g., XNAT, Human Imaging Database). Greater investment in the development and/or maturation of user-friendly, high-capacity databases is required for the CWA era. 4. The Jack-of-All-Trades Phenomenon. The modern-day neuroscientist feels increasingly pressured to be proficient in a growing array of scientific domains (e.g., cognitive neuroscience, clinical neuroscience, computer science, statistics,

and biophysics). Unfortunately, existing centralized educational resources cannot cover the broad gamut of interdisciplinary domains with which a researcher must be familiar. Although interdisciplinary training and fluency in multiple domains is essential, mastery of all is unlikely. Success in the CWA era will require the involvement of the broader scientific community see more and greater focus on active interdisciplinary collaboration. Open science initiatives serve to both inspire and facilitate collaborative research efforts within and across scientific disciplines. 5. Analytic Inertia. To date, imaging analysis has predominantly relied upon univariate statistical approaches. Unfortunately, such analytic frameworks fail to consider the complexities of the connectome. Similarly, conventional statistical models are poorly equipped for high-dimensional

data sets. Novel analytic approaches to characterizing and exploring Gefitinib purchase the connectome, as well as linking its properties to phenotypic variation, are needed. Recent applications of multivariate pattern analytic techniques based in graph theory and statistical or machine learning have highlighted the potential value of more complex analytic approaches ( Bullmore and Bassett, 2011, Craddock et al., 2009, Dosenbach et al., 2010 and Poldrack, 2011). Once again, the expertise and input of the broader scientific community will be needed to ensure appropriate implementation. Every significant innovation entails a new set of challenges and opens new avenues of research—often larger in scale. Although neuroimaging researchers could once work in silos, with only a limited set of developers supporting the community (e.g., Analysis of Functional NeuroImages

[AFNI], FMRIB software library [FSL], and Statistical Parameter Mapping [SPM]), the demands of the CWA era have changed the game. Fortunately, the community is mobilizing and shifting toward 3-mercaptopyruvate sulfurtransferase open science at a rapid pace. A full review of all the emerging initiatives would be too extensive for the present work. Instead, I provide descriptions (see Table 1) of selected initiatives that (1) guide users to or host open science initiatives (e.g., Neuroscience Information Framework, The Neuroimaging Tools and Resources Clearinghouse [NITRC]), (2) actively promote communication and collaboration (e.g., International Neuroinformatics Coordinating Facility, Neuro Bureau [NB]), (3) promote infrastructure pipeline development using nonproprietary platforms (e.g., NiPype, Niak), or (4) provide novel analytic platforms for the connectome (e.g.