Cognitive aging is accompanied by a range of structural and functional differences in the brain, even in the absence of neurodegenerative disease. Functional magnetic resonance imaging (fMRI) studies have reported increased bilateral activation during task performance in elderly participants compared to their younger counterparts, particularly in frontal regions. Alterations have also been observed in the functional architecture of the resting brain, suggesting that aging is associated with changes in the organization of the networks of the brain. However, previous studies have largely focused on the default mode network, and little is known about the effects of age on other resting state-networks (RSNs). The aim of the present study was to investigate age-differences in resting state functional connectivity (RSFC) using fMRI data obtained during rest from 238 healthy participants aged 21-80 years. Using independent component analysis (ICA) and dual-regression, the results revealed age-related increases in RSFC across a range of RSNs, including task-positive networks in frontal and parietal regions. In contrast, age-related reductions in the default mode network and occipital visual networks were observed. Furthermore, whereas the effects of age on the various RSNs were found independent of age-related decreases in gray matter volume, sex and subject motion, we report strong positive and widespread effects of estimated subject motion on the RSFC across RSNs. The results provide support for the notion of network-specific effects in aging, manifested as increased tonic activation of task-positive networks, supporting higher-order cognitive functions and cognitive control, along with reduced task-negative default mode network and sensory visual networks during rest. The present results also corroborate recent evidence of strong influence of subject motion on estimated functional connectivity measures and strongly suggest that studies using RSFC measures as imaging phenotypes should adjust for individual differences in in-scanner subject motion.

It has been suggested that specific forms of cognition in older age rely largely on late-life specific mechanisms. Here instead, we tested using task-fMRI (n = 540, age 6-82 years) whether the functional foundations of successful episodic memory encoding adhere to a principle of lifespan continuity, shaped by developmental, structural, and evolutionary influences. We clustered regions of the cerebral cortex according to the shape of the lifespan trajectory of memory activity in each region so that regions showing the same pattern were clustered together. The results revealed that lifespan trajectories of memory encoding function showed a continuity through life but no evidence of age-specific mechanisms such as compensatory patterns. Encoding activity was related to general cognitive abilities and variations of grey matter as captured by a multi-modal independent component analysis, variables reflecting core aspects of cognitive and structural change throughout the lifespan. Furthermore, memory encoding activity aligned to fundamental aspects of brain organization, such as large-scale connectivity and evolutionary cortical expansion gradients. Altogether, we provide novel support for a perspective on memory aging in which maintenance and decay of episodic memory in older age needs to be understood from a comprehensive life-long perspective rather than as a late-life phenomenon only.

Memory performance results from plasticity, the ability to change with experience. We show that benefit from practice over a few trials, learning slope, is predictive of long-term recall and hippocampal volume across a broad age range and a long period of time, relates to memory training benefit, and is heritable. First, in a healthy lifespan sample (n = 1825, age 4-93 years), comprising 3483 occasions of combined magnetic resonance imaging (MRI) scans and memory tests over a period of up to 11 years, learning slope across 5 trials was uniquely related to performance on a delayed free recall test, as well as hippocampal volume, independent from first trial memory or total memory performance across the five learning trials. Second, learning slope was predictive of benefit from memory training across ten weeks in an experimental subsample of adults (n = 155). Finally, in an independent sample of male twins (n = 1240, age 51-50 years), learning slope showed significant heritability. Within-session learning slope may be a useful marker beyond performance per se, being heritable and having unique predictive value for long-term memory function, hippocampal volume and training benefit across the human lifespan.

We examined whether sleep quality and quantity are associated with cortical and memory changes in cognitively healthy participants across the adult lifespan. Associations between self-reported sleep parameters (Pittsburgh Sleep Quality Index, PSQI) and longitudinal cortical change were tested using five samples from the Lifebrain consortium (n = 2205, 4363 MRIs, 18-92 years). In additional analyses, we tested coherence with cell-specific gene expression maps from the Allen Human Brain Atlas, and relations to changes in memory performance. "PSQI # 1 Subjective sleep quality" and "PSQI #5 Sleep disturbances" were related to thinning of the right lateral temporal cortex, with lower quality and more disturbances being associated with faster thinning. The association with "PSQI #5 Sleep disturbances" emerged after 60 years, especially in regions with high expression of genes related to oligodendrocytes and S1 pyramidal neurons. None of the sleep scales were related to a longitudinal change in episodic memory function, suggesting that sleep-related cortical changes were independent of cognitive decline. The relationship to cortical brain change suggests that self-reported sleep parameters are relevant in lifespan studies, but small effect sizes indicate that self-reported sleep is not a good biomarker of general cortical degeneration in healthy older adults.

Major depressive disorder has been associated with lower prefrontal thickness and hippocampal volume, but it is unknown whether this association also holds for depressive symptoms in the general population. We investigated associations of depressive symptoms and depression status with brain structures across population-based and patient-control cohorts, and explored whether these associations are similar over the lifespan and across sexes.

Working memory (WM) supports several higher-level cognitive abilities, yet we know less about factors associated with development and decline in WM compared to other cognitive processes. Here, we investigated lifespan changes in WM capacity and their structural brain correlates, using a longitudinal sample including 2358 magnetic resonance imaging (MRI) scans and WM scores from 1656 participants (4.4-86.4 years, mean follow-up interval 4.3 years). 8764 participants (9.0-10.9 years) with MRI, WM scores and genetic information from the Adolescent Brain Cognitive Development study were used for follow-up analyses. Results showed that both the information manipulation component and the storage component of WM improved during childhood and adolescence, but the age-decline could be fully explained by reductions in passive storage capacity alone. Greater WM function in development was related to apparent thinner cortex in both samples, also when general cognitive function was accounted for. The same WM-apparent thickness relationship was found for young adults. The WM-thickness relationships could not be explained by SNP-based co-heritability or by socioeconomic status. A larger sample with genetic information may be necessary to disentangle the true gene-environment effects. In conclusion, WM capacity changes greatly through life and has anatomically extended rather than function-specific structural cortical correlates.

Insufficient suppression and connectivity of the default mode network (DMN) is a potential mediator of cognitive dysfunctions across various disorders, including attention deficit/hyperactivity disorder (ADHD). However, it remains unclear if alterations in sustained DMN suppression, variability and connectivity during prolonged cognitive engagement are implicated in adult ADHD pathophysiology, and to which degree methylphenidate (MPH) remediates any DMN abnormalities. This randomized, double-blinded, placebo-controlled, cross-over clinical trial of MPH (clinicaltrials.gov/ct2/show/NCT01831622) explored large-scale brain network dynamics in 20 adults with ADHD on and off MPH, compared to 27 healthy controls, while performing a reward based decision-making task. DMN task-related activation, variability, and connectivity were estimated and compared between groups and conditions using independent component analysis, dual regression, and Bayesian linear mixed models. The results show that the DMN exhibited more variable activation patterns in unmedicated patients compared to healthy controls. Group differences in functional connectivity both between and within functional networks were evident. Further, functional connectivity between and within attention and DMN networks was sensitive both to task performance and case-control status. MPH altered within-network connectivity of the DMN and visual networks, but not between-network connectivity or temporal variability. This study thus provides novel fMRI evidence of reduced sustained DMN suppression in adults with ADHD during value-based decision-making, a pattern that was not alleviated by MPH. We infer from multiple analytical approaches further support to the default mode interference hypothesis, in that higher DMN activation variability is evident in adult ADHD and associated with lower task performance.

Performance on recall tests improves through childhood and adolescence, in part due to structural maturation of the medial temporal cortex. Although partly different processes support successful recall over shorter vs. longer intervals, recall is usually tested after less than an hour. The aim of the present study was to test whether there are unique developmental changes in recall performance using extended retention intervals, and whether these are related to structural maturation of sub-regions of the hippocampus. 650 children and adolescents from 4.1 to 24.8 years were assessed in total 962 times (mean interval ≈ 1.8 years). The California Verbal Learning Test (CVLT) and the Rey Complex Figure Test (CFT) were used. Recall was tested 30 min and ≈ 10 days after encoding. We found unique developmental effects on recall in the extended retention interval condition independently of 30 min recall performance. For CVLT, major improvements happened between 10 and 15 years. For CFT, improvement was linear and was accounted for by visuo-constructive abilities. The relationships did not show anterior-posterior hippocampal axis differences. In conclusion, performance on recall tests using extended retention intervals shows unique development, likely due to changes in encoding depth or efficacy, or improvements of long-term consolidation processes.

Poor sleep is associated with multiple age-related neurodegenerative and neuropsychiatric conditions. The hippocampus plays a special role in sleep and sleep-dependent cognition, and accelerated hippocampal atrophy is typically seen with higher age. Hence, it is critical to establish how the relationship between sleep and hippocampal volume loss unfolds across the adult lifespan.

Analyzing data from multiple neuroimaging studies has great potential in terms of increasing statistical power, enabling detection of effects of smaller magnitude than would be possible when analyzing each study separately and also allowing to systematically investigate between-study differences. Restrictions due to privacy or proprietary data as well as more practical concerns can make it hard to share neuroimaging datasets, such that analyzing all data in a common location might be impractical or impossible. Meta-analytic methods provide a way to overcome this issue, by combining aggregated quantities like model parameters or risk ratios. Most meta-analytic tools focus on parametric statistical models, and methods for meta-analyzing semi-parametric models like generalized additive models have not been well developed. Parametric models are often not appropriate in neuroimaging, where for instance age-brain relationships may take forms that are difficult to accurately describe using such models. In this paper we introduce meta-GAM, a method for meta-analysis of generalized additive models which does not require individual participant data, and hence is suitable for increasing statistical power while upholding privacy and other regulatory concerns. We extend previous works by enabling the analysis of multiple model terms as well as multivariate smooth functions. In addition, we show how meta-analytic p-values can be computed for smooth terms. The proposed methods are shown to perform well in simulation experiments, and are demonstrated in a real data analysis on hippocampal volume and self-reported sleep quality data from the Lifebrain consortium. We argue that application of meta-GAM is especially beneficial in lifespan neuroscience and imaging genetics. The methods are implemented in an accompanying R package metagam, which is also demonstrated.

Short sleep is held to cause poorer brain health, but is short sleep associated with higher rates of brain structural decline? Analysing 8,153 longitudinal MRIs from 3,893 healthy adults, we found no evidence for an association between sleep duration and brain atrophy. In contrast, cross-sectional analyses (51,295 observations) showed inverse U-shaped relationships, where a duration of 6.5 (95% confidence interval, (5.7, 7.3)) hours was associated with the thickest cortex and largest volumes relative to intracranial volume. This fits converging evidence from research on mortality, health and cognition that points to roughly seven hours being associated with good health. Genome-wide association analyses suggested that genes associated with longer sleep for below-average sleepers were linked to shorter sleep for above-average sleepers. Mendelian randomization did not yield evidence for causal impacts of sleep on brain structure. The combined results challenge the notion that habitual short sleep causes brain atrophy, suggesting that normal brains promote adequate sleep duration-which is shorter than current recommendations.

Higher socio-economic status (SES) has been proposed to have facilitating and protective effects on brain and cognition. We ask whether relationships between SES, brain volumes and cognitive ability differ across cohorts, by age and national origin. European and US cohorts covering the lifespan were studied (4-97 years, N = 500 000; 54 000 w/brain imaging). There was substantial heterogeneity across cohorts for all associations. Education was positively related to intracranial (ICV) and total gray matter (GM) volume. Income was related to ICV, but not GM. We did not observe reliable differences in associations as a function of age. SES was more strongly related to brain and cognition in US than European cohorts. Sample representativity varies, and this study cannot identify mechanisms underlying differences in associations across cohorts. Differences in neuroanatomical volumes partially explained SES-cognition relationships. SES was more strongly related to ICV than to GM, implying that SES-cognition relations in adulthood are less likely grounded in neuroprotective effects on GM volume in aging. The relatively stronger SES-ICV associations rather are compatible with SES-brain volume relationships being established early in life, as ICV stabilizes in childhood. The findings underscore that SES has no uniform association with, or impact on, brain and cognition.

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.