Abstract
Understanding how the brain encodes information and performs computation requires statistical and functional analysis. Given the complexity of the human brain, simple methods that facilitate the interpretation of statistical correlations among different brain regions can be very useful. In this report we introduce a numerical correlation measure that may serve the interpretation of correlational neuronal data, and may assist in the evaluation of different brain states. The description of the dynamical brain system, through a global numerical measure may indicate the presence of an action principle which may facilitate a application of physics principles in the study of the human brain and cognition.
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References
Riddle, D.R., ed. 2007. Brain Aging: Models, Methods, and Mechanisms. Chapter 12. Boca Raton, FL: CRC Press/Taylor & Francis.
O’Sullivan, M., D.K. Jones, P.E. Summers, R.G. Morris, S.C. Williams, and H.S. Markus. 2001. Evidence for Cortical “Disconnection” as a Mechanism of Age-Related Cognitive Decline. Neurology 57: 632–638.
Park, D.C., and P. Reuter-Lorenz. 2009. The Adaptive Brain: Aging and Neurocognitive Scaffolding. Annual Review of Psychology 60: 173–196.
Raz, N., U. Lindenberger, K.M. Rodrigue, K.M. Kennedy, D. Head, et al. 2005. Regional Brain Changes in Aging Healthy Adults: General Trends, Individual Differences and Modifiers. Cerebral Cortex 15 (11): 1676–1689.
Head, D., R.L. Buckner, J.S. Shimony, L.E. Williams, and E. Akbudak. 2004. Differential Vulnerability of Anterior White Matter in Nondemented Aging with Minimal Acceleration in Dementia of the Alzheimer Type: Evidence from Diffusion Tensor Imaging. Cerebral Cortex 14 (4): 410–423.
Wen, W., and P. Sachdev. 2004. The Topography of White Matter Hyperintensities on Brain MRI in Healthy 60- to 64-Year-Old Individuals. NeuroImage 22 (1): 144–154.
Shadlen, M.N., and W.T. Newsome. 1998. The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding. The Journal of Neuroscience 18: 3870–3896.
Tolhurst, D.J., J.A. Movshon, and A.F. Dean. 1983. The Statistical Reliability of Signals in Single Neurons in Cat and Monkey Visual Cortex. Vision Research 23: 775–785.
Averbeck, B.B., P.E. Latham, and A. Pouget. 2006. Neural Correlations, Population Coding and Computation. Nature Reviews Neuroscience 7: 358–366.
Sporns, O., G. Tononi, and R. Kötter. 2005. The Human Connectome: A Structural Description of the Human Brain. PLoS Computational Biology 1 (4): 1–42.
Prasad, P., Burkart, J., Joshi, S.H., Talia, M.N., Toga, W.A., and Thompson, P.M.. 2013 Multimodal Brain Image Anal. 8159, 129–137, 2013. Physics Letters A, 28 (19), 2013.
Saslaw, W.C. 1987. Gravitational Physics of Stellar and Galactic Systems. Cambridge Monographs on Mathematical Physics: 245–248.
Iqbal, N., M.S. Khan, and T. Masood. 2011. Entropy Changes in the Clustering of Galaxies in an Expanding Universe. Natural Science 3 (1): 65–68.
Shoshani, J., W.J. Kupsky, and G.H. Marchant. 2006. Elephant Brain. Part I: Gross Morphology Functions, Comparative Anatomy, and Evolution. Brain Research Bulletin 70: 124–157.
Cosgrove, K.P., M.M. Calrolyne, and J.K. Staley. 2007. Evolving Knowledge of Sex Differences in Brain Structure, Function, and Chemistry. Biological Psychiatry 62 (8): 847–855.
Wang, H., B. Wang, K.P. Normoyle, K. Jackson, K. Spitler, F.M. Sharrock, M.C. Miller, C. Best, D. Llano, and R. Du. 2014. Brain Temperature and Its Fundamental Properties: A Review for Clinical Neuroscientists. Frontiers in Neuroscience 8: 307.
Simpson, S.L., F. DuBois, F. Bowman, and P.J. Laurienti. 2013. Analyzing Complex Functional Brain Networks: Fusing Statistics and Network Science to Understand the Brain. Statistics Surveys 7: 1–36.
Tsodyks, M. 2008. Computational Neuroscience Grand Challenges - A Humble Attempt at Future Forecast. Frontiers in Neuroscience 2 (1): 17–18.
Beggs, J. 2015. Can There Be a Physics of the Brain? Physical Review Letters 114: 220001.
Beggs, M.J. 2008. The Criticality Hypothesis: How Local Cortical Networks Might Optimize Information Processing. Philosophical Transactions of the Royal Society A 366: 329.
Brain Jan. 2014. 137 (1) 12–32; doi:10.1093/brain/awt162.
Brain Temperature and Its Fundamental Properties: A Review for Clinical Neuroscientists, Frontiers in Neuroscience 8: 307, 2014.
Haranas, I., and I. Gkigkitzis. 2013a. Bekestein Bound of Information Number N and Its Relation to Cosmological Parameters in a Universe with and without Cosmological Constant. Modern Modern Physics Letters A 62 (8): 1350077.
———. 2013b. Number of Information and Its Relation to the Cosmological Constant Resulting from Landauer’s Principle. Astrophysics and Space Science 348 (2): 553–557.
Oligodendrocytes, Astrocytes, and Microglial Cells, NG2+ Cells. Reference: Riddle DR, editor. Brain Aging: Models, Methods, and Mechanisms. Chapter 5, Boca Raton (FL): CRC Press/Taylor & Francis; 2007.
The role of the posterior cingulate cortex in cognition and disease, Robert Leech, David J. Sharp
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Haranas, I., Gkigkitzis, I., Kotsireas, I., Austerlitz, C. (2017). Neuronal Correlation Parameter and the Idea of Thermodynamic Entropy of an N-Body Gravitationally Bounded System. In: Vlamos, P. (eds) GeNeDis 2016. Advances in Experimental Medicine and Biology, vol 987. Springer, Cham. https://doi.org/10.1007/978-3-319-57379-3_4
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