Instantaneous brain dynamics mapped to a continuous state space

&NA; Measures of whole‐brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain's dynamical operations. However, interpretation of whole‐brain dynamics has been stymied by the inherently high‐dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel‐level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet‐ICA state vectors is a graph that may be embedded onto a lower‐dimensional space to assist the interpretation of state‐space dynamics. Applying this procedure to a large sample of resting‐state and task‐active data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus‐dependent brain states. Upon observing the local neighborhood of brain‐states adopted subsequent to each stimulus, we may conclude that resting brain activity includes brain states that are, at times, similar to those adopted during tasks, but that are at other times distinct from task‐active brain states. As task‐active brain states often populate a local neighborhood, back‐projection of segments of the dynamical state space onto the brain's surface reveals the patterns of brain activity that support many experimentally‐defined states. HighlightsWe demonstrate the construction and interrogation of a continuous, two‐dimensional map of fMRI dynamics.Map points represent an individual's multispectral, and multispectral BOLD state centered at a single point in time.Task‐based scans occupy focal state‐spaces, reinforcing the utility of study methods to capture salient BOLD dynamics.Resting‐state scans occupy a broad state‐space, reinforcing the view that the resting mind is highly active.

[1]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

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

[3]  Fernand Meyer,et al.  Topographic distance and watershed lines , 1994, Signal Process..

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

[5]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[6]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[7]  Wim Sweldens,et al.  The lifting scheme: a construction of second generation wavelets , 1998 .

[8]  Vince D. Calhoun,et al.  Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information , 2015, NeuroImage.

[9]  Biyu J. He Scale-free brain activity: past, present, and future , 2014, Trends in Cognitive Sciences.

[10]  Timothy S. Coalson,et al.  Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. , 2012, Cerebral cortex.

[11]  E. Bullmore,et al.  Wavelets and functional magnetic resonance imaging of the human brain , 2004, NeuroImage.

[12]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

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

[14]  H. Eichenbaum,et al.  Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. , 2010, Journal of neurophysiology.

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

[16]  C. Frith,et al.  Movement and Mind: A Functional Imaging Study of Perception and Interpretation of Complex Intentional Movement Patterns , 2000, NeuroImage.

[17]  Bruce R. Rosen,et al.  fMRI at 20: Has it changed the world? , 2012, NeuroImage.

[18]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[19]  Mark J. Lowe,et al.  The emergence of doing “nothing” as a viable paradigm design , 2012, NeuroImage.

[20]  C. Sparrow The Fractal Geometry of Nature , 1984 .

[21]  D. Jaeger,et al.  Phase-amplitude coupling and infraslow (<1 Hz) frequencies in the rat brain: relationship to resting state fMRI , 2014, Front. Integr. Neurosci..

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

[23]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[24]  V. Calhoun,et al.  The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery , 2014, Neuron.

[25]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[26]  Shella D. Keilholz,et al.  Multiscale FC analysis refines functional connectivity networks in individual brains , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[27]  Abraham Z. Snyder,et al.  Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.

[28]  William Bialek,et al.  Mapping the stereotyped behaviour of freely moving fruit flies , 2013, Journal of The Royal Society Interface.

[29]  Matemática,et al.  Society for Industrial and Applied Mathematics , 2010 .

[30]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

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

[32]  J. Binder,et al.  Scale-Free Functional Connectivity of the Brain Is Maintained in Anesthetized Healthy Participants but Not in Patients with Unresponsive Wakefulness Syndrome , 2014, PloS one.

[33]  Vince D. Calhoun,et al.  Deep learning for neuroimaging: a validation study , 2013, Front. Neurosci..

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

[35]  P. Abry,et al.  Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task , 2012, Front. Physio..

[36]  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.

[37]  G. Wornell Wavelet-based representations for the 1/f family of fractal processes , 1993, Proc. IEEE.

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