The article by Karl J. Friston reviews the concepts of functional and effective connectivity in neuroimaging, emphasizing their role in understanding the brain's functional organization. Functional connectivity refers to the temporal correlations between remote neurophysiological events, while effective connectivity involves the influence one neural system exerts over another. The paper discusses the use of eigenimages or spatial modes to characterize distributed systems and their application in assessing functional and effective connectivity. Key points include: 1. **Functional Connectivity**: Defined as temporal correlations between neurophysiological events, it is measured using techniques like singular value decomposition (SVD) to identify eigenimages or spatial modes. These modes represent patterns of activity that contribute significantly to the variance-covariance structure. 2. **Effective Connectivity**: More complex and often model-based, it involves the influence one neural system exerts over another, either at the synaptic or cortical level. The paper discusses linear and nonlinear models for effective connectivity and methods to validate these models. 3. **Data Sets**: The article uses PET and fMRI data to illustrate the concepts, demonstrating how functional and effective connectivity can be analyzed in real-world neuroimaging studies. 4. **Applications**: The techniques are applied to understand functional systems, cortical integration, and nonlinear interactions. The paper also explores the relationship between functional connectivity and information theory, using mutual information to quantify cortico-cortical integration. The article provides a comprehensive framework for understanding and analyzing functional and effective connectivity in neuroimaging, highlighting the mathematical and conceptual foundations behind these concepts.The article by Karl J. Friston reviews the concepts of functional and effective connectivity in neuroimaging, emphasizing their role in understanding the brain's functional organization. Functional connectivity refers to the temporal correlations between remote neurophysiological events, while effective connectivity involves the influence one neural system exerts over another. The paper discusses the use of eigenimages or spatial modes to characterize distributed systems and their application in assessing functional and effective connectivity. Key points include: 1. **Functional Connectivity**: Defined as temporal correlations between neurophysiological events, it is measured using techniques like singular value decomposition (SVD) to identify eigenimages or spatial modes. These modes represent patterns of activity that contribute significantly to the variance-covariance structure. 2. **Effective Connectivity**: More complex and often model-based, it involves the influence one neural system exerts over another, either at the synaptic or cortical level. The paper discusses linear and nonlinear models for effective connectivity and methods to validate these models. 3. **Data Sets**: The article uses PET and fMRI data to illustrate the concepts, demonstrating how functional and effective connectivity can be analyzed in real-world neuroimaging studies. 4. **Applications**: The techniques are applied to understand functional systems, cortical integration, and nonlinear interactions. The paper also explores the relationship between functional connectivity and information theory, using mutual information to quantify cortico-cortical integration. The article provides a comprehensive framework for understanding and analyzing functional and effective connectivity in neuroimaging, highlighting the mathematical and conceptual foundations behind these concepts.