The purpose of this article is to present a methodology to identify the sources of activity in brain networks from functional magnetic resonance imaging (fMRI) data using the multiset canonical correlation analysis algorithm. The aim is to lay the foundations for a screening marker to be used as indicator of mental diseases. Group analysis blind source separation methods have proved reliable to extract the latent sources underlying the brain activities but currently there is no recognized biomarker for mental disorders.

Recently, synthetic aperture radar interferometry (InSAR) has been recognized as a promising tool to generate high-resolution maps of atmospherical precipitable water vapor temporal changes (Delta PWV) from the propagation delay of radar signal in atmosphere. The relationship between Delta PWV and propagation delay mainly depends on the vertical profiles of temperature and water vapor pressure. In this letter, we present a methodology to study the spatial and temporal variations of the temperature's vertical profile and generate more accurate high-resolution Delta PWV maps by means of InSAR.