Mild Cognitive Impairment: A Concept and Diagnostic Entity in Need of Input from Neuropsychology

Abstract

AbstractThis virtual issue consists of studies previously published in the Journal of the International Neuropsychological Society and selected on the basis of their content related to one of the most highly researched concepts in behavioral neurology and neuropsychology over the past decade: mild cognitive impairment (MCI). The reliance on cognitive screening measures, staging-based rating scales, and limited neuropsychological testing in diagnosing MCI across most research studies may miss individuals with subtle cognitive declines or mis-diagnose MCI in those who are otherwise cognitively normal on a broader neuropsychological battery of tests. The assembled articles highlight the perils of relying on these conventional criteria for MCI diagnosis and reveal how the reliability of diagnosis is improved when sound neuropsychological approaches are adopted. When these requirements are met, we illustrate with a second series of articles that neuropsychological measures associate strongly with biomarkers and often reflect pathology beyond or instead of typical AD distributions. The final set of articles reveal that people with MCI demonstrate mild but identifiable functional difficulties, and a challenge for neuropsychology is how to incorporate this information to better define MCI and distinguish it from early dementia. Neuropsychology is uniquely positioned to improve upon the state of the science in MCI research and practice by providing critically important empirical information on the specific cognitive domains affected by the predominant neurodegenerative disorders of late life as well as on the diagnostic decision-making strategies used in studies. When such efforts to more comprehensively assess neuropsychological functions are undertaken, better characterizations of spared and impaired cognitive and functional abilities result and lead to more convincing associations with other biomarkers as well as to prediction of clinical outcomes. (JINS, 2014, 20, 129–134)