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. 2025 Aug 1;46(11):e70296. doi: 10.1002/hbm.70296

Maturation of Hippocampal Subfields in Young Adulthood and Its Relationship With Cognition

Klara Mareckova 1,2,✉, Gabriel A Devenyi 3,4, Lenka Andryskova 5, Mallar M Chakravarty 3,4, Yuliya S Nikolova 6
PMCID: PMC12314922  PMID: 40747970

ABSTRACT

The hippocampus is a key brain region for memory and cognitive functions, which consists of distinct subregions with different developmental trajectories throughout adolescence. However, trajectories of hippocampal subfield change in young adulthood remain uncharacterized, as is their potential relationship with cortical brain aging and cognitive ability during this time. We conducted two magnetic resonance imaging (MRI) follow‐ups of a prenatal birth cohort in young adulthood and studied the effects of chronological age and cortical brain age on the volume of hippocampal subfields in the early 20s (n = 109; 51% men) and late 20s (n = 251; 53% men) and how these age‐related volumetric changes might relate to full‐scale IQ (FSIQ). We showed that CA1 and CA4DG subfields continue to grow in the third decade of life and that this growth can be observed both at the level of chronological age as well as estimated cortical brain age at both MRI timepoints. Moreover, in men, a larger size of these age‐related subfields was associated with higher FSIQ, and the deviations between cortical brain age and chronological age mediated the relationships between right CA1 and FSIQ, as well as right CA4DG and FSIQ. These findings reveal that coordinated patterns of advanced cortical brain aging and hippocampal maturation may confer a cognitive advantage in young adulthood.

Keywords: brain aging, CA1, CA4DG, full‐scale IQ, hippocampal subfields, young adulthood

Hippocampal subfields continue to develop in young adulthood, and in young adult men, the deviations between one's global cortical brain age and chronological age mediate the relationship between the size of CA1 and CA4DG and full‐scale IQ. These findings suggest that faster brain maturation early on is beneficial.

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Summary.

  • Hippocampal subfields CA1 and CA4DG continue to grow in young adulthood, and in men, a larger size of these subfields is associated with higher IQ.

  • Advanced cortical brain aging predicts larger CA1 and CA4DG in early and late 20s, and in men, cortical brain aging mediates the relationship between the subfields and IQ.

  • Advanced brain aging and hippocampal maturation are a cognitive advantage for men.

1. Introduction

The world population is aging rapidly. The percentage of people aged 65 almost doubled between 1974 and 2024, and according to the United Nations population projections, it is expected to double again by 2074, thus increasing from 5.5% to 20.7% within 100 years (United Nations Populations Fund 2004). Given this sharp increase in the percentage of older adults, successful aging becomes a critical issue, and an early identification of those at risk for age‐related cognitive decline is essential to provide early intervention. Many age‐related disorders may have their origins early in development or young adulthood (Danese et al. 2009; Guo et al. 2022; Mareckova et al. 2023; Poranen‐Clark et al. 2016). Therefore, characterizing individual differences in brain aging and cognition emerging as early as young adulthood may offer a promising opportunity for better understanding and, ultimately, preventing aging‐related disorders.

Cognitive decline has been associated with the shrinkage of the hippocampus (Bettio et al. 2017), which might stem from impairments of hippocampal neurogenesis (Babcock et al. 2021). New neurons are formed in the dentate gyrus of the hippocampus (Toda et al. 2019) and while this hippocampal neurogenesis persists throughout life, it declines with age and is known to be compromised in mild cognitive impairment (Mostafa et al. 2025) and strikingly impaired in Alzheimer's disease (Babcock et al. 2021). The hippocampal volume decreases on average by 0.18% per year in middle age and by 0.3% per year in older age (Fraser et al. 2021). Adults who age successfully might experience preserved neurogenesis and slower hippocampal atrophy, leading to greater cognitive reserve.

Anatomically, the hippocampus consists of subregions with distinct cytoarchitecture, which might follow different developmental trajectories and be differentially affected by pathological processes (Braak and Braak 1991; Zheng et al. 2018). The examination of the entire hippocampus might mask these spatially specific effects, necessitating subfield‐specific approaches for greater mechanistic insight. Therefore, atlases of the human hippocampus allow reliable segmentation of its subfields based on T1‐weighted data acquired with a 3T scanner and can accurately segment the cornu ammonis (CA)1, CA2CA3, CA4DG, subiculum, and strata radiatum lacunosum and moleculare (SRLM) (Winterburn et al. 2013).

Using some of these novel segmentation approaches, several studies reported age‐related differences in hippocampal subfield volumes. Lee et al. (Lee et al. 2014) performed manual segmentation of hippocampal subfields, corrected them for brain size, and found age‐related increases specific to the right CA1 and CA3/DG volumes in early adolescence. Further cross‐sectional study of age‐related differences in hippocampal subfield volumes (Daugherty et al. 2016) evaluated participants aged 8–82 years and also reported subfield‐specific effects of age. While the CA1‐2 subfields showed a linear negative relationship with age, the CA3‐DG showed a non‐linear relationship, and the subiculum showed no relationship (Daugherty et al. 2016).

Research on reduced hippocampal volume and its associations with cognitive impairment (Izzo et al. 2020; Tondelli et al. 2012) as well as mild cognitive impairment and Alzheimer's disease (Kälin et al. 2017) has also been extended by further research using hippocampal subfields. These studies reported subfield‐specific associations with the general cognitive ability of participants from 8 to 28 years (Tamnes et al. 2018) as well as global cognitive decline in older healthy adults (Doran et al. 2023). The subfield CA1 was described as a critical structure in memory decline (Zammit et al. 2017) and it was suggested that CA1 atrophy might be a sensitive biomarker for early AD detection (de Flores et al. 2017).

However, as pointed out by Keresztes et al. (Keresztes et al. 2022), the literature is inconsistent and cross‐sectional associations are not supported by longitudinal data on the volume of hippocampal subfields. Thus, structural brain development and its relationship to cognition cannot be inferred from cross‐sectional age comparisons. Longitudinal studies of hippocampal subfields using a precise method of subfield definition are needed to resolve the inconsistencies in the literature. Moreover, to the best of our knowledge, all research on the development of hippocampal subfields so far has focused on associations with chronological age. Since biological aging of the brain varies among individuals, irrespective of their chronological age (Soumya Kumari and Sundarrajan 2024), biological age might be a valuable predictor of hippocampal subfield volume.

The deviation between one's brain age, inferred from brain structure, and chronological age, is often referred to as the brain age gap estimate (BrainAGE). BrainAGE describes deviations from typical brain development claimed to represent a biomarker of neuropathology (Franke and Gaser 2012; Pardoe and Kuzniecky 2018). Due to ongoing hippocampal neurogenesis (Bergmann et al. 2015; Spalding et al. 2013), hippocampal subfield volumes may follow lifelong developmental trajectories distinct from other age‐sensitive structural properties in the brain, such as cortical thickness. In fact, most brain age calculators are heavily weighted towards cortical thickness and do not include hippocampal subfields. For example, the cortical thickness‐based NAPR model (Pardoe and Kuzniecky 2018) is based on neuroanatomical changes over the lifespan and allows us to estimate one's neuroanatomical brain age independently of hippocampal volume. The relationship between cortical brain aging and hippocampal subfield anatomy in young adulthood may be cognitively relevant but remains uncharacterized.

Therefore, the current study aimed to describe the changes in the volume of hippocampal subfields in the third decade of life and determine their potential relationship with cortical brain aging and cognitive ability during this time. We hypothesized that the volume of the hippocampal subfields might continue to grow in the third decade of life, and these age‐related volumetric changes might be associated with full‐scale IQ (FSIQ). We also hypothesized that accelerated brain aging, as indexed by a greater positive BrainAGE (brain age > chronological age), will be associated with a larger volume of the age‐related hippocampal subfields and that these relationships will be present in both the early and late 20s. Finally, we hypothesized that the BrainAGE would mediate the relationship between the volume of the age‐related hippocampal subfields and FSIQ. Given the literature on sex differences in hippocampal volume and brain aging, we also explored the role of sex as a possible moderator of these relationships.

2. Materials and Methods

2.1. Participants

Participants included members of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC), a prenatal birth cohort born in South Moravia, the Czech Republic, between 1991 and 1992, who also participated in its neuroimaging follow‐ups—the Biomarkers and underlying mechanisms of vulnerability to depression (VULDE) study at the age of 23–24 years, and the Health Brain Age (HBA) study at the age of 28–30 years—at the Central European Institute of Technology, Masaryk University (CEITEC MU). A total of 109 participants had hippocampal subfield and brain age data from the early 20s as well as late 20s. A total of 251 participants (53% men) had complete brain age, hippocampal subfield, and IQ data from the late 20s. Demographic information regarding these two samples of young adults is provided in Tables S1A and S1B, respectively. Both the VULDE as well as the HBA study were approved by the ELSPAC Ethics Committee and all participants provided written informed consent and agreed to merge their HBA, VULDE, and ELSPAC‐CZ data.

2.2. Acquisition of Magnetic Resonance Imaging Data

Structural magnetic resonance imaging (MRI) data were acquired at the age of 23–24 as well as the age of 28–30 years using the same 3T Siemens Prisma MRI Scanner with 64‐channel head/neck coil at CEITEC MU and the same T1‐weighted acquisition sequence: voxel size = 1 mm3, 240 slices per slab, repetition time (TR) = 2300 ms, echo time (TE) > 2.34 ms, inversion time (TI) = 900 ms, flip angle = 8°. The scanner hardware was not upgraded or changed between the two MRI assessments.

2.3. Analysis of Cortical Brain Age in the Early and Late 20s

Brain age was calculated using the Neuroanatomical Age Prediction (NAPR) model (Pardoe and Kuzniecky 2018), which is estimated using cortical thickness maps and is thus independent of hippocampal volume. As described previously (Mareckova et al. 2023, 2020), we processed T1‐weighted data with FreeSurfer version 7.1.1 (Fischl and Dale 2000), and after quality control fed the output to the Neuroanatomical Age Prediction using R (NAPR) platform (Pardoe and Kuzniecky 2018) (Cloud‐based tool, Amazon Web Services), which estimates an individual's brain age using cortical thickness maps derived from their own locally processed T1‐weighted whole‐brain MRI scans. This model has been trained on data from 2367 participants aged 6 to 89 years using relevance vector machine regression (Tipping 2001) and Gaussian processes machine learning (Rasmussen and Williams 2005) methods. Finally, we calculated every participant's brain age gap (BrainAGE) as the difference between each participant's estimated brain age and chronological age. A positive BrainAGE thus indicates that one's structural brain age was older than chronological age, and a negative BrainAGE indicates that one's structural brain age was younger than chronological age. The same steps were used to calculate the BrainAGE in the early 20s as well as the late 20s.

2.4. Analysis of Hippocampal Subfields Volume in the Early and Late 20s

Size of the hippocampal subfields in the early and late 20s was calculated using the longitudinal stream of the Multiple Automatically Generated Templates (MAGeT‐Brain) pipeline, a reliable multi‐atlas approach calculating volumes of left and right CA1, CA2CA3, CA4DG, SRLM, and subiculum subfields (Winterburn et al. 2013; Pipitone et al. 2014). Visual quality control of the MAGeT‐Brain segmentations of the hippocampal subfields indicated that all images passed the quality control inspection and thus no manual editing was done. Finally, volumetric data were corrected for the total intracranial volume (TIV), and residuals were used in further analyses.

2.5. Assessment of FSIQ in the Late 20s

FSIQ in the young adult offspring was tested using the seven‐subtest short form (Tam 2004) of the Wechsler Adult Intelligence Scale (WAIS‐IV), fourth edition, only at the second timepoint—at the age of 28–30 years. The FSIQ was calculated based on the following subtests: Information, Arithmetic, Similarity, Digit Span, Picture completion, Digit Symbol Coding, and Matrix Reasoning.

3. Statistical Analyses

Statistical analyses were performed in JMP version 10.0.0 (SAS Institute Inc., Cary, NC). First, a repeated measures mixed model was used in the sample of 109 individuals with longitudinal data to test the effect of age and sex on the volume of the different hippocampal subfields, and multiple comparisons were corrected using False Discovery Rate (FDR). Age was treated as a within‐subject factor, sex as a between‐subject factor, and the volumes of the different hippocampal subfields were treated as dependent variables.

Second, linear regression tested the relationships between BrainAGE, sex, and volume of the age‐related hippocampal subfields. Sex was treated as a moderating factor. To use the maximum sample size available, these analyses were first done in the sample of 109 participants with MRI data in the early 20s and then in the sample of 251 participants with MRI data in the late 20s. Multiple comparisons were corrected using FDR. Post hoc control analyses also corrected these models for current substance use (cannabis use, cigarette smoking, and alcohol use).

Next, linear regression tested the possible relationships between the volume of the age‐related hippocampal subfields, sex, and FSIQ of the 251 participants in their late 20s. Again, sex was treated as a moderating factor, and post hoc control analyses corrected the models for current substance use (cannabis use, cigarette smoking, and alcohol use).

Finally, the PROCESS macro (Hayes 2018) for SPSS, version 27 (IBM SPSS Statistics, IBM Corp, Armonk NY) was used to perform a mediation analysis using bootstrapping and assess whether brain age might mediate the relationship between the size of the age‐related hippocampal subfield and FSIQ. Conditional indirect effects were assessed for significance using bootstrapped bias‐corrected 95% confidence intervals constructed around indirect effect estimates, based on 10,000 bootstrapping iterations. Given the sex‐specific findings of the linear regression models, the mediation was run in men only. Again, post hoc control analyses used current substance use (cannabis use, cigarette smoking, and alcohol use) as covariates.

4. Results

4.1. Changes in Hippocampal Subfield Volume Between the Early and Late 20s

A significant increase of volume (brain size‐corrected) between the early and late 20s was observed in the CA1 (right: F (1,107) = 25.42, FDRp = 0.0005, left: F (1,107) = 9.23, FDRp = 0.0075) and CA4DG (right: F(1,107) = 22.08, FDRp = 0.0005, left: F(1,107) = 9.45, FDRp = 0.0005) subfields, and these effects were independent of sex. Interestingly, the other subfields did not show any significant effects of age. While some subfields, such as the right CA2CA3 or left subiculum, showed at least a trend, others were not even close to significance, providing strong evidence for spatial specificity. See Table 1 for the exact statistics regarding the effects of chronological age on the volume of all the hippocampal subfields.

TABLE 1.

Effect of age on volume of hippocampal subfields.

Subfield Hemisphere Exact F p FDRp
CA1 Right F (1,107)   = 25.42 p < 0.0001 0.0005
Left F (1,107)   = 9.23 0.003 0.0075
CA2CA3 Right F (1,107) = 5.09 0.026 0.052
Left F (1,107) = 1.16 0.285 0.356
CA4DG Right F (1,107)  = 22.08 p < 0.0001 0.0005
Left F (1,107)  = 9.45 0.002 0.0075
SRLM Right F (1,107) = 0.25 0.944 0.944
Left F (1,107) = 0.93 0.338 0.376
Subiculum Right F (1,107) = 1.23 0.269 0.356
Left F (1,107) = 3.96 0.049 0.082
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Note: Bold values highlights the significant findigns p < 0.05 and FDR p < 0.05, respectively.

4.2. Brain Aging and Hippocampal Subfield Volume in the Early and Late 20s

Further analyses focusing on these four age‐related hippocampal subfields revealed that greater positive BrainAGE (i.e., more mature‐like global cortical thickness pattern) in the early 20s was associated with greater volume of the left CA1 (beta = 0.21, FDRp = 0.04) and right CA4DG (beta = 0.23, FDRp = 0.04) measured concurrently in the early 20s. There were also non‐significant trends for the relationships in the other hemisphere (right CA1: beta = 0.19, FDRp = 0.053; left CA4DG: beta = 0.18, FDRp = 0.06) in the early 20s. There were no interactions between BrainAGE and sex on the volume of any of the hippocampal subfields in the early 20s (p > 0.19). The effects of BrainAGE on the volume left CA1 (beta = 0.20, p = 0.04) and right CA4DG (beta = 0.22, p = 0.02) measured concurrently in the early 20s remained significant also when correcting for cannabis use, cigarette smoking, and alcohol use in the early 20s.

Consistently, greater positive BrainAGE in the late 20s was associated with greater volume of the right CA1 (beta = 0.15, FDRp = 0.04) and right CA4DG (beta = 0.18, FDRp = 0.02) in the late 20s. The relationships with left CA1 and left CA4DG in the late 20s did not reach significance (FDRp > 0.29). An overview regarding the effects of brain age on the volume of age‐related hippocampal subfields in the early and late 20s is provided in Figure 1 and Table 2. There were no interactions between BrainAGE and sex on the volume of any of the hippocampal subfields in the late 20s (p > 0.15). The effects of BrainAGE on the volume of right CA1 (beta = 0.14, p = 0.02) and right CA4DG (beta = 0.17, p = 0.01) subfields measured concurrently in the late 20s remained significant also when correcting for cannabis use, cigarette smoking, and alcohol use in the late 20s.

FIGURE 1.

FIGURE 1

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BrainAGE and volume of hippocampal subfields CA1 and CA4DG in the early and late 20s. Relationships that survived the FDR correction for multiple comparisons are highlighted with a grey background (A—Early 20s: Left CA1: Adj R 2 = 0.04, beta = 0.21, FDRp = 0.04, right CA4DG: Adj R2 = 0.03, beta = 0.23, FDRp = 0.04; B—Late 20s: Right CA1: Adj R 2 = 0.02, beta = 0.15, FDRp = 0.04; right CA4DG: Adj R 2 = 0.03, beta = 0.18, FDRp = 0.02).

TABLE 2.

Effect of BrainAGE on volume of the age‐related hippocampal subfields in the early 20s.

Subfield Hemisphere Early 20s Late 20s
beta p FDRp beta p FDRp
CA1 Right 0.19 0.04 0.053 0.15 0.019 0.04
Left 0.21 0.02 0.04 0.08 0.23 0.29
CA4DG Right 0.23 0.02 0.04 0.18 0.005 0.02
Left 0.18 0.06 0.06 0.07 0.29 0.29
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Note: Bold values highlights the significant findigns p < 0.05 and FDR p < 0.05, respectively.

4.3. Volume of Hippocampal Subfields and FSIQ in the Late 20s

Focusing on the two hippocampal subfields, which showed a significant relationship with BrainAGE in the late 20s, we showed that both right CA1 and CA4DG interacted with sex to predict FSIQ in the late 20s (right CA1: beta = 0.14, FDRp = 0.03; right CA4DG: beta = 0.15, FDRp = 0.03). The interaction between the volume of right CA1 and sex reduced to a trend (beta = 0.10, p = 0.09), but the interaction between the volume of right CA4DG and sex remained significant (beta = 0.13, p = 0.04) when correcting for cannabis use, cigarette smoking, and alcohol use in the late 20s. Post hoc analyses revealed that in men, both greater right CA1 (R 2 = 0.04, p = 0.23; Figure 2) and CA4DG (R 2 = 0.04, p = 0.02; Figure 2) were associated with greater FSIQ. There were no similar relationships in women (p > 0.32).

FIGURE 2.

FIGURE 2

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In young adult men (blue), greater volumes of right CA1 (A; R 2 = 0.04, p = 0.23) and right CA4DG (B; R 2 = 0.04, p = 0.02) were associated with higher FSIQ. No significant relationships were found in women (red; p > 0.32).

4.4. Does Accelerated Brain Aging Mediate the Relationship Between the Volume of Hippocampal Subfields and FSIQ in Men in the Late 20s?

In men, BrainAGE mediated the relationships between the volume of right CA1 and FSIQ (ab = 0.01, SE = 0.007, 95% CI [0.0004; 0.0265]; Figure 3A) as well as the volume of right CA4DG and FSIQ (ab = 0.02, SE = 0.009, 95% CI [0.0006; 0.0388]; Figure 3B). The mediation of the relationship between right CA1 and FSIQ by BrainAGE (ab = 0.01, SE = 0.007, 95% CI [0.0000; 0.0267]) as well as the mediation between right CA4DG and FSIQ by BrainAGE (ab = 0.02, SE = 0.009, 95% CI [0.0009; 0.0385]) remained significant even when correcting for cannabis use, cigarette smoking, and alcohol use in the late 20s.

FIGURE 3.

FIGURE 3

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In young adult men, BrainAGE mediated the relationships between the volume of right CA1 and FSIQ (A; ab = 0.01, SE = 0.007, 95% CI [0.0004; 0.0265]) as well as the volume of right CA4DG and FSIQ (B; ab = 0.02, SE = 0.009, 95% CI [0.0006; 0.0388]).

5. Discussion

We have conducted a prospective study of a prenatal birth cohort with a longitudinal neuroimaging component in young adulthood and studied the effects of chronological age (early 20s vs. late 20s) and brain age, which varied from 15.7 to 41.1 years in the early 20s and from 18.46 to 43.25 years in the late 20s, on the volume of hippocampal subfields. Given the relatively equal proportion of men and women in the current study, we could also evaluate the possible interactions between age and sex. Our results indicate significant changes in CA1 and CA4DG subfields from early to late twenties in the whole sample, suggesting these two subfields continue to grow relatively to the brain size in both men and women in the third decade of life. We also showed that participants' current brain age gap predicts the size of these hippocampal subfields in both the early 20s and late 20s. Those with faster brain maturation (Brain age > Chronological age) had larger volumes of the CA1 and CA4DG subfields, suggesting that faster brain maturation might be beneficial for the young adults. We also showed that larger hippocampal subfields CA1 and CA4DG were associated with higher FSIQ in men and that this relationship was mediated by brain age. The overall pattern of results did not change even when correcting for substance use in young adulthood. These findings on spatially specific relationships among hippocampal subfield growth, global cortical brain age, and cognition in young adulthood reveal that coordinated patterns of advanced cortical brain aging and hippocampal maturation, specifically in the CA1 and CA4DG subfields, may confer a relative cognitive advantage in young adult men.

These findings substantially extend the study of Lee et al. (Lee et al. 2014) who performed manual segmentation of hippocampal subfields, corrected them for brain size, and found age‐related increases in the right CA1 and CA3/DG volumes but not the other subfields in early adolescence. Our findings also support the study of Kempermann et al. (Kempermann et al. 2015), who demonstrated that hippocampal neurogenesis plays a key role in cognitive functioning. The fact that we found the effects of chronological as well as global cortical brain age in the CA1 and CA4DG subfields supports and extends the previous research, which pointed out the significant impact of age, specifically on CA1 (Daugherty et al. 2016; de Flores et al. 2017; Shing et al. 2011; Wisse et al. 2014; Wolf et al. 2015) and which described CA1 as a critical structure for memory (Zammit et al. 2017).

The spatial specificity of our findings is striking, especially in the context of subfield‐specific neurogenesis. The dentate gyrus of the hippocampus is the main location of neurogenesis (Toda et al. 2019). We speculate that the greater volume of CA1 and CA3/DG subfields, which was associated with faster cortical maturation of the brain in young adults and higher FSIQ in men, is a manifestation of a greater neurogenesis. While this speculation should be tested in future research using preclinical models, it is in agreement with the accumulating evidence suggesting that dysregulation of adult hippocampal neurogenesis may be associated with cognitive decline in neurological disorders and psychological symptoms in psychiatric disorders and that this cognitive decline might be reversed by increasing neurogenesis (Toda et al. 2019), possibly through the CREB pathway (Villeda et al. 2014).

Our findings also extend the previous research linking the volume of CA1 and CA3/DG subfields to memory (Zammit et al. 2017; Aumont et al. 2023) and demonstrate that the volume of these subfields is related to a broader metric of cognitive ability, specifically FSIQ. This is consistent with Zhu et al. (Zhu et al. 2017) who compared the relationships between volumes of hippocampal subfields and performance on Raven's Advanced Progressive Matrices (RAMP) with performance on an n‐back task and concluded that RAMP had similar correlations with hippocampal subfields as the visual n‐back task and even larger correlations than the verbal n‐back task. They also pointed out that the highest correlations with RAMP performance were present in the right CA1 (Zhu et al. 2017). Given these findings, they suggested that the volume of hippocampal subfields might be more relevant to fluid intelligence than verbal working memory (Zhu et al. 2017).

The fact that faster brain maturation and thus less cortical thickness was associated with a larger volume of the CA1 and CA4DG subfields might be explained by the fact that cortical thickness peaks in adolescence, but the volume of the hippocampus peaks in adulthood (Ducharme et al. 2016; Shaw et al. 2008). While these relationships between BrainAGE and volume of hippocampal subfields did not interact with sex, men experienced slower brain aging than women (see Demographics table in the Supporting Information) and the BrainAGE mediated the relationship between the volume of hippocampal subfields and FSIQ in men only. Thus, it might be that the sex differences in the latter relationships might be, at least in part, related to the protracted brain maturation in men. Alternatively, the faster brain aging in women and the lack of a relationship between maturation of the CA1 and CA4DG hippocampal subfields and higher IQ in women might be related to the greater prevalence of anxiety and depression in women versus men, which might stem from hormonal changes and/or greater sensitivity to interpersonal relationships (Cyranowski et al. 2000). Hippocampal neurogenesis plays an important role in the pathophysiology of depression (Paizanis et al. 2007), and hippocampal atrophy has been linked to depressive symptoms (Sapolsky 2001) as well as cognitive deficits (Harvey et al. 2005; McEwen and Magarinos 2001).

The longitudinal design of our study and the fact that the volume of hippocampal subfields was assessed using the MAGeT‐Brain pipeline (Winterburn et al. 2013; Pipitone et al. 2014), which offers a very precise but easily replicable delineation of the subfields, are notable strengths of the study. Moreover, to the best of our knowledge, this is the first longitudinal study tracking the development of hippocampal subfield volume not only as a function of chronological age but also as a function of estimated global cortical brain aging, which spanned from 15.7 to 43.25 years. Still, our findings are limited by the fact that we have collected MRI data at only two time points in young adulthood and thus could test linear but not non‐linear relationships. Future longitudinal research should consider conducting MRI at more time points throughout the lifespan and also evaluate the presence of nonlinear trajectories. While our findings suggest that earlier maturation may be beneficial for young adults, at least in the short term, only future research can test whether this may turn into a disadvantage when moving into middle age and later life. Such future research might also try to conduct longitudinal neuroimaging in a larger sample size and use specific measures of spatial and temporal memory. Given the definition of our prenatal birth cohort, all our participants were young adults of European ancestry, born in the same region during an early post‐communist era in Czechoslovakia (1991 or 1992) and thus in families with a very similar socioeconomic status. Future research might thus try to replicate our findings at other stages of life, such as adolescence, in other ethnic groups, and in more socio‐economically diverse cohorts. Overall, our study demonstrates that hippocampal subfields continue to develop in young adulthood and that in young adult men, the deviations between one's global cortical brain age and chronological age mediate the relationship between the size of hippocampal volume and FSIQ. These findings suggest that faster brain maturation early on is beneficial and may manifest as a larger volume in specific hippocampal subfields, possibly indicating more neurogenesis and higher FSIQ. This in turn highlights the importance of considering individual differences in brain development well into young adulthood and adopting a lifespan perspective in future studies tracking cognitive trajectories.

Ethics Statement

This study was approved by the ELSPAC Ethics Committee and all participants signed informed consent.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1.

HBM-46-e70296-s001.docx (20.3KB, docx)

Acknowledgements

This work has received funding from the Czech Science Foundation, project no. 24‐12183M, Czech Health Research Council (No. NU20J‐04‐00022), Research Executive Agency (FP7‐IEF‐2013, 6485124), the European Union (Marie Curie Intra‐European Fellowship for Career Development, FP7‐PEOPLE‐IEF‐2013, grant #6485124), the Ministry of Education, Youth and Sports (MEYS CR) (Nos. CZ.02.1.01/0.0/0.0/17 043/0009632; CEITEC 2020, LQ1601, LM2018121), and by project nr. LX22NPO5107 (MEYS): Funded by the European Union—Next Generation EU. Support with obtaining scientific data presented in this paper came from the core facility Multimodal and Functional Imaging Laboratory of the Central European Institute of Technology, Masaryk University, supported by the Czech‐BioImaging large RI project (No. LM2018129, funded by MEYS CR). Authors also thank the RECETOX Research Infrastructure (No LM2023069) financed by the Ministry of Education, Youth and Sports for supportive background. This work was also supported by the European Union's Horizon 2020 research and innovation program under grant agreement No 857560 (CETOCOEN Excellence). This publication reflects only the author's view, and the European Commission is not responsible for any use that may be made of the information it contains. Yuliya S. Nikolova is supported by a Koerner New Scientist Award and a Paul Garfinkel New Investigator Catalyst Award administered by the Centre for Addiction and Mental Health Foundation, and a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada.

Mareckova, K. , Devenyi G. A., Andryskova L., Chakravarty M. M., and Nikolova Y. S.. 2025. “Maturation of Hippocampal Subfields in Young Adulthood and Its Relationship With Cognition.” Human Brain Mapping 46, no. 11: e70296. 10.1002/hbm.70296.

Funding: This work has received funding from the Czech Science Foundation, project no. 24‐12183M, Czech Health Research Council (No. NU20J‐04‐00022), Research Executive Agency (FP7‐IEF‐2013, 6485124), the European Union (Marie Curie Intra‐European Fellowship for Career Development, FP7‐PEOPLE‐IEF‐2013, grant #6485124), the Ministry of Education, Youth and Sports (MEYS CR) (Nos. CZ.02.1.01/0.0/0.0/17 043/0009632; CEITEC 2020, LQ1601, LM2018121), and by project no. LX22NPO5107 (MEYS): Funded by the European Union—Next Generation EU. Support with obtaining scientific data presented in this paper came from the core facility Multimodal and Functional Imaging Laboratory of the Central European Institute of Technology, Masaryk University, supported by the Czech‐BioImaging large RI project (No. LM2018129, funded by MEYS CR). Authors also thank the RECETOX Research Infrastructure (No LM2023069) financed by the Ministry of Education, Youth and Sports for supportive background. This work was also supported by the European Union's Horizon 2020 research and innovation program under grant agreement No 857560 (CETOCOEN Excellence).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

HBM-46-e70296-s001.docx (20.3KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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