Structural equation modelling (SEM) of neuroimaging data can be evaluated both for the goodness of fit of the model and for the strength of path coefficients (as an index of effective connectivity). SEM of auditory fMRI data is made difficult by the necessary sparse temporal sampling of the time series (to avoid contamination of auditory activation by the response to scanner noise), and by the paucity of well-defined anatomical information to constrain the functional model. We used SEM (i.e. a model incorporating latent variables) to investigate how well fMRI data in four adjacent cortical fields can be described as an auditory network. Seven out of 14 models (2 hemispheres x (6 subjects and 1 group)) produced a plausible description of the measured data. Since the auditory model to be tested is not fully validated by anatomical data, our approach requires that goodness of fit must be confirmed to assure generalisability of connectivity patterns. For good-fitting models, connectivity patterns varied significantly across subjects and were not replicable across stimulus conditions. SEM of central auditory function therefore appears to be highly sensitive to the voxel-selection procedure and/or the sampling of the time series
