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The Study of Sailors’ Brain Activity Difference Before and After Sailing Using Activated Functional Connectivity Pattern

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Abstract

The maritime environment is significantly different compared with the terrestrial environment, which will inevitably have a certain impact on the brain functional activities of sailors and lead to differential changes in the brain functional connectivity (FC). Therefore, it has a great significance to explore the impact of the marine environment on the brain functional activities of sailors. It is not only the need for more detailed research work of sailors, but also an inevitable requirement for accurately revealing the impact of the marine environment. In this paper, the functional magnetic resonance image data of 33 sailors before and after sailing were used to study the brain FCs changes of sailors at the activated voxels level, in which the activated voxels were obtained by independent component analysis combined with Anatomical Automatic Labelling template. Then, the FCs between the corresponding brain regions of these activated voxels were statistically analyzed to obtain the FCs with significant differences (DFCs) between sailors before and after sailing. Finally, the classification evaluation of sailors before and after sailing was realized by using the FCs and DFCs as the characteristic samples in support vector machine. The results indicated that the DFCs between the activated brain regions had better discriminative performance for sailors before and after sailing, especially for the FCs within Prefrontal lobe and Occipital lobe as well as those between them which showed a significant difference between sailors before and after sailing.

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References

  1. Wadsworth EJ, Allen PH, McNamara RL, Smith AP (2008) Fatigue and health in a seafaring population. Occup Med 58:198–204

    Google Scholar 

  2. Iversen RTB (2010) The mental health of seafarers: a brief review. In: Paper presented at the maritime medicine-an international challenge. 11th International Symposiumon Maritime Health, Odessa

  3. Shi Y, Zeng W, Wang N, Wang S, Huang Z (2015) Early warning for human mental sub-health based on fMRI data analysis: an example from a seafarers’ resting-data study. Front Psychol 6:1030

    Google Scholar 

  4. Wang N, Zeng W, Shi Y, Yan H (2017) Brain functional plasticity driven by career experience: a resting-state fMRI study of the seafarer. Front Psychol 8:1786

    Google Scholar 

  5. Wang N, Wu H, Xu M, Yang Y, Chang C, Zeng W, Yan H (2018) Occupational functional plasticity revealed by brain entropy: A resting-state fMRI study of seafarers. Hum Brain Mapp 39:2997–3004

    Google Scholar 

  6. Poldrack RA, Mumford JA, Nichols TE (2011) Handbook of functional MRI data analysis. Cambridge University Press, New York

    MATH  Google Scholar 

  7. Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT (2015) Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat Neurosci 18:1664–1671

    Google Scholar 

  8. Dubois J, Adolphs R (2016) Building a science of individual differences from fMRI. Trends Cogn Sci 20:425–443

    Google Scholar 

  9. Cui Z, Gong G (2018) The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage 178:622–637

    Google Scholar 

  10. Nielsen AN, Greene DJ, Gratton C, Dosenbach NUF, Petersen SE, Schlaggar B (2018) Evaluating the prediction of brain maturity from functional connectivity after motion artifact denoising. Cereb Cortex 11:25–35

    Google Scholar 

  11. Wei G, Zhang Y, Jiang T, Luo J (2011) Increased cortical thickness in sports experts: a comparison of diving players with the controls. PLoS ONE 6:e17112

    Google Scholar 

  12. Dong M, Li J, Shi X, Gao S, Fu S, Liu Z, Liang F, Gong Q, Shi G, Tian J (2015) Altered baseline brain activity in experts measured by amplitude of low frequency fluctuations (ALFF): a resting state fMRI study using expertise model of acupuncturists. Front Hum Neurosci 9:99

    Google Scholar 

  13. Hervais-Restelman A, Moser-Mercer B, Michel CM, Golestani N (2014) fMRI of simultaneous interpretation reveals the neural basis of extreme language control. Cereb Cortex 25:4727–4739

    Google Scholar 

  14. Shen H, Li Z, Qin J, Liu Q, Wang L, Zeng LL, Li H, Hu D (2016) Changes in functional connectivity dynamics associated with vigilance network in taxi drivers. Neuroimage 124:367–378

    Google Scholar 

  15. Van Dijk KRA, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL (2010) Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol 103:297–321

    Google Scholar 

  16. Rosenberg MD, Finn ES, Scheinost D, Papademetris X, Shen X, Todd Constable R, Chun MM (2016) A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci 19:165–171

    Google Scholar 

  17. De Lacy N, Kodish I, Rachakonda S, Calhoun VD (2018) Novel in silico multivariate mapping of intrinsic and anticorrelated connectivity to neurocognitive functional maps supports the maturational hypothesis of ADHD. Hum Brain Mapp 39:3449–3467

    Google Scholar 

  18. Yu M, Linn KA, Cook PA, Phillips ML, Mclnnis M, Fava M, Trivedi MH, Weissman MM, Shinohara RT, Sheline YI (2018) Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum Brain Mapp 39:4213–4227

    Google Scholar 

  19. Koelsch S, Skouras S, Lohmann G (2018) The auditory cortex hosts network nodes influential for emotion processing: An fMRI study on music-evoked fear and joy. PLoS ONE 13:e0190057

    Google Scholar 

  20. Rubia K, Criaud M, Wulff M, Alegria A, Brinson H, Barker G, Stahl D, Giampietro V (2019) Functional connectivity changes associated with fMRI neurofeedback of right inferior frontal cortex in adolescents with ADHD. Neuroimage 188:43–58

    Google Scholar 

  21. Armañanzas R, Iglesias M, Morales DA, Alonso-Nanclares L (2017) Voxel-based diagnosis of alzheimer’s disease using classifier ensembles. IEEE J Biomed Health Inform 21:778–878

    Google Scholar 

  22. Irajia A, Calhoun VD, Wiseman NM, Davoodi-Bojd E, Avanaki MRN, Mark Haacke E, Kou Z (2016) The connectivity domain: Analyzing resting state fMRI data using feature-based data-driven and model-based methods. Neuroimage 34:494–507

    Google Scholar 

  23. Yan CG, Zang YF (2010) DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 4:13

    Google Scholar 

  24. Shao L, You Y, Du H, Fu D (2020) Classification of ADHD with fMRI data and multi-objective optimization. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2020.105676

    Article  Google Scholar 

  25. Li G, Liu Y, Zheng Y, Li D, Shen D (2020) Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging. Hum Brain Mapp 41:865–881

    Google Scholar 

  26. Jun E, Na KS, Kang W, Lee J, Suk HI, Ham BJ (2020) Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks. Hum Brain Mapp 41(17):4997–5014

    Google Scholar 

  27. Friston KJ, Frith CD, Frackowiak RS, Turner R (1995) Characterizing dynamic brain responses with fMRI: a multivariate approach. Neuroimage 2:166–172

    Google Scholar 

  28. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Nat Acad Sci 102:9673–9678

    Google Scholar 

  29. Fransson P (2005) Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting-state default mode of brain function hypothesis. Hum Brain Mapp 26:15–29

    Google Scholar 

  30. Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Nat Acad Sci 100:253–258

    Google Scholar 

  31. Kelly A, Uddin LQ, Biswal BB, Castellanos FX, Milham MP (2008) Competition between functional brain networks mediates behavioral variability. Neuroimage 39:527–537

    Google Scholar 

  32. Khazaee A, Ebrahimzadeh A, Babajaniferemi A (2017) Classification of patients with MCI and ad from healthy controls using directed graph measures of resting-state fMRI. Behav Brain Res 322:339–350

    Google Scholar 

  33. Shi Y, Zeng W, Tang X, Kong W, Yin J (2017) An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis. Med Biol Eng Comput 56:683–694

    Google Scholar 

  34. Himberg J, Hyvärinen A (2003) Icasso: software for investigating the reliability of ICA estimates by clustering and visualization. In: Proceedings of IEEE workshop on neural networks for signal processing (NNSP2003)

  35. Rissanen J (1978) Modeling by the shortest data description. Automatica 14:465–471

    MATH  Google Scholar 

  36. Shi Y, Zeng W, Wang N, Zhao L (2017) A new method for independent component analysis with priori information based on multi-objective optimization. J Neurosci Methods 283:72–82

    Google Scholar 

  37. Blasi BD, Caciagli L, Storti SF, Galovic M, Galazzo IB (2020) Noise removal in resting-state and task fMRI: functional connectivity and activation maps. J Neural Eng 17:046040

    Google Scholar 

  38. Salman MS, Du Y, Lin D, Fu Z, Fedorov A, Damaraju E, Sui J, Chen J, Yu Q, Mayer A, Posse S, Mathalon DH, Ford JM, Van Erp T, Calhoun VD (2019) Group ICA for identifying biomarkers in schizophrenia: ‘Adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression. NeuroImage-Clinical 22:101747

    Google Scholar 

  39. Shi Y, Zeng W, Deng J, Nie W, Zhang Y (2020) The identification of Alzheimer’s disease using functional connectivity between activity voxels in resting-state fMRI data. IEEE J Transl Eng Health 8:1400211

    Google Scholar 

  40. Calhoun VD, Adali T (2016) Time-varying brain connectivity in fMRI data: whole-brain data-driven approaches for capturing and characterizing dynamic states. IEEE Signal Proc Mag 33:52–66

    Google Scholar 

  41. Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45:S199-209

    Google Scholar 

  42. Maldjian JA, Laurienti PJ, Burdette JH (2004) Precentral gyrus discrepancy in electronic versions of the talairach atlas. Neuroimage 21:450–455

    Google Scholar 

  43. Planetta PJ, Servos P (2012) The postcentral gyrus shows sustained fMRI activation during the tactile motion aftereffect. Exp Brain Res 216:535–544

    Google Scholar 

  44. Hadland KA, Rushworth MF, Gaffan D, Passingham RE (2003) The effect of cingulate lesions on social behaviour and emotion. Neuropsychologia 41:919–931

    Google Scholar 

  45. Kozlovskiy S, Vartanov A, Pyasik M, Nikonova E, Velichkovsky B (2013) Anatomical characteristics of cingulate cortex and neuropsychological memory tests performance. Procedia Soc Behav Sci 86:128–133

    Google Scholar 

  46. Kozlovskiy SA, Vartanov AV, Nikonova EY, Pyasik MM, Velichkovsky BM (2012) The cingulate cortex and human memory processes. Psychol Russia State Art 5:231–243

    Google Scholar 

  47. Radua J, Phillips ML, Russell T, Lawrence N, Marshall N, Kalidindi S, El-Hage W, McDonald C, Giampietro V, Brammer MJ, David AS, Surguladze SA (2010) Neural response to specific components of fearful faces in healthy and schizophrenic adults. Neuroimage 49:939–946

    Google Scholar 

  48. Chilosi AM, Brovedani P, Moscatelli M, Bonanni P, Guerrini R (2006) Neuropsychological findings in idiopathic occipital lobe epilepsies. Epilepsia 47:76–78

    Google Scholar 

  49. Goldberg I, Harel M, Malach R (2006) When the brain loses its self: prefrontal inactivation during sensorimotor processing. Neuron 50:329–339

    Google Scholar 

  50. Sharot T, Kanai R, Marston D, Korn CW, Rees G, Dolan RJ (2012) Selectively altering belief formation in the human brain. Proc Nat Acad Sci 109:17058–17062

    Google Scholar 

  51. Talati A, Hirsch J (2014) Functional specialization within the medial frontal gyrus for perceptual go/no-go decisions based on “what,” “when,” and “where” related information: an fmri study. J Cogn Neuro 17:981–993

    Google Scholar 

  52. Crockford DN, Goodyear B, Edwards J, Quickfall J, el-Guebaly N (2005) Cue-induced brain activity in pathological gamblers. Biol Psychiat 58:787–795

    Google Scholar 

  53. Kozlovskiy SA, Pyasik MM, Korotkova AV, Vartanov AV, Glozman JM, Kiselnikov AA (2014) Activation of left lingual gyrus related to working memory for schematic faces. Int J Psychophysiol 94:241

    Google Scholar 

  54. Shi Y, Zeng W, Wang N, Chen D (2015) A novel fMRI group data analysis method based on data-driven reference extracting from group subjects. Comput Methods Programs Biomed 122:362–371

    Google Scholar 

  55. Shi Y, Zeng W, Wang N, Zhao L (2018) A new constrained spatiotemporal ICA method based on multi-objective optimization for fMRI data analysis. IEEE T Neur Sys Reh Eng 26:1690–1699

    Google Scholar 

  56. Shi Y, Zeng, W (2017) An fMRI data analysis strategy for Seafarer's brain functional network study. In: International conference on photonics & imaging in biology & medicine

  57. Shi Y, Zeng W (2018) The study of seafarer's brain functional connectivity before and after sailling using fMRI. In: International conference on artificial intelligence and pattern recognition, pp 48–51

  58. Shi Y, Zeng W, Guo S (2019) The occupational brain plasticity study using dynamic functional connectivity between multi-networks: take seafarers for example. IEEE Access 7:148098–148107

    Google Scholar 

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Acknowledgements

This work was sponsored by Shanghai Sailing Program (Grant No. 19YF1419000), and National Natural Science Foundation of China (Grants No. 61906117, 31870979).

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Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China

    Yuhu Shi, Weiming Zeng, Jin Deng, Ying Li & Jia Lu

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  1. Yuhu Shi
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  3. Jin Deng
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Correspondence to Yuhu Shi.

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Shi, Y., Zeng, W., Deng, J. et al. The Study of Sailors’ Brain Activity Difference Before and After Sailing Using Activated Functional Connectivity Pattern. Neural Process Lett 53, 3253–3265 (2021). https://doi.org/10.1007/s11063-021-10545-3

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