Characterisation of Cognitive Activity Using Minimum Connected Component

Ramasamy, Vijayalakshmi; Nandagopal, D; Thilaga, M.; Cocks, Bernadine

doi:10.1007/978-3-319-26561-2_63

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Cognition is a mental process of transforming sensed information into action. Besides an active research in unveiling the abnormality of brain functioning in the literature, understanding of normal brain functioning has gained significant attention in addressing various mental health issues [1][2][3][4][5][6]. Characterization of the neuronal interaction patterns of the brain during different activities is a challenging problem and many studies have used graph theory as a tool to model and analyze the organizational principles of the brain to thereby understand the complexity of cognitive function.…”

Section: Introductionmentioning

confidence: 99%

Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks

Thilaga

Ramasamy

Nadarajan

et al. 2018

J. Integr. Neurosci.

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Understanding and analyzing the dynamic interactions among millions of spatially distributed and functionally connected regions in the human brain constituting a massively parallel communication system is one of the major challenges in computational neuroscience. Many studies in the recent past have employed graph theory to efficiently model, quantitatively analyze, and understand the brain's electrical activity. Since, the human brain is believed to broadcast information with reduced material and metabolic costs, identifying various brain regions in the shortest pathways of information dissemination becomes essential to understand the intricacies of brain function. This paper proposes a graph theoretic approach using the concept of shortest communication paths between various brain regions (electrode sites) to identify the significant communication pathways of information exchange between various nodes in the functional brain networks constructed from multi-channel electroencephalograph data. A special weighted network called the Shortest Path Network is constructed from a functional brain network where the edge weight is computed as the sum of frequency of occurrence of that edge in all possible shortest paths between every pair of electrodes. The weighted Shortest Path Networks thus constructed portray information on the number of times the edges are used in information propagation during different cognitive states. This network is further analyzed by computing the influential communication paths to characterize the information dissemination among brain regions during different cognitive load conditions. The experimental results presented demonstrate the efficacy of this novel graph theoretic approach in identifying, quantifying, and comparing the significant shortest pathways of information exchange during mild and heavy cognitive load conditions. Analysis also suggests that future research should consider the biological inferences of the ability of the human brain to use reduced material and metabolic cost during the instantaneous transmission of information.

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Section: Introductionmentioning

confidence: 99%

Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks

Thilaga

Ramasamy

Nadarajan

et al. 2018

J. Integr. Neurosci.

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Comparison of data-driven thresholding methods using directed functional brain networks

Manickam,

Ramasamy,

Doraisamy

2024

Reviews in the Neurosciences

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Over the past two centuries, intensive empirical research has been conducted on the human brain. As an electroencephalogram (EEG) records millisecond-to-millisecond changes in the electrical potentials of the brain, it has enormous potential for identifying useful information about neuronal transactions. The EEG data can be modelled as graphs by considering the electrode sites as nodes and the linear and nonlinear statistical dependencies among them as edges (with weights). The graph theoretical modelling of EEG data results in functional brain networks (FBNs), which are fully connected (complete) weighted undirected/directed networks. Since various brain regions are interconnected via sparse anatomical connections, the weak links can be filtered out from the fully connected networks using a process called thresholding. Multiple researchers in the past decades proposed many thresholding methods to gather more insights about the influential neuronal connections of FBNs. This paper reviews various thresholding methods used in the literature for FBN analysis. The analysis showed that data-driven methods are unbiased since no arbitrary user-specified threshold is required. The efficacy of four data-driven thresholding methods, namely minimum spanning tree (MST), minimum connected component (MCC), union of shortest path trees (USPT), and orthogonal minimum spanning tree (OMST), in characterizing cognitive behavior of the normal human brain is analysed using directed FBNs constructed from EEG data of different cognitive load states. The experimental results indicate that both MCC and OMST thresholding methods can detect cognitive load-induced changes in the directed functional brain networks.

show abstract

Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks

Thilaga

Ramasamy

Nadarajan

et al. 2018

JIN

View full text Add to dashboard Cite

Understanding and analyzing the dynamic interactions among millions of spatially distributed and functionally connected regions in the human brain constituting a massively parallel communication system is one of the major challenges in computational neuroscience. Many studies in the recent past have employed graph theory to efficiently model, quantitatively analyze, and understand the brain’s electrical activity. Since, the human brain is believed to broadcast information with reduced material and metabolic costs, identifying various brain regions in the shortest pathways of information dissemination becomes essential to understand the intricacies of brain function. This paper proposes a graph theoretic approach using the concept of shortest communication paths between various brain regions (electrode sites) to identify the significant communication pathways of information exchange between various nodes in the functional brain networks constructed from multi-channel electroencephalograph data. A special weighted network called the Shortest Path Network is constructed from a functional brain network where the edge weight is computed as the sum of frequency of occurrence of that edge in all possible shortest paths between every pair of electrodes. The weighted Shortest Path Networks thus constructed portray information on the number of times the edges are used in information propagation during different cognitive states. This network is further analyzed by computing the influential communication paths to characterize the information dissemination among brain regions during different cognitive load conditions. The experimental results presented demonstrate the efficacy of this novel graph theoretic approach in identifying, quantifying, and comparing the significant shortest pathways of information exchange during mild and heavy cognitive load conditions. Analysis also suggests that future research should consider the biological inferences of the ability of the human brain to use reduced material and metabolic cost during the instantaneous transmission of information.

show abstract

Characterisation of Cognitive Activity Using Minimum Connected Component

Cited by 3 publications

References 9 publications

Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks

Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks

Comparison of data-driven thresholding methods using directed functional brain networks

Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks

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