Abstract
Inter-subject correlation (ISC) analysis for functional
magnetic resonance imaging (fMRI)is a data driven
approach to detect the brain activity during complex
stimuli. TheISC measures are computed as a correlation
between the fMRI time courses of thestudied group of
subjects. The ISC analysis is developed especially for
fMRI studies withnaturalistic stimuli, like movies,
music, video games or annotated stories. The
naturalisticstimuli are typically used to study the
higher cognitive functions of a human brain suchas
emotions or humor.
This thesis investigates the properties of ISC analysis
for fMRI. Three major aspects ofthe ISC analysis were
studied: the accuracy of the analysis when compared with
thegeneral linear model (GLM) based analysis, the effects
of spatial smoothing and the effectof sample size on the
results. In addition, the openly available implementation
of ISCanalysis that was used in this study, ISCtoolbox
for Matlab, was improved by developinga built-in support
for cluster computing environments. The improvements were
publishedwith the new version of the ISCtoolbox. These
improvements were mandatory due to thehigh computational
costs of ISC analysis and the high number of ISC analyses
requiredfor the studies of this thesis.
Four international journal publications are included in
the thesis. The ?rst one describesthe properties of the
ISC analysis and ISCtoolbox for Matlab implementation.
The secondone investigates the accuracy of the ISC
analysis with a block design fMRI data when compared with
the GLM analysis. The third study investigates the
effects of spatialsmoothing on the ISC analysis and uses
the GLM analysis as a reference for the testing. The
fourth study tests how the sample size affects the ISC
analysis results.
The ISC analysis was veri?ed to be an effcient
non-parametric data analysis method forfMRI data
especially in studies with naturalistic stimuli. In
addition to this the studiesindicated that ISC can
successfully be applied also to the traditional block
design data,where it is able to detect activations with
similar accuracy as the GLM analysis. Thespatial
smoothing was found to be a mandatory pre-processing step
for the ISC analysis.When the thresholds for ISC results
were corrected with false discovery rate
multiplecomparisons correction, the ISC analysis was able
to tolerate slightly larger Gaussiansmoothing kernels
than the GLM analysis. The sample size investigation
veri?ed that theISC analysis can produce fairly stable
results when the number of subjects included in thestudy
was more than 20. At least 30 subjects were found to
guarantee the high similarityof the results. The
implementation of the ISC analysis as ISCtoolbox was
found to behighly efficient in cluster computing
environments.
Original language | English |
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Qualification | Doctor Degree |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 22 Apr 2016 |
Place of Publication | Tampere |
Publisher | |
Print ISBNs | 978-952-15-3721-9 |
Electronic ISBNs | 978-952-15-3723-3 |
Publication status | Published - 2016 |
MoE publication type | G5 Doctoral dissertation (article) |