Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease

Haan, Willem de; Flier, Wiesje M. van der; Koene, Teddy; Smits, Lieke; Scheltens, Philip; Stam, Cornelis J.

doi:10.1016/j.neuroimage.2011.11.055

Cited by 210 publications

(151 citation statements)

References 75 publications

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“…Hierarchical clustering analysis of the MST showed that the connections between the parahippocampal gyrus and posterior cingulate gyrus were disrupted in Alzheimer patients [42]. Impaired communication between functional clusters in AD was found in one MEG study [46]. Alexander-Bloch et al [47] found disrupted modularity and local connectivity of the brain functional networks in childhood-onset schizophrenia using hierarchical clustering analysis of the MST.…”

Section: Complexitymentioning

confidence: 99%

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Jing

et al. 2017

Complexity

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A large number of studies demonstrated that major depressive disorder (MDD) is characterized by the alterations in brain functional connections which is also identifiable during the brain's "resting-state." But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST) analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG) sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls.

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Section: Complexitymentioning

confidence: 99%

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Jing

et al. 2017

Complexity

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“…The Local Research Ethics Committee approved the study, and all participants provided written informed consent. The subjects and recordings were identical to a recent graph theoretical study focusing on modularity (de Haan et al, 2012).…”

Section: Patients and Controlsmentioning

confidence: 99%

Disruption of Functional Brain Networks in Alzheimer's Disease: What Can We Learn from Graph Spectral Analysis of Resting-State Magnetoencephalography?

et al. 2012

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In Alzheimer's disease (AD), structural and functional brain network organization is disturbed. However, many of the present network analysis measures require a priori assumptions and methodological choices that influence outcomes and interpretations. Graph spectral analysis (GSA) is a more direct algebraic method that describes network properties, which might lead to more reliable results. In this study, GSA was applied to magnetoencephalography (MEG) data to explore functional network integrity in AD. Sensor-level resting-state MEG was performed in 18 Alzheimer patients (age 67 -9, 6 women) and 18 healthy controls (age 66 -9, 11 women). Weighted, undirected graphs were constructed based on functional connectivity analysis using the Synchronization likelihood, and GSA was performed with a focus on network connectivity, synchronizability, and node centrality. The main outcomes were a global loss of network connectivity and altered synchronizability in most frequency bands. Eigenvector centrality mapping confirmed the hub status of the parietal areas, and demonstrated a low centrality of the left temporal region in the theta band in AD patients that was strongly related to the mini mental state examination (global cognitive function test) score (r = 0.67, p = 0.001). Summarizing, GSA is a theoretically solid approach that is able to detect the disruption of functional network topology in AD. In addition to the previously reported overall connectivity losses and parietal area hub status, impaired network synchronizability and a clinically relevant left temporal centrality loss were found in AD patients. Our findings imply that GSA is valuable for the purpose of studying altered brain network topology and dynamics in AD.

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“…Previous fMRI studies have reported functional properties of the resting state brain network [8][9]. MEG studies have also demonstrated a variety of characteristics of the brain network in healthy individuals and patients [15,17,19,36]. A recent study demonstrated the properties of the resting state brain network on MEG recordings, with global hubs being in the medial temporal lobe [14].…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have extensively examined task-related changes in connectivity among the different brain regions [1][2][3][4][5][6][7]. Very recently, studies started to demonstrate how the brain works as a functional network or a set of sub-networks using functional magnetic resonance imaging (fMRI) [8][9][10][11][12][13] and magnetoencephalogaphy (MEG) [14][15][16][17][18][19][20]. Network measures most commonly used in these studies are derived from graph-theoretical analysis [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Task-related changes in functional properties of the human brain network underlying attentional control

Kida

Tanaka²,

Kakigi

2014

International Journal of Psychophysiology

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Previous studies have demonstrated task-related changes in brain activation and inter-regional connectivity but the temporal dynamics of functional properties of the brain during task execution is still unclear. In the present study, we investigated task-related changes in functional properties of the human brain network by applying graph-theoretical analysis to magnetoencephalography (MEG). Subjects performed a cue-target attention task in which a visual cue informed them of the direction of focus for incoming auditory or tactile target stimuli, but not the sensory modality. We analyzed the MEG signal in the cue-target interval to examine network properties during attentional control. Cluster-based nonparametric permutation tests with the Monte-Carlo method showed that in the cue-target interval, beta activity was desynchronized in the sensori-motor region including premotor and posterior parietal regions in the hemisphere contralateral to the attended side. Graph-theoretical analysis revealed that, in beta frequency, global hubs were found around the sensori-motor and prefrontal regions, and functional segregation over the entire network was decreased during attentional control compared to the baseline. Thus, network measures revealed task-related temporal changes in functional properties of the human brain network, leading to the understanding of how the brain dynamically responds to task execution as a network.

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Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease

Cited by 210 publications

References 75 publications

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Disruption of Functional Brain Networks in Alzheimer's Disease: What Can We Learn from Graph Spectral Analysis of Resting-State Magnetoencephalography?

Task-related changes in functional properties of the human brain network underlying attentional control

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