Hub distribution of the brain functional networks of newborns prenatally exposed to maternal depression and SSRI antidepressants

2019 ◽  
Vol 36 (8) ◽  
pp. 753-765 ◽  
Author(s):  
Naama Rotem‐Kohavi ◽  
Lynne J. Williams ◽  
Angela M. Muller ◽  
Hervé Abdi ◽  
Naznin Virji‐Babul ◽  
...  
Keyword(s):  
Maternal Depression ◽  
Functional Networks ◽  
Prenatally Exposed ◽  
Brain Functional Networks ◽  
The Brain

Windows Into the Brain: Functional Networks Informing Treatment in Late-Life Depression

2017 ◽  
Vol 25 (3) ◽  
pp. S14-S15
Author(s):  
Warren D. Taylor ◽  
Sara Weisenbach ◽  
Faith Gunning ◽  
Olu Ajilore
Keyword(s):  
Late Life ◽  
Functional Networks ◽  
Late Life Depression ◽  
Brain Functional Networks ◽  
The Brain

Self-Adaptive Skeleton Approaches to Detect Self-Organized Coalitions From Brain Functional Networks Through Probabilistic Mixture Models

2021 ◽  
Vol 15 (5) ◽  
pp. 1-26
Author(s):  
Kai Liu ◽  
Hongbo Liu ◽  
Tomas E. Ward ◽  
Hua Wang ◽  
Yu Yang ◽  
...  
Keyword(s):  
Mixture Models ◽  
Density Variation ◽  
Functional Networks ◽  
Enumeration Algorithm ◽  
Self Organized ◽  
Functional Relations ◽  
Brain Functional Networks ◽  
Clique Enumeration ◽  
The Brain ◽  
Self Adaptive

Detecting self-organized coalitions from functional networks is one of the most important ways to uncover functional mechanisms in the brain. Determining these raises well-known technical challenges in terms of scale imbalance, outliers and hard-examples. In this article, we propose a novel self-adaptive skeleton approach to detect coalitions through an approximation method based on probabilistic mixture models. The nodes in the networks are characterized in terms of robust k -order complete subgraphs ( k -clique ) as essential substructures. The k -clique enumeration algorithm quickly enumerates all k -cliques in a parallel manner for a given network. Then, the cliques, from max -clique down to min -clique, of each order k , are hierarchically embedded into a probabilistic mixture model. They are self-adapted to the corresponding structure density of coalitions in the brain functional networks through different order k . All the cliques are merged and evolved into robust skeletons to sustain each unbalanced coalition by eliminating outliers and separating overlaps. We call this the k -CLIque Merging Evolution (CLIME) algorithm. The experimental results illustrate that the proposed approaches are robust to density variation and coalition mixture and can enable the effective detection of coalitions from real brain functional networks. There exist potential cognitive functional relations between the regions of interest in the coalitions revealed by our methods, which suggests the approach can be usefully applied in neuroscientific studies.

scholarly journals Mental Fatigue Has Great Impact on the Fractal Dimension of Brain Functional Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Gang Li ◽  
Yanting Xu ◽  
Yonghua Jiang ◽  
Weidong Jiao ◽  
Wanxiu Xu ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Mental Fatigue ◽  
Functional Network ◽  
Functional Networks ◽  
Fractal Structures ◽  
Brain Functional Network ◽  
Brain Functional Networks ◽  
Negative Impacts ◽  
Binary Network ◽  
The Brain

Mental fatigue has serious negative impacts on the brain cognitive functions and has been widely explored by the means of brain functional networks with the neuroimaging technique of electroencephalogram (EEG). Recently, several researchers reported that brain functional network constructed from EEG signals has fractal feature, raising an important question: what are the effects of mental fatigue on the fractal dimension of brain functional network? In the present study, the EEG data of alpha1 rhythm (8-10 Hz) at task state obtained by a mental fatigue model were chosen to construct brain functional networks. A modified greedy colouring algorithm was proposed for fractal dimension calculation in both binary and weighted brain functional networks. The results indicate that brain functional networks still maintain fractal structures even when the brain is at fatigue state; fractal dimension presented an increasing trend along with the deepening of mental fatigue fractal dimension of the weighted network was more sensitive to mental fatigue than that of binary network. Our current results suggested that mental fatigue has great regular impacts on the fractal dimension in both binary and weighted brain functional networks.

scholarly journals Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison

2021 ◽  
Vol 11 (6) ◽  
pp. 735
Author(s):  
Ilinka Ivanoska ◽  
Kire Trivodaliev ◽  
Slobodan Kalajdziski ◽  
Massimiliano Zanin
Keyword(s):  
Machine Learning ◽  
Computational Cost ◽  
Learning Task ◽  
Functional Networks ◽  
Large Set ◽  
Selection Methods ◽  
Machine Learning Methods ◽  
Brain Functional Networks ◽  
The Cost ◽  
The Brain

Network-based representations have introduced a revolution in neuroscience, expanding the understanding of the brain from the activity of individual regions to the interactions between them. This augmented network view comes at the cost of high dimensionality, which hinders both our capacity of deciphering the main mechanisms behind pathologies, and the significance of any statistical and/or machine learning task used in processing this data. A link selection method, allowing to remove irrelevant connections in a given scenario, is an obvious solution that provides improved utilization of these network representations. In this contribution we review a large set of statistical and machine learning link selection methods and evaluate them on real brain functional networks. Results indicate that most methods perform in a qualitatively similar way, with NBS (Network Based Statistics) winning in terms of quantity of retained information, AnovaNet in terms of stability and ExT (Extra Trees) in terms of lower computational cost. While machine learning methods are conceptually more complex than statistical ones, they do not yield a clear advantage. At the same time, the high heterogeneity in the set of links retained by each method suggests that they are offering complementary views to the data. The implications of these results in neuroscience tasks are finally discussed.

scholarly journals Revealing the Relevant Spatiotemporal Scale Underlying Whole-Brain Dynamics

2021 ◽  
Vol 15 ◽  
Author(s):  
Xenia Kobeleva ◽  
Ane López-González ◽  
Morten L. Kringelbach ◽  
Gustavo Deco
Keyword(s):  
Spatial Scales ◽  
Brain Dynamics ◽  
Functional Networks ◽  
Whole Brain ◽  
Spatiotemporal Scale ◽  
Brain Functional Networks ◽  
New Information ◽  
Brain Modeling ◽  
Spatiotemporal Scales ◽  
The Brain

The brain rapidly processes and adapts to new information by dynamically transitioning between whole-brain functional networks. In this whole-brain modeling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100–900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify the richness of the dynamic repertoire at each spatiotemporal scale by computing the entropy of transitions between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 ms. Overall, this study provides much needed evidence for the relevant spatiotemporal scales and recommendations for analyses of brain dynamics.

scholarly journals The Brain Functional Networks Associated to Human and Animal Suffering Differ among Omnivores, Vegetarians and Vegans

PLoS ONE ◽  
2010 ◽  
Vol 5 (5) ◽  
pp. e10847 ◽  
Author(s):  
Massimo Filippi ◽  
Gianna Riccitelli ◽  
Andrea Falini ◽  
Francesco Di Salle ◽  
Patrik Vuilleumier ◽  
...  
Keyword(s):  
Functional Networks ◽  
Animal Suffering ◽  
Brain Functional Networks ◽  
The Brain

scholarly journals Correlation between the oral microbiome and brain resting state connectivity in smokers

2018 ◽  
Author(s):  
Dongdong Lin ◽  
Kent E. Hutchison ◽  
Salvador Portillo ◽  
Victor Vegara ◽  
Jarrod M. Ellingson ◽  
...  
Keyword(s):  
Microbial Community ◽  
Network Connectivity ◽  
Oral Microbiome ◽  
Functional Network ◽  
Functional Networks ◽  
Functional Network Connectivity ◽  
Brain Functional Network ◽  
Brain Functional Networks ◽  
The Brain

AbstractRecent studies have shown a critical role of the gastrointestinal microbiome in brain and behavior via the complex gut–microbiome–brain axis, however, the influence of the oral microbiome in neurological processes is much less studied, especially in response to the stimuli in the oral microenvironment such as smoking. Additionally, given the complex structural and functional networks in brain system, our knowledge about the relationship between microbiome and brain function in specific brain circuits is still very limited. In this pilot work, we leveraged next generation microbial sequencing with functional neuroimaging techniques to enable the delineation of microbiome-brain network links as well as their relationship to cigarette smoking. Thirty smokers and 30 age- and sex- matched non-smokers were recruited for measuring both microbial community and brain functional networks. Statistical analyses were performed to demonstrate the influence of smoking on the abundance of the constituents within the oral microbial community and functional network connectivity among brain regions as well as the associations between microbial shifts and the brain functional network connectivity alternations. Compared to non-smokers, we found a significant decrease in beta diversity (p = 6×10−3) in smokers and identified several classes (Betaproteobacteria, Spirochaetia, Synergistia, and Mollicutes) as having significant alterations in microbial abundance. Taxonomic analyses demonstrate that the microbiota with altered abundance are mainly involved in pathways related to cell processes, DNA repair, immune system, and neurotransmitters signaling. One brain functional network connectivity component was identified to have a significant difference between smokers and nonsmokers (p = 0.033), mainly including connectivity between brain default network and other task-positive networks. The brain functional component was also significantly associated with some smoking related oral microbiota, suggesting a potential link between smoking-induced oral microbiome dysbiosis and brain functional connectivity, possibly through immunological and neurotransmitter signaling pathways. This work is the first attempt to link oral microbiome and brain functional networks, and provides support for future work in characterizing the role of oral microbiome in mediating smoking effects on brain activity.

Functional networks of the brain: from connectivity restoration to dynamic integration

2020 ◽  
Author(s):  
Aleksandr E. Hramov ◽  
Nikita S. Frolov ◽  
Vladimir A. Maksimenko ◽  
Semen A. Kurkin ◽  
Viktor B. Kazantsev ◽  
...  
Keyword(s):  
Functional Networks ◽  
Connectivity Restoration ◽  
Dynamic Integration ◽  
The Brain
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