Oligodendrocyte positioning in cerebral cortex is independent of projection neuron layering
ABSTRACT The factors affecting normal oligodendrocyte positioning in the cerebral cortex are unknown. Apart from the white matter, the highest numbers of oligodendrocytes in the rodent cortex are found in Layers V/VI, where the infragranular neurons normally reside. Few, if any, oligodendrocytes are normally found in the superficial cortical layers. To test whether or not this asymmetric positioning of oligodendrocytes is linked to the lamina positions of Layer V/VI projection neurons, mutant mice that cause neuronal layer inversion were examined. In three lines of mutant mice (Reeler, disabled-1, and p35) examined, representing two different genetic signaling pathways, the oligodendrocyte distribution was altered from an asymmetric to a symmetric distribution pattern. Unlike cortical neurons that are inverted in these mutant mice, the lack of oligodendrocyte inversion suggests a decoupling of the genetic mechanisms governing neuronal versus oligodendrocyte patterning. We conclude that oligodendrocyte positioning is not linked to the layer positions of V/VI projection neurons.
- SourceAvailable from: Zoltán Molnár
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- "In contrast, astrocyte-enriched genes (R1.5-fold; Cahoy et al., 2008) were 17% less likely to be patterned than unpatterned (p = 0.0007; two-tailed Chi-square test with Yates correction). Oligodendrocyte-enriched genes (Cahoy et al., 2008) were found almost exclusively in the deepest samples (see Belgard et al., 2011), matching previous observations that oligodendrocytes are rare in the neocortex except in the deepest layers (Tan et al., 2009). Likewise, the gene encoding the specific and robust microglia marker F4/80 (Cucchiarini et al., 2003; Perry et al., 1985) monotonically increased in expression with samples derived from deeper layers. "
ABSTRACT: In the mammalian cortex, neurons and glia form a patterned structure across six layers whose complex cytoarchitectonic arrangement is likely to contribute to cognition. We sequenced transcriptomes from layers 1-6b of different areas (primary and secondary) of the adult (postnatal day 56) mouse somatosensory cortex to understand the transcriptional levels and functional repertoires of coding and noncoding loci for cells constituting these layers. A total of 5,835 protein-coding genes and 66 noncoding RNA loci are differentially expressed ("patterned") across the layers, on the basis of a machine-learning model (naive Bayes) approach. Layers 2-6b are each associated with specific functional and disease annotations that provide insights into their biological roles. This new resource (http://genserv.anat.ox.ac.uk/layers) greatly extends currently available resources, such as the Allen Mouse Brain Atlas and microarray data sets, by providing quantitative expression levels, by being genome-wide, by including novel loci, and by identifying candidate alternatively spliced transcripts that are differentially expressed across layers.Neuron 08/2011; 71(4):605-16. DOI:10.1016/j.neuron.2011.06.039 · 15.98 Impact Factor
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ABSTRACT: As part of a computer-based cockpit design and analysis workstation, a prototype training assessment tool called the Training Assessment Module (TAM) has been developed which estimates the training resources and time imposed by the anticipated mission and cockpit design. Embedding instructional system and training analysis domain knowledge in a production system environment, this tool allows crew station designers to readily determine the training ramifications of their choices for cockpit equipment, mission tasks, and operator qualifications. Initial results have been validated by comparison to an existing training program, demonstrating the tool's utility as a conceptual design aid and illuminating areas for future development. The combined ART and Symbolics Common Lisp environment was found to be ideally suited to quickly capturing and representing the target domain. Despite its rather small size in comparison to other knowledge-based systems, TAM's initial recommendations show a very high correlation with empirical data from the AH-64 qualification courseSystems, Man and Cybernetics, 1989. Conference Proceedings., IEEE International Conference on; 12/1989
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ABSTRACT: Microelectrode arrays used to record local field potentials from the brain are being built with increasingly more spatial resolution, ranging from the initially developed laminar arrays to those with planar and three-dimensional (3D) formats. In parallel with such development in recording techniques, current source density (CSD) analyses have recently been expanded up to the continuous-3D form. Unfortunately, the effect of the conductivity profile on the CSD analysis performed with contemporary microelectrode arrays has not yet been evaluated and most of the studies assumed it was homogeneous and isotropic. In this study, we measured the conductivity profile in the somatosensory barrel cortex of Wistar rats. To that end, we combined multisite electrophysiological data recorded with a homemade assembly of silicon-based probes and a nonlinear least-squares algorithm that implicitly assumed that the cerebral cortex of rodents could be locally approximated as a layered anisotropic spherical volume conductor. The eccentricity of the six cortical layers in the somatosensory barrel cortex was evaluated from postmortem histological images. We provided evidence for the local spherical character of the entire barrels field, with concentric cortical layers. We found significant laminar dependencies in the conductivity values with radial/tangential anisotropies. These results were in agreement with the layer-dependent orientations of myelinated axons, but hardly related to densities of cells. Finally, we demonstrated through simulations that ignoring the real conductivity profile in the somatosensory barrel cortex of rats caused considerable errors in the CSD reconstruction, with pronounced effects on the continuous-3D form and charge-unbalanced CSD. We concluded that the conductivity profile must be included in future developments of CSD analysis, especially for rodents.Journal of Neurophysiology 12/2010; 104(6):3388-412. DOI:10.1152/jn.00122.2010 · 3.04 Impact Factor