Zoller, Daniela M.Bolton, Thomas A. W.Karahanoglu, Fikret IsikEliez, StephanSchaer, MarieVan De Ville, Dimitri2019-01-232019-01-232019-01-232019-01-0110.1109/TMI.2018.2863944https://infoscience.epfl.ch/handle/20.500.14299/153973WOS:000455110500028Functional magnetic resonance imaging is a non-invasive tomographic imaging modality that has provided insights into system-level brain function. New analysis methods are emerging to study the dynamic behavior of brain activity. The innovation-driven co-activation pattern (iCAP) approach is one such approach that relies on the detection of timepoints with a significant transient activity to subsequently retrieve spatially and temporally overlapping large-scalebrain networks. To recover temporal profiles of the iCAPs for further time-resolved analysis, spatial patterns are fitted back to the activity-inducing signals. In this crucial step, spatial dependences can hinder the recovery of temporal overlapping activity. To overcome this effect, we propose a novel back-projection method that optimally fits activity-inducing signals given a set of transient timepoints and spatial maps of iCAPs, thus taking into account both spatial and temporal constraints. Validation on simulated data shows that transient-based constraints improve the quality of fitted time courses. Further evaluation on experimental data demonstrates that overfitting and underfitting are prevented by the use of optimized spatio-temporal constraints. Spatial and temporal properties of resulting iCAPs support that brain activity is characterized by the recurrent co-activation and co-deactivation of spatially overlapping large-scale brain networks. This new approach opens new avenues to explore the brain's dynamic core.Computer Science, Interdisciplinary ApplicationsEngineering, BiomedicalEngineering, Electrical & ElectronicImaging Science & Photographic TechnologyRadiology, Nuclear Medicine & Medical ImagingComputer ScienceEngineeringImaging Science & Photographic TechnologyRadiology, Nuclear Medicine & Medical Imagingfmridynamic functional connectivityinnovation-driven co-activation patternslarge-scale brain network dynamicsspatio-temporal regressiondynamic functional connectivitybrain connectivityfmri datastatesactivationtrackingsubjectrevealstext::journal::journal article::research article