Rationale and Design of the National Neuropsychology Network. (4th January 2022)

• Record Type: Journal Article
• Authors: Loring, David W.; Bauer, Russell M.; Cavanagh, Lucia; Drane, Daniel L.; Enriquez, Kristen D.; Reise, Steven P.; Shih, KuoChung; Umfleet, Laura Glass; Wahlstrom, Dustin; Whelan, Fiona; Widaman, Keith F.; Bilder, Robert M.
• Other Names: collab.
• Is Part Of: Journal of the International Neuropsychological Society. Volume 28:Number 1(2022)
• Page Start: 1
• Page End: 11
• Publication Date: 2022-01-04
• Subjects: Collaborative neuropsychology -- Psychological tests -- Clinical decision-making -- Modern psychometric methods; Neuropsychology -- Periodicals; 616.8
• DOI: 10.1017/S1355617721000199
• Languages: English
• ISSNs: 1355-6177

Abstract

Abstract: Objective: The National Neuropsychology Network (NNN) is a multicenter clinical research initiative funded by the National Institute of Mental Health (NIMH; R01 MH118514) to facilitate neuropsychology's transition to contemporary psychometric assessment methods with resultant improvement in test validation and assessment efficiency. Method: The NNN includes four clinical research sites (Emory University; Medical College of Wisconsin; University of California, Los Angeles (UCLA); University of Florida) and Pearson Clinical Assessment. Pearson Q-interactive (Q-i) is used for data capture for Pearson published tests; web-based data capture tools programmed by UCLA, which serves as the Coordinating Center, are employed for remaining measures. Results: NNN is acquiring item-level data from 500–10, 000 patients across 47 widely used Neuropsychology (NP) tests and sharing these data via the NIMH Data Archive. Modern psychometric methods (e.g., item response theory) will specify the constructs measured by different tests and determine their positive/negative predictive power regarding diagnostic outcomes and relationships to other clinical, historical, and demographic factors. The Structured History Protocol for NP (SHiP-NP) helps standardize acquisition of relevant history and self-report data. Conclusions: NNN is a proof-of-principle collaboration: by addressing logistical challenges, NNN aims to engage other clinics to create a national and ultimately an international network. The mature NNN will provide mechanisms for data aggregation enabling shared analysis and collaborative research. NNN promises ultimately to enable robust diagnostic inferences about neuropsychological test patterns and to promote the validation of novel adaptive assessment strategies that will be more efficient, more precise, and more sensitive to clinical contexts and individual/cultural differences.