Functional Connectivity: Eigenimages and multivariate analyses

Eigenimages and multivariate analyses

A multivariate analysis of PET activation studies.

In this paper we present a general multivariate approach to the analysis of functional imaging studies. This analysis uses standard multivariate techniques to make statistical inferences about activation effects and to describe the important features of these effects. More specifically, the proposed analysis uses multivariate analysis of covariance (ManCova) with Wilk's lambda to test for speci...

Functional topography: multidimensional scaling and functional connectivity in the brain.

In neuroimaging, functional mapping usually implies mapping function into an anatomical space, for example, using statistical parametric mapping to identify activation foci, or the characterization of distributed changes with spatial modes (eigenimages or principal components) (Friston et al., 1993a). This article is about a complementary approach, namely, mapping anatomy into a functional spac...

Identification of mild cognitive impairment disease using brain functional connectivity and graph analysis in fMRI data

Background: Early diagnosis of patients in the early stages of Alzheimer's, known as mild cognitive impairment, is of great importance in the treatment of this disease. If a patient can be diagnosed at this stage, it is possible to treat or delay Alzheimer's disease. Resting-state functional magnetic resonance imaging (fMRI) is very common in the process of diagnosing Alzheimer's disease. In th...

Effective connectivity and neuroimaging

In the past decade functional neuroimaging has been extremely successful in establishing functional segregation as a principle of organisation in the human brain. Functional segregation is usually inferred by the presence of activation foci in change or statistical parametric maps. The nature of the functional specialisation is then attributed to the sensorimotor or cognitive process that has b...