Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis

Wang, Chaoyan; Yang, Ling; Yi, Lin; Wang, Caihong; Tian, Peng

doi:10.3389/fnint.2022.922577

Cited by 3 publications

(2 citation statements)

References 70 publications

(96 reference statements)

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“…Altered rsFC in the SMN could potentially contribute to these sensory processing differences. Moreover, altered rsFC in SMN may contribute to the motor coordination difficulties in individuals with ASD ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder

Kim,

Ravid,

Zheng

et al. 2024

Front. Psychiatry

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BackgroundResting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD).MethodsOne-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures.ResultsWe quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2.ConclusionThis study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

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Section: Discussionmentioning

confidence: 99%

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder

Kim,

Ravid,

Zheng

et al. 2024

Front. Psychiatry

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show abstract

Section: Discussionmentioning

confidence: 99%

Explaining Deep Learning-Based Representations of Resting State Functional Connectivity Data: Focusing on Interpreting Nonlinear Patterns in Autism Spectrum Disorder

Kim,

Ravid,

Zhang

et al. 2023

Preprint

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BackgroundResting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used to study brain function in psychiatric disorders, yielding insight into brain organization. However, the high dimensionality of the rs-fMRI data presents challenges, and requires dimensionality reduction before applying machine learning techniques. Neural networks, specifically variational autoencoders (VAEs), have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity patterns, addressing the complex nonlinear structure of rs-fMRI. However, interpreting those latent representations remains a challenge. This paper aims to address this gap by creating explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD).MethodsOne-thousand one hundred and fifty participants (601 HC and 549 patients with ASD) were included in the analysis. We extracted functional connectivity correlation matrices from the preprocessed rs-fMRI data using Power atlas with 264 ROIs. Then VAEs were trained in an unsupervised fashion. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures.ResultsWe quantified the latent contribution scores for the ASD and control groups at the network level. We found that both ASD and control groups share the top network connectivity that contribute to all estimated latent components. For example, latent 0 was driven by resting state functional connectivity patterns (rsFC) within ventral attention network in both the ASD and control. However, significant differences in the latent contribution scores between the ASD and control groups were discovered within the ventral attention network in latent 0 and the sensory/somatomotor network in latent 2.ConclusionThis study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC features as estimated latent representation changes, enabling an explainable deep learning model to better understand the underlying neural mechanism of ASD.

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Alterations of resting-state network dynamics in Alzheimer’s disease based on leading eigenvector dynamics analysis

Yang,

Liu,

Wei

et al. 2024

Journal of Neurophysiology

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Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called Leading Eigenvector Dynamics Analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, inter group differences in brain dynamic activity indicators are examined. And the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual regions activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state, and more active in limbic regions activity state and visual regions activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.

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Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis

Cited by 3 publications

References 70 publications

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder

Explaining Deep Learning-Based Representations of Resting State Functional Connectivity Data: Focusing on Interpreting Nonlinear Patterns in Autism Spectrum Disorder

Alterations of resting-state network dynamics in Alzheimer’s disease based on leading eigenvector dynamics analysis

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