Event Diary
CRiSM Seminar
Location: A1.01
Dr Cliona Golden, UCD, Dublin
On the validity of ICA for fMRI data
Functional Magnetic Resonance Imaging (fMRI) is a brain-imaging technique which, over time, records changes in blood oxygenation level that can be associated with underlying neural activity. However, fMRI images are very noisy and extracting useful information from them calls for a variety of methods of analysis.
I will discuss the validity of the use of two popular Independent Component Analysis (ICA) algorithms, InfoMax and FastICA, which are commonly used for fMRI data analysis.
Tests of the two algorithms on simulated, as well as real, fMRI data, suggest that their successes are related to their ability to detect "sparsity" rather than the independence which ICA is designed to seek.
On the validity of ICA for fMRI data
Functional Magnetic Resonance Imaging (fMRI) is a brain-imaging technique which, over time, records changes in blood oxygenation level that can be associated with underlying neural activity. However, fMRI images are very noisy and extracting useful information from them calls for a variety of methods of analysis.
I will discuss the validity of the use of two popular Independent Component Analysis (ICA) algorithms, InfoMax and FastICA, which are commonly used for fMRI data analysis.
Tests of the two algorithms on simulated, as well as real, fMRI data, suggest that their successes are related to their ability to detect "sparsity" rather than the independence which ICA is designed to seek.