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Apathy is common after stroke and has been associated with cognitive impairment. However, causality between post-stroke apathy and cognitive impairment remains unclear. We assessed the course of apathy in relation to changes in cognitive functioning in stroke survivors. Using the Apathy Scale (AS) and cognitive tests on memory, processing speed and executive functioning at six- and 15 months post-stroke we tested for associations between (1) AS-scores and (change in) cognitive scores; (2) apathy course (persistent/incident/resolved) and cognitive change scores. Of 117 included participants, 29% had persistent apathy, 13% apathy resolving over time and 10% apathy emerging between 6-15 months post-stroke. Higher AS-scores were cross-sectionally and longitudinally associated with lower cognitive scores. Relations between apathy and cognitive change scores were ambiguous. These inconsistent relations between apathy and changes in cognition over time suggest that post-stroke apathy does not directly impact cognitive performance. Both these sequelae of stroke require separate attention. [ABSTRACT FROM AUTHOR]

Validation studies of symptom validity tests (SVTs) in children are uncommon. However, since children's cognitive abilities are not yet fully developed, their performance may provide additional support for the validity of these measures in adult populations. Four SVTs, the Test of Memory Malingering (TOMM), the Word Memory Test (WMT), the Amsterdam Short-Term Memory (ASTM) test, and the Word Completion Memory Test (WCMT), along with several neuropsychological instruments were administered to 48 Dutch school children aged 7–12. All children scored above the established adult cut-offs on the TOMM and the WMT. They could pass the ASTM test if their reading skills were at a level equivalent to that of 9 year olds. All children passed our criterion of a negative WCMT score. However, the WCMT does seem sensitive to the level of verbal fluency. Implications for the applicability of these SVTs in adult populations are discussed. [ABSTRACT FROM PUBLISHER]

The authors studied whether the ability of cognitive functioning to predict mortality is pervasive or specific, and they considered the role of health in the cognition-mortality association. Data were taken from a sample of 2,380 persons aged 55–85 years who took part in the Netherlands' Longitudinal Aging Study Amsterdam in 1992–1993. Five cognitive measures were distinguished: general cognitive functioning, information processing speed, fluid intelligence, learning, and proportion retained. Mortality data were obtained during an average follow-up period of 1,215 days. Cox proportional hazards regression models revealed that all cognitive functions predicted mortality independent of age, sex, education, and depressive symptoms. When health (self-rated health, medication use, physical performance, functional limitations, lung function, specific chronic diseases) was also taken into account, information processing speed, fluid intelligence, and proportion retained remained independent predictors of mortality, whereas the ability of general cognitive functioning and learning to determine mortality was lost. The authors concluded that the ability of cognitive functioning to predict mortality is pervasive to all cognitive functions that were included in the study when age, sex, education, and depressive symptoms are considered and is more specific to some functions when also controlling for health. Am J Epidemiol 1999;150:978-86. [ABSTRACT FROM PUBLISHER]

Objective Following recent (meta-)analyses showing that the Cattell-Horn-Carroll (CHC) model outperforms traditional neuropsychological models of cognition, we investigated the relative performance of the traditional neuropsychological model (TNM) of cognitive functioning and the CHC model using the digital Philips IntelliSpace Cognition (ISC) test battery. Method The ISC battery consists of 11 conventional cognitive tests, which were administered to 221 healthy participants. We used confirmatory factor analysis (CFA) to map outcome measures to cognitive domains. Both models contained processing speed, working memory, memory, and visuospatial ability. TNM included executive functioning consisting of TMT B, Stroop interference, and Phonetic and Semantic Fluency. In the CHC model, these outcome measures were distributed over processing speed and a word fluency domain. The models' fit was compared through Vuong test for non-nested models. Results Both models were a good fit (TNM: χ2(64) = 91.95, p =.013, CFI =.971, RMSEA =.044; CHC: χ2(64) = 83.66, p =.050, CFI =.979, RMSEA =.037). The Vuong test showed that the models could be significantly distinguished based on the observed data, ω2 = 0.21, p <.001. However, the non-nested likelihood ratio test did not offer evidence that either model was a better fit, z = -0.61, p =.727. The 95% confidence interval of the AIC difference contained zero [-18.60, 35.20]. Conclusions Recent findings showing that the CHC model outperforms the TNM are not corroborated by our findings. Instead, both models captured the data equally well, suggesting that the two theoretical frameworks are not necessarily mutually exclusive. [ABSTRACT FROM AUTHOR]