Alterations of Brain Networks in Alzheimer’s Disease and Mild Cognitive Impairment: A Resting State fMRI Study Based on a Population-specific Brain Template

Luo, Yuan; Sun, Tongtong; Ma, Chunchao; Zhang, Xianchang; Ji, Yong; Fu, Xiuwei; Ni, Hongbo

doi:10.1016/j.neuroscience.2020.10.023

Cited by 22 publications

(16 citation statements)

References 84 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: 57%

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.

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Section: Complex Network Measuressupporting

confidence: 57%

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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“…Elevated assortativity has been observed in neurodegenerative disorders including Alzheimer's disease 25 and frontotemporal dementia 26 . Here, we quantify this graph metric in speci c, previously validated disease networks identi ed in the resting state with either FDG PET or rs-fMRI 3,8,27 .…”

Section: Discussionmentioning

confidence: 99%

Adaptive and pathological connectivity responses in Parkinson’s disease brain networks

Schindlbeck

Nguyen

et al. 2022

Cerebral Cortex

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Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, whether the connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the 2 responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson’s disease (PD), network assortativity increased over time. Assoratitivty was high in clinically aggressive genetic variants but was low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Stereotyped changes in connectivity patterns underlie disease progression and treatment responses in PD networks.

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Section: Discussionmentioning

confidence: 99%

Adaptive and pathological connectivity responses in Parkinson’s disease brain networks

Schindlbeck

Nguyen

et al. 2021

Preprint

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Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, if connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the two responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson’s disease (PD), network assortativity increased with over time. Assoratitivty was high in clinically aggressive genetic variants, but low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Assortativity may therefore be useful in evaluating disease-modifying therapies.

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Alterations of Brain Networks in Alzheimer’s Disease and Mild Cognitive Impairment: A Resting State fMRI Study Based on a Population-specific Brain Template

Cited by 22 publications

References 84 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

Adaptive and pathological connectivity responses in Parkinson’s disease brain networks

Adaptive and pathological connectivity responses in Parkinson’s disease brain networks

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