BackgroundA cross-sectional validation study was conducted in several urban and rural communities in Beijing, China, to evaluate the effectiveness of the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) as a screening tool to detect mild cognitive impairment (MCI) among Chinese older adults.MethodsThe MoCA-BJ and the Mini-Mental State Examination (MMSE) were administered to 1001 Chinese elderly community dwellers recruited from three different regions (i.e., newly developed, old down-town, and rural areas) in Beijing. Twenty-one of these participants were diagnosed by experienced psychiatrists as having dementia, 115 participants were diagnosed as MCI, and 865 participants were considered to be cognitively normal. To analyze the effectiveness of the MoCA-BJ, we examined its psychometric properties, conducted item analyses, evaluated the sensitivity and specificity of the scale, and compared the scale with the MMSE. Demographic and regional differences among our subjects were also taken into consideration.ResultsUnder the recommended cut-off score of 26, the MoCA-BJ demonstrated an excellent sensitivity of 90.4%, and a fair specificity (31.3%). The MoCA-BJ showed optimal sensitivity (68.7%) and specificity (63.9%) when the cut-off score was lowered to 22. Among all the seven cognitive sub-domains, delayed recall was shown to be the best index to differentiate MCI from the normal controls. Regional differences disappeared when the confounding demographic variables (i.e., age and education) were controlled. Item analysis showed that the internal consistency was relatively low in both naming and sentence repetition tasks, and the diagnostic accuracy was similar between the MoCA-BJ and the MMSE.ConclusionsIn general, the MoCA-BJ is an acceptable tool for MCI screening in both urban and rural regions of Beijing. However, presumably due to the linguistic and cultural differences between the original English version and the Chinese version of the scale, and the lower education level of Chinese older adults, the MoCA-BJ is not much better than the MMSE in detecting MCI, at least for this study sample. Further modifications to several test items of the MoCA-BJ are recommended in order to improve the applicability and effectiveness of the MoCA-BJ in MCI screening among the Chinese population.

Extracting named entities in text and linking extracted names to a given knowledge base are fundamental tasks in applications for text understanding. Existing systems typically run a named entity recognition (NER) model to extract entity names first, then run an entity linking model to link extracted names to a knowledge base. NER and linking models are usually trained separately, and the mutual dependency between the two tasks is ignored. We propose JERL, Joint Entity Recognition and Linking, to jointly model NER and linking tasks and capture the mutual dependency between them. It allows the information from each task to improve the performance of the other. To the best of our knowledge, JERL is the first model to jointly optimize NER and linking tasks together completely. In experiments on the CoNLL'03/AIDA data set, JERL outperforms state-of-art NER and linking systems, and we find improvements of 0.4% absolute F 1 for NER on CoNLL'03, and 0.36% absolute precision@1 for linking on AIDA.

The prefrontal cortex and medial temporal lobe are particularly vulnerable to the effects of aging. The disconnection between them is suggested to be an important cause of cognitive decline in normal aging. Here, using multimodal intervention training, we investigated the functional plasticity in resting-state connectivity of these two regions in older adults. The multimodal intervention, comprised of cognitive training, Tai Chi exercise, and group counseling, was conducted to explore the regional connectivity changes in the default-mode network, as well as changes in prefrontal-based voxel-wise connectivity in the whole brain. Results showed that the intervention selectively affected resting-state functional connectivity between the medial prefrontal cortex and medial temporal lobe. Moreover, the strength of resting-state functional connectivity between these regions correlated with individual cognitive performance. Our results suggest that multimodal intervention could postpone the effects of aging and improve the function of the regions that are most heavily influenced by aging, as well as play an important role in preserving the brain and cognition during old age.

Mounting evidence suggests that enriched mental, physical, and socially stimulating activities are beneficial for counteracting age-related decreases in brain function and cognition in older adults. Here, we used functional magnetic resonance imaging (fMRI) to demonstrate the functional plasticity of brain activity in response to a combined cognitive-psychological-physical intervention and investigated the contribution of the intervention-related brain changes to individual performance in healthy older adults. The intervention was composed of a 6-week program of combined activities including cognitive training, Tai Chi exercise, and group counseling. The results showed improved cognitive performance and reorganized regional homogeneity of spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signals in the superior and middle temporal gyri, and the posterior lobe of the cerebellum, in the participants who attended the intervention. Intriguingly, the intervention-induced changes in the coherence of local spontaneous activity correlated with the improvements in individual cognitive performance. Taken together with our previous findings of enhanced resting-state functional connectivity between the medial prefrontal cortex and medial temporal lobe regions following a combined intervention program in older adults, we conclude that the functional plasticity of the aging brain is a rather complex process, and an effective cognitive-psychological-physical intervention is helpful for maintaining a healthy brain and comprehensive cognition during old age.