Inference of direct and multistep effective connectivities from functional connectivity of the brain and of relationships to cortical geometry

BACKGROUND The problem of inferring effective brain connectivity from functional connectivity is under active investigation, and connectivity via multistep paths is poorly understood. NEW METHOD A method is presented to calculate the direct effective connection matrix (deCM), which embodies direct connection strengths between brain regions, from functional CMs (fCMs) by minimizing the difference between an experimental fCM and one calculated via neural field theory from an ansatz deCM based on an experimental anatomical CM. RESULTS The best match between fCMs occurs close to a critical point, consistent with independent published stability estimates. Residual mismatch between fCMs is identified to be largely due to interhemispheric connections that are poorly estimated in an initial ansatz deCM due to experimental limitations; improved ansatzes substantially reduce the mismatch and enable interhemispheric connections to be estimated. Various levels of significant multistep connections are then imaged via the neural field theory (NFT) result that these correspond to powers of the deCM; these are shown to be predictable from geometric distances between regions. COMPARISON WITH EXISTING METHODS This method gives insight into direct and multistep effective connectivity from fCMs and relating to physiology and brain geometry. This contrasts with other methods, which progressively adjust connections without an overarching physiologically based framework to deal with multistep or poorly estimated connections. CONCLUSIONS deCMs can be usefully estimated using this method and the results enable multistep connections to be investigated systematically.

[1]  P. Robinson,et al.  Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  P A Robinson,et al.  Estimation of multiscale neurophysiologic parameters by electroencephalographic means , 2004, Human brain mapping.

[3]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[4]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[5]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[6]  Peter A. Robinson,et al.  Stability and structural constraints of random brain networks with excitatory and inhibitory neural populations , 2009, Journal of Computational Neuroscience.

[7]  Donald L Rowe,et al.  Estimation of neurophysiological parameters from the waking EEG using a biophysical model of brain dynamics. , 2004, Journal of theoretical biology.

[8]  Jochen Triesch,et al.  Spike avalanches in vivo suggest a driven, slightly subcritical brain state , 2014, Front. Syst. Neurosci..

[9]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[10]  Sacha Jennifer van Albada,et al.  Neurophysiological changes with age probed by inverse modeling of EEG spectra , 2010, Clinical Neurophysiology.

[11]  Edward T. Bullmore,et al.  Broadband Criticality of Human Brain Network Synchronization , 2009, PLoS Comput. Biol..

[12]  P A Robinson,et al.  Interrelating anatomical, effective, and functional brain connectivity using propagators and neural field theory. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  R. F. Galán,et al.  On How Network Architecture Determines the Dominant Patterns of Spontaneous Neural Activity , 2008, PLoS ONE.

[14]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[15]  Ravi S. Menon,et al.  Resting-state connectivity identifies distinct functional networks in macaque cingulate cortex. , 2012, Cerebral cortex.

[16]  S. Laughlin,et al.  An Energy Budget for Signaling in the Grey Matter of the Brain , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[17]  Piet Van Mieghem,et al.  A Mapping Between Structural and Functional Brain Networks , 2016, Brain Connect..

[18]  P A Robinson,et al.  Determination of effective brain connectivity from functional connectivity with application to resting state connectivities. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Edward T. Bullmore,et al.  On the use of correlation as a measure of network connectivity , 2012, NeuroImage.

[20]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[21]  Peter A. Robinson,et al.  Stability of small-world networks of neural populations , 2009, Neurocomputing.

[22]  Peter A. Robinson,et al.  Relations Between the Geometry of Cortical Gyrification and White-Matter Network Architecture , 2014, Brain Connect..

[23]  M. Fiedler Special matrices and their applications in numerical mathematics , 1986 .

[24]  O. Sporns Networks of the Brain , 2010 .

[25]  James J. Wright,et al.  Propagation and stability of waves of electrical activity in the cerebral cortex , 1997 .

[26]  Gustavo Deco,et al.  Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.

[27]  Stefan Rotter,et al.  How Structure Determines Correlations in Neuronal Networks , 2011, PLoS Comput. Biol..

[28]  Fernando Calamante,et al.  The contribution of geometry to the human connectome , 2016, NeuroImage.

[29]  Jeremy D. Schmahmann,et al.  Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers , 2008, NeuroImage.

[30]  Ravi S. Menon,et al.  Identification of Optimal Structural Connectivity Using Functional Connectivity and Neural Modeling , 2014, The Journal of Neuroscience.

[31]  G Tononi,et al.  Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. , 2000, Cerebral cortex.

[32]  P A Robinson,et al.  Geometric effects on complex network structure in the cortex. , 2011, Physical review letters.

[33]  Karl J. Friston,et al.  The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields , 2008, PLoS Comput. Biol..

[34]  Peter A. Robinson,et al.  Using Geometry to Uncover Relationships Between Isotropy, Homogeneity, and Modularity in Cortical Connectivity , 2013, Brain Connect..

[35]  Prof. Dr. Dr. Valentino Braitenberg,et al.  Cortex: Statistics and Geometry of Neuronal Connectivity , 1998, Springer Berlin Heidelberg.

[36]  Michael Breakspear,et al.  Hemodynamic Traveling Waves in Human Visual Cortex , 2012, PLoS Comput. Biol..

[37]  Olaf Sporns,et al.  Can structure predict function in the human brain? , 2010, NeuroImage.

[38]  Olaf Sporns,et al.  Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons , 2011, PLoS Comput. Biol..

[39]  O. Sporns,et al.  Organization, development and function of complex brain networks , 2004, Trends in Cognitive Sciences.

[40]  John M. Beggs,et al.  Neuronal Avalanches in Neocortical Circuits , 2003, The Journal of Neuroscience.

[41]  P Riley,et al.  Dynamical reconnection and stability constraints on cortical network architecture. , 2009, Physical review letters.

[42]  P. Robinson Propagator theory of brain dynamics. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  O. Sporns,et al.  White matter maturation reshapes structural connectivity in the late developing human brain , 2010, Proceedings of the National Academy of Sciences.

[44]  A. R. McIntosh,et al.  The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models , 2009, Journal of Neuroscience Methods.

[45]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[46]  Richard F. Betzel,et al.  Resting-brain functional connectivity predicted by analytic measures of network communication , 2013, Proceedings of the National Academy of Sciences.

[47]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[48]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[49]  E. Bullmore,et al.  Neurophysiological architecture of functional magnetic resonance images of human brain. , 2005, Cerebral cortex.

[50]  C. Stam,et al.  Scale‐free dynamics of global functional connectivity in the human brain , 2004, Human brain mapping.

[51]  Somwrita Sarkar,et al.  Eigenmodes of brain activity: Neural field theory predictions and comparison with experiment , 2016, NeuroImage.

[52]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[53]  Matthieu Gilson,et al.  Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome , 2016, PLoS Comput. Biol..

[54]  Marcus Kaiser,et al.  Hierarchy and Dynamics of Neural Networks , 2010, Front. Neuroinform..

[55]  Olaf Sporns,et al.  Modeling the Impact of Lesions in the Human Brain , 2009, PLoS Comput. Biol..

[56]  K. Linkenkaer-Hansen,et al.  Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations , 2001, The Journal of Neuroscience.

[57]  J. Doyle,et al.  Essentials of Robust Control , 1997 .

[58]  Ronald F. Boisvert,et al.  NIST Handbook of Mathematical Functions , 2010 .

[59]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[60]  Olaf Sporns,et al.  Symbiotic relationship between brain structure and dynamics , 2009, BMC Neuroscience.

[61]  Thomas T. Liu,et al.  A geometric view of global signal confounds in resting-state functional MRI , 2012, NeuroImage.

[62]  I. S. Gradshteyn Table of Integrals, Series and Products, Corrected and Enlarged Edition , 1980 .

[63]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[64]  E. Bullmore,et al.  Adaptive reconfiguration of fractal small-world human brain functional networks , 2006, Proceedings of the National Academy of Sciences.

[65]  Karl J. Friston Functional and Effective Connectivity: A Review , 2011, Brain Connect..