Bibliography on Independent Component Analysis
in Functional
Neuroimaging
Finn Årup
Nielsen
CIMBI
at
DTU Informatics
and
NRU Rigshospitalet
Lyngby and Copenhagen, Denmark
$Revision: 1.42 $
$Date: 2009/03/19 21:39:06 $
Abstract:
References for
independent component analysis (ICA) applied in
functional neuroimaging are collected.
Functional neuroimaging here includes functional magnetic resonance
imaging (fMRI), positron emission tomography (PET),
electroencephalography (EEG) and magnetoencephalography (MEG).
The original URL to this document is
This structured bibliography is part of a larger collection of
bibliographies that was begun in 2001 see
http://www.imm.dtu.dk/~
fn/bib/Nielsen2001Bib/.
The bibliography is written in
LATEX and BIBTeX and should be
available both as HTML and PostScript.
The bibliography is probably far from complete, but new references
are added whenever the author finds new material and has the time to
add them.
You can email the author if corrections are required or you have
found some references that you fell ought to be included:
fn@imm.dtu.dk.
Note that there is a index at the very end of this document.
Thanks to Evan
D. Morris.
Funding through Lundbeck Foundation, EU project
MAPAWAMO,
International Neuroimaging
Consortium
(INC) American HBM project and Danish
THOR Center for Neuroinformatics.
A general introduction is (Hyvärinen and Oja, 2000).
A list of papers from the Third International Conference on Independent
Component Analysis and Signal Separation (ICA2001) is available from
http://ica2001.org.
Overviews of ICA for neuroimaging are available in
(McKeown et al., 2003; Calhoun et al., 2003) for functional magnetic
resonance imaging (fMRI), and Stone (2002) discuss
ICA for EEG, fMRI and optical imaging.
- Independent component analysis
- ``Bell and Sejnowski ICA'' (BS-ICA) --
``Infomax'' (Bell and Sejnowski, 1995).
- ``Maximum likelihood ICA'' (MacKay, 1999; Lee et al., 1999). (Usually)
the same as infomax but developed from maximum likelihood rather
than information theory with the sources as, e.g., hyperbolic
secant distributions.
- ``Extended infomax'' (Lee et al., 1999) is able to
model heavy- and light-tailed distributions.
- `Probabilistic ICA' (PICA) or `Noisy ICA'
(Beckmann et al., 2001a),
(Kolenda, 2002, section 3.3 and 3.6)
|
(1) |
The noise is usually assumed to be isotropic Gaussian distributed
.
A silimiar noise assumption is made in `probabilistic principal
component analysis' (PICA) -- a special factor analysis
model -- where model selection can be based on AIC and test set
(Hansen et al., 1999a,1997),
Minka, Bishop, Zoubin G.
- FastICA (Hyvärinen and Oja, 2000; Hyvärinen, 1999)
- Mean field ICA (MF-ICA) (Højen-Sørensen et al., 2001,2002).
- Decorrelation methods -- Dynamic component analysis (DCA)
Table 1:
Independent component analysis in functional
neuroimaging. sICA is spatial ICA, tICA is temporal ICA and stICA
is spatiotemporal ICA.
Modality |
ICA Type |
Purpose |
Reference |
fMRI |
sICA |
|
(McKeown et al., 1998b) |
fMRI |
sICA |
|
(McKeown et al., 1998a) |
fMRI |
|
Demonstration of estimation of noisy ICA with mean
field approximation |
(Hansen, 1998) |
fMRI |
tICA |
|
(Biswal and Ulmer, 1999) |
fMRI |
stICA |
|
(Muraki and Nakai, 1999) |
fMRI |
|
Comparison of different analysis methods |
(Lange et al., 1999) |
fMRI |
|
Separate task and non-task |
(Ulmer and Biswal, 2000) |
fMRI |
sICA(?) |
Separation of task-related and head movement signal |
(Netsiri et al., 2000) |
fMRI |
|
|
(Matsuo et al., 2000) |
Dynamic PET |
sICA (McKeown) |
Determination of arterial input
function |
(Jang et al., 2000) |
fMRI |
sICA, tICA (FastICA) |
Investigates sICA and tICA
capability to separate paradigm signal |
(Calhoun and Pekar, 2000) |
fMRI |
sICA with FastICA |
Artifact detection: Gradient waveform
corruption, bistable mean signal intensity change, Nyquist
ghosting, high frequency, motion |
(Beckmann et al., 2000a) |
EEG |
|
|
(Jung et al., 2001) |
fMRI |
sICA, tICA, stICA |
Comparison of different ICA
methods |
(Caprihan and Anderson, 2001) |
fMRI |
|
Model selection |
(Beckmann et al., 2001a) |
fMRI |
FastICA |
Compares ICA with GLM modeling |
(Beckmann et al., 2001b) |
fMRI |
|
Compared ICA with GLM modeling |
(Calhoun et al., 2001b) |
fMRI |
|
Identification of motion artifacts |
(Bannister et al., 2001) |
fMRI |
? |
Group inference |
(Calhoun et al., 2001a) |
fMRI |
sICA |
Artifact detection |
(Chuang and Chen, 2001) |
fMRI |
sICA infomax |
Investigation of variation in the
hemodynamic response function |
(Duann et al., 2001a,2002a,2001b) |
efMRI |
stICA |
with ``skewed probability density functions'' |
(Stone et al., 2002) |
fMRI |
Complex Infomax |
|
(Calhoun et al., 2002c,a; Calhoun and Adali, 2002; Calhoun et al., 2002b) |
pMRI |
|
|
(Tasciyan et al., 2001) |
|
Table 1 displays some of the
`methods' papers in ICA for functional neuroimaging.
Hansen (1998) develops noisy ICA with a mean field
approximation and applies it together with PCA on an fMRI data set.
Multisubject data may be analyzed in a variety of ways
(Calhoun et al., 2008; Schmitthort and Holland, 2004):
- Pre-average data and then perform a single ICA
(Schmitthort and Holland, 2004).
- Perform single subject ICAs and then combine or correlate the
subject specific independent component with each other (Calhoun et al., 2001b).
- ICA with temporal concatenation. Here the subjects need to
be in the same space, i.e., spatially normalized.
- ICA with spatial concatenation. Here the design in the temporal
dimension need to be the same, e.g., the stimulus need to occure at
the same time point across subjects
- Tensor-methods, e.g., PARAFAC.
A comparison on simulated data is reported in
(Schmitthort and Holland, 2004).
Petersen et al. (2000) and Petersen (2000)
compared spatial and temporal ICA with the infomax, DCA and
Molgedey-Schuster (MS-ICA): MS-ICA was found to be much faster than
BS-ICA and DCA and DCA much slower. BS-tICA and MS-sICA both had
difficulties in separating the `interesting' component.
Esposito et al. (2002) compared the
Bell and Sejnowski (1995) algorithm with the
Hyvärinen (1999).
Table 2 display some of the programs that are in use
for independent component analysis of brain signals.
Table 2:
ICA tools in functional neuroimaginging.
Tool |
Implementation |
Description |
Reference |
AnalyzeFMRI |
R |
FastICA implementation(?) |
http://www.stats.ox.ac.uk/~
marchini/software.html |
BrainVoyager |
Compiled for Windows, UNIX, Linux, Mac |
``Cortex-based ICA''&
http://www.brainvoyager.com/ |
|
EEGLAB |
Matlab |
EGG processing including ICA with GUI. Related to
FMRLAB. |
(Delorme and Makeig, 2003),
http://www.sccn.ucsd.edu/eeglab/ |
FMRLAB |
Matlab |
Extended Infomax Algorithm |
http://www.sccn.ucsd.edu/fmrlab/,
(Bell and Sejnowski, 1995; Amari, 1999; Lee et al., 1999; Duann et al., 2002b) |
GIFT |
Matlab |
``Group ICA of fMRI Toolbox'' |
(Egolf et al., 2004),
http://icatb.sourceforge.net/ |
ICA:DTU toolbox |
Matlab |
Implements Bell and Sejnowski Maximum
likelihood (Infomax) ICA and Mean Field ICA as well as
Molgedey-Schuster ICA. With model selection. |
http://mole.imm.dtu.dk/toolbox/ica/,
(Kolenda et al., 2001; Hansen et al., 2000; Højen-Sørensen et al., 2002; Petersen et al., 2000; Hansen et al., 2001; Kolenda et al., 2000) |
Lyngby |
Matlab |
The Bell-Sejnowski and
Molgedey-Schuster algorithms
are presently implemented |
(Hansen et al., 1999b),
http://hendrix.imm.dtu.dk/software/lyngby/ |
MELODIC |
C |
Part of FSL. Model order selection
(number of sources). Inference on image map with mixture modelling |
http://www.fmrib.ox.ac.uk/fsl/melodic2/
(Beckmann et al., 2001b; Beckmann and Smith, 2002a; Beckmann et al., 2001a,2000b,a; Beckmann and Smith, 2003,2002b) |
|
Apart from those listed in the table there are other programs that is
not specifically targeted for neuroimaging applications, e.g.,
ICALAB
(http://www.bsp.brain.riken.jp/ICALAB/).
(Makeig et al., 2001,2002) used ICA to show that
event-related potentials (ERPs) are linked to ``stationary'' EEG
(alpha) activity of the brain through ``partial phase resetting of the
EEG processes''.
A further ICA application is (Moritz et al., 2005).
- (Shi et al., 2004).
- (Arfanakis et al., 2000)
- (Calhoun and Pekar, 2000): ```self-evident'' spatiotemporal components' .
- (Calhoun et al., 2001c)
- (Calhoun et al., 2001d)
- V. D. Calhoun, T. Adali, G. D. Pearlson, ``Independent component
analysis applied to fMRI data: A generative model for validating
results,'' to appear Journal of VLSI Signal Processing Systems for
Signal, Image, and Video Technology, Special Issue: Data Mining and
Biomedical Applications of Neural Networks.
- (Dodel et al., 2001)
- (Esposito et al., 2001): rank ordering of ICs.
- (Formisano et al., 2001,2004): Cortex-based ICA.
- (Friston, 1998)
- Gu H, Engelien W, Feng H, Silbersweig DA, Stern E, Yang Y.
Mapping transient, randomly occurring neuropsychological events
using independent component analysis. Neuroimage. 2001 Dec;14(6):1432-43.
PMID: 11707099
- (McKeown, 2000)
- McKeown MJ, Sejnowski TJ.
Independent component analysis of fMRI data: examining the assumptions.
Hum Brain Mapp. 1998;6(5-6):368-72.
PMID: 9788074
- Moritz CH, Haughton VM, Cordes D, Quigley M, Meyerand E (2000):
Whole-brain functional MR imaging activation from a finger-tapping
task examined with independent component analysis. Am J Neuroradiol
21: 1629-1635
- Suzuki K, Kiryu T, Nakada T.
Fast and precise independent component analysis for high field
fMRI time series tailored using prior information on
spatiotemporal structure.
Hum Brain Mapp. 2002 Jan;15(1):54-66.
PMID: 11747100
- Independent component analysis for noisy data - MEG data analysis
S. Ikeda, K. Toyama
NEURAL NETWORKS 13(10)
- Consistency of Infomax ICA Decomposition of Functional Brain Imaging
Data Jeng-Ren Duann, Tzyy-Ping Jung, Scott Makeig (Institute for
Neural Computation, University of California San Diego), Terrence
J. Sejnowski (Institute for Neural Computation, University of
California San Diego, Computational Neurobiology Laboratory, The Salk
Institute for Biological Studies), ICA2003
- Independent Component Analysis of Auditory fMRI Responses Fabrizio
Esposito (Second Division of Neurology - Second University of Naples,
Italy), Elia Formisano (Department of Cognitive Neuroscience,
Maastricht University, The Netherlands), Erich Seifritz (Department of
Psychiatry - University of Basel, Switzerland), Raffaele Elefante
(Department of Neurological Sciences, University of Naples, Italy),
Rainer Goebel (Department of Cognitive Neuroscience, Maastricht
University, The Netherlands), Francesco Di Salle (Department of
Neurological Sciences, University of Naples, Italy), ICA2003
- Combining ICA and Cortical Surface Reconstruction in Functional MRI
Investigations of Human Brain Functions Elia Formisano (Department of
Cognitive Neuroscience, Faculty of Psychology, Maastricht University,
The Netherlands), Fabrizio Esposito (Second Division of Neurology -
Second University of Naples, Italy), Francesco Di Salle (Department of
Neurological Sciences, University of Naples, Italy), Rainer Goebel
(Department of Cognitive Neuroscience, Faculty of Psychology,
Maastricht University, The Netherlands), ICA2003
- Independent Component Analysis with Joint Speedup and Supervisory
Concept Injection: Applications to Brain fMRI Map Distillation Yasuo
Matsuyama, Ryo Kawamura, Naoto Katsumata (Waseda University), ICA2003
- Blind Identification of SEF Dynamics from MEG Data by using
Decorrelation Method of ICA Kuniharu Kishida, Kenji Kato (Gifu
University), Kazuhiro Shinosaki, Satoshi Ukai (Osaka University
Graduate School of Medicine), ICA2003
- Classification of Single Trial EEG Signals by a Combined Principal +
Independent Component Analysis and Probabilistic Neural Network
Approach Tetsuya Hoya, Gen Hori, Hovagim Bakardjian (BSI RIKEN),
Tomoaki Nishimura, Taiji Suzuki (Dept. of Mathematical Engi., and
Info. Physics Sch. of Engi., Univ. Tokyo), Yoichi Miyawaki, Arao
Funase (BSI RIKEN), Jianting Cao (Dept. Elec. Engi., Saitama Institute
of Technology), ICA2003
- Deterministic and stochastic features of fMRI data: implications for
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Martin J. McKeown
-
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ICA.
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-
Arfanakis, K., Cordes, D., Haughton, V. M., Moritz, C. H., Quigley, M. A., and
Meyerand, M. E. (2000).
- Combining independent component analysis and correlation analysis to
probe interregional connectivity in fMRI task activation datasets.
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-
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-
Bannister, P. R., Beckmann, C., and Jenkinson, M. (2001).
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NeuroImage, 13(6):S69.
-
Beckmann, C. F., Noble, J. A., and Smith, S. M. (2000a).
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analysis.
In Fox and Lancaster (2000), page S614.
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with FastICA in fMRI.
-
Beckmann, C. F., Noble, J. A., and Smith, S. M. (2001a).
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-
Beckmann, C. F., Noble, J. A., and Smith, S. M. (2001b).
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-
Beckmann, C. F. and Smith, S. M. (2002a).
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Beckmann, C. F. and Smith, S. S. (2002b).
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-
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-
Biswal, B. B. and Ulmer, J. L. (1999).
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analysis for multiple subjects.
-
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domain.
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Calhoun, V. and Pekar, J. (2000).
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different ICA methods for fMRI.
-
Chuang, K.-H. and Chen, J.-H. (2001).
- Independent component analysis in the detection and correction of
physiological artifacts in fMRI.
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Dodel, S., Herrmann, J. M., and Geisel, T. (2001).
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-
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and Sejnowski, T. (2001a).
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hemodynamic response in fMRI with Independent component analysis and find
that it varies with site, subject and task and may vary widely from trial to
trial.
-
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- Criteria for the rank ordering of fMRI independent components.
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-
Esposito, F., Formisano, E., Seifritz, E., Goebel, R., Morrone, R., Tedeschi,
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-
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- AnalyzeFMRI
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Finn Årup Nielsen
2010-04-23