2016
DOI: 10.3389/fnins.2016.00235
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Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients

Abstract: Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM). However, the DM-related changes in the topological properties in functional brain networks are unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET) data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs), followed by a graph theoretical analysis. We found that both DM patients and NCs … Show more

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Cited by 13 publications
(11 citation statements)
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“…The ability of PET to track biomarkers with high sensitivity makes it a powerful tool for studying cancer staging, therapeutic response, and recurrence [12]. Resting-state PET techniques can also be applied to validate hypotheses concerning the changes in functional connectivity that occur in various kinds of diseases such as schizophrenia [13], Alzheimer’s disease [14], depression [15], diabetic patients [16] and normal aging [17]. However, it remains largely unknown whether cancer and/or chemotherapy alter the topological organization of metabolic brain networks using 18 F-FDG PET data.…”
Section: Introductionmentioning
confidence: 99%
“…The ability of PET to track biomarkers with high sensitivity makes it a powerful tool for studying cancer staging, therapeutic response, and recurrence [12]. Resting-state PET techniques can also be applied to validate hypotheses concerning the changes in functional connectivity that occur in various kinds of diseases such as schizophrenia [13], Alzheimer’s disease [14], depression [15], diabetic patients [16] and normal aging [17]. However, it remains largely unknown whether cancer and/or chemotherapy alter the topological organization of metabolic brain networks using 18 F-FDG PET data.…”
Section: Introductionmentioning
confidence: 99%
“…In this study, “1” represents an edge with a correlation connection, and “0” represents an edge without a correlation connection 41 . Based on a previous study 42 , we thresholded each correlation matrix over a wide range of densities (10–40% with a 1% increment) and then estimated the properties of the resulting graphs at each threshold value. In the present study, the lowest density where the largest component size was 90 was 10%.…”
Section: Methodsmentioning
confidence: 99%
“…“Small-world” network: To quantify the “small-world” characteristics of these networks, a random network was used as a reference. If the network being studied has a larger Cp and a higher estimated shortest Lp relative to those of a random network, then the network is a “small-world” network 42 . Some researchers have proposed unifying the two metrics into one metric, the small-world index (σ), σ = γ/λ, to measure the “small-world” characteristics of the network 45 .…”
Section: Methodsmentioning
confidence: 99%
“…Random network is obtained by random reconnection of nodes in the original network. Clustering coefficient γ can be calculated using Equations () through () 33 Cibadbreak=2Liki(ki1)\begin{equation}{C_i} = \frac{{2{L_i}}}{{{k_i}({k_i} - 1)}}\end{equation} Cbadbreak=1NCi\begin{equation}C = \frac{1}{N}\sum {{C_i}} \end{equation} γbadbreak=CCrand\begin{equation}\gamma = \frac{C}{{{C_{rand}}}}\end{equation}…”
Section: Brain Wave Analysismentioning
confidence: 99%