Modeling the Antipodal Connectivity Structure of Neural Communities

Bayazit Karaman; R. Murat Demirer; Coskun Bayrak; M. Mert Su; Bayazit Karaman; R. Murat Demirer; Coskun Bayrak; M. Mert Su

doi:10.3934/Neuroscience.2016.2.163

AIMS Neuroscience

2016, Volume 3, Issue 2: 163-180. doi: 10.3934/Neuroscience.2016.2.163

Previous Article Next Article

Research article

Modeling the Antipodal Connectivity Structure of Neural Communities

1.
Computer Science Department, University of Arkansas at Little Rock, 2801 S University Ave, Little Rock, AR, USA
2.
Industrial and System Engineering, Uskudar University, Istanbul, TURKEY
3.
Computer Science Department, University of Arkansas at Little Rock, 2801 S University Ave, Little Rock, AR, USA
4.
University of Arkansas at Little Rock, 2801 S University Ave, Little Rock, AR, USA

Received: 25 January 2016 Accepted: 28 April 2016 Published: 04 May 2016

Recent studies support the theory of the brain being composed of modules and certain nodes establishing connections between the modules [1,2,3]. The existence of such connections can only be identified by conducting a detailed investigation with sophisticated tools. Therefore, in this manuscript we provide a new mathematical model to indicate the functional dependency, which supports the idea of information exchange between the neural modules at the highest spatial and hierarchical level of bottom-up processes using EEG (ElectroEncephaloGraphy) [4]. The developed model is to study the functional dependencies between di erent regions of the cortex is based on the Borsuk-Ulam's antipodal symmetry theorem. It is a mathematical model complemented with an innovative algorithm, called Projection based on Normalized Transformation (PNT), to show the existence of unique neural activity pattern known as the Antipodal Connectivity. For validating of the model, EEG data collected from a total of 50 experiments with the participation of 18 di erent test subjects was used to measure the e ectiveness and accuracy of method. Using the data collected from the subjects in di erent stages (active or resting) of the brain, the Antipodal Hub Neurons (AHNs) were captured and compared to determine the ratio of fluctuation under di erent conditions and whether or not the stimulus has any role in antipodal neural connectivity. Although the preliminary results are not conclusive, we have successfully identified the existence of antipodal behavioral patterns in neural activities.
- EEG,
- Antipodal Symmetry,
- Borsuk-Ulam Theorem,
- Antipodal Connectivity,
- PNT,
- AHNs,
- Neural Communities,
- Antipodal Hub Neurons
Citation: Bayazit Karaman, R. Murat Demirer, Coskun Bayrak, M. Mert Su. Modeling the Antipodal Connectivity Structure of Neural Communities[J]. AIMS Neuroscience, 2016, 3(2): 163-180. doi: 10.3934/Neuroscience.2016.2.163

Related Papers:

Abstract

Recent studies support the theory of the brain being composed of modules and certain nodes establishing connections between the modules [1,2,3]. The existence of such connections can only be identified by conducting a detailed investigation with sophisticated tools. Therefore, in this manuscript we provide a new mathematical model to indicate the functional dependency, which supports the idea of information exchange between the neural modules at the highest spatial and hierarchical level of bottom-up processes using EEG (ElectroEncephaloGraphy) [4]. The developed model is to study the functional dependencies between di erent regions of the cortex is based on the Borsuk-Ulam's antipodal symmetry theorem. It is a mathematical model complemented with an innovative algorithm, called Projection based on Normalized Transformation (PNT), to show the existence of unique neural activity pattern known as the Antipodal Connectivity. For validating of the model, EEG data collected from a total of 50 experiments with the participation of 18 di erent test subjects was used to measure the e ectiveness and accuracy of method. Using the data collected from the subjects in di erent stages (active or resting) of the brain, the Antipodal Hub Neurons (AHNs) were captured and compared to determine the ratio of fluctuation under di erent conditions and whether or not the stimulus has any role in antipodal neural connectivity. Although the preliminary results are not conclusive, we have successfully identified the existence of antipodal behavioral patterns in neural activities.

References

[1]	Chen ZJ, He Y, Rosa-Neto P, et al. (2008) Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cerebral cortex 18: 2374-2381. doi: 10.1093/cercor/bhn003
[2]	Ferri R, Rundo F, Bruni O, et al. (2008) The functional connectivity of di erent EEG bands moves towards small-world network organization during sleep. Clinical Neurophysiology 119: 2026-2036. doi: 10.1016/j.clinph.2008.04.294
[3]	Meunier D, Achard S, Morcom A, et al. (2009) Age-related changes in modular organization of human brain functional networks. Neuroimage 44: 715-723. doi: 10.1016/j.neuroimage.2008.09.062
[4]	Niedermeyer E, da Silva FL (2005) Electroencephalography: basic principles, clinical applications, and related fields: Lippincott Williams & Wilkins.
[5]	Nigam S, Shimono M, Ito S, et al. (2016) Rich-Club Organization in E ective Connectivity among Cortical Neurons. The Journal of Neuroscience 36: 670-684. doi: 10.1523/JNEUROSCI.2177-15.2016
[6]	Hagmann P, Cammoun L, Gigandet X, et al. (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6: e159. doi: 10.1371/journal.pbio.0060159
[7]	Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52: 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003
[8]	Sporns O, Tononi G, Kotter R (2005) The human connectome: a structural description of the human. Brain PLos Comput Biol(2005) 1.
[9]	Leergaard TB (2012) Mapping the connectome: multi-level analysis of brain connectivity: Frontiers E-books.
[10]	Balanis CA (2012) Advanced engineering electromagnetics: Wiley Online Library.
[11]	Feynman RP, Leighton RB, Sands M (2013) The Feynman Lectures on Physics, Desktop Edition Volume I: Basic Books.
[12]	Serway R, Jewett J (2013) Physics for scientists and engineers with modern physics: Cengage learning.
[13]	Baumann SB, Wozny DR, Kelly SK, et al. (1997) The electrical conductivity of human cerebrospinal fluid at body temperature. Biomedical Engineering, IEEE Transactions on 44: 220-223. doi: 10.1109/10.554770
[14]	Prescott T (2002) Extensions of the Borsuk-Ulam Theorem. Harvey Mudd College, http://citeseerx.ist.psu.edu.
[15]	Browder A (2006) Complex Numbers and the Ham Sandwich Theorem. The American Mathematical Monthly: 935-937.
[16]	Matousek J (2008) Using the Borsuk-Ulam theorem: lectures on topological methods in combinatorics and geometry: Springer Science & Business Media.
[17]	Aler R, Galvn IM, Valls JM (2012) Applying evolution strategies to preprocessing EEG signals for braincomputer interfaces. Information Sciences 215: 53-66. doi: 10.1016/j.ins.2012.05.012
[18]	Yang S, Deravi F. Wavelet-based EEG preprocessing for biometric applications; 2013. IEEE. pp. 43-46.
[19]	Hahn SL (1996) Hilbert transforms in signal processing: Artech House on Demand.
[20]	Yuvaraj R, Murugappan M, Ibrahim NM, et al. (2014) On the analysis of EEG power, frequency and asymmetry in Parkinsons disease during emotion processing. Behav brain Funct 10: 12. doi: 10.1186/1744-9081-10-12
[21]	Sanei S (2013) Adaptive processing of brain signals: John Wiley & Sons.
[22]	Iversen JR, Makeig S (2014) MEG/EEG Data Analysis Using EEGLAB. Magnetoencephalography: Springer. pp. 199-212.
[23]	Nater UM, Abbruzzese E, Krebs M, et al. (2006) Sex di erences in emotional and psychophysiological responses to musical stimuli. International journal of psychophysiology 62: 300-308. doi: 10.1016/j.ijpsycho.2006.05.011
[24]	KABUTO M, KAGEYAMA T, NITTA H (1993) EEG Power Spectrum Changes due to Listening to Pleasant Musics and Their Relation to Relaxation E ects. Nippon Eiseigaku Zasshi 48: 807- 818. doi: 10.1265/jjh.48.807
[25]	Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods 134: 9-21. doi: 10.1016/j.jneumeth.2003.10.009
[26]	Sanei S, Chambers JA (2013) EEG signal processing: John Wiley & Sons.
[27]	Whitham EM, Lewis T, Pope KJ, et al. (2008) Thinking activates EMG in scalp electrical recordings. Clinical Neurophysiology 119: 1166-1175. doi: 10.1016/j.clinph.2008.01.024
[28]	Whitham EM, Pope KJ, Fitzgibbon SP, et al. (2007) Scalp electrical recording during paralysis: quantitative evidence that EEG frequencies above 20Hz are contaminated by EMG. Clinical Neurophysiology 118: 1877-1888. doi: 10.1016/j.clinph.2007.04.027
[29]	Yuval-Greenberg S, Tomer O, Keren AS, et al. (2008) Transient induced gamma-band response in EEG as a manifestation of miniature saccades. Neuron 58: 429-441. doi: 10.1016/j.neuron.2008.03.027
[30]	Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience 8: 700-711. doi: 10.1038/nrn2201
[31]	Shulman GL, Fiez JA, Corbetta M, et al. (1997) Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. Journal of cognitive neuroscience 9: 648-663.
[32]	Raichle ME, MacLeod AM, Snyder AZ, et al. (2001) A default mode of brain function. Proceedings of the National Academy of Sciences 98: 676-682. doi: 10.1073/pnas.98.2.676
[33]	Greicius MD, Krasnow B, Reiss AL, et al. (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences 100: 253-258. doi: 10.1073/pnas.0135058100
[34]	Fox MD, Snyder AZ, Zacks JM, et al. (2006) Coherent spontaneous activity accounts for trial-totrial variability in human evoked brain responses. Nature neuroscience 9: 23-25. doi: 10.1038/nn1616
[35]	Fox MD, Snyder AZ, Vincent JL, et al. (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America 102: 9673-9678. doi: 10.1073/pnas.0504136102
[36]	Fox MD, Corbetta M, Snyder AZ, et al. (2006) Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences 103: 10046-10051. doi: 10.1073/pnas.0604187103

Reader Comments

Your name:*

Email:*
© 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)