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Register a new userThree-dimensional alteration of neurites in schizophrenia
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This paper reports nano-CT analysis of brain tissues of schizophrenia and control cases. The analysis revealed that: (1) neuronal structures vary between individuals, (2) the mean curvature of distal neurites of the schizophrenia cases was 1.5 times higher than that of the controls, and (3) dendritic spines were categorized into two geometrically distinctive groups, though no structural differences were observed between the disease and control cases. The differences in the neurite curvature result in differences in the spatial trajectory and hence alter neuronal circuits. We suggest that the structural alteration of neurons in the schizophrenia cases should reflect psychiatric symptoms of schizophrenia.
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Modularity plays an important role in brain networks architecture and influences its dynamics and the ability to integrate and segregate different modules of cerebral regions. Alterations in community structure are associated with several clinical disorders, specially schizophrenia, although its time evolution is not clear yet. In the present work, we analyze fMRI functional networks of $65$ healthy subjects (HC) and $44$ patients of schizophrenia (SZ), $28$ of them in a chronic state (CR) of illness, and $16$ at early stage (ES). We find clear differences in edges weights distribution, networks density, community structure consistency and robustness against edge removal. In comparison to healthy subjects, we found that networks from SZ patients exhibits wider weight distribution, larger overall connectivity, and are more consistent in the community structure across subjects. We also showed that the networks of SZ patients tend to be more robust to edge removal than healthy subjects, while having lower network density. In the case of early stages patients, we found that their networks exhibit topological features consistently in between the ones obtained from the other two groups, resulting in a tendency towards the chronic group state.
Can three-dimensional, microvasculature networks still ensure blood supply if individual links fail? We address this question in the sinusoidal network, a plexus-like microvasculature network, which transports nutrient-rich blood to every hepatocyte in liver tissue, by building on recent advances in high-resolution imaging and digital reconstruction of adult mice liver tissue. We find that the topology of the three-dimensional sinusoidal network reflects its two design requirements of a space-filling network that connects all hepatocytes, while using shortest transport routes: sinusoidal networks are sub-graphs of the Delaunay graph of their set of branching points, and also contain the corresponding minimum spanning tree, both to good approximation. To overcome the spatial limitations of experimental samples and generate arbitrarily-sized networks, we developed a network generation algorithm that reproduces the statistical features of 0.3-mm-sized samples of sinusoidal networks, using multi-objective optimization for node degree and edge length distribution. Nematic order in these simulated networks implies anisotropic transport properties, characterized by an empirical linear relation between a nematic order parameter and the anisotropy of the permeability tensor. Under the assumption that all sinusoid tubes have a constant and equal flow resistance, we predict that the distribution of currents in the network is very inhomogeneous, with a small number of edges carrying a substantial part of the flow. We quantify network resilience in terms of a permeability-at-risk, i.e. permeability as function of the fraction of removed edges. We find that sinusoidal networks are resilient to random removal of edges, but vulnerable to the removal of high-current edges. Our findings suggest the existence of a mechanism counteracting flow inhomogeneity to balance metabolic load on the liver.
We have recognized that 2D codes, i.e., a group of strongly connected neurosomes that can be simultaneously excited, are the basic data carriers for memory in a brain. An echoing mechanism between two neighboring layers of neurosomes is assumed to establish temporary memory, and repeating processes enhance the formation of long-term memory. Creation and degradation of memory information are statistically. The maximum capacity of memory storage in a human brain is estimated to be one billion of 2D codes. By triggering one or more neurosomes in a neurosome-based 2D code, the whole strongly connected neurosome network is capable of exciting simultaneously and projecting its excitation onto an analysis layer of neurons in cortex, thus retrieving the stored memory data. The capability of comparing two 2D codes in the analysis layer is one of the major brain functions.
Purpose: Auditory verbal hallucinations (AVHs) are speech perceptions in the absence of a external stimulation. An influential theoretical account of AVHs in schizophrenia claims that a deficit in inner speech monitoring would cause the verbal thoughts of the patient to be perceived as external voices. The account is based on a predictive control model, in which verbal self-monitoring is implemented. The aim of this study was to examine lip muscle activity during AVHs in schizophrenia patients, in order to check whether inner speech occurred. Methods: Lip muscle activity was recorded during covert AVHs (without articulation) and rest. Surface electromyography (EMG) was used on eleven schizophrenia patients. Results: Our results show an increase in EMG activity in the orbicularis oris inferior muscle, during covert AVHs relative to rest. This increase is not due to general muscular tension since there was no increase of muscular activity in the forearm muscle. Conclusion: This evidence that AVHs might be self-generated inner speech is discussed in the framework of a predictive control model. Further work is needed to better describe how the inner speech monitoring dysfunction occurs and how inner speech is controlled and monitored. This will help better understanding how AVHs occur.
Network neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in forty-four medicated schizophrenic patients and forty healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones.
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