Occupational functional plasticity revealed by brain entropy: A resting‐state fMRI study of seafarers

Recently, functional magnetic resonance imaging (fMRI) has been increasingly used to assess brain function. Brain entropy is an effective model for evaluating the alteration of brain complexity. Specifically, the sample entropy (SampEn) provides a feasible solution for revealing the brain's complexity. Occupation is one key factor affecting the brain's activity, but the neuropsychological mechanisms are still unclear. Thus, in this article, based on fMRI and a brain entropy model, we explored the functional complexity changes engendered by occupation factors, taking the seafarer as an example. The whole‐brain entropy values of two groups (i.e., the seafarers and the nonseafarers) were first calculated by SampEn and followed by a two‐sample t test with AlphaSim correction (p < .05). We found that the entropy of the orbital‐frontal gyrus (OFG) and superior temporal gyrus (STG) in the seafarers was significantly higher than that of the nonseafarers. In addition, the entropy of the cerebellum in the seafarers was lower than that of the nonseafarers. We conclude that (1) the lower entropy in the cerebellum implies that the seafarers’ cerebellum activity had strong regularity and consistency, suggesting that the seafarer's cerebellum was possibly more specialized by the long‐term career training; (2) the higher entropy in the OFG and STG possibly demonstrated that the seafarers had a relatively decreased capability for emotion control and auditory information processing. The above results imply that the seafarer occupation indeed impacted the brain's complexity, and also provided new neuropsychological evidence of functional plasticity related to one's career.

[1]  R. Bergström An entropy model of the developing brain. , 1969, Developmental psychobiology.

[2]  L. Derogatis,et al.  The SCL-90 and the MMPI: A Step in the Validation of a New Self-Report Scale , 1976, British Journal of Psychiatry.

[3]  J. Bernstein,et al.  Syntax and speech , 1984, Proceedings of the IEEE.

[4]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[5]  J. F. Stein,et al.  Role of the cerebellum in the visual guidance of movement , 1986, Nature.

[6]  D. Tank,et al.  Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[7]  R. Scheibel,et al.  Case report: acquired antisocial personality disorder associated with unilateral left orbital frontal lobe damage. , 1992, Journal of psychiatry & neuroscience : JPN.

[8]  A. L. Leiner,et al.  Cognitive and language functions of the human cerebellum , 1993, Trends in Neurosciences.

[9]  K. Coburn,et al.  EEG-based, neural-net predictive classification of Alzheimer's disease versus control subjects is augmented by non-linear EEG measures. , 1994, Electroencephalography and clinical neurophysiology.

[10]  A. Damasio,et al.  Insensitivity to future consequences following damage to human prefrontal cortex , 1994, Cognition.

[11]  J. Staiger,et al.  Increased corpus callosum size in musicians , 1995, Neuropsychologia.

[12]  G. Edelman,et al.  Complexity and coherency: integrating information in the brain , 1998, Trends in Cognitive Sciences.

[13]  J. Desmond,et al.  Neuroimaging studies of the cerebellum: language, learning and memory , 1998, Trends in Cognitive Sciences.

[14]  A. Damasio,et al.  Emotion, decision making and the orbitofrontal cortex. , 2000, Cerebral cortex.

[15]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[16]  J. E. Hind,et al.  Auditory cortex on the human posterior superior temporal gyrus , 2000, The Journal of comparative neurology.

[17]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[18]  C. Peng,et al.  What is physiologic complexity and how does it change with aging and disease? , 2002, Neurobiology of Aging.

[19]  Paul J. Laurienti,et al.  An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets , 2003, NeuroImage.

[20]  J. Duncan,et al.  Prefrontal cortical function and anxiety: controlling attention to threat-related stimuli , 2004, Nature Neuroscience.

[21]  S. M. Morton,et al.  Cerebellar Control of Balance and Locomotion , 2004, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[22]  Joseph A Maldjian,et al.  Precentral gyrus discrepancy in electronic versions of the Talairach atlas , 2004, NeuroImage.

[23]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Chris I. Baker,et al.  Integration of Visual and Auditory Information by Superior Temporal Sulcus Neurons Responsive to the Sight of Actions , 2005, Journal of Cognitive Neuroscience.

[25]  Atsushi Sato,et al.  Association of neural and physiological responses during voluntary emotion suppression , 2006, NeuroImage.

[26]  J. Horck A Mixed Crew Complement - A maritime safety challenge and its impact on maritime education and training , 2006 .

[27]  D. Drachman Aging of the brain, entropy, and Alzheimer disease , 2006, Neurology.

[28]  Thomas J. Eluvathingal,et al.  Abnormal Brain Connectivity in Children After Early Severe Socioemotional Deprivation: A Diffusion Tensor Imaging Study , 2006, Pediatrics.

[29]  D. Abásolo,et al.  Entropy analysis of the EEG background activity in Alzheimer's disease patients , 2006, Physiological measurement.

[30]  W. McMahon,et al.  Superior Temporal Gyrus, Language Function, and Autism , 2007, Developmental neuropsychology.

[31]  B. Mesquita,et al.  The experience of emotion. , 2007, Annual review of psychology.

[32]  N. Schor Abnormal Brain Connectivity in Children After Early Severe Socioemotional Deprivation: A Diffusion Tensor Imaging StudyEluvathingal TJ, Chugani HT, Behen ME, et al (Wayne State Univ, Detroit) Pediatrics 117:2093–2100, 2006§ , 2007 .

[33]  H. Barbas Flow of information for emotions through temporal and orbitofrontal pathways , 2007, Journal of anatomy.

[34]  M. Struys,et al.  Behavior of Entropy/Complexity Measures of the Electroencephalogram during Propofol-induced Sedation: Dose-dependent Effects of Remifentanil , 2007, Anesthesiology.

[35]  D. Angelaki,et al.  Vestibular system: the many facets of a multimodal sense. , 2008, Annual review of neuroscience.

[36]  W. Lee,et al.  Experience-Dependent Plasticity of Cerebellar Vermis in Basketball Players , 2009, The Cerebellum.

[37]  Weiting Chen,et al.  Measuring complexity using FuzzyEn, ApEn, and SampEn. , 2009, Medical engineering & physics.

[38]  Wolf Singer,et al.  The Brain, a Complex Self-organizing System , 2009, European Review.

[39]  P. Rakic Evolution of the neocortex: Perspective from developmental biology , 2010 .

[40]  R. Hornero,et al.  Entropy and Complexity Analyses in Alzheimer’s Disease: An MEG Study , 2010, The open biomedical engineering journal.

[41]  Hong-Bo Xie,et al.  Fuzzy Approximate Entropy Analysis of Chaotic and Natural Complex Systems: Detecting Muscle Fatigue Using Electromyography Signals , 2010, Annals of Biomedical Engineering.

[42]  Ian J. Deary,et al.  Inter-individual Differences in fMRI Entropy Measurements in Old Age , 2011, IEEE Transactions on Biomedical Engineering.

[43]  D. Gee,et al.  Anxiety dissociates dorsal and ventral medial prefrontal cortex functional connectivity with the amygdala at rest. , 2011, Cerebral cortex.

[44]  W. Lee,et al.  Basketball training increases striatum volume. , 2011, Human movement science.

[45]  T. Jiang,et al.  Increased Cortical Thickness in Sports Experts: A Comparison of Diving Players with the Controls , 2011, PloS one.

[46]  Xi-Nian Zuo,et al.  REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing , 2011, PloS one.

[47]  Olaf Sporns,et al.  THE HUMAN CONNECTOME: A COMPLEX NETWORK , 2011, Schizophrenia Research.

[48]  D. Linden,et al.  Resting state fMRI entropy probes complexity of brain activity in adults with ADHD , 2013, Psychiatry Research: Neuroimaging.

[49]  P. Tu,et al.  Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: a multiscale entropy analysis , 2013, Neurobiology of Aging.

[50]  K. Yuan,et al.  Length of Acupuncture Training and Structural Plastic Brain Changes in Professional Acupuncturists , 2013, PloS one.

[51]  H. Smet,et al.  The cerebellum: Its role in language and related cognitive and affective functions , 2013, Brain and Language.

[52]  J. Detre,et al.  Brain Entropy Mapping Using fMRI , 2014, PloS one.

[53]  Moses O. Sokunbi,et al.  Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets , 2014, Front. Neuroinform..

[54]  Moses O. Sokunbi,et al.  Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span. , 2015, Medical engineering & physics.

[55]  Q. Gong,et al.  Altered baseline brain activity in experts measured by amplitude of low frequency fluctuations (ALFF): a resting state fMRI study using expertise model of acupuncturists , 2015, Front. Hum. Neurosci..

[56]  C. Michel,et al.  fMRI of Simultaneous Interpretation Reveals the Neural Basis of Extreme Language Control. , 2015, Cerebral cortex.

[57]  Y. Dezhong,et al.  Functional Brain Network Study on Resting State of Composers , 2016 .

[58]  Lubin Wang,et al.  Changes in functional connectivity dynamics associated with vigilance network in taxi drivers , 2016, NeuroImage.

[59]  Ze Wang,et al.  Hyper-resting brain entropy within chronic smokers and its moderation by Sex , 2016, Scientific Reports.

[60]  Liberty S. Hamilton,et al.  Intonational speech prosody encoding in the human auditory cortex , 2017, Science.

[61]  Weiming Zeng,et al.  Brain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer , 2017, Front. Psychol..