Dynamic Generative Model of the Human Brain in Resting-State

The human connectome is constructed by translating neuron activities into a complex network with nodes and edges. Although the topology of such networks is well studied, the formation rules and dynamics are not fully understood. It is challenging to develop a generative model for large scale dynamic complex networks due to the computational obstacle and nontrivial network structure. To study the node-based and subject-based network dynamics in resting-state brain, we divide the brain scan session into several sliding windows and generate a network for each segment. Then, an action-based model generator, which generates edges according to a topology manipulating actions, is fitted to the network series. This model, presumably as the first applicable approach for synthesizing the brain network dynamics, is shown capable of synthesizing a network series of the action-based model. Also, the estimated parameters shows that the actions of brain regions are related to their functionality.

[1]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[2]  C. Stam,et al.  Small-world networks and disturbed functional connectivity in schizophrenia , 2006, Schizophrenia Research.

[3]  Larry H Hollier,et al.  Review of "A Multi-Modal Parcellation of Human Cerebral Cortex" by Glasser M, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, Ugurbil K, Andersson J, Beckmann CF, Jenkinson M, Smith SM, Van Essen DC in Nature 536: 171-181, 2016. , 2017, The Journal of craniofacial surgery.

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

[5]  Edward T. Bullmore,et al.  The discovery of population differences in network community structure: New methods and applications to brain functional networks in schizophrenia , 2012, NeuroImage.

[6]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Karl J. Friston,et al.  The slice-timing problem in event-related fMRI , 1999 .

[8]  Tzyy-Ping Jung,et al.  Imaging brain dynamics using independent component analysis , 2001, Proc. IEEE.

[9]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[10]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[11]  E. Bullmore,et al.  Wiring cost and topological participation of the mouse brain connectome , 2015, Proceedings of the National Academy of Sciences.

[12]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[13]  Olaf Sporns,et al.  Generative models of the human connectome , 2015, NeuroImage.

[14]  Satrajit S. Ghosh,et al.  Region of interest based analysis of functional imaging data , 2003, NeuroImage.

[15]  Paul J. Laurienti,et al.  Exponential Random Graph Modeling for Complex Brain Networks , 2010, PloS one.

[16]  Wenjie Fu,et al.  Recovering temporally rewiring networks: a model-based approach , 2007, ICML '07.

[17]  J. Polimeni,et al.  Blipped‐controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g‐factor penalty , 2012, Magnetic resonance in medicine.

[18]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[19]  Mario Ventresca,et al.  Action-based Modeling of Complex Networks , 2017, Scientific Reports.

[20]  Marián Boguñá,et al.  Extracting the multiscale backbone of complex weighted networks , 2009, Proceedings of the National Academy of Sciences.

[21]  Dimitri Van De Ville,et al.  Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest , 2013, NeuroImage.

[22]  J. Hajnal,et al.  Artifacts due to stimulus correlated motion in functional imaging of the brain , 1994, Magnetic resonance in medicine.

[23]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

[24]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[25]  Stephen M. Smith,et al.  Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.

[26]  Mark Holden,et al.  A Review of Geometric Transformations for Nonrigid Body Registration , 2008, IEEE Transactions on Medical Imaging.

[27]  R. Poldrack.  Region of interest analysis for fMRI. , 2007, Social cognitive and affective neuroscience.

[28]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[29]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[30]  Olaf Sporns,et al.  Connectivity and complexity: the relationship between neuroanatomy and brain dynamics , 2000, Neural Networks.

[31]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[32]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[33]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[35]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.