Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways

Powder diffraction and synchrotron radiation.

Powder diffraction is one of the fundamental techniques for the investigation of materials. Its sensitivity to long range order makes it ideal for the identification, quantification and structural characterization of crystalline phases. Powder diffraction experiments performed at synchrotron sources make ample use of the intrinsic characteristics of synchrotron radiation in terms of energy tunability, brilliance, natural divergence, and excellent signal/noise ratio. Synchrotron radiation powder diffraction (SR-PD) enhances and optimizes the traditional applications of laboratory XRPD, such as phase identification, phase quantification, texture analysis, and peak broadening analysis in terms of stress/strain. However, the properties of the synchrotron X-rays also allow a number of experiments not accessible with laboratory sources, especially in terms of time-resolution, the use of non-ambient sample environments, and simultaneous and combined experiments. The mapping of the physical, chemical, and crystallographic properties of the sample in 2D and 3D using smart combinations of diffraction imaging spectroscopy is the natural current evolution of many synchrotron instruments, and one that is bound to have a great impact on many aspects of materials studies