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 Characterization of functional diversity of the human brain based on intrinsic connectivity networks

2018 ◽  
Author(s):  
Congying Chu ◽  
Lingzhong Fan ◽  
Tianzi Jiang
Keyword(s):  
Human Brain ◽  
Functional Diversity ◽  
Functional Properties ◽  
Large Scale ◽  
Primary Motor Cortex ◽  
Brain Activity ◽  
Brain Regions ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
The Brain

AbstractSpontaneous fluctuations underlying the brain activity can reflect the intrinsic organization of the system, such as the functional brain networks. In large scale, a network perspective has emerged as a new avenue to explore the functional properties of human brain. Here, we studied functional diversity in healthy subjects based on the network perspective. We hypothesized that the patterns of participation of different functional networks were related with the functional diversity of particular brain regions. Independent component analysis (ICA) was adopted to detect the intrinsic connectivity networks (ICNs) based on the data of resting-state functional MRI. An index of functional diversity (FD index) was proposed to quantitatively describe the degree of anisotropic distribution related with participation of various ICNs. We found that FD index continuously varied across the brain, for example, the primary motor cortex with low FD value and the precuneus with significantly high FD value. The FD values indicated the different functional roles of the corresponding brain regions, which were reflected by the various patterns of participation of ICNs. The FD index can be used as a new approach to quantitatively characterize the functional diversity of human brain, even for the changed functional properties caused by the psychiatric disorders.

scholarly journals STAB: a spatio-temporal cell atlas of the human brain

2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Brain Regions ◽  
Molecular Characteristics ◽  
Great Effort ◽  
Brain Functions ◽  
Spatio Temporal ◽  
The Brain

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

scholarly journals Organization of Propagated Intrinsic Brain Activity in Individual Humans

2019 ◽  
Vol 30 (3) ◽  
pp. 1716-1734 ◽  
Author(s):  
Ryan V Raut ◽  
Anish Mitra ◽  
Scott Marek ◽  
Mario Ortega ◽  
Abraham Z Snyder ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Cortical Excitability ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Organization ◽  
Individual Level ◽  
Slow Activity ◽  
Lag Structure

Abstract Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

scholarly journals Mapping Pavlovian Conditioning Effects on the Brain: Blocking, Contiguity, and Excitatory Effects

2001 ◽  
Vol 86 (2) ◽  
pp. 809-823 ◽  
Author(s):  
Dirk Jones ◽  
F. Gonzalez-Lima
Keyword(s):  
Pavlovian Conditioning ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Blocking Effect ◽  
Anterior Cingulate ◽  
Caudate Putamen ◽  
Fdg Uptake ◽  
The Brain ◽  
Previous Learning

Pavlovian conditioning effects on the brain were investigated by mapping rat brain activity with fluorodeoxyglucose (FDG) autoradiography. The goal was to map the effects of the same tone after blocking or eliciting a conditioned emotional response (CER). In the tone-blocked group, previous learning about a light blocked a CER to the tone. In the tone-excitor group, the same pairings of tone with shock US resulted in a CER to the tone in the absence of previous learning about the light. A third group showed no CER after pseudorandom presentations of these stimuli. Brain systems involved in the various associative effects of Pavlovian conditioning were identified, and their functional significance was interpreted in light of previous FDG studies. Three conditioning effects were mapped: 1) blocking effects: FDG uptake was lower in medial prefrontal cortex and higher in spinal trigeminal and cuneate nuclei in the tone-blocked group relative to the tone-excitor group. 2) Contiguity effects: relative to pseudorandom controls, similar FDG uptake increases in the tone-blocked and -excitor groups were found in auditory regions (inferior colliculus and cortex), hippocampus (CA1), cerebellum, caudate putamen, and solitary nucleus. Contiguity effects may be due to tone-shock pairings common to the tone-blocked and -excitor groups rather than their different CER. And 3) excitatory effects: FDG uptake increases limited to the tone-excitor group occurred in a circuit linked to the CER, including insular and anterior cingulate cortex, vertical diagonal band nucleus, anterior hypothalamus, and caudoventral caudate putamen. This study provided the first large-scale map of brain regions underlying the Kamin blocking effect on conditioning. In particular, the results suggest that suppression of prefrontal activity and activation of unconditioned stimulus pathways are important neural substrates of the Kamin blocking effect.

scholarly journals Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ni Shu ◽  
Yaou Liu ◽  
Yunyun Duan ◽  
Kuncheng Li
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Functional Integration ◽  
Brain Regions ◽  
Hemispheric Asymmetries ◽  
Characteristic Path Length ◽  
Topological Measures ◽  
Structural Connections ◽  
The Brain

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain.

scholarly journals Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

2017 ◽  
Author(s):  
Giri P. Krishnan ◽  
Oscar C. González ◽  
Maxim Bazhenov
Keyword(s):  
Computational Model ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Ion Concentration ◽  
Brain State ◽  
The Brain

AbstractResting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

scholarly journals A Probabilistic, Distributed, Recursive Mechanism for Decision-making in the Brain

2016 ◽  
Author(s):  
Javier A. Caballero ◽  
Mark D. Humphries ◽  
Kevin N. Gurney
Keyword(s):  
Decision Making ◽  
Basal Ganglia ◽  
Neural Activity ◽  
Large Scale ◽  
Single Equation ◽  
Brain Regions ◽  
Compulsive Gambling ◽  
Choice Behaviour ◽  
Usable Information ◽  
The Brain

AbstractDecision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics.Author SummaryDecision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinson’s and Huntington’s diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decisionmaking has, hitherto, remained elusive. Here we show the essential composition of the brain’s decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys’ choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decisionmaking in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.

scholarly journals The dynamic basis of cognition: an integrative core under the control of the ascending neuromodulatory system

2018 ◽  
Author(s):  
J.M. Shine ◽  
M. Breakspear ◽  
P.T. Bell ◽  
K. Ehgoetz Martens ◽  
R. Shine ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
System Level ◽  
Dimensional Manifold ◽  
Brain Organization ◽  
Human Connectome Project ◽  
Functional Brain Organization ◽  
Low Dimensional ◽  
Dynamic Execution

AbstractThe human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here, we used multi-task fMRI data from the Human Connectome Project to examine the spatiotemporal architecture of cognition in the human brain. By investigating the spatial, dynamic and molecular signatures of system-wide neural activity across a range of cognitive tasks, we show that large-scale neuronal activity converges onto a low dimensional manifold that facilitates the dynamic execution of diverse task states. Flow within this attractor space is associated with dissociable cognitive functions, and with unique patterns of network-level topology and information processing complexity. The axes of the low-dimensional neurocognitive architecture align with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between low dimensional neural activity, network topology, neuromodulatory systems and cognitive function.One Sentence SummaryA diverse set of neuromodulators facilitates the formation of a dynamic, low-dimensional integrative core in the brain that is recruited by diverse cognitive demands

Combining brain imaging with brain stimulation: causality and connectivity

2012 ◽  
Author(s):  
Tomáš Paus
Keyword(s):  
Brain Imaging ◽  
Brain Stimulation ◽  
Neural Circuits ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Effective Connectivity ◽  
Methodological Approach ◽  
Brain Regions ◽  
Future Studies ◽  
The Brain

This article establishes the concept of a methodological approach to combine brain imaging with brain stimulation. Transcranial magnetic stimulation (TMS) is a tool that allows perturbing neural activity, in time and space, in a noninvasive manner. This approach allows the study of the brain-behaviour relationship. Under certain circumstances, the influence of one region on other, called the effective connectivity, can be measured. Functional connectivity is the extent of correlation in brain activity measured across a number of spatially distinct brain regions. This tool of connectivity can be applied to any dataset acquired with brain-mapping tools. However, its interpretation is complex. Also, the technical complexity of the combined studies needs to be resolved. Future studies may benefit from focusing on neurochemical transmission in specific neural circuits and on temporal dynamics of cortico-cortical interactions.

Large Scale Neurocognitive Networks Underlying Episodic Memory

2000 ◽  
Vol 12 (1) ◽  
pp. 163-173 ◽  
Author(s):  
Lars Nyberg ◽  
Jonas Persson ◽  
Reza Habib ◽  
Endel Tulving ◽  
Anthony R. McIntosh ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Target Density ◽  
Functional Connections ◽  
Positron Emission ◽  
Episodic Encoding

Large-scale networks of brain regions are believed to mediate cognitive processes, including episodic memory. Analyses of regional differences in brain activity, measured by functional neuroimaging, have begun to identify putative components of these networks. To more fully characterize neurocognitive networks, however, it is necessary to use analytical methods that quantify neural network interactions. Here, we used positron emission tomography (PET) to measure brain activity during initial encoding and subsequent recognition of sentences and pictures. For each type of material, three recognition conditions were included which varied with respect to target density (0%, 50%, 100%). Analysis of large-scale activity patterns identified a collection of foci whose activity distinguished the processing of sentences vs. pictures. A second pattern, which showed strong prefrontal cortex involvement, distinguished the type of cognitive process (encoding or retrieval). For both pictures and sentences, the manipulation of target density was associated with minor activation changes. Instead, it was found to relate to systematic changes of functional connections between material-specific regions and several other brain regions, including medial temporal, right prefrontal and parietal regions. These findings provide evidence for large-scale neural interactions between material-specific and process-specific neural substrates of episodic encoding and retrieval.

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