It is established that a substantial portion of functional variation through the lifespan, including in older age, is of neurodevelopmental origin (Kovacs et al., 2014; Fjell et al., 2015; Bale, 2015; Muller et al., 2014). Evidence converges on early-life factors being important for normal individual differences in brain, mental health, and cognition across the lifespan (Walhovd et al., 2016; Walhovd et al., 2012; Vidal-Pineiro et al., 2021; Dooley et al., 2022; Elliott et al., 2021), as well as risk of psychiatric (Anderson et al., 2021) and neurodegenerative disease in older age (Tuovinen et al., 2013). Obtaining reliable indicators of individual early-life factors is a major challenge. In this regard, birth weight (BW) stands out as a solid available measure. BW reflects fetal and maternal genetic, but also other in utero environmental factors affecting fetal growth (Beaumont et al., 2018; Li et al., 2021; Willis et al., 2022), including brain growth (Walhovd et al., 2012; Raznahan et al., 2012; Casey et al., 2017; Halevy et al., 2021). By now, a series of studies have established that BW relates positively to mental health, cognitive function, and brain characteristics, including neuroanatomical volumes and cortical surface area as measured in different age groups (Muller et al., 2014; Walhovd et al., 2016; Walhovd et al., 2012; Raznahan et al., 2012; Wheater et al., 2021; Eriksson et al., 2000). However, it is unknown whether and how BW relates to brain characteristics through the lifespan, how consistent effects are within and across samples, whether BW is associated with lifespan brain changes, and to what extent lifespan effects of BW on the brain are of an environmental, rather than genetic nature. We address these questions, which are critical to understand how and when the human brain can be influenced through the lifespan, in the present study. On an overarching level, this study also addresses current debates in the field of lifespan cognitive neuroscience, namely (1) whether consistent, reproducible relationships between phenotypes relevant for mental health and function and inter-individual differences in brain characteristics can be found (Marek et al., 2022), and (2) to what extent the effects found in and ascribed to brain aging may actually reflect early-life influences, rather than longitudinal changes in older age (Walhovd et al., 2016; Vidal-Pineiro et al., 2021; Walhovd et al., 2020; Walhovd et al., 2023).

There are at least two different ways by which the effects of fetal growth, as indexed by BW, could work to produce the brain effects observed so far. (1) In line with a brain reserve model (Katzman et al., 1988; Schofield et al., 1997), higher BW could be associated with greater brain growth before birth. This seems likely, given that the effects are seen also in young populations (Walhovd et al., 2012; Raznahan et al., 2012). However, from the so far largely cross-sectional, or mixed models, several questions remain unanswered: Is this a fixed effect at the time of birth? Does higher BW also have carry-over effects to greater development in childhood and adolescence? In line with a brain maintenance model (Nyberg et al., 2012), is higher BW associated with better maintenance of brain volumes in the face of age-related changes in older adulthood? While effects are found in young populations (Walhovd et al., 2016; Walhovd et al., 2012; Raznahan et al., 2012), reduced atrophy in aging is a possible additional effect of higher BW that should be investigated, given the known relationships between birth size and brain volumes also in older age (Muller et al., 2014). The possible effects of BW on later brain development and brain maintenance in adulthood can only be investigated by longitudinal brain imaging spanning all stages of human life. Furthermore, as BW normally reflects both genetic and prenatal environmental factors, and an environmental BW contribution to brain differences has been shown in young monozygotic (MZ) twins (Raznahan et al., 2012; Casey et al., 2017; Halevy et al., 2021), we need to study brain effects of BW discordance in MZ twins in this context to disentangle possible non-genetic contributions of BW through the lifespan.

We hypothesized that there are persistent effects of BW on brain characteristics through the lifespan, and hence, that these would be consistent within and across samples of varying age and origin. We test this in a Norwegian sample covering the lifespan (Lifespan Changes in Brain and Cognition [LCBC]) (Walhovd et al., 2016; Walhovd et al., 2020), the US developmental sample Adolescent Brain Cognitive Development (ABCD) (Casey et al., 2018; Garavan et al., 2018), and the older adult UK Biobank (UKB) (Alfaro-Almagro et al., 2018; Nobis et al., 2019) sample. The associations of BW and cortical surface, thickness, volume, and their change were investigated vertex-wise in a total of 5794 persons (of whom 5718 with repeated scans and 386 MZ twins) with 12,088 longitudinal observations, 4–82 y of age at baseline, followed for up to ~8.3 y. Based on previous results (Walhovd et al., 2016; Walhovd et al., 2012; Raznahan et al., 2012), we hypothesized such effects to be driven primarily by positive associations between BW and cortical area, with lesser, if any, effects on cortical thickness. We expected positive effects on cortical volume corresponding to positive effects on cortical area. We hypothesized that effects would be stable, so that BW mainly affects the brain ‘intercept’ and does not relate much to brain changes. That is, we hypothesize a threshold model, whereby higher BW yields greater cortical area, and hence cortical volume, to begin with, rather than a maintenance model, whereby higher BW serves to protect against atrophy in aging. Moreover, we hypothesized that effects could not be explained solely by genetics, so that BW discordance in a subsample of MZ twins would also result in differences in brain characteristics through the lifespan.

Cortical surfaces were reconstructed from T1-weighted anatomical MRIs by use of FreeSurfer v6.0 (LCBC and UKB) and 7.1. (ABCD) (https://surfer.nmr.mgh.harvard.edu/; Dale et al., 1999; Fischl et al., 2002; Reuter et al., 2012; Jovicich et al., 2013), yielding maps of cortical area, thickness, and volume. Vertex-wise analyses were run with spatiotemporal linear mixed effects modeling (FreeSurfer v6.0.0 ST-MLE package) to assess regional variation in the relationships between BW and cortical structure and its change. All analyses were run with baseline age, sex, scanner site, and time (scan interval) as covariates. For ABCD specifically, ethnicity was also included as a covariate. For consistency of multiple comparison corrections across analyses, the results were thresholded at a cluster-forming threshold of 2.0, p<0.01, with a cluster-wise probability of p<0.0.25 (p<.05/2 hemispheres).

The lifespan relationship of BW and cortical volume, surface area, and thickness

Associations of BW and cortical characteristics are shown in Figure 1 (for the right hemisphere) and in Figure 1—figure supplement 1 (for both hemispheres). Across all cohorts, widespread positive associations were observed between BW and cortical area. These were highly consistent across lifespan (LCBC), developmental (ABCD), and aging (UKB) cohorts, and there were bilateral overlapping effects across most of the cortical mantle. As expected, BW had, in general, lesser effects on cortical thickness and no significant effects on thickness were observed in the UKB. There were, however, some lateral-positive and medial-negative effects in the LCBC and ABCD cohorts. We note that corresponding effects with increased medial frontal and occipital cortical thickness have been found associated with white matter alterations (reduced FA) in young adults born preterm with very low BW compared to term-born controls (Rimol et al., 2019). BW was significantly positively associated with cortical volume across much of the cortical mantle. In sum, broad, bilateral, positive associations were observed across cohorts for cortical area and volume.

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Additionally controlling for education level had little effect on results (see Figure 1—figure supplement 2). Information on gestational length (i.e., whether there was premature birth) was not available for all participants. Importantly, this information was lacking for the older participants, that is, this information is not available in detail for UKB, and since this information for the LCBC was drawn from the Medical Birth Registry of Norway (MBRN), only established in 1967, this was not available for the older part of the LCBC sample either. The majority of the LCBC sample and the ABCD sample had information on gestational length, however (LCBC: n = 514; gestational length in weeks: M = 40.0 wk, SD = 1.9, range = 25–44; ABCD: n = 3306; weeks premature: M = 1.0 weeks premature, SD = 2.1, range = 0.0–13.0). Controlling for gestational age in these subsamples had relatively little effect on results (see Figure 1—figure supplement 3). However, as expected with reduced power in the LCBC sample, the effects in this analysis were somewhat narrower. Effects in ABCD, where almost all participants were retained for analysis, showed no sign of decrease with control for gestational length. When restricting all samples to participants with BW between 2.5 and 5.0 kg, results were also very similar (see Figure 1—figure supplement 4). As expected from the widespread effects on cortical area and volume, effects were partly generic, with analyses controlling for intracranial volume (ICV) showing more restricted effects (see Figure 1—figure supplement 5). However, consistent significant positive effects of BW on cortical area also when controlling for ICV were observed across all three cohorts in lateral temporal and frontal areas.

BW effects on cortical change

To test the effect of BW on cortical change, we reran the analyses with BW × time and age × time interactions. Note that BW × time (i.e., within-subject follow-up time) represents the contrasts of interest while age – and age interactions – is used to account for the differences in age across individuals. Significant BW × time interactions on cortical characteristics were observed in restricted and non-overlapping regions across samples (see Figure 2 [depicting right hemisphere results]; for visualization of effects in both hemispheres, see Figure 2—figure supplement 1). Per direction of effect, the effect of BW differences was apparently reduced over time for area in LCBC and ABCD, whereas no interaction effects on area were significant in UKB. A mixture of positive (ABCD) and negative (LCBC, UKB) interaction effects were significant for thickness and volume.

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Visualization of the interaction effects as seen in Figure 2 and Figure 2—figure supplement 1, by splitting the sample into two based on BW, did not yield convincing evidence for these interactions, as shown in Figure 2—figure supplements 2 and 4. In plots of LCBC data, where the number of follow-ups varied, and a select portion had longer follow-up, it appeared that the effect of BW was reduced over time in these restricted regions. However, virtually parallel trajectories for the ABCD and UKB subsamples with lower and higher BW suggested that the effect size even within the areas of significant interactions of BW and time was negligible. Since the UKB and ABCD samples here consisted of samples having two time points only, whereas LCBC consisted of a mix of number of follow-ups over a longer time period, there might be sample-specific selection effects also regarding other characteristics than BW that can influence these effects in LCBC. For instance, participants who do not drop out tend to have better health, cognitive ability, and education, which again may relate positively to the brain measures studied here (Nyberg et al., 2010; Wolke et al., 2009). Thus, caution is advised in interpreting effects seen only with longer follow-up in the LCBC sample.

Both BW effects on cortical characteristics and cortical change were rerun (ROI-wise) using spline models that accounted for possible nonlinear effects of age on cortical structure. The results were comparable to those reported in Figures 1 and 2. See Figure 1—figure supplement 6 and Figure 2—figure supplement 6 for BW effects on cortical characteristics and cortical change, respectively.

Consistency of spatial relationships across and within samples

Next, we assessed whether the cortical correlates of BW (βeta-maps) showed a similar topographic pattern across the three independent datasets (UKB, ABCD, and LCBC). The results showed that all the spatial comparisons were statistically significant (p<0.05, FDR-corrected). That is, the topography of the effects of BW on cortical structure was comparable across datasets – the pairwise spatial correlation of a given cortical correlate of BW (e.g., BW effects on cortical area) was similar when estimated from two different datasets. The spatial correlations were the highest for the volume measures (r = 0.64–0.79), and overall also high (r = 0.51–0.71) for area measures, whereas for cortical thickness, they were more moderate (r = 0.24–0.45). See spatial correlations for the right hemisphere cortical volume in Figure 3 and the full model summary in Supplementary file 1. The results were qualitatively comparable when using -log₁₀ (p) significance values instead of βeta estimates, as shown in Supplementary file 1. The same pattern of results was largely seen also for spatial correlation of the maps capturing BW-associated cortical characteristics when controlling for ICV. The correlations were then on average somewhat lower, but there were still only significant positive correlations across LCBC, ABCD, and UKB (see Supplementary file 1).

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In contrast, the spatial correlation of the maps capturing BW-associated cortical change (i.e., BW × time contrast) was either unrelated (n = 7) or showed negative associations between cohorts (n = 2). The spatial correlations of BW on cortical change were r = –35 to –0.05 for area, r = –0.35 to –0.08 for volume, and r = –0.20 to –0.04 for thickness. See a visual representation in Figure 3—figure supplement 1 and full stats in Supplementary file 1.

In sum, the spatial correlation analyses imply that the different datasets show a comparable topography of BW effects across the cortical mantle; that is, the areas more and less affected by BW were common across datasets. Thus, the BW effects on cortical structure are robust and replicable across very different datasets. In contrast, the effects of BW on cortical change are not robust across datasets, showing dissimilar topographies.

Additionally, we performed replicability analyses both across and within samples to further investigate the robustness of the effects of BW on cortical characteristics and cortical change. Split-half analyses within datasets were performed to investigate the replicability of significant effects (Kharabian Masouleh et al., 2019; Open Science, 2015) of BW on cortical characteristics within samples (refer to Figure 1). These analyses further confirmed that the significant effects were largely replicable for volume and area, but not for thickness (see Figure 2—figure supplement 5). Split-half analyses of BW on cortical change (refer to Figure 2) showed, in general, a very low degree of replicability on the three different cortical measures. See Supplementary file 2. Replicability across datasets showed a similar pattern, that is, replicability was high for the effect of brain weight on cortical characteristics but very low for the effects of cortical change. See Supplementary file 3 for stats. These analyses provide complementary evidence of robust associations of BW with cortical area and volume – but not cortical change – across and within samples.

Effects of BW discordance on brain characteristics and changes in MZ twins

BW discordance analyses on twins specifically were run as described for the main analyses above, with the exception that twin scans were reconstructed using FS v6.0.1. for ABCD and the addition of the twin’s mean BW as a covariate. BW discordance was associated with cortical area, where the heavier twins had greater area in some frontal, temporal, and occipitotemporal regions, with effects in the right hemisphere only surviving corrections for multiple comparisons. We note that these regions mostly overlap with regions where positive effects of BW were also seen in the bigger sample. Strikingly, the effect of BW discordance, as shown in Figure 4, appeared similar in size to the effect of BW itself in the MZ twin sample. However, note that this plot is merely for illustrating effects, the effect size is inflated for the BW discordance plot, since the values are derived from areas already identified as significantly related to BW. There was no association of BW discordance and cortical area changes over time.

Figure 4

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BW discordance also had a significant negative effect on cortical thickness in restricted right frontotemporal regions, where being the lighter twin yielded greater thickness. These significant effects did not appear to overlap with regions where significant negative associations with BW were seen in the bigger sample. BW had little effect on cortical thickness in the significant region, and the effect of BW discordance in the identified regions, as shown in Figure 5, appeared greater than the effect of BW itself here in the MZ twin sample. However, this plot is merely for illustrating effects; it should be noted that the effect size is inflated for the BW discordance plot since the values are derived from areas already identified as significantly related to BW.

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In a very small area of the right hemisphere, there was a significant association of BW discordance and cortical thickness change, meaning that the lighter twin had greater cortical thickness over time, but this effect was both regionally and quantitatively minor, as shown in Figure 5—figure supplement 1. There were no significant effects of BW discordance on cortical volume or volume change over time.

Finally, to formally assess whether the cortical correlates (βeta-maps) of BW discordance in the twin subsample corresponded to cortical correlates of BW in the bigger samples, we did a meta-analysis of these estimates for area, thickness, and volume in the UKB, ABCD, and LCBC, and then assessed whether the cortical correlates of BW and BW discordance (βeta-maps) showed a similar topographic pattern across the datasets. The results of this meta-analysis-twin comparison showed only positive relationships, for area, r = 0.23, thickness r = 0.19, and volume r = 0.22. However, the respective uncorrected p-values were 0.08, 0.12, and 0.04, so the spatial comparisons would not be statistically significant (p<0.05, FDR-corrected). However, the positive correlations are suggestive that the topography of the effects of BW discordance in genetically identical twins on cortical structure was to some extent comparable to effects of individual differences in BW in the bigger sample.

The present results indicate that BW, the earliest widely and easily obtainable congenital metric, shows robust, persistent, and chiefly stable associations with brain characteristics through life. Especially, BW was associated with cortical area and volume in an age and time-invariant fashion. The robustness of this effect is quite remarkable, given the wealth of different influences individuals meet after birth, which are repeatedly assumed and reported to have a major impact on the brain through the protracted human lifespan (Livingston et al., 2020; Livingston et al., 2017). It is also in quantitative terms outstanding compared to the consistency of cortical topographies reported for other phenotypical factors (Marek et al., 2022). This is also special for a phenotype known to be environmentally influenced, unlike biologically hardcoded phenotypes such as sex or age, for which there are known brain-wide association studies (BWAS) patterns (Kharabian Masouleh et al., 2019; Liu et al., 2020).

Typically, other factors relating to later socioeconomic status, lifestyle, and health get the most attention in adult and aging brain research (Livingston et al., 2020; Livingston et al., 2017). Such factors, which are then targeted for prevention and intervention at different stages of the life course, often do not show consistent relationships to brain characteristics (Walhovd et al., 2022), may not actually be causal (Walhovd et al., 2022), and may themselves be related to prenatal growth (Eriksson et al., 2000). Another phenotype that obviously, like BW, reflects both genetic and environmental influences (Beaumont et al., 2018; Li et al., 2021; Willis et al., 2022) is body mass index (BMI) (Couto Alves et al., 2019). Consistent BWAS patterns have been reported for BMI (Kharabian Masouleh et al., 2019). BW stands out as the single chronological earliest phenotype, and besides BMI, BW appears to have the most replicable and consistent relations to cortical morphology, as shown here both across and within samples. It has been claimed that smaller-than-expected brain–phenotype associations and variability across population subsamples can explain widespread replication failures for BWAS (Marek et al., 2022). However, this is necessarily a question of which phenotypes are the most relevant to relate to brain characteristics. Also, the temporal order of factors needs to be considered if causal interpretations are to be made. Chronologically later factors necessarily do not cause earlier ones. While we cannot claim that BW itself causes the cortical characteristics observed in aging, the cortical variance explained by BW after one decade, and seven or eight decades of life alike, is unlikely to be explained by influences only present at some point in adulthood or aging. BW, as further discussed below, depends on genetic, as well as prenatal environmental influences (Beaumont et al., 2018; Li et al., 2021; Willis et al., 2022), which likely have causal effects on early brain morphological features. Here we find that these effects are substantial also in the aging brain.

We calculated that a BW difference of 1 SD (about 600 g) equaled a difference in cortical volume on the order of 1.1–1.6% in these cohorts. This is a quite big effect of a magnitude relevant for explaining a substantial portion of the differences typically seen between patients with neurodevelopmental or neurodegenerative diseases and healthy controls. As noted, BW differences have been reported for neurodevelopmental disorders such as attention deficit hyperactovity disorder (ADHD) (Momany et al., 2018), and also other neurodevelopmental disorders, such as schizophrenia or more general psychopathology, with the cortical effects detected not invariably being very large in absolute terms (Patel et al., 2022). There is a limit to the range of variation that can apply to human cortical volumes, in general, (e.g., virtually none have cortical volumes below 0.45 or above 0.65 l). In terms of sample representativity, one may assume that there can be a restriction relative to the actual range of human cortical volume variation as the present samples specifically are largely healthy (Carretta and Ree, 2022). Much of the differences within this limited range of variation are explained by the factors we controlled for, such as age and sex. With the present effect, on the restricted range of variation, combined with big samples, it is obvious that BW differences of much less than our example magnitude (600 g) may be detectable in the cortical morphology of patients versus controls. In the context of aging and neurodegenerative change, the estimated cortical effect of ≈600 g difference in BW is of a magnitude many times the annual cortical reduction estimated to take place from, for example, 50–60 y in the adult cohorts. This is a substantial effect in brain imaging and may illuminate why metrics such as ‘brain age’, assumed to index aging-related processes, may rather largely capture variance already determined at birth (Vidal-Pineiro et al., 2021; Franke et al., 2018; Wagen et al., 2022). Neglecting this especially consistent and early factor is likely to lead to a substantial portion of human brain variance being either erroneously ascribed to factors only present at later life stages (Vidal-Pineiro et al., 2021; Walhovd et al., 2023) or left unaccounted for.

The solidity, replicability, and universality of effects as shown here for a partly environmentally influenced metric (Beaumont et al., 2018; Li et al., 2021; Willis et al., 2022) appear exceptional in human brain imaging. The within-sample replicability results are not fully comparable to other studies assessing the replicability of brain–phenotype associations due to analytical differences (e.g., sample size, multiple-comparison correction method) (Marek et al., 2022; Kharabian Masouleh et al., 2019). Still, these results too clearly show that the rate of replicability of BW associations with cortical area and volume is comparable to benchmark brain–phenotype associations such as age and BMI with brain structure (Kharabian Masouleh et al., 2019). The BW–cortical volume and area associations may be among the topographically broadest and most consistent effects so far seen as stable across the lifespan of the human brain. The three cohorts studies differ on a range of features known to be highly and reliably related to cortical characteristics, first and foremost age (Fjell et al., 2015; Walhovd et al., 2016; Kharabian Masouleh et al., 2019; Kharabian Masouleh et al., 2022; Schnack, 2019), but also country of origin and representativity of the populations from which they are drawn (Garavan et al., 2018; Stamatakis et al., 2021). Yet there is a comparable topography of BW effects across the samples. This is so despite the samples collectively spanning the entire human age range, within which there are always substantial age-related changes in cortical structure (Fjell et al., 2015; Walhovd et al., 2016; Mills et al., 2016; Storsve et al., 2014; Tamnes et al., 2010). The present results thus indicate that fetal growth influences an offset of brain reserve (Katzman et al., 1988; Schofield et al., 1997) and that this brain reserve effect is persistent and stable through the lifespan.

In contrast, the cortical maps capturing change in cortical structure associated with BW were not robust across datasets; that is, the most positive and negative association with BW on cortical change did not overlap at all between the different cohorts. While there was evidence from ABCD that BW affected regional cortical development in the narrow age range covered, there were limited and no consistent effects of BW on cortical change across cohorts. Importantly, there was no indication whatsoever that BW could be associated with better brain maintenance (Nyberg et al., 2012) in the face of age-related changes in older adulthood. Thus, the data seem to indicate that any effect of BW on cortical change may be of relatively more temporary nature. The ‘offset effect’ of BW, on the other hand, appears persistent and consistent, especially in terms of stable and widespread effects on cortical area and volume across the lifespan.

The sensitivity analyses indicate that the associations between BW and cortical characteristics are seen irrespective of not only sex and age, but also education, head size (ICV), and cases of abnormal BW. Such patterns could point to an underlying genetic pleiotropy of BW and brain characteristics. Interestingly, however, recent findings indicate that the effects of exposure to environmental adversity on epigenetic programming in aging may be localized to the in utero period (Schmitz and Duque, 2022). The effects of BW discordance in MZ twins in this context align with other studies (Casey et al., 2017; Halevy et al., 2021) pointing to also non-genetic, that is, environmental, influences in the womb, associated with the pattern observed for cortical area effects. These analyses also account for multiple possibly confounding variables that could represent a mix of genetic-environmental effects, such as parental socioeconomic status, parity, or prenatal exposures shared between twins in the same womb such as maternal smoking or use of alcohol.

The neural basis for the observed association cannot readily be ascertained from human imaging studies tracking change (Walhovd et al., 2016; Walhovd et al., 2012; Raznahan et al., 2012). While the ‘fetal origins hypothesis’, proposing that cardiovascular disease in adulthood is related to undernourishment in utero (Barker, 1990; Barker, 1995), is well known, there has been focus on ‘brain-sparing’ adaptations under such conditions (Eriksson et al., 2000). However, our finding that early human development in utero appears to be associated with a persistent and stable brain reserve effect is largely in correspondence with what is known of human nervous system development and change through the lifespan: while synaptogenesis, synaptic remodeling, and myelination are known to be protracted processes long after infancy (Greenough et al., 1987; Huttenlocher, 1979; Petanjek et al., 2011; Yeung et al., 2014), numerous processes in brain development appear to be exclusively or almost exclusively happening before birth. For instance, neurogenesis takes place almost only in fetal development (Rakic, 1988). Even if controversies remain, evidence suggests that any adult human neurogenesis must be severely restricted in location and amount (Bhardwaj et al., 2006; Paredes et al., 2016). Thus, human beings appear to be born with almost all cortical neurons they will have through life, and neuronal migration and differentiation are also defined early, by the place and time the neuron is born during fetal life (Rakic, 1988). Factors that affect placental function and uterine and/or umbilical blood flow on a chronic basis may lead to restricted fetal growth, including brain growth, and given the timing of brain development, it may not be surprising that effects would be stable across years. Animal studies of chronic placental insufficiency have shown effects on brain development that persist with age (Rehn et al., 2004). Hence, the relationship between BW and cortical characteristics in the normal population could likely have a twofold etiology: it is likely to in part be based on normal variation in genetically determined body and brain size, but it also may be based on variations in environmental prenatal conditions, yielding differences in optimality of early brain development persisting through the lifespan.

These results indicate that there is potential to increase brain reserve throughout the lifespan, also in aging, by combating factors affecting fetal growth negatively. While it is unknown to what extent results from these specific US and European samples can be generalized to other populations, the current potential to improve prenatal factors may be especially high in low- and middle-income countries, where the demographic changes will also be more marked in terms of the aging population (WHO, 2019). About 200 million children in developing countries are not meeting their growth potential, and improving the prenatal environment is likely important to help children reach their full potential (McGovern, 2019) – and ultimately also to help them stay above a functional threshold into older age. Also in industrialized countries, including the United States, environmental factors are associated with BW. The opioid epidemic is increasingly affecting pregnant women, and in utero opioid exposure is associated with higher risk of fetal growth restriction (Azuine et al., 2019). Among highly common exposures, air pollution may, for instance, be of relevance. Recently, local traffic congestion-pollution exposure during pregnancy, independently of a series of maternal sociodemographic characteristics, was associated with reductions in term BW in a large US sample (Willis et al., 2022). Programs and policies to limit such environmental factors reducing fetal growth may thus enhance brain reserve and ultimately prevent more people from falling below a functional threshold even in advanced age.

The code used for vertex-wise ST-LME analyses and spatial correlations analyses can be accessed here https://github.com/LCBC-UiO/paper-birthweight-brainchange-2022 (copy archived at Vidal-Pineiro and Amlien, 2024). Two of the datasets used, the ABCD https://abcdstudy.org and UKB https://www.ukbiobank.ac.uk databases are open to all researchers given appropriate application, please see instructions for how to gain access here: https://nda.nih.gov/abcd/ and https://www.ukbiobank.ac.uk/enable-your-research. The LCBC dataset has restricted access, requests can be made to the corresponding author, and some of the data can be made available given appropriate ethical and data protection approvals. However, the registry data on birth weight connected to this sample are not shareable by the authors, as these data are owned by the Medical Birth Registry of Norway, https://www.fhi.no/en/hn/health-registries/medical-birth-registry-of-norway/medical-birth-registry-of-norway/, the Norwegian Mother, Father, and Child Cohort Study https://www.fhi.no/en/studies/moba/, and the Norwegian Twin Registry https://www.fhi.no/en/more/health-studies/norwegian-twin-registry/ so that any access to data must be approved by them. Group-level unthresholded p-maps, F-maps, Beta-maps, and degrees of freedom for the univariate analyses accompany this manuscript as additional material.

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Author details

1. Kristine B Walhovd

   1. Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
   2. Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing – review and editing

   For correspondence

   k.b.walhovd@psykologi.uio.no

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1918-1123

2. Stine K Krogsrud

   Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

   Contribution

   Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

3. Inge K Amlien

   Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Øystein Sørensen

   Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Yunpeng Wang

   Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Anne Cecilie S Bråthen

   Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Knut Overbye

   Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Jonas Kransberg

   Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Athanasia M Mowinckel

   Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

   Contribution

   Data curation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5756-0223

10. Fredrik Magnussen

    Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

    Contribution

    Formal analysis, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2574-1705

11. Martine Herud

    Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Asta K Håberg

    Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Oslo, Norway

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9007-1202

13. Anders Martin Fjell

    1. Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
    2. Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway

    Contribution

    Conceptualization, Funding acquisition, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2502-8774

14. Didac Vidal-Pineiro

    Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway

    Contribution

    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9997-9156

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