Functional and Effective Connectivity in Neuroimaging: A Synthesis Karl J. Friston The brain adheres to two principles of functional organization: functional segregation and integration. Functional integration within and between specialized areas is mediated by functional or effective connectivity. This concept is central in neuroscience. The article discusses functional and effective connectivity, their roles in functional organization, and their relationship to similar concepts in electrophysiology. It emphasizes the role of eigenimages or spatial modes in diverse applications. Functional connectivity refers to temporal correlations between remote neurophysiological events, while effective connectivity refers to the influence one neural system exerts over another. The paper explores these concepts, their applications in neuroimaging, and their relationship to information theory and nonlinear interactions. The PET data were obtained from six subjects during an activation study of intrinsic work generation. The fMRI data came from a single-subject photic stimulation study. The paper discusses the fundamental distinction between functional and effective connectivity and their relationship to similar concepts in electrophysiology. Functional connectivity is defined in terms of correlations or covariance. Eigenimages or spatial modes are used to characterize distributed systems. These are derived using singular value decomposition (SVD) or related techniques. Eigenimages represent a mapping of function into anatomical space and can be used to map anatomy into a functional space. The paper discusses the application of these concepts to neuroimaging data, including the analysis of functional connectivity and effective connectivity. It also addresses the validation of these concepts and their biological mechanisms. The PET data matrix was subjected to SVD, revealing two spatial modes that accounted for most of the observed variance-covariance structure. The first mode accounted for 68% and the second 16% of the variance. These modes were associated with different functional tasks and regions of the brain. The paper also discusses the mapping of function into anatomical space and the use of multidimensional scaling to transform anatomical space into a functional space. This approach allows for the analysis of functional topography and the identification of regions with high functional connectivity. The paper concludes with a discussion of functional connectivity and information theory, highlighting the relationship between mutual information and functional connectivity. It also addresses the validation of effective connectivity models and their application in neuroimaging.Functional and Effective Connectivity in Neuroimaging: A Synthesis Karl J. Friston The brain adheres to two principles of functional organization: functional segregation and integration. Functional integration within and between specialized areas is mediated by functional or effective connectivity. This concept is central in neuroscience. The article discusses functional and effective connectivity, their roles in functional organization, and their relationship to similar concepts in electrophysiology. It emphasizes the role of eigenimages or spatial modes in diverse applications. Functional connectivity refers to temporal correlations between remote neurophysiological events, while effective connectivity refers to the influence one neural system exerts over another. The paper explores these concepts, their applications in neuroimaging, and their relationship to information theory and nonlinear interactions. The PET data were obtained from six subjects during an activation study of intrinsic work generation. The fMRI data came from a single-subject photic stimulation study. The paper discusses the fundamental distinction between functional and effective connectivity and their relationship to similar concepts in electrophysiology. Functional connectivity is defined in terms of correlations or covariance. Eigenimages or spatial modes are used to characterize distributed systems. These are derived using singular value decomposition (SVD) or related techniques. Eigenimages represent a mapping of function into anatomical space and can be used to map anatomy into a functional space. The paper discusses the application of these concepts to neuroimaging data, including the analysis of functional connectivity and effective connectivity. It also addresses the validation of these concepts and their biological mechanisms. The PET data matrix was subjected to SVD, revealing two spatial modes that accounted for most of the observed variance-covariance structure. The first mode accounted for 68% and the second 16% of the variance. These modes were associated with different functional tasks and regions of the brain. The paper also discusses the mapping of function into anatomical space and the use of multidimensional scaling to transform anatomical space into a functional space. This approach allows for the analysis of functional topography and the identification of regions with high functional connectivity. The paper concludes with a discussion of functional connectivity and information theory, highlighting the relationship between mutual information and functional connectivity. It also addresses the validation of effective connectivity models and their application in neuroimaging.