The gendered self: Evidence for differences in whole-brain dynamics

How the brain constructs gender identity is largely unknown, but some neural differences have recently been discovered. Here, we used an intrinsic-ignition framework to investigate if gender identity changes the propagation of the neural activity across the whole-brain network and within resting-state networks. Studying 29 transmen and 17 transwomen with gender incongruence, 22 ciswomen, and 19 cismen, we computed the capability of a given brain area in space to propagate activity to other areas (mean-ignition) and its variability across time (node-metastability). We found that both measures differentiated all four groups across the whole-brain network. Furthermore, at the network level, we found that compared to the other groups, cismen showed higher mean-ignition of the dorsal attention network and node-metastability of the dorsal and ventral attention, executive control, and temporal parietal networks. We also found mean-ignition differences between cismen and ciswomen within the executive control network, but higher in ciswomen than cismen and transmen for the default-mode network. For the node-metastability, this was higher in cismen compared to ciswomen in the somatomotor network, while both mean-ignition and node-metastability were higher for cismen than transmen in the limbic network. Finally, we computed correlations between both measures and their body image scores. Transmen dissatisfaction, cismen, and ciswomen satisfaction towards their own body image were distinctively associated with specific networks per group. Overall, the study of the whole-brain network dynamical complexity discriminates binary gender identity groups, and functional connectivity dynamics approaches are needed to disentangle the complex understanding of the gendered self.

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2092-P: Impact of Insulin Resistance on Response to a Meal by Salience, Executive Control, and Default Mode Resting State Networks in Humans

Diabetes ◽

10.2337/db19-2092-p ◽

2019 ◽

Vol 68 (Supplement 1) ◽

pp. 2092-P

Author(s):

LETICIA ESPOSITO SEWAYBRICKER ◽

SUSAN J. MELHORN ◽

MARY K. ASKREN ◽

MARY WEBB ◽

VIDHI TYAGI ◽

...

Keyword(s):

Insulin Resistance ◽

Executive Control ◽

Resting State ◽

Resting State Networks ◽

Default Mode

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ALTERED STRUCTURAL WHOLE-BRAIN NETWORK ORGANIZATION IN EXTREMELY PRETERM CHILDREN AT 10 YEARS OF AGE

10.26226/morressier.57d034cfd462b8029238321f ◽

2016 ◽

Author(s):

Nelly Padilla

Keyword(s):

Brain Network ◽

Network Organization ◽

Whole Brain ◽

Preterm Children ◽

Extremely Preterm

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Eating Out in the Seventeenth and Eighteenth Centuries: The Contest between English and French Food

10.1093/oso/9780198826187.003.0004 ◽

2018 ◽

Author(s):

Christel Lane

Keyword(s):

Gender Identity ◽

Social Economic ◽

Country House ◽

Class Background ◽

Eating Out ◽

Identity Changes ◽

Food Consumed

This chapter examines the food eaten at this time in taverns, inns, and public houses. It focuses on how allegiance to either English or French cuisine expresses patriotism and cosmopolitanism respectively. Patriotism and the consumption of large amounts of beef receive particular emphasis. An examination of food consumed nevertheless finds a considerable variety in the types of food enjoyed, as well as noting the quality, particularly of country house cooking. Divergent national identifications, in turn, are related to the class background of diners, as well as to gender identity. Changes in modes of dining out are viewed in their social, economic, and political contexts.

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Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

10.1101/344473 ◽

2018 ◽

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Resting State ◽

Dynamic Properties ◽

Brain Activity ◽

Temporal Structure ◽

Structural Connectivity ◽

Network Models ◽

Resting State Fmri ◽

Brain Network ◽

Dynamical Analysis ◽

Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

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A whole-brain and cross-diagnostic perspective on functional brain network dysfunction

10.1101/326728 ◽

2018 ◽

Cited By ~ 8

Author(s):

Marjolein Spronk ◽

Kaustubh Kulkarni ◽

Jie Lisa Ji ◽

Brian P. Keane ◽

Alan Anticevic ◽

...

Keyword(s):

Mental Illness ◽

Resting State ◽

Brain Network ◽

Functional Network ◽

Graph Distance ◽

Network Organization ◽

Whole Brain ◽

Functional Brain ◽

Functional Brain Network ◽

Mental Diseases

AbstractA wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations have seemed to support various theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in broad, whole-brain perspective. Using a graph distance measure – connectome-wide correlation – we found that whole-brain resting-state functional network organization in humans is highly similar across a variety of mental diseases and healthy controls. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease those differences are informative. Such small network alterations may reflect the fact that most psychiatric patients maintain overall cognitive abilities similar to those of healthy individuals (relative to, e.g., the most severe schizophrenia cases), such that whole-brain functional network organization is expected to differ only subtly even for mental diseases with devastating effects on everyday life. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases.

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Childhood Trauma Associated With Abnormal Resting-State Brain Network Connectivity in First-Episode Schizophrenia Patients

10.21203/rs.3.rs-73545/v1 ◽

2020 ◽

Author(s):

Xiangyun Long ◽

Jiaxin Wu ◽

Fei Liu ◽

Ansi Qi ◽

Nan Huang ◽

...

Keyword(s):

Childhood Trauma ◽

Resting State ◽

Network Connectivity ◽

Brain Network ◽

Traumatic Experiences ◽

First Episode ◽

Resting State Networks ◽

Attention Network ◽

Brain Network Connectivity ◽

First Episode Schizophrenia

Abstract Childhood trauma is a central risk factor for schizophrenia. We explored the correlation between early traumatic experiences and the functional connectivity of resting-state networks. This fMRI study included 28 first-episode schizophrenia patients and 27 healthy controls. In first-episode schizophrenia patients, higher levels of childhood trauma associated with abnormal connections of resting-state networks, and these anomalies distributed among task-positive networks (i.e., ventral attention network, dorsal-ventral attention network and frontal-parietal network), and sensory networks (i.e., visual network and auditory network). These findings mentioned that childhood traumatic experiences may impact resting-state network connectivity in adulthood, mainly involving systems related to attention and execution control.

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Neuronal cascades shape whole-brain functional dynamics at rest

10.1101/2020.12.25.424385 ◽

2020 ◽

Author(s):

Giovanni Rabuffo ◽

Jan Fousek ◽

Christophe Bernard ◽

Viktor Jirsa

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Blood Oxygen Level Dependent ◽

Brain Network ◽

Neural Activation ◽

Data Set ◽

Whole Brain ◽

Dynamic Component ◽

Leading Role ◽

Co Activation

AbstractAt rest, mammalian brains display a rich complex spatiotemporal behavior, which is reminiscent of healthy brain function and has provided nuanced understandings of several major neurological conditions. Despite the increasingly detailed phenomenological documentation of the brain’s resting state, its principle underlying causes remain unknown. To establish causality, we link structurally defined features of a brain network model to neural activation patterns and their variability. For the mouse, we use a detailed connectome-based model and simulate the resting state dynamics for neural sources and whole brain imaging signals (Blood-Oxygen-Level-Dependent (BOLD), Electroencephalography (EEG)). Under conditions of near-criticality, characteristic neuronal cascades form spontaneously and propagate through the network. The largest neuronal cascades produce short-lived but robust co-fluctuations at pairs of regions across the brain. During these co-activation episodes, long-lasting functional networks emerge giving rise to epochs of stable resting state networks correlated in time. Sets of neural cascades are typical for a resting state network, but different across. We experimentally confirm the existence and stability of functional connectivity epochs comprising BOLD co-activation bursts in mice (N=19). We further demonstrate the leading role of the neuronal cascades in a simultaneous EEG/fMRI data set in humans (N=15), explaining a large part of the variability of functional connectivity dynamics. We conclude that short-lived neuronal cascades are a major robust dynamic component contributing to the organization of the slowly evolving spontaneous fluctuations in brain dynamics at rest.

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Whole Brain Network Analysis Based on Morphological and Diffusion Tensor Imaging Reveals Connectivity Deficits in Autistics

NeuroImage ◽

10.1016/s1053-8119(09)71817-8 ◽

2009 ◽

Vol 47 ◽

pp. S169

Author(s):

H Li ◽

Z Xue ◽

L Guo ◽

JV Hunter ◽

STC Wong

Keyword(s):

Diffusion Tensor Imaging ◽

Network Analysis ◽

Diffusion Tensor ◽

Brain Network ◽

Whole Brain ◽

And Diffusion

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How small can the epileptogenic region be?

Neurology ◽

10.1212/wnl.0000000000003962 ◽

2017 ◽

Vol 88 (21) ◽

pp. 2017-2019 ◽

Cited By ~ 9

Author(s):

Graeme D. Jackson ◽

Mangor Pedersen ◽

A. Simon Harvey

Keyword(s):

Large Scale ◽

Intractable Epilepsy ◽

Brain Network ◽

Local Connectivity ◽

Whole Brain ◽

Network Characteristics ◽

Small Areas ◽

Language Deficit ◽

Large Scale Networks ◽

Combine Information

Objective:To present a case that demonstrates that seizures and interictal disturbances can be driven by a small area of functionally abnormal cortex.Methods:Two novel functional MRI network analysis methods were used to supplement conventional seizure and lesion localization methods: (1) regional homogeneity to quantify local connectivity, or synchrony, with a resolution of less than 1 cm3 of cortex; and (2) small-worldness to combine information about whole brain network segregation and integration.Results:After a small corticectomy in the dominant supramarginal gyrus (13 × 7 × 6 mm) limited to the area of abnormal local connectivity, and smaller than the PET and SPECT abnormalities, the patient has been seizure-free for 3 years with no language deficit. Whole brain network characteristics normalized (small-worldness) to that of healthy controls.Conclusions:This case demonstrates that small areas of cortex may be highly epileptogenic, drive intractable epilepsy, and disrupt large-scale networks likely to be involved in core cognitive functions.

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