Adolescence is a time of profound and genetically mediated changes in whole-brain network organization (Larsen and Luna, 2018; Menon, 2013). Adolescent development is important for the maturation in cognitive and educational functions and brain health more generally, a notion reinforced by the overlapping onset of several neurodevelopmental and psychiatric disorders (Hong et al., 2019; Khundrakpam et al., 2017; Paus et al., 2008). With increased capacity to carry out longitudinal studies in large samples, it is now possible to track changes in brain network organization within subjects, providing insights into maturational processes, their biological underpinnings, and their effects on behavior and cognition.

By offering an in vivo window into brain organization, neuroimaging techniques, such as magnetic resonance imaging (MRI), offer the ability to track adolescent brain development over time. Several cross-sectional and longitudinal studies in neurodevelopmental cohorts have focused on the analysis of morphological changes (Gogtay et al., 2004; Shaw et al., 2006; Tamnes et al., 2017), including MRI-based cortical thickness (Shaw et al., 2006; Tamnes et al., 2017) and volumetric measures (Gogtay et al., 2004; Tamnes et al., 2017). Studies robustly show initial gray matter increases until mid-late childhood followed by a decline for the rest of the lifespan. During adolescence, cortical thickness decreases in widespread brain regions (Khundrakpam et al., 2013; Shaw et al., 2006; Sotiras et al., 2017; Tamnes et al., 2017). Thus, contextualizing connectome alterations relative to established patterns of cortical thickness findings may establish whether inter-regional network changes occur above and beyond these diffuse effects of regional morphological maturation. More recent work explored changes in intracortical microstructure, capitalizing on myelin-sensitive contrasts such as magnetization transfer ratio (MT) mapping, which generally suggest overall increases in adolescence (Paquola et al., 2019a; Whitaker et al., 2016) together with depth-dependent shifts in intracortical myelin profiles (Paquola et al., 2019a). Besides the increasingly recognized changes in cortico-cortical connectivity, studying subcortical regions offer additional insights for understanding brain maturation during adolescence. Indeed, an increasing body of connectome-level studies emphasizes that subcortical structures contribute significantly to patterns of whole-brain organization, dynamics, and cognition (Hwang et al., 2017; Müller et al., 2020; Shine et al., 2019). In prior neurodevelopmental studies, it has been shown that the volumes of the striatum and thalamus decrease between adolescence and adulthood, potentially paralleling processes resulting in cortical gray matter reduction during this time window (Herting et al., 2018). A close inter-relationship between cortical and subcortical development is also suggested by recent functional connectivity work suggesting that cortico-subcortical pathways are intrinsically remodeled during adolescence (Váša et al., 2020), and these changes affect cognitive functioning. Collectively, these prior findings suggest measurable trajectories of cortical and subcortical structural organization and support associations to cognitive development (Baum et al., 2020; Shaw et al., 2006).

Recent conceptual and methodological advances enable the study of brain organization, development, and substrates underlying cognitive trajectories in humans. One key modality to track developmental changes in structural connectivity is diffusion MRI (dMRI), a technique sensitive to the displacement of water in tissue that allows for the non-invasive approximation of inter-regional white matter tracts. Prior cross-sectional and longitudinal studies in children and adolescents outlined changes in the microstructure of major white matter tracts during development based on the analysis of dMRI-derived tissue parameters (Lebel and Beaulieu, 2011; Schmithorst and Yuan, 2010). These findings have been complemented by assessments of brain network topology using graph-theoretical analysis (Baker et al., 2015; Hagmann et al., 2010; Lebel and Beaulieu, 2011; Oldham and Fornito, 2019), which reported a relatively preserved spatial layout of structural hubs across adolescent development on the one hand (Hagmann et al., 2010), yet with a continued strengthening of their connectivity profiles, likely underpinned by the ongoing maturation of long-range association fibers (Baker et al., 2015; Lebel and Beaulieu, 2011; Oldham and Fornito, 2019).

One emerging approach to address connectome organization and development comes from the application of manifold learning techniques to connectivity datasets. By decomposing whole-brain structural and functional connectomes into a series of lower dimensional axes capturing spatial gradients of connectivity variations, these techniques provide a compact perspective on large-scale connectome organization (Margulies et al., 2016; Paquola et al., 2019b; Vos de Wael et al., 2020a). In addition, these techniques capture multiple, potentially overlapping gradients in connectivity along cortical mantle, which can represent both subregional heterogeneity and multiplicity within a brain region (Haak and Beckmann, 2020). In prior work, we showed that multiple dMRI gradients can illustrate structural underpinnings of dynamic functional communication in the adult human connectome (Park et al., 2021b). In line with prior conceptual accounts, the low-dimensional eigenvectors (i.e., gradients) derived from these techniques provide continuous dimensions of cortical organization, and thus the eigenvectors can jointly generate intrinsic coordinate systems of the brain based on connectivity (Bijsterbosch et al., 2020; Haak et al., 2018; Huntenburg et al., 2018; Margulies et al., 2016; Mars et al., 2018). Beyond these methodological considerations, prior work has shown that the principal gradients estimated from resting-state functional (Margulies et al., 2016), microstructural (Paquola et al., 2019b), and diffusion MRI (Park et al., 2021a) all converge broadly along an established model of sensory-fugal hierarchy and laminar differentiation (Mesulam, 1998), allowing gradient mapping techniques to make conceptual contact to theories of cortical organization, development, and evolution (Buckner and Krienen, 2013; Goulas et al., 2018; Huntenburg et al., 2018; Sanides, 1969; Sanides, 1962). An emerging literature has indeed shown utility of the gradient framework to study primate evolution and cross-species alignment (Blazquez Freches et al., 2020; Valk et al., 2020; Xu et al., 2020), neurodevelopment (Hong et al., 2019; Paquola et al., 2019a), as well as plasticity and structure-function coupling (Park et al., 2021b; Valk Sofie et al., 2020; Vázquez-Rodríguez et al., 2019). In a recent assessment by our team, manifold learning techniques have been applied to myelin sensitive intracortical MT data, showing an increasing myeloarchitectural differentiation of association cortex throughout adolescence (Paquola et al., 2019a). Still, the longitudinal maturation of dMRI connectomes in children and adolescents using manifold techniques has not been tracked.

Imaging-transcriptomics approaches allow for the identification of cellular and molecular factors that co-vary with imaging-based findings (Arnatkeviciute et al., 2019; Fornito et al., 2019; Gorgolewski et al., 2014; Hawrylycz et al., 2015; Thompson et al., 2013). Recently established resources, such as the Allen Human Brain Atlas (Arnatkeviciute et al., 2019; Hawrylycz et al., 2015), can be utilized to spatially associate macroscale imaging/connectome data with the expression patterns of thousands of genes. These findings have already been applied in the study of healthy adults (Hawrylycz et al., 2015; Park et al., 2020) and typically developing adolescents (Mascarell Maričić et al., 2020; Padmanabhan and Luna, 2014; Paquola et al., 2019a; Vértes et al., 2016; Whitaker et al., 2016), as well as individuals suffering from prevalent brain disorders (Altmann et al., 2018; Hashimoto et al., 2015; Klein et al., 2017; Park et al., 2021a; Patel et al., 2021; Romero-Garcia et al., 2019). The gene sets that co-vary with in vivo findings can furthermore be subjected to gene set enrichment analyses to discover potentially implicated molecular, cellular, and pathological processes (Ashburner et al., 2000; Carbon et al., 2019; Chen et al., 2013; Dougherty et al., 2010; Kuleshov et al., 2016; Morgan et al., 2019; Romero-Garcia et al., 2018; Subramanian et al., 2005). For example, studies in newborns have shown that cortical morphology reflects spatiotemporal patterns of gene expression in fetuses, linking molecular mechanisms to in vivo measures of cortical development in early life (Ball et al., 2020). Work in adolescents has furthermore shown that developmental changes in regional cortical thickness measures and myelin proxies spatially co-localize with the expression patterns of genes involved in synaptic and oligodendroglial function (Paquola et al., 2019a; Whitaker et al., 2016). Building on these prior investigations, the current study aimed at exploring whether adolescent structural connectome reconfigurations, assessed using manifold learning techniques, reflect the expression patterns of specific genes in order to identify potential molecular signatures of macroscale structural network development.

Here, we charted developmental changes in structural connectome organization, based on an accelerated longitudinal neuroimaging study involving 208 participants investigated between 14 and 26 years of age (Kiddle et al., 2018; Whitaker et al., 2016). Compared to cross-sectional designs, longitudinal studies track within-subject change, separating developmental effects from between-subject variability (Louis et al., 1986). We first estimated longitudinal changes in structural connectome manifolds across age. This compact and lower dimensional space furthermore allowed for the integration of connectome-level findings with changes in MRI-based measures of cortical morphology and intracortical myelin. We furthermore projected subcortico-cortical connectivity patterns into the manifold space to assess parallel developmental shifts of these pathways in the studied time window. Connectome manifold changes were contextualized at the molecular level via transcriptomic association and developmental enrichment analyses based on post-mortem datasets, which furthermore allowed for data-driven exploration of time windows of spatially co-localized gene sets. To also assess behavioral associations of connectome manifold changes, we utilized supervised machine learning to predict future measures of cognitive function quantified via the intelligence quotient (IQ). IQ is a widely used marker of general cognitive abilities, which shows good test–retest reliability (Brown and May, 1979; Watkins and Smith, 2013; Catron, 1978; G.-Matarazzo et al., 1973; Snow et al., 1989; Wagner and Caldwell, 1979) and has previously been applied to index overall cognitive function during development (Crespi, 2016; Garde et al., 2005; Garde et al., 2000; Koenis et al., 2018; Park et al., 2016; Ramsden et al., 2011; Shaw et al., 2006; Suprano et al., 2020). In the study of neurodevelopment, neuroimaging reports have previously assessed associations between IQ and large-scale network measures in children to adolescents (Koenis et al., 2018; Ramsden et al., 2011; NSPN Consortium et al., 2018; Shaw et al., 2006; Suprano et al., 2020). Multiple sensitivity analyses were conducted at several steps to verify the robustness of our findings, and analytical code is made fully accessible to allow for independent replication of our findings.

These findings were based on the Neuroscience in Psychiatry Network (NSPN) cohort (Kiddle et al., 2018; Whitaker et al., 2016). In brief, we studied 208 healthy individuals enrolled in an accelerated longitudinal study, with approximately equal numbers of males and females in each of five age-related strata that collectively spanned the time period from 14 to 25 years coinciding with transition from adolescence to young adulthood. Participants (48% female) had a mean age of 18.82 years (range = 14–25 years) at baseline and 19.95 years (15–26 years) at follow-up. The average interval between baseline and follow-up scan was 11.28 months (range = 6–12 months). See Materials and methods for details on participant selection, image processing, and analysis.

Macroscale structural connectome manifold

For every participant, we built cortex-wide structural connectome manifolds formed by the eigenvectors displaying spatial gradients in structural connectome organization using non-linear dimensionality reduction techniques (Vos de Wael et al., 2020a; Vos de Wael et al., 2020b, https://github.com/MICA-MNI/BrainSpace). Individual manifolds were aligned to a template manifold estimated from a hold-out dataset (see Materials and methods) (Langs et al., 2015; Vos de Wael et al., 2020a). Three eigenvectors (E1, E2, and E3) explained approximately 50% of information in the template affinity matrix (i.e., 20.7/15.8/13.5% for E1/E2/E3, respectively), with each eigenvector showing a different axis of spatial variation across the cortical mantle (Figure 1A). Eigenvectors depicted a continuous differentiation between medial and lateral cortices (E1), between inferior and superior cortices (E2), and between anterior and posterior areas (E3). For each participant and time point, we calculated manifold eccentricity, which depicts how far each node is located from the center of the template manifold (see Materials and methods). It thus quantifies the changes in eigenvectors between the time points in terms of expansion and contraction instead of comparing multidimensional connectome manifolds (Bethlehem et al., 2020). The manifold eccentricity showed high values in frontal and somatomotor regions, while temporoparietal, visual, and limbic regions showed low values (Figure 1B).

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Effects of cortical morphology and microstructure

Previous studies demonstrated significant changes in cortical morphology and microstructure during adolescence, showing co-occurring reductions in cortical thickness and MT skewness, the latter being an index of depth-dependent intracortical myelin changes in multiple lobes (Gogtay et al., 2004; Khundrakpam et al., 2017; Paquola et al., 2019a; Shaw et al., 2006). We replicated these findings by showing cortical thinning in almost all brain regions across the studied age window as well as reductions in depth-dependent MT skewness, suggestive of supragranular enrichment of myelin (Figure 2A). To evaluate whether the age-related changes in manifold eccentricity were robust above and beyond these regional changes in cortical thickness and MT, we implemented linear mixed effect models including cortical thickness and MT as covariates in the analysis of developmental change in manifold eccentricity (Figure 2B). While we observed virtually identical spatial patterns of manifold eccentricity changes in models that controlled for thickness, MT skewness, and both, age-related effects in regions of significant manifold eccentricity findings (see Figure 1C) were reduced in models that additionally controlled for these covariates (average reduction of t-value in models controlling for thickness/MT skewness/both = 42/18/68%).

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Age-related changes in subcortico-cortical connectivity

Besides visualizing these changes in cortico-cortical connectivity, we also capitalized on the manifold representation to assess adolescent changes in the connectivity of subcortical regions, to obtain a more holistic insight into whole-brain connectome reconfigurations during this time period, and to examine whether subcortical connectivity patterns undergo parallel developmental trajectories (Hwang et al., 2017; Shine et al., 2019). Specifically, we assessed changes in subcortical-weighted manifolds across age, defined by projecting the streamline strength of subcortical regions to cortical targets to the manifold space (see Materials and methods). Such an analysis situates changes in subcortico-cortical pathways in the macroscale context of cortico-cortical connectivity identified in the previous analyses. After multiple comparisons correction, the caudate and thalamus showed significant age-related effects on subcortical-weighted manifolds (FDR < 0.05; Figure 3), and marginal effects were observed in the putamen, pallidum, and hippocampus (FDR < 0.1).

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Transcriptomic association analysis

Connectome organization, in general, and macroscale gradients, in particular, have been argued to reflect genetic expression profiles, underscoring the close link between the physical layout of the brain and innate transcriptional patterning (Buckner and Krienen, 2013; Fornito et al., 2019). Here, we carried out a transcriptomic association analysis and developmental enrichment analyses to contextualize the age-related manifold eccentricity changes with respect to patterns of post-mortem gene expression from a sample of independent adults (Figure 4A). Specifically, leveraging mixed effect models, we associated the spatial patterns of manifold change across age in the NSPN sample (controlling for covariation of cortical thickness and MT) with cortical maps of post-mortem gene expression data from the Allen Institute for Brain Sciences (Arnatkeviciute et al., 2019; Gorgolewski et al., 2015; Gorgolewski et al., 2014; Hawrylycz et al., 2012; Markello et al., 2020). Among the list of most strongly associated genes (FDR < 0.05), we selected only genes that were consistently expressed across different donors (r > 0.5) (Arnatkeviciute et al., 2019; Hawrylycz et al., 2012; Markello et al., 2020; Supplementary file 1). We performed developmental gene set enrichment analysis using the cell-type-specific expression analysis (CSEA) tool, which compares the selected gene list with developmental enrichment profiles (see Materials and methods) (Dougherty et al., 2010; Xu et al., 2014). This analysis highlights developmental time windows across macroscopic brain regions in which genes are strongly expressed. We found marked expression of the genes enriched from childhood onward in the cortex, thalamus, and cerebellum (FDR < 0.001; Figure 4B). Although signal was reduced, genes were also enriched for expression in the striatum at the transition from childhood to adolescence (FDR < 0.05). On the other hand, identified genes were not found to be expressed in the hippocampus and amygdala.

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Association between connectome manifold and cognitive function

Finally, to establish associations between connectome reconfigurations and cognitive functioning, we utilized supervised machine learning to predict full IQ at follow-up using manifold eccentricity features. Independent variables were combinations of cortical and subcortical manifold features at baseline and their age-related trajectory data. We used elastic net regularization with nested ten-fold cross-validation (Cawley and Talbot, 2010; Parvandeh et al., 2020; Tenenbaum et al., 2000; Varma and Simon, 2006; Zou and Hastie, 2005) (see Materials and methods), and repeated the prediction 100 times with different training and test dataset compositions to mitigate subject selection bias. Across cross-validation and iterations, 6.24 ± 5.74 (mean ± SD) features were selected to predict IQ using manifold eccentricity of cortical regions at baseline, 6.20 ± 5.14 cortical features at baseline and maturational change, 5.45 ± 5.99 cortical and subcortical features at baseline, and 5.16 ± 5.43 at baseline and maturational change, suggesting that adding more independent variables may not per se lead to improvement in prediction accuracy. The manifold eccentricity of cortical regions at baseline significantly predicted future IQ score (mean ± SD r = 0.14 ± 0.04; mean absolute error [MAE] = 8.93 ± 0.16, p=0.09). Prediction performance was slightly improved when we combined the manifold eccentricity both at baseline and differences between follow-up and baseline (r = 0.18 ± 0.04; MAE = 9.10 ± 0.19, p=0.04) (Figure 5A). Notably, prediction accuracy was improved if we additionally considered subcortical manifold features (baseline: r = 0.17 ± 0.03; MAE = 8.74 ± 0.11, p=0.04; baseline and maturational change: r = 0.21 ± 0.02; MAE = 8.86 ± 0.14, p=0.01) (Figure 5B). The regions showing strongest predictive validity for IQ were prefrontal, parietal, and temporal cortices, as well as the caudate and thalamus. The probability map of the selected brain regions (bottom right of Figure 5B) was further decoded using Neurosynth (Yarkoni et al., 2011), revealing strong associations with higher-order cognitive and social terms (Figure 5—figure supplement 1). We compared the prediction performance of our model with a baseline model, where IQ of the test set was simple average of training set (r = −0.15 ± 0.06, MAE = 8.98 ± 0.04, p=0.12; see Materials and methods). We found that our model outperformed this baseline model (Meng’s z-test p < 0.001) (Meng et al., 1992). We also predicted the change of IQ between the baseline and follow-up, instead of IQ at follow-up, using the imaging features. However, we could not find significant results.

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The current study tracked whole-brain structural connectome maturation from adolescence to young adulthood in an accelerated longitudinal imaging cohort (Kiddle et al., 2018; Whitaker et al., 2016). Capitalizing on advanced manifold learning techniques applied to dMRI-derived connectomes, we established that higher-order association cortices in prefrontal, medial and superior temporal areas, as well as parieto-occipital regions, expanded in their connectome manifold representation indicative of an increased differentiation of these systems from the rest of the brain in adolescence. Parallel topological analysis based on graph theory indicated that these changes anatomically coincided with increases in the within-module connectivity of transmodal cortices. Findings were consistent across the different acquisition sites and biological sexes, and similar albeit slightly weaker when correcting connectivity manifolds for MRI-based measures of macrostructure (cortical thickness) and microstructure (skewness of MT depth profile). In addition to the cortical manifold expansion, we found parallel reconfigurations of subcortical connectivity patterns for the caudate and thalamus. Decoding our findings with post-mortem gene expression maps implicated genes enriched in adolescence and young adulthood, again pointing to both cortical as well as subcortical targets. Finally, the combination of both cortical and subcortical manifold measures predicted behavioral measures of intelligence at follow-up, with higher performance than cortical or subcortical data alone. Collectively, our findings provide new insights into adolescent structural connectome maturation and indicate how multiple scales of cortical and subcortical organization can interact in typical neurodevelopment.

Leveraging advanced manifold learning, we depicted macroscale connectome organization along continuous cortical axes. Similar approaches have previously been harnessed to decompose microstructural (Paquola et al., 2019b; Paquola et al., 2019a) and functional MRI (Bethlehem et al., 2020; Hong et al., 2019; Margulies et al., 2016; Murphy et al., 2019; Vos de Wael et al., 2020a). These techniques are appealing as they offer a low-dimensional perspective on connectome reconfigurations in a data-driven and spatially unconstrained manner. In our longitudinal study, we could identify marked connectome expansion during adolescence, mainly encompassing transmodal and heteromodal association cortex in prefrontal, temporal, and posterior regions, the territories known to mature later in development (Gogtay et al., 2004; Shaw et al., 2006). Findings remained consistent when we considered a linear dimensionality reduction technique, suggesting robustness to methodological details of this analysis. Connectome expansion can be understood as an overall greater differentiation of the connectivity of these areas from the rest of the brain as they would then cover wider portions of the corresponding manifold space. Manifold expansion in higher-order areas correlated with an increase in their within-module connectivity, but not with participation coefficient and connectivity distance measures that would be more reflective of their between-module connectivity. In light of potential limitations of dMRI tractography in detecting long-distance fiber tracts (Betzel et al., 2019; Maier-Hein et al., 2017), we cannot rule out a reduced sensitivity of our approach for the study of long-range inter-regional connections. Nevertheless, our diffusion modeling was based on constrained spherical-deconvolution approaches together with SIFT2-based tractogram filtering, in addition to using a distance-dependent thresholding approach that may have partially mitigated these limitations (Betzel et al., 2019). Thus, our findings do overall confirm and extend prior dMRI studies that have focused on specific tracts and that have indicated considerable developmental shifts in diffusion parameters, such as increases in fractional anisotropy and decreases in mean diffusivity in early and late adolescence (Olson et al., 2009). Other studies have furthermore reported increased streamline count estimates (Genc et al., 2020). In this context, our macroscale manifold findings likely reflect an ongoing consolidation of transmodal cortical communities. These findings align with prior graph-theoretical studies, which have pointed to concurrent increases in network integration and consolidation of network hubs from late childhood to early adulthood (Baker et al., 2015; Lebel and Beaulieu, 2011; Oldham and Fornito, 2019). Considering their distributed regional substrate, these network effects are likely driven by the ongoing maturation of fiber bundles that interconnect these higher-order cortices, including superior longitudinal fascicules, but also thalamic and basal ganglia pathways (Tamnes et al., 2010), throughout adolescence.

Projecting manifold solutions back onto cortical surfaces allowed us to integrate our connectome manifold results with morphometric and intracortical intensity indices obtained via structural and quantitative MRI contrasts in the same participants. We were thus able to balance the network-level effects against trajectories of intracortical remodeling. Longitudinal changes in these cortical features were overall in agreement with prior work, suggesting marked reductions in cortical thickness in adolescence (Khundrakpam et al., 2017; Shaw et al., 2006), possibly reflecting synaptic pruning processes (Petanjek et al., 2011) together with decreases in the skewness of intracortical MT profiles, a feature sensitive to preferential myelination of supragranular layers (Paquola et al., 2019a). Although we still observed significant age-related changes in manifold eccentricity after controlling for these intracortical and morphological measures, the effect sizes of our findings were reduced. This effect was particularly evident when running a parallel analysis that additionally controlled for depth-dependent shifts in cortical microstructure, a finding in line with more generally demonstrated links between cortical microstructural depth profiles and inter-regional connectivity (Paquola et al., 2019b). In the context of adolescence and prior findings in the NSPN dataset (Paquola et al., 2019a), these results thus suggest a coupled process that affects depth-dependent shifts in cortical myeloarchitecture, on the one hand, and adolescent shifts in macroscale connectome organization, on the other hand, as shown by our longitudinal manifold analyses.

In addition to emphasizing a distributed set of association cortices and their cortico-cortical connections, analysis of subcortico-cortical connectivity patterns highlighted parallel developmental processes in several subcortical structures and their connections, particularly the caudate and thalamus. These findings were independently supported by transcriptomic association studies and developmental enrichment analyses, which implicated genes expressed in cortical regions and these subcortical structures during late childhood, adolescence, and young adulthood. The caudate nucleus of the striatum has long been recognized to play an important role in mediating large-scale cortical network organization (Aglioti, 1997; Alexander and Crutcher, 1990; Graybiel, 1995), a finding also increasingly recognized in the connectome literature (Hwang et al., 2017; Müller et al., 2020; Shine et al., 2019). It is known to modulate activity in prefrontal association areas during memory-driven internal thought processes (Aglioti, 1997), and higher-order cognitive functions, notably motivational processes, decision making, as well as cognitive control and executive functions more generally (Aglioti, 1997; Graybiel, 1995). Regions of the striatum participate in dense cortico-subcortical feedback loops and exchange neural activity through dense connections with adjacent basal ganglia structures as well as the thalamus (Aglioti, 1997; Alexander and Crutcher, 1990; Shine, 2021). Associating macroscopic changes in manifold eccentricity with post-mortem microarray data provided by the Allen Human Brain Atlas (Arnatkeviciute et al., 2019; Fornito et al., 2019; Gorgolewski et al., 2014; Hawrylycz et al., 2015; Thompson et al., 2013), we identified gene sets expressed in cortical regions and subcortical structures of the thalamus and striatum during late childhood, adolescence, and young adulthood. Despite these findings being associative and based on separate datasets, they overall support our results that brain network maturation from late childhood until early adulthood implicates micro- and macroscale factors in both subcortical and cortical networks. Coupled network and molecular changes may ultimately change subcortical and cortical circuit properties, including the balance of excitation and inhibition (E/I). Human brain development involves spatio-temporal waves of gene expression changes across different brain regions and developmental time windows (Ip et al., 2010; Kang et al., 2011; Shin et al., 2018). In the study of adolescent development, prior studies have suggested shifts in E/I balance, evolving from a dominant inhibitory bias in early developmental stages towards stronger excitatory drive in later stages, and suggested that these may underlie the maturation of cognitive functions such as working memory and executive control (Dorrn et al., 2010; Lander et al., 2017; Liu et al., 2007). In common neurodevelopmental disorders, including autism, schizophrenia, and attention-deficit hyperactivity disorder, imbalances in cortical E/I and cortico-subcortical network function have been demonstrated (Cellot and Cherubini, 2014; Gandal et al., 2018; Lee et al., 2017; Lewis et al., 2005; Nelson and Valakh, 2015; Park et al., 2021a; Sohal and Rubenstein, 2019; Trakoshis et al., 2020), potentially downstream to perturbations of different neurotransmitter systems, such as interneuron-mediated GABA transmission (Bonaventura et al., 2017; Kilb, 2012; Liu et al., 2007; Park et al., 2021a; Silveri et al., 2013; Trakoshis et al., 2020; Tziortzi et al., 2014).

Higher-order cognitive function implicates functionally relevant whole-brain network mechanisms, and its prediction may thus leverage structurally governed principles of network integration and segregation. Application of a supervised machine learning framework with cross-validation and regularization to our cohort demonstrated that it is possible to predict inter-individual variations in future IQ from structural connectome manifold data. These findings complement conceptual accounts linking brain organization to cognitive function (Margulies et al., 2016; Mesulam, 1998) and earlier efforts to predict IQ measures from inter-regional measures of connectivity and graph-theoretical indices of network topology (Greene et al., 2018). Notably, evaluations of several feature combinations highlighted that predictive performance was highest when including both baseline and trajectory data, and when complementing cortical and subcortical manifold features. These findings re-emphasize the benefits of incorporating subcortical nodes in the characterization of large-scale cortical network organization and overall cognitive function (Alves et al., 2019; Müller et al., 2020; Shine, 2021; Shine et al., 2019). Of note, although our model significantly outperformed a baseline model, the relationship between the actual and predicted IQ scores did not locate on the equality line and the strength of the association was rather weak. Further improvements in brain-based IQ prediction in adolescence, for example, through combinations of structural and functional imaging features, will be a focus of future work.

Adolescence is a time characterized by ongoing brain changes (Baum et al., 2020; Gogtay et al., 2004; Larsen and Luna, 2018; Menon, 2013; Shaw et al., 2006), gradually increasing independence from caregivers, accompanied by strong increments in knowledge and our ability to think more abstractly and to cooperate with our peers to achieve common goals. On the other hand, adolescence is also a sensitive time window for risk taking, the development of addictions, and is associated with high rates of onset of several psychiatric disorders (Hong et al., 2019; Khundrakpam et al., 2017). Our study has shown that structural brain network organization continues to mature significantly during this time period, with higher-order association cortices in prefrontal and posterior regions especially showing an expansion of their corresponding connectome manifold signature. Findings were related to an increased strengthening of intra-community connectivity as well as cortico-subcortical connectivity to thalamo-striatal regions. Although the current work was restricted to a longitudinal sample of typically developing adolescents, our framework may be useful to explore multiscale network perturbations in cohorts with a psychiatric diagnosis or those at risk for addiction or atypical neurodevelopment.

The imaging and phenotypic data were provided by the NSPN 2400 cohort. As stated in https://doi.org/10.1093/ije/dyx117, the NSPN project is committed to make the anonymised dataset fully available to the research community, and participants have consented to their de-identified data being made available to other researchers. A data request can be made to openNSPN@medschl.cam.ac.uk. Codes for connectome manifold generation are available at https://doi.org/10.1038/s42003-020-0794-7; https://github.com/MICA-MNI/BrainSpace (copy archived at https://archive.softwareheritage.org/swh:1:rev:1fb001f4961d3c0b05b7715f42bcc362b31b96a5/), and those for calculating manifold eccentricity and subcortical-weighted manifold, as well as performing linear mixed effect modeling to assess age-effects on these features at our GitHub (https://github.com/MICA-MNI/micaopen/tree/master/manifold_features; copy archived at https://archive.softwareheritage.org/swh:1:rev:d3988d51e01940007595761dab6b846ce2506433/).

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Author details

1. Bo-yong Park

   1. McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
   2. Department of Data Science, Inha University, Incheon, Republic of Korea

   Contribution

   Conceptualization, Software, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   by9433@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7096-337X

2. Richard AI Bethlehem

   1. Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   2. Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0714-0685

3. Casey Paquola

   1. McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
   2. Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany

   Contribution

   Conceptualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0190-4103

4. Sara Larivière

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Raul Rodríguez-Cruces

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Reinder Vos de Wael

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Neuroscience in Psychiatry Network (NSPN) Consortium

   Contribution

   Data curation

   Additional information

   A complete list of investigators from the Neuroscience in Psychiatry Network (NSPN) Consortium can be found in the Supplementary Information.

   1. Edward Bullmore, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   2. Raymond Dolan, Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
   3. Ian Goodyer, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   4. Peter Fonagy, Research Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
   5. Peter Jones, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   6. Michael Moutoussis, Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
   7. Tobias Hauser, Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
   8. Sharon Neufeld, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   9. Rafael Romero-Garcia, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   10. Michelle St Clair, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   11. Petra Vértes, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   12. Kirstie Whitaker, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   13. Becky Inkster, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   14. Gita Prabhu, Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
   15. Cinly Ooi, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   16. Umar Toseeb, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   17. Barry Widmer, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   18. Junaid Bhatti, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   19. Laura Villis, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   20. Ayesha Alrumaithi, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   21. Sarah Birt, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   22. Aislinn Bowler, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   23. Kalia Cleridou, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   24. Hina Dadabhoy, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   25. Emma Davies, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   26. Ashlyn Firkins, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   27. Sian Granville, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   28. Elizabeth Harding, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   29. Alexandra Hopkins, Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
   30. Daniel Isaacs, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   31. Janchai King, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   32. Danae Kokorikou, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   33. Christina Maurice, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   34. Cleo McIntosh, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   35. Jessica Memarzia, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   36. Harriet Mills, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   37. Ciara O’Donnell, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   38. Sara Pantaleone, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   39. Jenny Scott, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   40. Beatrice Kiddle, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   41. Ela Polek, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   42. Pasco Fearon, Research Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
   43. John Suckling, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   44. Anne-Laura van Harmelen, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
   45. Rogier Kievit, Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
   46. Sam Chamberlain, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

8. Edward T Bullmore

   Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Resources, Methodology, Writing - review and editing

   Competing interests

   ETB. serves on the scientific advisory board of Sosei Heptares and as a consultant for GlaxoSmithKline.

9. Boris C Bernhardt

   McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Supervision, Writing - original draft, Writing - review and editing

   For correspondence

   boris.bernhardt@mcgill.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9256-6041