scholarly journals Delineating neural responses and functional connectivity changes during vestibular and nociceptive stimulation reveal the uniqueness of cortical vestibular processing

2021 ◽  
Author(s):  
Judita Huber ◽  
Maxine Ruehl ◽  
Virginia Flanagin ◽  
Peter zu Eulenburg
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Vestibular Stimulation ◽  
Brain Regions ◽  
Global Network ◽  
Proprioceptive Information ◽  
Nociceptive Stimulation ◽  
Vestibular Information ◽  
Nociceptive Responses ◽  
Vestibular Perception

AbstractVestibular information is ubiquitous and often processed jointly with visual, somatosensory and proprioceptive information. Among the cortical brain regions associated with human vestibular processing, area OP2 in the parietal operculum has been proposed as vestibular core region. However, delineating responses uniquely to vestibular stimulation in this region using neuroimaging is challenging for several reasons: First, the parietal operculum is a cytoarchitectonically heterogeneous region responding to multisensory stimulation. Second, artificial vestibular stimulation evokes confounding somatosensory and nociceptive responses blurring responses contributing to vestibular perception. Furthermore, immediate effects of vestibular stimulation on the organization of functional networks have not been investigated in detail yet. Using high resolution neuroimaging in a task-based and functional connectivity approach, we compared two equally salient stimuli—unilateral galvanic vestibular (GVS) and galvanic nociceptive stimulation (GNS)—to disentangle the processing of both modalities in the parietal operculum and characterize their effects on functional network architecture. GNS and GVS gave joint responses in area OP1, 3, 4, and the anterior and middle insula, but not in area OP2. GVS gave stronger responses in the parietal operculum just adjacent to OP3 and OP4, whereas GNS evoked stronger responses in area OP1, 3 and 4. Our results underline the importance of considering this common pathway when interpreting vestibular neuroimaging experiments and underpin the role of area OP2 in central vestibular processing. Global network changes were found during GNS, but not during GVS. This lack of network reconfiguration despite the saliency of GVS may reflect the continuous processing of vestibular information in the awake human.

scholarly journals Delineating Neural Responses and Functional Connectivity Changes During Vestibular and Nociceptive Stimulation Reveal the Uniqueness of Cortical Vestibular Processing

2021 ◽  
Author(s):  
Judita Huber ◽  
Ria Maxine Rühl ◽  
Virginia Flanagin ◽  
Peter zu Eulenburg
Keyword(s):  
Network Architecture ◽  
Vestibular Stimulation ◽  
Brain Regions ◽  
Global Network ◽  
Proprioceptive Information ◽  
Nociceptive Stimulation ◽  
Vestibular Information ◽  
Nociceptive Responses ◽  
Vestibular Perception ◽  
Heterogeneous Region

Abstract Vestibular information is ubiquitous and often processed jointly with visual, somatosensory and proprioceptive information. Among the cortical brain regions associated with human vestibular processing, area OP2 in the parietal operculum has been proposed as vestibular core region. However, delineating responses uniquely to vestibular stimulation in this region using neuroimaging is challenging for several reasons: Firstly, the parietal operculum is a cytoarchitectonically heterogeneous region responding to multisensory stimulation. Secondly, artificial vestibular stimulation evokes confounding somatosensory and nociceptive responses blurring responses contributing to vestibular perception. Furthermore, immediate effects of vestibular stimulation on the organization of functional networks have not been investigated in detail yet.Here, we compared two equally salient stimuli - galvanic vestibular stimulation (GVS) and galvanic nociceptive stimulation (GNS)- to disentangle the processing of both modalities in the parietal operculum and characterize their effects on functional network architecture. GNS and GVS gave joint responses in area OP1,3,4, and the anterior and middle insula, but not in area OP2. Contrasting both stimulation modalities resulted in stronger responses in parts of the parietal operculum adjacent to OP3 and OP4 during GVS, whereas GNS evoked stronger responses in area OP1,3 and 4. Our results underline the importance of considering this common multisensory trunk when interpreting vestibular neuroimaging experiments and further underpin the role of area OP2 in central vestibular processing. Global network changes were found during GNS, but not during GVS. This lack of network reconfiguration despite the saliency of GVS may reflect the continuous processing of vestibular information in the awake human.

scholarly journals Brain Resting State Functional Networks in Infants with Prenatal Opioid Exposure

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicole Steinhardt ◽  
Ramana Vishnubhotla ◽  
Yi Zhao ◽  
David M. Haas ◽  
Gregory M. Sokol ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Gestational Age ◽  
Resting State ◽  
Brain Regions ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Global Network ◽  
Neurodevelopmental Outcomes ◽  
Distinct Component

Purpose: Infants of mothers with opioid and substance use can present with postnatal withdrawal symptoms and are at risk of poor neurodevelopmental outcomes in later childhood. Identifying methods to evaluate the consequences of substance exposure on the developing brain can help initiate proactive therapies to improve outcomes for opioid-exposed neonates. Additionally, early brain imaging in infancy has the potential to identify early brain developmental alterations that could prognosticate neurodevelopmental outcomes in these children. In this study, we aim to identify differences in global brain network connectivity in infants with prenatal opioid exposure compared to healthy control infants, using resting-state functional MRI performed at less than 2 months completed gestational age.   Materials and Methods: In this prospective, IRB-approved study, we recruited 20 infants with prenatal opioid exposure and 20 healthy, opioid naïve infants. Anatomic imaging and resting-state functional MRI were performed at less than 48 weeks corrected gestational age, and rs-fMRI images were co-registered to the UNC neonate brain template and 90 anatomic atlas-labelled regions. Covariate Assisted Principal (CAP) regression was performed to identify brain network functional connectivity that was significantly different among infants with prenatal opioid exposure compared to healthy neonates.   Results: Of the 5 significantly different CAP components identified, the most distinct component (CAP5, p= 3.86 x 10-6) spanned several brain regions, including the right inferior temporal gyrus, bilateral Hesch’s gyrus, left thalamus, left supramarginal gyrus, left inferior parietal lobule, left superior parietal gyrus, right anterior cingulate gyrus, right gyrus rectus, left supplementary motor area, and left pars triangularis. Functional connectivity in this network was lower in the infants with prenatal opioid exposure compared to non-opioid exposed infants.   Conclusion: This study demonstrates global network alterations in infants with prenatal opioid exposure compared to non-opioid exposed infants. Future studies should be aimed at identifying clinical significance of this altered connectivity.

scholarly journals Behavioural relevance of spontaneous, transient brain network interactions in fMRI

2019 ◽  
Author(s):  
D. Vidaurre ◽  
A. Llera ◽  
S.M. Smith ◽  
M.W. Woolrich
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Brain Networks ◽  
Structural Data ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Human Connectome Project ◽  
Brain Data ◽  
Structural Brain

AbstractHow spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

Vestibular Perception is Slow: A Review

2013 ◽  
Vol 26 (4) ◽  
pp. 387-403 ◽  
Author(s):  
Michael Barnett-Cowan
Keyword(s):  
Empirical Investigation ◽  
Physiological Responses ◽  
Vestibular Stimulation ◽  
Surprising Result ◽  
Perceptual Awareness ◽  
Sensory Stimuli ◽  
Vestibular Information ◽  
Theoretical Predictions ◽  
Vestibular Perception ◽  
The Brain

Multisensory stimuli originating from the same event can be perceived asynchronously due to differential physical and neural delays. The transduction of and physiological responses to vestibular stimulation are extremely fast, suggesting that other stimuli need to be presented prior to vestibular stimulation in order to be perceived as simultaneous. There is, however, a recent and growing body of evidence which indicates that the perceived onset of vestibular stimulation is slow compared to the other senses, such that vestibular stimuli need to be presented prior to other sensory stimuli in order to be perceived synchronously. From a review of this literature it is speculated that this perceived latency of vestibular stimulation may reflect the fact that vestibular stimulation is most often associated with sensory events that occur following head movement, that the vestibular system rarely works alone, that additional computations are required for processing vestibular information, and that the brain prioritizes physiological response to vestibular stimulation over perceptual awareness of stimulation onset. Empirical investigation of these theoretical predictions is encouraged in order to fully understand this surprising result, its implications, and to advance the field.

scholarly journals Neuron Geometry Underlies Universal Network Features in Cortical Microcircuits

2019 ◽  
Author(s):  
Eyal Gal ◽  
Rodrigo Perin ◽  
Henry Markram ◽  
Michael London ◽  
Idan Segev
Keyword(s):  
Network Architecture ◽  
Brain Regions ◽  
Global Network ◽  
Local Geometry ◽  
Network Motifs ◽  
Neuron Network ◽  
Spatial Alignment ◽  
Cortical Microcircuits ◽  
General Network

ABSTRACTWhy do cortical microcircuits in a variety of brain regions express similar, highly nonrandom, network motifs? To what extent this structure is innate and how much of it is molded by plasticity and learning processes? To address these questions, we developed a general network science framework to quantify the contribution of neurons’ geometry and their embedding in cortical volume to the emergence of three-neuron network motifs. Applying this framework to a dense in silico reconstructed cortical microcircuits showed that the innate asymmetric neuron’s geometry underlies the universally recurring motif architecture. It also predicted the spatial alignment of cells composing the different triplets-motifs. These predictions were directly validated via in vitro 12-patch whole-cell recordings (7,309 triplets) from rat somatosensory cortex. We conclude that the local geometry of neurons imposes an innate, already structured, global network architecture, which serves as a skeleton upon which fine-grained structural and functional plasticity processes take place.

scholarly journals Complexity of intrinsic brain dynamics shaped by multiscale structural constraints

2020 ◽  
Author(s):  
Mengsen Zhang ◽  
Manish Saggar
Keyword(s):  
Functional Connectivity ◽  
Brain Regions ◽  
Synergistic Interaction ◽  
Global Network ◽  
Brain Dynamics ◽  
Underlying Structure ◽  
Energy Costs ◽  
Structural Constraints ◽  
Temporal Diversity ◽  
The Brain

AbstractThe brain is a complex, nonlinear system, exhibiting ever-evolving patterns of activities even without external inputs or tasks. Such intrinsic dynamics play a key role in cognitive functions and psychiatric disorders. A challenge is to link the intrinsic dynamics to the underlying structure, given the nonlinearity. Here we use a biophysically constrained, nonlinear-dynamical model to show how the complexity of intrinsic brain dynamics, manifested as its multistability and temporal diversity, can be sculpted by structural properties across scales. At a local level, multistability and temporal diversity can be induced by sufficient recurrent excitatory connectivity and its heterogeneity. At a global level, such functional complexity can also be created by the synergistic interaction between monostable, locally identical regions. Coordination between model brain regions across attractors in the multistable landscape predicts human functional connectivity. Compared to dynamics near a single attractor, cross-attractor coordination better accounts for functional links uncorrelated with structural connectivity. Energy costs of cross-attractor coordination are modulated by both local and global connectivity, and higher in the Default Mode Network. These findings hint that functional connectivity underscores transitions between alternative patterns of activity in the brain—even more than the patterns themselves. This work provides a systematic framework for characterizing intrinsic brain dynamics as a web of cross-attractor transitions and their energy costs. The framework may be used to predict transitions and energy costs associated with experimental or clinical interventions.Significance StatementThe brain is a multifunctional system: different brain regions can coordinate flexibly to perform different tasks. Understanding the regional and global structural constraints on brain function is critical to understand cognition. Here, using a unified biophysical network model, we show how structural constraints across scales jointly shape the brain’s intrinsic functional repertoire – the set of all possible patterns that a brain could generate. The modeled functional repertoire is enriched by both the flexibility of individual brain regions and their synergistic interaction in a global network. By carefully examining the modeled repertoire, we found that human resting-state functional connectivity was better predicted by transitions between brain activity patterns rather than any specific pattern per se.

scholarly journals Event-related network changes unfold the dynamics of cortical integration during face processing

2020 ◽  
Author(s):  
Antonio Maffei ◽  
Paola Sessa
Keyword(s):  
Face Perception ◽  
Network Topology ◽  
Face Processing ◽  
Visual Processing ◽  
Network Architecture ◽  
Time Window ◽  
Brain Regions ◽  
Data Availability ◽  
Global Network ◽  
State And Local

AbstractFace perception arises from a collective activation of brain regions in the occipital, parietal and temporal cortices. Despite wide acknowledgement that these regions act in an intertwined network, the network behavior itself is poorly understood. Here we present a study in which time-varying connectivity estimated from EEG activity elicited by facial expressions presentation was characterized using graph-theoretical measures of node centrality and global network topology. Results revealed that face perception results from a dynamic reshaping of the network architecture, characterized by the emergence of hubs located in the occipital and temporal regions of the scalp. The importance of these nodes can be observed from early stages of visual processing and reaches a climax in the same time-window in which the face-sensitive N170 is observed. Furthermore, using Granger causality, we found that the time-evolving centrality of these nodes is associated with ERP amplitude, providing a direct link between the network state and local neural response. Additionally, investigating global network topology by means of small-worldness and modularity, we found that face processing requires a functional network with a strong small-world organization that maximizes integration, at the cost of segregated subdivisions. Interestingly, we found that this architecture is not static, but instead it is implemented by the network from stimulus onset to ~200 msec. Altogether, this study reveals the event-related changes underlying face processing at the network level, suggesting that a distributed processing mechanism operates through dynamically weighting the contribution of the cortical regions involved.Data AvailabilityData and code related to this manuscript can be accessed through the OSF at this link https://osf.io/hc3sk/?view_only=af52bc4295c044ffbbd3be019cc083f4

scholarly journals Mapping individual differences across brain network structure to function and behavior with connectome embedding

2021 ◽  
Author(s):  
Gidon Levakov ◽  
Joshua Faskowitz ◽  
Galia Avidan ◽  
Olaf Sporns
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Predictive Accuracy ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Global Network ◽  
Age Related ◽  
And Behavior ◽  
The Brain

AbstractThe connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n=542; Cam-CAN: n=601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.

scholarly journals Cerebral Energy Metabolism and the Brain’s Functional Network Architecture: An Integrative Review

2013 ◽  
Vol 33 (9) ◽  
pp. 1347-1354 ◽  
Author(s):  
Louis-David Lord ◽  
Paul Expert ◽  
Jeremy F Huckins ◽  
Federico E Turkheimer
Keyword(s):  
Functional Connectivity ◽  
High Frequency ◽  
Network Architecture ◽  
Large Scale ◽  
Brain Networks ◽  
Glucose Consumption ◽  
Brain Regions ◽  
Functional Brain ◽  
Neurobiological Substrates ◽  
Functional Brain Networks

Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain’s ‘functional connectivity’ is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks.

scholarly journals Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model

2021 ◽  
Author(s):  
Oscar Portoles ◽  
Yuzhen Qin ◽  
Jonathan Hadida ◽  
Mark Woolrich ◽  
Ming Cao ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Global Network ◽  
Resting State Functional Connectivity ◽  
Time Resolved ◽  
Networks Structure ◽  
Over Time

AbstractBiophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network.Author summaryHere we present and evaluate a parsimonious model of large-scale brain activity. The model represents the brain as a network-of-networks structure. The sub-networks describe the neural activity within a brain region, and the global network encodes interactions between brain regions. Unlike other models, it capture progressive changes of local synchrony and local dynamics can be tuned with one parameter. Therefore the model could be used not only to model resting-state, but also behavioural tasks. Furthermore, we describe a simple framework that can deal with the arduous task of identifying global and local parameters.

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