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•Brain structure-function coupling captured by network communication models.•Stronger structure-function coupling is linked to higher general cognitive ability.•Region-specific coupling strategies predict individual cognitive ability scores.•Prediction model generalizes across independent samples.•Efficient cognitive processing may depend on region-specific coupling strategies. Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

Significance Differences among children in educational achievement are highly heritable from the early school years until the end of compulsory education at age 16, when UK students are assessed nationwide with standard achievement tests [General Certificate of Secondary Education (GCSE)]. Genetic research has shown that intelligence makes a major contribution to the heritability of educational achievement. However, we show that other broad domains of behavior such as personality and psychopathology also account for genetic influence on GCSE scores beyond that predicted by intelligence. Together with intelligence, these domains account for 75% of the heritability of GCSE scores. These results underline the importance of genetics in educational achievement and its correlates. The results also support the trend in education toward personalized learning. Because educational achievement at the end of compulsory schooling represents a major tipping point in life, understanding its causes and correlates is important for individual children, their families, and society. Here we identify the general ingredients of educational achievement using a multivariate design that goes beyond intelligence to consider a wide range of predictors, such as self-efficacy, personality, and behavior problems, to assess their independent and joint contributions to educational achievement. We use a genetically sensitive design to address the question of why educational achievement is so highly heritable. We focus on the results of a United Kingdom-wide examination, the General Certificate of Secondary Education (GCSE), which is administered at the end of compulsory education at age 16. GCSE scores were obtained for 13,306 twins at age 16, whom we also assessed contemporaneously on 83 scales that were condensed to nine broad psychological domains, including intelligence, self-efficacy, personality, well-being, and behavior problems. The mean of GCSE core subjects (English, mathematics, science) is more heritable (62%) than the nine predictor domains (35–58%). Each of the domains correlates significantly with GCSE results, and these correlations are largely mediated genetically. The main finding is that, although intelligence accounts for more of the heritability of GCSE than any other single domain, the other domains collectively account for about as much GCSE heritability as intelligence. Together with intelligence, these domains account for 75% of the heritability of GCSE. We conclude that the high heritability of educational achievement reflects many genetically influenced traits, not just intelligence.

INTRODUCTION We address the extent to which adolescent cognition predicts dementia risk in later life, mediated by educational attainment and occupational complexity. METHODS Using data from Project Talent Aging Study (PTAS), we fitted two structural equation models to test whether adolescent cognition predicts cognitive impairment (CI) and Ascertain Dementia 8 (AD8) status simultaneously (NCognitive Assessment = 2477) and AD8 alone (NQuestionnaire = 6491) 60 years later, mediated by education and occupational complexity. Co‐twin control analysis examined 82 discordant pairs for CI/AD8. RESULTS Education partially mediated the effect of adolescent cognition on CI in the cognitive assessment aample and AD8 in the questionnaire sample (Ps < 0.001). Within twin pairs, differences in adolescent cognition were small, but intrapair differences in education predicted CI status. DISCUSSION Adolescent cognition predicted dementia risk 60 years later, partially mediated through education. Educational attainment, but not occupational complexity, contributes to CI risk beyond its role as a mediator of adolescent cognition, further supported by the co‐twin analyses. Highlights Project Talent Aging Study follows enrollees from high school for nearly 60 years. General cognitive ability in high school predicts later‐life cognitive impairment. Low education is a risk partially due to its association with cognitive ability.

The aim of the present study was to explore whether individual characteristics such as age, education, vocabulary, and baseline performance in a working memory (WM) task-similar to the one used in the training (criterion task)-predict the short- and long-term specific gains and transfer effects of a verbal WM training for older adults. Four studies that adopted the Borella et al. (2010) verbal WM training procedure were found eligible for our analysis as they included: healthy older adults who attended either the training sessions (WM training group), or alternative activities (active control group); the same measures for assessing specific gains (on the criterion WM task), and transfer effects (nearest on a visuo-spatial WM task, near on short-term memory tasks and far on a measure of fluid intelligence, a measure of processing speed and two inhibitory measures); and a follow-up session. Linear mixed models confirmed the overall efficacy of the training, in the short-term at least, and some maintenance effects. In the trained group, the individual characteristics considered were found to contribute (albeit only modestly in some cases) to explaining the effects of the training. Overall, our findings suggest the importance of taking individual characteristics and individual differences into account when examining WM training gains in older adults.

•Brain connectivity states identified by cofluctuation strength.•CMEP as new method to robustly predict human traits from brain imaging data.•Network-identifying connectivity ‘events’ are not predictive of cognitive ability.•Sixteen temporally independent fMRI time frames allow for significant prediction.•Neuroimaging-based assessment of cognitive ability requires sufficient scan lengths. Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.

As a novel measure for irregularity and complexity of the spontaneous fluctuations of brain activities, brain entropy (BEN) has attracted much attention in resting-state functional magnetic resonance imaging (rs-fMRI) studies during the last decade. Previous studies have shown its associations with cognitive and mental functions. While most previous research assumes BEN is approximately stationary during scan sessions, the brain, even at its resting state, is a highly dynamic system. Such dynamics could be characterized by a series of reoccurring whole-brain patterns related to cognitive and mental processes. The present study aims to explore the time-varying feature of BEN and its potential links with general cognitive ability. We adopted a sliding window approach to derive the dynamical brain entropy (dBEN) of the whole-brain functional networks from the HCP (Human Connectome Project) rs-fMRI dataset that includes 812 young healthy adults. The dBEN was further clustered into 4 reoccurring BEN states by the k-means clustering method. The fraction window (FW) and mean dwell time (MDT) of one BEN state, characterized by the extremely low overall BEN, were found to be negatively correlated with general cognitive abilities (i.e., cognitive flexibility, inhibitory control, and processing speed). Another BEN state, characterized by intermediate overall BEN and low within-state BEN located in DMN, ECN, and part of SAN, its FW, and MDT were positively correlated with the above cognitive abilities. The results of our study advance our understanding of the underlying mechanism of BEN dynamics and provide a potential framework for future investigations in clinical populations.

INTRODUCTION We addressed whether higher education plays a causal role in reducing dementia risk by comparing two indices of cognitive reserve: education and young adult general cognitive ability (GCA). METHODS We conducted a 52‐year survival analysis to examine associations of GCA and education with dementia in 16,619 male conscripts identified in Swedish national health registries born between 1936 and 1958 and with available data for models including midlife occupational complexity, physical activity, and socioeconomic status (SES). RESULTS Higher GCA was associated with lower dementia risk (hazard ratio = 0.865, 95% confidence interval = 0.756 to 0.990). After accounting for GCA, no other measure contributed significantly to dementia risk. DISCUSSION Putative reserve effects of education or occupational complexity likely reflect largely downstream effects of prior GCA. From a lifespan perspective on reducing dementia risk, the results may suggest favoring interventions aimed at enhancing cognitive development during childhood when there is substantial brain development as opposed to later‐life cognitive training. Highlights Education and GCA serve as indices of cognitive reserve. Education is a modifiable risk factor that is associated with dementia risk. The effect of higher education on reduced dementia risk is not directly causal. Education is largely a downstream effect of prior level of GCA. Increasing GCA during childhood may be more effective than later cognitive training.

Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n = 873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction (r = 0.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=∼.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities (r = 0.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r = 0.56) while still providing excellent test-retest reliability (ICC=∼.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities. •Non-task MRI (sMRI, rs-fMRI) are often used for the brain-cognition relationship.•Task-based fMRI has been deemed unreliable for capturing individual differences.•We tested if drawing task-based fMRI information across regions/tasks improves prediction and reliability of the brain-cognition relationship.•Our approach boosts prediction of task-based fMRI over non-task MRI.•Our approach renders task-based fMRI reliable over time.•Our approach shows the importance of the fronto-parietal areas in cognition.

General cognitive ability (GCA) has substantial explanatory power for behavioral and health outcomes, but its cortical substrate is still not fully established. GCA is highly polygenic and research to date strongly suggests that its cortical substrate is highly polyregional. We show in map-based and region-of-interest-based analyses of adult twins that a complex cortical configuration underlies GCA. Having relatively greater surface area in evolutionary and developmentally high-expanded prefrontal, lateral temporal, and inferior parietal regions is positively correlated with GCA, whereas relatively greater surface area in low-expanded occipital, medial temporal, and motor cortices is negatively correlated with GCA. Essentially the opposite pattern holds for relative cortical thickness. The phenotypic positive-to-negative gradients in our cortical-GCA association maps were largely driven by a similar pattern of genetic associations. The patterns are consistent with regional cortical stretching whereby relatively greater surface area is related to relatively thinner cortex in high-expanded regions. Thus, the typical “bigger is better” view does not adequately capture cortical-GCA associations. Rather, cognitive ability is influenced by complex configurations of cortical development patterns that are strongly influenced by genetic factors. Optimal cognitive ability appears to be driven both by the absolute size and the polyregional configuration of the entire cortex rather than by small, circumscribed regions. •Cortical surface area (SA)/thickness (CT)-cognition associations are positive.•Scaling for total SA and mean CT yields a different pattern.•Genetically driven gradients of positive-to-negative associations are observed.•SA gradients parallel to high/low-expanding regions in evolution/human development•CT gradients are largely orthogonal to SA gradients.