This review discusses the distinction between functional and effective connectivity in neuroimaging. Functional connectivity refers to statistical dependencies among remote neurophysiological events, typically inferred from correlations in neuronal activity. Effective connectivity, on the other hand, refers to the influence one neural system exerts over another, often modeled through causal relationships. The review highlights the importance of these distinctions in understanding brain function and connectivity. It also discusses the role of structural connectivity, or the connectome, in providing constraints on effective connectivity models. The review emphasizes the need for model comparison in effective connectivity analysis, as it allows for the testing of hypotheses about brain function. It also addresses the limitations of functional connectivity in capturing causal relationships and the potential of effective connectivity in this regard. The review concludes by discussing the relationship between functional and effective connectivity, and the importance of considering both in understanding brain function. It also touches on the use of structural equation modeling and Granger causality in analyzing connectivity, and the challenges associated with these methods in fMRI data. The review underscores the importance of considering both functional and effective connectivity in understanding brain function and connectivity.This review discusses the distinction between functional and effective connectivity in neuroimaging. Functional connectivity refers to statistical dependencies among remote neurophysiological events, typically inferred from correlations in neuronal activity. Effective connectivity, on the other hand, refers to the influence one neural system exerts over another, often modeled through causal relationships. The review highlights the importance of these distinctions in understanding brain function and connectivity. It also discusses the role of structural connectivity, or the connectome, in providing constraints on effective connectivity models. The review emphasizes the need for model comparison in effective connectivity analysis, as it allows for the testing of hypotheses about brain function. It also addresses the limitations of functional connectivity in capturing causal relationships and the potential of effective connectivity in this regard. The review concludes by discussing the relationship between functional and effective connectivity, and the importance of considering both in understanding brain function. It also touches on the use of structural equation modeling and Granger causality in analyzing connectivity, and the challenges associated with these methods in fMRI data. The review underscores the importance of considering both functional and effective connectivity in understanding brain function and connectivity.