Introduction to Connectivity Analyses Jennie Newton Marieke Schölvinck Functional architecture of the brain Functional integration Functional segregation Where are regional responses to experimental input? Univariate analyses of regionally specific.
Download ReportTranscript Introduction to Connectivity Analyses Jennie Newton Marieke Schölvinck Functional architecture of the brain Functional integration Functional segregation Where are regional responses to experimental input? Univariate analyses of regionally specific.
Introduction to Connectivity Analyses Jennie Newton Marieke Schölvinck Functional architecture of the brain Functional integration Functional segregation Where are regional responses to experimental input? Univariate analyses of regionally specific effects How does one region influence another (coupling b/w regions)? How is coupling effected by experimental manipulation (e.g. attention)? Multivariate analyses of regional interactions Experimentally designed input Functional integration Functional integration can be further subdivided into: Functional connectivity different ways of summarising patterns of correlations among brain systems operational/observational definition Effective connectivity the influence one neuronal system exerts upon others mechanistic/model-based definition Overview Functional Connectivity – – – – Basic concepts Eigenimages Singular Value Decomposition Limitations Effective connectivity – Basic concepts – Regression-based models: PPIs – Psycho-Physiological Interactions SEM – Structural Equation Modelling – Limitations – Dynamic Causal Modelling Functional Connectivity: Basics Aims – Summarise patterns of correlations among brain systems – Find those spatio-temporal patterns of activity which explain most of the variance in a series of repeated measurements (e.g. several scans in multiple voxels) Procedure – Select those voxels whose activation levels show a significant difference between the conditions of interest – From the time series of those voxels, extract the most important components which describe the intercorrelations between them – We do this by using Eigenimage / Principal Component Analysis……… Functional Connectivity: Eigenimages Time (scans) time-series of 1D images: 128 fMRI scans of 32 voxels Extracted voxels Eigenvariates: time-dependent profiles associated with each eigenimage Spectral decomposition: shows that only few eigenvariates are required to explain most of observed variance Eigenimages: show contribution of each eigenvariate to time series of each individual voxel Reconstruction: time-series are reconstructed from only 3 principal components Functional Connectivity: Singular Value Decomposition V1 voxels V2 time = Y (DATA) Y = USVT = s1 U1 APPROX. OF Y by P1 s1U1V1T U : “Eigenvariates” S : “Singular Values” or “Eigenvalues” (2) V : “Eigenimages” + s2 + U2 s2U2V2T APPROX. OF Y by P2 +… + ... (p < n!) Expression of p patterns in n scans Variance the p patterns account for Expression of p patterns in m voxels Data reduction: components explain less and less variance Functional Connectivity: example from PET 5 subjects, each scanned 12 times Alternated b/w two tasks: (1) repeat a letter presented aurally (2) generate a word beginning with letter Voxels with significant differences between the two conditions were extracted Singular Value Decomposition (SVD) used to extract eigenimages and eigenvariates Spectral decomposition shows only 2 eigenimages are required to explain most of the variance; 1st eigenimage accounts for 64.4 % 2nd eigenimage accounts for 16.0 % Friston et al. Functional connectivity; the principal component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 1993 Functional Connectivity: example from PET temporal eigenvariate reflecting the expression of the first eigenimage over the 12 conditions SPMs of the positive and negative components of the first eigenimage Functional Connectivity: limitations Data-driven method – Covariation of patterns with experimental conditions not always dominant functional interpretation not always possible Patterns need to be orthogonal – Biologically implausible because of interactions among the different systems Correlations can arise from many sources – May not reflect meaningful connectivity between cortical areas example: thalamus can send projections to multiple cortical regions, leading to highly correlated brain activity between these areas, despite fact they are not directly connected