Building a shared infrastructure for cognitive assessment in the service of cognitive training research

有效测量认知能力是有效诊断、风险评估和治疗的基础

Effective measurement of cognitive abilities is fundamental to effective diagnostics, risk assessment and evaluation of interventions targeted towards older adults (OA) and in particular those with Alzheimer's Disease and related dementias (ADRD). With the ubiquitous availability of smartphone/tablet technology in modern society a proliferation of mobile cognitive assessments from companies, healthcare providers, and researchers are being developed. However, a difficulty in evaluating such interventions, and in particular making comparison between them is the lack of standardization/interoperability of assessment tools. This is especially the case for early stage/mechanistic studies where it is common for investigators to each use their own labs' toolset to evaluate intervention outcomes. Here we address particular needs in the field of cognitive training, as well as for other longitudinal assessments focused on OA, where the limited standardization and accessibility of cognitive outcome measures makes it difficult to evaluate effectiveness of interventions. This R21/R33 infrastructure proposal seeks to develop shared tools to facilitate effective translation and sharing of cognitive assessment and training procedures. We accomplish this by leveraging technologies, existing assessment batteries, and know- how from 3 groups that have each independently developed robust systems for cognitive assessment and training that can run on mobile devices (UCR Brain Game Center, UCSF Neuroscape Center, and UCI Working Memory and Plasticity Lab). We target development of systems that allow for interoperability of assessments, enrollment/participant tracking systems, data visualization, and participant compliance systems. In the R21 phase, we aim to develop such systems and demonstrate that they can be effectively shared across labs, and in the R33 phase, these systems will be both tested for robustness in large scale-research projects that will now be able to share outcome measures, and for developing personalized, precision training approaches for participants based upon these assessments. Further, these systems will be documented and will be shared with other scientists groups to reduce the barrier of entry for other groups. The long-term impact of this work will be an infrastructure that will support better comparison across studies of cognitive training, as well as other interventions that are increasingly being used to ameliorate cognitive declines in older adults such as those related to ADRD. The key value of this system compared to others is that it will simultaneously support the flexibility required for basic research, by facilitating groups to continue to use their own lab's software systems, while at the same time providing them with a powerful infrastructure for sharing that allows them to incorporate assessments, server infrastructure and compliance tools into their own studies. This will facilitate comparisons across studies using common outcome measures as well as the ability to use the same assessments in numerous other domains including risk-assessment and longitudinal testing in older adults at risk for ADRD.