The image and data archive at the laboratory of neuro imaging.

The Image and Data Archive (IDA) is a secure online resource for archiving, exploring, and sharing neuroscience data run by the Laboratory of Neuro Imaging (LONI). The laboratory first started managing neuroimaging data for multi-centered research studies in the late 1990's and since has become a nexus for many multi-site collaborations. By providing management and informatics tools and resources for de-identifying, integrating, searching, visualizing, and sharing a diverse range of neuroscience data, study investigators maintain complete control over data stored in the IDA while benefiting from a robust and reliable infrastructure that protects and preserves research data to maximize data collection investment.

Apolipoprotein E Genotype and Sex Risk Factors for Alzheimer Disease: A Meta-analysis.

Importance: It is unclear whether female carriers of the apolipoprotein E (APOE) ε4 allele are at greater risk of developing Alzheimer disease (AD) than men, and the sex-dependent association of mild cognitive impairment (MCI) and APOE has not been established. Objective: To determine how sex and APOE genotype affect the risks for developing MCI and AD. Data Sources: Twenty-seven independent research studies in the Global Alzheimer's Association Interactive Network with data on nearly 58 000 participants. Study Selection: Non-Hispanic white individuals with clinical diagnostic and APOE genotype data. Data Extraction and Synthesis: Homogeneous data sets were pooled in case-control analyses, and logistic regression models were used to compute risks. Main Outcomes and Measures: Age-adjusted odds ratios (ORs) and 95% confidence intervals for developing MCI and AD were calculated for men and women across APOE genotypes. Results: Participants were men and women between ages 55 and 85 years. Across data sets most participants were white, and for many participants, racial/ethnic information was either not collected or not known. Men (OR, 3.09; 95% CI, 2.79-3.42) and women (OR, 3.31; CI, 3.03-3.61) with the APOE ε3/ε4 genotype from ages 55 to 85 years did not show a difference in AD risk; however, women had an increased risk compared with men between the ages of 65 and 75 years (women, OR, 4.37; 95% CI, 3.82-5.00; men, OR, 3.14; 95% CI, 2.68-3.67; P = .002). Men with APOE ε3/ε4 had an increased risk of AD compared with men with APOE ε3/ε3. The APOE ε2/ε3 genotype conferred a protective effect on women (OR, 0.51; 95% CI, 0.43-0.61) decreasing their risk of AD more (P value = .01) than men (OR, 0.71; 95% CI, 0.60-0.85). There was no difference between men with APOE ε3/ε4 (OR, 1.55; 95% CI, 1.36-1.76) and women (OR, 1.60; 95% CI, 1.43-1.81) in their risk of developing MCI between the ages of 55 and 85 years, but women had an increased risk between 55 and 70 years (women, OR, 1.43; 95% CI, 1.19-1.73; men, OR, 1.07; 95% CI, 0.87-1.30; P = .05). There were no significant differences between men and women in their risks for converting from MCI to AD between the ages of 55 and 85 years. Individuals with APOE ε4/ε4 showed increased risks vs individuals with ε3/ε4, but no significant differences between men and women with ε4/ε4 were seen. Conclusions and Relevance: Contrary to long-standing views, men and women with the APOE ε3/ε4 genotype have nearly the same odds of developing AD from age 55 to 85 years, but women have an increased risk at younger ages.

The Global Alzheimer's Association Interactive Network.

INTRODUCTION: The Global Alzheimer's Association Interactive Network (GAAIN) is consolidating the efforts of independent Alzheimer's disease data repositories around the world with the goals of revealing more insights into the causes of Alzheimer's disease, improving treatments, and designing preventative measures that delay the onset of physical symptoms. METHODS: We developed a system for federating these repositories that is reliant on the tenets that (1) its participants require incentives to join, (2) joining the network is not disruptive to existing repository systems, and (3) the data ownership rights of its members are protected. RESULTS: We are currently in various phases of recruitment with over 55 data repositories in North America, Europe, Asia, and Australia and can presently query >250,000 subjects using GAAIN's search interfaces. DISCUSSION: GAAIN's data sharing philosophy, which guided our architectural choices, is conducive to motivating membership in a voluntary data sharing network.

The Image and Data Archive at the Laboratory of Neuro Imaging.

The LONI Image and Data Archive (IDA)(1) is a repository for sharing and long-term preservation of neuroimaging and biomedical research data. Originally designed to archive strictly medical image files, the IDA has evolved over the last ten years and now encompasses the storage and dissemination of neuroimaging, clinical, biospecimen, and genetic data. In this article, we report upon the genesis of the IDA and how it currently securely manages data and protects data ownership.

Sharing data in the global alzheimer's association interactive network.

The Global Alzheimer's Association Interactive Network (GAAIN) aims to be a shared network of research data, analysis tools, and computational resources for studying the causes of Alzheimer's disease. Central to its design are policies that honor data ownership, prevent unauthorized data distribution, and respect the boundaries of contributing institutions. The results of data queries are displayed in graphs and summary tables, which protects data ownership while providing sufficient information to view trends in aggregated data and discover new data sets. In this article we report on our progress in sharing data through the integration of geographically-separated and independently-operated Alzheimer's disease research studies around the world.

Practical management of heterogeneous neuroimaging metadata by global neuroimaging data repositories.

Rapidly evolving neuroimaging techniques are producing unprecedented quantities of digital data at the same time that many research studies are evolving into global, multi-disciplinary collaborations between geographically distributed scientists. While networked computers have made it almost trivial to transmit data across long distances, collecting and analyzing this data requires extensive metadata if the data is to be maximally shared. Though it is typically straightforward to encode text and numerical values into files and send content between different locations, it is often difficult to attach context and implicit assumptions to the content. As the number of and geographic separation between data contributors grows to national and global scales, the heterogeneity of the collected metadata increases and conformance to a single standardization becomes implausible. Neuroimaging data repositories must then not only accumulate data but must also consolidate disparate metadata into an integrated view. In this article, using specific examples from our experiences, we demonstrate how standardization alone cannot achieve full integration of neuroimaging data from multiple heterogeneous sources and why a fundamental change in the architecture of neuroimaging data repositories is needed instead.

Automatic localization of anatomical point landmarks for brain image processing algorithms.

Many brain image processing algorithms require one or more well-chosen seed points because they need to be initialized close to an optimal solution. Anatomical point landmarks are useful for constructing initial conditions for these algorithms because they tend to be highly-visible and predictably-located points in brain image scans. We introduce an empirical training procedure that locates user-selected anatomical point landmarks within well-defined precisions using image data with different resolutions and MRI weightings. Our approach makes no assumptions on the structural or intensity characteristics of the images and produces results that have no tunable run-time parameters. We demonstrate the procedure using a Java GUI application (LONI ICE) to determine the MRI weighting of brain scans and to locate features in T1-weighted and T2-weighted scans.

The LONI Debabeler: a mediator for neuroimaging software.

Brain image analysis often involves processing neuroimaging data with different software packages. Using different software packages together requires exchanging files between them; the output files of one package are used as input files to the next package in the processing sequence. File exchanges become problematic when different packages use different file formats or different conventions within the same file format. Although comprehensive medical image file formats have been developed, no one format exists that satisfies the needs of analyses that involve multiple processing algorithms. The LONI Debabeler acts as a mediator between neuroimaging software packages by automatically using an appropriate file translation to convert files between each pair of linked packages. These translations are built and edited using the Debabeler graphical interface and compensate for package-dependent variations that result in intrapackage incompatibilities. The Debabeler gives neuroimaging processing environments a configurable automaton for file translation and provides users a flexible application for developing robust solutions to translation problems.