The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models.

Item does not contain fulltextFunctionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix

Small-World Properties of Nonlinear Brain Activity in Schizophrenia

Abstract: A disturbance in the interactions between distributed cortical regions may underlie the cogni-tive and perceptual dysfunction associated with schizophrenia. In this article, nonlinear measures of cortical interactions and graph-theoretical metrics of network topography are combined to investigate this schizophrenia ‘‘disconnection hypothesis.’ ’ This is achieved by analyzing the spatiotemporal struc-ture of resting state scalp EEG data previously acquired from 40 young subjects with a recent first epi-sode of schizophrenia and 40 healthy matched controls. In each subject, a method of mapping the to-pography of nonlinear interactions between cortical regions was applied to a widely distributed array of these data. The resulting nonlinear correlation matrices were converted to weighted graphs. The path length (a measure of large-scale network integration), clustering coefficient (a measure of ‘‘cliqu-ishness’’), and hub structure of these graphs were used as metrics of the underlying brain network ac-tivity. The graphs of both groups exhibited high levels of local clustering combined with comparatively short path lengths—features consistent with a ‘‘small-world’ ’ topology—as well as the presence of strong, central hubs. The graphs in the schizophrenia group displayed lower clustering and shorter path lengths in comparison to the healthy group. Whilst still ‘‘small-world,’ ’ these effects are consistent with a subtle randomization in the underlying network architecture—likely associated with a greate

Low-Frequency Type-II Radio Detections and Coronagraph Data Employed to Describe and Forecast the Propagation of 71 CMEs/Shocks

The vulnerability of technology on which present society relies demands that a solar event, its time of arrival at Earth, and its degree of geoeffectiveness be promptly forecasted. Motivated by improving predictions of arrival times at Earth of shocks driven by coronal mass ejections (CMEs), we have analyzed 71 Earth-directed events in different stages of their propagation. The study is primarily based on approximated locations of interplanetary (IP) shocks derived from type II radio emissions detected by the Wind/WAVES experiment during 1997-2007. Distance-time diagrams resulting from the combination of white-light corona, IP type II radio, and in situ data lead to the formulation of descriptive profiles of each CME's journey toward Earth. Furthermore, two different methods to track and predict the location of CME-driven IP shocks are presented. The linear method, solely based on Wind/WAVES data, arises after key modifications to a pre-existing technique that linearly projects the drifting low-frequency type II emissions to 1 AU. This upgraded method improves forecasts of shock arrival time by almost 50%. The second predictive method is proposed on the basis of information derived from the descriptive profiles, and relies on a single CME height-time point and on low-frequency type II radio emissions to obtain an approximate value of the shock arrival time at Earth. In addition, we discuss results on CME-radio emission associations, characteristics of IP propagation, and the relative success of the forecasting methods.Comment: Solar Physics; Accepted for publication 2015-Apr-2