Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics

Nie, Yunlong; Opoku, Eugene A.; Yasmin, Laila; Song, Yin; Wang, Jie; Wu, Sidi; Scarapicchia, Vanessa; Gawryluk, Jodie R.; Wang, Liangliang; Cao, Jiguo; Nathoo, Farouk S.

doi:10.1515/sagmb-2019-0058

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…This data comprises longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) effective brain connectivity network and genetic study [26] data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The 111 subjects comprise 36 cognitively normal (CN), 63 mild cognitive impairment (MCI) and 12 Alzheimer's Disease (AD) subjects.…”

Section: Resting-state Effective Brain Connectivity and Genetic Datamentioning

confidence: 99%

Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application

Opoku

Ahmed

Nathoo

2021

Entropy

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In a host of business applications, biomedical and epidemiological studies, the problem of multicollinearity among predictor variables is a frequent issue in longitudinal data analysis for linear mixed models (LMM). We consider an efficient estimation strategy for high-dimensional data application, where the dimensions of the parameters are larger than the number of observations. In this paper, we are interested in estimating the fixed effects parameters of the LMM when it is assumed that some prior information is available in the form of linear restrictions on the parameters. We propose the pretest and shrinkage estimation strategies using the ridge full model as the base estimator. We establish the asymptotic distributional bias and risks of the suggested estimators and investigate their relative performance with respect to the ridge full model estimator. Furthermore, we compare the numerical performance of the LASSO-type estimators with the pretest and shrinkage ridge estimators. The methodology is investigated using simulation studies and then demonstrated on an application exploring how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease.

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Section: Resting-state Effective Brain Connectivity and Genetic Datamentioning

confidence: 99%

Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application

Opoku

Ahmed

Nathoo

2021

Entropy

Self Cite

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A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis

Dai

Zhou

et al. 2022

Phys Eng Sci Med

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Early detection of dementia with default-mode network effective connectivity

Ereira,

Waters,

Razi

et al. 2024

Nat. Mental Health

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Altered functional connectivity precedes structural brain changes and symptoms in dementia. Alzheimer’s disease is the largest contributor to dementia at the population level, and disrupts functional connectivity in the brain’s default-mode network (DMN). We investigated whether a neurobiological model of DMN effective connectivity could predict a future dementia diagnosis at the single-participant level. We applied spectral dynamic causal modeling to resting-state functional magnetic resonance imaging data in a nested case–control group from the UK Biobank, including 81 undiagnosed individuals who developed dementia up to nine years after imaging, and 1,030 matched controls. Dysconnectivity predicted both future dementia incidence (AUC = 0.82) and time to diagnosis (R = 0.53), outperforming models based on brain structure and functional connectivity. We also evaluated associations between DMN dysconnectivity and major risk factors for dementia, revealing strong relationships with polygenic risk for Alzheimer’s disease and social isolation. Neurobiological models of effective connectivity may facilitate early detection of dementia at population level, supporting rational deployment of targeted dementia-prevention strategies.

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Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics

Cited by 4 publications

References 39 publications

Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application

Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application

A strategy of model space search for dynamic causal modeling in task fMRI data exploratory analysis

Early detection of dementia with default-mode network effective connectivity

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