Body context and posture affect mental imagery of hands.

Different visual stimuli have been shown to recruit different mental imagery strategies. However the role of specific visual stimuli properties related to body context and posture in mental imagery is still under debate. Aiming to dissociate the behavioural correlates of mental processing of visual stimuli characterized by different body context, in the present study we investigated whether the mental rotation of stimuli showing either hands as attached to a body (hands-on-body) or not (hands-only), would be based on different mechanisms. We further examined the effects of postural changes on the mental rotation of both stimuli. Thirty healthy volunteers verbally judged the laterality of rotated hands-only and hands-on-body stimuli presented from the dorsum- or the palm-view, while positioning their hands on their knees (front postural condition) or behind their back (back postural condition). Mental rotation of hands-only, but not of hands-on-body, was modulated by the stimulus view and orientation. Additionally, only the hands-only stimuli were mentally rotated at different speeds according to the postural conditions. This indicates that different stimulus-related mechanisms are recruited in mental rotation by changing the bodily context in which a particular body part is presented. The present data suggest that, with respect to hands-only, mental rotation of hands-on-body is less dependent on biomechanical constraints and proprioceptive input. We interpret our results as evidence for preferential processing of visual- rather than kinesthetic-based mechanisms during mental transformation of hands-on-body and hands-only, respectively.

The protracted Permo-Triassic crisis and multi-episode extinction around the Permian-Triassic boundary

The Permo-Triassic crisis was a major turning point in geological history. Following the end-Guadalupian extinction phase, the Palaeozoic biota underwent a steady decline through the Lopingian (Late Permian), resulting in their decimation at the level that is adopted as the Permian-Triassic boundary (PTB). This trend coincided with the greatest Phanerozoic regression. The extinction at the end of the Guadalupian and that marking the end of the Permian are therefore related. The subsequent recovery of the biota occupied the whole of the Early Triassic. Several phases of perturbations in [delta]13Ccarb occurred through a similar period, from the late Wuchiapingian to the end of the Early Triassic. Therefore, the Permian-Triassic crisis was protracted, and spanned Late Permian and Early Triassic time. The extinction associated with the PTB occurred in two episodes, the main act with a prelude and the epilogue. The prelude commenced prior to beds 25 and 26 at Meishan and coincided with the end-Permian regression. The main act itself happened in beds 25 and 26 at Meishan. The epilogue occurred in the late Griesbachian and coincided with the second volcanogenic layer (bed 28) at Meishan. The temporal distribution of these episodes constrains the interpretation of mechanisms responsible for the greatest Phanerozoic mass extinction, particularly the significance of a postulated bolide impact that to our view may have occurred about 50,000[no-break space]Myr after the prelude. The prolonged and multi-phase nature of the Permo-Triassic crisis favours the mechanisms of the Earth's intrinsic evolution rather than extraterrestrial catastrophe. The most significant regression in the Phanerozoic, the palaeomagnetic disturbance of the Permo-Triassic Mixed Superchron, widespread extensive volcanism, and other events, may all be related, through deep-seated processes that occurred during the integration of Pangea. These combined processes could be responsible for the profound changes in marine, terrestrial and atmospheric environments that resulted in the end-Permian mass extinction. Bolide impact is possible but is neither an adequate nor a necessary explanation for these changes.

ConnectomeViewer - Multi-Modal Multi-Level Network Visualization and Analysis.

Introduction: The field of Connectomic research is growing rapidly, resulting from methodological advances in structural neuroimaging on many spatial scales. Especially progress in Diffusion MRI data acquisition and processing made available macroscopic structural connectivity maps in vivo through Connectome Mapping Pipelines (Hagmann et al, 2008) into so-called Connectomes (Hagmann 2005, Sporns et al, 2005). They exhibit both spatial and topological information that constrain functional imaging studies and are relevant in their interpretation. The need for a special-purpose software tool for both clinical researchers and neuroscientists to support investigations of such connectome data has grown. Methods: We developed the ConnectomeViewer, a powerful, extensible software tool for visualization and analysis in connectomic research. It uses the novel defined container-like Connectome File Format, specifying networks (GraphML), surfaces (Gifti), volumes (Nifti), track data (TrackVis) and metadata. Usage of Python as programming language allows it to by cross-platform and have access to a multitude of scientific libraries. Results: Using a flexible plugin architecture, it is possible to enhance functionality for specific purposes easily. Following features are already implemented: * Ready usage of libraries, e.g. for complex network analysis (NetworkX) and data plotting (Matplotlib). More brain connectivity measures will be implemented in a future release (Rubinov et al, 2009). * 3D View of networks with node positioning based on corresponding ROI surface patch. Other layouts possible. * Picking functionality to select nodes, select edges, get more node information (ConnectomeWiki), toggle surface representations * Interactive thresholding and modality selection of edge properties using filters * Arbitrary metadata can be stored for networks, thereby allowing e.g. group-based analysis or meta-analysis. * Python Shell for scripting. Application data is exposed and can be modified or used for further post-processing. * Visualization pipelines using filters and modules can be composed with Mayavi (Ramachandran et al, 2008). * Interface to TrackVis to visualize track data. Selected nodes are converted to ROIs for fiber filtering The Connectome Mapping Pipeline (Hagmann et al, 2008) processed 20 healthy subjects into an average Connectome dataset. The Figures show the ConnectomeViewer user interface using this dataset. Connections are shown that occur in all 20 subjects. The dataset is freely available from the homepage (connectomeviewer.org). Conclusions: The ConnectomeViewer is a cross-platform, open-source software tool that provides extensive visualization and analysis capabilities for connectomic research. It has a modular architecture, integrates relevant datatypes and is completely scriptable. Visit www.connectomics.org to get involved as user or developer.