The Worldwide Alzheimer's Disease Neuroimaging Initiative: An update

Hendrix, James A.; Finger, Brad; Weiner, Michael W.; Frisoni, Giovanni B.; Iwatsubo, Takeshi; Rowe, Christopher C.; Kim, Seong Yoon; Guinjoan, Salvador M.; Sevlever, Gustavo; Carrillo, María C.

doi:10.1016/j.jalz.2015.05.008

Cited by 44 publications

(34 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more ambitious, yet potential ground-breaking step for neuropathological approaches would be to digitize research data and make them openly available along with detailed tissue collection and analysis protocols. A model for this could be the Alzheimer's Disease Neuroimaging Initiative (ADNI), which provides in-depth information in their neuroimaging, biomarkers and genetics data collected from large multicenter collaborations [94]. This level of sharing and standardization would maximize the usage of these precious tissue resources and accelerate the improvement of neuropathologic approaches by increasing interactions between investigators.…”

Section: Limitations Of Alternative Approaches and Strategies To Overmentioning

confidence: 99%

Alzheimer disease research in the 21st century: past and current failures, new perspectives and funding priorities

et al. 2016

View full text Add to dashboard Cite

Much of Alzheimer disease (AD) research has been traditionally based on the use of animals, which have been extensively applied in an effort to both improve our understanding of the pathophysiological mechanisms of the disease and to test novel therapeutic approaches. However, decades of such research have not effectively translated into substantial therapeutic success for human patients. Here we critically discuss these issues in order to determine how existing human-based methods can be applied to study AD pathology and develop novel therapeutics. These methods, which include patient-derived cells, computational analysis and models, together with large-scale epidemiological studies represent novel and exciting tools to enhance and forward AD research. In particular, these methods are helping advance AD research by contributing multifactorial and multidimensional perspectives, especially considering the crucial role played by lifestyle risk factors in the determination of AD risk. In addition to research techniques, we also consider related pitfalls and flaws in the current research funding system. Conversely, we identify encouraging new trends in research and government policy. In light of these new research directions, we provide recommendations regarding prioritization of research funding. The goal of this document is to stimulate scientific and public discussion on the need to explore new avenues in AD research, considering outcome and ethics as core principles to reliably judge traditional research efforts and eventually undertake new research strategies.

show abstract

Section: Limitations Of Alternative Approaches and Strategies To Overmentioning

confidence: 99%

Alzheimer disease research in the 21st century: past and current failures, new perspectives and funding priorities

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The open source data from persons with AD were provided by the North American Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the European’s AddNeuroMed Study. The ADNI dataset is longitudinal multicenter study to develop clinical genetic and biomedical biomarkers for AD early detection (22). The study began in 2004 and experienced three phases ADNI1, ADNI GO, and ADNI2.…”

Section: Introductionmentioning

confidence: 99%

Advancing Alzheimer’s research: A review of big data promises

Zhang

Simon

2017

International Journal of Medical Informatics

View full text Add to dashboard Cite

Objective To review the current state of science using big data to advance AD research and practice. In particular, we analyzed the types of research foci addressed and corresponding methods employed and study findings reported using big data in AD. Method Systematic review was conducted for articles published in PubMed from January 1, 2010 through December 31, 2015. Keywords with AD and big data analytics were used for literature retrieval. Articles were reviewed and included if they met the eligibility criteria. Results Thirty-eight articles were included in this review. They can be categorized into six research foci: diagnosing AD or mild cognitive impairment (MCI) (n=10), predicting MCI to AD conversion (n=13), stratifying risks for AD (n=5), mining the literature for knowledge discovery (n=4), predicting AD progression (n=2), describing clinical care for persons with AD (n=3) and understanding the relationship between cognition and AD (n=3). The most commonly used datasets are Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n= 16), electronic health records (EHR) (n=11), MEDLINE (n=3), and other research datasets (n=8). Logistic regression (n=9) and support vector machine (n=8) are the most used methods for data analysis. Conclusion Big data are increasingly used to address AD related research questions. While existing research datasets are frequently used, other datasets such as EHR data provide a unique, yet under-tapped opportunity for advancing AD research.

show abstract

“…Argentinian researchers are also studying, in collaboration with the Banner Sun Health Institute in Arizona, the biochemistry of successful aging in individuals with AD pathology. Argentina also participates in the Worldwide Alzheimer's Disease Neuroimaging Initiative (WW-ADNI) [50], an international effort to validate imaging and CSF biomarkers to advance AD clinical trials. Brazil is also hoping to join WW-ADNI soon.…”

Section: Ongoing Clinical Research Studiesmentioning

confidence: 99%