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Register a new userThe unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia
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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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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.
The structural human connectome (i.e. the network of fiber connections in the brain) can be analyzed at ever finer spatial resolution thanks to advances in neuroimaging. Here we analyze several large data sets for the human brain network made available by the Open Connectome Project. We apply statistical model selection to characterize the degree distributions of graphs containing up to $simeq 10^6$ nodes and $simeq 10^8$ edges. A three-parameter generalized Weibull (also known as a stretched exponential) distribution is a good fit to most of the observed degree distributions. For almost all networks, simple power laws cannot fit the data, but in some cases there is statistical support for power laws with an exponential cutoff. We also calculate the topological (graph) dimension $D$ and the small-world coefficient $sigma$ of these networks. While $sigma$ suggests a small-world topology, we found that $D < 4$ showing that long-distance connections provide only a small correction to the topology of the embedding three-dimensional space.
Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. From the behavioral data in both tasks, we found that learners were sensitive to the community structure of the networks, as evidenced by a slower reaction time on trials transitioning between clusters than on trials transitioning within a cluster. From the neuroimaging data collected during the social network learning task, we observed that the functional connectivity of the hippocampus and temporoparietal junction was significantly greater when transitioning between clusters than when transitioning within a cluster. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions during the social task than during the non-social task. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.
Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such K-pairwise models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the populations capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.
Higher socioeconomic status (SES) in childhood is associated with increased cognitive abilities, higher academic achievement, and decreased incidence of mental illness later in development. Accumulating evidence suggests that these effects may be due to changes in brain development induced by environmental factors. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8-22 years, n=1012) to examine developmental changes in functional brain network topology as estimated from resting state functional magnetic resonance imaging data. We quantitatively characterize this topology using a local measure of network segregation known as the clustering coefficient, and find that it accounts for a greater degree of SES-associated variance than meso-scale segregation captured by modularity. While whole-brain clustering increased with age, high-SES youth displayed faster increases in clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The effect of SES on developmental increases in clustering was strongest for connections of intermediate physical length, consistent with faster decreases in local connectivity in these regions in low-SES youth, and tracked changes in BOLD signal complexity in the form of regional homogeneity. Our findings suggest that neighborhood SES may fundamentally alter intrinsic patterns of inter-regional interactions in the human brain in a manner that is consistent with greater segregation of information processing in late childhood and adolescence.
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