Out-of-Body Experiences and Other Complex Dissociation Experiences in a Patient with Unilateral Peripheral Vestibular Damage and Deficient Multisensory Integration

Out-of-body experiences (OBEs) are illusory perceptions of one's body from an elevated disembodied perspective. Recent theories postulate a double disintegration process in the personal (visual, proprioceptive and tactile disintegration) and extrapersonal (visual and vestibular disintegration) space as the basis of OBEs. Here we describe a case which corroborates and extends this hypothesis. The patient suffered from peripheral vestibular damage and presented with OBEs and lucid dreams. Analysis of the patient's behaviour revealed a failure of visuo-vestibular integration and abnormal sensitivity to visuo-tactile conflicts that have previously been shown to experimentally induce out-of-body illusions (in healthy subjects). In light of these experimental findings and the patient's symptomatology we extend an earlier model of the role of vestibular signals in OBEs. Our results advocate the involvement of subcortical bodily mechanisms in the occurrence of OBEs.

Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis.