Biomarker extraction of sustained attention based on brain functional network_Journal of Biomedical Engineering

Authors：

JIA Wenxiao , SHAN Siyuan ,  ZHANG Jicong

School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P.R.China;

Corresponding author：

ZHANG Jicong, Email: jicongzhang@buaa.edu.cn

Keywords：

wavelet coherence; force control task; sustained attention; brain functional network

DOI：

10.7507/1001-5515.201611045

Video：

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Although attention plays an important role in cognitive and perception, there is no simple way to measure one's attention abilities. We identified that the strength of brain functional network in sustained attention task can be used as the physiological indicator to predict behavioral performance. Behavioral and electroencephalogram (EEG) data from 14 subjects during three force control tasks were collected in this paper. The reciprocal of the product of force tolerance and variance were used to calculate the score of behavioral performance. EEG data were used to construct brain network connectivity by wavelet coherence method and then correlation analysis between each edge in connectivity matrices and behavioral score was performed. The linear regression model combined those with significantly correlated network connections into physiological indicator to predict participant's performance on three force control tasks, all of which had correlation coefficients greater than 0.7. These results indicate that brain functional network strength can provide a widely applicable biomarker for sustained attention tasks.

Citation： JIA Wenxiao, SHAN Siyuan, ZHANG Jicong. Biomarker extraction of sustained attention based on brain functional network. Journal of Biomedical Engineering, 2018, 35(2): 176-181. doi: 10.7507/1001-5515.201611045 Copy

1.	姚小芳, 方小燕, 黄晓艳. 铁缺乏症学龄儿童注意力缺陷多动障碍认知行为训练的效果研究. 护理实践与研究, 2017, 14(12): 83-85.
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14.	单炎炎, 贾艳滨, 李雪果, 等. 注意缺陷多动障碍的脑网络连接研究进展. 中华精神科杂志, 2017, 50(3): 235-238.
15.	Anguera J A, Boccanfuso J, Rintoul J L, et al. Video game training enhances cognitive control in older adults. Nature, 2013, 501(7465): 97-101.
16.	Ciaramelli E, Grady C, Levine B, et al. Top-down and bottom-up attention to memory are dissociated in posterior parietal cortex: neuroimaging and neuropsychological evidence. J Neurosci, 2010, 30(14): 4943-4956.
17.	Baluchi F, Itti L. Mechanisms of top-down attention. Trends Neurosci, 2011, 34(4): 210-224.
18.	Sarter M, Givens B, Bruno J P. The cognitive neuroscience of sustained attention: where top-down meets bottom-up. Brain Res Rev, 2001, 35(2): 146-160.
19.	Marshall T R, Bergmann T O, Jensen O. Frontoparietal structural connectivity mediates the top-down control of neuronal synchronization associated with selective attention. PLoS Biol, 2015, 13(10): e1002272.
20.	Scolari M, Seidl-Rathkopf K N, Kastner S. Functions of the human frontoparietal attention network: Evidence from neuroimaging. Current Opinion in Behavioral Sciences. 2015, 1: 32-39.
21.	Esterman M, Noonan S K, Rosenberg M, et al. In the zone or zoning out? Tracking behavioral and neural fluctuations during sustained attention. Cereb Cortex, 2013, 23(11): 2712-2723.
22.	deBettencourt M T, Cohen J D, Lee R F, et al. Closed-loop training of attention with real-time brain imaging. Nat Neurosci, 2015, 18(3): 470-475.
23.	于洪丽, 徐桂芝, 付灵弟, 等. 基于脑电图的不同频率磁刺激内关穴脑网络分析. 中国生物医学工程学报, 2016, 35(6): 749-753.

1. 姚小芳, 方小燕, 黄晓艳. 铁缺乏症学龄儿童注意力缺陷多动障碍认知行为训练的效果研究. 护理实践与研究, 2017, 14(12): 83-85.
2. Arnsten A F. Fundamentals of attention-deficit/hyperactivity disorder: circuits and pathways. J Clin Psychiatry, 2006, 67(Suppl 8): 7-12.
3. 郭润蒲, 杜亚松. 注意缺陷多动障碍相关脑网络结构及功能磁共振研究进展. 中国神经精神疾病杂志, 2016, 42(9): 556-559.
4. Bonnelle V, Leech R, Kinnunen K M, et al. Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J Neurosci, 2011, 31(38): 13442-13451.
5. Rosenberg M D, Finn E S, Scheinost D, et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci, 2016, 19(1): 165-171.
6. Clayton M S, Yeung N, Cohen Kadosh R. The roles of cortical oscillations in sustained attention. Trends Cogn Sci, 2015, 19(4): 188-195.
7. de Vico Fallani F, Astolfi L, Cincotti F, et al. Evaluation of the brain network organization from EEG signals: a preliminary evidence in stroke patient. Anat Rec (Hoboken), 2009, 292(12): 2023-2031.
8. Li Ling, Jin Zhenlan, Li Bin. Analysis of a phase synchronized functional network based on the rhythm of brain activities. Chin Phys B, 2011, 20(3): 512-518.
9. Bowyer S M. Coherence a measure of the brain networks: past and present. Neuropsychiatric Electrophysiology, 2016, 2(1): 1-12.
10. Li Xiaowei, Jing Zhuang, Hu Bin, et al. An EEG-based study on coherence and brain networks in mild depression cognitive process//2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Shenzhen, China: IEEE, 2016: 1275-1282.
11. Tyagi R. EEG coherence biomarkers for differentiating attention states. Singapore: National University of Singapore, 2014.
12. 张方风. 大脑功能连接的复杂网络研究. 北京: 对外经济贸易大学出版社, 2011.
13. 梁夏, 王金辉, 贺永. 人脑连接组研究: 脑结构网络和脑功能网络. 科学通报, 2010, 55(16): 1565-1583.
14. 单炎炎, 贾艳滨, 李雪果, 等. 注意缺陷多动障碍的脑网络连接研究进展. 中华精神科杂志, 2017, 50(3): 235-238.
15. Anguera J A, Boccanfuso J, Rintoul J L, et al. Video game training enhances cognitive control in older adults. Nature, 2013, 501(7465): 97-101.
16. Ciaramelli E, Grady C, Levine B, et al. Top-down and bottom-up attention to memory are dissociated in posterior parietal cortex: neuroimaging and neuropsychological evidence. J Neurosci, 2010, 30(14): 4943-4956.
17. Baluchi F, Itti L. Mechanisms of top-down attention. Trends Neurosci, 2011, 34(4): 210-224.
18. Sarter M, Givens B, Bruno J P. The cognitive neuroscience of sustained attention: where top-down meets bottom-up. Brain Res Rev, 2001, 35(2): 146-160.
19. Marshall T R, Bergmann T O, Jensen O. Frontoparietal structural connectivity mediates the top-down control of neuronal synchronization associated with selective attention. PLoS Biol, 2015, 13(10): e1002272.
20. Scolari M, Seidl-Rathkopf K N, Kastner S. Functions of the human frontoparietal attention network: Evidence from neuroimaging. Current Opinion in Behavioral Sciences. 2015, 1: 32-39.
21. Esterman M, Noonan S K, Rosenberg M, et al. In the zone or zoning out? Tracking behavioral and neural fluctuations during sustained attention. Cereb Cortex, 2013, 23(11): 2712-2723.
22. deBettencourt M T, Cohen J D, Lee R F, et al. Closed-loop training of attention with real-time brain imaging. Nat Neurosci, 2015, 18(3): 470-475.
23. 于洪丽, 徐桂芝, 付灵弟, 等. 基于脑电图的不同频率磁刺激内关穴脑网络分析. 中国生物医学工程学报, 2016, 35(6): 749-753.

Journal of Biomedical Engineering

Biomarker extraction of sustained attention based on brain functional network

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