Topological impact of negative links on the stability of resting-state brain network

Saberi, Majid; Khosrowabadi, Reza; Khatibi, Ali; Mišić, Bratislav; Jafari, G. R.

doi:10.1038/s41598-021-81767-7

Cited by 38 publications

(61 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Global hubness demonstrates the multiplicity of hubs in a network. We assessed the global hubness of brain negative subnetworks using Tendency to Make Hub (TMH) [ 27 ] then we compared global hubness of lifespan stages. Kruskal-Wallis test shows significant differences between global hubness of subjects between lifespan stages (H = 34.1, d.f = 4, p-value = 7.09e-07).…”

Section: Resultsmentioning

confidence: 99%

“…In our previous work [ 27 ], we had fixed negative link density and studied the effect of the negative link topology on the brain network balance. In this research, we studied topological features of brain negative subnetworks and their relations with frustration in a case of inconsistency of negative link density and where network frustration and negative link density are correlated.…”

Section: Discussionmentioning

confidence: 99%

“…If we model the brain as a signed network where synchrony and anti-synchrony between regional brain activations determine signed links [ 27 ] ( Fig 1A ), we can expect that the brain signed network tries to resolve its frustrations by changing the sign of links. Therefore, we can regard the number of triadic frustrations presented in the functional signed network as a measure of the requirement to change of functional brain network.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Requirement to change of functional brain network across the lifespan

et al. 2021

Self Cite

View full text Add to dashboard Cite

Many studies have focused on neural changes and neuroplasticity, while the signaling demand for neural modification needs to be explored. In this study, we traced this issue in the organization of brain functional links where the conflictual arrangement of signed links makes a request to change. We introduced the number of frustrations (unsatisfied closed triadic interactions) as a measure for assessing "requirement to change" of functional brain network. We revealed that the requirement to change of the resting-state network has a u-shape functionality over the lifespan with a minimum in early adulthood, and it’s correlated with the presence of negative links. Also, we discovered that brain negative subnetwork has a special topology with a log-normal degree distribution in all stages, however, its global measures are altered by adulthood. Our results highlight the study of collective behavior of functional negative links as the source of the brain’s between-regions conflicts and we propose exploring the attribute of the requirement to change besides other neural change factors.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Requirement to change of functional brain network across the lifespan

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Negative correlation values were set to zero because the neurobiological interpretation of positive and negative edges are very different (Parente et al, 2017; Schwarz and McGonigle, 2011). In addition, since the distributions of network variables (such as degree) are different for positive and negative edges (Fraiman et al, 2009; Saberi et al, 2021). Although negative correlations are not regularly used in network neuroscience, if they are used, positive and negative networks should be generated and assessed separately (Schwarz and McGonigle, 2011).…”

Section: Experimental Studiesmentioning

confidence: 99%

A Regression Framework for Brain Network Distance Metrics

Tomlinson

Laurienti

Lyday

et al. 2021

Preprint

View full text Add to dashboard Cite

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances between connection matrices, and adapt several standard methods for estimation and inference within our framework: Standard F-test, F-test with individual level effects (ILE), Generalized Least Squares (GLS), Mixed Model, and Permutation. We also propose a novel modification of the standard GLS approach for estimation and inference. We assess all approaches for estimation and inference via simulation studies, and illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

show abstract

“…In graph theory, node centrality measure has a diverse set of practical applications in a wide area of fields, such as but not limited to identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, superspreaders of a disease, and brain networks [24,25]. A set of graph centrality measures has been defined so far, such as degree centrality, closeness centrality, betweeness centrality, eigenvector centrality, etc.…”

Section: Identification Of Important Pathwaysmentioning

confidence: 99%

Novel graph theoretic biological pathway network analytics methods for analyzing and discovering Alzheimer’s disease related genes

Saha

Soliman

Rajasekaran

2021

Preprint

View full text Add to dashboard Cite

BackgroundAlzheimer’s disease (AD) is the most common form of dementia among older people. It is a complex disease and the genetics and environmental factors behind it are not conclusive yet. Traditional statistical analyses are inadequate to identify variants, genes, or pathways capable of explaining AD as a unit. In this context, pathway network analysis based on a set of curated AD-specific genes identified in the literature can elucidate biological mechanisms underneath AD. Through the network, we can infer influential pathways that can together explain AD. Consequently, we can target those pathways and corresponding genes for further analysis to develop new drugs, discover novel AD-related genes, combine multiple hypotheses, and so forth.MethodsWe have developed a novel graph theoretic algorithm that can elucidate complex biology from a given set of disease-related genes. It constructs a weighted network of enriched pathways where similarity score between a pair of pathways is defined in a context-specific manner. To make the network robust, we employ topological overlap techniques on top of the raw similarity measure. We then provide the importance of each pathway with respect to the entire network, functional modules and importance of each pathway in a specific module, gene clusters, and so forth. We also provide a method to identify a set of novel genes that can further explain the disease-related genes and the disease itself.ResultsWe have employed our algorithms onto a set of AD-specific genes. It identified three distinct functional modules that are related to metabolism, cancer, and infectious disease related pathways. These findings are matched with three recognized hypotheses in Alzheimer’s disease, e.g. “metabolism hypothesis,” “cell cycle hypothesis,” and “infectious disease hypothesis.” By analyzing the curated genes common among those functional modules, we can attain more understanding about this fateful disease. We have also identified 24 novel AD-related genes of which at least 14 genes are known to be involved in AD.ConclusionsWe developed a computational framework for analyzing biological pathways in a context-specific manner. It can be used in any sets of disease-related genes. We manifest its efficacy, reliability, and accuracy by employing a set of AD-specific genes.

show abstract

Topological impact of negative links on the stability of resting-state brain network

Cited by 38 publications

References 65 publications

Requirement to change of functional brain network across the lifespan

Requirement to change of functional brain network across the lifespan

A Regression Framework for Brain Network Distance Metrics

Novel graph theoretic biological pathway network analytics methods for analyzing and discovering Alzheimer’s disease related genes

Contact Info

Product

Resources

About