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/).

1. 1. Aglioti S

   (1997) The role of the thalamus and basal ganglia in human cognition

   Journal of Neurolinguistics 10:255–265.

   https://doi.org/10.1016/S0911-6044(97)00020-1

   • Google Scholar
2. 1. Alexander GE
   2. Crutcher MD

   (1990) Functional architecture of basal ganglia circuits: neural substrates of parallel processing

   Trends in Neurosciences 13:266–271.

   https://doi.org/10.1016/0166-2236(90)90107-L

   • PubMed
   • Google Scholar
3. 1. Alexander-Bloch AF
   2. Shou H
   3. Liu S
   4. Satterthwaite TD
   5. Glahn DC
   6. Shinohara RT
   7. Vandekar SN
   8. Raznahan A

   (2018) On testing for spatial correspondence between maps of human brain structure and function

   NeuroImage 178:540–551.

   https://doi.org/10.1016/j.neuroimage.2018.05.070

   • PubMed
   • Google Scholar
4. Preprint

   1. Altmann A
   2. Ryten M
   3. Di NM
   4. Ravizza T
   5. Tolomeo D
   6. Reynolds RH
   7. Somani A
   8. Bacigaluppi M
   9. Iori V
   10. Micotti E
   11. Botia JA
   12. Absil J
   13. Alhusaini S
   14. Alvim MKM
   15. Auvinen P
   16. Bargallo N
   17. Bartolini E
   18. Bender B
   19. Bergo F
   20. Bernardes T
   21. Bernasconi A
   22. Bernasconi N
   23. Bernhardt BC
   24. Blackmon K
   25. Braga B
   26. Caligiuri ME
   27. Cavalo A
   28. Carlson C
   29. Carr S
   30. Cavalleri G
   31. Cendes F
   32. Chen J
   33. Chen S
   34. Cherubini A
   35. Concha L
   36. David P
   37. Delanty N
   38. Depondt C
   39. Devinsky O
   40. Doherty CP
   41. Domin M
   42. Focke N
   43. Foley S
   44. Franca W
   45. Gambardella A
   46. Guerrini R
   47. Hamandi K
   48. Hibar DP
   49. Isaev DY
   50. Jackson G
   51. Jahanshad N
   52. Kalviainen R
   53. Keller S
   54. Kochunov P
   55. Kotikalapudi R
   56. Kowalczyk MA
   57. Kuzniecky R
   58. Kwan P
   59. Labate A
   60. Langer S
   61. Lenge M
   62. Liu M
   63. Martin P
   64. Mascalchi M
   65. Meletti S
   66. Morita ME
   67. O’Brien TJ
   68. Pariente JC
   69. Richardson MP
   70. Rodriguez-Cruces R
   71. Rummel C
   72. Saavalainen T
   73. Semmelroch MK
   74. Severino M
   75. Striano P
   76. Thesen T
   77. Thomas RH
   78. Tondelli M
   79. Tortora D
   80. Vaudano AE
   81. Vivash L
   82. von PF
   83. Wagner J
   84. Weber B
   85. Wiest R
   86. Yasuda CL
   87. Zhang G
   88. Zhang J
   89. Group E-EW
   90. Leu C
   91. Avbersek A
   92. Consortium E
   93. Thom M
   94. Whelan CD
   95. Thompson P
   96. McDonald C
   97. Vezzani A
   98. Sisodiya SM

   (2018) A systems-level analysis highlights microglial activation as a modifying factor in common forms of human epilepsy

   bioRxiv.

   https://doi.org/10.1101/470518

   • Google Scholar
5. 1. Alves PN
   2. Foulon C
   3. Karolis V
   4. Bzdok D
   5. Margulies DS
   6. Volle E
   7. Thiebaut de Schotten M

   (2019) An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings

   Communications Biology 2:370.

   https://doi.org/10.1038/s42003-019-0611-3

   • PubMed
   • Google Scholar
6. 1. Arnatkeviciute A
   2. Fulcher BD
   3. Fornito A

   (2019) A practical guide to linking brain-wide gene expression and neuroimaging data

   NeuroImage 189:353–367.

   https://doi.org/10.1016/j.neuroimage.2019.01.011

   • PubMed
   • Google Scholar
7. 1. Ashburner M
   2. Ball CA
   3. Blake JA
   4. Botstein D
   5. Butler H
   6. Cherry JM
   7. Davis AP
   8. Dolinski K
   9. Dwight SS
   10. Eppig JT
   11. Harris MA
   12. Hill DP
   13. Issel-Tarver L
   14. Kasarskis A
   15. Lewis S
   16. Matese JC
   17. Richardson JE
   18. Ringwald M
   19. Rubin GM
   20. Sherlock G

   (2000) Gene ontology: tool for the unification of biology

   Nature Genetics 25:25–29.

   https://doi.org/10.1038/75556

   • Google Scholar
8. 1. Avants BB
   2. Tustison NJ
   3. Song G
   4. Cook PA
   5. Klein A
   6. Gee JC

   (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration

   NeuroImage 54:2033–2044.

   https://doi.org/10.1016/j.neuroimage.2010.09.025

   • PubMed
   • Google Scholar
9. 1. Baker ST
   2. Lubman DI
   3. Yücel M
   4. Allen NB
   5. Whittle S
   6. Fulcher BD
   7. Zalesky A
   8. Fornito A

   (2015) Developmental changes in brain network hub connectivity in late adolescence

   Journal of Neuroscience 35:9078–9087.

   https://doi.org/10.1523/JNEUROSCI.5043-14.2015

   • PubMed
   • Google Scholar
10. 1. Ball G
    2. Seidlitz J
    3. O'Muircheartaigh J
    4. Dimitrova R
    5. Fenchel D
    6. Makropoulos A
    7. Christiaens D
    8. Schuh A
    9. Passerat-Palmbach J
    10. Hutter J
    11. Cordero-Grande L
    12. Hughes E
    13. Price A
    14. Hajnal JV
    15. Rueckert D
    16. Robinson EC
    17. Edwards AD

    (2020) Cortical morphology at birth reflects spatiotemporal patterns of gene expression in the fetal human brain

    PLOS Biology 18:e3000976.

    https://doi.org/10.1371/journal.pbio.3000976

    • PubMed
    • Google Scholar
11. 1. Baum GL
    2. Cui Z
    3. Roalf DR
    4. Ciric R
    5. Betzel RF
    6. Larsen B
    7. Cieslak M
    8. Cook PA
    9. Xia CH
    10. Moore TM
    11. Ruparel K
    12. Oathes DJ
    13. Alexander-Bloch AF
    14. Shinohara RT
    15. Raznahan A
    16. Gur RE
    17. Gur RC
    18. Bassett DS
    19. Satterthwaite TD

    (2020) Development of structure-function coupling in human brain networks during youth

    PNAS 117:771–778.

    https://doi.org/10.1073/pnas.1912034117

    • PubMed
    • Google Scholar
12. 1. Benjamini Y
    2. Hochberg Y

    (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing

    Journal of the Royal Statistical Society: Series B 57:289–300.

    https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    • Google Scholar
13. Preprint

    1. Benkarim O
    2. Paquola C
    3. Park B
    4. Hong S-J
    5. Royer J
    6. de WRV
    7. Lariviere S
    8. Valk S
    9. Bzdok D
    10. Mottron L
    11. Bernhardt B

    (2020) Functional idiosyncrasy has a shared topography with group-level connectivity alterations in autism

    bioRxiv.

    https://doi.org/10.1101/2020.12.18.423291

    • Google Scholar
14. 1. Bethlehem RAI
    2. Paquola C
    3. Seidlitz J
    4. Ronan L
    5. Bernhardt B
    6. Consortium CC
    7. Tsvetanov KA

    (2020) Dispersion of functional gradients across the adult lifespan

    NeuroImage 222:117299.

    https://doi.org/10.1016/j.neuroimage.2020.117299

    • PubMed
    • Google Scholar
15. 1. Betzel RF
    2. Griffa A
    3. Hagmann P
    4. Mišić B

    (2019) Distance-dependent consensus thresholds for generating group-representative structural brain networks

    Network Neuroscience 3:475–496.

    https://doi.org/10.1162/netn_a_00075

    • PubMed
    • Google Scholar
16. 1. Bijsterbosch J
    2. Harrison SJ
    3. Jbabdi S
    4. Woolrich M
    5. Beckmann C
    6. Smith S
    7. Duff EP

    (2020) Challenges and future directions for representations of functional brain organization

    Nature Neuroscience 23:1484–1495.

    https://doi.org/10.1038/s41593-020-00726-z

    • PubMed
    • Google Scholar
17. 1. Blazquez Freches G
    2. Haak KV
    3. Bryant KL
    4. Schurz M
    5. Beckmann CF
    6. Mars RB

    (2020) Principles of temporal association cortex organisation as revealed by connectivity gradients

    Brain Structure and Function 225:1245–1260.

    https://doi.org/10.1007/s00429-020-02047-0

    • PubMed
    • Google Scholar
18. 1. Blondel VD
    2. Guillaume J-L
    3. Lambiotte R
    4. Lefebvre E

    (2008) Fast unfolding of communities in large networks

    Journal of Statistical Mechanics: Theory and Experiment 2008:P10008.

    https://doi.org/10.1088/1742-5468/2008/10/P10008

    • Google Scholar
19. 1. Bonaventura J
    2. Quiroz C
    3. Cai NS
    4. Rubinstein M
    5. Tanda G
    6. Ferré S

    (2017) Key role of the dopamine D₄ receptor in the modulation of corticostriatal glutamatergic neurotransmission

    Science Advances 3:e1601631.

    https://doi.org/10.1126/sciadv.1601631

    • PubMed
    • Google Scholar
20. Book

    1. Breiman L
    2. Friedman J
    3. Stone CJ
    4. Olshen RA

    (1984)

    Classification and Regression Trees

    Wadsworth, Inc.

    • Google Scholar
21. 1. Brown HS
    2. May AE

    (1979) A test–retest reliability study of the Wechsler Adult Intelligence Scale

    Journal of Consulting and Clinical Psychology 47:601–602.

    https://doi.org/10.1037/0022-006X.47.3.601

    • Google Scholar
22. 1. Buckner RL
    2. Krienen FM

    (2013) The evolution of distributed association networks in the human brain

    Trends in Cognitive Sciences 17:648–665.

    https://doi.org/10.1016/j.tics.2013.09.017

    • PubMed
    • Google Scholar
23. 1. Carbon S
    2. Douglass E
    3. Dunn N
    4. Good B
    5. Harris NL
    6. Lewis SE
    7. Mungall CJ
    8. Basu S
    9. Chisholm RL
    10. Dodson RJ
    11. Hartline E
    12. Fey P
    13. Thomas PD
    14. Albou LP
    15. Ebert D
    16. Kesling MJ
    17. Mi H
    18. Muruganujan A
    19. Huang X
    20. Poudel S
    21. Mushayahama T
    22. Jc H
    23. LaBonte SA
    24. Siegele DA
    25. Antonazzo G
    26. Attrill H
    27. Brown NH
    28. Fexova S
    29. Garapati P
    30. Jones TEM
    31. Marygold SJ
    32. Millburn GH
    33. Rey AJ
    34. Trovisco V
    35. Dos Santos G
    36. Emmert DB
    37. Falls K
    38. Zhou P
    39. Goodman JL
    40. Strelets VB
    41. Thurmond J
    42. Courtot M
    43. Osumi DS
    44. Parkinson H
    45. Roncaglia P
    46. Acencio ML
    47. Kuiper M
    48. Lreid A
    49. Logie C
    50. Lovering RC
    51. Huntley RP
    52. Denny P
    53. Campbell NH
    54. Kramarz B
    55. Acquaah V
    56. Ahmad SH
    57. Chen H
    58. Rawson JH
    59. Chibucos MC
    60. Giglio M
    61. Nadendla S
    62. Tauber R
    63. Duesbury MJ
    64. Del NT
    65. Meldal BHM
    66. Perfetto L
    67. Porras P
    68. Orchard S
    69. Shrivastava A
    70. Xie Z
    71. Chang HY
    72. Finn RD
    73. Mitchell AL
    74. Rawlings ND
    75. Richardson L
    76. Sangrador-Vegas A
    77. Blake JA
    78. Christie KR
    79. Dolan ME
    80. Drabkin HJ
    81. Hill DP
    82. Ni L
    83. Sitnikov D
    84. Harris MA
    85. Oliver SG
    86. Rutherford K
    87. Wood V
    88. Hayles J
    89. Bahler J
    90. Lock A
    91. Bolton ER
    92. De Pons J
    93. Dwinell M
    94. Hayman GT
    95. Laulederkind SJF
    96. Shimoyama M
    97. Tutaj M
    98. Wang SJ
    99. D’Eustachio P
    100. Matthews L
    101. Balhoff JP
    102. Aleksander SA
    103. Binkley G
    104. Dunn BL
    105. Cherry JM
    106. Engel SR
    107. Gondwe F
    108. Karra K
    109. MacPherson KA
    110. Miyasato SR
    111. Nash RS
    112. Pc N
    113. Sheppard TK
    114. Shrivatsav Vp A
    115. Simison M
    116. Skrzypek MS
    117. Weng S
    118. Wong ED
    119. Feuermann M
    120. Gaudet P
    121. Bakker E
    122. Berardini TZ
    123. Reiser L
    124. Subramaniam S
    125. Huala E
    126. Arighi C
    127. Auchincloss A
    128. Axelsen K
    129. Argoud GP
    130. Bateman A
    131. Bely B
    132. Blatter MC
    133. Boutet E
    134. Breuza L
    135. Bridge A
    136. Britto R
    137. Bye-A-Jee H
    138. Casals-Casas C
    139. Coudert E
    140. Estreicher A
    141. Famiglietti L
    142. Garmiri P
    143. Georghiou G
    144. Gos A
    145. Gruaz-Gumowski N
    146. Hatton-Ellis E
    147. Hinz U
    148. Hulo C
    149. Ignatchenko A
    150. Jungo F
    151. Keller G
    152. Laiho K
    153. Lemercier P
    154. Lieberherr D
    155. Lussi Y
    156. Mac-Dougall A
    157. Magrane M
    158. Martin MJ
    159. Masson P
    160. Natale DA
    161. Hyka NN
    162. Pedruzzi I
    163. Pichler K
    164. Poux S
    165. Rivoire C
    166. Rodriguez-Lopez M
    167. Sawford T
    168. Speretta E
    169. Shypitsyna A
    170. Stutz A
    171. Sundaram S
    172. Tognolli M
    173. Tyagi N
    174. Warner K
    175. Zaru R
    176. Wu C
    177. Chan J
    178. Cho J
    179. Gao S
    180. Grove C
    181. Harrison MC
    182. Howe K
    183. Lee R
    184. Mendel J
    185. Muller HM
    186. Raciti D
    187. Van Auken K
    188. Berriman M
    189. Stein L
    190. Sternberg PW
    191. Howe D
    192. Toro S
    193. Westerfield M
    194. The Gene Ontology Consortium

    (2019) The gene ontology resource: 20 years and still GOing strong

    Nucleic Acids Research 47:D330–D338.

    https://doi.org/10.1093/nar/gky1055

    • PubMed
    • Google Scholar
24. 1. Catron DW

    (1978) Immediate Test-Retest changes in WAIS scores among college males

    Psychological Reports 43:279–290.

    https://doi.org/10.2466/pr0.1978.43.1.279

    • Google Scholar
25. 1. Cawley GC
    2. Talbot NLC

    (2010)

    On over-fitting in model selection and subsequent selection Bias in performance evaluation

    Journal of Machine Learning Research : JMLR 11:2079–2107.

    • Google Scholar
26. 1. Cellot G
    2. Cherubini E

    (2014) GABAergic signaling as therapeutic target for autism spectrum disorders

    Frontiers in Pediatrics 2:70.

    https://doi.org/10.3389/fped.2014.00070

    • PubMed
    • Google Scholar
27. 1. Chen EY
    2. Tan CM
    3. Kou Y
    4. Duan Q
    5. Wang Z
    6. Meirelles GV
    7. Clark NR
    8. Ma'ayan A

    (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

    BMC Bioinformatics 14:128.

    https://doi.org/10.1186/1471-2105-14-128

    • PubMed
    • Google Scholar
28. 1. Christiaens D
    2. Reisert M
    3. Dhollander T
    4. Sunaert S
    5. Suetens P
    6. Maes F

    (2015) Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model

    NeuroImage 123:89–101.

    https://doi.org/10.1016/j.neuroimage.2015.08.008

    • PubMed
    • Google Scholar
29. 1. Coifman RR
    2. Lafon S

    (2006) Diffusion maps

    Applied and Computational Harmonic Analysis 21:5–30.

    https://doi.org/10.1016/j.acha.2006.04.006

    • Google Scholar
30. 1. Cox RW

    (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages

    Computers and Biomedical Research 29:162–173.

    https://doi.org/10.1006/cbmr.1996.0014

    • PubMed
    • Google Scholar
31. 1. Crespi BJ

    (2016) Autism as a disorder of high intelligence

    Frontiers in Neuroscience 10:300.

    https://doi.org/10.3389/fnins.2016.00300

    • PubMed
    • Google Scholar
32. 1. Dale AM
    2. Fischl B
    3. Sereno MI

    (1999) Cortical surface-based analysis. I. segmentation and surface reconstruction

    NeuroImage 9:179–194.

    https://doi.org/10.1006/nimg.1998.0395

    • PubMed
    • Google Scholar
33. 1. Desikan RS
    2. Ségonne F
    3. Fischl B
    4. Quinn BT
    5. Dickerson BC
    6. Blacker D
    7. Buckner RL
    8. Dale AM
    9. Maguire RP
    10. Hyman BT
    11. Albert MS
    12. Killiany RJ

    (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

    NeuroImage 31:968–980.

    https://doi.org/10.1016/j.neuroimage.2006.01.021

    • PubMed
    • Google Scholar
34. 1. Dorrn AL
    2. Yuan K
    3. Barker AJ
    4. Schreiner CE
    5. Froemke RC

    (2010) Developmental sensory experience balances cortical excitation and inhibition

    Nature 465:932–936.

    https://doi.org/10.1038/nature09119

    • PubMed
    • Google Scholar
35. 1. Dougherty JD
    2. Schmidt EF
    3. Nakajima M
    4. Heintz N

    (2010) Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells

    Nucleic Acids Research 38:4218–4230.

    https://doi.org/10.1093/nar/gkq130

    • PubMed
    • Google Scholar
36. Conference

    1. Errity A
    2. McKenna J

    (2007) A comparative study of linear and nonlinear dimensionality reduction for speaker identification 2007 

    15th International Conference on Digital Signal Processing IEEE. pp. 587–590.

    https://doi.org/10.1109/ICDSP.2007.4288650

    • Google Scholar
37. 1. Fischl B

    (2012) FreeSurfer

    NeuroImage 62:774–781.

    https://doi.org/10.1016/j.neuroimage.2012.01.021

    • PubMed
    • Google Scholar
38. 1. Fisher RA

    (1922) On the interpretation of χ 2 from contingency tables, and the calculation of P

    Journal of the Royal Statistical Society 85:87.

    https://doi.org/10.2307/2340521

    • Google Scholar
39. Book

    1. Fornito A
    2. Zalesky A
    3. Bullmore E

    (2016) Fundamentals of Brain Network Analysis

    Academic Press.

    https://doi.org/10.1016/C2012-0-06036-X

    • Google Scholar
40. 1. Fornito A
    2. Arnatkevičiūtė A
    3. Fulcher BD

    (2019) Bridging the gap between connectome and transcriptome

    Trends in Cognitive Sciences 23:34–50.

    https://doi.org/10.1016/j.tics.2018.10.005

    • PubMed
    • Google Scholar
41. 1. G.-Matarazzo R
    2. Wiens AN
    3. Matarazzo JD
    4. Manaugh TS

    (1973) Test-retest reliability of the WAIS in a normal population

    Journal of Clinical Psychology 29:194–197.

    https://doi.org/10.1002/1097-4679(197304)29:2<194::AID-JCLP2270290212>3.0.CO;2-W

    • Google Scholar
42. 1. Gallos IK
    2. Galaris E
    3. Siettos CI

    (2020) Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

    Cognitive Neurodynamics 43:e09645.

    https://doi.org/10.1007/s11571-020-09645-y

    • Google Scholar
43. 1. Gandal MJ
    2. Zhang P
    3. Hadjimichael E
    4. Walker RL
    5. Chen C
    6. Liu S
    7. Won H
    8. van Bakel H
    9. Varghese M
    10. Wang Y
    11. Shieh AW
    12. Haney J
    13. Parhami S
    14. Belmont J
    15. Kim M
    16. Moran Losada P
    17. Khan Z
    18. Mleczko J
    19. Xia Y
    20. Dai R
    21. Wang D
    22. Yang YT
    23. Xu M
    24. Fish K
    25. Hof PR
    26. Warrell J
    27. Fitzgerald D
    28. White K
    29. Jaffe AE
    30. Peters MA
    31. Gerstein M
    32. Liu C
    33. Iakoucheva LM
    34. Pinto D
    35. Geschwind DH
    36. PsychENCODE Consortium

    (2018) Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder

    Science 362:eaat8127.

    https://doi.org/10.1126/science.aat8127

    • PubMed
    • Google Scholar
44. 1. Garde E
    2. Mortensen EL
    3. Krabbe K
    4. Rostrup E
    5. Larsson HB

    (2000) Relation between age-related decline in intelligence and cerebral white-matter hyperintensities in healthy octogenarians: a longitudinal study

    The Lancet 356:628–634.

    https://doi.org/10.1016/S0140-6736(00)02604-0

    • PubMed
    • Google Scholar
45. 1. Garde E
    2. Lykke Mortensen E
    3. Rostrup E
    4. Paulson OB

    (2005) Decline in intelligence is associated with progression in white matter hyperintensity volume

    Journal of Neurology, Neurosurgery & Psychiatry 76:1289–1291.

    https://doi.org/10.1136/jnnp.2004.055905

    • PubMed
    • Google Scholar
46. Preprint

    1. Genc S
    2. Malpas CB
    3. Gulenc A
    4. Sciberras E
    5. Efron D
    6. Silk TJ
    7. Seal ML

    (2020) Longitudinal white matter development in children is associated with puberty, attentional difficulties, and mental health

    bioRxiv.

    https://doi.org/10.1101/607671

    • Google Scholar
47. 1. Glasser MF
    2. Sotiropoulos SN
    3. Wilson JA
    4. Coalson TS
    5. Fischl B
    6. Andersson JL
    7. Xu J
    8. Jbabdi S
    9. Webster M
    10. Polimeni JR
    11. Van Essen DC
    12. Jenkinson M
    13. WU-Minn HCP Consortium

    (2013) The minimal preprocessing pipelines for the human connectome project

    NeuroImage 80:105–124.

    https://doi.org/10.1016/j.neuroimage.2013.04.127

    • PubMed
    • Google Scholar
48. 1. Gogtay N
    2. Giedd JN
    3. Lusk L
    4. Hayashi KM
    5. Greenstein D
    6. Vaituzis AC
    7. Nugent TF
    8. Herman DH
    9. Clasen LS
    10. Toga AW
    11. Rapoport JL
    12. Thompson PM

    (2004) Dynamic mapping of human cortical development during childhood through early adulthood

    PNAS 101:8174–8179.

    https://doi.org/10.1073/pnas.0402680101

    • PubMed
    • Google Scholar
49. 1. Gorgolewski KJ
    2. Fox AS
    3. Chang L
    4. Schäfer A
    5. Arélin K
    6. Burmann I
    7. Sacher J
    8. Margulies DS

    (2014) Tight fitting genes: finding relations between statistical maps and gene expression patterns

    F1000Research 1:1607.

    https://doi.org/10.7490/F1000RESEARCH.1097120.1

    • Google Scholar
50. 1. Gorgolewski KJ
    2. Varoquaux G
    3. Rivera G
    4. Schwarz Y
    5. Ghosh SS
    6. Maumet C
    7. Sochat VV
    8. Nichols TE
    9. Poldrack RA
    10. Poline JB
    11. Yarkoni T
    12. Margulies DS

    (2015) NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain

    Frontiers in Neuroinformatics 9:8.

    https://doi.org/10.3389/fninf.2015.00008

    • PubMed
    • Google Scholar
51. 1. Goulas A
    2. Zilles K
    3. Hilgetag CC

    (2018) Cortical gradients and laminar projections in mammals

    Trends in Neurosciences 41:775–788.

    https://doi.org/10.1016/j.tins.2018.06.003

    • PubMed
    • Google Scholar
52. 1. Graybiel AM

    (1995) Building action repertoires: memory and learning functions of the basal ganglia

    Current Opinion in Neurobiology 5:733–741.

    https://doi.org/10.1016/0959-4388(95)80100-6

    • PubMed
    • Google Scholar
53. 1. Greene AS
    2. Gao S
    3. Scheinost D
    4. Constable RT

    (2018) Task-induced brain state manipulation improves prediction of individual traits

    Nature Communications 9:2807.

    https://doi.org/10.1038/s41467-018-04920-3

    • PubMed
    • Google Scholar
54. 1. Haak KV
    2. Marquand AF
    3. Beckmann CF

    (2018) Connectopic mapping with resting-state fMRI

    NeuroImage 170:83–94.

    https://doi.org/10.1016/j.neuroimage.2017.06.075

    • PubMed
    • Google Scholar
55. 1. Haak KV
    2. Beckmann CF

    (2020) Understanding brain organisation in the face of functional heterogeneity and functional multiplicity

    NeuroImage 220:117061.

    https://doi.org/10.1016/j.neuroimage.2020.117061

    • PubMed
    • Google Scholar
56. 1. Hagmann P
    2. Sporns O
    3. Madan N
    4. Cammoun L
    5. Pienaar R
    6. Wedeen VJ
    7. Meuli R
    8. Thiran JP
    9. Grant PE

    (2010) White matter maturation reshapes structural connectivity in the late developing human brain

    PNAS 107:19067–19072.

    https://doi.org/10.1073/pnas.1009073107

    • PubMed
    • Google Scholar
57. 1. Hashimoto R
    2. Ohi K
    3. Yamamori H
    4. Yasuda Y
    5. Fujimoto M
    6. Umeda-Yano S
    7. Watanabe Y
    8. Fukunaga M
    9. Takeda M

    (2015) Imaging genetics and psychiatric disorders

    Current Molecular Medicine 15:168–175.

    https://doi.org/10.2174/1566524015666150303104159

    • PubMed
    • Google Scholar
58. 1. Hawrylycz MJ
    2. Lein ES
    3. Guillozet-Bongaarts AL
    4. Shen EH
    5. Ng L
    6. Miller JA
    7. van de Lagemaat LN
    8. Smith KA
    9. Ebbert A
    10. Riley ZL
    11. Abajian C
    12. Beckmann CF
    13. Bernard A
    14. Bertagnolli D
    15. Boe AF
    16. Cartagena PM
    17. Chakravarty MM
    18. Chapin M
    19. Chong J
    20. Dalley RA
    21. David Daly B
    22. Dang C
    23. Datta S
    24. Dee N
    25. Dolbeare TA
    26. Faber V
    27. Feng D
    28. Fowler DR
    29. Goldy J
    30. Gregor BW
    31. Haradon Z
    32. Haynor DR
    33. Hohmann JG
    34. Horvath S
    35. Howard RE
    36. Jeromin A
    37. Jochim JM
    38. Kinnunen M
    39. Lau C
    40. Lazarz ET
    41. Lee C
    42. Lemon TA
    43. Li L
    44. Li Y
    45. Morris JA
    46. Overly CC
    47. Parker PD
    48. Parry SE
    49. Reding M
    50. Royall JJ
    51. Schulkin J
    52. Sequeira PA
    53. Slaughterbeck CR
    54. Smith SC
    55. Sodt AJ
    56. Sunkin SM
    57. Swanson BE
    58. Vawter MP
    59. Williams D
    60. Wohnoutka P
    61. Zielke HR
    62. Geschwind DH
    63. Hof PR
    64. Smith SM
    65. Koch C
    66. Grant SGN
    67. Jones AR

    (2012) An anatomically comprehensive atlas of the adult human brain transcriptome

    Nature 489:391–399.

    https://doi.org/10.1038/nature11405

    • PubMed
    • Google Scholar
59. 1. Hawrylycz M
    2. Miller JA
    3. Menon V
    4. Feng D
    5. Dolbeare T
    6. Guillozet-Bongaarts AL
    7. Jegga AG
    8. Aronow BJ
    9. Lee CK
    10. Bernard A
    11. Glasser MF
    12. Dierker DL
    13. Menche J
    14. Szafer A
    15. Collman F
    16. Grange P
    17. Berman KA
    18. Mihalas S
    19. Yao Z
    20. Stewart L
    21. Barabási AL
    22. Schulkin J
    23. Phillips J
    24. Ng L
    25. Dang C
    26. Haynor DR
    27. Jones A
    28. Van Essen DC
    29. Koch C
    30. Lein E

    (2015) Canonical genetic signatures of the adult human brain

    Nature Neuroscience 18:1832–1844.

    https://doi.org/10.1038/nn.4171

    • PubMed
    • Google Scholar
60. 1. Herting MM
    2. Johnson C
    3. Mills KL
    4. Vijayakumar N
    5. Dennison M
    6. Liu C
    7. Goddings AL
    8. Dahl RE
    9. Sowell ER
    10. Whittle S
    11. Allen NB
    12. Tamnes CK

    (2018) Development of subcortical volumes across adolescence in males and females: a multisample study of longitudinal changes

    NeuroImage 172:194–205.

    https://doi.org/10.1016/j.neuroimage.2018.01.020

    • PubMed
    • Google Scholar
61. 1. Hong SJ
    2. Vos de Wael R
    3. Bethlehem RAI
    4. Lariviere S
    5. Paquola C
    6. Valk SL
    7. Milham MP
    8. Di Martino A
    9. Margulies DS
    10. Smallwood J
    11. Bernhardt BC

    (2019) Atypical functional connectome hierarchy in autism

    Nature Communications 10:1022.

    https://doi.org/10.1038/s41467-019-08944-1

    • PubMed
    • Google Scholar
62. 1. Hong SJ
    2. Xu T
    3. Nikolaidis A
    4. Smallwood J
    5. Margulies DS
    6. Bernhardt B
    7. Vogelstein J
    8. Milham MP

    (2020) Toward a connectivity gradient-based framework for reproducible biomarker discovery

    NeuroImage 223:117322.

    https://doi.org/10.1016/j.neuroimage.2020.117322

    • PubMed
    • Google Scholar
63. 1. Huntenburg JM
    2. Bazin PL
    3. Goulas A
    4. Tardif CL
    5. Villringer A
    6. Margulies DS

    (2017) A systematic relationship between functional connectivity and intracortical myelin in the human cerebral cortex

    Cerebral Cortex 27:981–997.

    https://doi.org/10.1093/cercor/bhx030

    • PubMed
    • Google Scholar
64. 1. Huntenburg JM
    2. Bazin PL
    3. Margulies DS

    (2018) Large-Scale gradients in human cortical organization

    Trends in Cognitive Sciences 22:21–31.

    https://doi.org/10.1016/j.tics.2017.11.002

    • PubMed
    • Google Scholar
65. 1. Hwang K
    2. Bertolero MA
    3. Liu WB
    4. D'Esposito M

    (2017) The human thalamus is an integrative hub for functional brain networks

    The Journal of Neuroscience 37:5594–5607.

    https://doi.org/10.1523/JNEUROSCI.0067-17.2017

    • PubMed
    • Google Scholar
66. 1. Ip BK
    2. Wappler I
    3. Peters H
    4. Lindsay S
    5. Clowry GJ
    6. Bayatti N

    (2010) Investigating gradients of gene expression involved in early human cortical development

    Journal of Anatomy 217:300–311.

    https://doi.org/10.1111/j.1469-7580.2010.01259.x

    • PubMed
    • Google Scholar
67. 1. Jenkinson M
    2. Beckmann CF
    3. Behrens TE
    4. Woolrich MW
    5. Smith SM

    (2012) FSL

    NeuroImage 62:782–790.

    https://doi.org/10.1016/j.neuroimage.2011.09.015

    • PubMed
    • Google Scholar
68. 1. Jeurissen B
    2. Tournier JD
    3. Dhollander T
    4. Connelly A
    5. Sijbers J

    (2014) Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data

    NeuroImage 103:411–426.

    https://doi.org/10.1016/j.neuroimage.2014.07.061

    • PubMed
    • Google Scholar
69. 1. Kang HJ
    2. Kawasawa YI
    3. Cheng F
    4. Zhu Y
    5. Xu X
    6. Li M
    7. Sousa AM
    8. Pletikos M
    9. Meyer KA
    10. Sedmak G
    11. Guennel T
    12. Shin Y
    13. Johnson MB
    14. Krsnik Z
    15. Mayer S
    16. Fertuzinhos S
    17. Umlauf S
    18. Lisgo SN
    19. Vortmeyer A
    20. Weinberger DR
    21. Mane S
    22. Hyde TM
    23. Huttner A
    24. Reimers M
    25. Kleinman JE
    26. Sestan N

    (2011) Spatio-temporal transcriptome of the human brain

    Nature 478:483–489.

    https://doi.org/10.1038/nature10523

    • PubMed
    • Google Scholar
70. 1. Kannan SR
    2. Ramathilagam S
    3. Sathya A
    4. Pandiyarajan R

    (2010) Effective fuzzy c-means based kernel function in segmenting medical images

    Computers in Biology and Medicine 40:572–579.

    https://doi.org/10.1016/j.compbiomed.2010.04.001

    • PubMed
    • Google Scholar
71. 1. Khundrakpam BS
    2. Reid A
    3. Brauer J
    4. Carbonell F
    5. Lewis J
    6. Ameis S
    7. Karama S
    8. Lee J
    9. Chen Z
    10. Das S
    11. Evans AC
    12. Brain Development Cooperative Group

    (2013) Developmental changes in organization of structural brain networks

    Cerebral Cortex 23:2072–2085.

    https://doi.org/10.1093/cercor/bhs187

    • PubMed
    • Google Scholar
72. 1. Khundrakpam BS
    2. Lewis JD
    3. Kostopoulos P
    4. Carbonell F
    5. Evans AC

    (2017) Cortical thickness abnormalities in autism spectrum disorders through late childhood, adolescence, and adulthood: a Large-Scale MRI study

    Cerebral Cortex 27:1721–1731.

    https://doi.org/10.1093/cercor/bhx038

    • PubMed
    • Google Scholar
73. 1. Kiddle B
    2. Inkster B
    3. Prabhu G
    4. Moutoussis M
    5. Whitaker KJ
    6. Bullmore ET
    7. Dolan RJ
    8. Fonagy P
    9. Goodyer IM
    10. Jones PB

    (2018) Cohort profile: the NSPN 2400 cohort: a developmental sample supporting the wellcome trust NeuroScience in psychiatry network

    International Journal of Epidemiology 47:18–19.

    https://doi.org/10.1093/ije/dyx117

    • PubMed
    • Google Scholar
74. 1. Kilb W

    (2012) Development of the GABAergic system from birth to adolescence

    The Neuroscientist 18:613–630.

    https://doi.org/10.1177/1073858411422114

    • PubMed
    • Google Scholar
75. 1. Klein M
    2. Onnink M
    3. van Donkelaar M
    4. Wolfers T
    5. Harich B
    6. Shi Y
    7. Dammers J
    8. Arias-Vásquez A
    9. Hoogman M
    10. Franke B

    (2017) Brain imaging genetics in ADHD and beyond - Mapping pathways from gene to disorder at different levels of complexity

    Neuroscience & Biobehavioral Reviews 80:115–155.

    https://doi.org/10.1016/j.neubiorev.2017.01.013

    • PubMed
    • Google Scholar
76. 1. Koenis MMG
    2. Brouwer RM
    3. Swagerman SC
    4. van Soelen ILC
    5. Boomsma DI
    6. Hulshoff Pol HE

    (2018) Association between structural brain network efficiency and intelligence increases during adolescence

    Human Brain Mapping 39:822–836.

    https://doi.org/10.1002/hbm.23885

    • PubMed
    • Google Scholar
77. 1. Kuleshov MV
    2. Jones MR
    3. Rouillard AD
    4. Fernandez NF
    5. Duan Q
    6. Wang Z
    7. Koplev S
    8. Jenkins SL
    9. Jagodnik KM
    10. Lachmann A
    11. McDermott MG
    12. Monteiro CD
    13. Gundersen GW
    14. Ma'ayan A

    (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

    Nucleic Acids Research 44:W90–W97.

    https://doi.org/10.1093/nar/gkw377

    • PubMed
    • Google Scholar
78. 1. Lander SS
    2. Linder-Shacham D
    3. Gaisler-Salomon I

    (2017) Differential effects of social isolation in adolescent and adult mice on behavior and cortical gene expression

    Behavioural Brain Research 316:245–254.

    https://doi.org/10.1016/j.bbr.2016.09.005

    • PubMed
    • Google Scholar
79. Conference

    1. Langs G
    2. Golland P
    3. Ghosh SS

    (2015) Predicting activation across individuals with Resting-State functional connectivity based Multi-Atlas label fusion

    International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 313–320.

    https://doi.org/10.1007/978-3-319-24571-3_38

    • Google Scholar
80. 1. Larivière S
    2. Vos de Wael R
    3. Hong SJ
    4. Paquola C
    5. Tavakol S
    6. Lowe AJ
    7. Schrader DV
    8. Bernhardt BC

    (2020a) Multiscale Structure-Function gradients in the neonatal connectome

    Cerebral Cortex 30:47–58.

    https://doi.org/10.1093/cercor/bhz069

    • PubMed
    • Google Scholar
81. 1. Larivière S
    2. Weng Y
    3. Vos de Wael R
    4. Royer J
    5. Frauscher B
    6. Wang Z
    7. Bernasconi A
    8. Bernasconi N
    9. Schrader DV
    10. Zhang Z
    11. Bernhardt BC

    (2020b) Functional connectome contractions in temporal lobe epilepsy: microstructural underpinnings and predictors of surgical outcome

    Epilepsia 61:1221–1233.

    https://doi.org/10.1111/epi.16540

    • PubMed
    • Google Scholar
82. 1. Larsen B
    2. Luna B

    (2018) Adolescence as a neurobiological critical period for the development of higher-order cognition

    Neuroscience & Biobehavioral Reviews 94:179–195.

    https://doi.org/10.1016/j.neubiorev.2018.09.005

    • PubMed
    • Google Scholar
83. 1. Lebel C
    2. Beaulieu C

    (2011) Longitudinal development of human brain wiring continues from childhood into adulthood

    Journal of Neuroscience 31:10937–10947.

    https://doi.org/10.1523/JNEUROSCI.5302-10.2011

    • PubMed
    • Google Scholar
84. 1. Lee E
    2. Lee J
    3. Kim E

    (2017) Excitation/Inhibition imbalance in animal models of autism spectrum disorders

    Biological Psychiatry 81:838–847.

    https://doi.org/10.1016/j.biopsych.2016.05.011

    • PubMed
    • Google Scholar
85. 1. Lewis DA
    2. Hashimoto T
    3. Volk DW

    (2005) Cortical inhibitory neurons and schizophrenia

    Nature Reviews Neuroscience 6:312–324.

    https://doi.org/10.1038/nrn1648

    • PubMed
    • Google Scholar
86. 1. Liu Y
    2. Zhang LI
    3. Tao HW

    (2007) Heterosynaptic scaling of developing GABAergic synapses: dependence on glutamatergic input and developmental stage

    Journal of Neuroscience 27:5301–5312.

    https://doi.org/10.1523/JNEUROSCI.0376-07.2007

    • PubMed
    • Google Scholar
87. 1. Lohmann G
    2. Margulies DS
    3. Horstmann A
    4. Pleger B
    5. Lepsien J
    6. Goldhahn D
    7. Schloegl H
    8. Stumvoll M
    9. Villringer A
    10. Turner R

    (2010) Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain

    PLOS ONE 5:e10232.

    https://doi.org/10.1371/journal.pone.0010232

    • PubMed
    • Google Scholar
88. 1. Louis TA
    2. Robins J
    3. Dockery DW
    4. Spiro A
    5. Ware JH

    (1986) Explaining discrepancies between longitudinal and cross-sectional models

    Journal of Chronic Diseases 39:831–839.

    https://doi.org/10.1016/0021-9681(86)90085-8

    • PubMed
    • Google Scholar
89. 1. Maier-Hein KH
    2. Neher PF
    3. Houde JC
    4. Côté MA
    5. Garyfallidis E
    6. Zhong J
    7. Chamberland M
    8. Yeh FC
    9. Lin YC
    10. Ji Q
    11. Reddick WE
    12. Glass JO
    13. Chen DQ
    14. Feng Y
    15. Gao C
    16. Wu Y
    17. Ma J
    18. He R
    19. Li Q
    20. Westin CF
    21. Deslauriers-Gauthier S
    22. González JOO
    23. Paquette M
    24. St-Jean S
    25. Girard G
    26. Rheault F
    27. Sidhu J
    28. Tax CMW
    29. Guo F
    30. Mesri HY
    31. Dávid S
    32. Froeling M
    33. Heemskerk AM
    34. Leemans A
    35. Boré A
    36. Pinsard B
    37. Bedetti C
    38. Desrosiers M
    39. Brambati S
    40. Doyon J
    41. Sarica A
    42. Vasta R
    43. Cerasa A
    44. Quattrone A
    45. Yeatman J
    46. Khan AR
    47. Hodges W
    48. Alexander S
    49. Romascano D
    50. Barakovic M
    51. Auría A
    52. Esteban O
    53. Lemkaddem A
    54. Thiran JP
    55. Cetingul HE
    56. Odry BL
    57. Mailhe B
    58. Nadar MS
    59. Pizzagalli F
    60. Prasad G
    61. Villalon-Reina JE
    62. Galvis J
    63. Thompson PM
    64. Requejo FS
    65. Laguna PL
    66. Lacerda LM
    67. Barrett R
    68. Dell'Acqua F
    69. Catani M
    70. Petit L
    71. Caruyer E
    72. Daducci A
    73. Dyrby TB
    74. Holland-Letz T
    75. Hilgetag CC
    76. Stieltjes B
    77. Descoteaux M

    (2017) The challenge of mapping the human connectome based on diffusion tractography

    Nature Communications 8:1349.

    https://doi.org/10.1038/s41467-017-01285-x

    • PubMed
    • Google Scholar
90. 1. Margulies DS
    2. Ghosh SS
    3. Goulas A
    4. Falkiewicz M
    5. Huntenburg JM
    6. Langs G
    7. Bezgin G
    8. Eickhoff SB
    9. Castellanos FX
    10. Petrides M
    11. Jefferies E
    12. Smallwood J

    (2016) Situating the default-mode network along a principal gradient of macroscale cortical organization

    PNAS 113:12574–12579.

    https://doi.org/10.1073/pnas.1608282113

    • PubMed
    • Google Scholar
91. Software

    1. Markello R
    2. Shafiei G
    3. Zheng Y-Q
    4. Mišić B

    (2020) Abagen: a toolbox for the allen brain atlas genetics data

    Zenodo.

    https://doi.org/10.5281/zenodo.3688800
92. 1. Mars RB
    2. Sotiropoulos SN
    3. Passingham RE
    4. Sallet J
    5. Verhagen L
    6. Khrapitchev AA
    7. Sibson N
    8. Jbabdi S

    (2018) Whole brain comparative anatomy using connectivity blueprints

    eLife 7:e35237.

    https://doi.org/10.7554/eLife.35237

    • PubMed
    • Google Scholar
93. 1. Marshall WA
    2. Tanner JM

    (1969) Variations in pattern of pubertal changes in girls

    Archives of Disease in Childhood 44:291–303.

    https://doi.org/10.1136/adc.44.235.291

    • PubMed
    • Google Scholar
94. 1. Marshall WA
    2. Tanner JM

    (1970) Variations in the pattern of pubertal changes in boys

    Archives of Disease in Childhood 45:13–23.

    https://doi.org/10.1136/adc.45.239.13

    • PubMed
    • Google Scholar
95. 1. Mascarell Maričić L
    2. Walter H
    3. Rosenthal A
    4. Ripke S
    5. Quinlan EB
    6. Banaschewski T
    7. Barker GJ
    8. Bokde ALW
    9. Bromberg U
    10. Büchel C
    11. Desrivières S
    12. Flor H
    13. Frouin V
    14. Garavan H
    15. Itterman B
    16. Martinot JL
    17. Martinot MP
    18. Nees F
    19. Orfanos DP
    20. Paus T
    21. Poustka L
    22. Hohmann S
    23. Smolka MN
    24. Fröhner JH
    25. Whelan R
    26. Kaminski J
    27. Schumann G
    28. Heinz A
    29. IMAGEN consortium

    (2020) The IMAGEN study: a decade of imaging genetics in adolescents

    Molecular Psychiatry 25:2648–2671.

    https://doi.org/10.1038/s41380-020-0822-5

    • PubMed
    • Google Scholar
96. 1. Meng X-li
    2. Rosenthal R
    3. Rubin DB

    (1992) Comparing correlated correlation coefficients

    Psychological Bulletin 111:172–175.

    https://doi.org/10.1037/0033-2909.111.1.172

    • Google Scholar
97. 1. Menon V

    (2013) Developmental pathways to functional brain networks: emerging principles

    Trends in Cognitive Sciences 17:627–640.

    https://doi.org/10.1016/j.tics.2013.09.015

    • PubMed
    • Google Scholar
98. 1. Mesulam MM

    (1998) From sensation to cognition

    Brain 121 (Pt 6):1013–1052.

    https://doi.org/10.1093/brain/121.6.1013

    • PubMed
    • Google Scholar
99. 1. Morgan SE
    2. Seidlitz J
    3. Whitaker KJ
    4. Romero-Garcia R
    5. Clifton NE
    6. Scarpazza C
    7. van Amelsvoort T
    8. Marcelis M
    9. van Os J
    10. Donohoe G
    11. Mothersill D
    12. Corvin A
    13. Pocklington A
    14. Raznahan A
    15. McGuire P
    16. Vértes PE
    17. Bullmore ET

    (2019) Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes

    PNAS 116:9604–9609.

    https://doi.org/10.1073/pnas.1820754116

    • PubMed
    • Google Scholar
100. 1. Müller EJ
     2. Munn B
     3. Hearne LJ
     4. Smith JB
     5. Fulcher B
     6. Arnatkevičiūtė A
     7. Lurie DJ
     8. Cocchi L
     9. Shine JM

     (2020) Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients

     NeuroImage 222:117224.

     https://doi.org/10.1016/j.neuroimage.2020.117224

     • PubMed
     • Google Scholar
101. 1. Murphy C
     2. Wang HT
     3. Konu D
     4. Lowndes R
     5. Margulies DS
     6. Jefferies E
     7. Smallwood J

     (2019) Modes of operation: a topographic neural gradient supporting stimulus dependent and independent cognition

     NeuroImage 186:487–496.

     https://doi.org/10.1016/j.neuroimage.2018.11.009

     • PubMed
     • Google Scholar
102. 1. Nelson SB
     2. Valakh V

     (2015) Excitatory/Inhibitory balance and circuit homeostasis in autism spectrum disorders

     Neuron 87:684–698.

     https://doi.org/10.1016/j.neuron.2015.07.033

     • PubMed
     • Google Scholar
103. 1. NSPN Consortium
     2. Seidlitz J
     3. Váša F
     4. Shinn M
     5. Romero-Garcia R
     6. Whitaker KJ
     7. Vértes PE
     8. Wagstyl K
     9. Kirkpatrick Reardon P
     10. Clasen L
     11. Liu S
     12. Messinger A
     13. Leopold DA
     14. Fonagy P
     15. Dolan RJ
     16. Jones PB
     17. Goodyer IM
     18. Raznahan A
     19. Bullmore ET

     (2018) Morphometric similarity networks detect microscale cortical organization and predict Inter-Individual cognitive variation

     Neuron 97:231–247.

     https://doi.org/10.1016/j.neuron.2017.11.039

     • PubMed
     • Google Scholar
104. 1. Oldham S
     2. Fornito A

     (2019) The development of brain network hubs

     Developmental Cognitive Neuroscience 36:100607.

     https://doi.org/10.1016/j.dcn.2018.12.005

     • PubMed
     • Google Scholar
105. 1. Oligschläger S
     2. Xu T
     3. Baczkowski BM
     4. Falkiewicz M
     5. Falchier A
     6. Linn G
     7. Margulies DS

     (2019) Gradients of connectivity distance in the cerebral cortex of the macaque monkey

     Brain Structure and Function 224:925–935.

     https://doi.org/10.1007/s00429-018-1811-1

     • Google Scholar
106. 1. Olson EA
     2. Collins PF
     3. Hooper CJ
     4. Muetzel R
     5. Lim KO
     6. Luciana M

     (2009) White matter integrity predicts delay discounting behavior in 9- to 23-year-olds: a diffusion tensor imaging study

     Journal of Cognitive Neuroscience 21:1406–1421.

     https://doi.org/10.1162/jocn.2009.21107

     • PubMed
     • Google Scholar
107. 1. Osmanlıoğlu Y
     2. Tunç B
     3. Parker D
     4. Elliott MA
     5. Baum GL
     6. Ciric R
     7. Satterthwaite TD
     8. Gur RE
     9. Gur RC
     10. Verma R

     (2019) System-level matching of structural and functional connectomes in the human brain

     NeuroImage 199:93–104.

     https://doi.org/10.1016/j.neuroimage.2019.05.064

     • PubMed
     • Google Scholar
108. 1. Padmanabhan A
     2. Luna B

     (2014) Developmental imaging genetics: linking dopamine function to adolescent behavior

     Brain and Cognition 89:27–38.

     https://doi.org/10.1016/j.bandc.2013.09.011

     • PubMed
     • Google Scholar
109. 1. Paquola C
     2. Bethlehem RA
     3. Seidlitz J
     4. Wagstyl K
     5. Romero-Garcia R
     6. Whitaker KJ
     7. Vos de Wael R
     8. Williams GB
     9. Vértes PE
     10. Margulies DS
     11. Bernhardt B
     12. Bullmore ET
     13. NSPN Consortium

     (2019a) Shifts in myeloarchitecture characterise adolescent development of cortical gradients

     eLife 8:e50482.

     https://doi.org/10.7554/eLife.50482

     • PubMed
     • Google Scholar
110. 1. Paquola C
     2. Vos De Wael R
     3. Wagstyl K
     4. Bethlehem RAI
     5. Hong SJ
     6. Seidlitz J
     7. Bullmore ET
     8. Evans AC
     9. Misic B
     10. Margulies DS
     11. Smallwood J
     12. Bernhardt BC

     (2019b) Microstructural and functional gradients are increasingly dissociated in transmodal cortices

     PLOS Biology 17:e3000284.

     https://doi.org/10.1371/journal.pbio.3000284

     • PubMed
     • Google Scholar
111. 1. Paquola C
     2. Benkarim O
     3. DeKraker J
     4. Larivière S
     5. Frässle S
     6. Royer J
     7. Tavakol S
     8. Valk S
     9. Bernasconi A
     10. Bernasconi N
     11. Khan A
     12. Evans AC
     13. Razi A
     14. Smallwood J
     15. Bernhardt BC

     (2020) Convergence of cortical types and functional motifs in the human mesiotemporal lobe

     eLife 9:e60673.

     https://doi.org/10.7554/eLife.60673

     • PubMed
     • Google Scholar
112. 1. Park BY
     2. Hong J
     3. Lee SH
     4. Park H

     (2016) Functional connectivity of child and adolescent attention deficit hyperactivity disorder patients: correlation with IQ

     Frontiers in Human Neuroscience 10:565.

     https://doi.org/10.3389/fnhum.2016.00565

     • PubMed
     • Google Scholar
113. 1. Park BY
     2. Byeon K
     3. Park H

     (2019) FuNP (Fusion of neuroimaging preprocessing) Pipelines: a fully automated preprocessing software for functional magnetic resonance imaging

     Frontiers in Neuroinformatics 13:5.

     https://doi.org/10.3389/fninf.2019.00005

     • PubMed
     • Google Scholar
114. Preprint

     1. Park BY
     2. Park H
     3. Morys F
     4. Kim M
     5. Byeon K
     6. Lee H
     7. Kim S-H
     8. Valk S
     9. Dagher A
     10. Bernhardt BC

     (2020) Body mass variations relate to fractionated functional brain hierarchies

     bioRxiv.

     https://doi.org/10.1101/2020.08.07.241794

     • Google Scholar
115. Software

     1. Park B

     (2021) micaopen, version swh:1:rev:d3988d51e01940007595761dab6b846ce2506433

     Software Heritage.

     https://archive.softwareheritage.org/swh:1:dir:f1603a47661f1ae09726cce22cc32f7287bb92ac;origin=https://github.com/MICA-MNI/micaopen/;visit=swh:1:snp:aee93ca0f477461e59611646a315883f561adbe1;anchor=swh:1:rev:d3988d51e01940007595761dab6b846ce2506433/
116. 1. Park BY
     2. Hong SJ
     3. Valk SL
     4. Paquola C
     5. Benkarim O
     6. Bethlehem RAI
     7. Di Martino A
     8. Milham MP
     9. Gozzi A
     10. Yeo BTT
     11. Smallwood J
     12. Bernhardt BC

     (2021a) Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

     Nature Communications 12:2225.

     https://doi.org/10.1038/s41467-021-21732-0

     • PubMed
     • Google Scholar
117. 1. Park BY
     2. Vos de Wael R
     3. Paquola C
     4. Larivière S
     5. Benkarim O
     6. Royer J
     7. Tavakol S
     8. Cruces RR
     9. Li Q
     10. Valk SL
     11. Margulies DS
     12. Mišić B
     13. Bzdok D
     14. Smallwood J
     15. Bernhardt BC

     (2021b) Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function

     NeuroImage 224:117429.

     https://doi.org/10.1016/j.neuroimage.2020.117429

     • PubMed
     • Google Scholar
118. 1. Parvandeh S
     2. Yeh HW
     3. Paulus MP
     4. McKinney BA

     (2020) Consensus features nested cross-validation

     Bioinformatics 36:3093–3098.

     https://doi.org/10.1093/bioinformatics/btaa046

     • PubMed
     • Google Scholar
119. 1. Patel Y
     2. Parker N
     3. Shin J
     4. Howard D
     5. French L
     6. Thomopoulos SI
     7. Pozzi E
     8. Abe Y
     9. Abé C
     10. Anticevic A
     11. Alda M
     12. Aleman A
     13. Alloza C
     14. Alonso-Lana S
     15. Ameis SH
     16. Anagnostou E
     17. McIntosh AA
     18. Arango C
     19. Arnold PD
     20. Asherson P
     21. Assogna F
     22. Auzias G
     23. Ayesa-Arriola R
     24. Bakker G
     25. Banaj N
     26. Banaschewski T
     27. Bandeira CE
     28. Baranov A
     29. Bargalló N
     30. Bau CHD
     31. Baumeister S
     32. Baune BT
     33. Bellgrove MA
     34. Benedetti F
     35. Bertolino A
     36. Boedhoe PSW
     37. Boks M
     38. Bollettini I
     39. Del Mar Bonnin C
     40. Borgers T
     41. Borgwardt S
     42. Brandeis D
     43. Brennan BP
     44. Bruggemann JM
     45. Bülow R
     46. Busatto GF
     47. Calderoni S
     48. Calhoun VD
     49. Calvo R
     50. Canales-Rodríguez EJ
     51. Cannon DM
     52. Carr VJ
     53. Cascella N
     54. Cercignani M
     55. Chaim-Avancini TM
     56. Christakou A
     57. Coghill D
     58. Conzelmann A
     59. Crespo-Facorro B
     60. Cubillo AI
     61. Cullen KR
     62. Cupertino RB
     63. Daly E
     64. Dannlowski U
     65. Davey CG
     66. Denys D
     67. Deruelle C
     68. Di Giorgio A
     69. Dickie EW
     70. Dima D
     71. Dohm K
     72. Ehrlich S
     73. Ely BA
     74. Erwin-Grabner T
     75. Ethofer T
     76. Fair DA
     77. Fallgatter AJ
     78. Faraone SV
     79. Fatjó-Vilas M
     80. Fedor JM
     81. Fitzgerald KD
     82. Ford JM
     83. Frodl T
     84. Fu CHY
     85. Fullerton JM
     86. Gabel MC
     87. Glahn DC
     88. Roberts G
     89. Gogberashvili T
     90. Goikolea JM
     91. Gotlib IH
     92. Goya-Maldonado R
     93. Grabe HJ
     94. Green MJ
     95. Grevet EH
     96. Groenewold NA
     97. Grotegerd D
     98. Gruber O
     99. Gruner P
     100. Guerrero-Pedraza A
     101. Gur RE
     102. Gur RC
     103. Haar S
     104. Haarman BCM
     105. Haavik J
     106. Hahn T
     107. Hajek T
     108. Harrison BJ
     109. Harrison NA
     110. Hartman CA
     111. Whalley HC
     112. Heslenfeld DJ
     113. Hibar DP
     114. Hilland E
     115. Hirano Y
     116. Ho TC
     117. Hoekstra PJ
     118. Hoekstra L
     119. Hohmann S
     120. Hong LE
     121. Höschl C
     122. Høvik MF
     123. Howells FM
     124. Nenadic I
     125. Jalbrzikowski M
     126. James AC
     127. Janssen J
     128. Jaspers-Fayer F
     129. Xu J
     130. Jonassen R
     131. Karkashadze G
     132. King JA
     133. Kircher T
     134. Kirschner M
     135. Koch K
     136. Kochunov P
     137. Kohls G
     138. Konrad K
     139. Krämer B
     140. Krug A
     141. Kuntsi J
     142. Kwon JS
     143. Landén M
     144. Landrø NI
     145. Lazaro L
     146. Lebedeva IS
     147. Leehr EJ
     148. Lera-Miguel S
     149. Lesch KP
     150. Lochner C
     151. Louza MR
     152. Luna B
     153. Lundervold AJ
     154. MacMaster FP
     155. Maglanoc LA
     156. Malpas CB
     157. Portella MJ
     158. Marsh R
     159. Martyn FM
     160. Mataix-Cols D
     161. Mathalon DH
     162. McCarthy H
     163. McDonald C
     164. McPhilemy G
     165. Meinert S
     166. Menchón JM
     167. Minuzzi L
     168. Mitchell PB
     169. Moreno C
     170. Morgado P
     171. Muratori F
     172. Murphy CM
     173. Murphy D
     174. Mwangi B
     175. Nabulsi L
     176. Nakagawa A
     177. Nakamae T
     178. Namazova L
     179. Narayanaswamy J
     180. Jahanshad N
     181. Nguyen DD
     182. Nicolau R
     183. O'Gorman Tuura RL
     184. O'Hearn K
     185. Oosterlaan J
     186. Opel N
     187. Ophoff RA
     188. Oranje B
     189. García de la Foz VO
     190. Overs BJ
     191. Paloyelis Y
     192. Pantelis C
     193. Parellada M
     194. Pauli P
     195. Picó-Pérez M
     196. Picon FA
     197. Piras F
     198. Piras F
     199. Plessen KJ
     200. Pomarol-Clotet E
     201. Preda A
     202. Puig O
     203. Quidé Y
     204. Radua J
     205. Ramos-Quiroga JA
     206. Rasser PE
     207. Rauer L
     208. Reddy J
     209. Redlich R
     210. Reif A
     211. Reneman L
     212. Repple J
     213. Retico A
     214. Richarte V
     215. Richter A
     216. Rosa PGP
     217. Rubia KK
     218. Hashimoto R
     219. Sacchet MD
     220. Salvador R
     221. Santonja J
     222. Sarink K
     223. Sarró S
     224. Satterthwaite TD
     225. Sawa A
     226. Schall U
     227. Schofield PR
     228. Schrantee A
     229. Seitz J
     230. Serpa MH
     231. Setién-Suero E
     232. Shaw P
     233. Shook D
     234. Silk TJ
     235. Sim K
     236. Simon S
     237. Simpson HB
     238. Singh A
     239. Skoch A
     240. Skokauskas N
     241. Soares JC
     242. Soreni N
     243. Soriano-Mas C
     244. Spalletta G
     245. Spaniel F
     246. Lawrie SM
     247. Stern ER
     248. Stewart SE
     249. Takayanagi Y
     250. Temmingh HS
     251. Tolin DF
     252. Tomecek D
     253. Tordesillas-Gutiérrez D
     254. Tosetti M
     255. Uhlmann A
     256. van Amelsvoort T
     257. van der Wee NJA
     258. van der Werff SJA
     259. van Haren NEM
     260. van Wingen GA
     261. Vance A
     262. Vázquez-Bourgon J
     263. Vecchio D
     264. Venkatasubramanian G
     265. Vieta E
     266. Vilarroya O
     267. Vives-Gilabert Y
     268. Voineskos AN
     269. Völzke H
     270. von Polier GG
     271. Walton E
     272. Weickert TW
     273. Weickert CS
     274. Weideman AS
     275. Wittfeld K
     276. Wolf DH
     277. Wu MJ
     278. Yang TT
     279. Yang K
     280. Yoncheva Y
     281. Yun JY
     282. Cheng Y
     283. Zanetti MV
     284. Ziegler GC
     285. Franke B
     286. Hoogman M
     287. Buitelaar JK
     288. van Rooij D
     289. Andreassen OA
     290. Ching CRK
     291. Veltman DJ
     292. Schmaal L
     293. Stein DJ
     294. van den Heuvel OA
     295. Turner JA
     296. van Erp TGM
     297. Pausova Z
     298. Thompson PM
     299. Paus T
     300. Writing Committee for the Attention-Deficit/Hyperactivity Disorder
     301. Autism Spectrum Disorder
     302. Bipolar Disorder
     303. Major Depressive Disorder
     304. Obsessive-Compulsive Disorder
     305. and Schizophrenia ENIGMA Working Groups

     (2021) Virtual histology of cortical thickness and shared neurobiology in 6 psychiatric disorders

     JAMA Psychiatry 78:47.

     https://doi.org/10.1001/jamapsychiatry.2020.2694

     • PubMed
     • Google Scholar
120. 1. Patenaude B
     2. Smith SM
     3. Kennedy DN
     4. Jenkinson M

     (2011) A bayesian model of shape and appearance for subcortical brain segmentation

     NeuroImage 56:907–922.

     https://doi.org/10.1016/j.neuroimage.2011.02.046

     • PubMed
     • Google Scholar
121. 1. Paus T
     2. Keshavan M
     3. Giedd JN

     (2008) Why do many psychiatric disorders emerge during adolescence?

     Nature Reviews Neuroscience 9:947–957.

     https://doi.org/10.1038/nrn2513

     • PubMed
     • Google Scholar
122. 1. Petanjek Z
     2. Judaš M
     3. Šimic G
     4. Rasin MR
     5. Uylings HB
     6. Rakic P
     7. Kostovic I

     (2011) Extraordinary neoteny of synaptic spines in the human prefrontal cortex

     PNAS 108:13281–13286.

     https://doi.org/10.1073/pnas.1105108108

     • PubMed
     • Google Scholar
123. 1. Ramsden S
     2. Richardson FM
     3. Josse G
     4. Thomas MS
     5. Ellis C
     6. Shakeshaft C
     7. Seghier ML
     8. Price CJ

     (2011) Verbal and non-verbal intelligence changes in the teenage brain

     Nature 479:113–116.

     https://doi.org/10.1038/nature10514

     • PubMed
     • Google Scholar
124. 1. Rodríguez-Cruces R
     2. Bernhardt BC
     3. Concha L

     (2020) Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy

     NeuroImage 213:116706.

     https://doi.org/10.1016/j.neuroimage.2020.116706

     • PubMed
     • Google Scholar
125. 1. Romero-Garcia R
     2. Whitaker KJ
     3. Váša F
     4. Seidlitz J
     5. Shinn M
     6. Fonagy P
     7. Dolan RJ
     8. Jones PB
     9. Goodyer IM
     10. Bullmore ET
     11. Vértes PE
     12. NSPN Consortium

     (2018) Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex

     NeuroImage 171:256–267.

     https://doi.org/10.1016/j.neuroimage.2017.12.060

     • PubMed
     • Google Scholar
126. 1. Romero-Garcia R
     2. Warrier V
     3. Bullmore ET
     4. Baron-Cohen S
     5. Bethlehem RAI

     (2019) Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism

     Molecular Psychiatry 24:1053–1064.

     https://doi.org/10.1038/s41380-018-0023-7

     • PubMed
     • Google Scholar
127. 1. Rubinov M
     2. Sporns O

     (2010) Complex network measures of brain connectivity: uses and interpretations

     NeuroImage 52:1059–1069.

     https://doi.org/10.1016/j.neuroimage.2009.10.003

     • PubMed
     • Google Scholar
128. Book

     1. Sanides F

     (1962)

     Die Architektonik des Menschlichen Stirnhirns, Monographien aus dem Gesamtgebiete der Neurologie und Psychiatrie

     Berlin, Heidelberg: Springer Berlin Heidelberg.

     • Google Scholar
129. 1. Sanides F

     (1969) Comparative architectonics of the neocortex of mammals and their evolutionary interpretation*

     Annals of the New York Academy of Sciences 167:404–423.

     https://doi.org/10.1111/j.1749-6632.1969.tb20459.x

     • Google Scholar
130. 1. Schaefer A
     2. Kong R
     3. Gordon EM
     4. Laumann TO
     5. Zuo XN
     6. Holmes AJ
     7. Eickhoff SB
     8. Yeo BTT

     (2018) Local-Global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI

     Cerebral Cortex 28:3095–3114.

     https://doi.org/10.1093/cercor/bhx179

     • PubMed
     • Google Scholar
131. 1. Schmithorst VJ
     2. Yuan W

     (2010) White matter development during adolescence as shown by diffusion MRI

     Brain and Cognition 72:16–25.

     https://doi.org/10.1016/j.bandc.2009.06.005

     • PubMed
     • Google Scholar
132. 1. Shaw P
     2. Greenstein D
     3. Lerch J
     4. Clasen L
     5. Lenroot R
     6. Gogtay N
     7. Evans A
     8. Rapoport J
     9. Giedd J

     (2006) Intellectual ability and cortical development in children and adolescents

     Nature 440:676–679.

     https://doi.org/10.1038/nature04513

     • PubMed
     • Google Scholar
133. 1. Shin J
     2. French L
     3. Xu T
     4. Leonard G
     5. Perron M
     6. Pike GB
     7. Richer L
     8. Veillette S
     9. Pausova Z
     10. Paus T

     (2018) Cell-Specific Gene-Expression profiles and cortical thickness in the human brain

     Cerebral Cortex 28:3267–3277.

     https://doi.org/10.1093/cercor/bhx197

     • PubMed
     • Google Scholar
134. 1. Shine JM
     2. Hearne LJ
     3. Breakspear M
     4. Hwang K
     5. Müller EJ
     6. Sporns O
     7. Poldrack RA
     8. Mattingley JB
     9. Cocchi L

     (2019) The Low-Dimensional neural architecture of cognitive complexity is related to activity in medial thalamic nuclei

     Neuron 104:849–855.

     https://doi.org/10.1016/j.neuron.2019.09.002

     • PubMed
     • Google Scholar
135. 1. Shine JM

     (2021) The thalamus integrates the macrosystems of the brain to facilitate complex, adaptive brain network dynamics

     Progress in Neurobiology 199:101951.

     https://doi.org/10.1016/j.pneurobio.2020.101951

     • PubMed
     • Google Scholar
136. 1. Silveri MM
     2. Sneider JT
     3. Crowley DJ
     4. Covell MJ
     5. Acharya D
     6. Rosso IM
     7. Jensen JE

     (2013) Frontal lobe γ-aminobutyric acid levels during adolescence: associations with impulsivity and response inhibition

     Biological Psychiatry 74:296–304.

     https://doi.org/10.1016/j.biopsych.2013.01.033

     • PubMed
     • Google Scholar
137. 1. Smith RE
     2. Tournier JD
     3. Calamante F
     4. Connelly A

     (2012) Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information

     NeuroImage 62:1924–1938.

     https://doi.org/10.1016/j.neuroimage.2012.06.005

     • PubMed
     • Google Scholar
138. 1. Smith RE
     2. Tournier JD
     3. Calamante F
     4. Connelly A

     (2015) SIFT2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography

     NeuroImage 119:338–351.

     https://doi.org/10.1016/j.neuroimage.2015.06.092

     • PubMed
     • Google Scholar
139. 1. Snow WG
     2. Tierney MC
     3. Zorzitto ML
     4. Fisher RH
     5. Reid DW

     (1989) WAIS-R test-retest reliability in a normal elderly sample

     Journal of Clinical and Experimental Neuropsychology 11:423–428.

     https://doi.org/10.1080/01688638908400903

     • PubMed
     • Google Scholar
140. 1. Sohal VS
     2. Rubenstein JLR

     (2019) Excitation-inhibition balance as a framework for investigating mechanisms in neuropsychiatric disorders

     Molecular Psychiatry 24:1248–1257.

     https://doi.org/10.1038/s41380-019-0426-0

     • PubMed
     • Google Scholar
141. 1. Sotiras A
     2. Toledo JB
     3. Gur RE
     4. Gur RC
     5. Satterthwaite TD
     6. Davatzikos C

     (2017) Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion

     PNAS 114:3527–3532.

     https://doi.org/10.1073/pnas.1620928114

     • PubMed
     • Google Scholar
142. 1. Subramanian A
     2. Tamayo P
     3. Mootha VK
     4. Mukherjee S
     5. Ebert BL
     6. Gillette MA
     7. Paulovich A
     8. Pomeroy SL
     9. Golub TR
     10. Lander ES
     11. Mesirov JP

     (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles

     PNAS 102:15545–15550.

     https://doi.org/10.1073/pnas.0506580102

     • PubMed
     • Google Scholar
143. 1. Suprano I
     2. Kocevar G
     3. Stamile C
     4. Hannoun S
     5. Fourneret P
     6. Revol O
     7. Nusbaum F
     8. Sappey-Marinier D

     (2020) White matter microarchitecture and structural network integrity correlate with children intelligence quotient

     Scientific Reports 10:20722.

     https://doi.org/10.1038/s41598-020-76528-x

     • PubMed
     • Google Scholar
144. 1. Tamnes CK
     2. Ostby Y
     3. Fjell AM
     4. Westlye LT
     5. Due-Tønnessen P
     6. Walhovd KB

     (2010) Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure

     Cerebral Cortex 20:534–548.

     https://doi.org/10.1093/cercor/bhp118

     • PubMed
     • Google Scholar
145. 1. Tamnes CK
     2. Herting MM
     3. Goddings AL
     4. Meuwese R
     5. Blakemore SJ
     6. Dahl RE
     7. Güroğlu B
     8. Raznahan A
     9. Sowell ER
     10. Crone EA
     11. Mills KL

     (2017) Development of the cerebral cortex across adolescence: a multisample study of Inter-Related longitudinal changes in cortical volume, surface area, and thickness

     The Journal of Neuroscience 37:3402–3412.

     https://doi.org/10.1523/JNEUROSCI.3302-16.2017

     • PubMed
     • Google Scholar
146. 1. Tenenbaum JB
     2. de Silva V
     3. Langford JC

     (2000) A global geometric framework for nonlinear dimensionality reduction

     Science 290:2319–2323.

     https://doi.org/10.1126/science.290.5500.2319

     • PubMed
     • Google Scholar
147. 1. Thompson PM
     2. Ge T
     3. Glahn DC
     4. Jahanshad N
     5. Nichols TE

     (2013) Genetics of the connectome

     NeuroImage 80:475–488.

     https://doi.org/10.1016/j.neuroimage.2013.05.013

     • PubMed
     • Google Scholar
148. 1. Tournier JD
     2. Smith R
     3. Raffelt D
     4. Tabbara R
     5. Dhollander T
     6. Pietsch M
     7. Christiaens D
     8. Jeurissen B
     9. Yeh CH
     10. Connelly A

     (2019) MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation

     NeuroImage 202:116137.

     https://doi.org/10.1016/j.neuroimage.2019.116137

     • PubMed
     • Google Scholar
149. 1. Trakoshis S
     2. Martínez-Cañada P
     3. Rocchi F
     4. Canella C
     5. You W
     6. Chakrabarti B
     7. Ruigrok AN
     8. Bullmore ET
     9. Suckling J
     10. Markicevic M
     11. Zerbi V
     12. Baron-Cohen S
     13. Gozzi A
     14. Lai MC
     15. Panzeri S
     16. Lombardo MV
     17. MRC AIMS Consortium

     (2020) Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women

     eLife 9:e55684.

     https://doi.org/10.7554/eLife.55684

     • PubMed
     • Google Scholar
150. 1. Tziortzi AC
     2. Haber SN
     3. Searle GE
     4. Tsoumpas C
     5. Long CJ
     6. Shotbolt P
     7. Douaud G
     8. Jbabdi S
     9. Behrens TE
     10. Rabiner EA
     11. Jenkinson M
     12. Gunn RN

     (2014) Connectivity-based functional analysis of dopamine release in the striatum using diffusion-weighted MRI and positron emission tomography

     Cerebral Cortex 24:1165–1177.

     https://doi.org/10.1093/cercor/bhs397

     • PubMed
     • Google Scholar
151. 1. Valk SL
     2. Xu T
     3. Margulies DS
     4. Masouleh SK
     5. Paquola C
     6. Goulas A
     7. Kochunov P
     8. Smallwood J
     9. Yeo BTT
     10. Bernhardt BC
     11. Eickhoff SB

     (2020) Shaping brain structure: genetic and phylogenetic axes of macroscale organization of cortical thickness

     Science Advances 6:eabb3417.

     https://doi.org/10.1126/sciadv.abb3417

     • PubMed
     • Google Scholar
152. Preprint

     1. Valk Sofie L
     2. Kanske P
     3. Park B
     4. Hong S
     5. Raettig AB-
     6. Trautwein F-M
     7. Bernhardt BC
     8. Singer T

     (2020) A low-dimensional connectome manifold governs the organization and plasticity of social brain functions in humans

     bioRxiv.

     https://doi.org/10.1101/2020.11.11.377895

     • Google Scholar
153. 1. Van Essen DC
     2. Glasser MF
     3. Dierker DL
     4. Harwell J
     5. Coalson T

     (2012) Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases

     Cerebral Cortex 22:2241–2262.

     https://doi.org/10.1093/cercor/bhr291

     • PubMed
     • Google Scholar
154. 1. Varma S
     2. Simon R

     (2006) Bias in error estimation when using cross-validation for model selection

     BMC Bioinformatics 7:91.

     https://doi.org/10.1186/1471-2105-7-91

     • PubMed
     • Google Scholar
155. 1. Váša F
     2. Romero-Garcia R
     3. Kitzbichler MG
     4. Seidlitz J
     5. Whitaker KJ
     6. Vaghi MM
     7. Kundu P
     8. Patel AX
     9. Fonagy P
     10. Dolan RJ
     11. Jones PB
     12. Goodyer IM
     13. Vértes PE
     14. Bullmore ET
     15. NSPN Consortium

     (2020) Conservative and disruptive modes of adolescent change in human brain functional connectivity

     PNAS 117:3248–3253.

     https://doi.org/10.1073/pnas.1906144117

     • PubMed
     • Google Scholar
156. 1. Vázquez-Rodríguez B
     2. Suárez LE
     3. Markello RD
     4. Shafiei G
     5. Paquola C
     6. Hagmann P
     7. van den Heuvel MP
     8. Bernhardt BC
     9. Spreng RN
     10. Misic B

     (2019) Gradients of structure-function tethering across neocortex

     PNAS 116:21219–21227.

     https://doi.org/10.1073/pnas.1903403116

     • PubMed
     • Google Scholar
157. 1. Vértes PE
     2. Rittman T
     3. Whitaker KJ
     4. Romero-Garcia R
     5. Váša F
     6. Kitzbichler MG
     7. Wagstyl K
     8. Fonagy P
     9. Dolan RJ
     10. Jones PB
     11. Goodyer IM
     12. Bullmore ET
     13. NSPN Consortium

     (2016) Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks

     Philosophical Transactions of the Royal Society B: Biological Sciences 371:20150362.

     https://doi.org/10.1098/rstb.2015.0362

     • PubMed
     • Google Scholar
158. 1. Vos de Wael R
     2. Benkarim O
     3. Paquola C
     4. Lariviere S
     5. Royer J
     6. Tavakol S
     7. Xu T
     8. Hong SJ
     9. Langs G
     10. Valk S
     11. Misic B
     12. Milham M
     13. Margulies D
     14. Smallwood J
     15. Bernhardt BC

     (2020a) BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

     Communications Biology 3:103.

     https://doi.org/10.1038/s42003-020-0794-7

     • PubMed
     • Google Scholar
159. Software

     1. Vos de Wael R
     2. Benkarim O
     3. Paquola C
     4. Lariviere S
     5. Royer J
     6. Tavakol S
     7. Xu T
     8. Hong SJ
     9. Langs G
     10. Valk S
     11. Misic B
     12. Milham M
     13. Margulies D
     14. Smallwood J
     15. Bernhardt BC

     (2020b) BrainSpace

     GitHub.

     https://github.com/MICA-MNI/BrainSpace
160. 1. Wagner EE
     2. Caldwell MS

     (1979) WAIS test-retest reliability for a clinical out-patient sample

     Perceptual and Motor Skills 48:131–137.

     https://doi.org/10.2466/pms.1979.48.1.131

     • PubMed
     • Google Scholar
161. 1. Wang P
     2. Kong R
     3. Kong X
     4. Liégeois R
     5. Orban C
     6. Deco G
     7. van den Heuvel MP
     8. Thomas Yeo BT

     (2019) Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain

     Science Advances 5:eaat7854.

     https://doi.org/10.1126/sciadv.aat7854

     • PubMed
     • Google Scholar
162. 1. Watkins MW
     2. Smith LG

     (2013) Long-term stability of the Wechsler intelligence scale for children--fourth edition

     Psychological Assessment 25:477–483.

     https://doi.org/10.1037/a0031653

     • PubMed
     • Google Scholar
163. Book

     1. Wechsler D

     (1999)

     Wechsler Abbreviated Scales of Intelligence (WASI

     San Antonio, TX: Psychological Corporation.

     • Google Scholar
164. 1. Weiskopf N
     2. Suckling J
     3. Williams G
     4. Correia MM
     5. Inkster B
     6. Tait R
     7. Ooi C
     8. Bullmore ET
     9. Lutti A

     (2013) Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation

     Frontiers in Neuroscience 7:95.

     https://doi.org/10.3389/fnins.2013.00095

     • PubMed
     • Google Scholar
165. 1. Whitaker KJ
     2. Vértes PE
     3. Romero-Garcia R
     4. Váša F
     5. Moutoussis M
     6. Prabhu G
     7. Weiskopf N
     8. Callaghan MF
     9. Wagstyl K
     10. Rittman T
     11. Tait R
     12. Ooi C
     13. Suckling J
     14. Inkster B
     15. Fonagy P
     16. Dolan RJ
     17. Jones PB
     18. Goodyer IM
     19. Bullmore ET
     20. NSPN Consortium

     (2016) Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome

     PNAS 113:9105–9110.

     https://doi.org/10.1073/pnas.1601745113

     • PubMed
     • Google Scholar
166. 1. Wold S
     2. Esbensen K
     3. Geladi P

     (1987) Principal component analysis

     Chemometrics and Intelligent Laboratory Systems 2:37–52.

     https://doi.org/10.1016/0169-7439(87)80084-9

     • Google Scholar
167. 1. Worsley KJ
     2. Taylor JE
     3. Carbonell F
     4. Chung MK
     5. Duerden E
     6. Bernhardt B
     7. Lyttelton O
     8. Boucher M
     9. Evans AC

     (2009) SurfStat: a matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory

     NeuroImage 47:S102.

     https://doi.org/10.1016/S1053-8119(09)70882-1

     • Google Scholar
168. 1. Xu X
     2. Wells AB
     3. O'Brien DR
     4. Nehorai A
     5. Dougherty JD

     (2014) Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders

     Journal of Neuroscience 34:1420–1431.

     https://doi.org/10.1523/JNEUROSCI.4488-13.2014

     • PubMed
     • Google Scholar
169. 1. Xu T
     2. Nenning KH
     3. Schwartz E
     4. Hong SJ
     5. Vogelstein JT
     6. Goulas A
     7. Fair DA
     8. Schroeder CE
     9. Margulies DS
     10. Smallwood J
     11. Milham MP
     12. Langs G

     (2020) Cross-species functional alignment reveals evolutionary hierarchy within the connectome

     NeuroImage 223:117346.

     https://doi.org/10.1016/j.neuroimage.2020.117346

     • PubMed
     • Google Scholar
170. 1. Yarkoni T
     2. Poldrack RA
     3. Nichols TE
     4. Van Essen DC
     5. Wager TD

     (2011) Large-scale automated synthesis of human functional neuroimaging data

     Nature Methods 8:665–670.

     https://doi.org/10.1038/nmeth.1635

     • PubMed
     • Google Scholar
171. 1. Yeo BT
     2. Krienen FM
     3. Sepulcre J
     4. Sabuncu MR
     5. Lashkari D
     6. Hollinshead M
     7. Roffman JL
     8. Smoller JW
     9. Zöllei L
     10. Polimeni JR
     11. Fischl B
     12. Liu H
     13. Buckner RL

     (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity

     Journal of Neurophysiology 106:1125–1165.

     https://doi.org/10.1152/jn.00338.2011

     • PubMed
     • Google Scholar
172. 1. Zou H
     2. Hastie T

     (2005) Regularization and variable selection via the elastic net

     Journal of the Royal Statistical Society: Series B 67:301–320.

     https://doi.org/10.1111/j.1467-9868.2005.00503.x

     • Google Scholar
173. 1. Zuo XN
     2. Ehmke R
     3. Mennes M
     4. Imperati D
     5. Castellanos FX
     6. Sporns O
     7. Milham MP

     (2012) Network centrality in the human functional connectome

     Cerebral Cortex 22:1862–1875.

     https://doi.org/10.1093/cercor/bhr269

     • PubMed
     • Google Scholar

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

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