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Author Notes:

Corresponding Author. Address 1760 Haygood Dr, Suite W-230; Atlanta, GA 30322-4600. shella.keilholz@bme.gatech.edu

The authors gratefully acknowledge Dr. Dieter Jaeger and the Emory Center for Mind, Brain, and Culture whose Dimensionality Reduction Workshop inspired this work.

Special thanks go to Dr. Ying Guo who suggested the permutation test for statistical significance.


Research Funding:

These efforts were directly funded by: NIH 5-R01NS078095-02, and by Professional Development Supports Funds provided by the Laney Graduate School at Emory University.

Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neuroimaging
  • Radiology, Nuclear Medicine & Medical Imaging
  • Neurosciences & Neurology
  • fMRI
  • Connectivity dynamics
  • Functional connectivity
  • Multiscale systems
  • Dimensionality reduction

Instantaneous brain dynamics mapped to a continuous state space

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Journal Title:



Volume 162


, Pages 344-352

Type of Work:

Article | Post-print: After Peer Review


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.

Copyright information:

© 2017 Elsevier Inc.

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