Correlation of receptor density and mRNA expression patterns in the human cerebral cortex

Changes in distribution of associated molecular targets have been reported across several neuropsychiatric disorders. However, the high-resolution topology of most proteins is unknown and simultaneous in vivo measurement in multi-receptor systems is complicated. To account for the missing proteomic information, mRNA transcripts are typically used as a surrogate. Nonetheless, post-transcriptional and post-translational processes might cause the discrepancy between the final distribution of proteins and gene expression patterns. Therefore, this study aims to investigate ex vivo links between mRNA expression and corresponding receptor density in the human cerebral cortex. To this end, autoradiography data on the density of 15 different receptors in 38 brain regions were correlated with the expression patterns of 50 associated genes derived from microarray data (mA), RNA sequencing data (RNA-Seq) provided by the Allen Human Brain Atlas and predicted mRNA expression patterns (pred-mRNA). Spearman's rank correlation was used to evaluate the possible links between proteomic data and mRNA expression patterns. Correlations between mRNA and protein density varied greatly between targets: Positive associations were found for e.g. the serotonin 1A (pred-mRNA: rs = 0.708; mA: rs = 0.601) or kainate receptor (pred-mRNA: rs = 0.655; mA: rs = 0.601; RNA-Seq: rs = 0.575), while most of the investigated target receptors showed low or negative correlations. The high variability in the correspondence of mRNA expression and receptor warrants caution when inferring the topology of molecular targets in the brain from transcriptome data. This highlights the longstanding value of molecular imaging data and the need for comprehensive proteomic data.

MATHEMATICAL BASIS FOR ANALYSING SIGNALS REMOVED FROM FROM THE HUMAN CEREBRAL CORTEX USING DOBESHI AND HAAR METHODS

The article presents the results of the study of signals taken from the human cerebral cortex, and presents the mathematical foundations of analysis using the methods of Daubechy and Haar. A comparative analysis of the method of the Daubechy and Haar wavelet transform implemented in MATLAB and developed using the C++ programming language in the course of the study on the example of a recorded audio signal with natural interference is given.

THE USE OF LOW-FREQUENCY SIGNALS REMOVED FROM THE HUMAN CEREBRAL CORTEX TREATED BY THE WAVELET BY THE DOBESHA TRANSFORM

The article presents the results of a study on the possibility of using the Daubechy wavelet transform for processing low-frequency signals taken from the cerebral cortex. The advantages of using the Daubechy wavelet transform for audio signals with natural interference are given.

Asymmetry of cortical functional hierarchy in humans and macaques suggests phylogenetic conservation and adaptation

The human cerebral cortex is symmetrically organized along large-scale axes but also presents inter-hemispheric differences in structure and function. The quantified contralateral homologous difference, i.e., asymmetry, is a key feature of the human brain left-right axis supporting functional processes, such as language. Here, we assessed whether the asymmetry of cortical functional organization is heritable and phylogenetically conserved between humans and macaques. Our findings indicate asymmetric organization along an axis describing a hierarchical functional trajectory from perceptual/action to abstract cognition. Whereas language network showed leftward asymmetric organization, frontoparietal network showed rightward asymmetric organization. These asymmetries were heritable and comparable between humans and macaques, suggesting (phylo)genetic conservation. However, both language and frontoparietal networks showed a qualitatively larger asymmetry in humans relative to macaques and variable heritability in humans. This may reflect an evolutionary adaptation allowing for experience-dependent specialization, linked to higher-order cognitive functions uniquely developed in humans.

Constantin von Economo´s 90th death anniversary

ABSTRACT The year of 2021 marks 90 year since the death of the neuroscientist Constantin von Economo, whose research in various areas was extremely relevant for the field of neurology. He described lethargic epidemic encephalitis, published an atlas of the cytoarchitecture of the human cerebral cortex, and conducted multiple studies in neuroanatomy, neurophysiology, and clinical neurology. Von Economo's genius extended into other nonmedical fields such as aeronautics, and he had renowned artistic skills.

MULTIMODAL HUMAN BRAIN LONGITUDINAL PARCELLATION ACROSS LIFESPAN

Subdividing the human brain into several functionally distinct and spatially contiguous areas is important to understand the amazingly complex human cerebral cortex. However, adult aging is related to differences in the structure, function, and connectivity of brain areas, so that the single population subdivision does not apply to multiple age groups. Moreover, different modalities could provide affirmative and complementary information for the human brain subdivision. To obtain a more reasonable subdivision of the cerebral cortex, we make use of multimodal information to subdivide the human cerebral cortex across lifespan. Specifically, we first construct a population average functional connectivity matrix for each modality of each age group. Second, we separately calculate the population average similarity matrix for the cortical thickness and myelin modality of each age group. Finally, we fuse these population average matrixes to obtain the multimodal similarity matrix and feed it into the spectral clustering algorithm to generate the brain parcellation for each age group.

Cortex-Like Complex Systems: What Occurs Within?

We consider cortex-like complex systems in the form of strongly connected, directed networks-of-networks. In such a network, there are spiking dynamics at each of the nodes (modelling neurones), together with non-trivial time-lags associated with each of the directed edges (modelling synapses). The connections of the outer network are sparse, while the many inner networks, called modules, are dense. These systems may process various incoming stimulations by producing whole-system dynamical responses. We specifically discuss a generic class of systems with up to 10 billion nodes simulating the human cerebral cortex. It has recently been argued that such a system’s responses to a wide range of stimulations may be classified into a number of latent, internal dynamical modes. The modes might be interpreted as focussing and biasing the system’s short-term dynamical system responses to any further stimuli. In this work, we illustrate how latent modes may be shown to be both present and significant within very large-scale simulations for a wide and appropriate class of complex systems. We argue that they may explain the inner experience of the human brain.