Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

By Kaustubh Supekar, Vinod Menon, Daniel L. Rubin, Mark A. Musen, Michael D. Greicius. PLoS Comput. Biol., 2008

1,187 citations recorded by the source corpus.

Functional brain networks detected in task-free (“resting-state”) functional magnetic resonance imaging (fMRI) have a small-world architecture that reflects a robust functional organization of the brain. Here, we examined whether this functional organization is disrupted in Alzheimer's disease (AD). Task-free fMRI data from 21 AD subjects and 18 age-matched controls were obtained. Wavelet analysis was applied to the fMRI data to compute frequency-dependent correlation matrices. Correlation matrices were thresholded to create 90-node undirected-graphs of functional brain networks. Small-world metrics (characteristic path length and clustering coefficient) were computed using graph analytical methods. In the low frequency interval 0.01 to 0.05 Hz, functional brain networks in controls showed small-world organization of brain activity, characterized by a high clustering coefficient and a low characteristic path length. In contrast, functional brain networks in AD showed loss of small-world properties, characterized by a significantly lower clustering coefficient (p<0.01), indicative of disrupted local connectivity. Clustering coefficients for the left and right hippocampus were significantly lower (p<0.01) in the AD group compared to the control group. Furthermore, the clustering coefficient distinguished AD participants from the controls with a sensitivity of 72% and specificity of 78%. Our study provides new evidence that there is disrupted organization of functional brain networks in AD. Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/84cbf5e42744a14f5d2988c0cbb12b23778258ca

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Citations in the corpus

The Ubiquity of Small-World Networks (2011, background): “Specifically, the threshold was defined so that S=log(N)/log(k) with S=2.5 across all subjects for the study(Stam et al. 2007; Supekar et al. 2008).”
Complex brain networks: graph theoretical analysis of structural and functional systems (2009, background): “…between existing studies — for example, clustering is reportedly increased in the structural networks 120 but decreased in the functional networks of patients with aD 117 — which might be attributable to the clinical heterogeneity of the patient groups as well as to the differences in 86 .”
Decoding subject-driven cognitive states with whole-brain connectivity patterns. (2012, background): “A number of recent studies have used structurally defined ROIs to gain insights into the basic properties of both structural and functional brain networks (Hagmann et al. 2008; Supekar et al. 2008; Honey et al. 2009) and to classify brain states (Richiardi et al. 2010).”
GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, background): “In addition, resting state fMRI connectivity studies are sensitive to abnormal global network organization, revealing changes in several clinical populations with cognitive deficits including Alzheimer’s disease [5,9], schizophrenia [70,71], traumatic brain injury [72], temporal lobe epilepsy [73], ”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, result): “…with AD exhibited degeneration of speciﬁc brain hubs, reduced clustering coeﬃcients and path lengths very close to the values of random networks (Supekar et al., 2008; Sanz-Arigita et al., 2010; Dai et al., 2015; delEtoile and Adeli, 2017), similar to the results of researchers who worked on…”
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex (2017, background): “Analysing brain connectivity from a network theoretical point of view has shown significant potential for identifying organisational principles in the brain and their connections to cognitive procedures15 and brain disorders (Supekar et al., 2008; Bassett et al., 2008; Smith et al., 2009).”
Development of Large-Scale Functional Brain Networks in Children (2009, background): “05 Hz) for both children and young-adults, we focus on this frequency interval in subsequent analyses, consistent with other recent studies [34,35].”
Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, neutral): “The resulting correlation coefficients are usually converted to z-scores using the Fisher transformation and then thresholded to identify statistically significant network connections (Supekar et al., 2008).”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, positive): “Furthermore, a resting-state fMRI graph analysis study revealed a decreased overall clustering of the brain network of Alzheimer's patients in comparison to age-matched healthy controls, suggesting decreased efficiency of local information processing in Alzheimer's disease (Supekar et al., 2008).”
Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity (2010, background): “There is increasing evidence that the altered brain function in diseases as Schizophrenia, AD or attention deficit hyperactivity disorder is characterized by the conversion from small-world network architecture to less optimal functional topologies [17,18,33,34,35,36].”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “Previous reports have found altered static FCmeasures inAlzheimer's patients compared to healthy controls (Buckner et al., 2009; Greicius et al., 2004; Sorg et al., 2007; Supekar et al., 2008; reviewed in Greicius, 2008; Menon, 2011).”