previous work has focused on attempting to use the high spatial resolution of fMRI to determine the (spatial) origin of EEG/MEG signals, so that in future work this spatial information could be extracted from a unimodal EEG/MEG signal. While some studies have seen success in correlating signal origins between modalities to within a few millimeters, the results have not been uniformly positive. Another current limitation is the actual experimental setup: taking measurements using both modalities at once yields inferior signals, but the alternative of measuring each modality separately is confounded by trial-to-trial variability. Modes of analysis In functional integration, there is a distinction drawn between functional connectivity, and effective connectivity. Two brain regions are said to be functionally connected if there is a high correlation between the times that the two are firing, though this does not imply causality. Effective connectivity, on the other hand, is a description of the causal relationship between various brain regions. While statistical assessment of the functional connectivity of multiple brain regions is non-trivial, determining the causality of which brain regions influence which to fire is much thornier, and requires solutions to ill-posed optimization problems. Dynamic causal modelling Dynamic causal modeling (DCM) is a Bayesian method for deducing the structure of a neural system based on the observed hemodynamic (fMRI) or electrophysiologic (EEG/MEG) signal. The first step is to make a prediction as to the relationships between the brain regions of interest, and formulate a system of ordinary differential equations describing the causal relationship between them, although many parameters (and relationships) will be initially unknown. Using previous results on how neural activity is known to translate into fMRI or EEG signals, one can take the measured signal and determine the likelihood that model parameters have particular values. The elucidated model can then be used to predict relationships between the considered brain regions under different conditions. A key factor to consider during the design of neuroimaging experiments involving DCM is the relationship between the timing of tasks or stimuli presented to the subject and the ability of DCM to determine the underlying relationships between brain regions, which is partially determined by the temporal resolution of the imaging modality in use. Statistical parametric mapping Statistical parametric mapping (SPM) is a method for determining whether the activation of a particular brain region changes between experimental conditions, stimuli, or over time. The essential idea is simple, and consists of