A versatile software package for inter-subject correlation based analyses of fMRI

Jukka-Pekka Kauppi,Juha Pajula,Jussi Tohka

doi:10.3389/fninf.2014.00002

Jukka-Pekka Kauppi, Juha Pajula + Show 1 more

Open Access

https://doi.org/10.3389/fninf.2014.00002

Copy DOI

Abstract

In the inter-subject correlation (ISC) based analysis of the functional magnetic resonance imaging (fMRI) data, the extent of shared processing across subjects during the experiment is determined by calculating correlation coefficients between the fMRI time series of the subjects in the corresponding brain locations. This implies that ISC can be used to analyze fMRI data without explicitly modeling the stimulus and thus ISC is a potential method to analyze fMRI data acquired under complex naturalistic stimuli. Despite of the suitability of ISC based approach to analyze complex fMRI data, no generic software tools have been made available for this purpose, limiting a widespread use of ISC based analysis techniques among neuroimaging community. In this paper, we present a graphical user interface (GUI) based software package, ISC Toolbox, implemented in Matlab for computing various ISC based analyses. Many advanced computations such as comparison of ISCs between different stimuli, time window ISC, and inter-subject phase synchronization are supported by the toolbox. The analyses are coupled with re-sampling based statistical inference. The ISC based analyses are data and computation intensive and the ISC toolbox is equipped with mechanisms to execute the parallel computations in a cluster environment automatically and with an automatic detection of the cluster environment in use. Currently, SGE-based (Oracle Grid Engine, Son of a Grid Engine, or Open Grid Scheduler) and Slurm environments are supported. In this paper, we present a detailed account on the methods behind the ISC Toolbox, the implementation of the toolbox and demonstrate the possible use of the toolbox by summarizing selected example applications. We also report the computation time experiments both using a single desktop computer and two grid environments demonstrating that parallelization effectively reduces the computing time. The ISC Toolbox is available in https://code.google.com/p/isc-toolbox/

Highlights

Most neuroimaging studies, such as those based on functional magnetic resonance imaging, have so far utilized relatively simple static stimuli to analyze brain functions (Spiers and Maguire, 2007)
The used stimuli can be, for example, a movie. This kind of functional magnetic resonance imaging (fMRI) data cannot be straight-forwardly analyzed based on a general linear model (GLM), because a GLM requires a reference time course of the task that is impossible to obtain for a multi-dimensional stimulus such as a movie, unless focusing the data-analysis on a specific feature of the stimuli
We have previously introduced a framework for the basic inter-subject correlation (ISC) based analysis (Kauppi et al, 2010b) and started building an open source, graphical user interface (GUI) based Matlab toolbox, termed the ISC toolbox, for a generic, ISC based analysis of fMRI

Summary

Introduction

Most neuroimaging studies, such as those based on functional magnetic resonance imaging (fMRI), have so far utilized relatively simple static stimuli to analyze brain functions (Spiers and Maguire, 2007). The used stimuli can be, for example, a movie This kind of fMRI data cannot be straight-forwardly analyzed based on a general linear model (GLM), because a GLM requires a reference time course of the task that is impossible to obtain for a multi-dimensional stimulus such as a movie, unless focusing the data-analysis on a specific feature of the stimuli. For this reason, new data-driven methodologies are needed. The use of novel experimental setups involving rich stimuli and data-driven analysis methods which are designed to study complex brain functions opens up entire new fields for neuroscience research

Methods

Discussion

Conclusion