scholarly journals Heartbeat evokes a dynamically changing cortical theta-synchronized network in the resting state

2019 ◽  
Author(s):  
Jaejoong Kim ◽  
Bumseok Jeong
Keyword(s):  
Resting State ◽  
Phase Synchronization ◽  
Human Subjects ◽  
Predictive Coding ◽  
Regional Level ◽  
Human Connectome Project ◽  
Cortical Responses ◽  
Theta Frequency ◽  
Theta Phase ◽  
Cortical Regions

AbstractIn the resting state, heartbeats evoke cortical responses called heartbeat-evoked responses (HERs). While previous studies reported regional level HERs, researchers have not determined how heartbeat is processed at the cortical network level. Using resting-state magnetoencephalography data from 87 human subjects of both genders provided by the Human Connectome Project, we first showed that heartbeat increases the phase synchronization between cortical regions in the theta frequency, which forms a network structure, and we called this network a heartbeat-evoked network (HEN). The HEN was not an artefactual increase in phase synchronization. The HEN was partitioned into three modules with connector hubs in each module. The first module contained major interoception-related regions and thus was called a visceromotor-interoceptive network (VIN) displaying the strongest synchronization among modules, suggesting a major role for the VIN in processing heartbeat information. Two modules contained regions involved in the default mode network (DMN). The HEN structure was not fixed, but dynamically changed. The most prominent change was observed at approximately 200 ms after R-peak of the electrocardiogram, which was quantified based on the ‘flexibility’ of the network. Furthermore, the strongest synchronization within VIN was observed before heartbeat stimulated the cortex, which might be related to the prediction of an afferent heartbeat signal, thus supporting an interoceptive coding framework. Based on our results, the heartbeat is processed at the network level, and this result provides a useful framework that may potentially explain previous results of the regional level HER modulation through network-level processing.Significance statementThe resting-state network is composed of several networks supporting different functions. However, although the heartbeat is processed in the cortical regions, even in the resting state, the network supporting this function is unknown. Thus, we identified and investigated the heartbeat-evoked network (HEN), a network composed of significantly increased theta-phase synchronization between cortical regions after a heartbeat. The HEN comprised three modules. In particularly, the visceromotor-interoceptive network was likely to play a major role in network-level heartbeat processing and displayed the strongest synchronization immediately before the heartbeat enters the CNS, which supports an interoceptive predictive coding framework. These results provide a novel framework that may improve our understanding of cortical heartbeat processing from a network perspective.

scholarly journals Topology-preserving smoothing of retinotopic maps

2021 ◽  
Vol 17 (8) ◽  
pp. e1009216
Author(s):  
Yanshuai Tu ◽  
Duyan Ta ◽  
Zhong-Lin Lu ◽  
Yalin Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Accurate Analysis ◽  
Visual Neuroscience ◽  
Retinotopic Mapping ◽  
Retinotopic Maps ◽  
Topological Condition ◽  
Visual Inputs ◽  
Human Connectome Project ◽  
Cortical Responses ◽  
Cortical Regions

Retinotopic mapping, i.e., the mapping between visual inputs on the retina and neuronal activations in cortical visual areas, is one of the central topics in visual neuroscience. For human observers, the mapping is obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Although it is well known from neurophysiology that the mapping is topological (i.e., the topology of neighborhood connectivity is preserved) within each visual area, retinotopic maps derived from the state-of-the-art methods are often not topological because of the low signal-to-noise ratio and spatial resolution of fMRI. The violation of topological condition is most severe in cortical regions corresponding to the neighborhood of the fovea (e.g., < 1 degree eccentricity in the Human Connectome Project (HCP) dataset), significantly impeding accurate analysis of retinotopic maps. This study aims to directly model the topological condition and generate topology-preserving and smooth retinotopic maps. Specifically, we adopted the Beltrami coefficient, a metric of quasiconformal mapping, to define the topological condition, developed a mathematical model to quantify topological smoothing as a constrained optimization problem, and elaborated an efficient numerical method to solve the problem. The method was then applied to V1, V2, and V3 simultaneously in the HCP dataset. Experiments with both simulated and real retinotopy data demonstrated that the proposed method could generate topological and smooth retinotopic maps.

scholarly journals State-dependent differences in the frequency of TMS-evoked potentials

2019 ◽  
Author(s):  
Candice T. Stanfield ◽  
Martin Wiener
Keyword(s):  
Resting State ◽  
Right Hemisphere ◽  
Human Subjects ◽  
Dominant Frequency ◽  
Healthy Human ◽  
Cortical Areas ◽  
State Dependent ◽  
Concurrent Use ◽  
Cortical Regions ◽  
The Right

AbstractPrevious evidence suggests different cortical areas naturally oscillate at distinct frequencies, reflecting tuning properties of each region. The concurrent use of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been used to perturb cortical regions, resulting in an observed post-stimulation response that is maximal at the natural frequency of that region. However, little is known about the spatial extent of TMS-induced activation differences in cortical regions when comparing resting state (passive) versus active task performance. Here, we employed TMS-EEG to directly perturb three cortical areas in the right hemisphere while measuring the resultant changes in maximal evoked frequency in healthy human subjects during a resting state (N=12) and during an active sensorimotor task (N=12). Our results revealed that the brain engages a higher dominant frequency mode when actively engaged in a task, such that the frequency evoked during a task is consistently higher across cortical regions, regardless of the region stimulated. These findings suggest that a distinct characteristic of active performance versus resting state is a higher state of natural cortical frequencies.

PHASE SYNCHRONIZATION IN BRAIN NETWORKS DERIVED FROM CORRELATION BETWEEN PROBABILITIES OF RECURRENCES IN FUNCTIONAL MRI DATA

2013 ◽  
Vol 23 (02) ◽  
pp. 1350003 ◽  
Author(s):  
D. RANGAPRAKASH ◽  
XIAOPING HU ◽  
GOPIKRISHNA DESHPANDE
Keyword(s):  
Linear Correlation ◽  
Resting State ◽  
Brain Function ◽  
Phase Synchronization ◽  
Human Subjects ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fmri Data

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

scholarly journals State-dependent differences in the frequency of TMS-evoked potentials between resting and active states

2019 ◽  
Author(s):  
Candice T. Stanfield ◽  
Martin Wiener
Keyword(s):  
Resting State ◽  
Right Hemisphere ◽  
Human Subjects ◽  
Dominant Frequency ◽  
Healthy Human ◽  
Cortical Areas ◽  
State Dependent ◽  
Concurrent Use ◽  
Cortical Regions ◽  
The Right

AbstractPrevious evidence suggests different cortical areas naturally oscillate at distinct frequencies, reflecting tuning properties of each region. The concurrent use of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) has been used to perturb cortical regions, resulting in an observed post-stimulation response that is maximal at the natural frequency of that region. However, little is known about the spatial extent of TMS-induced activation differences in cortical regions when comparing resting state (passive) versus active task performance. Here, we employed TMS-EEG to directly perturb three cortical areas in the right hemisphere while measuring the resultant changes in maximal evoked frequency in healthy human subjects during a resting state (N=12) and during an active sensorimotor task (N=12). Our results revealed that the brain engages a higher dominant frequency mode when actively engaged in a task, such that the frequency evoked during a task is consistently higher across cortical regions, regardless of the region stimulated. These findings suggest that a distinct characteristic of active performance versus resting state is a higher state of natural cortical frequencies.

scholarly journals Theta Phase Synchronization between the Human Hippocampus and Prefrontal Cortex Increases during Encoding of Unexpected Information: A Case Study

2018 ◽  
Vol 30 (11) ◽  
pp. 1646-1656 ◽  
Author(s):  
Matthias J. Gruber ◽  
Liang-Tien Hsieh ◽  
Bernhard P. Staresina ◽  
Christian E. Elger ◽  
Juergen Fell ◽  
...  
Keyword(s):  
Phase Synchronization ◽  
Theta Oscillations ◽  
Pharmacoresistant Epilepsy ◽  
Intracranial Eeg ◽  
Human Hippocampus ◽  
Electrophysiological Recordings ◽  
Theta Frequency ◽  
Rhinal Cortex ◽  
Theta Phase

Events that violate predictions are thought to not only modulate activity within the hippocampus and PFC but also enhance communication between the two regions. Scalp and intracranial EEG studies have shown that oscillations in the theta frequency band are enhanced during processing of contextually unexpected information. Some theories suggest that the hippocampus and PFC interact during processing of unexpected events, and it is possible that theta oscillations may mediate these interactions. Here, we had the rare opportunity to conduct simultaneous electrophysiological recordings from the human hippocampus and PFC from two patients undergoing presurgical evaluation for pharmacoresistant epilepsy. Recordings were conducted during a task that involved encoding of contextually expected and unexpected visual stimuli. Across both patients, hippocampal–prefrontal theta phase synchronization was significantly higher during encoding of contextually unexpected study items, relative to contextually expected study items. Furthermore, the hippocampal–prefrontal theta phase synchronization was larger for contextually unexpected items that were later remembered compared with later forgotten items. Moreover, we did not find increased theta synchronization between the PFC and rhinal cortex, suggesting that the observed effects were specific to prefrontal–hippocampal interactions. Our findings are consistent with the idea that theta oscillations orchestrate communication between the hippocampus and PFC in support of enhanced encoding of contextually deviant information.

scholarly journals Learning Speech Production and Perception through Sensorimotor Interactions

2020 ◽  
Author(s):  
Shihab Shamma ◽  
Prachi Patel ◽  
Shoutik Mukherjee ◽  
Guilhem Marion ◽  
Bahar Khalighinejad ◽  
...  
Keyword(s):  
Vocal Tract ◽  
Human Subjects ◽  
Motor Task ◽  
Neural Processes ◽  
Mathematical Simulations ◽  
Predictive Role ◽  
Sensorimotor Interactions ◽  
Forward Projection ◽  
Cortical Regions

Abstract Action and Perception are closely linked in many behaviors necessitating a close coordination between sensory and motor neural processes so as to achieve a well-integrated smoothly evolving task performance. To investigate the detailed nature of these sensorimotor interactions, and their role in learning and executing the skilled motor task of speaking, we analyzed ECoG recordings of responses in the high-γ band (70 Hz-150 Hz) in human subjects while they listened to, spoke, or silently articulated speech. We found elaborate spectrotemporally-modulated neural activity projecting in both forward (motor-to-sensory) and inverse directions between the higher-auditory and motor cortical regions engaged during speaking. Furthermore, mathematical simulations demonstrate a key role for the forward projection in learning to control the vocal tract, beyond its commonly-postulated predictive role during execution. These results therefore offer a broader view of the functional role of the ubiquitous forward projection as an important ingredient in learning, rather than just control, of skilled sensorimotor tasks.

scholarly journals Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Model ◽  
Resting State ◽  
Early Life ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Noxious Stimulus ◽  
Noxious Stimulation ◽  
Human Connectome Project ◽  
And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

scholarly journals Anatomical and functional organization of the human substantia nigra and its connections

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Yu Zhang ◽  
Kevin Michel-Herve Larcher ◽  
Bratislav Misic ◽  
Alain Dagher
Keyword(s):  
Substantia Nigra ◽  
Functional Organization ◽  
Impulsive Choice ◽  
Behavioral Measures ◽  
Human Connectome Project ◽  
Motor Impulsivity ◽  
Cortical Regions ◽  
Value Coding ◽  
Decisional Impulsivity ◽  
Human Substantia Nigra

We investigated the anatomical and functional organization of the human substantia nigra (SN) using diffusion and functional MRI data from the Human Connectome Project. We identified a tripartite connectivity-based parcellation of SN with a limbic, cognitive, motor arrangement. The medial SN connects with limbic striatal and cortical regions and encodes value (greater response to monetary wins than losses during fMRI), while the ventral SN connects with associative regions of cortex and striatum and encodes salience (equal response to wins and losses). The lateral SN connects with somatomotor regions of striatum and cortex and also encodes salience. Behavioral measures from delay discounting and flanker tasks supported a role for the value-coding medial SN network in decisional impulsivity, while the salience-coding ventral SN network was associated with motor impulsivity. In sum, there is anatomical and functional heterogeneity of human SN, which underpins value versus salience coding, and impulsive choice versus impulsive action.

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