Spatiotemporal Modeling of Brain Dynamics Using Resting-State Functional Magnetic Resonance Imaging with Gaussian Hidden Markov Model

2016 ◽  
Vol 6 (4) ◽  
pp. 326-334 ◽  
Author(s):  
Shiyang Chen ◽  
Jason Langley ◽  
Xiangchuan Chen ◽  
Xiaoping Hu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Markov Model ◽  
Resting State ◽  
Hidden Markov ◽  
Brain Dynamics ◽  
Spatiotemporal Modeling ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Identification of Methamphetamine Abstainers by Resting-State Functional Magnetic Resonance Imaging

2021 ◽  
Vol 12 ◽  
Author(s):  
Tingting Dong ◽  
Qiuping Huang ◽  
Shucai Huang ◽  
Jiang Xin ◽  
Qiaolan Jia ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Time Domain ◽  
Resting State ◽  
Time Varying ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Normal People ◽  
The Time Domain

Methamphetamine (MA) can cause brain structural and functional impairment, but there are few studies on whether this difference will sustain on MA abstainers. The purpose of this study is to investigate the correlation of brain networks in MA abstainers. In this study, 47 people detoxified for at least 14 months and 44 normal people took a resting-state functional magnetic resonance imaging (RS-fMRI) scan. A dynamic (i.e., time-varying) functional connectivity (FC) is obtained by applying sliding windows in the time courses on the independent components (ICs). The windowed correlation data for each IC were then clustered by k-means. The number of subjects in each cluster was used as a new feature for individual identification. The results show that the classifier achieved satisfactory performance (82.3% accuracy, 77.7% specificity, and 85.7% sensitivity). We find that there are significant differences in the brain networks of MA abstainers and normal people in the time domain, but the spatial differences are not obvious. Most of the altered functional connections (time-varying) are identified to be located at dorsal default mode network. These results have shown that changes in the correlation of the time domain may play an important role in identifying MA abstainers. Therefore, our findings provide valuable insights in the identification of MA and elucidate the pathological mechanism of MA from a resting-state functional integration point of view.

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