Vascular contributions to cognitive impairment and dementia: Research consortia that focus on etiology and treatable targets to lessen the burden of dementia worldwide

Abstract

The research into vascular contributions to cognitive impairment and dementia (VCID) aims to understand the importance of cerebrovascular biology in cognitive decline. Prevention and treatment of VCID is poised to have major impact on dementia-related disease burden and is thus a critical emerging objective in dementia research. This article presents VCID consortia focused on multidisciplinary approaches to identify key pathologic targets and develop diagnostic tools with the goal of bridging the divide between basic research and clinical trials. Members of these multi-institute, multidisciplinary consortia provide a prospective on the history and emerging science of VCID and how VCID consortia can address some of the more complex questions in VCID and drive the field forward. These consortia, and others like them, are uniquely suited to tackle some of the most difficult obstacles in translating research to the clinic.

Fig. 1

VCID bibliometric topic map. Research topics are based on the results of the Latent Dirichlet Analysis (LDA) algorithm. LDA works by first creating a set of term vocabularies for each of a prespecified number of topics based on the terms’ co-occurrence in publication abstracts. The algorithm then uses those vocabularies to assign individual publications to one or more of these topics based on the frequency with which terms from that topic appear in the publication's abstract. Publications were pulled from PubMed using the following search string: (((dementia OR (cognitive impairment) OR (cognitive dysfunction)) AND (vascular OR cardiovascular OR cerebrovascular OR lacunar OR stroke)) OR CADASIL OR (cerebral autosomal dominant arteriopathy) OR (Binswanger[tiab] OR Binswanger's[tiab]) OR (cerebral amyloid angiopathy)) AND 2002:2016[dp]. For this analysis, the number of topics was set to 50, the algorithm was run on the publication abstracts in this data set. A topic similarity network was generated in which topics are connected if more than 45 articles in the data set were assigned to both of the connected topics. Descriptions were assigned to each topic based on the abstract and MeSH terms that most frequently appeared in each topic and on manual inspection of the papers assigned to each topic.