Proc. SPIE. 8922, IX International Seminar on Medical Information Processing and Analysis
KEYWORDS: Signal to noise ratio, Independent component analysis, Data acquisition, Data processing, Smoothing, Multiscale representation, Motion analysis, Functional magnetic resonance imaging, Mental disorders, Brain
The Default Mode Network (DMN) is a resting state network widely used for the analysis and diagnosis of mental disorders. It is normally detected in fMRI data, but for its detection in data corrupted by motion artefacts or low neuronal activity, the use of a robust analysis method is mandatory. In fMRI it has been shown that the signal-to-noise ratio (SNR) and the detection sensitivity of neuronal regions is increased with di erent smoothing kernels sizes. Here we propose to use a multiscale decomposition based of a linear scale-space representation for the detection of the DMN. Three main points are proposed in this methodology: rst, the use of fMRI data at di erent smoothing scale-spaces, second, detection of independent neuronal components of the DMN at each scale by using standard preprocessing methods and ICA decomposition at scale-level, and nally, a weighted contribution of each scale by the Goodness of Fit measurement. This method was applied to a group of control subjects and was compared with a standard preprocesing baseline. The detection of the DMN was improved at single subject level and at group level. Based on these results, we suggest to use this methodology to enhance the detection of the DMN in data perturbed with artefacts or applied to subjects with low neuronal activity. Furthermore, the multiscale method could be extended for the detection of other resting state neuronal networks.
Nowadays best Brain Computer Interface (BCI) methods are based on invasive recording of electrical brain activity. Surface electrodes methods are not as accurate. This is partially due to the filtering of the signal by the skull and to the distance to the sources. Surprisingly methods for solving the EEG inverse problem have seldom been used to overcome these limitations. Inverse solution methods can be adapted to either pre-process the data or as a classification method. In this paper we study the application of well-known Inverse Solution methods to the BCI. Methods are the Minimum Norm method, two methods based on respectively a laplacian and a location prior, as well as two parametric methods based on subspace correlation. Data are processed with an inverse solution method. Then the data are classified by measuring the activation in preselected areas. Processing with Inverse method can improve the classification obtained outside of the skull by more than 10%. Furthermore these methods can be used without increasing the computation time. With our simple paradigm we obtained 85% of good classification.
Conference Committee Involvement (1)
Tenth International Symposium on Medical Information Processing and Analysis