Multivariate pattern decoding of FMRI signals in disorders of consciousness

Evaluation of patients with disorder of consciousness (DOC) is conducted prevalently with bedside assessment. However, this approach has been known to produce misdiagnosis. Recent studies propose neuroimaging as a new diagnostic tool, potentially leading to the implementation of a more robust methodology to classify the patients depending on their brain activations, as minimally conscious status (MCS) or persistent vegetative state (PVS). A hierarchical protocol has been suggested for functional neuroimaging studies (Owen et al., 2005), progressing sequentially from the simplest form of brain processing to more complex cognitive functions. It was also demonstrated that pattern classification of brain signals in different behavioral tasks could enable DOC patients to use a binary fMRI-BCI (Boly et al., 2007). In light of the above, our study aimed to take this approach to a next step, by trying to assess new methodologies to classify the patients in the different clinical DOC categories, as minimally conscious status (MCS) or persistent vegetative state (PVS), by using functional Magnetic Resonance Imaging (fMRI) in a battery of experiments on intentional control, language competence, working memory, emotions and pain sensation. During this project, the MANAS 4 toolbox was implemented based on a revised version of a previously developed toolbox (Rana et al., 2013) for fMRI data classification. The toolbox used multivariate Support Vector Machine (SVM), and conducted a sub-study to assess the performances of different preprocessing steps (Tanabe et al., 2002) and feature selection algorithms, namely, Fisher Scoring (Gu, Li, & Han, 2012a), Fisher Scoring with Searchlight and Effect Mapping (Lee et al., 2010) using a motor task fMRI dataset (Rana et al., 2013). The toolbox allows a high degree of customization through configuration files, letting the researcher to focus on the analysis of the data, rather than the building of the processing. In the subsequent stages of this project, the MANAS 4 toolbox was used to analyze activations in a Classical Conditioning Paradigm with Emotional Sounds, to evaluate the possibility of a future implementation of an fMRI-BCI for communication in Alzheimer patients, by showing an overall good prediction of the conditioned stimuli.