About innovation-driven Co-Activation Patterns (iCAPs)

Dynamics of resting-state functional magnetic resonance imaging (fMRI) provide a new window onto the organizational principles of brain function. Using state-of-the-art sparsity-pursuing deconvolution, termed “Total Activation” (TA), we extract innovation-driven co-activation patterns (iCAPs) from resting-state fMRI. The iCAPs’ maps are spatially overlapping and their sustained-activity signals temporally overlapping. Decomposing resting-state fMRI using iCAPs reveals the rich spatiotemporal structure of functional components that dynamically assemble known resting-state networks. The temporal overlap between iCAPs is substantial and is consistent with their behavioral profiles. In contrast to conventional connectivity analysis, which suggests a negative correlation between fluctuations in the default-mode network (DMN) and task-positive networks, iCAPs show evidence for two DMN-related subnetworks consisting the posterior cingulate cortex that differentially interact with the attention network. This methodology demonstrates how fMRI resting state carries complex interactions between large-scale functional networks, a property that can be approached by decomposing the data into spatially and temporally overlapping building blocks using iCAPs.

References

Using iCAPs to reveal functional networks

• F. I. Karahanoglu, D. Van De Ville, “Transient Brain Activity Disentangles fMRI Resting-State Dynamics in Terms of Spatially and Temporally Overlapping Networks“, Nature Communications, vol. 6, art. 7751, 2015.
• D. Van De Ville, F. I. Karahanoglu, “Resting-State Neuroimaging Unravels Functional Organization in the Brain“, SPIE Newsroom, August 15, 2016.

Total activation deconvolution

• F. I. Karahanoglu, I. Bayram, D. Van De Ville, “A Signal Processing Approach to Generalized 1-D Total Variation“, IEEE Transactions on Signal Processing, vol. 59(11), pp. 5265-5274, 2011.
• F. I. Karahanoglu, C. Caballero-Gaudes, C., Lazeyras, F., and Van De Ville, “Total Activation: FMRI Deconvolution Through Spatio-Temporal Regularization“, Neuroimage, vol. 73, pp. 121-134, 2013.
• Y. Farouj, F. I. Karahanoglu, D. Van De Ville, “Regularized Spatiotemporal Deconvolution of fMRI Data Using Gray-Matter Constrained Total Variation“, Proc. 14th IEEE Int. Symp. Biomed. Imaging From Nano to Macro, pp. 472-475, 2017.

Transient-informed regression

• D. Zöller, T.A. Bolton, F. I. Karahanoglu, S. Eliez, M. Schaer, D. Van De Ville,  “Robust Recovery of Temporal Overlap Between Network Activity Using Transient-Informed Spatiotemporal Regression”, IEEE Transactions on Medical Imaging, in press.

More background about dynamic functional connectivity

• M. G. Preti, T. Bolton, D. Van De Ville, “The Dynamic Functional Connectome: State-of-the-Art and Perspectives“, NeuroImage, vol. 160, pp. 41-54, 2017.
• F. I. Karahanoglu, D. Van De Ville, “Dynamics of Large-Scale fMRI Networks: Deconstruct Brain Activity to Build Better Models of Brain Function“, Current Opinion in Biomedical Engineering, vol. 3, pp. 28-36, 2017.