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Inter‐subject phase synchronization for exploratory analysis of task‐fMRI

Taylor Bolt,Jason S. Nomi,S. Vij,Catie Chang,L. Uddin

Published 2018 in NeuroImage

ABSTRACT

ABSTRACT Analysis of task‐based fMRI data is conventionally carried out using a hypothesis‐driven approach, where blood‐oxygen‐level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data‐driven approach to detecting task‐driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter‐subject synchronization approach for exploratory analysis of task‐based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole‐brain task‐driven responses in terms of group‐wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter‐subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data‐driven approach for analysis of task‐driven BOLD activity. HIGHLIGHTSThe conventional approach to task‐based fMRI analysis relies on a hypothesized temporal structure that is difficult to specify in some contexts.This study utilizes an inter‐subject synchronization approach that allows for direct estimation of task‐driven brain responses.The inter‐subject synchronization approach applied to two different task paradigms yielded novel insights into the brain responses associated with each task.We suggest that this exploratory approach provides a framework in which the neural dynamics underlying task performance can be robustly characterized.

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