The effect of aging on the brain network for exception word reading

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

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The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

Scientific Reports ◽

10.1038/s41598-021-94825-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rossana Mastrandrea ◽

Fabrizio Piras ◽

Andrea Gabrielli ◽

Nerisa Banaj ◽

Guido Caldarelli ◽

...

Keyword(s):

Resting State ◽

Functional Organization ◽

Functional Integration ◽

Brain Network ◽

Modular Organization ◽

Node Degree ◽

Imaging Data ◽

Brain Disorder ◽

Functional Connections ◽

The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

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Energy constraints on brain network formation

Scientific Reports ◽

10.1038/s41598-021-91250-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kosuke Takagi

Keyword(s):

Network Formation ◽

Cost Minimization ◽

Brain Network ◽

Network Activity ◽

Rich Functionality ◽

Energy Constraints ◽

Dependent Form ◽

Statistical Similarity ◽

Simple Ratio ◽

The Brain

AbstractEnergy constraints are a fundamental limitation of the brain, which is physically embedded in a restricted space. The collective dynamics of neurons through connections enable the brain to achieve rich functionality, but building connections and maintaining activity come at a high cost. The effects of reducing these costs can be found in the characteristic structures of the brain network. Nevertheless, the mechanism by which energy constraints affect the organization and formation of the neuronal network in the brain is unclear. Here, it is shown that a simple model based on cost minimization can reproduce structures characteristic of the brain network. With reference to the behavior of neurons in real brains, the cost function was introduced in an activity-dependent form correlating the activity cost and the wiring cost as a simple ratio. Cost reduction of this ratio resulted in strengthening connections, especially at highly activated nodes, and induced the formation of large clusters. Regarding these network features, statistical similarity was confirmed by comparison to connectome datasets from various real brains. The findings indicate that these networks share an efficient structure maintained with low costs, both for activity and for wiring. These results imply the crucial role of energy constraints in regulating the network activity and structure of the brain.

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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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A Destruction Model of the Vascular and Lymphatic Systems in the Emergence of Psychiatric Symptoms

Biology ◽

10.3390/biology10010034 ◽

2021 ◽

Vol 10 (1) ◽

pp. 34

Author(s):

Kohei Segawa ◽

Yukari Blumenthal ◽

Yuki Yamawaki ◽

Gen Ohtsuki

Keyword(s):

Neurodegenerative Diseases ◽

Lymphatic System ◽

Psychiatric Symptoms ◽

Neurological Diseases ◽

Immune Surveillance ◽

Brain Network ◽

Cellular Mechanisms ◽

Postmortem Brains ◽

New Interpretation ◽

The Brain

The lymphatic system is important for antigen presentation and immune surveillance. The lymphatic system in the brain was originally introduced by Giovanni Mascagni in 1787, while the rediscovery of it by Jonathan Kipnis and Kari Kustaa Alitalo now opens the door for a new interpretation of neurological diseases and therapeutic applications. The glymphatic system for the exchanges of cerebrospinal fluid (CSF) and interstitial fluid (ISF) is associated with the blood-brain barrier (BBB), which is involved in the maintenance of immune privilege and homeostasis in the brain. Recent notions from studies of postmortem brains and clinical studies of neurodegenerative diseases, infection, and cerebral hemorrhage, implied that the breakdown of those barrier systems and infiltration of activated immune cells disrupt the function of both neurons and glia in the parenchyma (e.g., modulation of neurophysiological properties and maturation of myelination), which causes the abnormality in the functional connectivity of the entire brain network. Due to the vulnerability, such dysfunction may occur in developing brains as well as in senile or neurodegenerative diseases and may raise the risk of emergence of psychosis symptoms. Here, we introduce this hypothesis with a series of studies and cellular mechanisms.

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The brain network reflecting bodily self-consciousness: a functional connectivity study

Social Cognitive and Affective Neuroscience ◽

10.1093/scan/nst185 ◽

2014 ◽

Vol 9 (12) ◽

pp. 1904-1913 ◽

Cited By ~ 54

Author(s):

Silvio Ionta ◽

Roberto Martuzzi ◽

Roy Salomon ◽

Olaf Blanke

Keyword(s):

Functional Connectivity ◽

Brain Network ◽

The Brain

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The Brain Network in a Model of Thalamocortical Dysrhythmia

Brain Connectivity ◽

10.1089/brain.2018.0621 ◽

2019 ◽

Vol 9 (3) ◽

pp. 273-284 ◽

Cited By ~ 2

Author(s):

Mehrnoush Zobeiri ◽

Gilles van Luijtelaar ◽

Thomas Budde ◽

Ilya V. Sysoev

Keyword(s):

Brain Network ◽

Thalamocortical Dysrhythmia ◽

The Brain

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A Computational Network Control Theory Analysis of Depression Symptoms

Personality Neuroscience ◽

10.1017/pen.2018.15 ◽

2018 ◽

Vol 1 ◽

Cited By ~ 2

Author(s):

Yoed N. Kenett ◽

Roger E. Beaty ◽

John D. Medaglia

Keyword(s):

Control Theory ◽

Diffusion Tensor ◽

Brain Regions ◽

Brain Network ◽

Network Control ◽

Depression Symptoms ◽

Theory Approach ◽

Imaging Data ◽

The Brain ◽

Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

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