Spatial discriminant ICA for RS-fMRI characterisation

• Published in: 2014 International Workshop on Pattern Recognition in Neuroimaging pp. 1 - 4
• Main Authors: Tabas, Alejandro, Balaguer-Ballester, Emili, Igual, Laura
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Algorithm design and analysis; Brain; Computer architecture; Data mining; Independent component analysis; Noise
• DOI: 10.1109/PRNI.2014.6858546

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.