Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems

Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health Bethesda, MD, USA.
Frontiers in Systems Neuroscience 01/2011; 5:104. DOI: 10.3389/fnsys.2011.00104
Source: PubMed


Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need to be addressed. The issues are discussed within the framework of linear dynamic systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a "node" in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an "instantaneous" connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis.

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Available from: Kewei Chen
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    • "Prior attempts have relied on Granger causality [Brovelli et al., 2004] and dynamic causal modeling [Friston et al., 2003; McIntosh and Gonzalez-Lima, 1994]. However, these observational methods rely on statistical covariance [Smith et al., 2011] as opposed to interventional empiric testing. Combining transcranial magnetic stimulation (TMS) with electroencephalography (EEG), magnetoencephalography (MEG), or functional MRI (fMRI) provides a more empiric effective connectivity assessment [Massimini et al., 2005], but this method also has limitations related to the difficulty of inferring intracranial neural measurements from extracranial stimulation using assumptions of EEG/ MEG source modeling or indirect measures of neural activity with fMRI. "
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    • "Commonly studied resting-state networks (RSNs) include the sensorimotor, visual, default mode, and executive control networks (Beckmann et al., 2005; Damoiseaux et al., 2006; Raichle et al., 2001; Seeley et al., 2007). From TB-FMRI, a variety of inter-regional FC measures can be derived such as with structural equation modeling (McIntosh and Gonzalez-Lima, 1994), vector autoregressive modeling (Goebel et al., 2003), structural vector autoregressive analysis (Chen et al., 2011), psychophysiological interactions (Friston et al., 1997), dynamic causal modeling (Friston et al., 2003), and switching linear dynamic system for fMRI (Smith et al., 2011). While the models underlying these varied approaches differ considerably, the result is a measure of connectedness between regions. "
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    • "B t , G t ) can be represented as a weighted combination of the given A i (resp. B i , G i ) matrices (Smith et al., 2011b). "
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