A Mapping Between Structural and Functional Brain Networks

The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.

[1]  Gustavo Deco,et al.  How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model , 2012, Front. Comput. Neurosci..

[2]  Karl J. Friston,et al.  Degeneracy and cognitive anatomy , 2002, Trends in Cognitive Sciences.

[3]  C. J. Honeya,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009 .

[4]  Edwin van Dellen,et al.  Structural degree predicts functional network connectivity: A multimodal resting-state fMRI and MEG study , 2014, NeuroImage.

[5]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[6]  C. Stam Modern network science of neurological disorders , 2014, Nature Reviews Neuroscience.

[7]  Linda Douw,et al.  Dissociated multimodal hubs and seizures in temporal lobe epilepsy , 2015, Annals of clinical and translational neurology.

[8]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[9]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[10]  R. A. Silverman,et al.  Theory of Functions of a Complex Variable. Volume III. By A. I. Markushevich, trans, by R. A. Silverman. Pp. xi, 360. 104s. (Prentice-Hall Int., London, 1967.) , 1966, The Mathematical Gazette.

[11]  H. Voss,et al.  Network diffusion accurately models the relationship between structural and functional brain connectivity networks , 2014, NeuroImage.

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

[13]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[14]  Gustavo Deco,et al.  Rich club organization supports a diverse set of functional network configurations , 2014, NeuroImage.

[15]  P Tewarie,et al.  The relation between structural and functional connectivity patterns in complex brain networks. , 2016, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[16]  Darren Price,et al.  Investigating the electrophysiological basis of resting state networks using magnetoencephalography , 2011, Proceedings of the National Academy of Sciences.

[17]  J. Rapoport,et al.  Simple models of human brain functional networks , 2012, Proceedings of the National Academy of Sciences.

[18]  Scott T. Grafton,et al.  Structural foundations of resting-state and task-based functional connectivity in the human brain , 2013, Proceedings of the National Academy of Sciences.

[19]  R. Kahn,et al.  Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain , 2009, Human brain mapping.

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

[21]  E. C. Titchmarsh,et al.  The theory of functions , 1933 .

[22]  D. Leopold,et al.  Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited , 2014, Proceedings of the National Academy of Sciences.

[23]  P. V. Mieghem,et al.  Performance Analysis of Complex Networks and Systems , 2014 .

[24]  Gustavo Deco,et al.  Structure-Function Discrepancy: Inhomogeneity and Delays in Synchronized Neural Networks , 2014, PLoS Comput. Biol..

[25]  Arjan Hillebrand,et al.  Functional brain networks: Linking thalamic atrophy to clinical disability in multiple sclerosis, a multimodal fMRI and MEG Study , 2015, Human brain mapping.

[26]  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.

[27]  Piet Van Mieghem,et al.  Graph Spectra for Complex Networks , 2010 .

[28]  Habib Benali,et al.  Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities , 2014, PLoS Comput. Biol..

[29]  M. Greicius,et al.  Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity , 2009, Brain Structure and Function.

[30]  Nicholas J. Higham,et al.  Functions of matrices - theory and computation , 2008 .

[31]  Alan C. Evans,et al.  Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. , 2009, Cerebral cortex.

[32]  M. Hämäläinen,et al.  Feasibility of the homogeneous head model in the interpretation of neuromagnetic fields. , 1987, Physics in medicine and biology.

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

[34]  Jean-Philippe Thiran,et al.  The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes , 2011, Front. Neuroinform..

[35]  J. Rapoport,et al.  The anatomical distance of functional connections predicts brain network topology in health and schizophrenia. , 2013, Cerebral cortex.

[36]  Joel Yager Measuring Brain Connectivity to Predict Antidepressant Response , 2018 .

[37]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[38]  L. Cammoun,et al.  The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI , 2012, PloS one.

[39]  Vince D. Calhoun,et al.  Measuring brain connectivity: Diffusion tensor imaging validates resting state temporal correlations , 2008, NeuroImage.

[40]  Edmund Taylor Whittaker,et al.  A Course of Modern Analysis , 2021 .

[41]  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.