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| Methods | Purposes | Strengths | Limitations |
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| General linear model | Estimating to what extent each known predictor contributes to the variability observed in the voxel’s BOLD signal time course
| (i) Mathematically simple, easily interpreted, and readily available in standard packages (e.g., the SPM software)
(ii) Flexible to incorporate multiple quantitative and qualitative independent variables, such as low- frequency drifts and head motion | (i) Relies on assumptions such as appropriate repressors in the matrix and normality of the fMRI noise which are difficult to check |
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| Psychophysiological interaction | “Searching” for regions that correlate differently with a particular region under certain experimental context | (i) Can explore the connectivity of the source area to the rest of the brain and how it interacts with the psychological variables | (i) Only involves one region of interest in one model
(ii) Limited in the extent to which you can infer a causal relationship |
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| Structural equation model | Estimating the degree to which the activity between different brain regions is connected and how this connectivity is affected by an experimental variable | (i) Can examine interactions of several regions of interest simultaneously and offer estimations of causal relationships
(ii) Predetermined connections are based on prior anatomical or functional knowledge | (i) Causality is predetermined, and this might overlook several aspects of neural activity
(ii) Assumes the interactions are linear (i.e., structural equation models are not time-series)
(iii) Lacks temporal information |
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| Dynamic causal model | Estimating and making inferences about the coupling among brain regions and how this coupling is affected by changes in experimental context at the neuronal level | (i) Biologically more accurate and realistic than other methods because DCM models interactions at the neuronal rather than the hemodynamic level and complex connectivity patterns between regions can be arbitrarily postulated | (i) Prespecified models are needed
(ii) Requires much longer time to estimate parameters than SEM
(iii) Neurodynamics in each region are characterized by a single state variable (“neuronal activity”) |
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| Granger causality model | Measuring the predictability of one neural time-series from another | (i) No a priori specification of a model is needed. Thus this model can complement the hypothesis-driven methods and help to form directed graph models of regions and their interactions | (i) The causal relationship may be caused by the differences in hemodynamic latencies in different parts of the brain if long repetition times (TR) are used |
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| Multivoxel pattern analysis | Applying pattern- classification algorithms to demonstrate the relationship between measures of brain activity and a perceptual state and provide an information-theoretic framework for the isolation of regions that uniquely represent a behavior | (i) Simultaneously examines the disparate signals carried within a set of voxels rather than examining individual voxels in parallel
(ii) Can decode more complex information due to improved sensitivity and use of spatial information | (i) The possibility of overfitting increases as the classifier becomes more complex, which may result in poor performance in tests of generalization |
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