Network properties of healthy and Alzheimer brains

Coninck, José Carlos Pereira; Ferrari, Fernanda; Reis, Adriane S.; Iarosz, Kelly C.; Caldas, Iberê L.; Batista, Antônio M.; Viana, Ricardo L.

doi:10.1016/j.physa.2020.124475

Cited by 18 publications

(6 citation statements)

References 50 publications

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“…A positive assortativity coefficient indicates a network with a resilient core due to the interconnected nodes of high degree [124]. This measure was also associated with AD in several works [129,130] whose results showed an increase in the assortativity value in contrast to what was found here, where with the use of ayahuasca the assortativity value (median) decreased (see on the Figure 12-(b)). It should be noted that although the median value decreased, the upper confidence interval of the distribution increased.…”

Section: Complex Network Measuressupporting

confidence: 56%

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

Alves

Cury

Roster

et al. 2022

Preprint

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Ayahuasca is made from a mixture of Amazonian herbs and has been used for a few hundred years by the people of this region for traditional medicine. In addition, this plant has been shown to be a potential treatment for various neurological and psychiatric disorders. 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). 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 than connectivity changes within brain regions. The most activated areas were the frontal and temporal lobe, which is consistent with the literature. In terms of brain connections, the correlation between F3 and PO4 was the most important. This connection may point to a cognitive process similar 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. Overall, our results showed that machine learning methods were able to automatically detect changes in brain activity during ayahuasca consumption. The results also suggest that the application of machine learning and complex network measurements are useful methods to study the effects of ayahuasca on brain activity and medical use.

show abstract

Section: Complex Network Measuressupporting

confidence: 56%

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

Alves

Cury

Roster

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A positive assortativity coefficient indicates a network with a resilient core due to the interconnected nodes of high degree [128]. This measure was also associated with AD in several works [133,134] whose results showed an increase in the assortativity value in contrast to what was found here, where with the use of ayahuasca, the assortativity value (median) decreased (see on Fig 12). It should be noted that although the median value decreased, the upper confidence interval of the distribution increased.…”

Section: Complex Network Measuressupporting

confidence: 54%

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

et al. 2022

View full text Add to dashboard Cite

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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“…Graph theory serves as a foundational mathematical framework for examining graphs [37][38][39] , which are structures composed of nodes representing brain regions interconnected by edges signifying functional connectivity between these regions. We converted the undirected graphs into sparse ones by applying a threshold that retained the top 19% of the strongest weights 40,41 .…”

Section: Graph Theorymentioning

confidence: 99%

Between-networks hyperconnectivity is induced by beta-amyloid and may facilitate tau spread

Hojjati,

Butler,

Leon

et al. 2024

Preprint

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Alzheimer’s disease (AD) is characterized by the buildup of neurofibrillary tau tangles and beta-amyloid (Aβ) plaques. While it has been hypothesized that Aβ facilitates the spread of tau outside of the medial temporal lobe (MTL), the specific pathological processes and mechanisms by which this occurs remain poorly understood. Our study employed advanced neuroimaging techniques, integrating 18F-Florbetaben Aβ and 18F-MK6240 tau positron emission tomography (PET) with resting-state functional magnetic resonance imaging (rs-fMRI) to characterize these mechanisms in two distinct datasets, that included 481 healthy elderly subjects, 46 of whom came with longitudinal data. Our research highlighted an intricate internetwork relationship between Aβ and tau accumulation, across spatially distinct functional networks. Additionally, we observed compelling evidence supporting the existence of a compensatory mechanism triggered by Aβ accumulation, resulting in hyperconnectivity between functional networks. Finally, the longitudinal findings indicate that between-networks hyperconnectivity is associated with future tau elevation and mediates the relationship between cortical Aβ and early-stage tau. Understanding this early brain alteration in response to the accumulation of Aβ could guide treatments early in the disease course and potentially prevent future tau accumulation.

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Network properties of healthy and Alzheimer brains

Cited by 18 publications

References 50 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

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

Between-networks hyperconnectivity is induced by beta-amyloid and may facilitate tau spread

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