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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Wadsworth EJ, Allen PH, McNamara RL, Smith AP (2008) Fatigue and health in a seafaring population. Occup Med 58:198–204
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
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
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
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
Poldrack RA, Mumford JA, Nichols TE (2011) Handbook of functional MRI data analysis. Cambridge University Press, New York
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
Dubois J, Adolphs R (2016) Building a science of individual differences from fMRI. Trends Cogn Sci 20:425–443
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Yan CG, Zang YF (2010) DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 4:13
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
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
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
Friston KJ, Frith CD, Frackowiak RS, Turner R (1995) Characterizing dynamic brain responses with fMRI: a multivariate approach. Neuroimage 2:166–172
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
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
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
Kelly A, Uddin LQ, Biswal BB, Castellanos FX, Milham MP (2008) Competition between functional brain networks mediates behavioral variability. Neuroimage 39:527–537
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
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
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)
Rissanen J (1978) Modeling by the shortest data description. Automatica 14:465–471
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
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
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
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
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
Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45:S199-209
Maldjian JA, Laurienti PJ, Burdette JH (2004) Precentral gyrus discrepancy in electronic versions of the talairach atlas. Neuroimage 21:450–455
Planetta PJ, Servos P (2012) The postcentral gyrus shows sustained fMRI activation during the tactile motion aftereffect. Exp Brain Res 216:535–544
Hadland KA, Rushworth MF, Gaffan D, Passingham RE (2003) The effect of cingulate lesions on social behaviour and emotion. Neuropsychologia 41:919–931
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
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
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
Chilosi AM, Brovedani P, Moscatelli M, Bonanni P, Guerrini R (2006) Neuropsychological findings in idiopathic occipital lobe epilepsies. Epilepsia 47:76–78
Goldberg I, Harel M, Malach R (2006) When the brain loses its self: prefrontal inactivation during sensorimotor processing. Neuron 50:329–339
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
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
Crockford DN, Goodyear B, Edwards J, Quickfall J, el-Guebaly N (2005) Cue-induced brain activity in pathological gamblers. Biol Psychiat 58:787–795
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
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
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
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
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
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
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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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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DOI: https://doi.org/10.1007/s11063-021-10545-3