Sex Differences in Functional Brain Networks for Language

Abstract Men and women process language differently, but how the brain functions to support this difference is poorly understood. A few studies reported sex influences on brain activation for language, whereas others failed to detect the difference at the functional level. Recent advances of brain network analysis have shown great promise in picking up brain connectivity differences between sexes, leading us to hypothesize that the functional connections among distinct brain regions for language may differ in males and females. To test this hypothesis, we scanned 58 participants’ brain activities (28 males and 30 females) in a semantic decision task using functional magnetic resonance imaging. We found marked sex differences in dynamic interactions among language regions, as well as in functional segregation and integration of brain networks during language processing. The brain network differences were further supported by a machine learning analysis that accurately discriminated males from females using the multivariate patterns of functional connectivity. The sex-specific functional brain connectivity may constitute an essential neural basis for the long-held notion that men and women process language in different ways. Our finding also provides important implications for sex differences in the prevalence of language disorders, such as dyslexia and stuttering.

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Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

Brain Sciences ◽

10.3390/brainsci11010118 ◽

2021 ◽

Vol 11 (1) ◽

pp. 118

Author(s):

Blake R. Neyland ◽

Christina E. Hugenschmidt ◽

Robert G. Lyday ◽

Jonathan H. Burdette ◽

Laura D. Baker ◽

...

Keyword(s):

Older Adults ◽

Graph Theory ◽

Community Structure ◽

Resting State ◽

Brain Networks ◽

Brain Network ◽

Functional Brain ◽

Network Community ◽

Functional Brain Networks ◽

The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

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Brain network dynamics correlate with personality traits

10.1101/702266 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aya Kabbara ◽

Veronique Paban ◽

Arnaud Weill ◽

Julien Modolo ◽

Mahmoud Hassan

Keyword(s):

Functional Connectivity ◽

Personality Traits ◽

Resting State ◽

Brain Networks ◽

Neural Substrates ◽

Brain Network ◽

Functional Brain ◽

Human Connectome Project ◽

Functional Brain Networks ◽

The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

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Percolation Analysis of Brain Structural Network

Frontiers in Physics ◽

10.3389/fphy.2021.698077 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shu Guo ◽

Xiaoqi Chen ◽

Yimeng Liu ◽

Rui Kang ◽

Tao Liu ◽

...

Keyword(s):

Failure Modes ◽

Critical Infrastructure ◽

Network Reliability ◽

Brain Connectivity ◽

Brain Networks ◽

Structural Features ◽

Brain Network ◽

Structure Design ◽

Reliability Design ◽

The Brain

The brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation, and maintenance, we use the percolation methods of network reliability on brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core-based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown to be related to brain disorders, as well as the reliability design of other technological networks.

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Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

Neural Plasticity ◽

10.1155/2018/5340717 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Anna Lardone ◽

Marianna Liparoti ◽

Pierpaolo Sorrentino ◽

Rosaria Rucco ◽

Francesca Jacini ◽

...

Keyword(s):

Resting State ◽

Mindfulness Meditation ◽

Brain Connectivity ◽

Brain Networks ◽

Brain Network ◽

Long Term Effects ◽

Age Related ◽

Functional Topology ◽

The Right ◽

The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

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Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

International Journal of Image and Graphics ◽

10.1142/s0219467822500292 ◽

2021 ◽

pp. 2250029

Author(s):

Geng Zhang ◽

Qi Zhu ◽

Jing Yang ◽

Ruting Xu ◽

Zhiqiang Zhang ◽

...

Keyword(s):

Brain Connectivity ◽

Medical Image Analysis ◽

Brain Regions ◽

Brain Network ◽

Brain Diseases ◽

Functional Brain ◽

Functional Brain Network ◽

Structural Brain Network ◽

The Brain ◽

Structural Brain

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

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Sex Differences in the Associations Between Symptom Severity, Social Cognition, and Social Brain Network Connectivity in Alcohol Use Disorder

10.31234/osf.io/hm3n4 ◽

2020 ◽

Author(s):

Abigail Waters ◽

Sergey Chernyak ◽

Amy Janes ◽

William Killgore ◽

Shelly Greenfield ◽

...

Keyword(s):

Sex Differences ◽

Social Cognition ◽

Alcohol Use ◽

Symptom Severity ◽

Alcohol Use Disorder ◽

Brain Networks ◽

Network Connectivity ◽

Brain Network ◽

Men And Women ◽

Brain Network Connectivity

Background and Aims: Large-scale neurocognitive brain networks are necessary to coordinate social cognition. Regions of prefrontal cortex that are key nodes in these networks are highly vulnerable to alcohol neurotoxicity, which may link poor social function and alcohol use disorder (AUD). However, there is very little research on how brain networks associated with social cognition are affected by AUD, and no studies of how these effects may differ between men and women. The current study aims to address this gap by examining the interaction between sex and AUD on the connectivity between brain networks implicated in social cognition.Methods: Matched groups of men and women with and without AUD (N=156; N=39/group) were selected from the Human Connectome Project. Resting-state functional magnetic resonance imaging data were used to compute functional connectivity between prefrontal networks, including default mode sub-networks (anterior dorsomedial: aDMN, ventromedial: vmDMN, temporal lobe: tDMN, and posterior DMN: pDMN), and central executive, dorsal attention, ventral attention, salience, and striatal networks. Between-network connectivity was assessed for interactions between sex, AUD diagnosis and symptom severity, and a measure of composite social cognition using non-parametric permutation testing, corrected for number of network pairs tested (Benjamini-Hochberg procedure, p<0.05 corrected). Results: Connectivity between aDMN–tDMN (AUDcontrols, pcor=.030) differed between groups. An interaction between sex and AUD symptom severity was significantly associated with aDMN–VAN (pcor= .032) connectivity. Social cognition scores were associated with aDMN–vmDMN connectivity (pcor=.003), with the relationship being moderated by sex, AUD-status, and symptom severity. Conclusions: This study addresses a critical gap in the literature on how brain network connectivity that underpins social cognition may be impaired in men and women with AUD. Our findings show that vulnerabilities emerge in men and women even at mild symptom severity and that there are significant sex differences, suggesting sex-specific treatment considerations are warranted.

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Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics

Diagnostics ◽

10.3390/diagnostics8030051 ◽

2018 ◽

Vol 8 (3) ◽

pp. 51 ◽

Cited By ~ 5

Author(s):

Aitana Pascual-Belda ◽

Antonio Díaz-Parra ◽

David Moratal

Keyword(s):

Autism Spectrum Disorder ◽

Brain Connectivity ◽

Brain Networks ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Typical Development ◽

Functional Brain ◽

Functional Brain Networks ◽

Management Functions ◽

The Brain

The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

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Efficient search for informational cores in complex systems: Application to brain networks

10.1101/2020.04.06.027441 ◽

2020 ◽

Author(s):

Jun Kitazono ◽

Ryota Kanai ◽

Masafumi Oizumi

Keyword(s):

Mutual Information ◽

Loss Function ◽

Brain Networks ◽

Computation Time ◽

Exact Algorithm ◽

Brain Network ◽

Information Loss ◽

Brain Functions ◽

Large Systems ◽

The Brain

AbstractTo understand the nature of the complex behavior of the brain, one important step is to identify “cores” in the brain network, where neurons or brain areas strongly interact with each other. Cores can be considered as essential sub-networks for brain functions. In the last few decades, an information-theoretic approach to identifying cores has been developed. In this approach, many-to-many nonlinear interactions between parts are measured by an information loss function, which quantifies how much information would be lost if interactions between parts were removed. Then, a core called a “complex” is defined as a subsystem wherein the amount of information loss is locally maximal. Although identifying complexes can be a novel and useful approach to revealing essential properties of the brain network, its practical application is hindered by the fact that computation time grows exponentially with system size. Here we propose a fast and exact algorithm for finding complexes, called Hierarchical Partitioning for Complex search (HPC). HPC finds complexes by hierarchically partitioning systems to narrow down candidates for complexes. The computation time of HPC is polynomial, which is dramatically smaller than exponential. We prove that HPC is exact when an information loss function satisfies a mathematical property, monotonicity. We show that mutual information is one such information loss function. We also show that a broad class of submodular functions can be considered as such information loss functions, indicating the expandability of our framework to the class. In simulations, we show that HPC can find complexes in large systems (up to several hundred) in a practical amount of time when mutual information is used as an information loss function. Finally, we demonstrate the use of HPC in electrocorticogram recordings from monkeys. HPC revealed temporally stable and characteristic complexes, indicating that it can be reliably utilized to characterize brain networks.Author summaryAn important step in understanding the nature of the brain is to identify “cores” in the brain network, which can be considered as essential areas for brain functions and cognition. In the last few decades, a novel definition of cores has been developed, which takes account of many-to-many interactions among elements of the network. Although considering many-to-many interactions can be important in understanding the complex brain network, identifying cores in large systems has been impossible because of the extremely large computational costs required. Here, we propose a fast and exact algorithm for finding cores. We show that the proposed algorithm enables us to find cores in large systems consisting of several hundred elements in a practical amount of time. We applied our algorithm to electrocorticogram recordings from a monkey that monitored electrical activity of the brain with electrodes placed directly on the brain surface, and demonstrated that there are stable and characteristic core structures in the brain network. This result indicates that our algorithm can be reliably applied to uncovering the essential network structures of the brain.

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Graph analysis of functional brain networks: practical issues in translational neuroscience

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2013.0521 ◽

2014 ◽

Vol 369 (1653) ◽

pp. 20130521 ◽

Cited By ~ 173

Author(s):

Fabrizio De Vico Fallani ◽

Jonas Richiardi ◽

Mario Chavez ◽

Sophie Achard

Keyword(s):

Brain Networks ◽

Brain Network ◽

Graph Analysis ◽

Translational Neuroscience ◽

Functional Brain ◽

Functional Perspective ◽

Brain Signals ◽

Functional Brain Networks ◽

The One ◽

The Brain

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.

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Electroencephalography-based functional brain network analysis according to lesion location and motor function in stroke patients

10.21203/rs.2.20942/v1 ◽

2020 ◽

Author(s):

Da-Hye Kim ◽

Gyu Hyun Kwon ◽

Wanjoo Park ◽

Yun-Hee Kim ◽

Seong-Whan Lee ◽

...

Keyword(s):

Brain Networks ◽

Brain Network ◽

Control Group ◽

Stroke Patients ◽

Functional Brain ◽

Lesion Location ◽

Functional Brain Network ◽

Functional Brain Networks ◽

Stroke Lesion ◽

The Brain

Abstract Background. While numerous studies have investigated changes in brain activation after stroke, limited information exists on the association between functional brain networks and lesion location in stroke patients. Methods. We compared the characteristics of brain networks among patients with cortico-subcortical lesions (n = 5), subcortical lesions (n = 7), and age-matched healthy controls (n = 12) during the execution of hand movements. Functional brain networks were analyzed based on network parameters in beta frequency electroencephalography (EEG) bands. Results. Our results indicated that while the healthy control group had appropriate compensatory patterns on the brain network with an aging effect, the two stroke lesion groups exhibited different hyper-connected characteristics in the brain network within the sensorimotor regions, particularly the contralesional M1, during motor execution. In addition, the betweenness centrality on the contralesional motor area was identified as a promising biomarker for motor functional ability associated with stroke. Our findings further allowed us to identify the characteristics of the stroke lesion that could not be found with EEG power by using the EEG brain network on the cerebral cortex. Conclusions. We anticipate that our study will improve the understanding of the complex changes that occur in the brain network as a result of stroke, and support the development of more effective and efficient rehabilitation programs based on lesion location for stroke patients.

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