In neural circuitry, long-range (extracortical) interconnections among local (intracortical) microcircuits shape and constrain the large-scale functional organization of neural activity across the cortex (Vázquez-Rodríguez et al., 2019; Sarwar et al., 2021; Demirtaş et al., 2019; Deco et al., 2011; Breakspear, 2017). The coupling of structural connectome (SC) and functional connectome (FC) varies greatly across different cortical regions reflecting anatomical and functional hierarchies (Vázquez-Rodríguez et al., 2019; Valk et al., 2022; Zamani Esfahlani et al., 2022; Gu et al., 2021; Baum et al., 2020) and is regulated in part by genes (Valk et al., 2022; Gu et al., 2021), as well as its individual differences relates to cognitive function (Gu et al., 2021; Baum et al., 2020). Despite its fundamental importance, our understanding of the changes in SC–FC coupling with development is currently limited. Specifically, the alterations in SC–FC coupling during development, its association with cognitive functions, and the underlying spatial transcriptomic mechanisms remain largely unknown.

Network modelling of the brain enables the characterization of complex information interactions at a system level and provides natural correspondences between structure and function in the cortex (Zamani Esfahlani et al., 2022; Bassett and Sporns, 2017). Advances in diffusion MRI (dMRI) and tractography techniques have allowed the in vivo mapping of the white matter (WM) connectome (WMC), which depicts extracortical excitatory projections between regions (Feng et al., 2022). The T1- to T2-weighted (T1w/T2w) ratio of MRI has been proposed as a means of quantifying microstructure profile covariance (MPC), which reflects a simplified recapitulation in cellular changes across intracortical laminar structure (Valk et al., 2022; Paquola et al., 2019b; Liu et al., 2022; Paquola and Hong, 2023; Park et al., 2022). Resting state functional MRI (rs-fMRI) can be used to derive the FC, which captures the synchronization of neural activity (Honey et al., 2009). A variety of statistical (Valk et al., 2022; Gu et al., 2021; Baum et al., 2020), communication (Vázquez-Rodríguez et al., 2019; Zamani Esfahlani et al., 2022), and biophysical (Breakspear, 2017; Sanz-Leon et al., 2015) models have been proposed to study the SC–FC coupling. The communication model is particularly useful because it not only depicts indirect information transmission but also takes into account biodynamic information within acceptable computational complexity (Zamani Esfahlani et al., 2022; Avena-Koenigsberger et al., 2017). However, most studies have relied on WMC-derived extracortical communications as SC to predict FC, while ignoring the intracortical microcircuits, the MPC. In the present study, we propose that incorporating both intracortical and extracortical SC provides a more comprehensive perspective for characterizing the development of SC–FC coupling.

Previous studies in adults have revealed that the SC–FC coupling is strongest in sensory cortex regions and weakest in association cortex regions, following the general functional and cytoarchitectonic hierarchies of cortical organization (Vázquez-Rodríguez et al., 2019). This organization may occur due to structural constraints, wherein cortical areas with lower myelination and weaker WM connectivity tend to have more dynamic and complex functional connectivity (Vázquez-Rodríguez et al., 2019; Gu et al., 2021). Large-scale association networks emerged over evolution by breaking away from the rigid developmental programming found in lower-order sensory systems (Buckner and Krienen, 2013), facilitating regional and individual specialization (Preti and Van De Ville, 2019). In terms of developmental changes in SC–FC coupling, a statistical model-based study (Baum et al., 2020) identified positive age-related changes in some regions, while fewer regions exhibited negative changes. Furthermore, there is evidence that SC–FC coupling is linked to cognitive functions in healthy children (Chan et al., 2022), adults (Gu et al., 2021; Medaglia et al., 2018), and patients (Kuceyeski et al., 2019), suggesting that it may be a critical brain indicator that encodes individual cognitive differences. Nonetheless, a more comprehensive investigation is needed to understand the precise pattern of SC–FC coupling over development and its association with cognitive functions.

Cortical SC–FC coupling is highly heritable (Gu et al., 2021) and related to heritable connectivity profiles (Valk et al., 2022), suggesting that the development of coupling may be genetically regulated. The Allen Human Brain Atlas (AHBA) (Hawrylycz et al., 2012) is a valuable resource for identifying genes that co-vary with brain imaging phenotypes and for exploring potential functional pathways and cellular processes via enrichment analyses (Whitaker et al., 2016; Arnatkeviciute et al., 2021; Fornito et al., 2019). For instance, a myeloarchitectural study showed that enhanced myelin thickness in mid-to-deeper layers is specifically associated with the gene expression of oligodendrocytes (Paquola et al., 2019a). Another functional study found that the expression levels of genes involved in calcium ion-regulated exocytosis and synaptic transmission are associated with the development of a differentiation gradient (Xia et al., 2022). However, the transcriptomic architecture underlying the development of SC–FC coupling remains largely unknown.

In this study, we analysed data obtained from the Lifespan Human Connectome Project Development (HCP-D) (Somerville et al., 2018), which enrolled healthy participants ranging in age from 5.7 to 21.9 years. Our main objective was to investigate the SC–FC coupling of brain connectome and characterize its developmental landscapes. Specifically, we aimed to determine whether the SC–FC coupling encodes individual differences in cognition during development. Finally, we explored the genetic and cellular mechanisms underlying the development of SC–FC coupling of brain connectome. To assess the reproducibility of our findings, sensitivity and replication analyses were performed with different parcellation templates, different tractography strategies, and a split-half independent validation method.

We selected 439 participants (5.7–21.9 years of age, 207 males) in the HCP-D dataset who met our inclusion criteria: available high-quality T1/T2, dMRI, and rs-fMRI data that met the quality control thresholds. For each participant, we generated multiple connectomes using 210 cortical regions from the Human Brainnetome Atlas (BNA) (Fan et al., 2016), which comprised MPC, WMC, and FC. Intracortical connectivity was represented by MPC. According to the WMC, 27 weighted communication models (Zamani Esfahlani et al., 2022) were calculated to characterize geometric, topological, or dynamic connectivity properties. After analysis, we found that communicability (Crofts and Higham, 2009), mean first-passage times of random walkers (Noh and Rieger, 2004), and flow graphs (timescales = 1) provided the optimal combination of extracortical connectivity properties because of significantly predicting FC (p < 0.05, 1000 spin test permutations, Table 1). We used these three models to represent the extracortical connectivity properties in subsequent discovery and reproducibility analyses (Figure 1—figure supplement 1).

Spatial pattern of cortical SC–FC coupling

We used SCs (MPC and three WMC communication models) to predict FC per node based on a multilinear model (Vázquez-Rodríguez et al., 2019; Figure 1), and quantified the nodewise SC–FC coupling as an adjusted coefficient of determination $r^2$ . We observed that the grouped SC–FC coupling varied across cortical regions (mean adjusted $r^2$ = 0.14 ± 0.08, adjusted $r^2$ range = [0.03, 0.45], Figure 2A), and regions with significant coupling were located in the middle frontal gyrus, precentral gyrus, paracentral lobule, superior temporal gyrus, superior parietal lobule, postcentral gyrus, cingulate gyrus, and occipital lobe (p < 0.05, 1000 spin test permutations, Figure 2B). Similar heterogeneous patterns of coupling were observed when categorizing cortical regions into seven functional subnetworks (Yeo et al., 2011) (visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks). In the visual, somatomotor, default mode and ventral attention networks, SC significantly predict FC variance (p < 0.05, 1000 spin test permutations, Figure 2C). The visual and somatomotor networks had higher coupling values than the other networks (p < 0.05, Kruskal–Wallis ANOVA, Figure 2C). We further investigated the alignment between SC–FC coupling and three fundamental properties of brain organization: evolution expansion (Hill et al., 2010), myelin content (Glasser and Van Essen, 2011), and functional principal gradient (Margulies et al., 2016). Our findings reveal a negative association between regional distribution of SC–FC coupling and evolution expansion (Spearman’s r = −0.52, p < 0.001, 1000 spin test permutations, Figure 2D), as well as with the functional principal gradient (Spearman’s r = −0.46, p < 0.001, 1000 spin test permutations, Figure 2F). Conversely, nodes exhibiting higher SC–FC coupling tended to exhibit higher myelin content (Spearman’s r = 0.49, p < 0.001, 1000 spin test permutations, Figure 2E). In addition, the coupling pattern based on other models (using only MPC or only SCs to predict FC) and the comparison between the models are shown in Figure 2—figure supplement 1A–C.

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Additionally, we applied Haufe’s inversion transform (Haufe et al., 2014) to yield predictor weights of various SCs, where higher or lower values indicate stronger positive or negative correlations with FC. Our results demonstrated that different SCs had preferential contributions to FC variance across cortical regions to explain FC variance (p < 0.05, false discovery rate (FDR) corrected, Kruskal–Wallis ANOVA, Figure 2G). Specifically, in the MPC, regions with positive correlation were the orbital gyrus, precentral gyrus, right middle temporal gyrus, and temporoparietal junction, while regions with negative correlations were the left superior frontal gyrus, inferior parietal lobule, and bilateral cingulate gyrus. Regarding WMC communication models, the communicability and flow graphs tended to stronger higher positive correlations in the visual, limbic, and default mode networks, whereas the mean first-passage time had stronger negative correlations in the somatomotor, limbic, and frontoparietal networks.

Age-related changes in SC–FC coupling with development

To track changes in SC–FC coupling during development, we used a general linear model to assess the effect of age on nodal SC–FC coupling, while controlling for sex, intracranial volume, and in-scanner head motion. Our results revealed that the whole-cortex average coupling increased during development (${\beta }_{age}$ = 1.05E−03, F = 3.76, p = 1.93E−04, r = 0.20, p = 3.20E−05, Figure 3A). Regionally, the SC–FC coupling of most cortical regions increased with age (p < 0.05, FDR corrected, Figure 3B), particularly that in the frontal lobe, middle temporal gyrus, inferior temporal gyrus, parietal lobe, cingulate gyrus, and lateral occipital cortex. Conversely, cortical regions with significantly decreased SC–FC coupling (p < 0.05, FDR corrected, Figure 3B) were located in left orbital gyrus, left precentral gyrus, right superior and inferior temporal gyrus, left fusiform gyrus, left superior parietal lobule, left postcentral gyrus, insular gyrus, and cingulate gyrus. Age correlation coefficients distributed within functional subnetworks are shown in Figure 3C. Regarding mean SC–FC coupling within functional subnetworks, the somatomotor (${\beta }_{age}$ = 2.39E−03, F = 4.73, p = 3.10E−06, r = 0.25, p = 1.67E−07, Figure 3E), dorsal attention (${\beta }_{age}$ = 1.40E−03, F = 4.63, p = 4.86E−06, r = 0.24, p = 2.91E−07, Figure 3F), frontoparietal (${\beta }_{age}$ = 2.11E−03, F = 6.46, p = 2.80E−10, r = 0.33, p = 1.64E−12, Figure 3I) and default mode (${\beta }_{age}$ = 9.71E−04, F = 2.90, p = 3.94E−03, r = 0.15, p = 1.19E−03, Figure 3J) networks significantly increased with age and exhibited greater increase. No significant correlations were found between developmental changes in SC–FC coupling and the fundamental properties of cortical organization. Additionally, weights of different SCs varied with age, showing that MPC weight was positively correlated with age and that the weights of WMC communication models were stable (Figure 3—figure supplements 1–4). The age-related patterns of SC–FC coupling based other coupling models were shown in Figure 2—figure supplement 1D–F.

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SC–FC coupling predicts individual differences in cognitive functions

As we found that SC–FC coupling can encode brain maturation, we next evaluated the implications of coupling for individual cognition using Elastic-Net algorithm (Feng et al., 2022). After controlling for sex, intracranial volume and in-scanner head motion, we found the SC–FC coupling significantly predicted individual differences in fluid, crystal, and general intelligence (Pearson’s r = 0.3–0.4, p < 0.001, FDR corrected, Figure 4A). Furthermore, even after controlling for age, SC–FC coupling remained a significant predictor of general intelligence better than at chance (Pearson’s r = 0.11 ± 0.04, p = 0.01, FDR corrected, Figure 4A). For fluid and crystal intelligence, the predictive performances of SC–FC coupling were not better than at chance (Figure 4A). The predictive performances for other cognitive subscores are shown in Figure 4—figure supplement 1. To identify the regions with the greatest contributions to individual differences in age-adjusted general intelligence, we utilized Haufe’s inversion transform (Haufe et al., 2014) to extract predictor weights across various regions. Our analysis revealed that SC–FC coupling within the prefrontal, temporal, and lateral occipital lobes was the most predictive of individual differences in general intelligence (Figure 4B). In addition, we found that the weights of frontoparietal and default mode networks significantly contributed to the prediction of the general intelligence (p < 0.01, 1000 spin test permutations, Figure 4C).

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Transcriptomic and cellular architectures of SC–FC coupling development

We employed partial least square (PLS) analysis (Krishnan et al., 2011) to establish a link between the spatial pattern of SC–FC coupling development and gene transcriptomic profiles (Figure 5A) obtained from the AHBA using a recommended pipeline (Arnatkeviciute et al., 2019). The gene expression score of the first PLS component (PLS1) explained the most spatial variance, at 22.26%. After correcting for spatial autocorrelation (Vos de Wael et al., 2020), we found a positive correlation (Pearson’s r = 0.41, p = 0.006, 10,000 spin test permutations, Figure 5B) between the PLS1 score of genes and the spatial pattern of SC–FC coupling development. In addition, we identified potential transcriptomic architectures using a Gene Ontology (GO) enrichment analysis of biological processes and pathway (Zhou et al., 2019), analysing the significant positive and negative genes in PLS1. The positive weight genes (364 genes) were prominently enriched for ‘myelination’, ‘monoatomic cation transport’, ‘supramolecular fibre organization’, etc. (p < 0.05, FDR corrected, Figure 5C). The negative correlation genes (456 genes) were relatively weakly enriched in ‘cellular macromolecule biosynthetic process’ and other pathways (p < 0.05, FDR corrected, Figure 5C).

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To further investigate cell-specific expression patterns associated with SC–FC coupling development, the selected genes in the AHBA were agglomerated into seven canonical cell classes (Zhang et al., 2016; Lake et al., 2018; Habib et al., 2017; Darmanis et al., 2015; Li et al., 2018; Seidlitz et al., 2020): astrocytes, endothelial cells, excitatory neurons, inhibitory neurons, microglia, oligodendrocytes, and oligodendrocyte precursors (OPCs). Our findings showed that the genes with positive weights were significantly expressed in oligodendrocytes (75 genes, p < 0.001, permutation test, Figure 5D). The genes with negative weights were expressed in astrocytes (43 genes, p < 0.001, permutation test, Figure 5D). Additionally, genes enriched in positive pathways were intensively overexpressed in oligodendrocytes, while genes enriched in three negative pathways were expressed in astrocytes, inhibitory neurons and microglia (p < 0.05, permutation test, Figure 5—figure supplement 1).

Reproducibility analyses different parcellation templates

To evaluate the robustness of our findings to different parcellation templates, using the multimodal parcellation from the Human Connectome Project (HCPMMP) (Glasser et al., 2016), we repeated the analyses of the cortical patterns of SC–FC coupling, correlation of age with SC–FC coupling, and gene weights. We observed a similar distribution in SC–FC coupling in which visual and somatomotor networks had higher coupling values than other networks (Figure 6A). The SC–FC coupling of most cortical regions increased with age (Figure 6B), and the significant regions were similar to those in the main findings (Figure 6C, p < 0.05, FDR corrected). The gene weights of HCPMMP was consistent with that of BNA (r = 0.25, p < 0.001).

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Different tractography strategies

To evaluate the sensitivity of our results to tractography strategies, we reconstructed fibres using deterministic tractography with a ball-and-stick model and generated a fibre number-weighted network for each participant. This same pipeline was employed for subsequent SC–FC coupling, prediction, and gene analyses. These two tractography strategies yielded similar findings, as indicated by significant correlations in the mean SC–FC coupling (r = 0.85, p < 0.001, spin test, Figure 7A), the correlation of between age and SC–FC coupling (r = 0.79, p < 0.001, spin test, Figure 7B), predictive weights on the general intelligence (r = 0.85, p < 0.001, spin test, Figure 7C), and gene weights (r = 0.80, p < 0.001, Figure 7D).

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Split-half validation

To assess the reproducibility of our findings, we performed a split-half independent validation using the whole dataset (WD). Specifically, we randomly partitioned WD into two independent subsets (S1 and S2), and this process was repeated 1000 times to mitigate any potential bias due to data partitioning. We then quantified SC–FC coupling, correlation between age and SC–FC coupling, and gene weights in S1 and S2 using the same procedures. Remarkably, we observed high levels of agreement among the datasets (S1, S2, and the WD) as demonstrated in Figure 8.

Figure 8

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In the present study, we characterized alterations of SC–FC coupling of brain connectome during development by combining intra- and extracortical SC to predict FC based on the HCP-D dataset. We observed that SC–FC coupling was stronger in the visual and somatomotor networks than in other networks, and followed fundamental properties of cortical organization. With development, SC–FC coupling exhibited heterogeneous changes in cortical regions, with significant increases in the somatomotor, frontoparietal, dorsal attention, and default mode networks. Furthermore, we found that SC–FC coupling can predict individual differences in general intelligence, mainly with the frontoparietal and default mode networks contributing higher weights. Finally, we demonstrated that the spatial heterogeneity of changes in SC–FC coupling with age was associated with transcriptomic architectures, with genes with positive weights enriched in oligodendrocyte-related pathways and genes with negative weights expressed in astrocytes. Together, these findings characterized the spatial and temporal pattern of SC–FC coupling of brain connectome during development and the heterogeneity in the development of SC–FC coupling is associated with individual differences in intelligence and transcriptomic architecture.

Intracortical microcircuits are interconnected through extracortical WM connections, which give rise to richly patterned functional networks (Vázquez-Rodríguez et al., 2019; Demirtaş et al., 2019). Despite extensive research on this topic, the relationship between SC and FC remains unclear. Although many studies have attempted to directly correlate FC with the WMC, this correspondence is far from perfect due to the presence of polysynaptic (indirect) structural connections and circuit-level modulation of neural signals (Sarwar et al., 2021; Baum et al., 2020; Honey et al., 2009; Damoiseaux and Greicius, 2009). Biological models can realistically generate these complex structural interconnections, but they have significant temporal and spatial complexity when solving for model parameters (Wang et al., 2019; Woolrich and Stephan, 2013; Messé et al., 2015; Honey et al., 2007). Communication models using the WMC integrate the advantages of different communication strategies and are easy to construct (Avena-Koenigsberger et al., 2017). As there are numerous communication models, we identified an optimal combination consisting of three decentralized communication models based on predictive significance: communicability, mean first-passage times of random walkers and flow graphs. We excluded a centralized model (shortest paths), which was not biologically plausible since it requires global knowledge of the shortest path structure (Zamani Esfahlani et al., 2022; Goñi et al., 2014; Avena-Koenigsberger et al., 2019). In our study, we excluded the Euclidean and geodesic distance because spatial autocorrelation is inhibited. This study provides a complementary perspective (in addition to the role of WMC in shaping FC) that emphasizes the importance of intrinsic properties within intracortical circuit in shaping the large-scale functional organization of the human cortex. MPC can link intracortical circuits variance at specific cortical depths from a graph-theoretical perspective, enabling reflection of intracortical microcircuit differentiation at molecular, cellular, and laminar levels (Valk et al., 2022; Paquola et al., 2019b; Liu et al., 2022; Paquola and Hong, 2023; Park et al., 2022). Coupling models that incorporate these microarchitectural properties yield more accurate predictions of FC from SC (Demirtaş et al., 2019; Deco et al., 2021).

SC–FC coupling may reflect anatomical and functional hierarchies. SC–FC coupling in association areas, which have lower structural connectivity, was lower than that in sensory areas. This configuration effectively releases the association cortex from strong structural constraints imposed by early activity cascades, promoting higher cognitive functions that transcend simple sensorimotor exchanges (Buckner and Krienen, 2013). A macroscale functional principal gradient (Margulies et al., 2016; Huntenburg et al., 2018) in the human brain has been shown to align with anatomical hierarchies. Our study revealed a similar pattern, where SC–FC coupling was positively associated with evolutionary expansion and myelin content, and negatively associated with functional principal gradient during development. These findings are consistent with previous studies on WMC–FC (Baum et al., 2020) and MPC–FC coupling (Valk et al., 2022). Notably, we also found that the coupling pattern differed from that in adults, as illustrated by the moderate coupling of the sensorimotor network in the adult population (Gu et al., 2021). SC–FC coupling is dynamic and changes throughout the lifespan (Zamani Esfahlani et al., 2022), particularly during adolescence (Valk et al., 2022; Baum et al., 2020), suggesting that perfect SC–FC coupling may require sufficient structural descriptors. Moreover, our results suggested that regional preferential contributions across different SCs lead to variations in the underlying communication process. Interestingly, the two extremes of regions in terms of MPC correlations corresponded to the two anchor points of the gradient (Paquola et al., 2019a). The preferential regions in WM communication models were consistent with the adult results (Zamani Esfahlani et al., 2022).

In addition, we observed developmental changes in SC–FC coupling dominated by a positive increase in cortical regions (Baum et al., 2020), broadly distributed across somatomotor, frontoparietal, dorsal attention, and default mode networks (Baum et al., 2020). In a lifespan study, the global SC–FC coupling alterations with age were driven by reduced coupling in the sensorimotor network (Zamani Esfahlani et al., 2022). This finding is consistent across age ranges, indicating that sensorimotor coupling changes appear throughout development and ageing. Furthermore, we investigated the relationships of coupling alterations with evolutionary expansion and functional principal gradient but found no significant correlations, in contrast to a previous study (Baum et al., 2020). These discrepancies likely arise from differences in coupling methods. We also found the SC–FC coupling with age across regions within subnetworks has more variability than the differences between networks, suggesting that the coupling with age is more likely region-dependent than network-dependent.

The neural circuits in the human brain support a wide repertoire of human behaviour (Chen et al., 2022). Our study demonstrates that the degree of SC–FC coupling in cortical regions can significantly predict cognitive scores across various domains, suggesting that it serves as a sensitive indicator of brain maturity. Moreover, even after controlling for age effects, SC–FC coupling significantly predicted general intelligence, suggesting that it can partly explain individual differences in intelligence, as shown in previous studies (Gu et al., 2021). In another study (Baum et al., 2020), positive correlations between executive function and SC–FC coupling were mainly observed in the rostro-lateral frontal and medial occipital regions, whereas negative associations were found in only the right primary motor cortex. While SC–FC coupling was not found to predict age-adjusted executive function in our study, we observed that the frontoparietal network and the default mode network specifically contributed higher positive prediction weights for general intelligence, whereas the somatomotor network had negative prediction weights (Gu et al., 2021). The maturation of the frontoparietal network and default mode network continues into early adulthood, providing an extended window for the activity-dependent reconstruction of distributed neural circuits in the cross-modal association cortex (Buckner and Krienen, 2013). As we observed increasing coupling in these networks, this may have contributed to the improvements in general intelligence, highlighting the flexible and integrated role of these networks.

Classic twin studies have reported that the heritability of coupling differs among cortical regions, with higher heritability in the visual network than in other cortical networks (Gu et al., 2021). An inverse correlation between the pattern of SC–FC coupling and heritable connectivity profiles has been reported (Valk et al., 2022). This led us to hypothesize that the development of SC–FC coupling may be influenced by the expression patterns of the genetic transcriptome across various cell types with different spatial distributions. Our findings suggest that the spatial development of SC–FC coupling is associated with underlying transcriptome structure. Specifically, genes positively associated with the development of SC–FC coupling were enriched in oligodendrocyte-related pathways. Oligodendrocytes, specialized glial cells in the central nervous system, play a crucial role in myelination by producing myelin sheaths that enable saltatory conduction and provide metabolic support to axons (Simons and Nave, 2015). Defects in myelination have been linked to developmental disorders (Berry et al., 2020). This seems to indicate that significant alterations in SC–FC coupling during development may reflect neural plasticity, such as activity-dependent myelination of axons connecting functionally coupled regions (Gibson et al., 2014; Mount and Monje, 2017). Conversely, we found that genes negatively correlated with SC–FC coupling were enriched in two specific gene pathways within astrocytes, inhibitory neurons and microglia. Both astrocytes and microglia have been implicated in synaptic pruning, a critical developmental process for the formation of fully functional neuronal circuits that eliminates weak and inappropriate synapses (Kurshan and Shen, 2019; Van Horn and Ruthazer, 2019; Faust et al., 2021). Importantly, the precise establishment of synapses is crucial for establishing the intercellular connectivity patterns of GABAergic neurons (Favuzzi et al., 2019). These findings suggest that the subtle alterations observed in SC–FC coupling are closely associated with the refinement of mature neural circuits.

Several methodological issues must be addressed. First, we implemented a conservative quality control procedure to address head motion, which unavoidably resulted in the loss of some valuable data. Given the confounding influence of head motion in fMRI studies, especially those involving developing populations, we applied censoring of high-motion frames and included motion as a covariate in the generalized linear model (GLM) analysis and cognitive prediction to minimize its effects (Zamani Esfahlani et al., 2022; Chen et al., 2022; Ciric et al., 2017; Li et al., 2022). Second, although we observed SC–FC coupling across development by integrating intra- and extracortical SC to predict FC, it is worth noting that combining deep learning models (Sarwar et al., 2021), biophysical models (Breakspear, 2017; Sanz-Leon et al., 2015), or dynamic coupling (Demirtaş et al., 2019; Liu et al., 2022) perspectives may provide complementary insights. Third, the appropriateness of structurally defined regions for the functional analysis is also a topic of important debate. Fourth, we focused solely on cortico-cortical pathways, excluding subcortical nuclei from analysis. This decision stemmed from the difficulty of reconstructing the surface of subcortical regions (Glasser et al., 2013) and characterizing their connections using MPC technique, as well as the challenge of accurately resolving the connections of small structures within subcortical regions using whole-brain diffusion imaging and tractography techniques (Thomas et al., 2014; Reveley et al., 2015). In addition, the reconstruction of short connections between hemispheres is a notable challenge. Fifth, it is important to acknowledge that changes in gene expression levels during development may introduce bias in the results. Finally, validation of sensitivity across independent datasets is a crucial step in ensuring the reliability of our results. To address this, we employed an alternative split-half validation strategy and the results supported the reliability of the current findings. However, future verification of current findings on independent datasets are still needed.

The HCP-D 2.0 release data that support the findings of this study are publicly available at https://www.humanconnectome.org/study/hcp-lifespan-development. R4.1.2 software (https://www.r-project.org/) was used to construct the general linear model. MATLAB scripts used for preprocessing of the AHBA dataset can be found at https://github.com/BMHLab/AHBAprocessing (Arnatkeviciute, 2021). Python scripts used to perform PLS regression can be found at https://scikit-learn.org/. The minimal preprocessing pipelines can be accessed at https://github.com/Washington-University/HCPpipelines (Brown et al., 2024). The code relevant to this study can be accessed through the following GitHub repository: https://github.com/FelixFengCN/SC-FC-coupling-development (copy archived at Feng, 2024).

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Author details

1. Guozheng Feng

   1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
   2. BABRI Centre, Beijing Normal University, Beijing, China
   3. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6937-8592

2. Yiwen Wang

   1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
   2. BABRI Centre, Beijing Normal University, Beijing, China
   3. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

3. Weijie Huang

   1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
   2. BABRI Centre, Beijing Normal University, Beijing, China
   3. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2481-1188

4. Haojie Chen

   1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
   2. BABRI Centre, Beijing Normal University, Beijing, China
   3. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

5. Jian Cheng

   School of Computer Science and Engineering, Beihang University, Beijing, China

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

6. Ni Shu

   1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
   2. BABRI Centre, Beijing Normal University, Beijing, China
   3. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Writing - review and editing

   For correspondence

   nshu@bnu.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2420-2910

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