scholarly journals Intersubject brain connectivity dynamics encode the stream of affect at multiple timescales

2020 ◽  
Author(s):  
Giada Lettieri ◽  
Giacomo Handjaras ◽  
Emiliano Ricciardi ◽  
Pietro Pietrini ◽  
Luca Cecchetti
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Multiple Timescales ◽  
Control Networks ◽  
Past Experiences ◽  
And Control ◽  
The Relationship ◽  
The Brain ◽  
Connectivity Strength

AbstractThe stream of affect is the result of a constant interaction between past experiences, motivations, expectations and the unfolding of events. How the brain represents the relationship between time and affect has been hardly explored, as it requires modeling the complexity of everyday life in the laboratory. Movies condense into hours a multitude of emotional responses, synchronized across subjects and characterized by temporal dynamics alike real-world experiences.Here, using naturalistic stimulation, time-varying intersubject brain connectivity and behavioral reports, we demonstrate that connectivity strength of large-scale brain networks tracks changes in affect. The default mode network represents the pleasantness of the experience, whereas attention and control networks encode its intensity. Interestingly, these orthogonal descriptions of affect converge in right temporoparietal and fronto-polar cortex. Within these regions, the stream of affect is represented at multiple timescales by chronotopic maps, where connectivity of adjacent areas preferentially maps experiences in 3- to 11-minute segments.

scholarly journals Cerebral cartography and connectomics

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140173 ◽  
Author(s):  
Olaf Sporns
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Network Science ◽  
Temporal Dynamics ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Hierarchical Organization ◽  
Large Scale Networks ◽  
Definition Of ◽  
The Brain

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.

scholarly journals Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective

2021 ◽  
Vol 12 ◽  
Author(s):  
Cristiana Dimulescu ◽  
Serdar Gareayaghi ◽  
Fabian Kamp ◽  
Sophie Fromm ◽  
Klaus Obermayer ◽  
...  
Keyword(s):  
Schizoaffective Disorder ◽  
Large Scale ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Theoretical Perspective ◽  
Input Energy ◽  
Strong Reduction ◽  
Patient Groups ◽  
And Control ◽  
The Brain

The coordinated dynamic interactions of large-scale brain circuits and networks have been associated with cognitive functions and behavior. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts fundamental constraints on the dynamical landscape of brain activity, i.e., the different states, or patterns of regional activation, and transition between states the brain can display. Specifically, it has been shown that densely connected, central regions control the transition between states that are “easily” reachable (in terms of expended energy), whereas weakly connected areas control transitions to states that are hard-to-reach. Changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia, especially regarding cognitive symptoms. Therefore, an analysis of graph and control theoretic measures of large-scale brain connectivity in patients offers to give insight into the emergence of cognitive disturbances in the disorder. To investigate these potential differences between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we used structural MRI data to assess the microstructural organization of white matter. We first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability, which identifies brain areas that can guide brain activity into different, easily reachable states with little input energy and modal controllability, which characterizes regions that can push the brain into difficult-to-reach states, i.e., states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. We found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.

scholarly journals Using the past to estimate sensory uncertainty

2019 ◽  
Author(s):  
Ulrik Beierholm ◽  
Tim Rohe ◽  
Ambra Ferrari ◽  
Oliver Stegle ◽  
Uta Noppeney
Keyword(s):  
Temporal Dynamics ◽  
Visual Signals ◽  
Perceptual Inference ◽  
Reliability Estimates ◽  
Uncertainty Estimates ◽  
Classical Models ◽  
Past Experiences ◽  
Combine Information ◽  
Sensory Uncertainty ◽  
The Brain

AbstractTo form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discountingOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

scholarly journals Brain network dynamics are hierarchically organized in time

2017 ◽  
Vol 114 (48) ◽  
pp. 12827-12832 ◽  
Author(s):  
Diego Vidaurre ◽  
Stephen M. Smith ◽  
Mark W. Woolrich
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Network ◽  
Specific Measure ◽  
Brain Areas ◽  
Neuronal Populations ◽  
Subject Specific ◽  
Large Scale Networks ◽  
Network Patterns ◽  
The Brain

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

scholarly journals Message-Elicited Brain Response Moderates the Relationship Between Opportunities for Exposure to Anti-Smoking Messages and Message Recall

2019 ◽  
Vol 69 (6) ◽  
pp. 589-611
Author(s):  
Elissa C Kranzler ◽  
Ralf Schmälzle ◽  
Rui Pei ◽  
Robert C Hornik ◽  
Emily B Falk
Keyword(s):  
Large Scale ◽  
Communication Theory ◽  
Brain Regions ◽  
Message Processing ◽  
Memory Encoding ◽  
Brain Response ◽  
Real Cost ◽  
Social Processing ◽  
The Relationship ◽  
The Brain

Abstract Campaign success is contingent on adequate exposure; however, exposure opportunities (e.g., ad reach/frequency) are imperfect predictors of message recall. We hypothesized that the exposure-recall relationship would be contingent on message processing. We tested moderation hypotheses using 3 data sets pertinent to “The Real Cost” anti-smoking campaign: past 30-day ad recall from a rolling national survey of adolescents aged 13–17 (n = 5,110); ad-specific target rating points (TRPs), measuring ad reach and frequency; and ad-elicited response in brain regions implicated in social processing and memory encoding, from a separate adolescent sample aged 14–17 (n = 40). Average ad-level brain activation in these regions moderates the relationship between national TRPs and large-scale recall (p < .001), such that the positive exposure-recall relationship is more strongly observed for ads that elicit high levels of social processing and memory encoding in the brain. Findings advance communication theory by demonstrating conditional exposure effects, contingent on social and memory processes in the brain.

scholarly journals Relationship of the BOLD Signal with VEP for Ultrashort Duration Visual Stimuli (0.1 to 5 ms) in Humans

2009 ◽  
Vol 30 (2) ◽  
pp. 449-458 ◽  
Author(s):  
Barış Yeşilyurt ◽  
Kevin Whittingstall ◽  
Kâmil Uğurbil ◽  
Nikos K Logothetis ◽  
Kâmil Uludağ
Keyword(s):  
Brain Function ◽  
Temporal Dynamics ◽  
Visual Stimulation ◽  
Impulse Response Function ◽  
Blood Oxygenation ◽  
Context Dependency ◽  
Oxygenation Level ◽  
Relationship Of ◽  
The Relationship ◽  
The Brain

There is currently a great interest to combine electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to study brain function. Earlier studies have shown different EEG components to correlate well with the fMRI signal arguing for a complex relationship between both measurements. In this study, using separate EEG and fMRI measurements, we show that (1) 0.1 ms visual stimulation evokes detectable hemodynamic and visual-evoked potential (VEP) responses, (2) the negative VEP deflection at ∼80 ms (N2) co-varies with stimulus duration/intensity such as with blood oxygenation level-dependent (BOLD) response; the positive deflection at ∼120 ms (P2) does not, and (3) although the N2 VEP–BOLD relationship is approximately linear, deviation is evident at the limit of zero N2 VEP. The latter finding argues that, although EEG and fMRI measurements can co-vary, they reflect partially independent processes in the brain tissue. Finally, it is shown that the stimulus-induced impulse response function (IRF) at 0.1 ms and the intrinsic IRF during rest have different temporal dynamics, possibly due to predominance of neuromodulation during rest as compared with neurotransmission during stimulation. These results extend earlier findings regarding VEP–BOLD coupling and highlight the component- and context-dependency of the relationship between evoked potentials and hemodynamic responses.

scholarly journals Brain Connectivity Dynamics in Untreated Transmen and Cisgender Individuals

2021 ◽  
Author(s):  
Carme Uribe ◽  
Carme Junque ◽  
Esther Gómez-Gil ◽  
María Díez-Cirarda ◽  
Antonio Guillamon
Keyword(s):  
Large Scale ◽  
Brain Connectivity ◽  
Positive Association ◽  
Brain Network ◽  
Functional Explanation ◽  
Salience Network ◽  
Temporal Perspective ◽  
Attention Regulation ◽  
Sensorimotor Network ◽  
Control Networks

Abstract Large-scale brain network interactions have been described between trans- and cis-gender identities. However, a temporal perspective of the brain spontaneous fluctuations is missing. We investigated the functional connectivity dynamics in transmen with gender incongruence and its relationship with interoceptive awareness. We describe four states in native and meta-state spaces: i) one state highly prevalent with sparse overall connections; ii) a second with strong couplings mainly involving components of the salience, default and executive control networks. Two states with global sparse connectivity but positive couplings iii) within the sensorimotor network, and iv) between salience network regions. Transmen had more dynamical fluidity than cismen, while cismen presented less meta-state fluidity and range dynamism than transmen and ciswomen. A positive association between attention regulation and fluidity, and meta-state range dynamism was found in transmen. There exist gender differences in the temporal brain dynamism, characterized by distinct interrelations of the salience network as catalyst interacting with other networks. We provide a functional explanation to the neurodevelopmental hypothesis proposing different brain phenotypes in the construction of the gendered-self.

scholarly journals Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Dongya Wu ◽  
Xin Li ◽  
Jun Feng
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Face Area ◽  
The Face ◽  
The Individual ◽  
The Right ◽  
And Function ◽  
The Relationship ◽  
The Brain

Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.

scholarly journals Attention Networks and the Intrinsic Network Structure of the Human Brain

2021 ◽  
Author(s):  
Sebastian Markett ◽  
David Nothdurfter ◽  
Antonia Focsa ◽  
Martin Reuter ◽  
Philippe Jawinski
Keyword(s):  
Network Structure ◽  
Network Theory ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Relevant Information ◽  
Attention Network ◽  
Attention Networks ◽  
Large Scale Networks ◽  
The Brain

Attention network theory states that attention is not a unified construct but consists of three independent systems that are supported by separable distributed networks: an alerting network to deploy attentional resources in anticipation of upcoming events, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. It is well established that the brain is intrinsically organized into several large-scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks with highest spatial precision at the level of cifti-grayordinates. Resulting group maps were compared to the group-level topology of 23 intrinsic networks which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto-parietal and midcingulo-insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

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