Brain imaging data analysis
P. Ramkumar, L. Parkkonen, R. Hari, and A. Hyvärinen.
Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis.
Human Brain Mapping, in press.
pdf
[Shows an alternative way of applying ICA on EEG/MEG signals, based on the idea of spatial ICA well-known in fMRI literature.]
A. Hyvärinen.
Testing the ICA mixing matrix based on inter-subject or inter-session consistency. NeuroImage, 58:122-136, 2011.
pdf Matlab code
[A method for assigning a statistical significance (p-value) to each independent component, based on whether an independent component with the same mixing coefficients was found in different data sets. The datasets can be from different subjects in brain imaging, or just different parts of the same larger data set.]
A. Hyvärinen, P. Ramkumar, L. Parkkonen, and R. Hari.
Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis.
NeuroImage, 49(1):257-271, 2010.
pdf
Matlab code
[Proposes a source separation method tailor-made to EEG and MEG signals. Basically, preprocess the data by short-time Fourier transforms and then do ICA. Shows that this takes temporal correlations into account, and combines them with non-Gaussianity.]
F. Esposito, T. Scarabino, A. Hyvärinen, J. Himberg, E. Formisano, S. Comani, G. Tedeschi, R. Goebel, E. Seifritz and F. Di Salle. Independent component analysis of fMRI group studies by self-organizing clustering.
NeuroImage, 25(1):193-205, 2005.
pdf
[Proposes a new method for ICA analysis of fMRI group studies (i.e. several subjects)]
J. Himberg, A. Hyvärinen and F. Esposito. Validating the independent components of neuroimaging time-series via clustering and visualization.
NeuroImage 22(3):1214-1222, 2004.
pdf
Matlab code
[A method for analyzing the reliability and stability of estimated independent components by re-running the algorithm many times and visualizing the relationships of the obtained component. Features an easy-to-use software package for Matlab.]
V. Kiviniemi, J. H. Kantola, J. Jauhiainen, A. Hyvärinen and O. Tervonen. Independent component analysis of nondeterministic fMRI signal sources. NeuroImage, 19(2):253-260, 2003.
pdf
[Analyzes resting state activity in the brain using ICA. This was possibly the first paper to do so! ICA is especially suited for this kind of analysis where no stimulation schedule is known.]
|
