scholarly journals Comparison of functional connectivity between empirical and randomized structural brain networks

2016 ◽  
Author(s):  
Seyma Bayrak ◽  
Philipp Hövel ◽  
Vesna Vuksanovic
Keyword(s):  
Time Series ◽  
Human Brain ◽  
Random Graphs ◽  
Spatial Organization ◽  
Temporal Dynamics ◽  
Structural Connectivity ◽  
Signal Propagation ◽  
Brain Regions ◽  
Neuroimaging Data ◽  
Structural Connections

This study combines experimental and modeling approaches in order to investigate the temporal dynamics of the human brain at rest. The dynamics of the neuronal activity is modeled with FitzHugh-Nagumo oscillators and the blood-oxygen-level-dependent (BOLD) time series is inferred via the Balloon-Windkessel hemodynamic model. The simulations are based on structural connections that are derived from diffusion-weighted magnetic resonance imaging measurements yielding anatomical probabilities between the considered brain regions of interest. In addition, the length of the fiber tracks allows for inference of coupling delays due to finite signal propagation velocities. We aim (i) to investigate the network topology of our neuroimaging data and (ii) how randomization of structural connections influence dynamics on top of it. The network characteristics of the structural connectivity data are compared to density-matched Erdős-Rényi random graphs. Furthermore, the neuronal and BOLD activity are modeled on both real and random (Erdős-Rényi type) graphs. The simulated temporal dynamics on both graphs are compared statistically to capture whether the spatial organization of these network affects the modeled time series. Results supported that key topological network properties such as small-worldness of our neuroimaging data are distinguishable from random networks. Moreover, the simulated BOLD activity on real and random graphs are observed to be dissimilar. The difference of the modeled temporal dynamics on the brain and random graphs suggests that anatomical connections in the human brain together with dynamical self-organization are crucial for the temporal evolution of the resting-state activity.

scholarly journals Comparison of functional connectivity between empirical and randomized structural brain networks

2016 ◽  
Author(s):  
Seyma Bayrak ◽  
Philipp Hövel ◽  
Vesna Vuksanovic
Keyword(s):  
Time Series ◽  
Human Brain ◽  
Random Graphs ◽  
Spatial Organization ◽  
Temporal Dynamics ◽  
Structural Connectivity ◽  
Signal Propagation ◽  
Brain Regions ◽  
Neuroimaging Data ◽  
Structural Connections

This study combines experimental and modeling approaches in order to investigate the temporal dynamics of the human brain at rest. The dynamics of the neuronal activity is modeled with FitzHugh-Nagumo oscillators and the blood-oxygen-level-dependent (BOLD) time series is inferred via the Balloon-Windkessel hemodynamic model. The simulations are based on structural connections that are derived from diffusion-weighted magnetic resonance imaging measurements yielding anatomical probabilities between the considered brain regions of interest. In addition, the length of the fiber tracks allows for inference of coupling delays due to finite signal propagation velocities. We aim (i) to investigate the network topology of our neuroimaging data and (ii) how randomization of structural connections influence dynamics on top of it. The network characteristics of the structural connectivity data are compared to density-matched Erdős-Rényi random graphs. Furthermore, the neuronal and BOLD activity are modeled on both real and random (Erdős-Rényi type) graphs. The simulated temporal dynamics on both graphs are compared statistically to capture whether the spatial organization of these network affects the modeled time series. Results supported that key topological network properties such as small-worldness of our neuroimaging data are distinguishable from random networks. Moreover, the simulated BOLD activity on real and random graphs are observed to be dissimilar. The difference of the modeled temporal dynamics on the brain and random graphs suggests that anatomical connections in the human brain together with dynamical self-organization are crucial for the temporal evolution of the resting-state activity.

scholarly journals Recent advances in understanding object recognition in the human brain: deep neural networks, temporal dynamics, and context

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 590
Author(s):  
Susan G. Wardle ◽  
Chris I. Baker
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Human Brain ◽  
Spatial Organization ◽  
Deep Neural Networks ◽  
Temporal Dynamics ◽  
Brain Regions ◽  
Visual Features ◽  
Recent Advances ◽  
Ambient Lighting

Object recognition is the ability to identify an object or category based on the combination of visual features observed. It is a remarkable feat of the human brain, given that the patterns of light received by the eye associated with the properties of a given object vary widely with simple changes in viewing angle, ambient lighting, and distance. Furthermore, different exemplars of a specific object category can vary widely in visual appearance, such that successful categorization requires generalization across disparate visual features. In this review, we discuss recent advances in understanding the neural representations underlying object recognition in the human brain. We highlight three current trends in the approach towards this goal within the field of cognitive neuroscience. Firstly, we consider the influence of deep neural networks both as potential models of object vision and in how their representations relate to those in the human brain. Secondly, we review the contribution that time-series neuroimaging methods have made towards understanding the temporal dynamics of object representations beyond their spatial organization within different brain regions. Finally, we argue that an increasing emphasis on the context (both visual and task) within which object recognition occurs has led to a broader conceptualization of what constitutes an object representation for the brain. We conclude by identifying some current challenges facing the experimental pursuit of understanding object recognition and outline some emerging directions that are likely to yield new insight into this complex cognitive process.

scholarly journals An architectonic type principle in the development of laminar patterns of cortico-cortical connections

2021 ◽  
Author(s):  
Sarah F. Beul ◽  
Alexandros Goulas ◽  
Claus C. Hilgetag
Keyword(s):  
Structural Connectivity ◽  
Macaque Monkey ◽  
Brain Regions ◽  
Adult Brain ◽  
Mammalian Brain ◽  
Cortical Connections ◽  
Cortical Projections ◽  
Structural Connections ◽  
High Degree ◽  
The Brain

AbstractStructural connections between cortical areas form an intricate network with a high degree of specificity. Many aspects of this complex network organization in the adult mammalian cortex are captured by an architectonic type principle, which relates structural connections to the architectonic differentiation of brain regions. In particular, the laminar patterns of projection origins are a prominent feature of structural connections that varies in a graded manner with the relative architectonic differentiation of connected areas in the adult brain. Here we show that the architectonic type principle is already apparent for the laminar origins of cortico-cortical projections in the immature cortex of the macaque monkey. We find that prenatal and neonatal laminar patterns correlate with cortical architectonic differentiation, and that the relation of laminar patterns to architectonic differences between connected areas is not substantially altered by the complete loss of visual input. Moreover, we find that the degree of change in laminar patterns that projections undergo during development varies in proportion to the relative architectonic differentiation of the connected areas. Hence, it appears that initial biases in laminar projection patterns become progressively strengthened by later developmental processes. These findings suggest that early neurogenetic processes during the formation of the brain are sufficient to establish the characteristic laminar projection patterns. This conclusion is in line with previously suggested mechanistic explanations underlying the emergence of the architectonic type principle and provides further constraints for exploring the fundamental factors that shape structural connectivity in the mammalian brain.

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 A Hybrid Communication Pattern in Brain Structural Network Revealed by Evolutionary Computation

2021 ◽  
Author(s):  
QUANMIN LIANG ◽  
Ying Lin ◽  
Zhengjia Dai ◽  
Junji Ma ◽  
Xitian Chen
Keyword(s):  
Human Brain ◽  
Structure Function ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Communication Model ◽  
Functional Modules ◽  
Global Knowledge ◽  
Hybrid Routing ◽  
Routing Strategy ◽  
Functional Components

The human brain functional connectivity network (FCN) is constrained and shaped by the information communication processes in the structural connectivity network (SCN). The underlying communication model thus becomes a critical issue for understanding structure-function coupling in the human brain. A number of communication models featuring different point-to-point routing strategies have been proposed, with shortest path (SP), diffusion (DIF), and navigation (NAV) as the typical, respectively requiring network global knowledge, local knowledge, and their combination for path seeking. Yet these models all assumed the entire brain to use a uniform routing strategy, which contradicted lumping evidence supporting the wide variety of brain regions in both terms of biological substrates and functional exhibitions. In this study, we developed a novel communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB) for maximizing the structure-function coupling. The HYB-based model outperformed the three typical models in terms of predicting FCN and supporting robust communication. In HYB, brain regions in lower-order functional modules inclined to choose the routing strategies requiring more global knowledge, while those in higher-order functional components preferred to choose DIF. Additionally, compared to regions using SP and NAV, regions using DIF had denser structural connections, participated in more functional modules, but were less dominant within them. Together, our findings revealed and evidenced the possibility and advantages of hybrid routing underpinning efficient SCN communication.

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 Perturbation of resting-state network nodes preferentially propagates to structurally rather than functionally connected regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Davide Momi ◽  
Recep A. Ozdemir ◽  
Ehsan Tadayon ◽  
Pierre Boucher ◽  
Alberto Di Domenico ◽  
...  
Keyword(s):  
Resting State ◽  
Target Selection ◽  
Structural Connectivity ◽  
Signal Propagation ◽  
Brain Regions ◽  
Local Effect ◽  
High Temporal Resolution ◽  
Target Engagement ◽  
Network Nodes ◽  
A Minor

AbstractCombining Transcranial Magnetic Stimulation (TMS) with electroencephalography (EEG) offers the opportunity to study signal propagation dynamics at high temporal resolution in the human brain. TMS pulse induces a local effect which propagates across cortical networks engaging distant cortical and subcortical sites. However, the degree of propagation supported by the structural compared to functional connectome remains unclear. Clarifying this issue would help tailor TMS interventions to maximize target engagement. The goal of this study was to establish the contribution of functional and structural connectivity in predicting TMSinduced signal propagation after perturbation of two distinct brain networks. For this purpose, 24 healthy individuals underwent two identical TMS-EEG visits where neuronavigated TMS pulses were delivered to nodes of the default mode network (DMN) and the dorsal attention network (DAN). The functional and structural connectivity derived from each individual stimulation spot were characterized via functional magnetic resonance imaging (fMRI) and Diffusion Weighted Imaging (DWI), and signal propagation across these two metrics was compared. Direct comparison between the signal extracted from brain regions either functionally or structurally connected to the stimulation sites, shows a stronger activation over cortical areas connected via white matter pathways, with a minor contribution of functional projections. This pattern was not observed when analyzing spontaneous resting state EEG activity. Overall, results suggest that structural links can predict network-level response to perturbation more accurately than functional connectivity. Additionally, DWI-based estimation of propagation patterns can be used to estimate off-target engagement of other networks and possibly guide target selection to maximize specificity.

scholarly journals NIMG-13. GENETIC, CELLULAR, AND CONNECTOMIC CHARACTERIZATION OF THE ADULT HUMAN BRAIN REGIONS COMMONLY PLAGUED BY GLIOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii149-ii149
Author(s):  
Ayan Mandal ◽  
Rafael Romero-Garcia ◽  
Michael Hart ◽  
John Suckling
Keyword(s):  
Human Brain ◽  
Occipital Lobe ◽  
Brain Regions ◽  
Brain Dysfunction ◽  
Brain Atlas ◽  
Association Cortex ◽  
Low Grade ◽  
Expression Of Genes ◽  
Neuroimaging Data ◽  
Multiple Graph

Abstract For decades, it has been known that gliomas follow a nonrandom spatial distribution, appearing more often in some brain regions (e.g. the insula) compared to others (e.g. the occipital lobe). A better understanding of the glioma localization patterns could lend clues to the origins of these types of tumors, and consequently inform treatment targets. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by functional hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumor frequency map. Using this map, we demonstrate that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with putative cells-of-origin for glioma, neural stem cells and oligodendrocyte precursor cells, exhibited a high glioma frequency. Leveraging a human brain atlas of post-mortem gene expression, we found that gliomas were localized to brain regions enriched with expression of genes associated with chromatin organization and synaptic signaling. A set of glioma proto-oncogenes was enriched among the transcriptomic correlates of glioma distribution. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. These results add to previous literature reporting the vulnerability of hub regions to neurological disease, as well as provide support for cancer stem cell theories of glioma. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of brain dysfunction.

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 Rapid and dynamic processing of face pareidolia in the human brain

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Susan G. Wardle ◽  
Jessica Taubert ◽  
Lina Teichmann ◽  
Chris I. Baker
Keyword(s):  
Human Brain ◽  
Face Detection ◽  
Temporal Dynamics ◽  
Detection Mechanism ◽  
Multifaceted Approach ◽  
Face Representation ◽  
Ordinary Objects ◽  
Neuroimaging Data ◽  
Dynamic Processing ◽  
Human Faces

Abstract The human brain is specialized for face processing, yet we sometimes perceive illusory faces in objects. It is unknown whether these natural errors of face detection originate from a rapid process based on visual features or from a slower, cognitive re-interpretation. Here we use a multifaceted approach to understand both the spatial distribution and temporal dynamics of illusory face representation in the brain by combining functional magnetic resonance imaging and magnetoencephalography neuroimaging data with model-based analysis. We find that the representation of illusory faces is confined to occipital-temporal face-selective visual cortex. The temporal dynamics reveal a striking evolution in how illusory faces are represented relative to human faces and matched objects. Illusory faces are initially represented more similarly to real faces than matched objects are, but within ~250 ms, the representation transforms, and they become equivalent to ordinary objects. This is consistent with the initial recruitment of a broadly-tuned face detection mechanism which privileges sensitivity over selectivity.

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