Towards an automated selection of spontaneous co-activity maps in functional magnetic resonance imaging

Marion Sourty; Laurent Thoraval; Daniel Roquet; Jean-Paul Armspach; Jack Foucher

doi:10.1117/12.2075643

19 March 2015 Towards an automated selection of spontaneous co-activity maps in functional magnetic resonance imaging

Marion Sourty, Laurent Thoraval, Daniel Roquet, Jean-Paul Armspach, Jack Foucher

Proceedings Volume 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging; 94170K (2015) https://doi.org/10.1117/12.2075643
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Functional magnetic resonance imaging allows to assess large scale functional integration of the brain. One of the leading techniques to extract functionally relevant networks is spatial independent component analysis (ICA). Spatial ICA separates independent spatial sources, many of whom are noise or imaging artifacts, whereas some do correspond to functionally relevant Spontaneous co-Activity Maps (SAMs). For research purposes, ICA is generally performed on group data. This strategy is well adapted to uncover commonly shared networks, e.g. resting-state networks, but fails to capture idiosyncratic functional networks which may be related to pathological activity, e.g. epilepsy, hallucinations. To capture these subject specific networks, ICA has to be applied to single subjects using a large number of components, from which a tenth are SAMs. Up to now, SAMs have to be selected manually by an expert based on predefined criteria. We aim to semi-automate the selection process in order to save time. To this end, some approaches have been proposed but none with the near 100 % sensitivity required for clinical purposes. In this paper, we propose a computerized version of the SAM's criteria used by experts, based on frequential and spatial characteristics of functional networks. Here we present a pre-selection method and its results at different resolutions, with different scanners or imaging sequences. While preserving a near 100 % sensitivity, it allows an average of 70 % reduction of components to be classified which save 55% of experts' time. In comparison, group ICA fails to detect about 25% of the SAMs.

Citation Download Citation

Marion Sourty, Laurent Thoraval, Daniel Roquet, Jean-Paul Armspach, and Jack Foucher "Towards an automated selection of spontaneous co-activity maps in functional magnetic resonance imaging", Proc. SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 94170K (19 March 2015); https://doi.org/10.1117/12.2075643

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