Bayesian Network Analysis Reveals Alterations to Default Mode Network Connectivity in Individuals at Risk for Alzheimer's Disease

Li, Rui; Yu, Jing; Zhang, Shouzi; Bao, Feng; Wang, Pengyun; Huang, Xin; Li, Juan

doi:10.1371/journal.pone.0082104

Cited by 32 publications

(35 citation statements)

References 72 publications

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“…In the present study, by referring to previous studies of the DMN (Buckner et al, 2008, 2009; Li et al, 2012, 2013), we divided regions in the network into hub regions including the PCC, MPFC, and IPC, and non-hub regions including the ITC and HC, and then examined the connections within the hub region subsystem, the non-hub region subsystem and the ones between them. The BN result of the DMN shows that, except for the interactions with each other, the ITC and HC do not receive any connections but only generate connections pointing to the PCC, MPFC and IPC during both conditions.…”

Section: Discussionmentioning

confidence: 99%

Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state

Yao

et al. 2014

Front. Comput. Neurosci.

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Functional magnetic resonance imaging (fMRI) studies have converged to reveal the default mode network (DMN), a constellation of regions that display co-activation during resting-state but co-deactivation during attention-demanding tasks in the brain. Here, we employed a Bayesian network (BN) analysis method to construct a directed effective connectivity model of the DMN and compared the organizational architecture and interregional directed connections under both resting-state and task-state. The analysis results indicated that the DMN was consistently organized into two closely interacting subsystems in both resting-state and task-state. The directed connections between DMN regions, however, changed significantly from the resting-state to task-state condition. The results suggest that the DMN intrinsically maintains a relatively stable structure whether at rest or performing tasks but has different information processing mechanisms under varied states.

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Section: Discussionmentioning

confidence: 99%

Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state

Yao

et al. 2014

Front. Comput. Neurosci.

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“…10B1). In addition to the posterior cingulate cortex (PCC) (Li et al, 2013), which was detected by both methods, our proposed method extracted activation from the dorsal/ventral medial prefrontal cortex and left/right inferior parietal lobule (Li et al, 2013). Details are displayed in Fig.…”

Section: Comparison With Magnitude-only Iva-glmentioning

confidence: 99%

Adaptive independent vector analysis for multi-subject complex-valued fMRI data

Kuang

Qiu

Xiao

et al. 2017

Journal of Neuroscience Methods

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3 Highlights:An adaptive IVA algorithm was proposed for multi-subject complex-valued fMRI data.An MGGD-based nonlinear function was exploited to match varying SCV distributions.The MGGD shape parameter was estimated using maximum likelihood estimation.Subspace de-noising, post-IVA phase de-noising, and noncircularity were utilized.Our method detected more contiguous activations than magnitude-only methods. 4 Abstract BackgroundComplex-valued fMRI data can provide additional insights beyond magnitude-only data. However, independent vector analysis (IVA), which has exhibited great potential for group analysis of magnitudeonly fMRI data, has rarely been applied to complex-valued fMRI data. The main challenges in this application include the extremely noisy nature and large variability of the source component vector (SCV) distribution. New MethodTo address these challenges, we propose an adaptive fixed-point IVA algorithm for analyzing multiplesubject complex-valued fMRI data. We exploited a multivariate generalized Gaussian distribution (MGGD)-based nonlinear function to match varying SCV distributions in which the MGGD shape parameter was estimated using maximum likelihood estimation. To achieve our de-noising goal, we updated the MGGD-based nonlinearity in the dominant SCV subspace, and employed a post-IVA denoising strategy based on phase information in the IVA estimates. We also incorporated the pseudocovariance matrix of fMRI data into the algorithm to emphasize the noncircularity of complex-valued fMRI sources. ResultsResults from simulated and experimental fMRI data demonstrated the efficacy of our method. Comparison with Existing Method(s)Our approach exhibited significant improvements over typical complex-valued IVA algorithms, especially during higher noise levels and larger spatial and temporal changes. As expected, the proposed complex-valued IVA algorithm detected more contiguous and reasonable activations than the magnitudeonly method for task-related (393%) and default mode (301%) spatial maps. ConclusionsThe proposed approach is suitable for decomposing multi-subject complex-valued fMRI data, and has great potential for capturing additional subject variability.

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“…Recently, resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to study MCI and AD (Allen et al, 2007a;Bai et al, 2011;Bai et al, 2008;Celone et al, 2006;Greicius et al, 2004;Li et al, 2013a;Liang et al, 2012;Qi et al, 2010;Sorg et al, 2007;Wang et al, 2006;Wang et al, 2011;Zhang et al, 2010). Many studies analyze rs-fMRI data by calculating the Pearson's correlation coefficient to define the functional connectivity of brain regions.…”

Section: Introductionmentioning

confidence: 99%

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI

Khazaee

Ebrahimzadeh

Babajani‐Feremi

2017

Behavioural Brain Research

130

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Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain networks in patients with AD and mild cognitive impairment (MCI), and provide an accurate algorithm for classification of these patients from healthy control subjects (HC) by using a graph theoretical approach and advanced machine learning methods. Multivariate Granger causality analysis was performed on resting-state functional magnetic resonance imaging (rs-fMRI) data of 34 AD, 89 MCI, and 45 HC to calculate various directed graph measures. The graph measures were used as the original feature set for the machine learning algorithm. Filter and wrapper feature selection methods were applied to the original feature set to select an optimal subset of features. An accuracy of 93.3% was achieved for classification of AD, MCI, and HC using the optimal features and the naïve Bayes classifier. We also performed a hub node analysis and found that the number of hubs in HC, MCI, and AD were 12, 10, and 9, respectively, suggesting that patients with AD experience disturbance of critical communication areas in their brain network as AD progresses. The findings of this study provide insight into the neurophysiological mechanisms underlying MCI and AD. The proposed classification method highlights the potential of directed graph measures of rs-fMRI data for identification of the early stage of AD.

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Bayesian Network Analysis Reveals Alterations to Default Mode Network Connectivity in Individuals at Risk for Alzheimer's Disease

Cited by 32 publications

References 72 publications

Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state

Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state

Adaptive independent vector analysis for multi-subject complex-valued fMRI data

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI

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