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Abstract #1024

A Multi-Subject Deconvolution Algorithm for the Analysis of Naturalistic fMRI data

Eneko Uruñuela1, Clara Sava-Segal2, Megan Leung2, Emily S Finn2, and César Caballero-Gaudes1
1Basque Center on Cognition, Brain and Language, Donostia - San Sebastián, Spain, 2Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States

Synopsis

Keywords: Brain Connectivity, fMRI, naturalistic paradigmsCollecting fMRI data during naturalistic paradigms has drawn considerable attention in human neuroscience as a way to investigate brain function in ecologically valid conditions. We introduce a novel method (multi-subject paradigm free mapping) to decipher BOLD events in a temporally agnostic manner, and explore concordant group activations and individual idiosyncrasies. Besides, it can operate at the fastest temporal and spatial resolutions of the data. We validate it on simulated and real naturalistic fMRI data, revealing events that track expected features of the stimulus. Overall, this technique substantially increases sensitivity in linking moment-to-moment brain activity to its underlying cause(s).

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Keywords