Efficient small-world and scale-free functional brain networks at rest using k-nearest neighbors thresholding

AbstractIn recent years, there has been a massive effort to analyze the topological properties of brain networks. Yet, one of the challenging questions in the field is how to construct brain networks based on the connectivity values derived from neuroimaging methods. From a theoretical point of view, it is plausible that the brain would have evolved to minimize energetic costs of information processing, and therefore, maximizes efficiency as well as to redirect its function in an adaptive fashion, that is, resilience. A brain network with such features, when characterized using graph analysis, would present small-world and scale-free properties.In this paper, we focused on how the brain network is constructed by introducing and testing an alternative method: k-nearest neighbor (kNN). In addition, we compared the kNN method with one of the most common methods in neuroscience: namely the density threshold. We performed our analyses on functional connectivity matrices derived from resting state fMRI of a big imaging cohort (N=434) of young and older healthy participants. The topology of networks was characterized by the graph measures degree, characteristic path length, clustering coefficient and small world. In addition, we verified whether kNN produces scale-free networks. We showed that networks built by kNN presented advantages over traditional thresholding methods, namely greater values for small-world (linked to efficiency of networks) than those derived by means of density thresholds and moreover, it presented also scale-free properties (linked to the resilience of networks), where density threshold did not. A brain network with such properties would have advantages in terms of efficiency, rapid adaptive reconfiguration and resilience, features of brain networks that are relevant for plasticity and cognition as well as neurological diseases as stroke and dementia.HighlightsA novel thresholding method for brain networks based on k-nearest neighbors (kNN)kNN applied on resting state fMRI from a big cohort of healthy subjects BASE-IIkNN built networks present greater small world properties than density thresholdkNN built networks present scale-free properties whereas density threshold did not

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Hyperbolic disc embedding of functional human brain connectomes using resting state fMRI

10.1101/2021.03.25.436730 ◽

2021 ◽

Author(s):

Wonseok Whi ◽

Seunggyun Ha ◽

Hyejin Kang ◽

Dong Soo Lee

Keyword(s):

Resting State ◽

Brain Networks ◽

Small World ◽

Resting State Fmri ◽

Hyperbolic Distance ◽

Functional Brain ◽

Scale Free ◽

Functional Brain Networks ◽

Individual Scale ◽

The Brain

The brain presents a real complex network of modular, small-world, and hierarchical nature, which are features of non-Euclidean geometry. Using resting-state functional magnetic resonance imaging (rs-fMRI), we constructed a scale-free binary graph for each subject, using internodal time-series correlation of regions-of-interest (ROIs) as a proximity measure. The resulted network could be embedded onto manifolds of various curvature and dimensions. While maintaining the fidelity of embedding (low distortion, high mean average precision), functional brain networks were found to be best represented in the hyperbolic disc. Using a popularity-similarity optimization model (PSOM) on the hyperbolic plane, we reduced the dimension of the network into 2-D hyperbolic space and were able to efficiently visualize the internodal connections of the brain, preserving proximity as distances and angles on the PSOM discs. Each individual PSOM disc revealed decentralized nature of information flow and anatomic relevance. Using the hyperbolic distance on the PSOM disc, we could detect the anomaly of network in autistic spectrum disorder (ASD) subjects. This procedure of embedding grants us a reliable new framework for studying functional brain networks and the possibility of detecting anomalies of the network in the hyperbolic disc on an individual scale.

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Scale-Free Exponents of Resting State are Biomarkers of Neuro-Typical and Atypical Brain Activity

10.1101/068841 ◽

2016 ◽

Author(s):

S.J. Hanson ◽

D. Mastrovito ◽

C. Hanson ◽

J. Ramsey ◽

C. Glymour

Keyword(s):

Network Structure ◽

Resting State ◽

Fault Tolerant ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Network ◽

Single Individual ◽

Scale Free ◽

Scale Free Networks ◽

The Brain

AbstractScale-free networks (SFN) arise from simple growth processes, which can encourage efficient, centralized and fault tolerant communication (1). Recently its been shown that stable network hub structure is governed by a phase transition at exponents (>2.0) causing a dramatic change in network structure including a loss of global connectivity, an increasing minimum dominating node set, and a shift towards increasing connectivity growth compared to node growth. Is this SFN shift identifiable in atypical brain activity? The Pareto Distribution (P(D)∼D∧-β) on the hub Degree (D) is a signature of scale-free networks. During resting-state, we assess Degree exponents across a large range of neurotypical and atypical subjects. We use graph complexity theory to provide a predictive theory of the brain network structure. Results.We show that neurotypical resting-state fMRI brain activity possess scale-free Pareto exponents (1.8 se .01) in a single individual scanned over 66 days as well as in 60 different individuals (1.8 se .02). We also show that 60 individuals with Autistic Spectrum Disorder, and 60 individuals with Schizophrenia have significantly higher (>2.0) scale-free exponents (2.4 se .03, 2.3 se .04), indicating more fractionated and less controllable dynamics in the brain networks revealed in resting state. Finally we show that the exponent values vary with phenotypic measures of atypical disease severity indicating that the global topology of the network itself can provide specific diagnostic biomarkers for atypical brain activity.

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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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Brain network dynamics correlate with personality traits

10.1101/702266 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aya Kabbara ◽

Veronique Paban ◽

Arnaud Weill ◽

Julien Modolo ◽

Mahmoud Hassan

Keyword(s):

Functional Connectivity ◽

Personality Traits ◽

Resting State ◽

Brain Networks ◽

Neural Substrates ◽

Brain Network ◽

Functional Brain ◽

Human Connectome Project ◽

Functional Brain Networks ◽

The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

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Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

Neural Plasticity ◽

10.1155/2018/5340717 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Anna Lardone ◽

Marianna Liparoti ◽

Pierpaolo Sorrentino ◽

Rosaria Rucco ◽

Francesca Jacini ◽

...

Keyword(s):

Resting State ◽

Mindfulness Meditation ◽

Brain Connectivity ◽

Brain Networks ◽

Brain Network ◽

Long Term Effects ◽

Age Related ◽

Functional Topology ◽

The Right ◽

The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

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Exploring the brain network: A review on resting-state fMRI functional connectivity

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2010.03.008 ◽

2010 ◽

Vol 20 (8) ◽

pp. 519-534 ◽

Cited By ~ 1529

Author(s):

Martijn P. van den Heuvel ◽

Hilleke E. Hulshoff Pol

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Brain Network ◽

The Brain

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Loss of integration of brain networks after sleep deprivation relates to the worsening of cognitive functions

10.1101/2020.07.15.197590 ◽

2020 ◽

Author(s):

Pesoli Matteo ◽

Rucco Rosaria ◽

Liparoti Marianna ◽

Lardone Anna ◽

D’Aurizio Giula ◽

...

Keyword(s):

Sleep Deprivation ◽

Resting State ◽

Large Scale ◽

Brain Networks ◽

Brain Network ◽

Cognitive Tasks ◽

Functional Networks ◽

Long Sleep ◽

Before And After ◽

The Brain

AbstractThe topology of brain networks changes according to environmental demands and can be described within the framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty-two young men underwent resting-state MEG recording and assessments of attention and switching abilities before and after SD. We found loss of integration of brain network and a worsening of attention but not of switching abilities. These results show that brain network changes due to SD affect switching abilities, worsened attention and induce large-scale rearrangements in the functional networks.

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Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

ACM Transactions on Design Automation of Electronic Systems ◽

10.1145/3480961 ◽

2022 ◽

Vol 27 (1) ◽

pp. 1-30

Author(s):

Mengke Ge ◽

Xiaobing Ni ◽

Xu Qi ◽

Song Chen ◽

Jinglei Huang ◽

...

Keyword(s):

Large Scale ◽

Small World ◽

Brain Network ◽

Graph Processing ◽

Hop Count ◽

Scale Free ◽

Large Scale Network ◽

Average Latency ◽

Scale Network ◽

The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

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A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD

Neural Plasticity ◽

10.1155/2020/9436406 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Hao Liu ◽

Haimeng Hu ◽

Huiying Wang ◽

Jiahui Han ◽

Yunfei Li ◽

...

Keyword(s):

Regression Model ◽

Pearson Correlation ◽

Brain Networks ◽

Small World ◽

Brain Network ◽

Clustering Coefficient ◽

Elderly Subjects ◽

Small World Network ◽

L1 Norm ◽

The Brain

Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-world attributes of the brain networks of three groups of patients, namely, those with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and healthy controls (HCs); we attempted to identify the method that may detect minor differences in MCI and AD patients. Twenty-four AD patients, 33 MCI patients, and 27 HC elderly subjects were subjected to functional MRI (fMRI). We applied traditional Pearson correlation and the L1-norm to construct the brain networks and then tested the small-world attributes by calculating the following parameters: clustering coefficient (Cp), path length (Lp), global efficiency (Eg), and local efficiency (Eloc). As expected, L1 could detect slight changes, mainly in MCI patients expressing higher Cp and Eloc; however, no statistical differences were found between MCI patients and HCs in terms of Cp, Lp, Eg, and Eloc, using Pearson correlation. Compared with HCs, AD patients expressed a lower Cp, Eloc, and Lp and an increased Eg using both connectivity metrics. The statistical differences between the groups indicated the brain networks constructed by the L1-norm were more sensitive to detect slight small-world network changes in early stages of AD.

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