Changes in snoRNA and snRNA Abundance in the Human, Chimpanzee, Macaque, and Mouse Brain
Small nuclear and nucleolar RNAs (snRNAs and snoRNAs) are known to be functionally and evolutionarily conserved elements of transcript processing machinery. Here, we investigated the expression evolution of snRNAs and snoRNAs by measuring their abundance in the frontal cortex of humans, chimpanzees, rhesus monkeys, and mice. Although snRNA expression is largely conserved, 44% of the 185 measured snoRNA and 40% of the 134 snoRNA families showed significant expression divergence among species. The snRNA and snoRNA expression divergence included drastic changes unique to humans: A 10-fold elevated expression of U1snRNA and a 1,000-fold drop in expression ofSNORA29 The decreased expression of SNORA29 might be due to two mutations that affect secondary structure stability. Using in situ hybridization, we further localizedSNORA29expression to nucleolar regions of neuronal cells. Our study presents the first observation of snoRNA abundance changes specific to the human lineage and suggests a possible mechanism underlying these changes.
Many environmental stimuli present a quasi-rhythmic structure at different timescales that the brain needs to decompose and integrate. Cortical oscillations have been proposed as instruments of sensory de-multiplexing, i.e., the parallel processing of different frequency streams in sensory signals. Yet their causal role in such a process has never been demonstrated. Here, we used a neural microcircuit model to address whether coupled theta–gamma oscillations, as observed in human auditory cortex, could underpin the multiscale sensory analysis of speech. We show that, in continuous speech, theta oscillations can flexibly track the syllabic rhythm and temporally organize the phoneme-level response of gamma neurons into a code that enables syllable identification. The tracking of slow speech fluctuations by theta oscillations, and its coupling to gamma-spiking activity both appeared as critical features for accurate speech encoding. These results demonstrate that cortical oscillations can be a key instrument of speech de-multiplexing, parsing, and encoding.
Language is a uniquely human cognitive function which plays a defining role in our psychological and social traits. Despite the obvious importance of language and speech, they remain one of the least understood human cognitive functions with the cortical underpinnings of these crucial skills still obscure. In recent decades, a large amount of data that account for the neural bases of language processes in both children and adults have been acquired through the use of many advanced neurophysiology techniques. These include high-density electroencephalography, magnetoencephalography, functional magnetic-resonance tomography, transcranial magnetic stimulation, transcranial direct current stimulation, and eye-tracking. The combined use of these approaches continues to shed light on brain mechanisms of language acquisition, comprehension and processing, on speech disorders and their treatment, and on interactions between language and other neurocognitive systems and functions. The aim of this Research Topic in Frontiers in Human Neuroscience is to provide a state-of-the-art overview of this diverse and multidisciplinary area of research, with special emphasis on bridging the gap between different methodologies.
There has been compared behavior of rats, corvid birds, and monkeys of different species at their performance of the Revecz–Krushinskii test (RKT) developed by L.V. Krushinskii to estimate the human capability for revealing rule of discrete translocation of hidden target object. RKT was introduced as an addition to the test for extrapolation of the movement direction of the lure seen only at the initial pathway fragment; this test is close to Piaget’s test (stage 6) evaluating the capability for mental representation and location of the moving hidden object. During RKT, the lure, hidden from animals, was placed, near where it was previous time: at the first test presentation— under the 1st cylinder, at the 2nd one—under the 2nd cylinder, etc. The animals were tested once. It was shown that they did not catch the necessary for successful solution rule of the lure translocation, direction and step of its translocation at each presentation. Only some of the animals solved RKT, found the lure 3 and more times in succession with no errors or with one error. Nevertheless, in all groups the number of errors was lower than that in the model situation of random search. Such optimization was a consequence of universal for all groups’ strategy of search in the places where the lure was found recently. With the similar number of errors, rats, birds, and monkeys performed the search differently. Rats were looking for lure mainly among the cylinders where they had found it previously, whereas monkeys and birds the first the new cylinders located near the target one, which implies the existence, to the weak extent, of elements of prognosis. For all groups of animals, RKT turned out to be more difficult both of the test for extrapolation and of the Piaget’s test.
Proceedings of the conference "Cognitive Science in Moscow: New Research" (June 19, 2019).
Connectivity measures are (typically bivariate) statistical measures that may be used to estimate interactions between brain regions from electrophysiological data. We review both formal and informal descriptions of a range of such measures, suitable for the analysis of human brain electrophysiological data, principally electro- and magnetoencephalography. Methods are described in the space–time,space–frequency, and space–time–frequency domains. Signal processing and information theoretic measures are considered, and linear and nonlinear methods are distinguished. A novel set of crosstime–frequency measures is introduced, including a cross-time–frequency phase synchronization measure.
One of the key advances in genome assembly that has led to a significant improvement in contig lengths has been improved algorithms for utilization of paired reads (mate-pairs). While in most assemblers, mate-pair information is used in a post-processing step, the recently proposed Paired de Bruijn Graph (PDBG) approach incorporates the mate-pair information directly in the assembly graph structure. However, the PDBG approach faces difficulties when the variation in the insert sizes is high. To address this problem, we first transform mate-pairs into edge-pair histograms that allow one to better estimate the distance between edges in the assembly graph that represent regions linked by multiple mate-pairs. Further, we combine the ideas of mate-pair transformation and PDBGs to construct new data structures for genome assembly: pathsets and pathset graphs.