A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations

Noro, Yusuke; Li, Ruixiang; Matsui, Teppei; Jimura, Koji

doi:10.3389/fninf.2022.960607

Cited by 2 publications

(2 citation statements)

References 94 publications

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“…We however failed to find differences between the groups using variance and skewness measures. Actually, such a negative result aligns with the previous studies that showed static FC can explain most of the difference in connectivity between groups 54,56 ; yet, a small proportion of data variance cannot be extracted by static FC. Indeed, we found that kurtosis reflected a difference between iRBD and HC.…”

Section: Utility Of Dfc Metricessupporting

confidence: 89%

Dynamic and topological properties of large-scale brain networks in rapid eye movement behavior disorder

Li,

Yoshinaga,

Hanakawa

2024

Preprint

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Introduction: There is a lack of research in the existing literature when it comes to analyzing the dynamics of resting-state functional magnetic resonance imaging to understand the underlying mechanisms of isolated rapid eye movement sleep behavior disorder (iRBD). This study aims to contribute to our understanding of abnormalities in brain network dynamics in iRBD and their association with alpha-synucleinopathy. Additionally, I employed graph theoretical metrics to obtain a topological insight into the brain network of iRBD. Methods: Resting-state fMRI data from 55 iRBD patients and 97 healthy controls (HCs) were utilized. A sliding window approach, functional connectivity analysis, and graph theory analysis were applied to the data. I calculated the mean, standard deviation, skewness, and kurtosis of the time series for both dynamic functional connectivity (dFC) and four graph metrics (clustering coefficient, global efficiency, assortativity coefficients, and eigenvector centrality). Subsequently, I compared the those metrices between iRBDs and HCs. Relationships between clinical scales and abnormal dFC were assessed using a general linear model. Results: iRBD patients exhibited abnormal mean dFC, particularly in the default mode network, sensorimotor network, basal ganglia network, and cerebellum. Kurtosis of dFC revealed abnormalities between the middle temporal gyrus and cerebellum. Group differences were also observed in the mean eigenvector centrality of the precentral gyrus and thalamus. Conclusion: The mean of dFC identified impairments putatively in movement functions and various compensatory mechanisms. Moreover, mean eigenvector centrality revealed topological changes in motor-related network in iRBDs. The use of kurtosis as a potential index for extracting dynamic information may provide additional insights into pathophysiology in iRBDs.

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Section: Utility Of Dfc Metricessupporting

confidence: 89%

Dynamic and topological properties of large-scale brain networks in rapid eye movement behavior disorder

Li,

Yoshinaga,

Hanakawa

2024

Preprint

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“…Brain activity in the resting state, as measured by fMRI, is widely investigated for its potential applications in the non-invasive diagnosis of neuropsychiatric and neurological disorders (1). A common assumption regarding the dynamics of resting-brain activity is that it can be explained by transitions between multiple brain states (2, 3). Recent studies have reported that dynamic features (e.g., brain states) extracted from measured resting-state brain activity can better explain subject-specific phenotypes (e.g., fluid intelligence score) than static features (4, 5).…”

Section: Introductionmentioning

confidence: 99%

Linear models replicate the energy landscape and dynamics of resting-state brain activity

Hosaka,

Hieda,

Hayashi

et al. 2024

Preprint

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The brain is constantly and spontaneously active even in the absence of sensory stimulation or motor tasks. The spatiotemporal dynamics of this resting-state brain activity are commonly assumed to be non-stationary and non-linear. It has been proposed that dynamic features (e.g., brain states) extracted from measured resting-state brain activity can better explain subject-specific phenotypes (e.g., fluid intelligence score) than static features. However, several recent studies reported that some analysis methods used to extract dynamic features from resting-brain activity as measured by functional magnetic resonance imaging (fMRI) do not truly reflect non-stationarity and/or non-linear statistical properties of the data. In this study, we examined energy landscape analysis (ELA) to assess whether dynamic features extracted by ELA accurately represent the non-stationary and/or non-linear aspects of resting-brain fMRI activity. To specify the statistical properties being extracted, we applied ELA to both real fMRI data and surrogate data generated by linear and stationary models. We found that ELA results obtained with both the real and simulated data were almost indistinguishable, suggesting that the features extracted by ELA can be explained by covariance and autocorrelation structures. These results corroborate a recent proposal that resting-state fMRI activity can be adequately described by linear models.

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A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations

Cited by 2 publications

References 94 publications

Dynamic and topological properties of large-scale brain networks in rapid eye movement behavior disorder

Dynamic and topological properties of large-scale brain networks in rapid eye movement behavior disorder

Linear models replicate the energy landscape and dynamics of resting-state brain activity

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