Mirror neurons in the tree of life, development and evolution of sensorimotor matching responses

Mirror neurons in the tree of life rappresenta lo sviluppo e l' evoluzione del sistema dei neuroni specchio nei primati umani, non - umani e di alcune specie di uccelli, utilizzando metodi cooptati dalla filosofia della biologia e la biologia teorica, per integrare dati relativi al sistema nervoso e al comportamento delle specie in esame.

Laminar distribution of neurofilament inclusions and swollen achromatic neurons in neurofilament inclusion disease (NID)

The laminar distribution of the neurofilament inclusions (NI) and swollen achromatic neurons (SN) was studied in gyri of the temporal cortex in four patients with neurofilament inclusion disease (NID). In 84% of gyri analysed, the density of the NI was maximal in the lower cortical laminae. The distribution of the SN was more variable than the NI. Density was maximal in the lower cortex in 46% of gyri, in the upper cortical laminae in 8% of gyri, and a bimodal distribution in 15% of gyri. In the remaining gyri, there was a more even distribution of SN with cortical depth. In 31% of gyri, the vertical density of the NI was positively correlated with that of the SN. The data suggest that cortical degeneration in the temporal lobe of NID initially affects neurons in the lower laminae. Subsequently, the pathology may spread to affect much of the cortical profile, the SN preceding the appearance of the NI.

Hierarchical GTM: Constructing localized non-linear projection manifolds in a principled way

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis ¸iteBishop98a in several directions: bf(1) We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping. bf(2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. bf(3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the parent visualization plot which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 19-dimensional data sets.

Using directional curvatures to visualize folding patterns of the GTM projection manifolds

In data visualization, characterizing local geometric properties of non-linear projection manifolds provides the user with valuable additional information that can influence further steps in the data analysis. We take advantage of the smooth character of GTM projection manifold and analytically calculate its local directional curvatures. Curvature plots are useful for detecting regions where geometry is distorted, for changing the amount of regularization in non-linear projection manifolds, and for choosing regions of interest when constructing detailed lower-level visualization plots.

Hierarchical GTM: constructing localized non-linear projection manifolds in a principled way

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis ¸iteBishop98a in several directions: bf(1) We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping (GTM). bf(2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. bf(3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the ancestor visualization plots which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 18-dimensional data sets.

Spatial correlations between the vacuolation, prion protein deposits, and surviving neurons in the cerebral cortex in sporadic Creutzfeldt-Jakob disease

In the cerebral cortex of cases of sporadic Creutzfeldt-Jakob disease (sCJD), the vacuolation (spongiform change) and PrP deposits are aggregated into clusters which are regularly distributed parallel to the pia mater. The objective of the present study was to determine the spatial relationships between the clusters of the vacuoles and PrP deposits and between the pathological changes and variations in the density of surviving neurons. In areas with low densities of pathological change, clusters of vacuoles were spatially correlated with the surviving neurons and not with the PrP deposits. By contrast, in more significantly affected areas, clusters of vacuoles were spatially correlated with those of the PrP deposits and not with the surviving neurons. In addition, areas with a high density of vacuoles and a low density of PrP deposits exhibited no spatial correlations between the variables. These data suggest that the spatial relationships between the vacuolation, PrP deposits and surviving neurons in sCJD depend on the density of lesions present. Differences in the pattern of correlation may reflect the developmental stage of the pathology in particular cortical areas.