Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2008 Jun 27;4(6):e1000100.
doi: 10.1371/journal.pcbi.1000100.

Network analysis of intrinsic functional brain connectivity in Alzheimer's disease

Kaustubh Supekar  1 , Vinod Menon, Daniel Rubin, Mark Musen, Michael D Greicius
Affiliations

Network analysis of intrinsic functional brain connectivity in Alzheimer's disease

Kaustubh Supekar et al. PLoS Comput Biol. .

Abstract

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.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Graph metrics–degree, λ (L/Lran), γ (C/Cran), σ (γ/λ), for the AD group (Δ) and the control group (○) at three frequency intervals–0.01 to 005 Hz (green), 0.06 to 0.12 Hz (blue), and 0.13 to 0.25 Hz (red).
(A) For both groups, the mean degree–a measure of network connectivity is highest at Scale 3 for a wide range of correlation thresholds (0.011) showed a linear increase in small-worldness as the threshold increased (degree decreased). σ values for higher correlation thresholds are hard to interpret as at higher threshold values graphs of functional brain networks have fewer edges (smaller degree) and tend to split into isolated sub-graphs.
Figure 2
Figure 2. Small-world properties for networks derived by thresholding the correlation matrices such that the network has K′ edges.
Error bars represent values two standard deviations from the mean. (A) Mean λ (L/Lran) values for the AD group and the control group. No significant differences in the mean λ values are observed. (B) Mean γ (C/Cran) values for the AD group and the control group. γ values in AD group were significantly lower (indicated by *) than that in the control group (p<0.01). (C) Mean σ (γ/λ) values for the AD group and the control group. σ values in AD group were significantly lower (indicated by *) than that in the control group (p<0.01).
Figure 3
Figure 3. Global efficiency of whole-brain functional connectivity network.
(A) Global efficiency measure (Eglobal), for the AD group (Δ) and the control group (○) at three frequency intervals–0.01 to 005 Hz (green), 0.06 to 0.12 Hz (blue), and 0.13 to 0.25 Hz (red). The mean Eglobal value is low (0.78<λ<1) and shows similar trends at all the scales. (B) For the frequency interval 0.01 to 005 Hz (green)–mean Eglobal values for the AD group and the control group for networks derived by thresholding the correlation matrices such that the network has K′ edges. No significant differences in the mean Eglobal values were observed. Error bars represent values two standard deviations from the mean.
Figure 4
Figure 4. Receiver operating characteristic curve, plot of the sensitivity vs. (1-specificity) for distinguishing AD participants from controls as a function of varying normalized clustering coefficient (γ) threshold.
Using a cut-off value of 1.57, γ correctly classified 14 out of 18 controls and 15 of 21 AD subjects yielding 72% sensitivity and 78% specificity. The Area under the curve was 0.754 (95% CI Area 0.602 to 0.906).
Figure 5
Figure 5. Small-world property γ (C/Cran), the normalized clustering coefficient, for four regions of interest–left hippocampus (Hippocampus - Left), right hippocampus (Hippocampus - Right), left precentral gyrus (Precentral Gyrus - Left), right precentral gyrus (Precentral Gyrus - Right)–for the AD group (red) and the control group (blue) as a function of the correlation threshold.
In the left and the right hippocampus, for threshold values from 0.1 to 0.6, the clustering coefficient values in the AD group were significantly lower (p<0.01) than in the control group, while for the left and the right precentral gyrus, no significant differences in the clustering coefficient values were observed at any correlation threshold.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Jack CR, Jr, Petersen RC, Xu Y, O'Brien PC, Smith GE, et al. Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology. 2000;55:484–489. - PMC - PubMed
    1. Pearlson GD, Harris GJ, Powers RE, Barta PE, Camargo EE, et al. Quantitative changes in mesial temporal volume, regional cerebral blood flow, and cognition in Alzheimer's disease. Arch Gen Psychiatry. 1992;49:402–408. - PubMed
    1. Sperling RA, Bates JF, Chua EF, Cocchiarella AJ, Rentz DM, et al. fMRI studies of associative encoding in young and elderly controls and mild Alzheimer's disease. J Neurol Neurosurg Psychiatry. 2003;74:44–50. - PMC - PubMed
    1. Smith CD, Andersen AH, Kryscio RJ, Schmitt FA, Kindy MS, et al. Altered brain activation in cognitively intact individuals at high risk for Alzheimer's disease. Neurology. 1999;53:1391–1396. - PubMed
    1. Rombouts SA, Barkhof F, Veltman DJ, Machielsen WC, Witter MP, et al. Functional MR imaging in Alzheimer's disease during memory encoding. AJNR Am J Neuroradiol. 2000;21:1869–1875. - PMC - PubMed

Publication types