Resting state data from E. Allen, et al, “A baseline for the multivariate comparison of resting state networks,” Frontiers in Systems Neuroscience, vol. 5, p. 12, 2011.

T-maps of all 75 components

T-maps of 28 resting state networks. See Network Labels

Aggregate ICA components

Resting state data from E. Allen, E. Damaraju, S. M. Plis, E. Erhardt, T. Eichele, and V. D.Calhoun, “Tracking whole-brain connectivity dynamics in the resting state”, Cereb Cortex, Nov 2012.

Aggregate ICA components

T-maps of all 100 components See Component Labels

Composite T maps of 7 groups resampled to 1mm^3 space and their labels.

Mean ICA components from Stevens, V. D. Calhoun, G. D. Pearlson, and K. A. Kiehl, “Brain network dynamics during error commission,” Hum.Brain Map., vol. 30, pp. 24-37, 2009

Resting State Aggregate ICA maps used in papers:

  • B. Rashid, E. Damaraju, G. D. Pearlson and V. D. Calhoun, “Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects”, Front Hum Neurosci. 2014; 8: 897.
  • B. Rashid, M. Arabshirani, E. Damaraju, M. Cetin, R. Miller, G. D. Pearlson and V. D. Calhoun, “Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity”, NeuroImage, Volume 134, 1 July 2016, Pages 645-657.

Neuromark network templates and network labels

Large ICA networks used in paper A. Iraji et al., “The spatial chronnectome reveals a dynamic interplay between functional segregation and integration”, HBM, 2019.

Filtered Sliding Window Correlation. Please see Victor M. Vergara, PhD and Vince D Calhoun, PhD, “Filtered Correlation and Allowed Frequency Spectra in Dynamic Functional Connectivity”, Journal of Neuroscience Methods, July 2020.

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