How Older Adults Learn ICT—Guided and Self-Regulated Learning in Individuals With and Without Disabilities

Learning to use information and communication technologies (ICT) may be more difficult for older people due to decreases in fluid intelligence, generational effects, and other age-related effects. Especially older people with intellectual disabilities (ID) are at a high risk of digital exclusion. To enable all older adults to use ICT, individualized technology training may be provided. However, little is known about the ICT learning preferences among older people with ID. Based on semi-structured interviews with older adults (n = 7, mean age = 76.6 years) and older adults with ID (n = 14, mean age = 62.4 years), this paper analyzes learning strategies, preferences, and learning settings. The results from content analysis show that guided learning with personal explanations in a one-to-one setting is the most preferred learning format in both groups of older adults. While many older adults without ID additionally favor self-regulated learning (i.e., learning with manuals or videos), older adults with ID mostly rely on guided learning with personal assistance. The differences can be explained by different abilities (e.g., reading skills) and social networks (e.g., living situation, having children). Not all older adults have a family or an institutional support network to help them learn ICT and community organizations may provide additional support. Researchers and practitioners should be aware of the diverse knowledge backgrounds and competencies in the group of older adults. ICT training in old age should be ideally composed in a modular way embedding self-regulated learning formats into guided learning modules.

Identifying healthy individuals with Alzheimer neuroimaging phenotypes in the UK Biobank

Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease modifying trials. Evidence from genetic studies suggest the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be detected in sporadic disease. To address this challenge we train a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD-score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80), and demonstrate correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease. This cohort have a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and with some evidence of poorer performance on tests of numeric memory, reaction time, working memory and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

Are Older Adults’ Difficulties on Emotion Recognition and Theory of Mind Tasks Due to a Decline in General Cognitive Ability?

Older adults have both worse general cognition and worse social cognition. A frequent suggestion is that worse social cognition is due to worse general cognition. However, previous studies have often provided contradictory evidence. The current study examined this issue with a more extensive battery of tasks for both forms of cognition. We gave 47 young and 40 older adults three tasks to assess general cognition (processing speed, working memory, fluid intelligence) and three tasks to assess their social cognition (emotion and theory-of-mind). Older adults did worse on all tasks and there were correlations between general and social cognition. Although working memory and fluid intelligence were unique predictors of performance on the Emotion Photos task and the Eyes task, Age Group was a unique predictor on all three social cognitiaon tasks. Thus, there were relations between the two forms of cognition but older adults continued to do worse than young adults even after accounting for general cognition. We argue that this pattern of results is due to some overlap in brain areas mediating general and social cognition, but also independence, and with a differential rate of decline in brain areas dedicated to general cognition versus social cognition.

Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy ageing subjects

Cognitive ageing is a complex process which requires multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization and vascular dynamics. However, most neuroimaging studies of ageing have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and the interpretation of neural correlates of cognitive decline in ageing. Here, we used linked independent component analysis as a data-driven multimodal approach to jointly analyze magnetic resonance imaging of grey matter density, cerebrovascular, and functional network topographies. Neuroimaging and behavioural data (n = 215) from the Cambridge Centre for Ageing and Neuroscience study were used, containing healthy subjects aged 18 to 88. In the output components, fusion was found between structural and cerebrovascular topographies in multiple components with cognitive-relevance across the lifespan. A component reflecting global atrophy with regional cerebrovascular changes and a component reflecting right frontoparietal network activity were correlated with fluid intelligence over and above age and gender. No meaningful fusion between functional network topography and structural or cerebrovascular signals was observed. We propose that integrating multiple neuroimaging modalities allows to better characterize brain pattern variability and to differentiate brain changes in healthy ageing.

Personal Values & Decision-Making Biases

Decision-making is understood to be influenced by genetic and environmental factors related to need, as are personal values. Personal values are a component of personality known to influence decision-making in agreement with the circular structure of the Schwartz (1992) system. We set out to explore whether personal values also exert complementary linear patterns of influence on heuristics and performance in fluid intelligence and creativity tests. Such patterns are predicted by an evolutionary theory that proposes the influence of values described by Schwartz (1992) evolve sequentially and incrementally in living systems, internalising the schema of a pre-existing system of universal equivalents. Testing N=1317 individuals with challenges derived from Kahneman and Tversky and others, we found values exerted both circular and linear influences on intuitive and rational decision-making. These were apparent in overall value/response correlation patterns, and in the performance of individuals allocated to linear, values-based, quasi-Maslowian (1943) motivational types. Performance in fluid intelligence and creativity tests most strongly betrayed linear, developmental patterns of influence. In relation to a Bayesian inference challenge, tentative support was also forthcoming for the hypothesis that those most likely to be subject to values-related conflicts would be most likely to avoid giving erroneous intuitive responses by engaging rational system 2 thinking (Stanovich & West, 2000). This suggests values may also play a role in mediating between rational and irrational systems of thinking. These findings extend our understanding of the role values play in individual decision-making, and by extension, their importance in organizational and societal decision-making.

The relations between personality, components of executive functions, and intelligence in children and young adults

Previous studies in adults showed heterogeneous results regarding the associations of personality with intelligence and executive functions (EF). In children, there is a lack of studies investigating the relations between personality and EF. Therefore, the aim of our study was to examine the relations between the Big Five personality traits, EF, and intelligence in a sample of children (Experiment 1) and young adults (Experiment 2). A total of 155 children (Experiment 1, mean age = 9.54 years) and 91 young adults (Experiment 2, mean age = 23.49 years) participated in the two studies. In both studies, participants performed tasks measuring working memory (WM), inhibitory control, cognitive flexibility, and fluid intelligence and completed a personality questionnaire. In Experiment 1, we found a negative relation between neuroticism and intelligence. In Experiment 2, we found a positive relation between conscientiousness and intelligence and a positive relation between conscientiousness and cognitive flexibility. Our results suggest a complex interplay between personality factors, EF, and intelligence both in children as well as in young adults.

Predicting Fluid Intelligence via Naturalistic Functional Connectivity Using Weighted Ensemble Model and Network Analysis

Objectives: Functional connectivity triggered by naturalistic stimuli (e.g., movie clips), coupled with machine learning techniques provide great insight in exploring brain functions such as fluid intelligence. However, functional connectivity is multi-layered while traditional machine learning is based on individual model, which is not only limited in performance, but also fails to extract multi-dimensional and multi-layered information from the brain network. Methods: In this study, inspired by multi-layer brain network structure, we propose a new method, namely weighted ensemble model and network analysis, which combines machine learning and graph theory for improved fluid intelligence prediction. Firstly, functional connectivity analysis and graphical theory were jointly employed. The functional connectivity and graphical indices computed using the preprocessed fMRI data were then all fed into an auto-encoder parallelly for automatic feature extraction to predict the fluid intelligence. In order to improve the performance, tree regression and ridge regression models were stacked and fused automatically with weighted values. Finally, layers of auto-encoder were visualized to better illustrate the connectome patterns, followed by the evaluation of the performance to justify the mechanism of brain functions. Results: Our proposed method achieved the best performance with a 3.85 mean absolute deviation, 0.66 correlation coefficient and 0.42 R-squared coefficient; this model outperformed other state-of-the-art methods. It is also worth noting that the optimization of the biological pattern extraction was automated though the auto-encoder algorithm. Conclusion: The proposed method outperforms the state-of-the-art reports, also is able to effectively capture the biological patterns of functional connectivity during a naturalistic movie state for potential clinical explorations.

Novel Age-Dependent Cortico-Subcortical Morphologic Interactions Predict Fluid Intelligence: A Multi-Cohort Geometric Deep Learning Study

The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.

Rare genetic variants in dominant developmental disorder loci cause milder related phenotypes in the general population

Many rare diseases are known to be caused by deleterious variants in Mendelian genes, however the same variants can also be found in people without the associated clinical phenotypes. The penetrance of these monogenic variants is generally unknown in the wider population, as they are typically identified in small clinical cohorts of affected individuals and families with highly penetrant variants. Here, we investigated the phenotypic effect of rare, potentially deleterious variants in genes and loci that are known to cause monogenic developmental disorders (DD) in a large population cohort. We used UK Biobank to investigate phenotypes associated with rare protein-truncating and missense variants in 599 dominant DD genes using whole exome sequencing data from ~200,000 individuals, and rare copy number variants overlapping known DD loci using SNP-array data from ~500,000 individuals. We found that individuals with these likely deleterious variants had a mild DD-related phenotype, including lower fluid intelligence, slower reaction times, lower numeric memory scores and longer pairs matching times compared to the rest of the UK Biobank cohort. They were also shorter, with a higher BMI and had significant socioeconomic disadvantages, being less likely to be employed or be able to work, and having a lower income and higher deprivation index. Our findings suggest that many monogenic DD genes routinely tested within paediatric genetics have intermediate penetrance and may cause lifelong milder, sub-clinical phenotypes in the general adult population.