Biology subjects

Betzel, R.

Publications and source records attributed to Betzel, R..

42 records · Page 3Linked to original sources

bioRxiv2019

Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Network neuroscience has relied on a node-centric network model in which cells, populations, and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. Here, we develop an edge-centric network model, which generates the novel constructs of \"edge time series\" and \"edge functional connectivity\" (eFC). Using network analysis, we show that at rest eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We go on to show that eFC is systematically and consistently modulated by variation in sensory input. In future work, the edge-centric approach could be used to map the connectional architecture of brain circuits and for the development of brain-based biomarkers of disease and development.

neuroscience↗

bioRxiv2019

High-amplitude co-fluctuations in cortical activity drive resting-state functional connectivity

Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to correlated activity are, however, poorly understood. Here, we decompose resting-state functional connectivity using a "temporal unwrapping" procedure to assess the contributions of moment-to-moment activity co-fluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of co-fluctuations of network organization with fluctuations in the BOLD time series. We show that, surprisingly, only a small fraction of frames exhibiting the strongest co-fluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the networks modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Finally, we demonstrate that co-fluctuation amplitude synchronizes across subjects during movie-watching and that high-amplitude frames carry detailed information about individual subjects (whereas low-amplitude frames carry little). Our approach reveals fine-scale temporal structure of resting-state functional connectivity, and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of new directions for future research.

neuroscience↗

bioRxiv2019

Generative network models identify biological mechanisms of altered structural brain connectivity in schizophrenia

BackgroundAlterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms leading to those alterations remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of the formation of altered structural brain networks.\n\nMethodsWe evaluated different generative network models to investigate the formation of structural brain networks in healthy controls (n=152), schizophrenia patients (n=66) and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated the association of these factors to cognition and to polygenic risk for schizophrenia.\n\nResultsStructural brain networks can be best accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation for all groups analyzed. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. The model parameter for spatial constraint was correlated with the polygenic risk for schizophrenia and predicted reduced cognitive performance.\n\nConclusionsOur results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to normal brain development as well as altered connectomes in schizophrenia. Spatial constraints were linked to the genetic risk for schizophrenia and general cognitive functioning, thereby providing insights into their biological basis and behavioral relevance.

neuroscience↗

bioRxiv2019

Space-independent community and hub structure of functional brain networks

Coordinated brain activity reflects underlying cognitive processes and can be modeled as a network of inter-regional functional connections. The most costly connections in the network are long-distance correlations that, in the absence of underlying structural connections, are maintained by sustained energetic inputs. Here, we present a spatial modeling approach that amplifies contributions made by long-distance functional connections to whole-brain network architecture, while simultaneously suppressing contributions made by short-range connections. We use this method to characterize the long-distance architecture of functional networks and to identify aspects of community and hub structure that are driven by long-distance correlations and that, we argue, are of greater functional significance. We find that based only on patterns of long-distance connectivity, primary sensory cortices occupy increasingly central positions and appear more \"hub-like\". Additionally, we show that the community structure of long-distance connections spans multiple topological levels and differs from the community structure detected in networks that include both short-range and long-distance connections. In summary, these findings highlight the complex relationship between the brains physical layout and its functional architecture. The results presented here inform future analyses of community structure and network hubs in health, across development, and in the case of neuropsychiatric disorders.

neuroscience↗

bioRxiv2018

Organizing principles of whole-brain functional connectivity in zebrafish larvae

Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchical modular structure and hubs that span module boundaries. We go on to show that spon-taneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal, for the first time, basic organizing principles of whole-brain functional brain networks at the microscale. Our overarching methodological framework provides a blueprint for studying the correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and microscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.

neuroscience↗

bioRxiv2018

Stability of spontaneous, correlated activity in mouse auditory cortex

Neural systems can be modeled as complex networks in which neural elements are represented as nodes linked to one another through structural or functional connections. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the systems topological organization and to better understand its function. While the network-based approach has become common in the analysis of large-scale neural systems probed by non-invasive neuroimaging, few studies have used network science to study the organization of biological neuronal networks reconstructed at the cellular level, and thus many very basic and fundamental questions remain unanswered. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the maximum lagged correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work provides a careful methodological blueprint for future studies of spontaneous activity measured by two-photon calcium imaging using cutting-edge computational methods and machine learning algorithms informed by explicit graphical models from network science. The methods are flexible and easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease.

neuroscience↗