A new method for detecting causality in fMRI data of cognitive processing

被引：32

作者：

Londei A. ^{[1
,2
]}

D'Ausilio A. ^{[1
,2
]}

Basso D. ^{[3
,4
]}

Belardinelli M.O. ^{[1
,2
]}

机构：

[1] Department of Psychology 1, University of Rome La Sapienza, 78-00185 Rome, Via dei Marsi

[2] ECONA, Interuniversity Center for Research on Cognitive Processing in Natural and Artificial Systems, Rome

[3] Laboratory of Clinical Biochemistry and Molecular Biology, University of Pisa, Pisa

[4] Department of Psychology, University of Pavia, Pisa

来源：

Cognitive Processing | 2006年 / 7卷 / 1期

关键词：

Transcranial Magnetic Stimulation; Independent Component Analysis; fMRI Data; Functional Area; Granger Causality Test;

D O I：

10.1007/s10339-005-0019-5

中图分类号：

学科分类号：

摘要：

One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data. © Marta Olivetti Belardinelli and Springer-Verlag 2005.

引用

页码：42 / 52

页数：10

共 30 条

[1]

Adolphs R., Neural systems for recognizing emotion, Curr Opin Neurobiol, 12, pp. 169-177, (2002)

[2]

Basso D., Involvement of the Prefrontal Cortex in Visuospatial Planning, (2005)

[3]

Bell A.J., Sejnowski T.J., An information-maximization approach to blind separation and blind deconvolution, Neural Comp, 7, pp. 1129-1159, (1995)

[4]

Biswal B.B., Ulmer J.L., Blind source separation of multiple signal sources of fMRI data sets using independent component analysis, J Comput Assist Tomogr, 23, pp. 265-271, (1999)

[5]

Comon P., Independent component analysis - A new concept?, Signal Proc, 36, pp. 287-314, (1994)

[6]

Dagher A., Owen A.M., Boecker H., Brooks D.J., Mapping the network for planning: A correlational PET activation study with the "Tower of London" task, Brain, 122, pp. 1973-1987, (1999)

[7]

Doya K., What are the computations of the cerebellum, the basal ganglia, and the cerebral cortex, Neural Netw, 12, pp. 961-974, (1999)

[8]

Doya K., Complementary roles of basal ganglia and cerebellum in learning and motor control, Curr Opin Neurobiol, 10, 6, pp. 732-739, (2000)

[9]

Esposito F., Formisano E., Seifritz E., Goebel R., Morrone R., Tedeschi G., Di Salle F., Spatial independent component analysis of functional MRI time-series: To what extent do results depend on the algorithm used?, Hum Brain Mapp, 16, pp. 146-157, (2002)

[10]

Friston K.J., Modes or models: A critique on independent component analysis for fMRI, Trends Neurosci, 2, 10, pp. 373-375, (1998)

← 1 2 3 →