Neural Signaling and Communication

To understand the complex nature of the human brain, network science approaches have played an important role. Neural signaling and communication form the basis for studying the dynamics of brain activity and functions. The neuroscientific community is interested in the network architecture of the human brain its simulation and for prediction of emergent network states. In this chapter we focus on how neurosignaling and communication is playing its part in medical psychology, furthermore, we have also reviewed how the interaction of network topology and dynamic models of a brain network

Navigation of brain networks

Understanding the mechanisms of neural communication in large-scale brain networks remains a major goal in neuroscience. We investigated whether navigation is a parsimonious routing model for connectomics. Navigating a network involves progressing to the next node that is closest in distance to a desired destination. We developed a measure to quantify navigation efficiency and found that connectomes in a range of mammalian species (human, mouse and macaque) can be successfully navigated with near-optimal efficiency (>80% of optimal efficiency for typical connection densities). Rewiring network topology or repositioning network nodes resulted in 45%-60% reductions in navigation performance. Specifically, we found that brain networks cannot be progressively rewired (randomized or clusterized) to result in topologies with significantly improved navigation performance. Navigation was also found to: i) promote a resource-efficient distribution of the information traffic load, potentially relieving communication bottlenecks; and, ii) explain significant variation in functional connectivity. Unlike prevalently studied communication strategies in connectomics, navigation does not mandate biologically unrealistic assumptions about global knowledge of network topology. We conclude that the wiring and spatial embedding of brain networks is conducive to effective decentralized communication. Graph-theoretic studies of the connectome should consider measures of network efficiency and centrality that are consistent with decentralized models of neural communication

Introducing axonal myelination in connectomics: a preliminary analysis of g-ratio distribution in healthy subjects

Microstructural imaging and connectomics are two research areas that hold great potential for investigating brain structure and function. Combining these two approaches can lead to a better and more complete characterization of the brain as a network. The aim of this work is characterizing the connectome from a novel perspective using the myelination measure given by the g-ratio. The g-ratio is the ratio of the inner to the outer diameters of a myelinated axon, whose aggregated value can now be estimated in vivo using MRI. In two different datasets of healthy subjects, we reconstructed the structural connectome and then used the g-ratio estimated from diffusion and magnetization transfer data to characterise the network structure. Significant characteristics of g-ratio weighted graphs emerged. First, the g-ratio distribution across the edges of the graph did not show the power-law distribution observed using the number of streamlines as a weight. Second, connections involving regions related to motor and sensory functions were the highest in myelin content. We also observed significant differences in terms of the hub structure and the rich-club organization suggesting that connections involving hub regions present higher myelination than peripheral connections. Taken together, these findings offer a characterization of g-ratio distribution across the connectome in healthy subjects and lay the foundations for further investigating plasticity and pathology using a similar approach

Estimating the impact of structural directionality: How reliable are undirected connectomes?

Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Due to limitations of non-invasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (i) remove uni-directional connections, (ii) add reciprocal connections, and (iii) combine equal numbers of removed and added uni-directional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections (false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections (false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.Comment: 29 pages, 6 figures, 9 supplementary figures, 4 supplementary table

Narušení funkce neuronů a gliových buněk u schizofrenie s důrazem na jejich komunikaci

Katedra fyziologieDepartment of PhysiologyFaculty of SciencePřírodovědecká fakult

Frequency-Dependent Spatial Distribution of Functional Hubs in the Human Brain and Alterations in Major Depressive Disorder

Alterations in large-scale brain intrinsic functional connectivity (FC), i.e., coherence between fluctuations of ongoing activity, have been implicated in major depressive disorder (MDD). Yet, little is known about the frequency-dependent alterations of FC in MDD. We calculated frequency specific degree centrality (DC) – a measure of overall FC of a brain region – within 10 distinct frequency sub-bands accessible from the full range of resting-state fMRI BOLD fluctuations (i.e., 0.01–0.25 Hz) in 24 healthy controls and 24 MDD patients. In healthy controls, results reveal a frequency-specific spatial distribution of highly connected brain regions – i.e., hubs – which play a fundamental role in information integration in the brain. MDD patients exhibited significant deviations from the healthy DC patterns, with decreased overall connectedness of widespread regions, in a frequency-specific manner. Decreased DC in MDD patients was observed predominantly in the occipital cortex at low frequencies (0.01–0.1 Hz), in the middle cingulate cortex, sensorimotor cortex, lateral parietal cortex, and the precuneus at middle frequencies (0.1–0.175 Hz), and in the anterior cingulate cortex at high frequencies (0.175–0.25 Hz). Additionally, decreased DC of distinct parts of the insula was observed across low, middle, and high frequency bands. Frequency-specific alterations in the DC of the temporal, insular, and lateral parietal cortices correlated with symptom severity. Importantly, our results indicate that frequency-resolved analysis within the full range of frequencies accessible from the BOLD signal – also including higher frequencies (>0.1 Hz) – reveals unique information about brain organization and its changes, which can otherwise be overlooked

Grading of Frequency Spectral Centroid Across Resting-State Networks

Ongoing, slowly fluctuating brain activity is organized in resting-state networks (RSNs) of spatially coherent fluctuations. Beyond spatial coherence, RSN activity is governed in a frequency-specific manner. The more detailed architecture of frequency spectra across RSNs is, however, poorly understood. Here we propose a novel measure–the Spectral Centroid (SC)–which represents the center of gravity of the full power spectrum of RSN signal fluctuations. We examine whether spectral underpinnings of network fluctuations are distinct across RSNs. We hypothesize that spectral content differs across networks in a consistent way, thus, the aggregate representation–SC–systematically differs across RSNs. We therefore test for a significant grading (i.e., ordering) of SC across RSNs in healthy subjects. Moreover, we hypothesize that such grading is biologically significant by demonstrating its RSN-specific change through brain disease, namely major depressive disorder. Our results yield a highly organized grading of SC across RSNs in 820 healthy subjects. This ordering was largely replicated in an independent dataset of 25 healthy subjects, pointing toward the validity and consistency of found SC grading across RSNs. Furthermore, we demonstrated the biological relevance of SC grading, as the SC of the salience network–a RSN well known to be implicated in depression–was specifically increased in patients compared to healthy controls. In summary, results provide evidence for a distinct grading of spectra across RSNs, which is sensitive to major depression

Executive Dysufnctions in the Schizophrenia Spectrum Disorders

Schizophrenia is from a considerable part genetically conditioned. One of the heritable characteristics of this disorder is a cognitive deficit, which is to the some point even detected in healthly siblings of individuals with schizophrenia. One of the cognitive deficits, which according to recent research is a candidate for its consideration as a specific heritable characteristic as well, is also executive dysfunction. Executive dysfunction is correlated with changes of functional connectivity in the specific neural circuits. In this bachelor thesis, the functional changes are discussed and are associated with results of neuropsychological tests. Submitted research proposal aims to replicate the executive dysfunction (specifically in the domains of inhibitory control, working memory and cognitive flexibility) of schizophrenics and their first-degree relatives. In the matter of replication of this result, executive dysfunction and its neural correlation might be considered as a valid endophenotype, potentially useful in diagnostics and prevention of this serious disorder. Keywords executive dysfunction, endophenotype, schizophrenia, schizophrenia-spectrum disorders, MRISchizofrenie je ze značné části geneticky podmíněná. Jednou z heritabilních charakteristik této poruchy je kognitivní deficit, který je do jisté míry detekovatelný i u zdravých sourozenců jedinců se schizofrenií. Jedním z kognitivních deficitů, který je dle recentních výzkumů rovněž kandidátem na to být považován za specifickou heritabilní charakteristiku, je i exekutivní dysfunkce. Exekutivní dysfunkce je korelována se změnami funkční konektivity ve specifických neurálních okruzích. Funkční změny jsou v této bakalářské práci diskutovány a asociovány s výsledky v neuropsychologických testech. Předložený návrh výzkumného projektu si klade za cíl replikovat exekutivní dysfunkce (konkrétně v doménách inhibiční kontroly, pracovní paměti a kognitivní flexibility) u jedinců s diagnózou schizofrenního spektra a jejich příbuzných prvního stupně. V případě replikace tohoto výsledku by exekutivní dysfunkce a její neurální koreláty mohly být považovány jako validní endofenotyp potenciálně využitelný v diagnostice a prevenci této závažné poruchy. Klíčová slova exekutivní dysfunkce, endofenotyp, poruchy schizofrenního spektra, schizofrenie, MRIDepartment of PsychologyKatedra psychologieFilozofická fakultaFaculty of Art