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Dimensionality Reduction for fMRI Diagnostic Systems

Functional Magnetic Resonance Imaging (fMRI) measures the dynamic activity of each voxel of a brain. This dissertation addresses the challenge of learning a diagnostic classifier that uses a subject’s fMRI data to distinguish subjects with neuropsychiatric disorders from healthy controls. fMRI intrinsically possess spatial and temporal dimensions, given by a waveform over hundreds of time points at each of 10^5 spatial locations. Given training data of only dozens to hundreds of subjects, standard learning algorithms will over-fit – i.e., do well on the training data, but poorly on novel instances. We address this by reducing the dimensionality, using several variants of Principal Component Analysis (PCA). We evaluate the per- formance of the PCA Variants on two datasets: Attention-Deficit Hyperactivity Disorder (ADHD) [a large public dataset of 668 subjects, used for the ADHD200 competition] and First Episode Psy- chosis [involving 34 subjects]. Our empirical studies show that using non-linear PCA to reduce fMRI dimensionality over both the spatial and temporal dimensions is statistically better, with respect to the classification task, than using a linear mapping to reduce over only the spatial or only the temporal dimension.

Citation

G. Sidhu. "Dimensionality Reduction for fMRI Diagnostic Systems". MSc Thesis, University of Alberta, August 2012.

Keywords: fMRI, machine learning, bioinformatics, medical informatics, computational psychiatry
Category: MSc Thesis
Web Links: HDL (ERA)

BibTeX

@mastersthesis{Sidhu:12,
  author = {Gagan Sidhu},
  title = {Dimensionality Reduction for fMRI Diagnostic Systems},
  School = {University of Alberta},
  year = 2012,
}

Last Updated: July 25, 2017
Submitted by Russ Greiner

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