Use of Nuclear Medicine Molecular Neuroimaging to Model Brain Molecular Connectivity

We introduce basic concepts of brain networks and discuss methods to model and analyze brain molecular connectivity using positron emission tomography (PET) and single-photon emission computed tomography (SPECT). Basic elements of network analytic methods, including graph theory, and the connectivity matrix as a basis for network analysis will be discussed in more detail. Statistical methods to compare networks will be reviewed. A specific brain network analysis method called sparse inverse covariance estimation (SICE) is presented as an alternative to Pearson correlation to estimate the brain molecular connectivity matrix. Finally, we will discuss examples from published research to illustrate the practical application of brain molecular connectivity analysis concepts.