Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats.

Hosting 1460 plant and 126 vertebrate endemic species, the Great Escarpment (hereafter, Escarpment) forms a semi-circular "amphitheater" of mountains girdling southern Africa from arid west to temperate east. Since arid and temperate biota are usually studied separately, earlier studies overlooked the biogeographical importance of the Escarpment as a whole. Bats disperse more widely than other mammalian taxa, with related species and intraspecific lineages occupying both arid and temperate highlands of the Escarpment, providing an excellent model to address this knowledge gap. We investigated patterns of speciation and micro-endemism from modeled past, present, and future distributions in six clades of southern African bats from three families (Rhinolophidae, Cistugidae, and Vespertilionidae) having different crown ages (Pleistocene to Miocene) and biome affiliations (temperate to arid). We estimated mtDNA relaxed clock dates of key divergence events across the six clades in relation both to biogeographical features and patterns of phenotypic variation in crania, bacula and echolocation calls. In horseshoe bats (Rhinolophidae), both the western and eastern "arms" of the Escarpment have facilitated dispersals from the Afrotropics into southern Africa. Pleistocene and pre-Pleistocene "species pumps" and temperate refugia explained observed patterns of speciation, intraspecific divergence and, in two cases, mtDNA introgression. The Maloti-Drakensberg is a center of micro-endemism for bats, housing three newly described or undescribed species. Vicariance across biogeographic barriers gave rise to 29 micro-endemic species and intraspecific lineages whose distributions were congruent with those identified in other phytogeographic and zoogeographic studies. Although Köppen-Geiger climate models predict a widespread replacement of current temperate ecosystems in southern Africa by tropical or arid ecosystems by 2070-2100, future climate Maxent models for 13 bat species (all but one of those analyzed above) showed minimal range changes in temperate species from the eastern Escarpment by 2070, possibly due to the buffering effect of mountains to climate change.

Deepening biomedical research training: Community-Building Wellness Workshops for Post-Baccalaureate Research Education Program (PREP) Trainees.

Problem: All trainees, especially those from historically minoritized backgrounds, experience stresses that may reduce their continuation in science, technology, engineering, math, and medicine (STEMM) careers. The Johns Hopkins University School of Medicine is one of ~45 institutions with a National Institutes of Health funded Postbaccalaureate Research Education Program (PREP) that provides mentoring and a year of fulltime research to prepare students from historically excluded groups for graduate school. Having experienced the conflation of stresses during the COVID-19 pandemic and related shutdown, we realized our program lacked a component that explicitly helped PREP Scholars recognize and cope with non-academic stresses (financial, familial, social, mental) that might threaten their confidence and success as scientists and future in STEMM. Intervention: We developed an early-intervention program to help Scholars develop life-long skills to become successful and resilient scientists. We developed a year-long series comprised of 9 workshops focused on community, introspection, financial fitness, emotional intelligence, mental health, and soft-skills. We recruited and compensated a cohort of PhD students and postdoctoral fellows to serve as Peer Mentors, to provide a community and the safest 'space' for Scholars to discuss personal concerns. Peer Mentors were responsible for developing and facilitating these Community-Building Wellness Workshops (CBWW). Context: CBWW were created and exectued as part of the larger PREP program. Workshops included a PowerPoint presentation by Peer Mentors that featured several case studies that prompted discussion and provided time for small-group discussions between Scholars and Peer Mentors. We also included pre- and post-work for each workshop. These touch-points helped Scholars cultivate the habit of introspection. Impact: The CBWW exceeded our goals. Both Peer Mentors and Scholars experienced strong mutual support, and Scholars developed life-long skills. Notably, several Scholars who had been experiencing financial, mental or mentor-related stress immediately brought this to the attention of program leadership, allowing early and successful intervention. At the completion of CBWW, PREP Scholars reported implementing many workshop skills into practice, were reshaping their criteria for choosing future mentors, and evaluating career decisions. Strikingly, Peer Mentors found they also benefitted from the program as well, suggesting a potential larger scope for the role of CBWW in academia. Lessons Learned: Peer Mentors were essential in creating a safe supportive environment that facilitated discussions, self-reflection, and self-care. Providing fair compensation to Peer Mentors for their professional mentoring and teaching contributions was essential and contributed meaningfully to the positive energy and impact of this program.

Deep learning to extract the meteorological by-catch of wildlife cameras.

Microclimate-proximal climatic variation at scales of metres and minutes-can exacerbate or mitigate the impacts of climate change on biodiversity. However, most microclimate studies are temperature centric, and do not consider meteorological factors such as sunshine, hail and snow. Meanwhile, remote cameras have become a primary tool to monitor wild plants and animals, even at micro-scales, and deep learning tools rapidly convert images into ecological data. However, deep learning applications for wildlife imagery have focused exclusively on living subjects. Here, we identify an overlooked opportunity to extract latent, ecologically relevant meteorological information. We produce an annotated image dataset of micrometeorological conditions across 49 wildlife cameras in South Africa's Maloti-Drakensberg and the Swiss Alps. We train ensemble deep learning models to classify conditions as overcast, sunshine, hail or snow. We achieve 91.7% accuracy on test cameras not seen during training. Furthermore, we show how effective accuracy is raised to 96% by disregarding 14.1% of classifications where ensemble member models did not reach a consensus. For two-class weather classification (overcast vs. sunshine) in a novel location in Svalbard, Norway, we achieve 79.3% accuracy (93.9% consensus accuracy), outperforming a benchmark model from the computer vision literature (75.5% accuracy). Our model rapidly classifies sunshine, snow and hail in almost 2 million unlabelled images. Resulting micrometeorological data illustrated common seasonal patterns of summer hailstorms and autumn snowfalls across mountains in the northern and southern hemispheres. However, daily patterns of sunshine and shade diverged between sites, impacting daily temperature cycles. Crucially, we leverage micrometeorological data to demonstrate that (1) experimental warming using open-top chambers shortens early snow events in autumn, and (2) image-derived sunshine marginally outperforms sensor-derived temperature when predicting bumblebee foraging. These methods generate novel micrometeorological variables in synchrony with biological recordings, enabling new insights from an increasingly global network of wildlife cameras.

Perspective-The Feasibility of Continuous Protein Monitoring in Interstitial Fluid.

Real-time continuous monitoring of proteins in-vivo holds great potential for personalized medical applications. Unfortunately, a prominent knowledge gap exists in the fundamental biology regarding protein transfer and correlation between interstitial fluid and blood. Additionally, technological sensing will require affinity-based platforms that cannot be robustly protected in-vivo and will therefore be challenged in sensitivity, longevity, and fouling over multi-day to week timelines. Here we use electrochemical aptamer sensors as a model system to discuss further research necessary to achieve continuous protein sensing.

Explaining the Decay of Nucleic Acid-Based Sensors under Continuous Voltammetric Interrogation.

Nucleic acid-based electrochemical sensors (NBEs) can support continuous and highly selective molecular monitoring in biological fluids, both in vitro and in vivo, via affinity-based interactions. Such interactions afford a sensing versatility that is not supported by strategies that depend on target-specific reactivity. Thus, NBEs have significantly expanded the scope of molecules that can be monitored continuously in biological systems. However, the technology is limited by the lability of the thiol-based monolayers employed for sensor fabrication. Seeking to understand the main drivers of monolayer degradation, we studied four possible mechanisms of NBE decay: (i) passive desorption of monolayer elements in undisturbed sensors, (ii) voltage-induced desorption under continuous voltammetric interrogation, (iii) competitive displacement by thiolated molecules naturally present in biofluids like serum, and (iv) protein binding. Our results indicate that voltage-induced desorption of monolayer elements is the main mechanism by which NBEs decay in phosphate-buffered saline. This degradation can be overcome by using a voltage window contained between -0.2 and 0.2 V vs Ag|AgCl, reported for the first time in this work, where electrochemical oxygen reduction and surface gold oxidation cannot occur. This result underscores the need for chemically stable redox reporters with more positive reduction potentials than the benchmark methylene blue and the ability to cycle thousands of times between redox states to support continuous sensing for long periods. Additionally, in biofluids, the rate of sensor decay is further accelerated by the presence of thiolated small molecules like cysteine and glutathione, which can competitively displace monolayer elements even in the absence of voltage-induced damage. We hope that this work will serve as a framework to inspire future development of novel sensor interfaces aiming to eliminate the mechanisms of signal decay in NBEs.

Reproductive Ecology of the Invasive Alien Shrub Pyracantha angustifolia in the Grassland Biome, South Africa.

Knowledge on reproductive traits of problematic invasive alien plants, such as the woody invasive shrub Pyracantha angustifolia of temperate Chinese origin, can help better manage invasive species. To determine factors contributing to its invasion, we investigated floral visitors and pollen loads, self-compatibility, seed set, seed rain, soil seed banks, and seed longevity in the soil. Generalist insects were recorded visiting flowers and all carried pollen loads of high purity (>70%). Floral visitor exclusion experiments showed that P. angustifolia can set seed (66%) without pollen vectors, although natural pollination resulted in higher fruit set (91%). Fruit count surveys and seed set showed an exponentially increased relationship between seed set and plant size with high natural seed yield (±2 million seeds m-2). Soil core samples revealed a high seed density of 46,400 ± (SE) 8934 m-2 under shrubs, decreasing with distance away from the shrub. Bowl traps stationed under trees and fences confirmed that seeds were efficiently dispersed by animals. Buried seeds survived for less than six months in the soil. Due to high seed production, self-compatibility augmented by generalist pollen vectors, and effective seed dispersal by local frugivores, it is difficult to manage the spread manually. Management of this species should focus on the short life span of seeds.

Closed-Loop tACS Delivered during Slow-Wave Sleep Reduces Retroactive Interference on a Paired-Associates Learning Task.

Previous studies have found a benefit of closed-loop transcranial alternating current stimulation (CL-tACS) matched to ongoing slow-wave oscillations (SWO) during sleep on memory consolidation for words in a paired associates task (PAT). Here, we examined the effects of CL-tACS in a retroactive interference PAT (ri-PAT) paradigm, where additional stimuli were presented to increase interference and reduce memory performance. Thirty-one participants were tested on a PAT before sleep, and CL-tACS was applied over the right and left DLPFC (F3 and F4) vs. mastoids for five cycles after detection of the onset of each discrete event of SWO during sleep. Participants were awoken the following morning, learned a new PAT list, and then were tested on the original list. There was a significant effect of stimulation condition (p = 0.04297; Cohen's d = 0.768), where verum stimulation resulted in reduced retroactive interference compared with sham and a significant interaction of encoding strength and stimulation condition (p = 0.03591). Planned simple effects testing within levels of encoding revealed a significant effect of stimulation only for low-encoders (p = 0.0066; Cohen's d = 1.075) but not high-encoders. We demonstrate here for the first time that CL-tACS during sleep can enhance the protective benefits on retroactive interference in participants who have lower encoding aptitude.

Transcranial direct current stimulation modulates working memory and prefrontal-insula connectivity after mild-moderate traumatic brain injury.

Background: Persistent posttraumatic symptoms (PPS) may manifest after a mild-moderate traumatic brain injury (mmTBI) even when standard brain imaging appears normal. Transcranial direct current stimulation (tDCS) represents a promising treatment that may ameliorate pathophysiological processes contributing to PPS. Objective/Hypothesis: We hypothesized that in a mmTBI population, active tDCS combined with training would result in greater improvement in executive functions and post-TBI cognitive symptoms and increased resting state connectivity of the stimulated region, i.e., left dorsolateral prefrontal cortex (DLPFC) compared to control tDCS. Methods: Thirty-four subjects with mmTBI underwent baseline assessments of demographics, symptoms, and cognitive function as well as resting state functional magnetic resonance imaging (rsfMRI) in a subset of patients (n = 24). Primary outcome measures included NIH EXAMINER composite scores, and the Neurobehavioral Symptom Inventory (NSI). All participants received 10 daily sessions of 30 min of executive function training coupled with active or control tDCS (2 mA, anode F3, cathode right deltoid). Imaging and assessments were re-obtained after the final training session, and assessments were repeated after 1 month. Mixed-models linear regression and repeated measures analyses of variance were calculated for main effects and interactions. Results: Both active and control groups demonstrated improvements in executive function (EXAMINER composite: p < 0.001) and posttraumatic symptoms (NSI cognitive: p = 0.01) from baseline to 1 month. Active anodal tDCS was associated with greater improvements in working memory reaction time compared to control (p = 0.007). Reaction time improvement correlated significantly with the degree of connectivity change between the right DLPFC and the left anterior insula (p = 0.02). Conclusion: Anodal tDCS improved reaction time on an online working memory task in a mmTBI population, and decreased connectivity between executive network and salience network nodes. These findings generate important hypotheses for the mechanism of recovery from PPS after mild-moderate TBI.

Digitalized transcranial electrical stimulation: A consensus statement.

OBJECTIVE: Although relatively costly and non-scalable, non-invasive neuromodulation interventions are treatment alternatives for neuropsychiatric disorders. The recent developments of highly-deployable transcranial electric stimulation (tES) systems, combined with mobile-Health technologies, could be incorporated in digital trials to overcome methodological barriers and increase equity of access. The study aims are to discuss the implementation of tES digital trials by performing a systematic scoping review and strategic process mapping, evaluate methodological aspects of tES digital trial designs, and provide Delphi-based recommendations for implementing digital trials using tES. METHODS: We convened 61 highly-productive specialists and contacted 8 tES companies to assess 71 issues related to tES digitalization readiness, and processes, barriers, advantages, and opportunities for implementing tES digital trials. Delphi-based recommendations (>60% agreement) were provided. RESULTS: The main strengths/opportunities of tES were: (i) non-pharmacological nature (92% of agreement), safety of these techniques (80%), affordability (88%), and potential scalability (78%). As for weaknesses/threats, we listed insufficient supervision (76%) and unclear regulatory status (69%). Many issues related to methodological biases did not reach consensus. Device appraisal showed moderate digitalization readiness, with high safety and potential for trial implementation, but low connectivity. CONCLUSIONS: Panelists recognized the potential of tES for scalability, generalizability, and leverage of digital trials processes; with no consensus about aspects regarding methodological biases. SIGNIFICANCE: We further propose and discuss a conceptual framework for exploiting shared aspects between mobile-Health tES technologies with digital trials methodology to drive future efforts for digitizing tES trials.

A review of functional brain differences predicting relapse in substance use disorder: Actionable targets for new methods of noninvasive brain stimulation.

Neuroimaging studies have identified a variety of brain regions whose activity predicts substance use (i.e., relapse) in patients with substance use disorder (SUD), suggesting that malfunctioning brain networks may exacerbate relapse. However, this knowledge has not yet led to a marked improvement in treatment outcomes. Noninvasive brain stimulation (NIBS) has shown some potential for treating SUDs, and a new generation of NIBS technologies offers the possibility of selectively altering activity in both superficial and deep brain structures implicated in SUDs. The goal of the current review was to identify deeper brain structures involved in relapse to SUD and give an account of innovative methods of NIBS that might be used to target them. Included studies measured fMRI in currently abstinent SUD patients and tracked treatment outcomes, and fMRI results were organized with the framework of the Addictions Neuroclinical Assessment (ANA). Four brain structures were consistently implicated: the anterior and posterior cingulate cortices, ventral striatum and insula. These four deeper brain structures may be appropriate future targets for the treatment of SUD using these innovative NIBS technologies.

Human Cyclophilin B Nuclease Activity Revealed via Nucleic Acid-Based Electrochemical Sensors.

Human cyclophilin B (CypB) is oversecreted by pancreatic cancer cells, making it a potential biomarker for early-stage disease diagnosis. Our group is motivated to develop aptamer-based assays to measure CypB levels in biofluids. However, human cyclophilins have been postulated to have collateral nuclease activity, which could impede the use of aptamers for CypB detection. To establish if CypB can hydrolyze electrode-bound nucleic acids, we used ultrasensitive electrochemical sensors to measure CypB's hydrolytic activity. Our sensors use ssDNA and dsDNA in the biologically predominant d-DNA form, and in the nuclease resistant l-DNA form. Challenging such sensors with CypB and control proteins, we unequivocally demonstrate that CypB can cleave nucleic acids. To our knowledge, this is the first study to use electrochemical biosensors to reveal the hydrolytic activity of a protein that is not known to be a nuclease. Future development of CypB bioassays will require the use of nuclease-resistant aptamer sequences.

Transcranial Direct Current Stimulation Provides no Additional Benefit to Improvements in Self-Reported Craving Following Mindfulness-Based Relapse Prevention.

Objectives: Mindfulness-Based Relapse Prevention (MBRP) and transcranial direct current stimulation (tDCS) have each demonstrated efficacy in improving outcomes in those with alcohol use disorder (AUD), however a recent study that combined MBRP with tDCS found tDCS provided no additional benefit to MBRP for AUD. Differences in treatment adherence between active versus sham tDCS groups may have contributed to this result. The current study examined whether treatment adherence interacted with tDCS condition in predicting post-treatment mindfulness and craving. Methods: This study was a secondary data analysis from a randomized sham-controlled trial comparing MBRP paired with tDCS. Linear regression analyses were conducted examining the interaction between tDCS condition and two measures of treatment adherence (i.e., number of groups attended, number of tDCS administrations) on post-treatment mindfulness and craving. Results: There was no effect of treatment adherence by tDCS condition in predicting mindfulness, however the interaction between treatment adherence and tDCS condition significantly predicted post-treatment craving. There was a significant negative association between treatment adherence and post-treatment craving in the sham group, but there was no association in the active tDCS group. Conclusions: MBRP coupled with sham stimulation led to significant reductions in self-reported craving when patients attended more sessions and received a greater number of sham tDCS administrations, while no relationship was observed between treatment adherence and craving among those who received active tDCS. This result provides tentative evidence that, rather than improve the effects of MBRP on craving, this active tDCS protocol provides no additional benefit to MBRP in reducing craving. Pre-registration: This study was registered with clinicaltrials.gov (NCT02861807).

Cytosolic Protein Delivery Using Modular Biotin-Streptavidin Assembly of Nanocomposites.

Current strategies for the delivery of proteins into cells face general challenges of endosomal entrapment and concomitant degradation of protein cargo. Efficient delivery directly to the cytosol overcomes this obstacle: we report here the use of biotin-streptavidin tethering to provide a modular approach to the generation of nanovectors capable of a cytosolic delivery of biotinylated proteins. This strategy uses streptavidin to organize biotinylated protein and biotinylated oligo(glutamate) peptide into modular complexes that are then electrostatically self-assembled with a cationic guanidinium-functionalized polymer. The resulting polymer-protein nanocomposites demonstrate efficient cytosolic delivery of six biotinylated protein cargos of varying size, charge, and quaternary structure. Retention of protein function was established through efficient cell killing via delivery of the chemotherapeutic enzyme granzyme A. This platform represents a versatile and modular approach to intracellular delivery through the noncovalent tethering of multiple components into a single delivery vector.

Revisiting Hemispheric Asymmetry in Mood Regulation: Implications for rTMS for Major Depressive Disorder.

Hemispheric differences in emotional processing have been observed for over half a century, leading to multiple theories classifying differing roles for the right and left hemisphere in emotional processing. Conventional acceptance of these theories has had lasting clinical implications for the treatment of mood disorders. The theory that the left hemisphere is broadly associated with positively valenced emotions, while the right hemisphere is broadly associated with negatively valenced emotions, drove the initial application of repetitive transcranial magnetic stimulation (rTMS) for the treatment of major depressive disorder (MDD). Subsequent rTMS research has led to improved response rates while adhering to the same initial paradigm of administering excitatory rTMS to the left prefrontal cortex (PFC) and inhibitory rTMS to the right PFC. However, accumulating evidence points to greater similarities in emotional regulation between the hemispheres than previously theorized, with potential implications for how rTMS for MDD may be delivered and optimized in the near future. This review will catalog the range of measurement modalities that have been used to explore and describe hemispheric differences, and highlight evidence that updates and advances knowledge of TMS targeting and parameter selection. Future directions for research are proposed that may advance precision medicine and improve efficacy of TMS for MDD.

High affinity protein surface binding through co-engineering of nanoparticles and proteins.

Control over supramolecular recognition between proteins and nanoparticles (NPs) is of fundamental importance in therapeutic applications and sensor development. Most NP-protein binding approaches use 'tags' such as biotin or His-tags to provide high affinity; protein surface recognition provides a versatile alternative strategy. Generating high affinity NP-protein interactions is challenging however, due to dielectric screening at physiological ionic strengths. We report here the co-engineering of nanoparticles and protein to provide high affinity binding. In this strategy, 'supercharged' proteins provide enhanced interfacial electrostatic interactions with complementarily charged nanoparticles, generating high affinity complexes. Significantly, the co-engineered protein-nanoparticle assemblies feature high binding affinity even at physiologically relevant ionic strength conditions. Computational studies identify both hydrophobic and electrostatic interactions as drivers for these high affinity NP-protein complexes.

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Greater male than female variability in regional brain structure across the lifespan.

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.