The Human Connectome Project: A retrospective.

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools.

ConnectomeDB--Sharing human brain connectivity data.

ConnectomeDB is a database for housing and disseminating data about human brain structure, function, and connectivity, along with associated behavioral and demographic data. It is the main archive and dissemination platform for data collected under the WU-Minn consortium Human Connectome Project. Additional connectome-style study data is and will be made available in the database under current and future projects, including the Connectome Coordination Facility.

Hospital performance with myocardial reperfusion therapy: are hospitals capable of meeting established guidelines?

Objectives: To determine whether hospitals are capable of delivering myocardial reperfusion therapy in a manner consistent with the American College of Cardiology/American Heart Association guidelines. DATA SOURCE AND STUDY SETTING: Data from the National Registry of Myocardial Infarction (NRMI)-2 and NRMI-3 were used. NRMI is an observational study, sponsored by Genentech, conducted from June 1994 through June 2000 and involving 1876 hospitals and 1,310,030 patients across the United States.

Monitoring pharmacy expert system performance using statistical process control methodology.

Automated expert systems provide a reliable and effective way to improve patient safety in a hospital environment. Their ability to analyze large amounts of data without fatigue is a decided advantage over clinicians who perform the same tasks. As dependence on expert systems increase and the systems become more complex, it is important to closely monitor their performance.