Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study

Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over time, not the brain-state transition itself. Here, we propose a novel dynamic functional connectivity analysis method, brain-state extraction algorithm based on state transition (BEST), which constructs connectivity matrices from the duration of brain-states and decodes the proper number of brain-states in a data-driven way. To set the duration of each brain-state, we detected brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time. Furthermore, we also used Bayesian information criterion to the clustering method to estimate and extract the number of brain-states. Through validations, it was proved that BEST could find brain-state transition time-points and could estimate the proper number of brain-states without any a priori knowledge. It has also shown that BEST can be applied to resting state fMRI data and provide stable and consistent results.

[1]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[2]  Jukka-Pekka Onnela,et al.  Change Point Detection in Correlation Networks , 2014, Scientific Reports.

[3]  Meiling Li,et al.  Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure , 2017, Human brain mapping.

[4]  Oscar Vilarroya,et al.  An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD , 2014, Human brain mapping.

[5]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[6]  Andrew W. Moore,et al.  X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.

[7]  Wolfgang Skrandies,et al.  Global field power and topographic similarity , 2005, Brain Topography.

[8]  Gustavo Deco,et al.  Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? , 2016, NeuroImage.

[9]  S. Small,et al.  Lateralization of motor circuits and handedness during finger movements , 2001, European journal of neurology.

[10]  S. Balqis Samdin,et al.  Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models , 2018, IEEE Transactions on Medical Imaging.

[11]  B. Thompson,et al.  Fatigue-related impairments in oculomotor control are prevented by norepinephrine-dopamine reuptake inhibition , 2017, Scientific Reports.

[12]  S Makeig,et al.  Spatially independent activity patterns in functional MRI data during the stroop color-naming task. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Adrian E. Raftery,et al.  How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..

[14]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[15]  Stephen M Smith,et al.  Fast transient networks in spontaneous human brain activity , 2014, eLife.

[16]  Zhi-jun Zhang,et al.  Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer’s disease , 2009, Behavioural Brain Research.

[17]  William H. Thompson,et al.  A common framework for the problem of deriving estimates of dynamic functional brain connectivity , 2017, NeuroImage.

[18]  Danielle S Bassett,et al.  Detection of functional brain network reconfiguration during task-driven cognitive states , 2016, NeuroImage.

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

[20]  Mark W. Woolrich,et al.  Spectrally resolved fast transient brain states in electrophysiological data , 2016, NeuroImage.

[21]  Xenophon Papademetris,et al.  Groupwise whole-brain parcellation from resting-state fMRI data for network node identification , 2013, NeuroImage.

[22]  A. Fleisher,et al.  Altered default mode network connectivity in alzheimer's disease—A resting functional MRI and bayesian network study , 2011, Human brain mapping.

[23]  Chongwon Pae,et al.  Connectivity-based change point detection for large-size functional networks , 2016, NeuroImage.

[24]  M Corbetta,et al.  A Dynamic Core Network and Global Efficiency in the Resting Human Brain. , 2016, Cerebral cortex.

[25]  Timothy O. Laumann,et al.  Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation , 2018, Neuron.

[26]  Martin A. Lindquist,et al.  Modeling state-related fMRI activity using change-point theory , 2007, NeuroImage.

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

[28]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[29]  M. Sigman,et al.  Signature of consciousness in the dynamics of resting-state brain activity , 2015, Proceedings of the National Academy of Sciences.

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

[31]  Pablo Balenzuela,et al.  Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis , 2012, Front. Physio..

[32]  I. J. Rae,et al.  A diagnosis of the plasma waves responsible for the explosive energy release of substorm onset , 2018, Nature Communications.

[33]  Daniel L. Rubin,et al.  Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease , 2008, PLoS Comput. Biol..

[34]  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.

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

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

[37]  Danielle S. Bassett,et al.  Functional Network Dynamics of the Language System , 2016, Cerebral cortex.

[38]  Dimitri Van De Ville,et al.  On spurious and real fluctuations of dynamic functional connectivity during rest , 2015, NeuroImage.

[39]  Evelyne Sernagor,et al.  Dampening Spontaneous Activity Improves the Light Sensitivity and Spatial Acuity of Optogenetic Retinal Prosthetic Responses , 2016, Scientific Reports.

[40]  E. Formisano,et al.  Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest , 2004, Human brain mapping.

[41]  Aapo Hyvärinen,et al.  Independent component analysis of nondeterministic fMRI signal sources , 2003, NeuroImage.

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

[43]  R. K. Hutson,et al.  Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer's disease and mild cognitive impairment , 2008, Alzheimer's & Dementia.

[44]  Y. Jeong,et al.  Influence of ROI selection on resting state functional connectivity: an individualized approach for resting state fMRI analysis , 2015, Front. Neurosci..

[45]  E. Bullmore,et al.  Behavioral / Systems / Cognitive Functional Connectivity and Brain Networks in Schizophrenia , 2010 .

[46]  Marie T. Banich,et al.  Resting-state networks predict individual differences in common and specific aspects of executive function , 2015, NeuroImage.

[47]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[48]  Danielle Smith Bassett,et al.  Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[49]  S. Rombouts,et al.  Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity , 2010, PloS one.

[50]  Mark W. Woolrich,et al.  Discovering dynamic brain networks from big data in rest and task , 2017, NeuroImage.

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

[52]  Peter Fransson,et al.  Spatial confluence of psychological and anatomical network constructs in the human brain revealed by a mass meta-analysis of fMRI activation , 2017, Scientific Reports.

[53]  Marie T Banich,et al.  Functional connectivity at rest is sensitive to individual differences in executive function: A network analysis , 2016, Human brain mapping.

[54]  Edward T. Bullmore,et al.  Age-related changes in modular organization of human brain functional networks , 2009, NeuroImage.

[55]  Jaakko Astola,et al.  Adaptive window size image denoising based on ICI rule , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[56]  Dustin Scheinost,et al.  Can brain state be manipulated to emphasize individual differences in functional connectivity? , 2017, NeuroImage.

[57]  Olaf Sporns,et al.  Towards a new approach to reveal dynamical organization of the brain using topological data analysis , 2018, Nature Communications.

[58]  Gustavo Deco,et al.  Corrigendum to “Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?” [NeuroImage 127 (2016) 242–256] , 2016, NeuroImage.

[59]  Laura C. Buchanan,et al.  Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns , 2015, Proceedings of the National Academy of Sciences.

[60]  Robert Lindenberg,et al.  Differential Effects of Dual and Unihemispheric Motor Cortex Stimulation in Older Adults , 2013, The Journal of Neuroscience.

[61]  Evan M. Gordon,et al.  Individual-specific features of brain systems identified with resting state functional correlations , 2017, NeuroImage.

[62]  Timothy Edward John Behrens,et al.  Task-free MRI predicts individual differences in brain activity during task performance , 2016, Science.

[63]  Tianzi Jiang,et al.  Changes in hippocampal connectivity in the early stages of Alzheimer's disease: Evidence from resting state fMRI , 2006, NeuroImage.

[64]  Harald Hampel,et al.  Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease , 2012, Neurobiology of Aging.

[65]  Leslie M. Loew,et al.  Computational neurobiology is a useful tool in translational neurology: the example of ataxia , 2014, Front. Neurosci..

[66]  Krzysztof J. Gorgolewski,et al.  The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance , 2015, Neuron.

[67]  Dietrich Lehmann,et al.  Millisecond by Millisecond, Year by Year: Normative EEG Microstates and Developmental Stages , 2002, NeuroImage.

[68]  Chin-Hui Lee,et al.  Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states , 2016, NeuroImage.