Independent component analysis of functional MRI: what is signal and what is noise?

被引:284
作者
McKeown, MJ
Hansen, LK
Sejnowski, TJ [1 ]
机构
[1] Univ Calif San Diego, Dept Biol, La Jolla, CA 92093 USA
[2] Duke Univ, Dept Med Neurol, Durham, NC USA
[3] Tech Univ Denmark, DK-2800 Lyngby, Denmark
[4] Salk Inst Biol Studies, Howard Hughes Med Inst, La Jolla, CA 92037 USA
[5] Duke Univ, Brain Imaging & Anal Ctr, Durham, NC USA
关键词
D O I
10.1016/j.conb.2003.09.012
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI) signal, complicating attempts to infer those changes that are truly related to brain activation. Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform, data-driven methods, such as independent component analysis and clustering, attempt to find common features within the data. This exploratory approach can be revealing when the brain activation is difficult to predict beforehand, such as with complex stimuli and internal shifts of activation that are not time-locked to an easily specified sensory or motor event. These methods can be further improved by incorporating prior knowledge regarding the temporal and spatial extent of brain activation.
引用
收藏
页码:620 / 629
页数:10
相关论文
共 81 条
[1]  
[Anonymous], P INT C ART NEUR NET
[2]   Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets [J].
Arfanakis, K ;
Cordes, D ;
Haughton, VM ;
Moritz, CH ;
Quigley, MA ;
Meyerand, ME .
MAGNETIC RESONANCE IMAGING, 2000, 18 (08) :921-930
[3]  
Attias H, 1998, NEURAL COMPUT, V10, P1373, DOI 10.1162/neco.1998.10.6.1373
[4]   PROCESSING STRATEGIES FOR TIME-COURSE DATA SETS IN FUNCTIONAL MRI OF THE HUMAN BRAIN [J].
BANDETTINI, PA ;
JESMANOWICZ, A ;
WONG, EC ;
HYDE, JS .
MAGNETIC RESONANCE IN MEDICINE, 1993, 30 (02) :161-173
[5]   The architecture of the colour centre in the human visual brain: new results and a review [J].
Bartels, A ;
Zeki, S .
EUROPEAN JOURNAL OF NEUROSCIENCE, 2000, 12 (01) :172-190
[6]   Correlator beware: Correlation has limited selectivity for fMRI data analysis [J].
Baumgartner, R ;
Somorjai, R ;
Summers, R ;
Richter, W ;
Ryner, L .
NEUROIMAGE, 2000, 12 (02) :240-243
[7]   Dynamical cluster analysis of cortical fMRI activation [J].
Baune, A ;
Sommer, FT ;
Erb, M ;
Wildgruber, D ;
Kardatzki, B ;
Palm, G ;
Grodd, W .
NEUROIMAGE, 1999, 9 (05) :477-489
[8]   AN INFORMATION MAXIMIZATION APPROACH TO BLIND SEPARATION AND BLIND DECONVOLUTION [J].
BELL, AJ ;
SEJNOWSKI, TJ .
NEURAL COMPUTATION, 1995, 7 (06) :1129-1159
[9]   Blind source separation of multiple signal sources of fMRI data sets using independent component analysis [J].
Biswal, BB ;
Ulmer, JL .
JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 1999, 23 (02) :265-271
[10]   Independent component analysis at the neural cocktail party [J].
Brown, GD ;
Yamada, S ;
Sejnowski, TJ .
TRENDS IN NEUROSCIENCES, 2001, 24 (01) :54-63