Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia

2020 ◽  
pp. 1-11
Author(s):  
Yuchao Jiang ◽  
Dezhong Yao ◽  
Jingyu Zhou ◽  
Yue Tan ◽  
Huan Huang ◽  
...  
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Negative Symptoms ◽  
Functional Organization ◽  
Clustering Coefficient ◽  
Theoretical Approaches ◽  
Topological Organization ◽  
Large Scale Networks ◽  
Nodal Level

Abstract Background Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function. Methods Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks. Results At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions. Conclusions These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.

scholarly journals The role of anatomical connection strength for interareal communication in macaque cortex

2020 ◽  
Author(s):  
Julien Vezoli ◽  
Martin Vinck ◽  
Conrado A. Bosman ◽  
Andre M. Bastos ◽  
Christopher M Lewis ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Brain Regions ◽  
Connection Strength ◽  
Non Invasive ◽  
Large Scale Networks ◽  
Invasive Techniques ◽  
The Relationship ◽  
Anatomical Connection

What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.

scholarly journals Large-Scale Functional Brain Networks of Maladaptive Childhood Aggression Identified by Connectome-Based Predictive Modeling

2021 ◽  
Author(s):  
Karim Ibrahim ◽  
Stephanie Noble ◽  
George He ◽  
Cheryl Lacadie ◽  
Michael Crowley ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Emotion Processing ◽  
Childhood Aggression ◽  
Child Aggression ◽  
Targeted Treatments ◽  
Machine Learning Approach ◽  
Out Of Sample ◽  
Functional Brain Networks ◽  
Large Scale Networks

Abstract Disruptions in frontoparietal networks supporting emotion regulation have been long implicated in maladaptive childhood aggression. However, the association of connectivity between large-scale functional networks in the human connectome with aggressive behavior has not been tested. By using a data-driven, machine learning approach, we show that the functional organization of the connectome during emotion processing predicts severity of aggression in children (n=129). Connectivity predictive of aggression was identified within and between large-scale networks implicated in cognitive control (frontoparietal), social functioning (default mode), and emotion processing (subcortical). Out-of-sample replication and generalization of findings predicting aggression from the functional connectome was conducted in an independent sample of children from the Adolescent Brain Cognitive Development study (n=1,791; n=1,701). These results define novel connectivity-based networks of child aggression that can serve as biomarkers to inform targeted treatments for aggression.

scholarly journals Large-Scale Evidence for an Association Between Peripheral Inflammation and White Matter Free Water in Schizophrenia and Healthy Individuals

2020 ◽  
Author(s):  
Maria A Di Biase ◽  
Andrew Zalesky ◽  
Suheyla Cetin-Karayumak ◽  
Yogesh Rathi ◽  
Jinglei Lv ◽  
...  
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Interleukin 2 ◽  
Free Water ◽  
Markers Of Inflammation ◽  
Brain White Matter ◽  
Inflammatory Processes ◽  
Weighted Magnetic Resonance Imaging

Abstract Introduction Clarifying the role of neuroinflammation in schizophrenia is subject to its detection in the living brain. Free-water (FW) imaging is an in vivo diffusion-weighted magnetic resonance imaging (dMRI) technique that measures water molecules freely diffusing in the brain and is hypothesized to detect inflammatory processes. Here, we aimed to establish a link between peripheral markers of inflammation and FW in brain white matter. Methods All data were obtained from the Australian Schizophrenia Research Bank (ASRB) across 5 Australian states and territories. We first tested for the presence of peripheral cytokine deregulation in schizophrenia, using a large sample (N = 1143) comprising the ASRB. We next determined the extent to which individual variation in 8 circulating pro-/anti-inflammatory cytokines related to FW in brain white matter, imaged in a subset (n = 308) of patients and controls. Results Patients with schizophrenia showed reduced interleukin-2 (IL-2) (t = −3.56, P = .0004) and IL-12(p70) (t = −2.84, P = .005) and increased IL-6 (t = 3.56, P = .0004), IL-8 (t = 3.8, P = .0002), and TNFα (t = 4.30, P < .0001). Higher proinflammatory signaling of IL-6 (t = 3.4, P = .0007) and TNFα (t = 2.7, P = .0007) was associated with higher FW levels in white matter. The reciprocal increases in serum cytokines and FW were spatially widespread in patients encompassing most major fibers; conversely, in controls, the relationship was confined to the anterior corpus callosum and thalamic radiations. No relationships were observed with alternative dMRI measures, including the fractional anisotropy and tissue-related FA. Conclusions We report widespread deregulation of cytokines in schizophrenia and identify inflammation as a putative mechanism underlying increases in brain FW levels.

scholarly journals Microstructural damage of white-matter tracts connecting large-scale networks is related to impaired executive profile in alcohol use disorder

2020 ◽  
Vol 25 ◽  
pp. 102141 ◽  
Author(s):  
Chiara Crespi ◽  
Caterina Galandra ◽  
Nicola Canessa ◽  
Marina Manera ◽  
Paolo Poggi ◽  
...  
Keyword(s):  
White Matter ◽  
Alcohol Use ◽  
Alcohol Use Disorder ◽  
Large Scale ◽  
White Matter Tracts ◽  
Microstructural Damage ◽  
Large Scale Networks

The role of functional magnetic resonance imaging in brain surgery

2010 ◽  
Vol 28 (2) ◽  
pp. E4 ◽  
Author(s):  
Geert-Jan Rutten ◽  
Nick F. Ramsey
Keyword(s):  
Mr Imaging ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Imaging Techniques ◽  
Convincing Evidence ◽  
Brain Surgery ◽  
Wada Test ◽  
Functional Mr Imaging ◽  
Large Scale Networks

New functional neuroimaging techniques are changing our understanding of the human brain, and there is now convincing evidence to move away from the classic and clinical static concepts of functional topography. In a modern neurocognitive view, functions are thought to be represented in dynamic large-scale networks. The authors review the current (limited) role of functional MR imaging in brain surgery and the possibilities of new functional MR imaging techniques for research and neurosurgical practice. A critique of current clinical gold standard techniques (electrocortical stimulation and the Wada test) is given.

Synchrony of Neuronal Oscillations Controlled by GABAergic Reversal Potentials

2007 ◽  
Vol 19 (3) ◽  
pp. 706-729 ◽  
Author(s):  
Ho Young Jeong ◽  
Boris Gutkin
Keyword(s):  
Large Scale ◽  
Reversal Potential ◽  
Type I ◽  
Gabaergic Synapse ◽  
Neural Oscillators ◽  
Large Scale Network ◽  
Weakly Coupled ◽  
Scale Network ◽  
Large Scale Networks

GABAergic synapse reversal potential is controlled by the concentration of chloride. This concentration can change significantly during development and as a function of neuronal activity. Thus, GABA inhibition can be hyperpolarizing, shunting, or partially depolarizing. Previous results pinpointed the conditions under which hyperpolarizing inhibition (or depolarizing excitation) can lead to synchrony of neural oscillators. Here we examine the role of the GABAergic reversal potential in generation of synchronous oscillations in circuits of neural oscillators. Using weakly coupled oscillator analysis, we show when shunting and partially depolarizing inhibition can produce synchrony, asynchrony, and coexistence of the two. In particular, we show that this depends critically on such factors as the firing rate, the speed of the synapse, spike frequency adaptation, and, most important, the dynamics of spike generation (type I versus type II). We back up our analysis with simulations of small circuits of conductance-based neurons, as well as large-scale networks of neural oscillators. The simulation results are compatible with the analysis: for example, when bistability is predicted analytically, the large-scale network shows clustered states.

scholarly journals Algorithms for generating large-scale clustered random graphs

2014 ◽  
Vol 2 (3) ◽  
pp. 403-415 ◽  
Author(s):  
CHENG WANG ◽  
OMAR LIZARDO ◽  
DAVID HACHEN
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Real World ◽  
Local Structure ◽  
Topological Structure ◽  
Large Scale ◽  
Random Processes ◽  
Clustering Coefficient ◽  
Computation Efficiency ◽  
Large Scale Networks

AbstractReal-world networks are often compared to random graphs to assess whether their topological structure could be a result of random processes. However, a simple random graph in large scale often lacks social structure beyond the dyadic level. As a result we need to generate clustered random graph to compare the local structure at higher network levels. In this paper a generalized version of Gleeson's algorithm G(VS, VT, ES, ET, S, T) is advanced to generate a clustered random graph in large-scale which persists the number of vertices |V|, the number of edges |E|, and the global clustering coefficient CΔ as in the real network and it works successfully for nine large-scale networks. Our new algorithm also has advantages in randomness evaluation and computation efficiency when compared with the existing algorithms.

Open-Source Solution to Secure E-Government Services

2011 ◽  
pp. 1300-1305
Author(s):  
C. A. Ardagna
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Security Requirements ◽  
The Public ◽  
Security Environment ◽  
Public Responsibility ◽  
Large Scale Networks ◽  
Standard Integration

Nowadays, a global information infrastructure connects remote parties through the use of large scale networks, and many companies focus on developing e-services based on remote resources and on interactions between remote parties. In such a context, e-government (e-gov) systems became of paramount importance for the public administration, and many ongoing development projects are targeted on their implementation, security, and release (Bettini, Jajodia, Sean Wang, & Wijesekera, 2002). For open-source software to play an important role in this scenario, three main technological requirements must be fulfilled: (1) the identification and optimization of de facto standards for building e-gov open-source software components, (2) the adoption of open-source techniques to secure e-gov services and (3) the standard integration of these components into an open-source middleware layer, capable of conveying a completely open-source e-gov solution. This article highlights that e-gov systems should be constructed on an open-source middleware layer, providing full public responsibility in its development. The role of open-source middleware for secure e-gov services deployment is discussed, focusing on implementing a security environment without custom programming. An alternative solution is given and consists of the adoption of a stand-alone architecture that fulfils all security requirements.

A novel method for identifying influential nodes in complex networks based on multiple attributes

2018 ◽  
Vol 32 (28) ◽  
pp. 1850307 ◽  
Author(s):  
Dong Liu ◽  
Hao Nie ◽  
Baowen Zhang
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Entropy Method ◽  
Viral Marketing ◽  
Clustering Coefficient ◽  
Node Location ◽  
Influential Nodes ◽  
Multiple Attributes ◽  
Comparison Algorithms ◽  
Large Scale Networks

Identifying influential nodes is a crucial issue in epidemic spreading, controlling the propagation process of information and viral marketing. Thus, algorithms for exploring vital nodes have aroused more and more concern among researchers. Recently, scholars have proposed various types of algorithms based on different perspectives. However, each of these methods has their own strengths and weaknesses. In this work, we introduce a novel multiple attributes centrality for identifying significant nodes based on the node location and neighbor information attributes. We call our proposed method the MAC. Specifically, we utilize the information of the number of iterations per node to enhance the accuracy of the K-shell algorithm, so that the location attribute can be used to distinguish the important nodes more deeply. And the neighbor information attribute we selected can effectively avoid the overlapping problem of neighbor information propagation caused by large clustering coefficient of networks. Because these two indexes have different emphases, we use entropy method to assign them reasonable weights. In addition, MAC has low time complexity O(n), which makes the algorithm suitable for large-scale networks. In order to objectively assess its performance, we utilize the Susceptible-Infected-Recovered (SIR) model to verify the propagation capability of each node and compare the MAC method with several classic methods in six real-life datasets. Extensive experiments verify the superiority of our algorithm to other comparison algorithms.

scholarly journals Cerebral cartography and connectomics

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140173 ◽  
Author(s):  
Olaf Sporns
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Network Science ◽  
Temporal Dynamics ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Hierarchical Organization ◽  
Large Scale Networks ◽  
Definition Of ◽  
The Brain

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.

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