scholarly journals Development of Functional Connectome Gradients during Childhood and Adolescence

2021 ◽  
Author(s):  
Yunman Xia ◽  
Mingrui Xia ◽  
Jin Liu ◽  
Xuhong Liao ◽  
Tianyuan Lei ◽  
...  
Keyword(s):  
Developmental Disorders ◽  
Normal Development ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Brain Network ◽  
Adult Brain ◽  
Childhood And Adolescence ◽  
Imaging Data ◽  
Cognitive Growth ◽  
Magnetic Resonance Imaging Data

Connectome mapping studies have documented a principal primary-to-transmodal gradient in the adult brain network, capturing a functional spectrum which ranges from perception and action to abstract cognition. However, how this gradient pattern develops and whether its development is linked to cognitive growth, topological reorganization, and gene expression profiles remain largely unknown. Using longitudinal resting-state functional magnetic resonance imaging data from 305 children (ages 6-14), we describe substantial changes in the primary-to-transmodal gradient between childhood and adolescence, including emergence as the principal gradient, expansion of global topography, and focal tuning in primary and default-mode regions. These gradient changes are mediated by developmental changes in network integration and segregation, and are associated with abstract processing functions such as working memory and expression levels of calcium ion regulated exocytosis, synaptic transmission, and axon and synapse part related genes. Our findings have implications for understanding connectome maturation principles in normal development and developmental disorders.

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

2021 ◽  
Author(s):  
Tianyuan Lei ◽  
Xuhong Liao ◽  
Xiaodan Chen ◽  
Tengda Zhao ◽  
Yuehua Xu ◽  
...  
Keyword(s):  
Large Scale ◽  
Expression Profiles ◽  
Brain Networks ◽  
Network Dynamics ◽  
Brain Network ◽  
Global Network ◽  
Association Cortex ◽  
Childhood And Adolescence ◽  
Gene Expressions ◽  
Age Related

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.

scholarly journals Machine learning algorithms for the identification of potential non-syndromic intellectual disability associated miRNAs

2021 ◽  
Author(s):  
Julián González Betancur ◽  
José Guevara-Coto ◽  
Adarli Romero
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Developmental Disorders ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Temporal Gene Expression ◽  
Interesting Approach ◽  
Spatio Temporal

Abstract Background: Intellectual disabilities (IDs) are a group of developmental disorders with high phenotypic and genotypic heterogeneity. Association of genetic elements to IDs has typically been empirically accomplished, however recently, machine learning (ML) has proved to be an excellent instrument to elucidate these associations. miRNAs are short non-coding molecules that participate in spatiotemporal gene regulation, making them relevant for the understanding ID causality. Methods: In this study we used the BrainSpan spatio-temporal expression database to develop a series of machine learning predictors: SVM, RF, FF-ANN, and Stochastic Gradient Descent Classifier. These models were capable of recognizing gene expression profiles. The best classifier was used to label miRNAs associated with NS-IDs using the BrainSpan expression profiles. Results: The model with the best performance was a FF-ANN with 0.78 of F1-score, 0.78 of weighted recall and 0.78 of weighted precision. We used this model to identify miRNAs with high probability to be associated with NS-IDs using the spatio-temporal gene expression profile in the human brain. Labeled miRNAs that were annotated were associated with processes related to either IDs and-or neurodevelopmental processes. Conclusions: The development of a machine learning framework that identified potential NS-ID miRNAs represents an interesting approach for the identification of a potential list of on genes that could be subject for further experimental validation. This study also reinforces the potential of machine learning frameworks in their discovery of potential biomarkers that could improve disease detection and management.

scholarly journals Subtle left-right asymmetry of gene expression profiles in embryonic and foetal human brains

2018 ◽  
Author(s):  
Carolien G.F. de Kovel ◽  
Steven N. Lisgo ◽  
Simon E. Fisher ◽  
Clyde Francks
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Adult Brain ◽  
Human Embryos ◽  
Sequencing Data ◽  
Brain Organization ◽  
Central Nervous Systems ◽  
Left Right Asymmetry ◽  
Human Brains

AbstractLeft-right laterality is an important aspect of human brain organization for which the genetic basis is poorly understood. Using RNA sequencing data we contrasted gene expression in left- and right-sided samples from several structures of the anterior central nervous systems of post mortem human embryos and fetuses. While few individual genes stood out as significantly lateralized, most structures showed evidence of laterality of their overall transcriptomic profiles. These left-right differences showed overlap with age-dependent changes in expression, indicating lateralized maturation rates, but not consistently in left-right orientation over all structures. Brain asymmetry may therefore originate in multiple locations, or if there is a single origin, it is earlier than 5 weeks post conception, with structure-specific lateralized processes already underway by this age. This pattern is broadly consistent with the weak correlations reported between various aspects of adult brain laterality, such as language dominance and handedness.

scholarly journals Machine Learning Algorithms for the Identification of Potential Non-Syndromic Intellectual Disability Associated miRNAs

2021 ◽  
Author(s):  
Julián González Betancur ◽  
José A Guevara-Coto ◽  
Adarli Romero
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Intellectual Disability ◽  
Developmental Disorders ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Temporal Gene Expression ◽  
Spatio Temporal

Abstract Background: Intellectual disabilities (IDs) are a group of developmental disorders with high phenotypic and genotypic heterogeneity. Association of genetic elements to IDs has typically been empirically accomplished, however recently, machine learning (ML) has proved to be an excellent instrument to elucidate these associations. miRNAs are short non-coding molecules that participate in spatiotemporal gene regulation, making them relevant for the understanding ID causality. Methods: In this study we used the BrainSpan spatio-temporal expression database to develop a series of machine learning predictors: SVM, RF, FF-ANN, and Stochastic Gradient Descent Classifier. These models were capable of recognizing gene expression profiles. The best classifier was used to label miRNAs associated with NS-IDs using the BrainSpan expression profiles. Results: The model with the best performance was a FF-ANN with 0.78 of F1-score, 0.78 of weighted recall and 0.78 of weighted precision. We used this model to identify miRNAs with high probability to be associated with NS-IDs using the spatio-temporal gene expression profile in the human brain. Labeled miRNAs that were annotated were associated with processes related to either IDs and-or neurodevelopmental processes. Conclusions: The development of a machine learning framework that identified potential NS-ID miRNAs represents an interesting approach for the identification of a potential list of on genes that could be subject for further experimental validation. This study also reinforces the potential of machine learning frameworks in their discovery of potential biomarkers that could improve disease detection and management. Keywords: miRNA association; artificial intelligence; machine learning; intellectual disability; biomarker

scholarly journals Where the genome meets the connectome: understanding how genes shape human brain connectivity

2021 ◽  
Author(s):  
Aurina Arnatkeviciute ◽  
Ben Fulcher ◽  
Mark Bellgrove ◽  
Alex Fornito
Keyword(s):  
Human Brain ◽  
Phenotypic Variation ◽  
Molecular Mechanisms ◽  
Brain Connectivity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Brain Dysfunction ◽  
Brain Network ◽  
Clinical Disorders ◽  
And Function

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organisation of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

scholarly journals Riboceine Rescues Auranofin-Induced Craniofacial Defects in Zebrafish

Antioxidants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1964
Author(s):  
Megan Leask ◽  
Catherine Carleton ◽  
Bryony Leeke ◽  
Trent Newman ◽  
Joseph Antoun ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Developmental Disorders ◽  
Oral Surgery ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Zebrafish Embryos ◽  
Craniofacial Abnormalities ◽  
Antioxidant Genes ◽  
Common Group ◽  
Craniofacial Defects

Craniofacial abnormalities are a common group of congenital developmental disorders that can require intensive oral surgery as part of their treatment. Neural crest cells (NCCs) contribute to the facial structures; however, they are extremely sensitive to high levels of oxidative stress, which result in craniofacial abnormalities under perturbed developmental environments. The oxidative stress-inducing compound auranofin (AFN) disrupts craniofacial development in wildtype zebrafish embryos. Here, we tested whether the antioxidant Riboceine (RBC) rescues craniofacial defects arising from exposure to AFN. RBC rescued AFN-induced cellular apoptosis and distinct defects of the cranial cartilage in zebrafish larvae. Zebrafish embryos exposed to AFN have higher expression of antioxidant genes gstp1 and prxd1, with RBC treatment partially rescuing these gene expression profiles. Our data suggest that antioxidants may have utility in preventing defects in the craniofacial cartilage owing to environmental or genetic risk, perhaps by enhancing cell survival.

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

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.

scholarly journals Dynamic Characterization of Structural, Molecular, and Electrophysiological Phenotypes of Human-Induced Pluripotent Stem Cell-Derived Cerebral Organoids, and Comparison with Fetal and Adult Gene Profiles

Cells ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 1301 ◽  
Author(s):  
Sarah Logan ◽  
Thiago Arzua ◽  
Yasheng Yan ◽  
Congshan Jiang ◽  
Xiaojie Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Adult Brain ◽  
Brain Diseases ◽  
Gene Profiles ◽  
Health And Disease ◽  
Induced Pluripotent ◽  
First Time

Background: The development of 3D cerebral organoid technology using human-induced pluripotent stem cells (iPSCs) provides a promising platform to study how brain diseases are appropriately modeled and treated. So far, understanding of the characteristics of organoids is still in its infancy. The current study profiled, for the first time, the electrophysiological properties of organoids at molecular and cellular levels and dissected the potential age equivalency of 2-month-old organoids to human ones by a comparison of gene expression profiles among cerebral organoids, human fetal and adult brains. Results: Cerebral organoids exhibit heterogeneous gene and protein markers of various brain cells, such as neurons, astrocytes, and vascular cells (endothelial cells and smooth muscle cells) at 2 months, and increases in neural, glial, vascular, and channel-related gene expression over a 2-month differentiation course. Two-month organoids exhibited action potentials, multiple channel activities, and functional electrophysiological responses to the anesthetic agent propofol. A bioinformatics analysis of 20,723 gene expression profiles showed the similar distance of gene profiles in cerebral organoids to fetal and adult brain tissues. The subsequent Ingenuity Pathway Analysis (IPA) of select canonical pathways related to neural development, network formation, and electrophysiological signaling, revealed that only calcium signaling, cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB) signaling in neurons, glutamate receptor signaling, and synaptogenesis signaling were predicted to be downregulated in cerebral organoids relative to fetal samples. Nearly all cerebral organoid and fetal pathway phenotypes were predicted to be downregulated compared with adult tissue. Conclusions: This novel study highlights dynamic development, cellular heterogeneity and electrophysiological activity. In particular, for the first time, electrophysiological drug response recapitulates what occurs in vivo, and neural characteristics are predicted to be highly similar to the human brain, further supporting the promising application of the cerebral organoid system for the modeling of the human brain in health and disease. Additionally, the studies from these characterizations of cerebral organoids in multiple levels and the findings from gene comparisons between cerebral organoids and humans (fetuses and adults) help us better understand this cerebral organoid-based cutting-edge platform and its wide uses in modeling human brain in terms of health and disease, development, and testing drug efficacy and toxicity.

scholarly journals Olfactory bulb astrocytes mediate sensory circuit processing through Sox9 in the mouse brain

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kevin Ung ◽  
Teng-Wei Huang ◽  
Brittney Lozzi ◽  
Junsung Woo ◽  
Elizabeth Hanson ◽  
...  
Keyword(s):  
Olfactory Bulb ◽  
Sensory Processing ◽  
Neuronal Development ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Adult Brain ◽  
Sensory Function ◽  
Sensory Response ◽  
Sequencing Analysis ◽  
Functional Changes

AbstractThe role of transcription factors during astrocyte development and their subsequent effects on neuronal development has been well studied. Less is known about astrocytes contributions towards circuits and behavior in the adult brain. Astrocytes play important roles in synaptic development and modulation, however their contributions towards neuronal sensory function and maintenance of neuronal circuit architecture remain unclear. Here, we show that loss of the transcription factor Sox9 results in both anatomical and functional changes in adult mouse olfactory bulb (OB) astrocytes, affecting sensory processing. Indeed, astrocyte-specific deletion of Sox9 in the OB results in decreased odor detection thresholds and discrimination and it is associated with aberrant neuronal sensory response maps. At functional level, loss of astrocytic Sox9 impairs the electrophysiological properties of mitral and tufted neurons. RNA-sequencing analysis reveals widespread changes in the gene expression profiles of OB astrocytes. In particular, we observe reduced GLT-1 expression and consequential alterations in glutamate transport. Our findings reveal that astrocytes are required for physiological sensory processing and we identify astrocytic Sox9 as an essential transcriptional regulator of mature astrocyte function in the mouse OB.

scholarly journals Dynamic brain network changes in resting-state reflect neuroplasticity: molecular and cognitive evidence

2019 ◽  
Author(s):  
Zhaowen Liu ◽  
Xiao Xiao ◽  
Kai Zhang ◽  
Qi Zhao ◽  
Xinyi Cao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Functional Connectivity ◽  
Resting State ◽  
Temporal Variability ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Brain Network ◽  
Brain Gene Expression ◽  
Functional Brain Networks

AbstractResting-state functional brain networks demonstrate dynamic changes on the scale of seconds. However, their genetic mechanisms and profound cognitive relevance remain less explored. We identified 459 Bonferroni-corrected genes, by associating temporal variability of regional functional connectivity patterns with Allen Brain gene expression profiles across the whole brain. These genes are partially verified in developing human brain gene expression in the BrainSpan Atlas, and are found to be involved in the enrichment of short- and long-term plasticity processes. The former process depends on synaptic plasticity, involving ion transmembrane transport, action potential propagation, and modulation. The latter process depends on structural plasticity, including axonal genesis, development, and guidance. Results from a longitudinal cognitive training study further revealed that baseline variability of the hippocampal network predicted cognitive ability changes after three months of training. Our genetic association results suggest that the short-term plasticity processes may account for the rapid changes of regional functional connectivity, while the underlying long-term plasticity processes explain why temporal variability can predict long-term learning outcomes. To our knowledge, this is the first demonstration that measuring the dynamic brain network can lead to a non-invasive quantification of neuroplasticity in humans.

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