Community Structure Detection for the Functional Connectivity Networks of the Brain

Lung, Rodica Ioana; Suciu, Mihai Alexandru; Meszlényi, Regina; Búza, Krisztián; Gaskó, Noémi

doi:10.1007/978-3-319-45823-6_59

Cited by 2 publications

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“…Furthermore, the community detection algorithm (also referred to as the clustering graph) is a fundamental analysis technique that aims to identify densely connected structures within complex networks [28][29][30]. Several studies have used complex network measurements and community detection algorithms to detect brain activity in EEG data recently [31][32][33] Because of the increased amount of data related to health, such as medical records, exams of patients, and hospital resources, machine learning (ML) algorithms have become more applicable, primarily for medical diagnosis [34][35][36][37], in order to provide more accurate and automatic investigations of various diseases [38] and may be an important tool capable of detecting acute and permanent abnormalities in the brain. In addition, many studies have utilized machine learning algorithms to capture brain activity using raw EEG time series [39,40], the correlation between electrodes [41,42], and complex network measures [23].…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

et al. 2022

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Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by the inhabitants of this region for hundreds of years. Furthermore, this plant has been demonstrated to be a viable therapy for a variety of neurological and mental diseases. EEG experiments have found specific brain regions that changed significantly due to ayahuasca. Here, we used an EEG dataset to investigate the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures calculated from (B). Further, at the abstraction level of (C), we developed new measures of complex networks relating to community detection. As a result, the machine learning method was able to automatically detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are more important for the detection of ayahuasca. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. F3 and PO4 were the most important brain connections, a significant new discovery for psychedelic literature. This connection may point to a cognitive process akin to face recognition in individuals during ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assortativity were the most important complex network measures. These two measures are also associated with diseases such as Alzheimer’s disease, indicating a possible therapeutic mechanism. Moreover, the new measures were crucial to the predictive model and suggested larger brain communities associated with the use of ayahuasca. This suggests that the dissemination of information in functional brain networks is slower when this drug is present. Overall, our methodology was able to automatically detect changes in brain activity during ayahuasca consumption and interpret how these psychedelics alter brain networks, as well as provide insights into their mechanisms of action.

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Section: Introductionmentioning

confidence: 99%

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

et al. 2022

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“…Several studies have used complex network measurements and community detection algorithms to detect brain activity in EEG data recently 58 Chapter 3. Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments (LUNG et al, 2016;KHAJEHPOUR et al, 2019;VARLEY et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

On the application of Machine Learning and Complex Networks to Neuroscience

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In the midst of hate, I found there was, within me, an invincible love. In the midst of tears, I found there was, within me, an invincible smile. In the midst of chaos, I found there was, within me, an invincible calm.I realized, through it all, that. . . In the midst of winter, I found there was, within me, an invincible summer.And that makes me happy. For it says that no matter how hard the world pushes against me, within me, there's something stronger -something better, pushing right back. " (Albert Camus)

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Community Structure Detection for the Functional Connectivity Networks of the Brain

Cited by 2 publications

References 14 publications

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

On the application of Machine Learning and Complex Networks to Neuroscience

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