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Advances in Psychological Science    2019, Vol. 27 Issue (2) : 289-300     DOI: 10.3724/SP.J.1042.2019.00289
Regular Articles |
Altered resting-state brain networks in insomnia: Functional connectivities within and between networks
QIN Haixia,ZHAO Wenrui,YU Jing,LEI Xu()
Faculty of Psychology, Southwest University, Chongqing, 400715, China
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Abstract  

Insomnia has high incidence in modern society. The resting-state functional magnetic resonance (rs-fMRI) becomes one of the main imaging methods for the neuroimaging studies of insomnia, with its convenience and non-intrusive during data recording. Recent rs-fMRI studies showed that patients with insomnia had abnormalities in the prefrontal lobe, the temporal lobe, anterior cingulate gyrus and insula. Large-scale brain network is a brain structure that contains multiple brain regions and has relatively unique cognitive function. Based on the perspective of large-scale brain networks, patients with insomnia had abnormal activities and connectivities within the default network, the salience network, the cognitive control network and the negative affect network. More important, growing evidence presented an altered connectivities pattern among these four large-scale brain networks. Based on the symptoms, therapy, and the patterns of the large-scale brain networks, we proposed a "precision treatment" approach for insomnia. Future researches could integrate the big data with multimodal neuroimaging technology to verify the findings of rs-fMRI. Moreover, longitudinal and sequential design of insomnia can further benefit for the understanding of the neural mechanisms of insomnia.

Keywords insomnia      rs-fMRI      large-scale brain network      subtype of insomnia      precision treatment     
ZTFLH:  B845  
Corresponding Authors: Xu LEI     E-mail: xlei@swu.edu.cn
Issue Date: 25 December 2018
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Haixia QIN
Wenrui ZHAO
Jing YU
Xu LEI
Cite this article:   
Haixia QIN,Wenrui ZHAO,Jing YU, et al. Altered resting-state brain networks in insomnia: Functional connectivities within and between networks[J]. Advances in Psychological Science, 2019, 27(2): 289-300.
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http://journal.psych.ac.cn/xlkxjz/EN/10.3724/SP.J.1042.2019.00289     OR     http://journal.psych.ac.cn/xlkxjz/EN/Y2019/V27/I2/289
序号 作者/年份 被试(ID/HC) 年龄/年(ID/HC) ISI (ID/HC) PSG 病程 数据分析 失眠
类型
1 Lee et al., 2018 13/18 51.0±10.2/42.7±12.3 - Y - 基于种子点FC 1
2 Huang et al., 2017 27/26 40.07±11.62/41.19±11.69 - - - 基于种子点FC 2
3 Li et al., 2017 59/53 39.78±10.63/39.70±8.84 19.59±3.29/5.90±1.07 - 23.81±29.73(月) ICA 1
4 Li et al., 2017 50/40 39.64±10.42 /39.20±8.47 12.56±2.96/5.78±2.34 - 40.31±44.09(月) 全脑FC 1
5 Li et al., 2017 27/27 38.37±11.87/38.15±11.68 - - - 基于种子点FC 1
6 PaceSchott et al., 2017 13/13 35.2±15.7/35±15 18.83±3.79/0.77±1.67 Y - 基于种子点FC 1
7 Pang et al., 2017 39/28 52.21±11.74/51.25±12.47 15.52±0.52/0.59±0.62 Y - 全脑FC 2
8 Ran et al., 2017 21/20 40.62±7.52/38.65±7.40 - Y - ALFF 2
9 Dai et al., 2016 42/42 49.21±10.96/49.14±10.2 19.69±3.28/5.36±1.14 - - ALFF 1
10 Li et al., 2016 55/44 39.18±10.34/39.91±9.43 19.69±3.28/5.36±1.14 - 46.04±29.63(月) ALFF 1
11 Liu et al., 2016 31/71 38.58±12.09/14.00±3.31 - - - fALFF 1
12 Regen et al., 2016 20/20 42.7±13.4/44.1±10.6 12.8±3.1/1.6±1.9 Y - 基于种子点FC 1
13 Wang et al., 2016 59/47 39.3±10.7/40.0±9.1 - - - ReHo 1
14 Zhou et al., 2016 27/27 42.59±11.59/40.92±11.46 - - 10.98±8.85(年) ALFF 2
15 Nie et al., 2015 42/42 49.24±12.26/49.14±10.2 18.43±2.96/- - - 基于种子点FC 1
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19 Huang et al., 2012 10/10 37.50±12.38/35.50±8.67 - - 基于种子点FC 1
  
  
  
  
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