Table Info

Table 1.

Selected examples of productive consortia efforts

Consortium type	Weblink	Goals	Outcome
Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) Network	http://enigma.ini.usc.edu/	Fifty active working groups dedicated to sharing ideas, algorithms, data, and information	Replicating promising findings among a network of researchers in the field of imaging genetics
Institutional Neuroimaging Data-Sharing Initiative	http://fcon_1000.projects.nitrc.org/	Access to thousands of functional MRI datasets for their analysis	Standardized imaging data
Critical Path for Parkinson’s Consortium	https://c-path.org/programs/cpp/	Links academic researchers with scientists from the pharmaceutical industry, government agencies, and patient-advocacy organizations	Facilitate the development of therapies with improved clinical endpoints
Linguistic Data Consortium	https://www.ldc.upenn.edu/	Open network of universities, libraries, corporations, and government-research laboratories that supports language-related education, research and technology development	Creating and sharing linguistic resources, such as data, tools and standards
AphasiaBank	https://aphasia.talkbank.org/	The development of standardized evaluation methods to guide the development and evaluation of effective methods for improving language usage in people with aphasia	Improvement of patient-oriented treatment of aphasia

Declarations

Acknowledgments

The authors would like to thank the companies and investigators who provided their insightful expertise for the development of this commentary. The authors would also like to thank Dr. Niranjan Bose, Health and Life Sciences, Gates Ventures for thoughtful discussions and critical review of the manuscript. Also, the authors would like to thank Visar Berisha, Julie Liss, Shira Hahn, and Jessica Robin for their help in creating Figure 1. Writing assistance was provided by Emily Lieb.

Author contributions

NLB, LK, KM, SP and HF contributed to the conception and design of the commentary. NLB, LK and SP wrote the first draft of the manuscript. SP created the tables. All authors contributed to manuscript revision, read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Funding was provided through the Diagnostics Accelerator, an initiative funded by a coalition of funders including the Alzheimer’s Drug Discovery Foundation and Gates Ventures. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

References

Kourtis LC, Regele OB, Wright JM, Jones GB. Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity. npj Digital Med. 2019;2:9. [DOI] [PubMed] [PMC]

Boschi V, Catricalà E, Consonni M, Chesi C, Moro A, Cappa SF. Connected speech in neurodegenerative language disorders: a review. Front Psychol. 2017;8:269. [DOI] [PubMed] [PMC]

Mueller KD, Hermann B, Mecollari J, Turkstra LS. Connected speech and language in mild cognitive impairment and Alzheimer’s disease: a review of picture description tasks. J Clin Exp Neuropsychol. 2018;40:917–39. [DOI] [PubMed] [PMC]

Linguistics Data Consortium [Internet]. Linguistic Data Consortium, The Trustees of the University of Pennsylvania. c1992–2020 - [cited 2020 Nov 9]. Available from: https://www.ldc.upenn.edu/

DementiaBank [Internet]. DementiaBank Consortium, Carnegie Mellon University. c1999 - [cited 2020 Nov 9]. Available from: https://dementia.talkbank.org/