Support vector classification analysis of resting state functional connectivity fMRI
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Author(s)
Craddock, Richard Cameron
Advisor(s)
Hu, Xiaoping
Vachtsevanos, George J.
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Abstract
Since its discovery in 1995 resting state functional connectivity derived from functional
MRI data has become a popular neuroimaging method for study psychiatric disorders.
Current methods for analyzing resting state functional connectivity in disease involve
thousands of univariate tests, and the specification of regions of interests to employ in the
analysis. There are several drawbacks to these methods. First the mass univariate tests
employed are insensitive to the information present in distributed networks of functional
connectivity. Second, the null hypothesis testing employed to select functional connectivity
dierences between groups does not evaluate the predictive power of identified functional
connectivities. Third, the specification of regions of interests is confounded by experimentor
bias in terms of which regions should be modeled and experimental error in terms
of the size and location of these regions of interests. The objective of this dissertation is
to improve the methods for functional connectivity analysis using multivariate predictive
modeling, feature selection, and whole brain parcellation.
A method of applying Support vector classification (SVC) to resting state functional
connectivity data was developed in the context of a neuroimaging study of depression.
The interpretability of the obtained classifier was optimized using feature selection techniques
that incorporate reliability information. The problem of selecting regions of interests
for whole brain functional connectivity analysis was addressed by clustering whole brain
functional connectivity data to parcellate the brain into contiguous functionally homogenous
regions. This newly developed famework was applied to derive a classifier capable of
correctly seperating the functional connectivity patterns of patients with depression from
those of healthy controls 90% of the time. The features most relevant to the obtain classifier
match those previously identified in previous studies, but also include several regions not
previously implicated in the functional networks underlying depression.
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Date
2009-11-17
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Text
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Dissertation