Brain Connectivity: Gender Makes a Difference
TL;DR: The literature provides convergent evidence for a substantial gender difference in brain connectivity within the human brain that possibly underlies gender-related cognitive differences and should be mandatory to take gender into account when designing experiments or interpreting results of brain connectivity/network in health and disease.
Abstract: It has been well known that gender plays a critical role in the anatomy and function of the human brain, as well as human behaviors. Recent neuroimaging studies have demonstrated gender effects on not only focal brain areas but also the connectivity between areas. Specifically, structural MRI and diffusion MRI data have revealed substantial gender differences in white matter-based anatomical connectivity. Structural MRI data further demonstrated gender differences in the connectivity revealed by morphometric correlation among brain areas. Functional connectivity derived from functional neuroimaging (e.g., functional MRI and PET) data is also modulated by gender. Moreover, male and female human brains display differences in the network topology that represents the organizational patterns of brain connectivity across the entire brain. In this review, the authors summarize recent findings in the multimodal brain connectivity/network research with gender, focusing on large-scale data sets derived from modern neuroimaging techniques. The literature provides convergent evidence for a substantial gender difference in brain connectivity within the human brain that possibly underlies gender-related cognitive differences. Therefore, it should be mandatory to take gender into account when designing experiments or interpreting results of brain connectivity/network in health and disease. Future studies will likely be conducted to explore the interdependence between gender-related brain connectivity/network and the gender-specific nature of brain diseases as well as to investigate gender-related characteristics of multimodal brain connectivity/network in the normal brain.
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TL;DR: The notion that brain connectivity can be abstracted to a graph of nodes, representing neural elements linked by edges, representing some measure of structural, functional or causal interaction between nodes, brings connectomic data into the realm of graph theory.
702 citations
Cites background from "Brain Connectivity: Gender Makes a ..."
...The connectome was first defined as a “complete description of the structural [italics added] connectivity of an organism's nervous system” (Sporns, 2007, 2011a; Sporns et al., 2005), though the termhas since been adapted to refer to comprehensive maps of functional interactions as well (Alivisatos…...
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TL;DR: A literature review of the current knowledge surrounding individual and gender differences in STEM educational and career choices, using expectancy-value theory as a guiding framework to provide both a well-defined theoretical framework and complementary empirical evidence for linking specific sociocultural, contextual, biological, and psychological factors.
559 citations
TL;DR: In this article, the authors describe four major theories of gender differences (socio-cultural, evolutionary, hormone brain, and selectivity hypothesis) and assess relevant research from 2000 to 2013 in marketing, psychology, and biomedicine.
383 citations
TL;DR: This review describes the basic methodological strategies, the biological basis of the observed covariance as well as applications in normal brain and brain disease before a final review of future prospects for the technique.
343 citations
Cites background or methods from "Brain Connectivity: Gender Makes a ..."
...…employ diffusion-weighted imaging (DWI) and tractography to examine structural connectivity as a white matter (WM) phenomenon (Behrens et al., 2007; Gong et al., 2009a, 2011; Hofer and Frahm, 2006; Iturria-Medina, 2013; Jbabdi et al., 2007; Le Bihan and Johansen-Berg, 2012; Raj and Chen, 2011;…...
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...…interest in correlation of cortical grey matter (GM) morphology across the brain (Alexander-Bloch et al., 2013b; Chen et al., 2008, 2011a, 2011b; Gong et al., 2009b, 2011, 2012; He and Evans, 2010; He et al., 2007, 2008, 2009a, 2009b; Khundrakpam et al., in press; Lerch et al., 2006; Lo et al.,…...
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TL;DR: It is demonstrated for the first time that sex differences in patterns of cognition are in part represented on a neural level through divergent patterns of brain connectivity through rsfc-MRI.
Abstract: Sex differences in human cognition are marked, but little is known regarding their neural origins. Here, in a sample of 674 human participants ages 9-22, we demonstrate that sex differences in cognitive profiles are related to multivariate patterns of resting-state functional connectivity MRI (rsfc-MRI). Males outperformed females on motor and spatial cognitive tasks; females were faster in tasks of emotion identification and nonverbal reasoning. Sex differences were also prominent in the rsfc-MRI data at multiple scales of analysis, with males displaying more between-module connectivity, while females demonstrated more within-module connectivity. Multivariate pattern analysis using support vector machines classified subject sex on the basis of their cognitive profile with 63% accuracy (P Language: en
318 citations
Cites background from "Brain Connectivity: Gender Makes a ..."
...There is a growing consensus within the neuroscience field that sex differences are underappreciated and merit further study (Gong et al. 2011; Giedd et al. 2012; Sacher et al. 2013)....
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References
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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
39,297 citations
"Brain Connectivity: Gender Makes a ..." refers background in this paper
...The two metrics can be used to distinguish different classes of network such as regular, small-world, and random networks (Watts and Strogatz 1998)....
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...Clustering coefficient and characteristic path length are two basic measurements of a complex network (Watts and Strogatz 1998)....
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Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
9,700 citations
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
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TL;DR: Recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity are reviewed.
Abstract: The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.
6,135 citations
"Brain Connectivity: Gender Makes a ..." refers background in this paper
...R-fMRI has recently attracted a great deal of interest because it is able to detect intrinsic or spontaneous brain activity in health and disease (Fox and Raichle 2007)....
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TL;DR: Once Deff is estimated from a series of NMR pulsed-gradient, spin-echo experiments, a tissue's three orthotropic axes can be determined and the effective diffusivities along these orthotropic directions are the eigenvalues of Deff.
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