What we can and cannot do with fMRI

Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany, and Imaging Science and Biomedical Engineering, University of Manchester, Manchester M13 9PL, UK.
Nature (Impact Factor: 41.46). 06/2008; 453(7197):869-878. DOI: 10.1038/nature06976
Source: PubMed


Functional magnetic resonance imaging (fMRI) is currently the mainstay of neuroimaging in cognitive neuroscience. Advances in scanner technology, image acquisition protocols, experimental design, and analysis methods promise to push forward fMRI from mere cartography to the true study of brain organization. However, fundamental questions concerning the interpretation of fMRI data abound, as the conclusions drawn often ignore the actual limitations of the methodology. Here I give an overview of the current state of fMRI, and draw on neuroimaging and physiological data to present the current understanding of the haemodynamic signals and the constraints they impose on neuroimaging data interpretation.

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    • "Nevertheless, as a voxel contains nearly a million neurons (cf. Logothetis, 2008) including both superadditive and subadditive neurons in comparable numbers and spatially intermixed, these terms are unlikely to reflect true forms of multisensory integration at the neuronal level as applied in electrophysiology research (Beauchamp, 2005; Laurienti et al., 2005). Set 2 — Visual or olfactory-related gain via OV integration (Dolan et al., 2001): 2a) Visual perceptual gain: visual negative stimuli accompanied by olfactory negative stimuli minus visual negative stimuli accompanied by olfactory neutral stimuli (O Neg V Neg À O Neut V Neg ); and 2b) Olfactory perceptual gain: olfactory negative stimuli accompanied by visual negative stimuli minus olfactory negative stimuli accompanied by visual neutral stimuli (O Neg V Neg –O Neg V Neut ). "
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    • "Task-based fMRI has been widely used to identify brain regions that are functionally involved in specific task performance, and has significantly advanced our understanding of functional localizations within the brain (Friston et al., 1994; Heeger and Ress, 2002; Matthews and Jezzard, 2004; Logothetis, 2008). In the functional neuroimaging community, there have been a variety of modelbased or data-driven approaches for fMRI time series analysis and/ or activation detection, for instances, correlation analysis (Bandettini et al., 1993), general linear model (GLM) (Friston et al., 1994; Worsley, 1997), statistic testing (Ardekani and Kanno, 1998), principal component analysis (PCA) (Andersen et al., 1999), Markov random field (MRF) models (Descombes et al., 1998), mixture models (Hartvig and Jensen, 2000), independent component analysis (ICA) (McKeown et al., 1998), clustering analysis (Baumgartner et al., 1997), wavelet algorithms (Bullmore et al., 2003; Shimizu et al., 2004), autoregressive spatial models (Woolrich et al., 2004a), Bayesian approaches (Huaien and Puthusserypady, 2007; Bowman et al., 2008), and empirical mean curve decomposition (Deng et al., 2013). "
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    • "These neural assemblies become sequentially active throughout the WM task with each receding assembly passing its representational " content " to further assemblies. The integrated effect generates local field potentials [Okun et al., 2010] which are tightly linked to the fMRI-BOLD signal [Logothetis, 2008]. "
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