Significance The human brain is organized into a hierarchy of functional systems that evolve in childhood and adolescence to support the dynamic control of attention and behavior. However, it remains unknown how developing white-matter architecture supports coordinated fluctuations in neural activity underlying cognition. We document marked remodeling of structure–function coupling in youth, which aligns with cortical hierarchies of functional specialization and evolutionary expansion. Further, we demonstrate that structure–function coupling in rostrolateral prefrontal cortex supports age-related improvements in executive ability. These findings have broad relevance for accounts of experience-dependent plasticity in healthy development and abnormal development associated with neuropsychiatric illness.

Abstract The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as executive function. However, it remains unclear how white-matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure–function coupling using diffusion-weighted imaging and n-back functional MRI data in a sample of 727 individuals (ages 8 to 23 y). We found that spatial variability in structure–function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure–function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n = 294). Moreover, structure–function coupling in rostrolateral prefrontal cortex was associated with executive performance and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.

The human cerebral cortex is organized along a functional hierarchy extending from unimodal sensory cortex to transmodal association cortex (1, 2). This macroscale functional hierarchy is anchored by an anatomical backbone of white-matter pathways that coordinate synchronized neural activity and cognition. Both primate cortical evolution and human brain development have been characterized by the targeted expansion and remodeling of transmodal association areas (3, 4), which underpin the integration of sensory representations and abstract rules for executing goals. The protracted development of transmodal association cortex in humans provides an extended window for activity-dependent myelination (5) and synaptic pruning (6). This period of cortical plasticity sculpts functional specialization in transmodal association cortex and may be critical for developing higher-order executive functions such as working memory, mental flexibility, and inhibitory control (7).

Characterizing the functional specialization of cortical areas based on their patterns of connectivity has been central to understanding hierarchies of brain organization (8, 9). Network theory has provided a parsimonious framework for modeling structure–function mappings in neurobiological systems across species and spatial scales (10). Convergent evidence has highlighted the strong correspondence between measures of structural and functional brain connectivity at different spatiotemporal scales, including neural populations (11), specialized cortical regions (12), and large-scale brain networks (13⇓–15). However, only sparse data exist regarding how the maturation of white-matter architecture during human brain development supports coordinated fluctuations in neural activity underlying cognition. Furthermore, aberrant development of structural constraints on functional communication could contribute to deficits in executive function and the emergence of neuropsychiatric disorders during adolescence (16⇓–18).

Structure–function coupling describes structural support for functional communication and occurs when a cortical region's profile of interregional white-matter connectivity predicts the strength of interregional functional connectivity. Here, we describe the cortical topography of structure–function coupling and delineate how it evolves with development. To do this, we tested three related hypotheses. First, we hypothesized that structure–function coupling would reflect the functional specialization of a cortical area. Specifically, we predicted structure–function coupling would be high in somatosensory cortex, due to highly conserved programming that governs the early development of specialized sensory hierarchies (19). Conversely, we expected that structure–function coupling would be low in transmodal association cortex, where functional communication may have become untethered from genetic and anatomical constraints through rapid evolutionary expansion (19). Second, based on evidence of prolonged activity-dependent myelination during development (5), we hypothesized that developmental increases in structure–function coupling would be localized to transmodal association cortex. Third, under the premise that structure–function coupling reflects functional specialization of a cortical area (9), we hypothesized that higher structure–function coupling in frontoparietal association cortex would support specialized computations relevant for executive functioning (16, 20).

Discussion We leveraged multimodal neuroimaging in a large sample of youths to characterize how structure–function coupling evolves in development and reflects macroscale cortical hierarchies. Consistent with previous work characterizing the targeted expansion and remodeling of transmodal cortex in both primate evolution and human development, we observed age-related differences in coupling localized within a unique subset of transmodal regions spanning higher-order association networks. These findings fill a critical gap in our understanding of how white-matter architecture develops during human adolescence to support coordinated neural activity underlying executive processing. Cortical hierarchy has provided a unifying principle for understanding the multiscale organization of primate cortical anatomy and function (2, 8, 27). Anatomical hierarchies of intracortical myelin (28) and laminar patterns of interareal projections (29) have been shown to align with hierarchies of functional (2) and transcriptional (28) specialization. Here, we provide evidence that these cortical hierarchies are in part determined by anatomical constraints on functional communication, whereby highly myelinated sensory areas exhibit strong structure–function coupling, and less myelinated association areas exhibit weak structure–function coupling. The convergence of structural and functional connectivity profiles in unimodal sensory regions suggests that functional communication is directly supported by local white-matter pathways. In contrast, the divergence of structural and functional connectivity profiles in transmodal regions suggests that functional communication is untethered by structural constraints, relying on polysynaptic (indirect) structural connections or circuit-level modulation of neural signals. Lower structure–function coupling in transmodal brain regions may also support functional flexibility and dynamic recruitment during diverse task demands (30). One important exception to this trend was observed in transmodal regions of the default mode network, such as the medial prefrontal cortex, which exhibited both functional segregation and strong structure–function coupling. Tightly coupled structural and functional connectivity within transmodal regions of the medial prefrontal cortex could support efficient communication among strongly interconnected association areas within the default mode network. Further, high structure–function coupling in local hubs of the default network could reduce competitive interference between the (task-positive) central executive and (task-negative) default mode networks (31), allowing for the suppression of internally generated thoughts while maintaining and manipulating information in working memory. Our findings of regional variability in structure–function coupling are consistent with recent work that has described similar hierarchical differences between structural and functional connectivity (32) and between microstructural covariance profiles and functional connectivity (33). While these studies report convergent structure–function coupling in primary sensory cortex and divergent structure–function coupling in transmodal association cortex, the focus on group-averaged data precluded investigating how structure–function coupling changes over the course of brain development, and whether it is relevant for individual differences in cognitive ability. One recent study in adults found that lower switch costs during a cognitive switching task were linked with individual differences in the alignment between blood-oxygen-level-dependent signals and the eigenspectrum of structural brain networks (34). By demonstrating age-related differences in regional patterns of structure–function coupling that are linked with executive function, our findings build upon prior accounts of structure–function relationships in human neocortex. Developmental changes in coupling were preferentially localized within transmodal areas of frontoparietal and default mode networks, recapitulating evolutionary patterns of cortical areal expansion. In addition to having expanded association cortex relative to other primates, humans exhibit slower axonal myelination in association cortex during childhood, characterized by a prolonged period of maturation that extends into early adulthood (5). As posited by the tethering hypothesis (19), this protracted development provides an extended window for the activity-dependent remodeling of distributed neural circuits in transmodal association cortex, which may be critical for the maturation of complex cognitive abilities in humans. In our study, longitudinal changes in structure–function coupling in transmodal cortex were associated with developmental increases in the diversity of intermodular functional connectivity, underscoring the flexible and integrative role of these brain regions within the network. One outstanding question concerns whether existing white-matter architecture drives future changes in functional connectivity, or whether functional circuit changes sculpt the development of specific wiring patterns. We speculate that developmental changes in structure–function coupling could reflect processes of neural plasticity, such as the activity-dependent myelination of axons linking functionally coupled regions (35, 36). Alternatively, early myelination of axons could enhance signal conduction velocity and fidelity, enhancing neural signal-to-noise ratio and the coordination of distributed neural activity (36). Longitudinal inferences in our study were limited by only two time points of imaging data, precluding the characterization of lead–lag relationships between structural and functional brain connectivity. Future studies could leverage dense sampling of individuals during sensitive periods of development to delineate lead–lag relationships in the maturation of structural and functional connectivity within specialized circuits. Our results also suggest that structure–function coupling has implications for individual differences in executive function. The rlPFC has been consistently linked with abstract reasoning (37) and the hierarchical control of goal-directed behavior (38). From childhood through early adulthood, the development of structural and functional connectivity between the rlPFC and lateral parietal cortex has been associated with improvements in abstract reasoning ability (37, 39). In this study, we extend these findings by showing that individual differences in rlPFC structure–function coupling partially mediate age-related improvements in executive functioning. The capacity of rlPFC to support executive processing may be understood through its role in integrating information between frontoparietal and dorsal attention networks to regulate perceptual attention (40). Despite the strengths of this study, potential limitations should be noted. First, accurately reconstructing the complexity of human white-matter pathways from diffusion MRI and tractography remains challenging. Diffusion tractography algorithms face a well-characterized trade-off between connectome specificity and sensitivity (41). In this study, we attempted to overcome these limitations by replicating results with both deterministic and probabilistic tractography methods, while also applying a consistency-based thresholding procedure to minimize the influence of false-positive connections (42). Second, motion artifact remains an important confound for all neuroimaging-based studies of brain development (43, 44). In addition to rigorous quality assurance protocols and extensively validated image processing designed to mitigate the influence of head motion on functional connectivity (45), we address this issue by quantifying and controlling for the influence of in-scanner head motion in all group-level analyses. Third, while our approach for quantifying regional patterns of structure–function coupling allowed us to evaluate age-related differences and associations with cognitive ability, this approach was limited in its ability to discern the influence of individual network connections on regional measures.

Conclusion By quantifying regional patterns of structure–function coupling and characterizing their development during adolescence, our results inform network-level mechanisms of plasticity that support cognitive maturation. Describing how underlying white-matter architecture develops to support coordinated neural activity underlying executive function may offer critical insights into the basis for many sources of adolescent morbidity and mortality, such as risk taking and diverse neuropsychiatric syndromes which are prominently associated with failures of executive function.

Materials and Methods Neuroimaging was completed as part of the Philadelphia Neurodevelopmental Cohort (46). All participants, or their parent or guardian, provided informed consent, and minors provided assent; study procedures were approved by the institutional review boards of both the University of Pennsylvania and the Children’s Hospital of Philadelphia. All participants included in this study were medically healthy, were not taking psychotropic medication at the time of study, and passed strict quality-assurance procedures for four imaging modalities including T1-weighted structural images, diffusion-weighted imaging, resting-state functional MRI (fMRI), and n-back fMRI. The final sample included 727 youths ages 8 to 23 y (420 females; mean = 15.9 y, SD = 3.2 y). From the original study sample, 147 typically developing youths returned for longitudinal neuroimaging assessments ∼1.7 y after baseline (83 females; 294 total scans). For further details regarding image preprocessing and brain network construction see SI Appendix, SI Methods. To evaluate the relationship between structure–function coupling and previously characterized cortical hierarchies, evolutionary cortical areal expansion (3) and the principal gradient of intrinsic functional connectivity (2) were extracted from publicly available atlases. The significance of the spatial correspondence between brain maps was estimated using a conservative spatial permutation test, which generates a null distribution of randomly rotated brain maps that preserve spatial covariance structure of the original data (23). We used penalized splines within a GAM to estimate linear and nonlinear age-related changes in structure–function coupling for each brain region. Importantly, the GAM estimates nonlinearities using restricted maximum likelihood, penalizing nonlinearity in order to avoid overfitting the data (47). To evaluate regional associations between structure–function coupling and executive function, executive performance was measured as a factor score summarizing accuracy across mental flexibility, attention, working memory, verbal reasoning, and spatial ability tasks administered as part of the Penn Computerized Neurocognitive Battery (SI Appendix, SI Methods). Longitudinal developmental change in structure–function coupling was evaluated with two approaches. First, we estimated longitudinal age effects on coupling within a linear mixed effects model, including a random subject intercept in addition to other covariates. Second, we used linear regression models with longitudinal change scores. Longitudinal intraindividual change in coupling (ΔCoupling) and the participation coefficient (ΔPC) were calculated as the difference in regional brain measures between time points. Baseline age, sex, mean relative framewise displacement, and the number of years between time points were included as additional covariates in linear regression models. The data reported in this paper have been deposited in the database of Genotypes and Phenotypes under accession number dbGaP: phs000607.v2.p2 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000607.v2.p2).

Acknowledgments This study was supported by grants F31MH115709 (to G.L.B.), R01MH113550 (to T.D.S. and D.S.B.), and R01MH112847 (to R.T.S. and T.D.S.) from the National Institute of Mental Health (NIMH). The Philadelphia Neurodevelopmental Cohort was supported by MH089983 and MH089924. Additional support was provided by NIH Grants R01MH107703 (T.D.S.), R01MH107235 (to R.C.G.), P50MH096891 (to R.E.G.), K01MH102609 (to D.R.R.), R01NS085211 (to R.T.S.), and RF1MH116920 (to D.J.O., T.D.S., and D.S.B.); the Dowshen Program for Neuroscience; and the Lifespan Brain Institute at Penn and Children’s Hospital of Philadelphia.

Footnotes Author contributions: G.L.B., R.E.G., R.C.G., D.S.B., and T.D.S. designed research; G.L.B. performed research; Z.C., D.R.R., R.C., R.F.B., B.L., M.C., P.A.C., C.H.X., T.M.M., K.R., D.J.O., A.F.A.-B., R.T.S., A.R., D.S.B., and T.D.S. contributed new reagents/analytic tools; G.L.B., Z.C., R.C., and T.M.M. analyzed data; and G.L.B. and T.D.S. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

Data deposition: The data reported in this paper have been deposited in the database of Genotypes and Phenotypes (accession no. dbGaP: phs000607.v2.p2).

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1912034117/-/DCSupplemental.