Dataset Information

Improving comparability across cognitive training trials for brain aging: A focus on interoperability.

ABSTRACT: Cognitive training may promote healthy brain aging and prevent dementia, but results from individual studies are inconsistent. There are disagreements on how to evaluate cognitive training interventions between clinical and basic scientists. Individual labs typically create their own assessment and training materials, leading to difficulties reproducing methods. Here, we advocate for improved interoperability: the exchange and cooperative development of a consensus for cognitive training design, analysis, and result interpretation. We outline five guiding principles for improving interoperability: (i) design interoperability, developing standard design and analysis models; (ii) material interoperability, promoting sharing of materials; (iii) interoperability incentives; (iv) privacy and security norms, ensuring adherence to accepted ethical standards; and (v) interpretability prioritization, encouraging a shared focus on neurobiological mechanisms to improve clinical relevance. Improving interoperability will allow us to develop scientifically optimized, clinically useful cognitive training programs to slow/prevent brain aging.

Highlights

Interoperability facilitates progress via resource sharing and comparability.Better interoperability is needed in cognitive training for brain aging research.We adapt an interoperability framework to cognitive training research.We suggest five guiding principles for improved interoperability.We propose an open-source pipeline to facilitate interoperability.

SUBMITTER: Turnbull A

PROVIDER: S-EPMC10441567 | biostudies-literature | 2023 Jul-Sep

REPOSITORIES: biostudies-literature

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Improving comparability across cognitive training trials for brain aging: A focus on interoperability.

Turnbull Adam A Seitz Aaron A Lin Feng V FV

Alzheimer's & dementia (New York, N. Y.) 20230701 3

Cognitive training may promote healthy brain aging and prevent dementia, but results from individual studies are inconsistent. There are disagreements on how to evaluate cognitive training interventions between clinical and basic scientists. Individual labs typically create their own assessment and training materials, leading to difficulties reproducing methods. Here, we advocate for improved <b>interoperability</b>: the exchange and cooperative development of a consensus for cognitive training ...[more]

PMID: 37609454

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Project description:The lack of interoperability among biomedical text-mining tools is a major bottleneck in creating more complex applications. Despite the availability of numerous methods and techniques for various text-mining tasks, combining different tools requires substantial efforts and time owing to heterogeneity and variety in data formats. In response, BioC is a recent proposal that offers a minimalistic approach to tool interoperability by stipulating minimal changes to existing tools and applications. BioC is a family of XML formats that define how to present text documents and annotations, and also provides easy-to-use functions to read/write documents in the BioC format. In this study, we introduce our text-mining toolkit, which is designed to perform several challenging and significant tasks in the biomedical domain, and repackage the toolkit into BioC to enhance its interoperability. Our toolkit consists of six state-of-the-art tools for named-entity recognition, normalization and annotation (PubTator) of genes (GenNorm), diseases (DNorm), mutations (tmVar), species (SR4GN) and chemicals (tmChem). Although developed within the same group, each tool is designed to process input articles and output annotations in a different format. We modify these tools and enable them to read/write data in the proposed BioC format. We find that, using the BioC family of formats and functions, only minimal changes were required to build the newer versions of the tools. The resulting BioC wrapped toolkit, which we have named tmBioC, consists of our tools in BioC, an annotated full-text corpus in BioC, and a format detection and conversion tool. Furthermore, through participation in the 2013 BioCreative IV Interoperability Track, we empirically demonstrate that the tools in tmBioC can be more efficiently integrated with each other as well as with external tools: Our experimental results show that using BioC reduces >60% in lines of code for text-mining tool integration. The tmBioC toolkit is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/.

Dataset Information

Improving comparability across cognitive training trials for brain aging: A focus on interoperability.

Highlights

Publications

Improving comparability across cognitive training trials for brain aging: A focus on interoperability.

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