2015
DOI: 10.1038/jcbfm.2015.40
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Metabolic Connectivity as Index of Verbal Working Memory

Abstract: Positron emission tomography (PET) data are commonly analyzed in terms of regional intensity, while covariant information is not taken into account. Here, we searched for network correlates of healthy cognitive function in resting state PET data. PET with [18 F]-fluorodeoxyglucose and a test of verbal working memory (WM) were administered to 35 young healthy adults. Metabolic connectivity was modeled at a group level using sparse inverse covariance estimation. Among 13 WM-relevant Brodmann areas (BAs), 6 appea… Show more

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Cited by 18 publications
(23 citation statements)
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“…We used partial correlation‐based connectivity estimation using the sparse inverse covariance estimation to construct the metabolic networks. To determine the optimal sparsity control parameter, a stability approach to regularization selection was utilized . The advantage of sparse inverse covariance estimation is that it renders zero for very weak connectivity, making the network sparse, and does not need a threshold for network analysis.…”
Section: Methodsmentioning
confidence: 99%
“…We used partial correlation‐based connectivity estimation using the sparse inverse covariance estimation to construct the metabolic networks. To determine the optimal sparsity control parameter, a stability approach to regularization selection was utilized . The advantage of sparse inverse covariance estimation is that it renders zero for very weak connectivity, making the network sparse, and does not need a threshold for network analysis.…”
Section: Methodsmentioning
confidence: 99%
“…The regularization parameter λ was determined using Stability Approach to Regularization Selection ( 32 ). Since edges generally shrink after gLASSO, we adjusted the edges to reflect connectivity strength properly ( 33 ) using a sample covariance matrix and a sparse structure of original precision matrix ( 34 ). Functional networks were constructed by thresholding pFC > 10 −5 , since gLASSO shrinks unrelated edges to 0.…”
Section: Methodsmentioning
confidence: 99%
“…In analogy with previous studies, 11,22 the data were assumed to follow a multivariate Gaussian distribution. That is, x 1 , .…”
Section: Metabolic Connectivity Patternmentioning
confidence: 99%