We study complex networks in which the nodes are tagged with different colors depending on their function (colored graphs), using information theory applied to the distribution of motifs in such networks. We find that colored motifs can be viewed as the building blocks of the networks (much more than the uncolored structural motifs can be) and that the relative frequency with which these motifs appear in the network can be used to define its information content. This information is defined in such a way that a network with random coloration (but keeping the relative number of nodes with different colors the same) has zero color information content. Thus, colored motif information captures the exceptionality of coloring in the motifs that is maintained via selection. We study the motif information content of the C. elegans brain as well as the evolution of colored motif information in networks that reflect the interaction between instructions in genomes of digital life organisms. While we find that colored motif information appears to capture essential functionality in the C. elegans brain (where the color assignment of nodes is straightforward), it is not obvious whether the colored motif information content always increases during evolution, as would be expected from a measure that captures network complexity. For a single choice of color assignment of instructions in the digital life form Avida, we find rather that colored motif information content increases or decreases during evolution, depending on how the genomes are organized, and therefore could be an interesting tool to dissect genomic rearrangements. © 2011 Massachusetts Institute of Technology.
Proteoglycans are cell surface and extracellular matrix molecules to which long, unbranched glycosaminoglycan side chains are attached. Heparan sulphate, a type of glycosaminoglycan chain, has been proposed as a co-factor necessary for signalling by a range of growth factors. Here we provide evidence that loss of 2-O-sulphation in heparan sulphate leads to a significant reduction in cell proliferation in the developing cerebral cortex. The gene encoding heparan sulphate 2-sulphotransferase (Hs2st) is expressed in embryonic cortex and histological analysis of mice homozygous for a null mutation in Hs2st indicated a reduction in the thickness of the embryonic cerebral cortex. Using 5′-bromodeoxyuridine (BrdU) incorporation assays we found a reduction of approximately 40% in labelling indices of cortical precursor cells at E12. Comparison of the fates of cortical cells born on E13 and E15 in Hs2st−/− mutant and wildtype littermate embryos revealed no differences in the pattern of cell migration. Our findings suggest a critical role for 2-O-sulphation of heparan sulphate proteoglycan (HSPG) in regulating cell proliferation during development of the cerebral cortex, perhaps through the modulation of cellular responses to growth factor signalling.
Natural organisms have transitioned from one niche to another over the course of evolution and have adapted accordingly. In particular, if these transition go back and forth between two niches repeatedly, such as transitioning between diurnal and nocturnal lifestyles, this should over time result in adaptations that are beneficial to both environments. Furthermore, they should also adapt to the transitions themselves. Here we answer how Markov Brains, which are an analogue to natural brains, change structurally and functionally when experiencing periodic changes. We show that if environments change sufficiently fast, the structural components that form the brains become useful in both environments. However, brains evolve to perform different computations while using the same components, and thus have computational structures that are multifunctional. Copyright © ALIFE 2018.All rights reserved.
Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Methodology/Principal Findings: Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. Conclusions/Significance: The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available. © 2011 Qian et al.
Many real-life situations require flexible behavior in changing environments. Evidence suggests that anticipation of conflict or task difficulty results in behavioral and neural allocation of task-relevant resources. Here we used a high- and low-interference version of an item-recognition task to examine the neurobehavioral underpinnings of context-sensitive adjustment in working memory (WM). We hypothesized that task environments that included high-interference trials would require participants to allocate neurocognitive resources to adjust to the more demanding task context. The results of two independent behavioral experiments showed enhanced WM performance in the high-interference context, which indicated that a high-interference context improves performance on non-interference trials. A third behavioral experiment showed that when WM load was increased, this effect was no longer significant. Neuroimaging results further showed greater engagement of inferior frontal gyrus, striatum, parietal cortex, hippocampus, and midbrain in participants performing the task in the high- than in the low-interference context. This effect could arise from an active or dormant mode of anticipation that seems to engage fronto-striatal and midbrain regions to flexibly adjust resources to task demands. Our results extend the model of conflict adaptation beyond trial-to-trial adjustments by showing that a high interference context affects both behavioral and biological aspects of cognition.
We describe a case of late onset neurodegeneration with brain iron accumulation (NBIA) presenting as frontotemporal dementia (FTD) with amyotrophic lateral sclerosis (ALS). A male patient presented at age 66 with change of personality: disinhibition, emotional blunting, and socially inappropriate behavior, coupled with dysarthria, dystonia, and corticospinal tract involvement. Magnetic resonance imaging showed general cortical atrophy, iron deposits in the globus pallidus, and the “eye of the tiger” sign. Neuropsychologic performance was globally reduced, especially executive functions. Fluorodeoxyglucose positron emission tomography showed hypometabolism predominantly in frontal and temporal areas. Repeated neurophysiologic examinations showed signs of chronic denervation. The patient was diagnosed with NBIA but fulfilled consensus criteria for FTD and had a clinical picture of ALS, without neurophysiologic confirmation. Our finding introduces NBIA as a possible cause of FTD and as a differential diagnosis of the FTD-ALS complex.