Granger causality analysis of state dependent functional connectivity of neurons in orofacial motor cortex during chewing and swallowing

Primate feeding behavior is characterized by a series of jaw movement cycles of different types making it ideal for investigating the role of motor cortex in controlling transitions between different kinematic states. We recorded spiking activity in populations of neurons in the orofacial portion of primary motor cortex (MIo) of a macaque monkey and, using a Granger causality model, estimated their functional connectivity during transitions between chewing cycles and from chewing to swallowing cycles. We found that during rhythmic chewing, the network was dominated by excitatory connections and exhibited a few “out degree” hub neurons, while during transitions from rhythmic chews to swallows, the numbers of excitatory and inhibitory connections became comparable, and more temporarily varying “in degree” hub neurons emerged. Furthermore, based on shared connections between neurons between different networks, networks from same state transitions were quantitatively shown to be more similar. These results suggest that networks of functionally connected neurons in MIo change their operative states with changes in kinematically defined behavioral states.

[1]  Emery N. Brown,et al.  Computational Neuroscience: A Comprehensive Approach , 2022 .

[2]  Roger Sauter,et al.  In All Likelihood , 2002, Technometrics.

[3]  Emery N. Brown,et al.  A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity , 2011, PLoS Comput. Biol..

[4]  A J Thexton,et al.  Food consistency and bite size as regulators of jaw movement during feeding in the cat. , 1980, Journal of neurophysiology.

[5]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[6]  G. M. Murray,et al.  Neuronal activity patterns in primate primary motor cortex related to trained or semiautomatic jaw and tongue movements. , 2002, Journal of neurophysiology.

[7]  Todd P. Coleman,et al.  Information transfer between neurons in the motor cortex triggered by visual cues , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Yuji Masuda,et al.  Effects of reversible bilateral inactivation of face primary motor cortex on mastication and swallowing , 2002, Brain Research.

[9]  Annette J. Dobson,et al.  An introduction to generalized linear models , 1991 .

[10]  Anthony N. Burkitt,et al.  “Computational Neuroscience: A Comprehensive Approach”, J. Feng (Ed), London: CRC Press (2004). , 2007 .

[11]  J. Iriarte-Díaz,et al.  Sources of variance in temporal and spatial aspects of jaw kinematics in two species of primates feeding on foods of different properties. , 2011, Integrative and comparative biology.

[12]  C. Ross,et al.  The influence of food material properties on jaw kinematics in the primate, Cebus. , 2010, Archives of oral biology.

[13]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[14]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[15]  H. Akaike A new look at the statistical model identification , 1974 .

[16]  John P. Donoghue,et al.  Automated spike sorting using density grid contour clustering and subtractive waveform decomposition , 2007, Journal of Neuroscience Methods.

[17]  Uri T Eden,et al.  A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.

[18]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[19]  K. Hiiemae,et al.  The Effect of Food Consistency upon Jaw Movement in the Macaque: A Cineradiographic Study , 1997, Journal of dental research.

[20]  R. Meech,et al.  An introduction to generalized linear models , 1990 .

[21]  Daryl J. Daley,et al.  An Introduction to the Theory of Point Processes , 2013 .

[22]  Emery N. Brown,et al.  A general statistical framework for assessing Granger causality , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.