Establishing robust cognitive dimensions for characterization and differentiation of patients with Alzheimer's disease, mild cognitive impairment, frontotemporal dementia and depression

OBJECTIVE The diagnosis of mild cognitive impairment (MCI) and dementia requires detailed neuropsychological examinations. These examinations typically yield a large number of outcome variables, which may complicate the interpretation and communication of results. The purposes of this study were the following: (i) to reduce a large data set of interrelated neuropsychological variables to a smaller number of cognitive dimensions; (ii) to create a common metric for these dimensions (z-scores); and (iii) to study the ability of the cognitive dimensions to distinguish between groups of patients with different types of cognitive impairment. METHODS We tested 1646 patients with different forms of dementia or with a major depression with a standard (n = 632) or, if cognitively less affected, a challenging neuropsychological battery (n = 1014). To identify the underlying cognitive dimensions of the two test batteries, maximum likelihood factor analyses with a promax rotation were conducted. To interpret the sum scores of the factors as standard scores, we divided them by the standard deviation of a cognitively healthy sample (n = 1145). RESULTS The factor analyses yielded seven factors for each test battery. The cognitive dimensions in both test batteries distinguished patients with different forms of dementia (MCI, Alzheimer's dementia or frontotemporal dementia) and patients with major depression. Furthermore, patients with stable MCI could be separated from patients with progressing MCI. Discriminant analyses with an independent new sample of patients (n = 306) revealed that the new dimension scores distinguished new samples of patients with MCI from patients with Alzheimer's dementia with high accuracy. CONCLUSION These findings suggest that these cognitive dimensions may benefit neuropsychological diagnostics.