Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

NEUROINFORMATICSwith electroencephalography (MEG/EEG). A battery of behavioral and cognitive tests will also be included along with the collection of genetic material. This endeavor will yield valuable information about brain connectivity, its relationship to behavior, and the contributions of genetic and environmental factors to individual differences in brain circuitry. The data generated by the WU-Minn HCP consortium will be openly shared with the scientific community.The HCP has a broad informatics vision that includes support for the acquisition, analysis, visualization, mining, and sharing of connectome-related data. As it implements this agenda, the consortium seeks to engage the neuroinformatics community through open source software, open programming interfaces, open-access data-sharing, and standards-based development. The HCP informatics approach includes three basic domains.• Data support components include tools and services that manage data (e.g., data uploads from scanners and other data collection devices); execution and monitoring of quality assurance, image processing, and analysis pipelines and routines; secure long-term storage of acquired and processed data; search services to identify and select subsets of the data; and download mechanisms to distribute data to users around the globe. IntroductIonThe past decade has seen great progress in the refinement of noninvasive neuroimaging methods for assessing long-distance connections in the human brain. This has given rise to the tantalizing prospect of systematically characterizing human brain connectivity, i.e., mapping the connectome (Sporns et al., 2005 The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience research community, enabling discoveries of how the brain is wired and how it functions in different individuals. To fulfill its potential, the HCP consortium is developing an informatics platform that will handle: (1) storage of primary and processed data, (2) systematic processing and analysis of the data, (3) openaccess data-sharing, and (4) mining and exploration of the data. This informatics platform will include two primary components. ConnectomeDB will provide database services for storing and distributing the data, as well as data analysis pipelines. Connectome Workbench will provide visualization and exploration capabilities. The platform will be based on standard data formats and provide an open set of application programming interfaces (APIs) that will facilitate broad utilization of the data and integration of HCP services into a variety of external applications. Primary and processed data generated by the HCP will be openly shared with the scientific community, and the informatics platform will be available under an open source license. This paper describes the HCP informatics platform as currently envisioned and places it into the context of the o...

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Descent into sleep is accompanied by disengagement of the conscious brain from the external world. It follows that this process should be associated with reduced neural activity in regions of the brain known to mediate interaction with the environment. We examined blood oxygen dependent (BOLD) signal functional connectivity using conventional seed-based analyses in 3 primary sensory and 3 association networks as normal young adults transitioned from wakefulness to light sleep while lying immobile in the bore of a magnetic resonance imaging scanner. Functional connectivity was maintained in each network throughout all examined states of arousal. Indeed, correlations within the dorsal attention network modestly but significantly increased during light sleep compared to wakefulness. Moreover, our data suggest that neuronally mediated BOLD signal variance generally increases in light sleep. These results do not support the view that ongoing BOLD fluctuations primarily reflect unconstrained cognition. Rather, accumulating evidence supports the hypothesis that spontaneous BOLD fluctuations reflect processes that maintain the integrity of functional systems in the brain.default network ͉ fMRI ͉ neuroimaging ͉ non-rapid eye movement sleep T here is a physiologically distinct change in the state of the brain during sleep in comparison to wakefulness that is manifest subjectively as altered awareness and objectively as reduced responsiveness to environmental stimuli. The electrophysiological correlates of sleep are sufficiently pronounced and characteristic as to be defining (1, 2). Thus, natural sleep is characterized by a sequence of electroencephalographically defined stages that may be broadly divided into nonrapid eye movement (NREM) and rapid eye movement (REM) that cyclically alternate throughout the sleep period.Over the past decade, PET studies have shown that throughout NREM sleep cerebral blood flow and metabolism are reduced in cortical association areas (3-7), as well as in the brainstem, thalamus, basal ganglia, and basal forebrain (3, 4, 7). NREM sleep is accompanied by reduced responsiveness to stimuli in regions involved in executive function, attention, and perceptual processing (5,7,8). The deepest NREM sleep states are characterized by low frequency oscillations in the EEG during which cognition is thought to be greatly reduced (9-13). During REM, cerebral blood flow and metabolism remain decreased in prefrontal and parietal regions but are increased in paralimbic areas, anterior cingulate, and thalamus (3,7,14), a pattern consistent with the emotionality and reduced logicality notable in during dreaming (7,15,16). REM sleep is also marked by atonia in skeletal muscles, reducing the ability to overtly respond to external stimulation. Thus, the transitions from wakefulness to successively deeper stages of NREM and then REM sleep progressively disengage the self from the environment.It is now well-established that slow (Ͻ0.1 Hz) spontaneous fluctuations of the blood oxygen dependent (BOLD) signal show ph...