Tuesday, June 27, 2017: 12:45 PM
Monday, June 26 & Tuesday, June 27
Jussi Tohka1, Frank Pollick2, Juha Pajula3, Jukka-Pekka Kauppi4
1University of Eastern Finland, Kuopio, Finland, 2University of Glasgow, Glasgow, United Kingdom, 3VTT Technical Research Centre of Finland, Tampere, Finland, 4University of Jyvaskyla, Jyvaskyla, Finland
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. The freely available ISC toolbox (Kauppi 2014) provides a GUI-driven software framework to carry out various ISC based analysis such as mean, frequency band, time window and phase synchronization ISC analysis. We have extended the functionality of the ISC-Toolbox to analyze the differences of ISCs between two distinct groups of subjects. This abstract presents these novel features of the ISC-Toolbox as well as validation results concerning this new type of ISC analysis.
We rely largely on the methodology put forth by Chen 2016 with few important differences and additions. For every voxel, the test statistic is the difference between the average z-transformed subject-pair wise ISC values between the two groups. The hypothesis test utilized is a permutation test containing multiple comparisons correction. We implement three different types of permutations: 1) element-wise, where the subject-pairs (elements of correlation matrix) are permuted between the groups, 2) subject-wise, where the subjects are permuted between the two groups, and 3) optimized subject-wise permutations, where the subjects are permuted, but between group correlations that arise are ignored. Our experiments with synthetic data confirmed the findings by Chen 2016 that the element-wise permutations lead to too liberal hypothesis tests while the subject-wise permutations maintain the correct alpha level. Novel optimized subject-wise permutations are introduced due to the heavy memory burden of the subject-wise permutations and theoretically reduce the memory consumption to one third of the subject-wise permutation test.
We evaluated the method by using fMRIs from 36 healthy young adults during the auditory naming task of the ICBM functional reference battery, see (Pajula 2012) for the details of the data and its processing . We divided 36 subjects randomly into two groups of 18 subjects. We expected to see no difference between similar groups. The element-wise permutation was too lenient, indicating 240 voxels of the significant ISC difference . Two other permutation types correctly identified no significant difference (p < 0.05, voxel-wise FWER corrected over the whole brain in all cases).
Additionally, we have applied the method to compare inter-subject synchronisation of 12 male adults on the autism spectrum to 10 age and IQ matched typically developed male adults while they viewed a 90 second clip of a solo ballet dance. We found significant ISC differences (p < 0.05 voxel wise FWER-corrected using permutation test) involving greater synchronisation for the autism group in the Middle Temporal Gyrus, BA 37, at Talairach coordinates (-49, -62, 4).
We have introduced the implementation of between-group ISC analysis incorporated to ISC-Toolbox and validated the analysis. The ISC-Toolbox with these novel features is available at https://www.nitrc.org/projects/isc-toolbox/ .
Informatics Other 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Poster Session - Tuesday
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G. Chen, Y-W Shin, P A. Taylor, D R. Glen, R C. Reynolds, R B. Israel, R W. Cox, Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level, NeuroImage, 142, 248 - 259 2016.
J.-P. Kauppi, J. Pajula and J. Tohka . A Versatile Software Package for Inter-subject Correlation Based Analyses of fMRI. Frontiers in Neuroinformatics, 8:2, 2014.
J. Pajula, J.-P. Kauppi, and J. Tohka . Inter-Subject Correlation in fMRI: Method Validation against Stimulus-model Based Analysis, PLoS ONE, 7(8):e41196 2012.