%0 Conference Proceedings
%T Spatial Discriminant ICA for RS-fMRI characterisation
%A Alejandro Tabas
%A Emili Balaguer-Ballester
%A Laura Igual
%B 4th International Workshop on Pattern Recognition in Neuroimaging
%D 2014
%@ 978-1-4799-4150-6
%F Alejandro Tabas2014
%O OR;MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2493), last updated on Fri, 09 Sep 2016 10:08:08 +0200
%X Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments.
%U http://refbase.cvc.uab.es/files/TBI2014.pdf
%U http://dx.doi.org/10.1109/PRNI.2014.6858546
%P 1-4