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Frequency-Dependent Changes of the Resting BOLD Signals Predicts Cognitive Deficits in Asymptomatic Carotid Artery Stenosis

Overview of attention for article published in Frontiers in Neuroscience, June 2018
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Title
Frequency-Dependent Changes of the Resting BOLD Signals Predicts Cognitive Deficits in Asymptomatic Carotid Artery Stenosis
Published in
Frontiers in Neuroscience, June 2018
DOI 10.3389/fnins.2018.00416
Pubmed ID
Authors

Feng Xiao, Tao Wang, Lei Gao, Jian Fang, Zhenmeng Sun, Haibo Xu, Junjian Zhang

Abstract

"Asymptomatic" carotid artery stenosis (aCAS) patients usually have cognitive impairment in the domains of executive, psychomotor speed, and memory function. However, the pathophysiology of this impairment in aCAS patients is still unclear. In this study, amplitude of low-frequency fluctuation (ALFF) method was used based on resting-state blood oxygenation level dependent (BOLD) signals, to investigate local brain activity in 19 aCAS patients and 24 healthy controls, aimed to explore this pathophysiology mechanism. We analyzed this intrinsic activity in four individual frequency bands: Slow-2 (0.198-0.25 Hz), Slow-3 (0.073-0.198 Hz), Slow-4 (0.027-0.073 Hz), and Slow-5 (0.01-0.027 Hz). The aCAS-related ALFF changes were mainly distributed in (1) cortical midline structure, including bilateral dorsomedial prefrontal (dmPFC), cingulate cortex (CC) and precuneus (PCu); (2) hippocampus and its adjacent structures, including bilateral hippocampus, thalamus and medial temporal regions. We found these spatial patterns were frequency-dependent. Significant interaction between frequency and group was found distributed in left putamen, triangle part of inferior temporal and bilateral precentral/postcentral gyrus when Slow-4 and Slow-5 were considered. The delay recall ability of aCAS patient was significantly positive correlated to the mean ALFF in dmPFC within Slow-4 band and the mean ALFF in the bilateral hippocampus within Slow-3 band, respectively. We also found the Montreal Cognitive Assessme score of aCAS patient was significantly positive correlated to the mean ALFF in right fusiform and parahippocampus within Slow-3 band. Furthermore, we built the automatic diagnosis and prediction models based on support vector machine (SVM) and back propagation neural network (BPNN), respectively. Both two types of models could achieve relatively competent performance, which meant the frequency-dependent changes in ALFF could not only reveal the pathophysiology mechanism of cognitive impairment of aCAS, but also could be used as neuroimaging marker in the analysis of cognition impairment for aCAS patients.

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The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Other 3 15%
Student > Master 3 15%
Student > Ph. D. Student 2 10%
Student > Postgraduate 2 10%
Professor > Associate Professor 1 5%
Other 0 0%
Unknown 9 45%
Readers by discipline Count As %
Medicine and Dentistry 7 35%
Psychology 3 15%
Engineering 1 5%
Unknown 9 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 June 2018.
All research outputs
#20,663,600
of 25,385,509 outputs
Outputs from Frontiers in Neuroscience
#9,459
of 11,542 outputs
Outputs of similar age
#265,759
of 341,505 outputs
Outputs of similar age from Frontiers in Neuroscience
#210
of 225 outputs
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