Normative data for the 10/66 dementia research group neuropsychological test battery from Singapore's older adult population

A predictive model for the risk of cognitive impairment in community middle-aged and older adults

Identifying individuals at high risk of cognitive impairment is essential for treatment and prevention strategies. We aimed to develop and validate a prediction model for evaluating the risk of cognitive impairment. Data were from the China Family Panel Studies (CFPS) and China Health and Retirement Longitudinal Study (CHARLS). A total of 14,265 subjects were selected for model development. The area under the curve(AUC) for the training, internal, and external validation sets were 0.775, 0.920, and 0.727, respectively. This model could be used to identify middle-aged and older adults aged 45 years and older at high risk of cognitive impairment.

Neuropsychological Assessment of Community-Dwelling Older Adults in Almaty, Kazakhstan

Cognitive impairment in older adults is a major public concern for Kazakhstan's aging population. We aimed to (1) administer a neuropsychological test battery (NTB) in domains relevant to aging-associated cognitive impairment in a sample of adults aged 60+ without dementia in Almaty, Kazakhstan; (2) investigate the associations between demographic factors and test performance; and (3) provide information on the distribution of NTB scores as preliminary local normative data relevant for this population. A cross-sectional evaluation of 276 participants aged 60+ in Almaty, Kazakhstan, was conducted using cognitive instruments including tests of memory, attention, language, executive functions, visuospatial abilities, and processing speed. Multiple linear regression analyses were used to examine the association of demographic factors with neuropsychological test performance. The results from the regression analysis showed that those who are younger, have more years of education, are women, and are of Russian ethnicity had significantly better performance. The current study illustrated (1) the feasibility of administering the NTB to older adults in the general population in Kazakhstan; (2) the preliminary local normative neuropsychological measures; and (3) their independent associations with age, education, gender, and ethnicity. The findings are a platform for future research on dementia and cognitive impairment in older adults in Kazakhstan.

The individualized prediction of cognitive test scores in mild cognitive impairment using structural and functional connectivity features

Neuropsychological assessments are essential in diagnosing age-related neurocognitive disorders. However, they are lengthy in duration and can be unreliable at times. To this end, we explored a modified connectome-based predictive modeling approach to estimating individualized scores from multiple cognitive domains using structural connectivity (SC) and functional connectivity (FC) features. Multi-shell HARDI and resting-state functional magnetic resonance imaging scans, and scores from 10 cognitive measures were acquired from 91 older adults with mild cognitive impairment. SC and FC matrices were derived from these scans and, in various combinations, entered into models along with demographic covariates to predict cognitive scores. Leave-one-out cross-validation was performed. Predictive accuracy was assessed via the correlation between predicted and observed scores (r_{predicted-observed}). Across all cognitive measures, significant r_{predicted-observed} (0.402 to 0.654) were observed from the best-predicting models. Six of these models consisted of multimodal features. For three cognitive measures, their best-predicting models' r_{predicted-observed} were similar to that of a model that included only demographic covariates- suggesting that SC and/or FC features did not contribute significantly on top of demographics. Cross-prediction models revealed that the best-predicting models were similarly accurate in predicting scores of related cognitive measures- suggesting their limited specificity in predicting cognitive scores. Generally, multimodal connectomes together with demographics, can be exploited as sensitive markers, though with limited specificity, to predict cognitive performance across a spectrum in multiple cognitive domains. In certain situations, it may not be worthwhile to acquire neuroimaging data, considering that demographics alone can be similarly accurate in predicting cognitive scores.