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

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

Task-specific modulation of human auditory evoked responses in a delayed-match-to-sample task

In this study, we focus our investigation on task-specific cognitive modulation of early cortical auditory processing in human cerebral cortex. During the experiments, we acquired whole-head magnetoencephalography (MEG) data while participants were performing an auditory delayed-match-to-sample (DMS) task and associated control tasks. Using a spatial filtering beamformer technique to simultaneously estimate multiple source activities inside the human brain, we observed a significant DMS-specific suppression of the auditory evoked response to the second stimulus in a sound pair, with the center of the effect being located in the vicinity of the left auditory cortex. For the right auditory cortex, a non-invariant suppression effect was observed in both DMS and control tasks. Furthermore, analysis of coherence revealed a beta band (12 ~ 20 Hz) DMS-specific enhanced functional interaction between the sources in left auditory cortex and those in left inferior frontal gyrus, which has been shown to involve in short-term memory processing during the delay period of DMS task. Our findings support the view that early evoked cortical responses to incoming acoustic stimuli can be modulated by task-specific cognitive functions by means of frontal-temporal functional interactions