Graduate Thesis Or Dissertation

 

FMRI decoding using sparse neuronal networks Public Deposited

Downloadable Content

Download PDF
https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/n009w2607
Abstract
  • In this thesis we propose the use of Sparse Principal Component Analysis to recover neuronal areas in Brain Imaging. We work with functional magnetic resonance imaging data focusing our attention on the dimensionality reduction stage to represent the neuronal activation within the components that contain the maximum temporal variance, tightly related with the hemodynamic response of the neurons. The motivation for the sparse representation follows the idea of the massive modularity definition of the mind where "different neural circuits are specialized for solving adaptive problems''. The results show that the new sparse low dimensional basis (Eigenbrains) generated through novel unsupervised algorithms, such as Augmented Sparse Principal Component Analysis, perform competitively in terms of neuronal activity prediction. We push the limits of the brain understanding by describing a neuronal network through each Eigenbrain component and defining a prediction neuronal model using a linear combination of them.
Creator
Date Issued
  • 2012
Academic Affiliation
Advisor
Committee Member
Degree Grantor
Commencement Year
Subject
Last Modified
  • 2019-11-18
Resource Type
Rights Statement
Language

Relationships

Items