scholarly journals Stereotypical modulations in dynamic functional connectivity explained by changes in BOLD variance

2017 ◽  
Author(s):  
Katharina Glomb ◽  
Adrián Ponce-Alvarez ◽  
Matthieu Gilson ◽  
Petra Ritter ◽  
Gustavo Deco
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Random Noise ◽  
Surrogate Data ◽  
Global Dynamics ◽  
Resting State Networks ◽  
Sliding Windows ◽  
Data Phase ◽  
Time Courses ◽  
The Brain

AbstractSpontaneous activity measured in human subject under the absence of any task exhibits complex patterns of correlation that largely correspond to large-scale functional topographies obtained with a wide variety of cognitive and perceptual tasks. These “resting state networks” (RSNs) fluctuate over time, forming and dissolving on the scale of seconds to minutes. While these fluctuations, most prominently those of the default mode network, have been linked to cognitive function, it remains unclear whether they result from random noise or whether they index a non-stationary process which could be described as state switching.In this study, we use a sliding windows-approach to relate temporal dynamics of RSNs to global modulations in correlation and BOLD variance. We compare empirical data, phase-randomized surrogate data, and data simulated with a stationary model. We find that RSN time courses exhibit a large amount of coactivation in all three cases, and that the modulations in their activity are closely linked to global dynamics of the underlying BOLD signal.We find that many properties of the observed fluctuations in FC and BOLD, including their ranges and their correlations amongst each other, are explained by fluctuations around the average FC structure. However, we also encounter interesting characteristics that are not explained in this way. In particular, we find that the brain spends more time in the troughs of modulations than can be expected from stationary dynamics.

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 Communicability systematically explains transmission speed in a cortical macro-connectome

2017 ◽  
Author(s):  
Masanori Shimono ◽  
Naomichi Hatano
Keyword(s):  
Propagation Velocity ◽  
Single Unit ◽  
Temporal Dynamics ◽  
Global Dynamics ◽  
Theoretical Understanding ◽  
Graph Theoretic ◽  
Temporal Aspects ◽  
Single Unit Recordings ◽  
The Brain ◽  
Anatomical Constraints

AbstractGlobal dynamics in the brain can be captured using fMRI, MEG, or electrocorticography (ECoG), but models are often restricted by anatomical constraints. Complementary single-/multi-unit recordings have described local fast temporal dynamics. However, because of anatomical constraints, global fast temporal dynamics remain incompletely understood. Therefore, we compared temporal aspects of cross-area propagations of single-unit recordings and ECoG, and investigated their anatomical bases. First, we demonstrated how both evoked and spontaneous ECoGs can accurately predict latencies of single-unit recordings. Next, we estimated the propagation velocity (1.0–1.5 m/s) from brain-wide data and found that it was fairly stable among different conscious levels. We also found that the anatomical topology strongly predicted the latencies. Finally, Communicability, a novel graph-theoretic measure, could systematically capture the balance between shorter or longer pathways. These results demonstrate that macro-connectomic perspective is essential for evaluating detailed temporal dynamics in the brain.Author SummaryThis study produced four main findings: First, we demonstrated that ECoG signals could predict the timing of evoked electrical spikes of neurons elicited by visual stimuli. Second, we showed that spontaneous ECoG recorded under a blindfold condition (without any stimuli) could also predict the timing of visually evoked neuronal spikes. We also clarified that performance predictions from blindfold data are essentially supported by the constraints of structural paths. Third, we quantified the propagation velocity (conductance velocity) as 1.0–1.5 m/s, and found that the velocity was stable among different conscious levels. Fourth, Communicability successfully characterized the relative contributions of shorter and longer paths. This study represents an important contribution to the theoretical understanding of the brain in terms of connectomics, dynamical propagations, and multi-scale architectures.

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

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 A qualitative model of the rod photoreceptor in natural context

2016 ◽  
Author(s):  
Timothée T. Dubuc ◽  
Etienne B. Roesch
Keyword(s):  
Large Scale ◽  
Neural Circuit ◽  
Temporal Dynamics ◽  
Continuous Model ◽  
Retinal Function ◽  
Sensitivity Analyses ◽  
Qualitative Behavior ◽  
Rod Photoreceptor ◽  
The Brain

AbstractGiven the intricacies of the retinal neural circuit, which bears a striking resemblance to that of the brain, it is proposed that retinal function goes beyond mere spatiotemporal prefiltering. We hypothesise that aspects related to motion detection and discrimination, anticipation and adaptation to environmental and contextual conditions, which have traditionally been ascribed to the brain, may be supported by neurons in the retina. Such early computations may be dependent on compensative and adaptive mechanisms that stem from qualities intrinsic to the retinal neural circuit and its interaction with the environment (neural transduction time, connectivity patterns, regularities in the input signal, temporal dynamics and light variations).With a view to investigating the contribution of the photoreceptor population to the processing performed by the retina in natural scotopic conditions, we present a continuous model of the rod photoreceptor. Our model permits the reproduction and exploration of a set of qualitative features displayed in vitro, such as excitation-dependent activation level and time-to-membrane current integration. We captured qualitative aspects of key features selected for their presumed importance in early visual function. Further, we subjected our model to extensive parameter sensitivity analyses, aiming to provide a visual representation of their contribution to the observed qualitative behavior.Author SummaryPrimate rod photoreceptor cells constitute the very first processing step in retinal function. This layer influences most of the visual field and presents itself as a tightly packed photosensitive array. Yet, no computational formulation of this neuron is suited for large-scale, time continuous modeling. With a view to studying retinal function in natural contexts, we describe a qualitative, continuous model of the rod. We subject this model to parameter analyses against selected behavioral features. We aim to provide a model that can integrate in further experiments, as well as in large scale network simulation.

scholarly journals Temporal dynamics of neural activity in macaque frontal cortex assessed with large-scale recordings

2020 ◽  
Author(s):  
Thomas Decramer ◽  
Elsie Premereur ◽  
Irene Caprara ◽  
Tom Theys ◽  
Peter Janssen
Keyword(s):  
Frontal Cortex ◽  
Large Scale ◽  
Primary Motor Cortex ◽  
Temporal Dynamics ◽  
Peripheral Vision ◽  
Premotor Cortex ◽  
Hand Movement ◽  
The Gaze ◽  
Hand Coordination ◽  
The Brain

AbstractWe make eye movements to objects before grasping these objects, and the gaze direction generally indicates where the object will be grasped. Hence, the brain has to coordinate eye-, arm- and hand movements. We performed large-scale recordings (more than 2000 responsive sites) in frontal cortex of monkeys during a saccade-reach-grasp task. When an object appeared in peripheral vision, the first burst of activity emerged in prearcuate areas (the FEF and area 45B), followed by dorsal and ventral premotor cortex, and a buildup of activity in primary motor cortex. After the saccade, prearcuate activity remained elevated while primary motor and premotor activity rose in anticipation of the upcoming arm and hand movement. Some premotor and prearcuate sites were equally active when the object appeared in peripheral vision and at the fovea, suggesting a role in eye-hand coordination. Thus, a large part of lateral frontal cortex is active during a saccade-reach-grasp task.

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 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 Resting State Networks in empirical and simulated dynamic functional connectivity

2016 ◽  
Author(s):  
Katharina Glomb ◽  
Adrián Ponce-Alvarez ◽  
Matthieu Gilson ◽  
Petra Ritter ◽  
Gustavo Deco
Keyword(s):  
Resting State ◽  
Field Model ◽  
Temporal Dynamics ◽  
Mean Field ◽  
Tensor Decomposition ◽  
Resting State Networks ◽  
Mean Field Model ◽  
Spatio Temporal ◽  
Time Courses ◽  
Over Time

AbstractIt is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions (“communities”) that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.

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.

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