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•Generalized additive mixed models (GAMMs) fit lifespan brain trajectories more accurately than traditional methods.•Optimal formulation of GAMMs for longitudinal data analysis is discussed, and compared in realistic simulation experiments and two application examples.•We discuss and contrast questions which can be answered with a single measurement per participant, and which questions require repeated measurements.•R code shows how GAMMs can be used in practice, with packages “gamm4” and “mgcv”. We address the problem of estimating how different parts of the brain develop and change throughout the lifespan, and how these trajectories are affected by genetic and environmental factors. Estimation of these lifespan trajectories is statistically challenging, since their shapes are typically highly nonlinear, and although true change can only be quantified by longitudinal examinations, as follow-up intervals in neuroimaging studies typically cover less than 10% of the lifespan, use of cross-sectional information is necessary. Linear mixed models (LMMs) and structural equation models (SEMs) commonly used in longitudinal analysis rely on assumptions which are typically not met with lifespan data, in particular when the data consist of observations combined from multiple studies. While LMMs require a priori specification of a polynomial functional form, SEMs do not easily handle data with unstructured time intervals between measurements. Generalized additive mixed models (GAMMs) offer an attractive alternative, and in this paper we propose various ways of formulating GAMMs for estimation of lifespan trajectories of 12 brain regions, using a large longitudinal dataset and realistic simulation experiments. We show that GAMMs are able to more accurately fit lifespan trajectories, distinguish longitudinal and cross-sectional effects, and estimate effects of genetic and environmental exposures. Finally, we discuss and contrast questions related to lifespan research which strictly require repeated measures data and questions which can be answered with a single measurement per participant, and in the latter case, which simplifying assumptions that need to be made. The examples are accompanied with R code, providing a tutorial for researchers interested in using GAMMs.

The human brain undergoes extensive and dynamic growth during the first years of life. The UNC/UMN Baby Connectome Project (BCP), one of the Lifespan Connectome Projects funded by NIH, is an ongoing study jointly conducted by investigators at the University of North Carolina at Chapel Hill and the University of Minnesota. The primary objective of the BCP is to characterize brain and behavioral development in typically developing infants across the first 5 years of life. The ultimate goals are to chart emerging patterns of structural and functional connectivity during this period, map brain-behavior associations, and establish a foundation from which to further explore trajectories of health and disease. To accomplish these goals, we are combining state of the art MRI acquisition and analysis techniques, including high-resolution structural MRI (T1-and T2-weighted images), diffusion imaging (dMRI), and resting state functional connectivity MRI (rfMRI). While the overall design of the BCP largely is built on the protocol developed by the Lifespan Human Connectome Project (HCP), given the unique age range of the BCP cohort, additional optimization of imaging parameters and consideration of an age appropriate battery of behavioral assessments were needed. Here we provide the overall study protocol, including approaches for subject recruitment, strategies for imaging typically developing children 0–5 years of age without sedation, imaging protocol and optimization, a description of the battery of behavioral assessments, and QA/QC procedures. Combining HCP inspired neuroimaging data with well-established behavioral assessments during this time period will yield an invaluable resource for the scientific community. •Complete description of the UNC/UMN Baby Connectome Project (BCP) protocol.•The importanc'e of dense longitudinal sampling.•Protocol optimization and preliminary results of optimized imaging protocol.•BCP study data as a unique resource for the scientific community.

Age‐related changes in focal cortical morphology have been well documented in previous literature; however, how interregional coordination patterns of the focal cortical morphology reorganize with advancing age is not well established. In this study, we performed a comprehensive analysis of the topological changes in single‐subject morphological brain networks across the adult lifespan. Specifically, we constructed four types of single‐subject morphological brain networks for 650 participants (aged from 18 to 88 years old), and characterized their topological organization using graph‐based network measures. Age‐related changes in the network measures were examined via linear, quadratic, and cubic models. We found profound age‐related changes in global small‐world attributes and efficiency, local nodal centralities, and interregional similarities of the single‐subject morphological brain networks. The age‐related changes were mainly embodied in cortical thickness networks, involved in frontal regions and highly connected hubs, concentrated on short‐range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal, and ventral attention networks. Intriguingly, nonlinear (i.e., quadratic or cubic) age‐related changes were frequently found in the insula and limbic regions, and age‐related cubic changes preferred long‐range morphological connections. Finally, we demonstrated that the morphological similarity in cortical thickness between two frontal regions mediated the relationship between age and cognition measured by Cattell scores. Taken together, these findings deepen our understanding of adaptive changes of the human brain with advancing age, which may account for interindividual variations in behaviors and cognition. This study demonstrated profound age‐related changes on single‐subject morphological brain networks across the adult lifespan that were mainly embodied in cortical thickness‐based networks, involved in frontal regions and highly connected hubs, concentrated on short‐range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal and ventral attention networks.

It has been claimed that bilingual experience leads to an enhancement of cognitive control across the lifespan, a claim that has been investigated by comparing monolingual and bilingual groups performing standard executive function (EF) tasks. The results of these studies have been inconsistent, however, leading to controversy over the essential assumptions underlying the research program, namely, whether bilingualism produces cognitive change. We argue that the source of the inconsistency is not in the evidence but rather in the framework that has typically been used to motivate the research and interpret the results. We examine the componential view of EF with its central role for inhibition and argue that it provides a poor fit to both bilingual experience and the results of these studies. As an alternative, we propose a more holistic account based on attentional control that overrides the processes in the componential model of EF and applies to a wider range of tasks. The key element in our account is that behavioral differences between monolingual and bilingual individuals reflect differences in the efficiency and deployment of attentional control between the two language groups. In support of this point we show how attentional control provides a more satisfactory account for a range of findings that cannot reasonably be attributed to inhibition. We also suggest that group differences will emerge only when the attentional demands of a task exceed the control abilities of one of the groups, regardless of the EF components involved. We then review literature from across the lifespan to evaluate the extent to which this account is consistent with existing evidence, and conclude with some suggestions on how the field may be advanced by new lines of empirical enquiry.

Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the developmental timing of asymmetry and the extent to which it arises through genetic and later influences in childhood. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in seven datasets and chart asymmetry trajectories longitudinally across life (4–89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large–scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry is low-moderately heritable (max h 2 SNP ~19%) and correlates phenotypically and genetically in specific regions, indicating coordinated development of asymmetries partly through genes. In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises early in life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.

At rest, the brain's sensorimotor and higher cognitive systems engage in organized patterns of correlated activity forming resting-state networks. An important empirical question is how functional connectivity and structural connectivity within and between resting-state networks change with age. In this study we use network modeling techniques to identify significant changes in network organization across the human lifespan. The results of this study demonstrate that whole-brain functional and structural connectivity both exhibit reorganization with age. On average, functional connections within resting-state networks weaken in magnitude while connections between resting-state networks tend to increase. These changes can be localized to a small subset of functional connections that exhibit systematic changes across the lifespan. Collectively, changes in functional connectivity are also manifest at a system-wide level, as components of the control, default mode, saliency/ventral attention, dorsal attention, and visual networks become less functionally cohesive, as evidenced by decreased component modularity. Paralleling this functional reorganization is a decrease in the density and weight of anatomical white-matter connections. Hub regions are particularly affected by these changes, and the capacity of those regions to communicate with other regions exhibits a lifelong pattern of decline. Finally, the relationship between functional connectivity and structural connectivity also appears to change with age; functional connectivity along multi-step structural paths tends to be stronger in older subjects than in younger subjects. Overall, our analysis points to age-related changes in inter-regional communication unfolding within and between resting-state networks. •We model changes in functional and structural connectivity as a function of age.•Functional connections within/between RSNs decrease/increase with age.•Structural hubs become less-efficiently connected to other regions with age.•With age, functional connections are supported by longer structural paths.

Age‐related variations in many regions and/or networks of the human brain have been uncovered using resting‐state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large‐scale cross‐sectional adult lifespan dataset ( n  = 492). Both GS topography and its variation with age showed frequency‐specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age‐related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro‐vascular coupling and physiological noises. Together, these results provide the first evidence for age‐related effects on global brain activity and its topographic‐dynamic representation in terms of spatiotemporal dedifferentiation.

The operation of the human brain relies on functional networks enabled by inter‐areal oscillatory synchronization between neuronal populations. Although disruptions in this functional connectivity are associated with brain disorders, evidence on its healthy age‐dependent variation and behavioral relevance remains limited. Utilizing magnetoencephalography (MEG) recordings from 576 adults aged 18–87 years, we investigated the evolution of resting‐state functional connectivity (rs‐FC) across the healthy adult lifespan. We observed age‐related, frequency‐specific changes in widespread cortical networks. Alpha‐band (8–13 Hz) rs‐FC decreased, while delta (1–4 Hz), theta (4–8 Hz), and gamma‐band (40–90 Hz) rs‐FC increased with age. Beta‐band (13–30 Hz) rs‐FC followed a non‐linear trajectory, peaking in middle age. The global delta, theta, alpha, and beta‐band patterns differed from concurrent changes in oscillatory power, underscoring their dissociable contributions. Notably, reduced beta‐band rs‐FC was associated with increased sensorimotor attenuation, indicating that changes in rs‐FC are behaviorally relevant for sensorimotor function. These findings advance our understanding of healthy brain aging and highlight a link between resting‐state brain activity and sensorimotor integration. Key Points Functional connectivity is altered across the healthy adult lifespan in a frequency‐dependent manner. Changes in source power do not explain global functional connectivity trajectories. Beta‐band connectivity at rest is associated with sensorimotor attenuation independent of age‐related effects. An analysis of magnetoencephalography (MEG) recordings from 576 adults aged 18–87 years revealed frequency‐dependent changes in cortical functional connectivity across the adult lifespan, which are not explained by changes in source power. Beta‐band (13–30 Hz) connectivity at rest is associated with sensorimotor attenuation independent of these age‐related effects.

•Research on spatiotemporal self-organization throughout the entire lifespan.•Phase Coherent Graph Autoencoder framework determines the metastable brain states.•The reduction of global metastable state in middle and old individuals.•Linear and quadratic changes of brain dynamic trajectory indicators with age.•Excellent classification performance of spatiotemporal self-organizing features. The development of the human brain is a complex, lifelong process during which collective behaviors of neurons exhibit self-organizing dynamics. Metastable states play a crucial role in understanding the complex dynamical mechanisms of the brain, and analyzing them helps to reveal the mechanisms of functional changes in the brain throughout development and aging. Specifically, global metastable state provides a overall perspective on the flexibility of brain reorganization, while the evolution trajectories of transient functional patterns capture detailed changes in brain activity. The leading eigenvector dynamics analysis (LEiDA) method significantly reduces the dimensionality of data and is widely used to capture the temporal trajectory characteristics of transient functional patterns, i.e., metastable brain states. However, LEiDA's linear dimensionality reduction of high-dimensional raw brain data may overlook non-linear information and lose some relationships between features. We developed a framework based on Phase Coherence Graph Autoencoder (PCGAE) that employs graph autoencoders (GAE) for non-linear dimensionality reduction of phase coherence matrices. This approach clusters to identify more distinct metastable brain states and is applied to the analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data across the human lifespan. This paper investigates age-related differences and continuity changes from different perspectives: metastable state indicators and state trajectory indicators (occurrence probability, lifetime, and state transition metrics). Global metastable state shows a linear decline with age, while both linear and quadratic effects of age-related changes are observed in detailed state metastable and state trajectory indicators. Finally, the proposed feature extraction scheme demonstrates good classification performance for categorizing brain age groups. These findings can help us understand the self-organizing reorganization characteristics associated with aging and their complex dynamic changes, providing new insights into brain development throughout the entire lifespan.

Variation in the clinical manifestation of dementia has been associated with differences in cognitive reserve, although less is known about the cumulative effects of exposure to cognitive reserve factors over the life course. We examined the association of cognitive reserve-related factors over the lifespan with the risk of dementia in a community-based cohort of older adults. Information on early-life education, socioeconomic status, work complexity at age 20, midlife occupation attainment, and late-life leisure activities was collected in a cohort of dementia-free community dwellers aged 75+ y residing in the Kungsholmen district of Stockholm, Sweden, in 1987-1989. The cohort was followed up to 9 y (until 1996) to detect incident dementia cases. To exclude preclinical phases of disease, participants who developed dementia at the first follow-up examination 3 y after the baseline were excluded (n = 602 after exclusions). Structural equation modelling was used to generate latent factors of cognitive reserve from three periods over the life course: early (before 20 y), adulthood (around 30-55 y), and late life (75 y and older). The correlation between early- and adult-life latent factors was strong (γ = 0.9), whereas early-late (γ = 0.27) and adult-late (γ = 0.16) latent factor correlations were weak. One hundred forty-eight participants developed dementia during follow-up, and 454 remained dementia-free. The relative risk (RR) of dementia was estimated using Cox models with life-course cognitive reserve-enhancing factors modelled separately and simultaneously to assess direct and indirect effects. The analysis was repeated among carriers and noncarriers of the apolipoprotein E (APOE) ε4 allele. A reduced risk of dementia was associated with early- (RR 0.57; 95% CI 0.36-0.90), adult- (RR 0.60; 95% CI 0.42-0.87), and late-life (RR 0.52; 95% CI 0.37-0.73) reserve-enhancing latent factors in separate multivariable Cox models. In a mutually adjusted model, which may have been imprecisely estimated because of strong correlation between early- and adult-life factors, the late-life factor preserved its association (RR 0.65; 95% CI 0.45-0.94), whereas the effect of midlife (RR 0.73; 95% CI 0.50-1.06) and early-life factors (RR 0.76; 95% CI 0.47-1.23) on the risk of dementia was attenuated. The risk declined progressively with cumulative exposure to reserve-enhancing latent factors, and having high scores on cognitive reserve-enhancing composite factors in all three periods over the life course was associated with the lowest risk of dementia (RR 0.40; 95% CI 0.20-0.81). Similar associations were detected among APOE ε4 allele carriers and noncarriers. Limitations include measurement error and nonresponse, with both biases likely favouring the null. Strong correlation between early- and adult-life latent factors may have led to a loss in precision when estimating mutually adjusted effects of all periods. In this study, cumulative exposure to reserve-enhancing factors over the lifespan was associated with reduced risk of dementia in late life, even among individuals with genetic predisposition.