Temporal transitions of spontaneous brain activity

Spontaneous brain activity, typically investigated using resting-state fMRI (rsfMRI), provides a measure of inter-areal resting-state functional connectivity (RSFC). Although it has been established that RSFC is non-stationary, previous dynamic rsfMRI studies mainly focused on revealing the spatial characteristics of dynamic RSFC patterns, but the temporal relationship between these RSFC patterns remains elusive. Here we investigated the temporal organization of characteristic RSFC patterns in awake rats and humans. We found that transitions between RSFC patterns were not random but followed specific sequential orders. The organization of RSFC pattern transitions was further analyzed using graph theory, and pivotal RSFC patterns in transitions were identified. This study has demonstrated that spontaneous brain activity is not only nonrandom spatially, but also nonrandom temporally, and this feature is well conserved between rodents and humans. These results offer new insights into understanding the spatiotemporal dynamics of spontaneous activity in the mammalian brain.

[1]  M. Raichle,et al.  Disease and the brain's dark energy , 2010, Nature Reviews Neurology.

[2]  M. V. Centeno,et al.  Novel method for functional brain imaging in awake minimally restrained rats. , 2016, Journal of neurophysiology.

[3]  Wei Chen,et al.  Procedure for minimizing stress for fMRI studies in conscious rats , 2005, Journal of Neuroscience Methods.

[4]  Nanyin Zhang,et al.  Intrinsic Organization of the Anesthetized Brain , 2012, The Journal of Neuroscience.

[5]  M. Raichle,et al.  Lag threads organize the brain’s intrinsic activity , 2015, Proceedings of the National Academy of Sciences.

[6]  Zhifeng Liang,et al.  Mapping resting-state brain networks in conscious animals , 2010, Journal of Neuroscience Methods.

[7]  Itamar Kahn,et al.  The Organization of Mouse and Human Cortico-Hippocampal Networks Estimated by Intrinsic Functional Connectivity , 2016, Cerebral cortex.

[8]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Nanyin Zhang,et al.  Uncovering Intrinsic Connectional Architecture of Functional Networks in Awake Rat Brain , 2011, The Journal of Neuroscience.

[10]  Xiao Liu,et al.  EEG correlates of time-varying BOLD functional connectivity , 2013, NeuroImage.

[11]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[12]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[13]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[14]  Enzo Tagliazucchi,et al.  Dynamic BOLD functional connectivity in humans and its electrophysiological correlates , 2012, Front. Hum. Neurosci..

[15]  Stephen M. Smith,et al.  Brain network dynamics are hierarchically organized in time , 2017, Proceedings of the National Academy of Sciences.

[16]  Waqas Majeed,et al.  Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans , 2011, NeuroImage.

[17]  D. Leopold,et al.  Arousal transitions in sleep, wakefulness, and anesthesia are characterized by an orderly sequence of cortical events , 2015, NeuroImage.

[18]  Zhifeng Liang,et al.  Neuroplasticity to a single-episode traumatic stress revealed by resting-state fMRI in awake rats , 2014, NeuroImage.

[19]  Xiao Liu,et al.  Dynamic resting state functional connectivity in awake and anesthetized rodents , 2015, NeuroImage.

[20]  Bert Sakmann,et al.  Spontaneous persistent activity in entorhinal cortex modulates cortico-hippocampal interaction in vivo , 2012, Nature Neuroscience.

[21]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[22]  Ravi S. Menon,et al.  Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques , 2013, Human brain mapping.

[23]  Ying-Shing Chan,et al.  Long-range projections coordinate distributed brain-wide neural activity with a specific spatiotemporal profile , 2016, Proceedings of the National Academy of Sciences.

[24]  Marcus E Raichle,et al.  Neuroscience. The brain's dark energy. , 2006, Science.

[25]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[26]  G. La Camera,et al.  Dynamics of Multistable States during Ongoing and Evoked Cortical Activity , 2015, The Journal of Neuroscience.

[27]  M. Schölvinck,et al.  Tracking brain arousal fluctuations with fMRI , 2016, Proceedings of the National Academy of Sciences.

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

[29]  G. E. Smith.  The Human Brain , 1924, Nature.

[30]  Andrew C. N. Chen,et al.  Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons , 2017, eLife.

[31]  Leonardo L. Gollo,et al.  Time-resolved resting-state brain networks , 2014, Proceedings of the National Academy of Sciences.

[32]  J. Born,et al.  Hippocampal sharp wave-ripples linked to slow oscillations in rat slow-wave sleep. , 2006, Journal of neurophysiology.

[33]  Kwang Lee,et al.  Parvalbumin Interneurons Modulate Striatal Output and Enhance Performance during Associative Learning , 2018, Neuron.

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

[35]  Dimitri Van De Ville,et al.  Dynamic reorganization of intrinsic functional networks in the mouse brain , 2017, NeuroImage.

[36]  G. Paxinos.  The Rat nervous system , 1985 .

[37]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[38]  Celia M. Dong,et al.  Low-frequency hippocampal–cortical activity drives brain-wide resting-state functional MRI connectivity , 2017, Proceedings of the National Academy of Sciences.

[39]  Catie Chang,et al.  Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns , 2013, Front. Syst. Neurosci..

[40]  Rex E. Jung,et al.  A Baseline for the Multivariate Comparison of Resting-State Networks , 2011, Front. Syst. Neurosci..

[41]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[42]  J. Duyn,et al.  Time-varying functional network information extracted from brief instances of spontaneous brain activity , 2013, Proceedings of the National Academy of Sciences.

[43]  M. Mintun,et al.  Brain work and brain imaging. , 2006, Annual review of neuroscience.

[44]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

[45]  Alexander S. Tolpygo,et al.  Frequency-selective control of cortical and subcortical networks by central thalamus , 2015, eLife.

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

[47]  J. Born,et al.  Oscillations in Rat Slow-wave Sleep Hippocampal Sharp Wave-ripples Linked to Slow , 2006 .

[48]  Shella D. Keilholz,et al.  Dynamic Properties of Functional Connectivity in the Rodent , 2013, Brain Connect..

[49]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[50]  Shella D. Keilholz,et al.  The Neural Basis of Time-Varying Resting-State Functional Connectivity , 2014, Brain Connect..

[51]  R. Kahn,et al.  Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis , 2010, The Journal of Neuroscience.

[52]  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.

[53]  Mark Jenkinson,et al.  MSM: A new flexible framework for Multimodal Surface Matching , 2014, NeuroImage.

[54]  P. Redgrave,et al.  A New Framework for Cortico-Striatal Plasticity: Behavioural Theory Meets In Vitro Data at the Reinforcement-Action Interface , 2015, PLoS biology.

[55]  D. McVea,et al.  Spontaneous cortical activity alternates between motifs defined by regional axonal projections , 2013, Nature Neuroscience.

[56]  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.

[57]  G. Glover,et al.  Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior , 2016, Science.

[58]  Zhifeng Liang,et al.  Functional atlas of the awake rat brain: A neuroimaging study of rat brain specialization and integration , 2016, NeuroImage.

[59]  Zhifeng Liang,et al.  Anticorrelated resting-state functional connectivity in awake rat brain , 2012, NeuroImage.

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

[61]  Zhifeng Liang,et al.  Time to wake up: Studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal , 2016, NeuroImage.

[62]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[63]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[64]  A. Oeltermann,et al.  Hippocampal–cortical interaction during periods of subcortical silence , 2012, Nature.

[65]  Tao Li,et al.  Mapping thalamocortical networks in rat brain using resting-state functional connectivity , 2013, NeuroImage.

[66]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

[67]  Nanyin Zhang,et al.  Dynamic Connectivity Patterns in Conscious and Unconscious Brain , 2017, Brain Connect..

[68]  Demetris K. Roumis,et al.  Hippocampal sharp-wave ripples in waking and sleeping states , 2015, Current Opinion in Neurobiology.

[69]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

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

[71]  Hideyuki Okano,et al.  Physiological effects of a habituation procedure for functional MRI in awake mice using a cryogenic radiofrequency probe , 2016, Journal of Neuroscience Methods.

[72]  R. Cools,et al.  Human Choice Strategy Varies with Anatomical Projections from Ventromedial Prefrontal Cortex to Medial Striatum. , 2016, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[73]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[74]  Evan M. Gordon,et al.  On the Stability of BOLD fMRI Correlations , 2016, Cerebral cortex.

[75]  M. Raichle.  The Brain's Dark Energy , 2006, Science.

[76]  M. Jacomy,et al.  ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software , 2014, PloS one.