Biology subjects

Alnaes, D.

Publications and source records attributed to Alnaes, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Latent neural network representations of the brain reflect broad-scale adolescent phenotypic variation

The adolescent brain is attuned to social and environmental exploration, allowing behavioral adaptation as experiences shape lasting patterns of morphological organization. Using a convolutional neural network on longitudinal structural MRI data, we assess the early part of this developmental window and derive latent brain representations reflecting patterns of structural variability linked to personal, social, and neighborhood conditions in adolescence. These representations offer a flexible framework for mapping brain-trait associations in adolescence and beyond.

neuroscience↗

bioRxiv2026

A normative reference for large-scale human brain dynamics across the lifespan

Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed the study of static brain structure and connectivity, an equivalent normative framework for intrinsic brain dynamics has been lacking. This gap has limited our ability to characterize individual variability, development, ageing, and mental health conditions at scale. Here, we establish a population-level normative reference for large-scale human brain dynamics using resting-state fMRI data from more than 10,000 individuals spanning the lifespan and 91 scanning sites. We derive a compact set of recurring brain-state configurations that are reproducible across scanners and acquisition paradigms and that generalize to previously unseen cohorts. Anchoring these dynamic states to normative lifespan models enables the quantification of individual deviations relative to population reference distributions. We show that intrinsic brain dynamics undergo systematic reorganization across development and ageing, with pronounced changes before early adulthood and more gradual modulation thereafter. Applying this framework across multiple mental health conditions reveals disorder-specific and highly heterogeneous deviations in brain dynamics that are not captured by static neuroimaging measures. Robust transfer to independent cohorts and longitudinal analyses demonstrate that normative brain dynamics can be reliably assessed out of distribution. These results delineate a population-scale dynamic architecture of the human brain and extend normative brain mapping from static phenotypes to the temporal domain, providing a reference framework for studying brain function across the lifespan in health and disease.

neuroscience↗

bioRxiv2025

Gene dosage effects of 22q11.2 copy number variants on in-vivo measures of white matter axonal density and dispersion

22q11.2 deletion (22qDel) and duplication (22qDup) carriers have an increased risk of neurodevelopmental disorders and exhibit altered brain structure, including white matter microstructure. However, the underlying cellular architecture and age-related changes contributing to these white matter alterations remain poorly understood. Neurite orientation dispersion and density imaging (NODDI) was used on mixed cross-sectional and longitudinal data to examine group differences and age-related trajectories in measures of axonal density (i.e., intracellular volume fraction; ICVF), axonal orientation (orientation dispersion index; ODI) and free water diffusion (isotropic volume fraction; ISO) in 50 22qDel (n scans = 69, mean age = 20.7, age range = 7.4-51.1, 64.0% female) and 24 22qDup (n scans = 34, mean age = 21.6, age range = 8.3-49.4, 54.2% female) carriers, and 890 controls (n scans = 901, mean age = 21.9, age range = 7.8-51.1, 54.5% female). The results showed widespread gene dosage effects, with higher ICVF in 22qDel and lower ICVF in 22qDup compared to controls, and region-specific effects of the 22qDel and 22qDup on ODI and ISO measures. However, 22qDel and 22qDup carriers did not exhibit altered age-related trajectories relative to controls. Observed differences in ICVF suggest higher white matter axonal density in 22qDel and lower axonal density in 22qDup compared to controls. Conversely, differences in ODI are highly localized, indicating region-specific effects on axonal dispersion in white matter. We do not find evidence for altered developmental trajectories of axonal density or dispersion among 22q11.2 CNV carriers, suggesting stable disruptions to neurodevelopmental events before childhood.

neuroscience↗

bioRxiv2025

Thalamic Nuclei Volumes Across Psychiatric and Neurological Disorders: A Multi-Site Magnetic Resonance Imaging Study

The human thalamus is an integrative hub for multiple cortical and subcortical circuits involved in sensory processing and higher cognitive functions. Thalamic volume differences have been reported across multiple psychiatric and neurological disorders, but previous studies have typically relied on small samples, focused on one or a limited number of disorders, or investigated the thalamus as a whole without considering its functional subdivisions. In this multi-site study, we compared thalamic nuclei volumes across mild cognitive impairment (MCI), dementia (DEM), major depressive disorder, schizophrenia spectrum disorder (SCZ), clinical high risk for schizophrenia, bipolar spectrum disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder, Parkinsons disease, multiple sclerosis (MS), and healthy controls (N > 8 000). Using structural MRI, we segmented 25 bilateral thalamic nuclei, corresponding to six anatomical groups. Linear models revealed that anterior, medial and lateral regions of the thalamus were significantly smaller in several conditions, with largest effects observed for MCI, DEM, SCZ and MS. In contrast, the ventral and intralaminar groups were relatively normal. This pattern of effects largely corresponds to the canonical functional subdivision of the thalamus into higher-order and sensory regions. At the level of individual nuclei, the clinical conditions were associated with distinct patterns of alterations, while left and right lateral geniculate nuclei were implicated in six of the disorders, suggesting a possible relation with circadian and sleep disturbances. Together, the results highlight a role for the higher-order thalamus in common brain disorders and a differential involvement at the nuclei level, refining our understanding of thalamic pathology across common brain disorders.

neuroscience↗

bioRxiv2023

Genetic and brain similarity independently predict childhood anthropometrics and socioeconomic markers

Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ a novel approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance, with the assumption that similarity in cortical thickness may in part be driven by similarity in genetic variants. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, significant associations between cortical thickness similarity and traits were limited to anthropometrics such as height (r2 = 0.11, SE = 0.01), weight (r2 = 0.12, SE = 0.01), and birth weight (r2 = 0.19, SE = 0.01), as well as markers of socioeconomic status such as local area deprivation (r2 = 0.06, SE = 0.01). Analyses of the contribution from common genetic variants to traits revealed contributions across included outcomes, albeit somewhat lower than previous reports, possibly due to the young age of the sample. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as socioeconomic factors and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample. The approach provides a promising framework for analyses of neuroimaging genetics cohorts, which can be further expanded by including imaging derived phenotypes beyond cortical thickness.

neuroscience↗

bioRxiv2021

Mind the gap: performance metric evaluation in brain-age prediction

Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age based on neuroimaging data in two population-based datasets, and assessed the effects of age range, sample size, and age-bias correction on the model performance metrics r, R2, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy - also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics in general indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.

neuroscience↗

bioRxiv2020

Brain disconnectivity mapping of post-stroke fatigue

Stroke patients commonly suffer from post stroke fatigue (PSF). Despite a general consensus that brain perturbations constitute a precipitating event in the multifactorial etiology of PSF, the specific predictive value of conventional lesion characteristics such as size and localization remain unclear. The current study represents a novel approach to assess the neural correlates of PSF in chronic stroke patients. While previous research has focused primarily on lesion location or size, with mixed or inconclusive results, we targeted the extended structural network implicated by the lesion, and evaluated the added explanatory value of a disconnectivity approach with regards to the brain correlates of PSF. To this end, we estimated individual brain disconnectome maps in 84 stroke survivors in the chronic phase ([≥] 3 months post stroke) using information about lesion location and normative white matter pathways obtained from 170 healthy individuals. PSF was measured by the Fatigue Severity Scale (FSS). Voxel wise analyses using non-parametric permutation-based inference were conducted on disconnectome maps to estimate regional effects of disconnectivity. Associations between PSF and global disconnectivity and clinical lesion characteristics were tested by linear models, and we estimated Bayes factor to quantify the evidence for the null and alternative hypotheses, respectively. The results revealed no significant associations between PSF and disconnectome measures or lesion characteristics, with moderate evidence in favor of the null hypothesis. These results suggest that symptoms of post-stroke fatigue are not simply explained by lesion characteristics or brain disconnectome measures in stroke patients in a chronic phase, and are discussed in light of methodological considerations.

neuroscience↗

bioRxiv2020

Cortical Thickness Trajectories across the Lifespan: Data from 17,075 healthy individuals aged 3-90 years

Delineating age-related cortical trajectories in healthy individuals is critical given the association of cortical thickness with cognition and behaviour. Previous research has shown that deriving robust estimates of age-related brain morphometric changes requires large-scale studies. In response, we conducted a large-scale analysis of cortical thickness in 17,075 individuals aged 3-90 years by pooling data through the Lifespan Working group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium. We used fractional polynomial (FP) regression to characterize age-related trajectories in cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma (LMS) method. Inter-individual variability was estimated using meta-analysis and one-way analysis of variance. Overall, cortical thickness peaked in childhood and had a steep decrease during the first 2-3 decades of life; thereafter, it showed a gradual monotonic decrease which was steeper in men than in women particularly in middle-life. Notable exceptions to this general pattern were entorhinal, temporopolar and anterior cingulate cortices. Inter-individual 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 reconcile uncertainties about age-related trajectories of cortical thickness; the centile values provide estimates of normative variance in cortical thickness, and may assist in detecting abnormal deviations in cortical thickness, and associated behavioural, cognitive and clinical outcomes.

neuroscience↗

bioRxiv2020

Subcortical Volume Trajectories 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 capitalised on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine the age-related morphometric trajectories 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 early in life; the volume of the basal ganglia showed a gradual monotonic decline thereafter while the volumes of the thalamus, amygdala and the hippocampus remained largely stable (with some degree of decline in thalamus) until the sixth decade of life followed by a steep decline thereafter. The lateral ventricles showed a trajectory of continuous enlargement throughout the lifespan. Significant age-related increase in inter-individual variability was found for the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to derive risk predictions for the early identification of diverse clinical phenotypes.

neuroscience↗

bioRxiv2020

Dynamics of Brain Structure and its Genetic Architecture over the Lifespan

Human brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. Here, we identified common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal MRI data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene-set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and ageing.

genetics↗

bioRxiv2020

Population-based body-brain mapping links brain morphology and body composition

BackgroundUnderstanding complex body-brain processes, and putative interplay between adipose tissue and brain health, is of vital importance for brain and somatic disease prevention in the general population. We studied the link between body composition and brain structure through large-scale investigation in a healthy population without secondary disease effects. MethodsWe processed brain magnetic resonance imaging (MRI) data and extracted measures of brain morphometry from 19,330 healthy UK Biobank participants, of which a subset (n=2,703) had body MRI. We investigated associations between brain structure and (i) anthropometric body composition measures, and (ii) regional/specific body MRI measures of abdominal fat and muscle tissue. FindingsWe identified highly significant body-brain associations (p-values[≤]0{middle dot}0002). Anthropometric measures showed negative, nonlinear, associations with cerebellar/cortical gray matter, and brain stem structures, negative associations with white matter, and positive associations with ventricular volumes. Subcortical structures exhibited mixed effect directionality, with strongest positive association for accumbens. Among body MRI measures, liver fat was negatively associated with thinner/lower cortical gray matter thickness/volume, and thigh muscle volume positively associated with accumbens volume. InterpretationWe demonstrate significant body-brain associations, and map individual differences in body composition to brain morphology in healthy individuals. Common measures of body composition correlated negatively with cerebellar and cortical structures and positively with the accumbens, a dopamine rich structure involved in reward processing. These findings of a relationship between brain anatomy and body composition provide new insight into body-brain processes and suggest shared mechanisms of cardiometabolic risk factors and brain disorders. This may form the foundation for a new type of prevention studies, and provides a framework for studies of underlying mechanisms related to unhealthy lifestyle and obesity, with implications for public health and prevention. FundingThe Research Council of Norway, South-Eastern Norway Regional Health Authority, European Unions Horizon 2020 Research and Innovation Programme & European Research Council. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrior studies have indicated an association between brain structure and both obesity and fitness levels - of opposing directionality. Despite this, normal body-brain association patterns in healthy individuals have not been established, and the causal mechanisms are unclear. To enhance our understanding and establish the link between the body and the brain, we saw the need for large-scale investigations in healthy populations. For the study, we searched the PubMed database from March 12th, 2019, through February 25th, 2020, for scientific literature related to adipose tissue, body composition, brain morphology, and body and brain MRI. Search terms included: body fat, adipose tissue, subcutaneous/visceral adipose tissue, liver fat, body composition, anthropometric measures, body mass index, waist circumference, waist-to-hip ratio, adiposity, obesity, metabolic syndrome, cardiovascular, cardiometabolic, disease/disorder, muscle volume, fitness, brain structure, brain morphology, brain MRI, and body MRI. We based the scientific foundation on review studies, meta-analyses, and other larger studies, but generally excluded smaller studies, and thereby lowering the risk of evidential bias such as winners curse, although this does not eliminate the risk of publication bias. Added value of this studyIn the largest study, to date, including 19,330 healthy participants without secondary disease effects, we provide insight into normal body-brain processes by identifying body-brain associations that map normally varying body composition to brain morphology. Implications of all the available evidenceWe identified body-brain associations that give insight into normal physiological body-brain processes in healthy individuals, providing a reference point for studies of underlying mechanisms related to unhealthy lifestyle, obesity, and disorders of the body and the brain. Whereas the directionality and causal chain is unknown, these findings have potential implications for public health and disease prevention.

neuroscience↗

bioRxiv2020

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.

neuroscience↗

bioRxiv2019

Testing relationships between multimodal modes of brain structural variation and age, sex and polygenic scores for neuroticism in children and adolescents

Human brain development involves spatially and temporally heterogeneous changes, detectable across a wide range of magnetic resonance imaging (MRI) measures. Investigating the interplay between multimodal MRI and polygenic scores (PGS) for personality traits associated with mental disorders in youth may provide new knowledge about typical and atypical neurodevelopment. We derived independent components across cortical thickness, cortical surface area, and gray/white matter contrast (GWC) (n=2596, 3-23 years), and tested for associations between these components and age, sex and-, in a subsample (n=878), PGS for neuroticism. Age was negatively associated with a single-modality component reflecting higher global GWC, and additionally with components capturing common variance between global thickness and GWC, and several multimodal regional patterns. Sex differences were found for components primarily capturing global and regional surface area (boys>girls), but also regional cortical thickness. For PGS for neuroticism, we found weak and bidirectional associations with a component reflecting right prefrontal surface area. These results indicate that multimodal fusion is sensitive to age and sex differences in brain structure in youth, but only weakly to polygenic load for neuroticism.

neuroscience↗

bioRxiv2019

Patterns of socio-cognitive stratification and perinatal risk in the child brain

The expanding behavioral repertoire of the developing brain during childhood and adolescence is shaped by complex brain-environment interactions and flavored by unique life experiences. The transition into young adulthood offer opportunities for adaptation and growth, but also increased susceptibility to environmental perturbations, such as the characteristics of social relationships, family environment, quality of schools and activities, financial security, urbanization and pollution, drugs, cultural practices, and values, that all act in concert with our genetic architecture and biology. Our multivariate brain-behavior mapping in 7,577 children aged 9-11 years across 585 brain imaging phenotypes, and 617 cognitive, behavioral, psychosocial and socioeconomic measures revealed three population modes of brain co-variation, which were robust as assessed by cross-validation and permutation testing, taking into account siblings and twins, identified using genetic data. The first mode revealed traces of perinatal complications, including pre-term and twin-birth, eclampsia and toxemia, shorter period of breast feeding and lower cognitive scores, with higher cortical thickness and lower cortical areas and volumes. The second mode reflected a pattern of socio-cognitive stratification, linking lower cognitive ability and socioeconomic status to lower cortical thickness, area and volumes. The third mote captured a pattern related to urbanicity, with particulate matter pollution (PM25) inversely related to home value, walkability and population density, associated with diffusion properties of white matter tracts. These results underscore the importance of a multidimensional and interdisciplinary understanding, integrating social, psychological and biological sciences, to map the constituents of healthy development and to identify factors that may precede maladjustment and mental illness.

neuroscience↗

bioRxiv2019

The genetic architecture of human brainstem structures and their involvement in common brain disorders

Brainstem regions support critical bodily functions, yet their genetic architectures and involvement in brain disorders remain understudied. Here, we examined volumes of brainstem structures using magnetic resonance imaging in 43,353 individuals. In 27,034 genotyped healthy participants, we identified 16 genetic loci associated with whole brainstem volume and 10, 23, 3, and 9 loci associated with volumes of the midbrain, pons, superior cerebellar peduncle, and medulla oblongata, respectively. These loci were mapped to 305 genes, including genes linked to brainstem development and common brain disorders. We detected genetic overlap between the brainstem volumes and eight psychiatric and neurological disorders. Using imaging data from 16,319 additional individuals, we observed differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinsons disease. Together, our results provide new insights into the genetic underpinnings of brainstem structures and support their involvement in common brain disorders.

neuroscience↗

bioRxiv2019

Differences in directed functional brain connectivity related to age and sex

ObjectiveFunctional interconnections between brain regions define the connectome which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the brain connectome and age, sex, intellectual abilities and mental health. MethodsWe applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. ResultsOur analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. DiscussionOur findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.

neuroscience↗

bioRxiv2019

Clinical utility of brain age prediction in stroke patients: A longitudinal study

Cognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase.\n\nUsing brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke (>6 months since hospital admission, NIHSS<7 at hospital discharge) underwent 3-weeks CCT and MRI before and after the intervention. In addition, patients were randomized to one of two groups receiving either active or sham transcranial direct current stimulation (tDCS). We tested for main effects of brain age gap (estimated age - chronological age) on cognitive performance, and associations between brain age gap and task improvement. Finally, we tested if longitudinal changes in brain age gap during the intervention were sensitive to treatment response. Briefly, our results suggest that longitudinal brain age prediction based on automated brain morphometry is feasible and reliable in stroke patients. However, no significant association between brain age and both performance and response to cognitive training were found.

neuroscience↗

bioRxiv2019

Maturation of cortical microstructure and cognitive development in childhood and adolescence: a T1w/T2w ratio MRI study

The restructuring and optimization of the cerebral cortex from early childhood and through adolescence is an essential feature of human brain development, underlying immense cognitive improvements. Beyond established morphometric cortical assessments, the T1w/T2w ratio quantifies partly separate biological processes, and might inform models of typical neurocognitive development and developmental psychopathology. In the present study, we computed vertex-wise T1w/T2w ratio across the cortical surface in 621 youths (3-21 years) sampled from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and tested for associations with individual differences in age, sex, and both general and specific cognitive abilities. The results showed a near global linear age-related increase in T1w/T2w ratio across the brain surface, with a general posterior to anterior increasing gradient in association strength. Moreover, results indicated that boys in late adolescence had regionally higher T1w/T2w ratio as compared to girls. Across individuals, T1w/T2w ratio was negatively associated with general and several specific cognitive abilities mainly within anterior cortical regions. Our study indicates age-related differences in T1w/T2w ratio throughout childhood, adolescence and young adulthood, in line with the known protracted myelination of the cortex. Moreover, the study supports T1w/T2w ratio as a promising surrogate measure of individual differences in intracortical brain structure in neurodevelopment.

neuroscience↗