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 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 ADHD symptoms map onto noise-driven structure-function decoupling between hub and peripheral brain regions

2019 ◽  
Author(s):  
Luke J. Hearne ◽  
Hsiang-Yuan Lin ◽  
Paula Sanz-Leon ◽  
Wen-Yih Isaac Tseng ◽  
Susan Shur-Fen Gau ◽  
...  
Keyword(s):  
Structure Function ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Adhd Symptoms ◽  
Brain Abnormalities ◽  
Childhood Onset ◽  
Individual Level ◽  
Neural Noise ◽  
Diffusion Spectrum Imaging

AbstractAdults with childhood-onset attention-deficit hyperactivity disorder (ADHD) show altered whole-brain connectivity. However, the relationship between structural and functional brain abnormalities, the implications for the development of life-long debilitating symptoms, and the underlying mechanisms remain uncharted. We recruited a unique sample of 80 medication-naive adults with a clinical diagnosis of childhood-onset ADHD without psychiatric comorbidities, and 123 age-, sex-, and intelligence-matched healthy controls. Structural and functional connectivity matrices were derived from diffusion spectrum imaging and multi-echo resting-state functional MRI data. Hub, feeder, and local connections were defined using diffusion data. Individual-level measures of structural connectivity and structure-function coupling were used to contrast groups and link behavior to brain abnormalities. Computational modeling was used to test possible neural mechanisms underpinning observed group differences in the structure-function coupling. Structural connectivity did not significantly differ between groups but, relative to controls, ADHD showed a reduction in structure-function coupling in feeder connections linking hubs with peripheral regions. This abnormality involved connections linking fronto-parietal control systems with sensory networks. Crucially, lower structure-function coupling was associated with higher ADHD symptoms. Results from our computational model further suggest that the observed structure-function decoupling in ADHD is driven by heterogeneity in neural noise variability across brain regions. By highlighting a neural cause of a clinically meaningful breakdown in the structure-function relationship, our work provides novel information on the nature of chronic ADHD. The current results encourage future work assessing the genetic and neurobiological underpinnings of neural noise in ADHD, particularly in brain regions encompassed by fronto-parietal systems.

scholarly journals Local structure-function relationships in human brain networks across the human lifespan

2021 ◽  
Author(s):  
Farnaz Zamani Esfahlani ◽  
Joshua Faskowitz ◽  
Jonah Slack ◽  
Bratislav Misic ◽  
Richard Betzel
Keyword(s):  
Structure Function ◽  
Local Structure ◽  
Linear Models ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Functional Coupling ◽  
Regional Patterns ◽  
Human Lifespan ◽  
In Silico Studies ◽  
Wide Range

The human connectome is the set of physical pathways linking brain regions to one another. Empirical and in silico studies have demonstrated that the structure of this network helps shape patterns of functional coupling between brain regions. To better understand this link between structure and function, a growing number of studies have derived geometric, dynamic, and topological predictors from structural connectivity in order to make predictions about correlation structure. These studies, however, have typically focused on global (whole-brain) predictions using a restricted set of predictors. Here, we investigate a wide range of predictors and shift focus onto predictions of local (regional) patterns of functional coupling. We show that, globally, no individual predictor performs well and, that even the best predictors are largely driven by their ability to predict functional coupling between directly connected regions. We then use the same predictors to make predictions of local coupling and find marked improvement. Notably, the most predictable local FC is linked to sensorimotor regions, which are best predicted by measures based on topological similarity, mean first passage times of random walkers, and the brain's embedding in Euclidean space. We then show that by combining the predictive power of more than one predictor using multi-linear models, we can further improve local predictions. Finally, we investigate how global and local structure-function coupling changes across the human lifespan. We find that, globally, the magnitude of coupling decreases with biological age, which is paralleled by an increase in the number of multi-step pathways. We also show that, locally, structure function coupling is preserved in higher order cognitive systems, but preferentially decreases with age in sensorimotor systems. Our results illuminate the heterogeneous landscape of structure-function coupling across the cerebral cortex and help clarify its changes with age.

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 Brain Metabolic Correlates of Persistent Olfactory Dysfunction after SARS-Cov2 Infection

Biomedicines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 287
Author(s):  
Maria Isabella Donegani ◽  
Alberto Miceli ◽  
Matteo Pardini ◽  
Matteo Bauckneht ◽  
Silvia Chiola ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Structural Connectivity ◽  
Statistical Parametric Mapping ◽  
Brain Regions ◽  
Olfactory Dysfunction ◽  
Whole Body ◽  
Mapping Software ◽  
18F Fdg ◽  
Left Insula

We aimed to evaluate the brain hypometabolic signature of persistent isolated olfactory dysfunction after SARS-CoV-2 infection. Twenty-two patients underwent whole-body [18F]-FDG PET, including a dedicated brain acquisition at our institution between May and December 2020 following their recovery after SARS-Cov2 infection. Fourteen of these patients presented isolated persistent hyposmia (smell diskettes olfaction test was used). A voxel-wise analysis (using Statistical Parametric Mapping software version 8 (SPM8)) was performed to identify brain regions of relative hypometabolism in patients with hyposmia with respect to controls. Structural connectivity of these regions was assessed (BCB toolkit). Relative hypometabolism was demonstrated in bilateral parahippocampal and fusiform gyri and in left insula in patients with respect to controls. Structural connectivity maps highlighted the involvement of bilateral longitudinal fasciculi. This study provides evidence of cortical hypometabolism in patients with isolated persistent hyposmia after SARS-Cov2 infection. [18F]-FDG PET may play a role in the identification of long-term brain functional sequelae of COVID-19.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

scholarly journals Heritability and interindividual variability of regional structure-function coupling

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zijin Gu ◽  
Keith Wakefield Jamison ◽  
Mert Rory Sabuncu ◽  
Amy Kuceyeski
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Regional Differences ◽  
Regional Scale ◽  
Brain Regions ◽  
Diffusion Weighted Mri ◽  
Regional Structure ◽  
Human Connectome Project ◽  
Structural Connections ◽  
The Relationship

AbstractWhite matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

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