Stabilization of Brain Network Dynamics during Childhood and Adolescence is Associated with Gene Expressions

AbstractFunctional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.

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Age-differences in information flow in executive and sensorimotor brain networks during childhood and adolescence

10.1101/2020.10.09.20207936 ◽

2020 ◽

Author(s):

Martina J. Lund ◽

Dag Alnæs ◽

Jaroslav Rokicki ◽

Simon Schwab ◽

Ole A. Andreassen ◽

...

Keyword(s):

Information Flow ◽

Graphical Models ◽

Cognitive Abilities ◽

Large Scale ◽

Brain Networks ◽

Brain Network ◽

Childhood And Adolescence ◽

Imaging Data ◽

Fine Grained ◽

Age Related

AbstractObjectiveMental disorders often emerge during adolescence, and age-related differences in connection strengths of brain networks (static connectivity) have been identified. However, little is known about the directionality of information flow (directed connectivity) in this period of brain development.MethodsWe employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 979 participants aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on directionality.ResultsWe show robust bi-directionality in information flow between visual-medial and visual-lateral nodes of the network, in line with prior studies in adult samples. Furthermore, we found that age in this developmental sample was associated with directionality of information flow in sensorimotor and executive control networks, yet we did not find associations with cognitive abilities or psychopathology.DiscussionOur results revealed that directionality in information flow of large-scale brain networks is sensitive to age during adolescence, warranting further studies that may explore trajectories of development in more fine-grained network parcellations and in different populations.

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Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

Scientific Reports ◽

10.1038/s41598-020-78899-7 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Rieke Fruengel ◽

Timo Bröhl ◽

Thorsten Rings ◽

Klaus Lehnertz

Keyword(s):

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Theoretical Concept ◽

Global Network ◽

Time Resolved ◽

Functional Connections ◽

Node Centrality ◽

Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

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Large-scale brain network dynamics provide a measure of psychosis and anxiety in 22q11.2 deletion syndrome

10.1101/551796 ◽

2019 ◽

Author(s):

Daniela Zöller ◽

Corrado Sandini ◽

Fikret Işik Karahanoğlu ◽

Maria Carmela Padula ◽

Marie Schaer ◽

...

Keyword(s):

Large Scale ◽

Brain Networks ◽

Network Dynamics ◽

22Q11.2 Deletion Syndrome ◽

Brain Network ◽

Anterior Cingulate ◽

Deletion Syndrome ◽

Psychotic Symptoms ◽

22Q11.2 Deletion ◽

Network Activation

AbstractProdromal positive psychotic symptoms and anxiety are two strong risk factors for schizophrenia in 22q11.2 deletion syndrome (22q11DS). The analysis of large-scale brain network dynamics during rest is promising to investigate aberrant brain function and identify potentially more reliable biomarkers. We retrieved and examined dynamics of large-scale functional brain networks using innovation-driven co-activation patterns (iCAPs) and probed into functional signatures of prodromal psychotic symptoms and anxiety. Patients with 22q11DS had shorter activation in cognitive brain networks and longer activation in emotion processing networks. Functional signatures of prodromal psychotic symptoms confirmed an implication of cingulo-prefrontal salience network activation duration and coupling. Functional signatures of anxiety un-covered an implication of amygdala activation and coupling, indicating differential roles of dorsal and ventral sub-divisions of anterior cingulate and medial prefrontal cortices. These results confirm that the dynamic nature of brain network activation contains essential function to develop clinically relevant imaging markers of psychosis vulnerability.

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Trajectory of rich club properties in structural brain networks

10.1101/2021.11.16.468806 ◽

2021 ◽

Author(s):

Levin Riedel ◽

Martijn P van den Heuvel ◽

Sebastian Markett

Keyword(s):

Brain Networks ◽

Structural Connectivity ◽

Subsequent Development ◽

Brain Network ◽

Aging Brain ◽

Childhood And Adolescence ◽

Rich Club ◽

Age Related ◽

Connectivity Strength ◽

Structural Brain

Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8,066 participants aged 5 to 80 years. Across all brain regions, structural connectivity strength followed an ′inverted-U′-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a ′first come, last served′ model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.

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Discovering Distinct Patterns in Gene Expression Profiles

Journal of Integrative Bioinformatics ◽

10.1515/jib-2008-105 ◽

2008 ◽

Vol 5 (2) ◽

Cited By ~ 1

Author(s):

Li Teng ◽

Laiwan Chan

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Clustering Methods ◽

Gene Expressions ◽

Real Gene ◽

Large Scale Dataset ◽

Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

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Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

Frontiers in Neuroinformatics ◽

10.3389/fninf.2015.00002 ◽

2015 ◽

Vol 9 ◽

Cited By ~ 30

Author(s):

Xerxes D. Arsiwalla ◽

Riccardo Zucca ◽

Alberto Betella ◽

Enrique Martinez ◽

David Dalmazzo ◽

...

Keyword(s):

Human Brain ◽

Real Time ◽

Large Scale ◽

Network Dynamics ◽

Brain Network ◽

Large Scale Simulation ◽

Time Interaction

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A Neuroeconomic Framework for Creative Cognition

10.31234/osf.io/5pqy6 ◽

2017 ◽

Author(s):

Hause Lin ◽

Oshin Vartanian

Keyword(s):

Decision Making ◽

Locus Coeruleus ◽

Large Scale ◽

Network Dynamics ◽

Brain Network ◽

Creative Cognition ◽

Decision Making Processes

Neuroeconomics is the study of the neurobiological bases of subjective preferences and choices. We present a novel framework that synthesizes findings from the literatures on neuroeconomics and creativity to provide a neurobiological description of creative cognition. It proposes that value-based decision-making processes and activity in the locus coeruleus-norepinephrine (LC-NE) neuromodulatory system underlie creative cognition, as well as the large-scale brain network dynamics shown to be associated with creativity. This framework allows us to re-conceptualize creative cognition as driven by value-based decision making, in the process providing several falsifiable hypotheses that can further our understanding of creativity, decision making, and brain network dynamics.

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Amplification and Suppression of Distinct Brain-wide Activity Patterns by Catecholamines

10.1101/270645 ◽

2018 ◽

Author(s):

RL van den Brink ◽

S Nieuwenhuis ◽

TH Donner

Keyword(s):

Spatial Distribution ◽

Human Brain ◽

Spatial Patterns ◽

Large Scale ◽

Network Dynamics ◽

Brain Network ◽

Heterogeneous Distribution ◽

Neurotransmitter Systems ◽

The Brain ◽

Catecholamine Levels

ABSTRACTThe widely projecting catecholaminergic (norepinephrine and dopamine) neurotransmitter systems profoundly shape the state of neuronal networks in the forebrain. Current models posit that the effects of catecholaminergic modulation on network dynamics are homogenous across the brain. However, the brain is equipped with a variety of catecholamine receptors with distinct functional effects and heterogeneous density across brain regions. Consequently, catecholaminergic effects on brain-wide network dynamics might be more spatially specific than assumed. We tested this idea through the analysis of functional magnetic resonance imaging (fMRI) measurements performed in humans (19 females, 5 males) at ‘rest’ under pharmacological (atomoxetine-induced) elevation of catecholamine levels. We used a linear decomposition technique to identify spatial patterns of correlated fMRI signal fluctuations that were either increased or decreased by atomoxetine. This yielded two distinct spatial patterns, each expressing reliable and specific drug effects. The spatial structure of both fluctuation patterns resembled the spatial distribution of the expression of catecholamine receptor genes: α1 norepinephrine receptors (for the fluctuation pattern: placebo > atomoxetine), ‘D2-like’ dopamine receptors (pattern: atomoxetine > placebo), and β norepinephrine receptors (for both patterns, with correlations of opposite sign). We conclude that catecholaminergic effects on the forebrain are spatially more structured than traditionally assumed and at least in part explained by the heterogeneous distribution of various catecholamine receptors. Our findings link catecholaminergic effects on large-scale brain networks to low-level characteristics of the underlying neurotransmitter systems. They also provide key constraints for the development of realistic models of neuromodulatory effects on large-scale brain network dynamics.SIGNIFICANCE STATEMENTThe catecholamines norepinephrine and dopamine are an important class of modulatory neurotransmitters. Because of the widespread and diffuse release of these neuromodulators, it has commonly been assumed that their effects on neural interactions are homogenous across the brain. Here, we present results from the human brain that challenge this view. We pharmacologically increased catecholamine levels and imaged the effects on the spontaneous covariations between brain-wide fMRI signals at ‘rest’. We identified two distinct spatial patterns of covariations: one that was amplified and another that was suppressed by catecholamines. Each pattern was associated with the heterogeneous spatial distribution of the expression of distinct catecholamine receptor genes. Our results provide novel insights into the catecholaminergic modulation of large-scale human brain dynamics.

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