonance ◽  
Intra Class Correlation ◽  
Retest Reliability ◽  
Brain Signals ◽  
Human Connectome Project ◽  
Test Retest Reliability ◽  
The Brain

In recent years, interest has been growing in dynamic characteristic of brain signals from resting-state functional magnetic resonance imaging (rs-fMRI). Synchrony and metastability, as neurodynamic indexes, are considered as one of methods for analyzing dynamic characteristics. Although much research has studied the analysis of neurodynamic indices, few have investigated its reliability. In this paper, the datasets from the Human Connectome Project have been used to explore the test–retest reliabilities of synchrony and metastability from multiple angles through intra-class correlation (ICC). The results showed that both of these indexes had fair test–retest reliability, but they are strongly affected by the field strength, the spatial resolution, and scanning interval, less affected by the temporal resolution. Denoising processing can help improve their ICC values. In addition, the reliability of neurodynamic indexes was affected by the node definition strategy, but these effects were not apparent. In particular, by comparing the test–retest reliability of different resting-state networks, we found that synchrony of different networks was basically stable, but the metastability varied considerably. Among these, DMN and LIM had a relatively higher test–retest reliability of metastability than other networks. This paper provides a methodological reference for exploring the brain dynamic neural activity by using synchrony and metastability in fMRI signals.

scholarly journals Computation-Based Feature Representation of Body Expressions in the Human Brain

2020 ◽  
Vol 30 (12) ◽  
pp. 6376-6390
Author(s):  
Marta Poyo Solanas ◽  
Maarten Vaessen ◽  
Beatrice de Gelder
Keyword(s):  
Brain Activity ◽  
Action Observation ◽  
Body Movement ◽  
The Body ◽  
Feature Representation ◽  
Primate Species ◽  
Whole Body ◽  
Affective Neuroscience ◽  
Body Area ◽  
Extrastriate Body Area

Abstract Humans and other primate species are experts at recognizing body expressions. To understand the underlying perceptual mechanisms, we computed postural and kinematic features from affective whole-body movement videos and related them to brain processes. Using representational similarity and multivoxel pattern analyses, we showed systematic relations between computation-based body features and brain activity. Our results revealed that postural rather than kinematic features reflect the affective category of the body movements. The feature limb contraction showed a central contribution in fearful body expression perception, differentially represented in action observation, motor preparation, and affect coding regions, including the amygdala. The posterior superior temporal sulcus differentiated fearful from other affective categories using limb contraction rather than kinematics. The extrastriate body area and fusiform body area also showed greater tuning to postural features. The discovery of midlevel body feature encoding in the brain moves affective neuroscience beyond research on high-level emotion representations and provides insights in the perceptual features that possibly drive automatic emotion perception.

Identification of Brain Functional Networks Using a Model-Based Approach

2019 ◽  
Vol 34 (08) ◽  
pp. 2057004
Author(s):  
Vangelis P. Oikonomou ◽  
Konstantinos Blekas ◽  
Loukas Astrakas
Keyword(s):  
Time Series ◽  
Resting State ◽  
Probabilistic Method ◽  
Resting State Fmri ◽  
Model Parameters ◽  
Resting State Networks ◽  
Model Based ◽  
Functional Brain Networks ◽  
Regression Mixture ◽  
The Brain

Functional MRI (fMRI) is a valuable brain imaging technique. A significant problem, when analyzing fMRI time series, is the finding of functional brain networks when the brain is at rest, i.e. no external stimulus is applied to the subject. In this work, we present a probabilistic method to estimate the Resting State Networks (RSNs) using a model-based approach. More specifically, RSNs are assumed to be the result of a clustering procedure. In order to perform the clustering, a mixture of regression models are used. Furthermore, special care has been given in order to incorporate important functionalities, such as spatial and embedded sparsity enforcing properties, through the use of informative priors over the model parameters. Another interesting feature of the proposed scheme is the flexibility to handle all the brain time series at once, allowing more robust solutions. We provide comparative experimental results, using an artificial fMRI dataset and two real resting state fMRI datasets, that empirically illustrate the efficiency of the proposed regression mixture model.

Temporal order of signal propagation within and across intrinsic brain networks

2021 ◽  
Vol 118 (48) ◽  
pp. e2105031118
Author(s):  
Mike J. Veit ◽  
Aaron Kucyi ◽  
Wenhan Hu ◽  
Chao Zhang ◽  
Baotian Zhao ◽  
...  
Keyword(s):  
Resting State ◽  
Temporal Order ◽  
Temporal Dynamics ◽  
Single Pulse ◽  
Signal Propagation ◽  
Resting State Networks ◽  
Temporal Separation ◽  
Electrophysiological Responses ◽  
Intracranial Electrodes ◽  
Intracranial Electroencephalography

We studied the temporal dynamics of activity within and across functional MRI (fMRI)–derived nodes of intrinsic resting-state networks of the human brain using intracranial electroencephalography (iEEG) and repeated single-pulse electrical stimulation (SPES) in neurosurgical subjects implanted with intracranial electrodes. We stimulated and recorded from 2,133 and 2,372 sites, respectively, in 29 subjects. We found that N1 and N2 segments of the evoked responses are associated with intra- and internetwork communications, respectively. In a separate cognitive experiment, evoked electrophysiological responses to visual target stimuli occurred with less temporal separation across pairs of electrodes that were located within the same fMRI-defined resting-state networks compared with those located across different resting-state networks. Our results suggest intranetwork prior to internetwork information processing at the subsecond timescale.

scholarly journals How the stomach and the brain work together at rest

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Giuseppina Porciello ◽  
Alessandro Monti ◽  
Salvatore Maria Aglioti
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Low Frequency ◽  
Resting State Network ◽  
The Brain

Low-frequency electrical waves in the stomach seem to be synchronised with the activity of a newly discovered resting-state network in the human brain.

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