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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.

Multiple studies have found that children born to mothers with opioid or poly-substance use during pregnancy have more behavior and attention problems and lower cognitive functioning than non-exposed children. The present study aimed to investigate whether behavior and attention problems are more prominent than general cognitive deficits in this risk group and whether the problems wane or increase over time. This prospective longitudinal cross-informant study compared 72 children who were prenatally exposed to heroin and multiple drugs with a group of 58 children without known prenatal risk factors. Group differences in caregivers' and teachers' reports of the children's behavior and attention problems based on the Child Behavior Check List and the ADHD Rating Scale were compared based on group differences in general cognitive functioning at 4 ½ and 8 ½ years of age. Both parent and teacher reports suggest that the exposed group has significantly more problems in several behavioral areas than the comparison group, particularly with regard to attention problems. The preschool teachers had already reported these problems when the children were 4 ½ years old, whereas the caregivers reported these problems mainly when the children were 8 ½ years old. The group differences in behavioral and attentional problems were not significantly greater and some were even significantly smaller than the group differences in general cognitive abilities. These findings suggest that children subject to prenatally drug exposure have increasing problems in multiple areas related to behavior from preschool age to 8 ½ years but that these problems do not seem to be specific; i.e., they are not more severe than the problems with general cognitive abilities found for this group.

A causal link between decreases in white matter (WM) integrity and cortical degeneration is assumed, but there is scarce knowledge on the relationship between these changes across the adult human lifespan. We investigated changes in thickness throughout the cortical mantle and WM tract integrity derived from T1 and diffusion weighted magnetic resonance imaging (MRI) scans in 201 healthy adults aged 23-87 years over a mean interval of 3.6 years. Fractional anisotropy (FA), mean (MD), radial (RD) and axial (AD) diffusivity changes were calculated for forceps minor and major and eight major white matter tracts in each hemisphere by use of a novel automated longitudinal tractography constrained by underlying anatomy (TRACULA) approach. We hypothesized that increasing MD and decreasing FA across tracts would relate to cortical thinning, with some anatomical specificity. WM integrity decreased across tracts non-uniformly, with mean annual percentage decreases ranging from 0.20 in the Inferior Longitudinal Fasciculus to 0.65 in the Superior Longitudinal Fasciculus. For most tracts, greater MD increases and FA decreases related to more cortical thinning, in areas in part overlapping with but also outside the projected tract endings. The findings indicate a combination of global and tract-specific relationships between WM integrity and cortical thinning.

Cortical thinning occurs throughout the entire life and extends to late-life neurodegeneration, yet the neurobiological substrates are poorly understood. Here, we used a virtual-histology technique and gene expression data from the Allen Human Brain Atlas to compare the regional profiles of longitudinal cortical thinning through life (4004 magnetic resonance images [MRIs]) with those of gene expression for several neuronal and non-neuronal cell types. The results were replicated in three independent datasets. We found that inter-regional profiles of cortical thinning related to expression profiles for marker genes of CA1 pyramidal cells, astrocytes and, microglia during development and in aging. During the two stages of life, the relationships went in opposite directions: greater gene expression related to less thinning in development and vice versa in aging. The association between cortical thinning and cell-specific gene expression was also present in mild cognitive impairment and Alzheimer’s Disease. These findings suggest a role of astrocytes and microglia in promoting and supporting neuronal growth and dendritic structures through life that affects cortical thickness during development, aging, and neurodegeneration. Overall, the findings contribute to our understanding of the neurobiology underlying variations in MRI-derived estimates of cortical thinning through life and late-life disease.

Significance Many evolutionary–developmental models have attempted to relate development and aging, with one popular hypothesis proposing that healthy age-related brain decline mirrors developmental maturation. But this elegant hypothesis has so far lacked clear and direct data to support it. Here, we describe intrinsic, entirely data-driven evidence that healthy brain degeneration and developmental process mirror one another in certain brain regions. Specifically, a data-driven decomposition of structural brain images in 484 healthy participants reveals a network of mainly higher-order regions that develop relatively late during adolescence, demonstrate accelerated degeneration in old age, and show heightened vulnerability to disorders that impact on brain structure during adolescence and aging. These results provide a fundamental link between development, aging, and disease processes in the brain. Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8–85 y) reveals a largely—but not only—transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer’s disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer’s disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer’s disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.

There is a growing realization that early life influences have lasting impact on brain function and structure. Recent research has demonstrated that genetic relationships in adults can be used to parcellate the cortex into regions of maximal shared genetic influence, and a major hypothesis is that genetically programmed neurodevelopmental events cause a lasting impact on the organization of the cerebral cortex observable decades later. Here we tested how developmental and lifespan changes in cortical thickness fit the underlying genetic organizational principles of cortical thickness in a longitudinal sample of 974 participants between 4.1 and 88.5 y of age with a total of 1,633 scans, including 773 scans from children below 12 y. Genetic clustering of cortical thickness was based on an independent dataset of 406 adult twins. Developmental and adult age-related changes in cortical thickness followed closely the genetic organization of the cerebral cortex, with change rates varying as a function of genetic similarity between regions. Cortical regions with overlapping genetic architecture showed correlated developmental and adult age change trajectories and vice versa for regions with low genetic overlap. Thus, effects of genes on regional variations in cortical thickness in middle age can be traced to regional differences in neurodevelopmental change rates and extrapolated to further adult aging-related cortical thinning. This finding suggests that genetic factors contribute to cortical changes through life and calls for a lifespan perspective in research aimed at identifying the genetic and environmental determinants of cortical development and aging.

•Distinct profiles for the anterior and posterior hippocampus.•Stable connectivity during resting state and memory tasks.•Upregulation of hippocampal connectivity with recollection areas during retrieval.•Sparse task-general connectivity during encoding when compared with retrieval. Memory encoding and retrieval are critical sub-processes of episodic memory. While the hippocampus is involved in both, less is known about its connectivity with the neocortex during memory processing in humans. This is partially due to variations in demands in common memory tasks, which inevitably recruit cognitive processes other than episodic memory. Conjunctive analysis of data from different tasks with the same core elements of encoding and retrieval can reduce the intrusion of patterns related to subsidiary perceptual and cognitive processing. Leveraging data from two large-scale functional resonance imaging studies with different episodic memory tasks (514 and 237 participants), we identified hippocampal-cortical networks active during memory tasks. Whole-brain functional connectivity maps were similar during resting state, encoding, and retrieval. Anterior and posterior hippocampus had distinct connectivity profiles, which were also stable across resting state and memory tasks. When contrasting encoding and retrieval connectivity, conjunctive encoding-related connectivity was sparse. During retrieval hippocampal connectivity was increased with areas known to be active during recollection, including medial prefrontal, inferior parietal, and parahippocampal cortices. This indicates that the stable functional connectivity of the hippocampus along its longitudinal axis is superposed by increased functional connectivity with the recollection network during retrieval, while auxiliary encoding connectivity likely reflects contextual factors.

Advances in neuroimaging techniques have recently provided glimpse into the neurobiology of complex traits of human personality. Whereas some intriguing findings have connected aspects of personality to variations in brain morphology, the relations are complex and our current understanding is incomplete. Therefore, we aimed to provide a comprehensive investigation of brain–personality relations using a multimodal neuroimaging approach in a large sample comprising 265 healthy individuals. The NEO Personality Inventory was used to provide measures of core aspects of human personality, and imaging phenotypes included measures of total and regional brain volumes, regional cortical thickness and arealization, and diffusion tensor imaging indices of white matter (WM) microstructure. Neuroticism was the trait most clearly linked to brain structure. Higher neuroticism including facets reflecting anxiety, depression and vulnerability to stress was associated with smaller total brain volume, widespread decrease in WM microstructure, and smaller frontotemporal surface area. Higher scores on extraversion were associated with thinner inferior frontal gyrus, and conscientiousness was negatively associated with arealization of the temporoparietal junction. No reliable associations between brain structure and agreeableness and openness, respectively, were found. The results provide novel evidence of the associations between brain structure and variations in human personality, and corroborate previous findings of a consistent neuroanatomical basis of negative emotionality. ► Neuroticism was the NEO-PI trait most clearly linked to brain structure. ► Higher neuroticism was associated with smaller total brain volume. ► Higher neuroticism was associated with reduced frontotemporal surface area. ► Neuroticism was associated with widespread decrease in white matter microstructure. ► Multimodal imaging promising in delineating the neural substrate of personality.

Working memory capacity is pivotal for a broad specter of cognitive tasks and develops throughout childhood. This must in part rely on development of neural connections and white matter microstructure maturation, but there is scarce knowledge of specific relations between this and different aspects of working memory. Diffusion tensor imaging (DTI) enables us to study development of brain white matter microstructure. In a longitudinal DTI study of 148 healthy children between 4 and 11 years scanned twice with an on average 1.6 years interval, we characterized change in fractional anisotropy (FA), mean (MD), radial (RD) and axial diffusivity (AD) in 10 major white matter tracts hypothesized to be of importance for working memory. The results showed relationships between change in several tracts and change in visuospatial working memory. Specifically, improvement in visuospatial working memory capacity was significantly associated with decreased MD, RD and AD in inferior longitudinal fasciculus (ILF), inferior fronto-occipital fasciculus (IFOF) and uncinate fasciculus (UF) in the right hemisphere, as well as forceps major (FMaj). No significant relationships were found between change in DTI metrics and change in verbal working memory capacity. These findings yield new knowledge about brain development and corresponding working memory improvements in childhood.

The brain's ability to alter its functional and structural architecture in response to experience and learning has been extensively studied. Mental stimulation might serve as a reserve mechanism in brain aging, but macrostructural brain changes in response to cognitive training have been demonstrated in young participants only. We examined the short-term effects of an intensive memory training program on cognition and brain structure in middle-aged and elderly healthy volunteers. The memory trainers completed an 8-week training regimen aimed at improving verbal source memory utilizing the Method of Loci (MoL), while control participants did not receive any intervention. Both the memory trainers and the controls underwent magnetic resonance imaging (MRI) scans and memory testing pre and post 8weeks of training or no training, respectively. Cortical thickness was automatically measured across the cortical mantle, and data processing and statistical analyses were optimized for reliable detection of longitudinal changes. The results showed that memory training improved source memory performance. Memory trainers also showed regional increases in cortical thickness compared with controls. Furthermore, thickness change in the right fusiform and lateral orbitofrontal cortex correlated positively with improvement in source memory performance, suggesting a possible functional significance of the structural changes. These findings demonstrate that systematic mental exercise may induce short-term structural changes in the aging human brain, indicating structural brain plasticity in elderly. The present study included short-term assessments, and follow-up studies are needed in order to assess whether such training indeed alters the long-term structural trajectories.