scholarly journals Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8305
Author(s):  
César Covantes-Osuna ◽  
Jhonatan B. López ◽  
Omar Paredes ◽  
Hugo Vélez-Pérez ◽  
Rebeca Romo-Vázquez
Keyword(s):  
Motor Imagery ◽  
Brain Connectivity ◽  
Single Layer ◽  
Network Models ◽  
Brain Regions ◽  
Adaptive Threshold ◽  
Brain Network ◽  
Multilayer Network ◽  
Brain Models ◽  
The Brain

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

scholarly journals Detecting brain connectivity changes during seizures using Granger causality measures on scalp EEG

2015 ◽  
Author(s):  
Christos Koutlis
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Window ◽  
Brain Connectivity ◽  
Characteristic Curve ◽  
Multivariate Time Series ◽  
Brain Regions ◽  
Brain Network ◽  
Network Feature ◽  
The Brain

In this work the objective is to detect brain connectivity changes during epileptic seizures using methods of multivariate time series analysis on scalp multi-channel EEG. Different brain regions represented by the electrode positions interact in terms of Granger causality and these directed connections formulate the brain network at a certain time window. The numerous proposed network features are believed to capture the information of many network characteristics. The ability of a single network feature of the brain network to detect the transition of brain activity from preictal to ictal is examined. The connectivity of the brain is estimated by 13 Granger causality indices on 7 epochs from multivariate time series (19 channels per epoch) at 15 time windows of 20 seconds (5 min in total) before seizure and during the seizure. The characteristics of the networks are estimated by 379 network features. Finally, the discrimination task (preictal vs. ictal) for each network feature is evaluated by the area under receiver operating characteristic curve (AUROC).

Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

2021 ◽  
pp. 2250029
Author(s):  
Geng Zhang ◽  
Qi Zhu ◽  
Jing Yang ◽  
Ruting Xu ◽  
Zhiqiang Zhang ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Medical Image Analysis ◽  
Brain Regions ◽  
Brain Network ◽  
Brain Diseases ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Structural Brain Network ◽  
The Brain ◽  
Structural Brain

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

scholarly journals Quantifying structural connectivity in brain tumor patients

2021 ◽  
Author(s):  
Yiran Wei ◽  
Chao Li ◽  
Stephen John Price
Keyword(s):  
Brain Tumor ◽  
White Matter ◽  
Robust Estimation ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Mass Effect ◽  
Brain Regions ◽  
Brain Network ◽  
Tumor Patients ◽  
The Brain

AbstractBrain tumors are characterised by infiltration along the white matter tracts, posing significant challenges to precise treatment. Mounting evidence shows that an infiltrating tumor can interfere with the brain network diffusely. Therefore, quantifying structural connectivity has potential to identify tumor invasion and stratify patients more accurately. The tract-based statistics (TBSS) is widely used to measure the white matter integrity. This voxel-wise method, however, cannot directly quantify the connectivity of brain regions. Tractography is a fiber tracking approach, which has been widely used to quantify brain connectivity. However, the performance of tractography on the brain with tumors is complicated by the tumor mass effect. A robust method of quantifying the structural connectivity strength in brain tumor patients is still lacking.Here we propose a method which could provide robust estimation of tract strength for brain tumor patients. Specifically, we firstly construct an unbiased tract template in healthy subjects using tractography. The voxel projection of TBSS is employed to quantify the tract connectivity in patients, using the location of each tract fiber from the template. To further improve the standard TBSS, we propose an approach of iterative projection of tract voxels guided by the tract orientation measured using the voxel-wise eigenvectors. Compared to state-of-the-art tractography, our approach is more sensitive in reflecting more functional relevance. Further, the different extent of network disruption revealed by our approach correspond to the clinical prior knowledge of tumor histology. The proposed method could provide a robust estimation of the structural connectivity for brain tumor patients.

scholarly journals A Network Flow-based Analysis of Cognitive Reserve in Normal Ageing and Alzheimer’s Disease

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Sang Wook Yoo ◽  
Cheol E. Han ◽  
Joseph S. Shin ◽  
Sang Won Seo ◽  
Duk L. Na ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
High Education ◽  
Network Flow ◽  
Cognitive Reserve ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Education Levels ◽  
The Brain ◽  
Structural Brain

Abstract Cognitive reserve is the ability to sustain cognitive function even with a certain amount of brain damages. Here we investigate the neural compensation mechanism of cognitive reserve from the perspective of structural brain connectivity. Our goal was to show that normal people with high education levels (i.e., cognitive reserve) maintain abundant pathways connecting any two brain regions, providing better compensation or resilience after brain damage. Accordingly, patients with high education levels show more deterioration in structural brain connectivity than those with low education levels before symptoms of Alzheimer’s disease (AD) become apparent. To test this hypothesis, we use network flow measuring the number of alternative paths between two brain regions in the brain network. The experimental results show that for normal aging, education strengthens network reliability, as measured through flow values, in a subnetwork centered at the supramarginal gyrus. For AD, a subnetwork centered at the left middle frontal gyrus shows a negative correlation between flow and education, which implies more collapse in structural brain connectivity for highly educated patients. We conclude that cognitive reserve may come from the ability of network reorganization to secure the information flow within the brain network, therefore making it more resistant to disease progress.

scholarly journals Deep Neural Networks Carve the Brain at its Joints

2020 ◽  
Author(s):  
Maxwell A. Bertolero ◽  
Danielle S. Bassett
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Linear Models ◽  
Brain Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Linear Functions ◽  
Complex Relationships ◽  
And Behavior ◽  
The Brain

AbstractHow an individual’s unique brain connectivity determines that individual’s cognition, behavior, and risk for pathology is a fundamental question in basic and clinical neuroscience. In seeking answers, many have turned to machine learning, with some noting the particular promise of deep neural networks in modelling complex non-linear functions. However, it is not clear that complex functions actually exist between brain connectivity and behavior, and thus if deep neural networks necessarily outperform simpler linear models, or if their results would be interpretable. Here we show that, across 52 subject measures of cognition and behavior, deep neural networks fit to each brain region’s connectivity outperform linear regression, particularly for the brain’s connector hubs—regions with diverse brain connectivity—whereas the two approaches perform similarly when fit to brain systems. Critically, averaging deep neural network predictions across brain regions results in the most accurate predictions, demonstrating the ability of deep neural networks to easily model the various functions that exists between regional brain connectivity and behavior, carving the brain at its joints. Finally, we shine light into the black box of deep neural networks using multislice network models. We determined that the relationship between connector hubs and behavior is best captured by modular deep neural networks. Our results demonstrate that both simple and complex relationships exist between brain connectivity and behavior, and that deep neural networks can fit both. Moreover, deep neural networks are particularly powerful when they are first fit to the various functions of a system independently and then combined. Finally, deep neural networks are interpretable when their architectures are structurally characterized using multislice network models.

scholarly journals A Computational Network Control Theory Analysis of Depression Symptoms

2018 ◽  
Vol 1 ◽  
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.

scholarly journals Category representations in the brain are both discretely localized and widely distributed

2018 ◽  
Vol 119 (6) ◽  
pp. 2256-2264 ◽  
Author(s):  
Zarrar Shehzad ◽  
Gregory McCarthy
Keyword(s):  
Functional Mri ◽  
Brain Connectivity ◽  
Strong Support ◽  
Brain Regions ◽  
Distributed Representations ◽  
Domain Specific ◽  
Category Information ◽  
Shared Information ◽  
Unique Domain ◽  
The Brain

Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.

scholarly journals Percolation Analysis of Brain Structural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Shu Guo ◽  
Xiaoqi Chen ◽  
Yimeng Liu ◽  
Rui Kang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Failure Modes ◽  
Critical Infrastructure ◽  
Network Reliability ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Features ◽  
Brain Network ◽  
Structure Design ◽  
Reliability Design ◽  
The Brain

The brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation, and maintenance, we use the percolation methods of network reliability on brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core-based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown to be related to brain disorders, as well as the reliability design of other technological networks.

scholarly journals A Graphlet-Based Topological Characterization of the Resting-State Network in Healthy People

2021 ◽  
Vol 15 ◽  
Author(s):  
Paolo Finotelli ◽  
Carlo Piccardi ◽  
Edie Miglio ◽  
Paolo Dulio
Keyword(s):  
Resting State ◽  
Brain Regions ◽  
Brain Network ◽  
Induced Subgraphs ◽  
Topological Characterization ◽  
Network Information ◽  
The Subject ◽  
Euclidean Distances ◽  
The Brain

In this paper, we propose a graphlet-based topological algorithm for the investigation of the brain network at resting state (RS). To this aim, we model the brain as a graph, where (labeled) nodes correspond to specific cerebral areas and links are weighted connections determined by the intensity of the functional magnetic resonance imaging (fMRI). Then, we select a number of working graphlets, namely, connected and non-isomorphic induced subgraphs. We compute, for each labeled node, its Graphlet Degree Vector (GDV), which allows us to associate a GDV matrix to each one of the 133 subjects of the considered sample, reporting how many times each node of the atlas “touches” the independent orbits defined by the graphlet set. We focus on the 56 independent columns (i.e., non-redundant orbits) of the GDV matrices. By aggregating their count all over the 133 subjects and then by sorting each column independently, we obtain a sorted node table, whose top-level entries highlight the nodes (i.e., brain regions) most frequently touching each of the 56 independent graphlet orbits. Then, by pairwise comparing the columns of the sorted node table in the top-k entries for various values of k, we identify sets of nodes that are consistently involved with high frequency in the 56 independent graphlet orbits all over the 133 subjects. It turns out that these sets consist of labeled nodes directly belonging to the default mode network (DMN) or strongly interacting with it at the RS, indicating that graphlet analysis provides a viable tool for the topological characterization of such brain regions. We finally provide a validation of the graphlet approach by testing its power in catching network differences. To this aim, we encode in a Graphlet Correlation Matrix (GCM) the network information associated with each subject then construct a subject-to-subject Graphlet Correlation Distance (GCD) matrix based on the Euclidean distances between all possible pairs of GCM. The analysis of the clusters induced by the GCD matrix shows a clear separation of the subjects in two groups, whose relationship with the subject characteristics is investigated.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

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