The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined

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.

Long-term outcomes of early exposure to repeated general anaesthesia in children with cystic fibrosis (CF-GAIN): a multicentre, open-label, randomised controlled phase 4 trial

BACKGROUND

Long-term effects of early, recurrent human exposure to general anaesthesia remain unknown. The Australasian Cystic Fibrosis Bronchoalveolar Lavage (ACFBAL) trial provided an opportunity to examine this issue in children randomly assigned in infancy to either repeated bronchoalveolar-lavage (BAL)-directed therapy with general anaesthesia or standard care with no planned lavages up to 5 years of age when all children received BAL-directed therapy under general anaesthesia.

METHODS

This multicentre, randomised, open-label phase 4 trial (CF-GAIN) used the original ACFBAL trial randomisation at 3·6 months (SD 1·6) to BAL-directed therapy or standard-care groups to assess the impact of general anaesthesia exposures over early childhood. Children who completed the ACFBAL trial, with a mean age of 5·1 (SD 0·18) years, received standardised neurobehavioural and health-related-quality-of-life assessment and brain MRI scans between Oct 8, 2013, and June 30, 2017, at a mean age of 12·8 (SD 1·7) years at three hospitals in Australia and one hospital in New Zealand. The primary outcome was a composite score of performance on a standardised, computer-based assessment of child attention, processing speed, and response inhibition skills (Conners Continuous Performance test, second edition). Secondary outcomes included intellectual function, other neurobehavioural measures, and brain imaging as an exploratory outcome. The trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN 12613000057785) and is completed.

FINDINGS

At 2 years, the BAL-directed therapy group (n=52) and standard-care group (n=45) had a median of 2·0 (IQR 1·0-3·0) and 0·0 (0·0-0·0) exposures, respectively. At completion of the ACFBAL trial, the BAL-directed therapy group had a median of 6·0 (4·0-9·5) exposures and the standard-care group 2·0 (1·0-4·0) exposures. At CF-GAIN completion, the BAL-directed therapy group had a median of 10·0 (IQR 6·5-14·5) exposures and the standard-care group 4·0 (3·0-7·0) exposures. Cumulative general anaesthesia exposure time was not prospectively collected but, for those with complete cumulative exposure time data to the end of the ACFBAL trial, the median cumulative exposure time for the BAL-directed therapy group (n=29) was 180 (IQR 140-285) min and for the standard-care group (n=32) was 48 (30-122) min. The mean Conners Continuous Performance test, second edition composite score was 51 (SD 8·1) in BAL-directed therapy group and 53 (8·8) in the standard-care group; difference -1·7 (95% CI -5·2 to 1·7; p=0·32) with similar performance on other neurobehavioural measures, including measures of executive function, intellectual quotient scores, and brain imaging.

INTERPRETATION

Our findings suggest that repeated general anaesthesia exposure in young children with cystic fibrosis is not related to functional impairment in attention, intellectual quotient, executive function, or brain structure compared with a group with fewer and shorter cumulative anaesthesia durations.

FUNDING

National Health and Medical Research Council Australia, Queensland Government Health Service and Clinical Innovation Fellowship, and the Children's Hospital Foundation Queensland.

The 'middle-aging' brain

Middle age has historically been an understudied period of life compared to older age, when cognitive and brain health decline are most pronounced, but the scope for intervention may be limited. However, recent research suggests that middle age could mark a shift in brain aging. We review emerging evidence on multiple levels of analysis indicating that midlife is a period defined by unique central and peripheral processes that shape future cognitive trajectories and brain health. Informed by recent developments in aging research and lifespan studies in humans and animal models, we highlight the utility of modeling non-linear changes in study samples with wide subject age ranges to distinguish life stage-specific processes from those acting linearly throughout the lifespan.

The future of neuroscience in developmental psychopathology

Developmental psychopathology started as an intersection of fields and is now a field itself. As we contemplate the future of this field, we consider the ways in which a newer, interdisciplinary field - human developmental neuroscience - can inform, and be informed by, developmental psychopathology. To do so, we outline principles of developmental psychopathology and how they are and/or can be implemented in developmental neuroscience. In turn, we highlight how the collaboration between these fields can lead to richer models and more impactful translation. In doing so, we describe the ways in which models from developmental psychopathology can enrich developmental neuroscience and future directions for developmental psychopathology.

Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study

Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.

Adolescent substance use initiation and long-term neurobiological outcomes: insights, challenges and opportunities

The increased frequency of risk taking behavior combined with marked neuromaturation has positioned adolescence as a focal point of research into the neural causes and consequences of substance use. However, little work has provided a summary of the links between adolescent initiated substance use and longer-term brain outcomes. Here we review studies exploring the long-term effects of adolescent-initiated substance use with structural and microstructural neuroimaging. A quarter of all studies reviewed conducted repeated neuroimaging assessments. Long-term alcohol use, as well as tobacco use were consistently associated with smaller frontal cortices and altered white matter microstructure. This association was mostly observed in the ACC, insula and subcortical regions in alcohol users, and for the OFC in tobacco users. Long-term cannabis use was mostly related to altered frontal cortices and hippocampal volumes. Interestingly, cannabis users scanned more years after use initiation tended to show smaller measures of these regions, whereas those with fewer years since initiation showed larger measures. Long-term stimulant use tended to show a similar trend as cannabis in terms of years since initiation in measures of the putamen, insula and frontal cortex. Long-term opioid use was mostly associated with smaller subcortical and insular volumes. Of note, null findings were reported in all substance use categories, most often in cannabis use studies. In the context of the large variety in study designs, substance use assessment, methods, and sample characteristics, we provide recommendations on how to interpret these findings, and considerations for future studies.

Neural abnormalities of reward processing in adolescents with bipolar disorders: An ERP study

Adolescent onset is common in bipolar disorders (BDs) and is associated with a worse illness course in adulthood. A model of BDs suggests that a dysregulated behavioral approach system (BAS), a neural system that mobilizes reward-seeking behavior, is at the root of BDs. Normative adolescence is often accompanied by dynamic changes to neural structures underlying the BAS and related cognitive processes. It is possible that adolescent-onset BDs is associated with abnormal BAS neurodevelopment. Consistently, the present study is the first to compare specific BAS-relevant anticipatory and consummatory reward processes as indexed by event-related potentials (ERPs) in adolescents with BDs and typically developing peers. Using a sample of 43 adolescents with BDs and 56 without psychopathology, we analyzed N1 and P3 responses to anticipatory cues and feedback-related negativity (FRN) and P3 responses to feedback stimuli during a monetary incentive delay (MID) task. Hierarchical linear models examined relationships between ERP amplitudes and diagnostic group, MID condition, sex, and age. During anticipation phase, adolescent boys with BDs exhibited significantly larger N1 amplitudes in loss than even or gain trials. During feedback phase, compared to their healthy peers, adolescents with BDs had smaller FRN amplitudes across all conditions. Additional effects involving age, sex and trial type were observed. The findings indicate subtle, non-ubiquitous BAS-relevant neural abnormalities involving early attentional processes during reward anticipation and reward learning following feedback in adolescents with BDs. Adolescents with BDs did not show overall hypersensitive neural responses to monetary reward anticipation or feedback observed in adults with BDs.

Brain development mediates the relationship between self-reported poor parental monitoring and adolescent anxiety

Adolescence is the peak period for the onset of generalized anxiety disorder (GAD). Brain networks of cognitive and affective control in adolescents are not well developed when their exposure to external stimuli suddenly increases.Reasonable parental monitoring is especially important during this period.To examine the role of parental monitoring in the development of functional brain networks of GAD, we conducted a cross-validation-based predictive study based on the functional brain networks of 192 participants. We found that a set of functional brain networks, especially the default mode network and its connectivity with the frontoparietal network, could predict the ages of adolescents, which was replicated in three independent samples.Importantly, the difference between predicted age and chronological age significantly mediated the relationship between parental monitoring and anxiety levels. These findings suggest that inadequate parental monitoring plays a crucial role in the delayed development of specific brain networks associated with GAD in adolescents. Our work highlights the important role of parental monitoring in adolescent development.

Linking negative affect, personality and social conditions to structural brain development during the transition from late adolescent to young adulthood

BACKGROUND

The transition from late adolescence to early adulthood is a period that experiences a surge of life changes and brain reorganization caused by internal and external factors, including negative affect, personality, and social conditions.

METHODS

Non-imaging phenotype and structural brain variables were available on 497 healthy participants (279 females and 218 males) between 17 and 22 years old. We used sparse canonical correlation analysis (sCCA) on the high-dimensional and longitudinal data to extract modes with maximum covariation between structural brain changes and negative affect, personality, and social conditions.

RESULTS

Separate sCCAs for cortical volume, cortical thickness, cortical surface area and subcortical volume confirmed that each imaging phenotype was correlated with non-imaging features (sCCA |r| range: 0.21-0.38, all p_FDR < 0.01). Bilateral superior frontal, left caudal anterior cingulate and bilateral caudate had the highest canonical cross-loadings (|ρ| = 0.15-0.32). In longitudinal data analysis, scan-interval, negative affect, and enthusiasm had the highest association with structural brain changes (|ρ| = 0.07-0.38); at baseline, intellect and politeness were associated with individual variability in the structural brain (|ρ| = 0.10-0.25).

LIMITATIONS

The present study used non-imaging variables only at baseline, making it impossible to explore the relationship between changing behavior and structural brain development.

CONCLUSIONS

Individual structural brain changes are associated with multiple factors. In addition to time-dependent variables, we find that negative affect, enthusiasm and social support play a numerically weak but significant role in structural brain development during the transition from late adolescence to young adulthood.

A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework

Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.

Linking brain maturation and puberty during early adolescence using longitudinal brain age prediction in the ABCD cohort

The temporal characteristics of adolescent neurodevelopment are shaped by a complex interplay of genetic, biological, and environmental factors. Using a large longitudinal dataset of children aged 9-13 from the Adolescent Brain Cognitive Development (ABCD) study we tested the associations between pubertal status and brain maturation. Brain maturation was assessed using brain age prediction based on convolutional neural networks and minimally processed T1-weighted structural MRI data. Brain age prediction provided highly accurate and reliable estimates of individual age, with an overall mean absolute error of 0.7 and 1.4 years at the two timepoints respectively, and an intraclass correlation of 0.65. Linear mixed effects (LME) models accounting for age and sex showed that on average, a one unit increase in pubertal maturational level was associated with a 2.22 months higher brain age across time points (β = 0.10, p < .001). Moreover, annualized change in pubertal development was weakly related to the rate of change in brain age (β = .047, p = 0.04). These results demonstrate a link between sexual development and brain maturation in early adolescence, and provides a basis for further investigations of the complex sociobiological impacts of puberty on life outcomes.

The Genetically Informed Neurobiology of Addiction (GINA) model

Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

Functional connectivity directionality between large-scale resting-state networks across typical and non-typical trajectories in children and adolescence

Mental disorders often emerge during adolescence and have been associated with age-related differences in connection strengths of brain networks (static functional connectivity), manifesting in non-typical trajectories of brain development. However, little is known about the direction of information flow (directed functional connectivity) in this period of functional brain progression. We employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 1143 participants, aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on estimates of direction flow. Across participants, we show a pattern of reciprocal information flow between visual-medial and visual-lateral connections, in line with findings in adults. Investigating directed connectivity patterns between networks, we observed a positive association for age and direction flow from the cerebellar to the auditory network, and for the auditory to the sensorimotor network. Further, higher cognitive abilities were linked to lower information flow from the visual occipital to the default mode network. Additionally, examining the degree networks overall send and receive information to each other, we identified age-related effects implicating the right frontoparietal and sensorimotor network. However, we did not find any associations with psychopathology. Our results suggest that the directed functional connectivity of large-scale resting-state brain networks is sensitive to age and cognition during adolescence, warranting further studies that may explore directed relationships at rest and trajectories in more fine-grained network parcellations and in different populations.

Systematic evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth

Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the ML approach in estimating brain-age in youth is important because age-related brain changes in this age-group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5-22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9-10 years and another comprising 594 individuals aged 5-21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, number of extreme outliers, and sample size. Tree-based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain-age in youth.

Sex differences in predictors and regional patterns of brain age gap estimates

The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.

Brain Morphology Predictors of Alcohol, Tobacco, and Cannabis Use in Adolescence: A Systematic Review

In the last decade, extensive research has emerged on the predictive value of brain morphology for substance use initiation and related problems during adolescence. This systematic review provides an overview of longitudinal studies on pre-existing brain variations and later initiation of alcohol, tobacco, and cannabis use (N = 18). Adolescent structural neuroimaging studies that started before substance use initiation suggest that a smaller anterior cingulate cortex (ACC) volume, thicker or smaller superior frontal gyrus, and larger nucleus accumbens (NAcc) volume are associated with future alcohol use. Also, both smaller and larger orbitofrontal cortex (OFC) volumes were associated with future cannabis and combined alcohol/cannabis use. Smaller amygdala volumes were related to future daily tobacco smoking. These findings could point to specific vulnerabilities for adolescent substance use, as these brain areas are involved in cognitive control (ACC), reward (NAcc), motivation (OFC), and emotional memory (amygdala). However, the reported findings were inconsistent in directionality and laterality, and the largest study on alcohol use predictors reported null findings. Therefore, large population-based longitudinal studies should investigate the robustness and mechanisms of these associations. We suggested future research directions regarding sample selection, timing of baseline and follow-up measurements, and a harmonization approach of study methods.

The Impact of Genes and Environment on Brain Ageing in Males Aged 51 to 72 Years

Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer's Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51-72 years. Biometrical genetic analyses "twin models" revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences.

Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II

From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual's biological and chronological age of the brain ("brain age gap") can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.

Adverse childhood experiences and fronto-subcortical structures in the developing brain

The impact of adverse childhood experiences (ACEs) differs between individuals and depends on the type and timing of the ACE. The aim of this study was to assess the relation between various recently occurred ACEs and morphology in the developing brain of children between 8 and 11 years of age. We measured subcortical volumes, cortical thickness, cortical surface area and fractional anisotropy in regions of interest in brain scans acquired in 1,184 children from the YOUth cohort. ACEs were based on parent-reports of recent experiences and included: financial problems; parental mental health problems; physical health problems in the family; substance abuse in the family; trouble with police, justice or child protective services; change in household composition; change in housing; bereavement; divorce or conflict in the family; exposure to violence in the family and bullying victimization. We ran separate linear models for each ACE and each brain measure. Results were adjusted for the false discovery rate across regions of interest. ACEs were reported for 83% of children in the past year. Children were on average exposed to two ACEs. Substance abuse in the household was associated with larger cortical surface area in the left superior frontal gyrus, t(781) = 3.724, p _FDR = 0.0077, right superior frontal gyrus, t(781) = 3.409, p _FDR = 0.0110, left pars triangularis, t(781) = 3.614, p _FDR = 0.0077, left rostral middle frontal gyrus, t(781) = 3.163, p _FDR = 0.0195 and right caudal anterior cingulate gyrus, t(781) = 2.918, p _FDR = 0.0348. Household exposure to violence (was associated with lower fractional anisotropy in the left and right cingulum bundle hippocampus region t(697) = -3.154, p _FDR = 0.0101 and t(697) = -3.401, p _FDR = 0.0085, respectively. Lower household incomes were more prevalent when parents reported exposure to violence and the mean parental education in years was lower when parents reported substance abuse in the family. No other significant associations with brain structures were found. Longer intervals between adversity and brain measurements and longitudinal measurements may reveal whether more evidence for the impact of ACEs on brain development will emerge later in life.

Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms

OBJECTIVE

Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health.

METHODS

We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n = 1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance.

RESULTS

Our model was able to predict age in the independent test samples, with a model performance of r = 0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN.

DISCUSSION

Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.

Investigating brain structural maturation in children and adolescents born very preterm using the brain age framework

Very preterm (VP) birth is associated with an increased risk for later neurodevelopmental and behavioural challenges. Although the neurobiological underpinnings of such challenges continue to be explored, previous studies have reported brain volume and morphology alterations in children and adolescents born VP compared with full-term (FT)-born controls. How these alterations relate to the trajectory of brain maturation, with potential implications for later brain ageing, remains unclear. In this longitudinal study, we investigate the relationship between VP birth and brain development during childhood and adolescence. We construct a normative 'brain age' model to predict age over childhood and adolescence based on measures of brain cortical and subcortical volumes and cortical morphology from structural MRI of a dataset of typically developing children aged 3-21 years (n = 768). Using this model, we examined deviations from normative brain development in a separate dataset of children and adolescents born VP (<30 weeks' gestation) at two timepoints (ages 7 and 13 years) compared with FT-born controls (120 VP and 29 FT children at age 7 years; 140 VP and 47 FT children at age 13 years). Brain age delta (brain-predicted age minus chronological age) was, on average, higher in the VP group at both timepoints compared with controls, however this difference had a small to medium effect size and was not statistically significant. Variance in brain age delta was higher in the VP group compared with controls; this difference was significant at the 13-year timepoint. Within the VP group, there was little evidence of associations between brain age delta and perinatal risk factors or cognitive and motor outcomes. Under the brain age framework, our results may suggest that children and adolescents born VP have similar brain structural developmental trajectories to term-born peers between 7 and 13 years of age.

Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change

Brain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

Scientists who study the brain and aging are keen to find an effective way to measure brain health, which could help identify people at risk for dementia or memory problems. One popular marker is ‘brain age’. This measurement uses a brain scan to estimate a person’s chronological age, then compares the estimated brain age to the person’s actual age to determine whether their brain is aging faster or slower than expected for their age.

However, since brain age relies on one brain scan taken at one point in time, it is not clear whether it really measures brain aging or if it might capture brain differences that have been present throughout the individual’s life. Studies comparing individual brain scans over several years would be necessary to know for sure.

Now, Vidal-Piñeiro et al. show that the brain-age measurement does not reflect faster brain aging. In the experiments, the researchers compared repeated brain scans of thousands of individuals over 40 years of age. The experiments showed that deviations from normative brain age detected in a single scan reflected early life differences more than changes in the brain over time. For example, people with older-looking brains were more likely to have had a low birth weight or to have a combination of genes associated with having an older looking brain.

Vidal-Piñeiro et al. show that brain age mostly reflects a pre-existing brain condition rather than brain aging. The experiments also suggest that genetics and early brain development likely have a strong impact on brain health throughout life. Future studies trying to test or develop brain-aging measurements should use serial measurements to track brain changes over time.

Novel Volumetric and Surface-Based Magnetic Resonance Indices of the Aging Brain - Does Male and Female Brain Age in the Same Way?

Introduction: Novel post-processing methods allow not only for assessment of brain volumetry or cortical thickness based on magnetic resonance imaging (MRI) but also for more detailed analysis of cortical shape and complexity using parameters such as sulcal depth, gyrification index, or fractal dimension. The aim of this study was to analyze changes in brain volumetry and other cortical indices during aging in men and women.

Material and Methods: Material consisted of 697 healthy volunteers (aged 38–80 years; M/F, 264/443) who underwent brain MRI using a 1.5-T scanner. Voxel-based volumetry of total gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) was performed followed by assessment of cortical parameters [cortical thickness (CT), sulcal depth (SD), gyrification index (GI), and fractal dimension (FD)] in 150 atlas locations using surface-based morphometry with a region-based approach. All parameters were compared among seven age groups (grouped every 5 years) separately for men and women. Additionally, percentile curves for men and women were provided for total volumes of GM, WM, and CSF.

Results: In men and women, a decrease in GM and WM volumes and an increase in CSF volume seem to progress slowly since the age of 45. In men, significant GM and WM loss as well as CSF increase start above 55 years of age, while in women, significant GM loss starts above 50 and significant WM loss as well as CSF increase above 60. CT was found to significantly decrease with aging in 39% of locations in women and in 36% of locations in men, SD was found to increase in 13.5% of locations in women and in 1.3% of locations in men, GI was decreased in 3.4% of locations in women and in 2.0% of locations in men, and FD was changed in 2.7% of locations in women compared to 2.0% in men.

Conclusions: Male and female brains start aging at the similar age of 45. Compared to men, in women, the cortex is affected earlier and in the more complex pattern regarding not only cortical loss but also other alterations within the cortical shape, with relatively longer sparing of WM volume.