The brain is a complex network of anatomically connected and perpetually interacting neuronal populations (Sporns et al., 2005). Inter-regional connectivity promotes signaling via electrical impulses, generating patterned electrophysiological and haemodynamic activity (Avena-Koenigsberger et al., 2017; Suárez et al., 2020). Neuronal populations are organized into a hierarchy of increasingly polyfunctional neural circuits (Jones and Powell, 1970; Mesulam, 1998; Hilgetag and Goulas, 2020; Bazinet et al., 2020), manifesting as topographic gradients of molecular and cellular properties that smoothly vary between unimodal and transmodal cortices (Huntenburg et al., 2018). Recent studies have demonstrated cortical gradients of gene transcription (Fulcher et al., 2019; Burt et al., 2018), intracortical myelin (Huntenburg et al., 2017), cortical thickness (Wagstyl et al., 2015) and laminar profiles (Paquola et al., 2019).

The topological and physical embedding of neural circuits in macroscale networks and microscale gradients influence their dynamics (Kiebel et al., 2008; Gollo et al., 2015; Wang, 2020). For a neuronal population, the confluence of local micro-architectural properties and global connectivity shapes both the generation of local rhythms, as well as its propensity to communicate with other populations. Specifically, cell type composition, their morphology and their configuration in local circuits determine how signals are generated, transmitted and integrated (Payeur et al., 2019). These micro-architectural properties – increasingly measured directly from histology or inferred from other measurements, such as microarray gene expression – provide a unique opportunity to relate circuit architecture to temporal dynamics and computation. Indeed, multiple studies have focused on how intrinsic timescales vary in relation to microscale and macroscale attributes (Murray et al., 2014; Mahjoory et al., 2019; Shine et al., 2019; Gao et al., 2020; Ito et al., 2020; Raut et al., 2020). The primary functional consequence of this hierarchy of timescales is thought to be a hierarchy of temporal receptive windows: time windows in which a newly arriving stimulus will modify processing of previously presented (i.e. contextual) information (Hasson et al., 2008; Honey et al., 2012; Baldassano et al., 2017; Huntenburg et al., 2018; Chaudhuri et al., 2015; Chien and Honey, 2020). Thus, areas at the bottom of the hierarchy preferentially respond to immediate changes in the sensory environment, while responses in areas at the top of the hierarchy are modulated by prior context. Altogether, previous work highlights a hierarchy of a small number of manually selected time-series features, but it is possible that different types of local computations manifest as different organizational gradients.

The relationship between structure and dynamics is also observed at the network level (Suárez et al., 2020). Intrinsic or ‘resting state’ networks possess unique spectral fingerprints (Keitel and Gross, 2016). The signal variability of brain areas, measured in terms of standard deviations or temporal entropy, is closely related to their structural and functional connectivity profiles (i.e. network embedding) (Mišić et al., 2011; Burzynska et al., 2013; Garrett et al., 2017; Shafiei et al., 2019). More generally, the autocorrelation of blood oxygenation level-dependent (BOLD) signal is correlated with topological characteristics of structural brain networks, such that areas with greater connectivity generate signals with greater autocorrelation (Sethi et al., 2017; Fallon et al., 2020). Finally, in computational models of structurally coupled neuronal populations (neural mass and neural field models [Breakspear, 2017]), highly interconnected hubs exhibit slower dynamic fluctuations, while sensory areas exhibit fast fluctuating neural activity (Gollo et al., 2015). Indeed, these models offer better fits to empirical functional connectivity if they assume heterogeneous local dynamics (Cocchi et al., 2016; Demirtaş et al., 2019; Wang et al., 2019; Deco et al., 2020).

Altogether, multiple lines of evidence suggest that local computations may reflect systematic variation in microscale properties and macroscale network embedding, manifesting as diverse time-series features of regional neural activity. How molecular, cellular and connectomic architecture precisely shapes temporal dynamics, and ultimately, cortical patterns of functional specialization, is poorly understood. A significant limitation is that conventional computational analysis is based on specific, manually selected time-series features, such as the decay of the autocorrelation function, bands of the Fourier power spectrum, or signal variance. Yet the time-series analysis literature is vast and interdisciplinary; how do other metrics of temporal structure vary across the brain and what can they tell us about cortical organization?

Here we comprehensively chart summary features of spontaneous BOLD signals across the cerebral cortex (hereafter referred to as ‘intrinsic dynamics’), mapping temporal organization to structural organization. We apply massive temporal feature extraction to resting state BOLD signals to derive a near-exhaustive time-series profile for each brain region. We then systematically investigate the relationship between local time-series features and gene expression, microstructure, morphology, structural connectivity and functional connectivity. Finally, we map time-series features to a meta-analytic atlas of cognitive ontologies to investigate how temporal dynamics shape regional functional specialization. We show that intrinsic dynamics reflect molecular and cytoarchitectonic gradients, as well as patterns of structural and functional connectivity. These spatial variations in intrinsic dynamics ultimately manifest as patterns of distinct psychological functions.

All analyses were performed on four resting state fMRI runs from the Human Connectome Project (Van Essen et al., 2013). The data were pseudorandomly divided into two samples of unrelated participants to form Discovery and Validation samples with $n=201$ and $n=127$, respectively (Vos de Wael et al., 2018). External replication was then performed using data from the Midnight Scan Club (Gordon et al., 2017). Massive temporal feature extraction was performed using highly comparative time-series analysis, hctsa (Fulcher et al., 2013; Fulcher and Jones, 2017), yielding 6441 features per regional time-series, including measures of frequency composition, variance, autocorrelation, fractal scaling and entropy (Figure 1). The results are organized as follows. We first investigate whether regions that are structurally and functionally connected display similar intrinsic dynamics. We then characterize the topographic organization of time-series features in relation to microstructural attributes and cognitive ontologies.

Figure 1

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Inter-regional temporal profile similarity reflects network geometry and topology

We first assessed the extent to which intrinsic dynamics depend on inter-regional physical distance, anatomical connectivity and functional connectivity. We estimated similarity between inter-regional dynamics by computing Pearson correlation coefficients between regional time-series feature vectors (Figure 1). Two regional time-series are judged to be similar if they have similar temporal profiles, estimated across a comprehensive and diverse set of time-series features (e.g. similar entropy, stationarity, linear correlation properties) (Fulcher, 2018). This measure of similarity identifies pairs of regions that have similar dynamical features, but not necessarily coherent or synchronous dynamics (Figure 2a). We refer to correlations between regional time-series feature profiles as ‘temporal profile similarity’.

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Figure 2b shows a negative exponential relationship between spatial proximity and temporal profile similarity, meaning that regions that are spatially close exhibit similar intrinsic dynamics. Interestingly, regions that share an anatomical projection have greater temporal profile similarity than those that do not (Figure 2c; two-tailed t-test; $t(79,798)=40.234$, $p\approx 0$). To test whether this anatomically-mediated similarity of time-series features is not due to spatial proximity, we performed two additional comparisons. First, we regressed out the exponential trend identified above from the temporal profile similarity matrix, and repeated the analysis on the residuals, yielding a significant difference in temporal profile similarity between connected and non-connected regions (two-tailed t-test; $t(79,798)=9.916$, $p\approx 0$). Second, we generated an ensemble of 10,000 degree- and edge length-preserving surrogate networks (Betzel and Bassett, 2018), and compared the difference of the means between connected and non-connected pairs in the empirical and surrogate networks. Again, we observe a significant difference in temporal profile similarity between connected and non-connected regions (two-tailed; $p_{\mathrm{rewired}}=0.0001$; Figure 2c).

Likewise, regions belonging to the same intrinsic functional network have greater temporal profile similarity compared to regions in different networks (Figure 2d; two-tailed t-test; $t(79,798)=61.093$, $p\approx 0$). To confirm this finding is not driven by spatial proximity, we repeated the analysis with distance-residualized values (Mišić et al., 2014), finding a significant difference (two-tailed t-test; $t(79,798)=47.112$, $p\approx 0$). We also repeated the analysis using a nonparametric label-permutation null model with preserved spatial autocorrelation (10,000 repetitions) (Alexander-Bloch et al., 2018; Markello and Misic, 2020), again finding significantly greater within- compared to between-network temporal profile similarity (two-tailed; $p_{\mathrm{spin}}=0.006$; Figure 2d). These results are consistent when applying the 17 network partition of intrinsic networks (Yeo et al., 2011; Schaefer et al., 2018; Figure 2—figure supplement 1).

More generally, we find a weak positive correlation between temporal profile similarity and functional connectivity (original: Spearman rank $r_{\mathrm{s}}=0.23$, $p\approx 0$; distance-corrected: $r_{\mathrm{s}}=0.18$, $p\approx 0$; Figure 2e), suggesting that areas with similar time-series features exhibit coherent spontaneous fluctuations, but that the two are only weakly correlated. Figure 2e shows the correlation between temporal profile similarity and functional connectivity; points represent node pairs and are colored by their membership in intrinsic networks (Yeo et al., 2011; Schaefer et al., 2018). The results are consistent when functional connectivity is estimated using partial correlations (Figure 2—figure supplement 2). In other words, two regions could display similar time-series features, but they do not necessarily fluctuate coherently. Thus, representing time-series using sets of features provides a fundamentally different perspective compared to representing them as the raw set of ordered BOLD measurements.

As a final step, we sought to assess the distinct contributions of Euclidean distance, structural connectivity and functional connectivity to temporal profile similarity. Dominance analysis revealed the relative importance of each predictor (collective $R^2$ = 0.28; distance = 56%, structural connectivity = 20.4%, functional connectivity = 23.6%; Supplementary file 1), suggesting that distance contributes the most to temporal profile similarity, while structural and functional connectivity make distinct but approximately even contributions (Budescu, 1993; Azen and Budescu, 2003) (https://github.com/dominance-analysis/dominance-analysis). Altogether, we find that the organization of intrinsic dynamics is closely related to both the geometric and topological embedding of brain regions in macroscale networks.

Two distinct spatial gradients of intrinsic dynamics

We next investigate the topographic organization of time-series features. The hctsa library generates 6 441 time-series features, with the aim of being comprehensive in coverage across scientific time-series analysis algorithms and, as a result, contains groups of features with correlated outputs (Fulcher et al., 2013). We therefore sought to identify groups of correlated features that explain maximal variance and that span different conceptual types of time-series properties. Applying principal component analysis (PCA; Scikit-learn [Pedregosa et al., 2011]) to the region × feature matrix yielded mutually orthogonal patterns of intrinsic dynamics (Figure 1), with the top two components collectively accounting for more than 70% of the variance in time-series features (Figure 3a). Figure 3a shows the spatial distribution of the top two components. The first component (PC1) mainly captures differential intrinsic dynamics along a ventromedial-dorsolateral gradient, separating occipital-parietal cortex and anterior temporal cortex. The second component (PC2) captures a unimodal-transmodal gradient, reminiscent of recently reported miscrostructural and functional gradients (Huntenburg et al., 2018). Both components show considerable hemispheric symmetry. In the following sections, we focus on these two components because of their (a) effect size (percent variance accounted for), (b) close resemblance to previously reported topographic gradients, and (c) reproducibility (only the first two components were reproducible in both the HCP and MSC datasets; see Sensitivity and replication analyses below). Note that neither spatial maps were significantly correlated with temporal signal-to-noise ratio map, computed as the ratio of the time-series mean to standard deviation (tSNR; PC1: $r_{\mathrm{s}}=0.28$, $p_{\mathrm{spin}}=0.19$; PC2: $r_{\mathrm{s}}=0.21$, $p_{\mathrm{spin}}=0.16$).

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Which time-series features contribute most to these topographic gradients of intrinsic dynamics? To address this question, we systematically assess the feature composition of PC1 and PC2. We compute univariate correlations (i.e. loadings) between individual time-series feature vectors and PC scores (Figure 3b). Each loading is assessed against 10 000 spin tests and the results are corrected for multiple comparisons by controlling the false discovery rate (FDR [Benjamini and Hochberg, 1995]; $\alpha =0.001$). The top 5% positively and negatively correlated features are shown in word clouds. The complete list of features (ranked by loading), their definitions, loadings and p-values for both components is presented in machine-readable format in Supplementary files 3 and 4. Altogether, we find that PC1 is sensitive to temporal dependencies in BOLD signals, while PC2 is sensitive to the distribution shape of time-series amplitudes. For PC1, in line with previous reports, we observe strong contributions from multiple measures of autocorrelation (e.g. linear autocorrelation; nonlinear autocorrelation; automutual information). Short-lag autocorrelation measures load positively, while long-lag autocorrelation measures load negatively, consistent with the notion that autocorrelation decays with increasing time lag (Murray et al., 2014; Gao et al., 2020; Raut et al., 2020; Figure 3—figure supplement 1). For PC2, we observe strong contributions from measures of distribution shape, captured by measures of distributional entropy (e.g. entropy of kernel-smoothed distribution; kurtosis; distribution balance about the mean). In other words, PC2 captures the spread of time-series amplitudes away from the mean. Interestingly, none of the odd moments (distribution asymmetry) are high in the PC2 loading list, just even moments, suggesting that PC2 captures the shape of the deviations of time-series data points in both directions from the mean. Thus, PC2 indexes the range or diversity of values that a regional time-series can realize. Hereafter, we refer to the time-series profile of PC1 as ‘autocorrelation’ and PC2 as ‘dynamic range’.

To illustrate the spatial organization and time-series attributes of these components, Figure 3c shows the spatial distributions of two high-loading representative time-series features for each component. Ventromedial areas (lower in the PC1 gradient) have lower linear and nonlinear autocorrelation, while doroslateral areas (higher in the PC1 gradient) have greater autocorrelation. Sensory areas (lower in the PC2 gradient) have greater distributional entropy and kurtosis, while transmodal areas (higher in the PC2 gradient) have lower distributional entropy and kurtosis. Finally, to build intuition about what each component reflects about regional signals, we select three regional time-series from one participant based on their lag-1 autocorrelation and kurtosis (Figure 3c; pink = 5th percentile, green = 50th percentile, purple = 95th percentile). For the former, going from low-ranked to high-ranked regions results in a slowing down of BOLD fluctuations. For the latter, going from low-ranked to high-ranked regions results in increasingly heavier symmetric tails of the signal amplitude distributions.

Intrinsic dynamics reflect microscale and macroscale hierarchies

To assess whether the dominant variation in time-series features of BOLD dynamics varies spatially with structural and functional gradients, we next quantify the concordance between PC1/PC2 and multiple microstructural and functional attributes (Figure 4). Specifically, we compare PC1 and PC2 with the following microscale and macroscale features: (1) the first component of microarray gene expression computed from the Allen Institute Human Brain Atlas (Hawrylycz et al., 2012; Burt et al., 2018) using PCA analysis, (2) the principal gradient of functional connectivity estimated using diffusion map embedding (Margulies et al., 2016; Coifman et al., 2005; Langs et al., 2015) (https://github.com/satra/mapalign), (3) T1w/T2w ratio, a putative proxy for intracortical myelin (Huntenburg et al., 2017), (4) cortical thickness (Wagstyl et al., 2015). We use Spearman rank correlations ($r_{\mathrm{s}}$) throughout, as they do not assume a linear relationship among variables. Given the spatially autocorrelated nature of both hctsa features and other imaging features, we assess statistical significance with respect to nonparametric spatial autocorrelation-preserving null models (Alexander-Bloch et al., 2018; Markello and Misic, 2020).

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PC1 topography is correlated with the first principal component of gene expression ($r_{\mathrm{s}}=0.57$, $p_{\mathrm{spin}}=0.03$), but no other attributes. PC2 topography is significantly correlated with the first principal component of gene expression ($r_{\mathrm{s}}=-0.45$, $p_{\mathrm{spin}}=0.0008$), with the principal gradient of functional connectivity ($r_{\mathrm{s}}=0.77$, $p_{\mathrm{spin}}=0.0001$), with T1w/T2w ratio ($r_{\mathrm{s}}=-0.57$, $p_{\mathrm{spin}}=0.0001$), and with cortical thickness ($r_{\mathrm{s}}=0.43$, $p_{\mathrm{spin}}=0.008$). Altogether, the two topographic gradients of intrinsic dynamics closely mirror molecular and microstructural gradients, suggesting a link between regional structural properties and regional dynamical properties. Figure 4—figure supplement 1 further confirms this intuition, showing the mean score of each component for three well-known cortical partitions, including intrinsic functional networks (Yeo et al., 2011; Schaefer et al., 2018), cytoarchitectonic classes (von Economo and Koskinas, 1925; von Economo et al., 2008; Vértes et al., 2016) and laminar differentiation levels (Mesulam, 2000).

For completeness, we also tested associations with two maps that were previously related to cortical hierarchies: evolutionary expansion (indexing enlargement of cortical areas in the human relative to the macaque) (Hill et al., 2010; Baum et al., 2020) and node-wise functional participation coefficient (indexing the diversity of a node’s links) (Bertolero et al., 2017; Baum et al., 2020). PC2 is significantly correlated with evolutionary expansion ($r_{\mathrm{s}}=0.52$, $p_{\mathrm{spin}}=0.0002$), but neither component is correlated with participation coefficient (Figure 4—figure supplement 2).

Spatial gradients of intrinsic dynamics support distinct functional activations

Given that topographic patterns of intrinsic dynamics run parallel to microstructural and functional gradients, and are marked by specific time-series features, we next asked whether these topographic patterns of intrinsic dynamics are related to patterns of functional activation and psychological processes. To address this question, we used Neurosynth to derive probability maps for multiple psychological terms (Yarkoni et al., 2011). The term set was restricted to those in the intersection of terms reported in Neurosynth and in the Cognitive Atlas (Poldrack et al., 2011), yielding a total of 123 terms (Supplementary file 2). Each term map was correlated with the PC1 and PC2 score maps to identify topographic distributions of psychological terms that most closely correspond to patterns of intrinsic dynamics (Bonferroni corrected, $\alpha =0.05$; Figure 5). Consistent with the intuition developed from comparisons with intrinsic networks, PC1 intrinsic dynamics mainly defined a cognitive-affective axis (e.g. ‘attention’ versus ‘stress’, ‘fear’, ‘loss’, ‘emotion’; Figure 5a), while PC2 dynamics defined a sensory-cognitive axis (e.g. ‘perception’, ‘multisensory’, ‘facial expression’ versus ‘cognitive control’, ‘memory retrieval’, ‘reasoning’; Figure 5b).

Figure 5

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Sensitivity and replication analyses

As a final step, we sought to assess the extent to which the present findings are replicable under alternative processing choices and in other samples (Figure 6). For all comparisons, we correlated PC1 and PC2 scores and weights obtained in the original analysis and in each new analysis. Significance was assessed using spatial autocorrelation preserving nulls as before. We first replicated the results in individual subjects in the Discovery sample by applying PCA to individual region × feature matrices and aligning PCA results through an iterative process using Procrustes rotations (https://github.com/satra/mapalign [Langs et al., 2015]). The mean individual-level PC scores and weights were then compared to the original findings (Figure 6a). We next replicated the results by repeating the analysis after grey-matter signal regression (similar to global signal regression as the global signal is shown to be a grey-matter specific signal following sICA+FIX) (Glasser et al., 2018; Glasser et al., 2016), with near identical results (Figure 6b). To assess the extent to which results are influenced by choice of parcellation, we repeated the analysis using the 68-region Desikan-Killiany anatomical atlas (Desikan et al., 2006), which were then further divided into 200 approximately equally-sized cortical areas. Again, we find near-identical results (Figure 6c).

Figure 6

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In the last two analyses, we focused on out-of-sample validation. We first repeated the analysis on the held-out Validation sample of $n=127$ unrelated HCP subjects, with similar results (Figure 6d). Finally, we repeated the analysis using data from the independently collected Midnight Scan Club (MSC) dataset, again finding highly consistent results (Figure 6e).

In the present report, we comprehensively characterize intrinsic dynamics across the cortex, identifying two robust spatial patterns of time-series features. The patterns, capturing spatial variation in signal autocorrelation and dynamic range, follow microscale gradients and macroscale network architecture. Importantly, the two patterns underlie distinct psychological axes, demonstrating a link between brain architecture, intrinsic dynamics, and cognition. These findings are robust against a wide range of methodological choices and were validated in two held-out samples.

Our results demonstrate that regional haemodynamic activity, often overlooked in favour of electrophysiological measurements with greater temporal resolution, possesses a rich dynamic signature (Garrett et al., 2013; Uddin, 2020; Preti et al., 2017; Lurie et al., 2020; Li et al., 2019; Bolt et al., 2018). While multiple reports have suggested the existence of a timescale or temporal receptive window hierarchy (Kiebel et al., 2008; Murray et al., 2014; Honey et al., 2012; Hasson et al., 2008; Watanabe et al., 2019; Golesorkhi et al., 2020; Ito et al., 2020), these investigations typically involved (a) incomplete spatial coverage, making it difficult to quantitatively assess correspondence with other microscale and macroscale maps, and (b) a priori measures of interest, such as spectral power or temporal autocorrelation, potentially obscuring other important dynamical features. Here we comprehensively benchmark the entire dynamic profile of the brain, by near-exhaustively estimating 6000+ features from the wider time-series literature. We identify a much broader spectrum of time-series features that relate to microstructure, connectivity and behavior. As we discuss below, feature-based time-series phenotyping offers a powerful, fundamentally new and entirely data-driven method to quantify and articulate neural dynamics.

Applying a data-driven feature extraction method to high-resolution BOLD fMRI, we decompose regional signals into two intrinsic modes, with distinct topographic organization and time-series features. One pattern, characterized by variation in signal autocorrelation, follows a ventromedial-dorsolateral gradient, separating the limbic and paralimibic systems from posterior parietal cortex. Another pattern, characterized by dynamic range, follows a unimodal-transmodal gradient, separating primary sensory-motor cortices from association cortex. The first is closely associated with gene expression PC1 (itself closely related to cell type composition, synaptic physiology and cortical cytoarchitecture [Burt et al., 2018]), suggesting a molecular and cellular basis for regional differences in temporal autocorrelation. The second is closely associated with the principal functional gradient, as well as with intracortical myelin and cortical thickness, suggesting that the dynamic range of BOLD signals is related to regional variation in macroscale circuit organization. Taken together, we find evidence that molecular and cellular properties (gene expression PC1) relate to regional autocorrelation, while micro-circuit properties (T1w/T2w, cortical thickness) and macroscale network embedding (principal functional gradient) relate to regional dynamic range.

An emerging literature emphasizes the hierarchical organization of neural systems, whereby systematic variation in laminar architecture across the cortical sheet is mirrored by multiple cytological properties, including neuron density, spine count, branching and neurotransmitter receptor profiles (Mesulam, 1998; Margulies et al., 2016; Hilgetag and Goulas, 2020). These variations manifest as spatially ordered gradients of structural and functional attributes (Huntenburg et al., 2018), including gene expression (Burt et al., 2018; Fulcher et al., 2019; Hansen et al., 2020), cortical thickness (Wagstyl et al., 2015), intracortical myelin (Huntenburg et al., 2017), laminar differentiation (Paquola et al., 2019; Wagstyl et al., 2020) and excitability (Demirtaş et al., 2019; Wang, 2020; Markicevic et al., 2020; Straub et al., 2020). Indeed, we find that the two patterns of intrinsic dynamics are closely related to gene expression, intracortical myelin and cortical thickness. Our results build on this literature, demonstrating that microscale and connectional hierarchies leave an indelible mark on intrinsic dynamics (Lurie and D’Esposito, 2020), perhaps through variation in local excitability (Demirtaş et al., 2019; Wang et al., 2019; Wang, 2020; Deco et al., 2020). How these patterns are related to underlying cell types and subcortical afferent input – in particular, thalamocortical feedback – is an important ongoing question (Abeysuriya et al., 2015; Garrett et al., 2018; Shine et al., 2019; Wang et al., 2019; Muller et al., 2020; Paquola et al., 2020).

Importantly, the two patterns are related to two dominant axes of meta-analytic functional activation. We show that topographic variations in microcircuitry and connectomic embedding yield variations in intrinsic dynamics and may explain regional differences in functional specialization. The ventromedial-dorsolateral autocorrelation pattern differentiates affective versus cognitive activation (mainly visual cognition and visuo-spatial attention), whereas the unimodal-transmodal dynamic range pattern differentiates primary sensory versus higher-order cognitive processing. Collectively, these results provide evidence that local computations reflect systematic variation in multiple anatomical circuit properties, and can be measured as unique temporal signatures in regional activity and patterns of functional specialization.

More generally, the present findings are part of a larger trend in the field to understand structure-function relationships by considering molecular (Richiardi et al., 2015; Fulcher and Fornito, 2016; Anderson et al., 2018; Zheng et al., 2019), cellular (Scholtens et al., 2014; Anderson et al., 2020; Shafiei et al., 2020; Muller et al., 2020) and physiological (Sethi et al., 2017; Fallon et al., 2020) attributes of network nodes, thereby conceptually linking local and global brain organization (Khambhati et al., 2018; Suárez et al., 2020). In such ‘annotated networks’, macroscale network architecture is thought to reflect similarity in local properties, and vice versa, such that areas with similar properties are more likely to be anatomically connected and to functionally interact with one another (Beul et al., 2017; Goulas et al., 2019; Wei et al., 2019; Hilgetag et al., 2019). Indeed, we find that two regions are more likely to display similar intrinsic dynamics if they are anatomically connected and if they are part of the same functional community, suggesting that network organization and local intrinsic dynamics are intertwined (Gollo et al., 2015; Cocchi et al., 2016). A significant corollary of the present work is that functional connectivity – presently conceptualized as coherent fluctuations in neural activity and operationalized as correlated BOLD values over time – misses out on an important set of inter-regional relationships. Namely, two regions may display identical time-series profiles, suggesting common circuit dynamics and function, but unless they also display time-locked activity, current methods would miss out on this potentially biologically meaningful inter-regional relationship.

The present results are consistent with contemporary theories linking brain structure and function, but they must be interpreted with respect to several methodological caveats. First, all analyses were performed on BOLD time-series with lower sampling rate compared to electromagnetic recordings, potentially obscuring more subtle dynamics occurring on faster timescales. To mitigate this concern, all analyses were performed in high-resolution multiband HCP data with multiple runs, and replicated in MSC data, but in principle, these analyses could be repeated and validated in magnetoencephalographic recordings (Watanabe et al., 2019). Second, all analyses were performed on haemodynamic time courses that may not completely reflect the underlying neuronal population dynamics. Despite this caveat, we observe a close correspondence between the isolated patterns of intrinsic dynamics and molecular, structural, functional, and psychological gradients. Third, the pattern of temporal signal-to-noise ratio in the BOLD is known to be non-uniform, but it is not correlated with the intrinsic dynamics patterns observed in the present report. Fourth, the analysis included all features from hctsa, potentially biasing results towards specific properties of BOLD signals. We attempted to mitigate this challenge by applying PCA to directly examine correlation patterns among features, but PCA components may still lend greater weight to over-represented feature classes (Fulcher et al., 2013). This may obscure the contribution of under-represented feature classes, and should be investigated further in future work.

Altogether, the present results point towards highly patterned intrinsic dynamics across the neocortex. These patterns reflect prominent molecular and microstructural gradients, as well as macroscale structural and functional organization. Importantly, spatial variation of intrinsic dynamics parallels spatial variation of meta-analytic cognitive functional activation. These findings demonstrate that structural organization of the brain shapes patterns of intrinsic dynamics, ultimately manifesting as distinct axes of psychological processes.

All data used in this study is publicly available. Detailed information about the datasets is available in the manuscript.

1. 1. Van Essen DC
   2. Smith SM
   3. Barch DM
   4. Behrens TE
   5. Yacoub E
   6. Ugurbil K
   7. WU-Minn HCP Consortium

   (2013) ConnectomeDB

   Human Connectome Project (HCP).

   https://doi.org/10.1016/j.neuroimage.2013.05.041
2. 1. Gordon EM
   2. Laumann TO
   3. Gilmore AW
   4. Newbold DJ
   5. Greene DJ
   6. Berg JJ
   7. Ortega M
   8. Hoyt-Drazen C
   9. Gratton C
   10. Sun H
   11. Hampton JM
   12. Coalson RS
   13. Nguyen AL
   14. McDermott KB
   15. Shimony JS
   16. Snyder AZ
   17. Schlaggar BL
   18. Petersen SE
   19. Nelson SM
   20. Dosenbach NUF

   (2017) OpenfMRI database

   ID ds000224. Midnight Scan Club (MSC).

   https://openneuro.org/datasets/ds000224/versions/1.0.0
3. 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 DB
   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) Allen Institute Human Brain Atlas (AHBA)

   Allen Institute Human Brain Atlas (AHBA).

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

1. 1. Abeysuriya RG
   2. Rennie CJ
   3. Robinson PA

   (2015) Physiologically based arousal state estimation and dynamics

   Journal of Neuroscience Methods 253:55–69.

   https://doi.org/10.1016/j.jneumeth.2015.06.002

   • PubMed
   • Google Scholar
2. 1. Abraham A
   2. Pedregosa F
   3. Eickenberg M
   4. Gervais P
   5. Mueller A
   6. Kossaifi J
   7. Gramfort A
   8. Thirion B
   9. Varoquaux G

   (2014) Machine learning for neuroimaging with scikit-learn

   Frontiers in Neuroinformatics 8:14.

   https://doi.org/10.3389/fninf.2014.00014

   • 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. 1. Anderson KM
   2. Krienen FM
   3. Choi EY
   4. Reinen JM
   5. Yeo BTT
   6. Holmes AJ

   (2018) Gene expression links functional networks across cortex and striatum

   Nature Communications 9:1428.

   https://doi.org/10.1038/s41467-018-03811-x

   • PubMed
   • Google Scholar
5. 1. Anderson KM
   2. Collins MA
   3. Chin R
   4. Ge T
   5. Rosenberg MD
   6. Holmes AJ

   (2020) Transcriptional and imaging-genetic association of cortical interneurons, brain function, and schizophrenia risk

   Nature Communications 11:2889.

   https://doi.org/10.1038/s41467-020-16710-x

   • 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. Avena-Koenigsberger A
   2. Misic B
   3. Sporns O

   (2017) Communication dynamics in complex brain networks

   Nature Reviews Neuroscience 19:17–33.

   https://doi.org/10.1038/nrn.2017.149

   • PubMed
   • Google Scholar
8. 1. Azen R
   2. Budescu DV

   (2003) The dominance analysis approach for comparing predictors in multiple regression

   Psychological Methods 8:129–148.

   https://doi.org/10.1037/1082-989X.8.2.129

   • PubMed
   • Google Scholar
9. 1. Baldassano C
   2. Chen J
   3. Zadbood A
   4. Pillow JW
   5. Hasson U
   6. Norman KA

   (2017) Discovering event structure in continuous narrative perception and memory

   Neuron 95:709–721.

   https://doi.org/10.1016/j.neuron.2017.06.041

   • PubMed
   • Google Scholar
10. 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
11. Preprint

    1. Bazinet V
    2. de Wael R
    3. Hagmann P
    4. Bernhardt BC
    5. Misic B

    (2020) Multiscale communication in cortico-cortical networks

    bioRxiv.

    https://doi.org/10.1101/2020.10.02.323030

    • 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. 1. Bertolero MA
    2. Yeo BTT
    3. D'Esposito M

    (2017) The diverse club

    Nature Communications 8:1–11.

    https://doi.org/10.1038/s41467-017-01189-w

    • PubMed
    • Google Scholar
14. 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
15. 1. Betzel RF
    2. Bassett DS

    (2018) Specificity and robustness of long-distance connections in weighted, interareal connectomes

    PNAS 115:E4880–E4889.

    https://doi.org/10.1073/pnas.1720186115

    • PubMed
    • Google Scholar
16. 1. Beul SF
    2. Barbas H
    3. Hilgetag CC

    (2017) A predictive structural model of the primate connectome

    Scientific Reports 7:43176.

    https://doi.org/10.1038/srep43176

    • PubMed
    • Google Scholar
17. 1. Bolt T
    2. Anderson ML
    3. Uddin LQ

    (2018) Beyond the evoked/intrinsic neural process dichotomy

    Network Neuroscience 2:1–22.

    https://doi.org/10.1162/NETN_a_00028

    • PubMed
    • Google Scholar
18. 1. Breakspear M

    (2017) Dynamic models of large-scale brain activity

    Nature Neuroscience 20:340–352.

    https://doi.org/10.1038/nn.4497

    • PubMed
    • Google Scholar
19. 1. Budescu DV

    (1993) Dominance analysis: a new approach to the problem of relative importance of predictors in multiple regression

    Psychological Bulletin 114:542–551.

    https://doi.org/10.1037/0033-2909.114.3.542

    • Google Scholar
20. 1. Burt JB
    2. Demirtaş M
    3. Eckner WJ
    4. Navejar NM
    5. Ji JL
    6. Martin WJ
    7. Bernacchia A
    8. Anticevic A
    9. Murray JD

    (2018) Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography

    Nature Neuroscience 21:1251–1259.

    https://doi.org/10.1038/s41593-018-0195-0

    • PubMed
    • Google Scholar
21. 1. Burt JB
    2. Helmer M
    3. Shinn M
    4. Anticevic A
    5. Murray JD

    (2020) Generative modeling of brain maps with spatial autocorrelation

    NeuroImage 220:117038.

    https://doi.org/10.1016/j.neuroimage.2020.117038

    • PubMed
    • Google Scholar
22. 1. Burzynska AZ
    2. Garrett DD
    3. Preuschhof C
    4. Nagel IE
    5. Li SC
    6. Bäckman L
    7. Heekeren HR
    8. Lindenberger U

    (2013) A scaffold for efficiency in the human brain

    The Journal of Neuroscience 33:17150–17159.

    https://doi.org/10.1523/JNEUROSCI.1426-13.2013

    • PubMed
    • Google Scholar
23. 1. Chaudhuri R
    2. Knoblauch K
    3. Gariel MA
    4. Kennedy H
    5. Wang XJ

    (2015) A Large-Scale circuit mechanism for hierarchical dynamical processing in the primate cortex

    Neuron 88:419–431.

    https://doi.org/10.1016/j.neuron.2015.09.008

    • PubMed
    • Google Scholar
24. 1. Chien HS
    2. Honey CJ

    (2020) Constructing and forgetting temporal context in the human cerebral cortex

    Neuron 106:675–686.

    https://doi.org/10.1016/j.neuron.2020.02.013

    • PubMed
    • Google Scholar
25. 1. Cocchi L
    2. Sale MV
    3. L Gollo L
    4. Bell PT
    5. Nguyen VT
    6. Zalesky A
    7. Breakspear M
    8. Mattingley JB

    (2016) A hierarchy of timescales explains distinct effects of local inhibition of primary visual cortex and frontal eye fields

    eLife 5:e15252.

    https://doi.org/10.7554/eLife.15252

    • PubMed
    • Google Scholar
26. 1. Coifman RR
    2. Lafon S
    3. Lee AB
    4. Maggioni M
    5. Nadler B
    6. Warner F
    7. Zucker SW

    (2005) Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps

    PNAS 102:7426–7431.

    https://doi.org/10.1073/pnas.0500334102

    • PubMed
    • Google Scholar
27. 1. Daducci A
    2. Gerhard S
    3. Griffa A
    4. Lemkaddem A
    5. Cammoun L
    6. Gigandet X
    7. Meuli R
    8. Hagmann P
    9. Thiran JP

    (2012) The connectome mapper: an open-source processing pipeline to map connectomes with MRI

    PLOS ONE 7:e48121.

    https://doi.org/10.1371/journal.pone.0048121

    • PubMed
    • Google Scholar
28. 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
29. Preprint

    1. Deco G
    2. Aquino KM
    3. Arnatkeviciute A
    4. Oldham S
    5. Sabaroedin K
    6. Rogasch NC
    7. Kringelbach ML
    8. Fornito A

    (2020) Dynamical consequences of regional heterogeneity in the brains transcriptional landscape

    bioRxiv.

    https://doi.org/10.1101/2020.10.28.359943

    • Google Scholar
30. 1. Demirtaş M
    2. Burt JB
    3. Helmer M
    4. Ji JL
    5. Adkinson BD
    6. Glasser MF
    7. Van Essen DC
    8. Sotiropoulos SN
    9. Anticevic A
    10. Murray JD

    (2019) Hierarchical heterogeneity across human cortex shapes Large-Scale neural dynamics

    Neuron 101:1181–1194.

    https://doi.org/10.1016/j.neuron.2019.01.017

    • PubMed
    • Google Scholar
31. 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
32. Conference

    1. Dhollander T
    2. Raffelt D
    3. Connelly A

    (2016)

    Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion mr data without a co-registered t1 image

    ISMRM Workshop on Breaking the Barriers of Diffusion MRI.

    • Google Scholar
33. 1. Fallon J
    2. Ward PGD
    3. Parkes L
    4. Oldham S
    5. Arnatkevičiūtė A
    6. Fornito A
    7. Fulcher BD

    (2020) Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

    Network Neuroscience 4:788–806.

    https://doi.org/10.1162/netn_a_00151

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

    (1999) Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system

    NeuroImage 9:195–207.

    https://doi.org/10.1006/nimg.1998.0396

    • PubMed
    • Google Scholar
35. 1. Fulcher BD
    2. Little MA
    3. Jones NS

    (2013) Highly comparative time-series analysis: the empirical structure of time series and their methods

    Journal of the Royal Society Interface 10:20130048.

    https://doi.org/10.1098/rsif.2013.0048

    • PubMed
    • Google Scholar
36. Conference

    1. Fulcher BD

    (2018)

    Feature-based time-series analysis

    Feature Engineering for Machine Learning and Data Analytics.

    • Google Scholar
37. 1. Fulcher BD
    2. Murray JD
    3. Zerbi V
    4. Wang XJ

    (2019) Multimodal gradients across mouse cortex

    PNAS 116:4689–4695.

    https://doi.org/10.1073/pnas.1814144116

    • PubMed
    • Google Scholar
38. 1. Fulcher BD
    2. Fornito A

    (2016) A transcriptional signature of hub connectivity in the mouse connectome

    PNAS 113:1435–1440.

    https://doi.org/10.1073/pnas.1513302113

    • PubMed
    • Google Scholar
39. 1. Fulcher BD
    2. Jones NS

    (2017) Hctsa: a computational framework for automated Time-Series phenotyping using massive feature extraction

    Cell Systems 5:527–531.

    https://doi.org/10.1016/j.cels.2017.10.001

    • PubMed
    • Google Scholar
40. 1. Gao R
    2. van den Brink RL
    3. Pfeffer T
    4. Voytek B

    (2020) Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture

    eLife 9:e61277.

    https://doi.org/10.7554/eLife.61277

    • PubMed
    • Google Scholar
41. 1. Garrett DD
    2. Samanez-Larkin GR
    3. MacDonald SW
    4. Lindenberger U
    5. McIntosh AR
    6. Grady CL

    (2013) Moment-to-moment brain signal variability: a next frontier in human brain mapping?

    Neuroscience & Biobehavioral Reviews 37:610–624.

    https://doi.org/10.1016/j.neubiorev.2013.02.015

    • PubMed
    • Google Scholar
42. 1. Garrett DD
    2. Epp SM
    3. Lindenberger U

    (2017)

    Localtemporal variability reflects functional network integrationin the human brain: on the crucial role of thethalamus

    NeuroImage 183:e19.

    • Google Scholar
43. 1. Garrett DD
    2. Epp SM
    3. Perry A
    4. Lindenberger U

    (2018) Local temporal variability reflects functional integration in the human brain

    NeuroImage 183:776–787.

    https://doi.org/10.1016/j.neuroimage.2018.08.019

    • PubMed
    • Google Scholar
44. 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
45. 1. Glasser MF
    2. Smith SM
    3. Marcus DS
    4. Andersson JL
    5. Auerbach EJ
    6. Behrens TE
    7. Coalson TS
    8. Harms MP
    9. Jenkinson M
    10. Moeller S
    11. Robinson EC
    12. Sotiropoulos SN
    13. Xu J
    14. Yacoub E
    15. Ugurbil K
    16. Van Essen DC

    (2016) The human connectome project's neuroimaging approach

    Nature Neuroscience 19:1175–1187.

    https://doi.org/10.1038/nn.4361

    • PubMed
    • Google Scholar
46. 1. Glasser MF
    2. Coalson TS
    3. Bijsterbosch JD
    4. Harrison SJ
    5. Harms MP
    6. Anticevic A
    7. Van Essen DC
    8. Smith SM

    (2018) Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data

    NeuroImage 181:692–717.

    https://doi.org/10.1016/j.neuroimage.2018.04.076

    • PubMed
    • Google Scholar
47. Preprint

    1. Golesorkhi M
    2. Tumati S
    3. Gomez-Pilar J
    4. Stamatakis E
    5. Northoff G

    (2020) Time meets space: brain dynamics drive spatial topography

    bioRxiv.

    https://doi.org/10.1101/2020.06.11.106476

    • Google Scholar
48. 1. Gollo LL
    2. Zalesky A
    3. Hutchison RM
    4. van den Heuvel M
    5. Breakspear M

    (2015) Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

    Philosophical Transactions of the Royal Society B: Biological Sciences 370:20140165.

    https://doi.org/10.1098/rstb.2014.0165

    • PubMed
    • Google Scholar
49. 1. Gordon EM
    2. Laumann TO
    3. Gilmore AW
    4. Newbold DJ
    5. Greene DJ
    6. Berg JJ
    7. Ortega M
    8. Hoyt-Drazen C
    9. Gratton C
    10. Sun H
    11. Hampton JM
    12. Coalson RS
    13. Nguyen AL
    14. McDermott KB
    15. Shimony JS
    16. Snyder AZ
    17. Schlaggar BL
    18. Petersen SE
    19. Nelson SM
    20. Dosenbach NUF

    (2017) Precision functional mapping of individual human brains

    Neuron 95:791–807.

    https://doi.org/10.1016/j.neuron.2017.07.011

    • PubMed
    • Google Scholar
50. 1. Goulas A
    2. Majka P
    3. Rosa MGP
    4. Hilgetag CC

    (2019) A blueprint of mammalian cortical connectomes

    PLOS Biology 17:e2005346.

    https://doi.org/10.1371/journal.pbio.2005346

    • PubMed
    • Google Scholar
51. Preprint

    1. Hansen JY
    2. Markello RD
    3. Vogel JW
    4. Seidlitz J
    5. Bzdok D
    6. Misic B

    (2020) Molecular signatures of cognition and affect

    bioRxiv.

    https://doi.org/10.1101/2020.07.16.203026

    • Google Scholar
52. 1. Hasson U
    2. Yang E
    3. Vallines I
    4. Heeger DJ
    5. Rubin N

    (2008) A hierarchy of temporal receptive windows in human cortex

    Journal of Neuroscience 28:2539–2550.

    https://doi.org/10.1523/JNEUROSCI.5487-07.2008

    • PubMed
    • Google Scholar
53. 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
54. 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
55. 1. Hilgetag CC
    2. Beul SF
    3. van Albada SJ
    4. Goulas A

    (2019) An architectonic type principle integrates macroscopic cortico-cortical connections with intrinsic cortical circuits of the primate brain

    Network Neuroscience 3:905–923.

    https://doi.org/10.1162/netn_a_00100

    • PubMed
    • Google Scholar
56. 1. Hilgetag CC
    2. Goulas A

    (2020) 'Hierarchy' in the organization of brain networks

    Philosophical Transactions of the Royal Society B: Biological Sciences 375:20190319.

    https://doi.org/10.1098/rstb.2019.0319

    • PubMed
    • Google Scholar
57. 1. Hill J
    2. Inder T
    3. Neil J
    4. Dierker D
    5. Harwell J
    6. Van Essen D

    (2010) Similar patterns of cortical expansion during human development and evolution

    PNAS 107:13135–13140.

    https://doi.org/10.1073/pnas.1001229107

    • PubMed
    • Google Scholar
58. 1. Honey CJ
    2. Thesen T
    3. Donner TH
    4. Silbert LJ
    5. Carlson CE
    6. Devinsky O
    7. Doyle WK
    8. Rubin N
    9. Heeger DJ
    10. Hasson U

    (2012) Slow cortical dynamics and the accumulation of information over long timescales

    Neuron 76:423–434.

    https://doi.org/10.1016/j.neuron.2012.08.011

    • PubMed
    • Google Scholar
59. 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
60. 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
61. 1. Ito T
    2. Hearne LJ
    3. Cole MW

    (2020) A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales

    NeuroImage 221:117141.

    https://doi.org/10.1016/j.neuroimage.2020.117141

    • PubMed
    • Google Scholar
62. 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
63. 1. Jones EG
    2. Powell TP

    (1970) An anatomical study of converging sensory pathways within the cerebral cortex of the monkey

    Brain 93:793–820.

    https://doi.org/10.1093/brain/93.4.793

    • PubMed
    • Google Scholar
64. 1. Keitel A
    2. Gross J

    (2016) Individual human brain Areas can be identified from their characteristic spectral activation fingerprints

    PLOS Biology 14:e1002498.

    https://doi.org/10.1371/journal.pbio.1002498

    • PubMed
    • Google Scholar
65. 1. Khambhati AN
    2. Sizemore AE
    3. Betzel RF
    4. Bassett DS

    (2018) Modeling and interpreting mesoscale network dynamics

    NeuroImage 180:337–349.

    https://doi.org/10.1016/j.neuroimage.2017.06.029

    • PubMed
    • Google Scholar
66. 1. Kiebel SJ
    2. Daunizeau J
    3. Friston KJ

    (2008) A hierarchy of time-scales and the brain

    PLOS Computational Biology 4:e1000209.

    https://doi.org/10.1371/journal.pcbi.1000209

    • PubMed
    • Google Scholar
67. 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.

    • Google Scholar
68. 1. Li J
    2. Bolt T
    3. Bzdok D
    4. Nomi JS
    5. Yeo BTT
    6. Spreng RN
    7. Uddin LQ

    (2019) Topography and behavioral relevance of the global signal in the human brain

    Scientific Reports 9:14286.

    https://doi.org/10.1038/s41598-019-50750-8

    • PubMed
    • Google Scholar
69. 1. Lurie DJ
    2. Kessler D
    3. Bassett DS
    4. Betzel RF
    5. Breakspear M
    6. Kheilholz S
    7. Kucyi A
    8. Liégeois R
    9. Lindquist MA
    10. McIntosh AR
    11. Poldrack RA
    12. Shine JM
    13. Thompson WH
    14. Bielczyk NZ
    15. Douw L
    16. Kraft D
    17. Miller RL
    18. Muthuraman M
    19. Pasquini L
    20. Razi A
    21. Vidaurre D
    22. Xie H
    23. Calhoun VD

    (2020) Questions and controversies in the study of time-varying functional connectivity in resting fMRI

    Network Neuroscience 4:30–69.

    https://doi.org/10.1162/netn_a_00116

    • PubMed
    • Google Scholar
70. Conference

    1. Lurie DJ
    2. D’Esposito M

    (2020)

    Gradients of intrinsic dynamics follow connectomic, anatomical and microstructural hierarchies

    Annual Meeting of the Organization for Human Brain Mapping.

    • Google Scholar
71. Preprint

    1. Mahjoory K
    2. Schoffelen J-M
    3. Keitel A
    4. Gross J

    (2019) The frequency gradient of human resting-state brain oscillations follows cortical hierarchies

    bioRxiv.

    https://doi.org/10.1101/857656

    • Google Scholar
72. 1. Marcus DS
    2. Harwell J
    3. Olsen T
    4. Hodge M
    5. Glasser MF
    6. Prior F
    7. Jenkinson M
    8. Laumann T
    9. Curtiss SW
    10. Van Essen DC

    (2011) Informatics and data mining tools and strategies for the human connectome project

    Frontiers in Neuroinformatics 5:4.

    https://doi.org/10.3389/fninf.2011.00004

    • PubMed
    • Google Scholar
73. 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
74. Software

    1. Markello R
    2. Shafiei G
    3. Zheng YQ
    4. Mišić B

    (2020) A toolbox for the Allen Brain Atlas genetics data

    Zenodo.

    https://doi.org/10.5281/zenodo.4091537
75. Preprint

    1. Markello R
    2. Misic B

    (2020) Comparing spatially-constrained null models for parcellated brain maps

    bioRxiv.

    https://doi.org/10.1101/2020.08.13.249797

    • Google Scholar
76. 1. Markicevic M
    2. Fulcher BD
    3. Lewis C
    4. Helmchen F
    5. Rudin M
    6. Zerbi V
    7. Wenderoth N

    (2020) Cortical excitation:inhibition imbalance causes abnormal brain network dynamics as observed in neurodevelopmental disorders

    Cerebral Cortex 30:4922–4937.

    https://doi.org/10.1093/cercor/bhaa084

    • PubMed
    • Google Scholar
77. 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
78. 1. Mesulam M-M

    (2000)

    Behavioral neuroanatomy

    Principles of behavioral and cognitive neurology 2:1–120.

    • Google Scholar
79. 1. Mišić B
    2. Vakorin VA
    3. Paus T
    4. McIntosh AR

    (2011) Functional embedding predicts the variability of neural activity

    Frontiers in Systems Neuroscience 5:90.

    https://doi.org/10.3389/fnsys.2011.00090

    • PubMed
    • Google Scholar
80. 1. Mišić B
    2. Fatima Z
    3. Askren MK
    4. Buschkuehl M
    5. Churchill N
    6. Cimprich B
    7. Deldin PJ
    8. Jaeggi S
    9. Jung M
    10. Korostil M
    11. Kross E
    12. Krpan KM
    13. Peltier S
    14. Reuter-Lorenz PA
    15. Strother SC
    16. Jonides J
    17. McIntosh AR
    18. Berman MG

    (2014) The functional connectivity landscape of the human brain

    PLOS ONE 9:e111007.

    https://doi.org/10.1371/journal.pone.0111007

    • PubMed
    • Google Scholar
81. 1. Mišić B
    2. Betzel RF
    3. Nematzadeh A
    4. Goñi J
    5. Griffa A
    6. Hagmann P
    7. Flammini A
    8. Ahn YY
    9. Sporns O

    (2015) Cooperative and competitive spreading dynamics on the human connectome

    Neuron 86:1518–1529.

    https://doi.org/10.1016/j.neuron.2015.05.035

    • PubMed
    • Google Scholar
82. 1. Mišic B
    2. Betzel RF
    3. Griffa A
    4. de Reus MA
    5. He Y
    6. Zuo XN
    7. van den Heuvel MP
    8. Hagmann P
    9. Sporns O
    10. Zatorre RJ

    (2018) Network-Based asymmetry of the human auditory system

    Cerebral Cortex 28:2655–2664.

    https://doi.org/10.1093/cercor/bhy101

    • PubMed
    • Google Scholar
83. Preprint

    1. Muller EJ
    2. Munn B
    3. Hearne LJ
    4. Smith JB
    5. Fulcher B
    6. Cocchi L
    7. Shine J

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

    bioRxiv.

    https://doi.org/10.1101/2020.02.28.970350

    • Google Scholar
84. 1. Murray JD
    2. Bernacchia A
    3. Freedman DJ
    4. Romo R
    5. Wallis JD
    6. Cai X
    7. Padoa-Schioppa C
    8. Pasternak T
    9. Seo H
    10. Lee D
    11. Wang XJ

    (2014) A hierarchy of intrinsic timescales across primate cortex

    Nature Neuroscience 17:1661–1663.

    https://doi.org/10.1038/nn.3862

    • PubMed
    • Google Scholar
85. 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

    (2019) 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
86. Preprint

    1. Paquola C
    2. Seidlitz J
    3. Benkarim O
    4. Royer J
    5. Klimes P
    6. Bethlehem RA
    7. Lariviere S
    8. de Wael R
    9. Hall J
    10. Frauscher B

    (2020) A cortical wiring space links cellular architecture. funtional dynamics and hierarchies in humans

    bioRxiv.

    https://doi.org/10.1101/2020.01.08.899583

    • Google Scholar
87. 1. Payeur A
    2. Béïque JC
    3. Naud R

    (2019) Classes of dendritic information processing

    Current Opinion in Neurobiology 58:78–85.

    https://doi.org/10.1016/j.conb.2019.07.006

    • PubMed
    • Google Scholar
88. 1. Pedregosa F
    2. Varoquaux G
    3. Gramfort A
    4. Michel V
    5. Thirion B
    6. Grisel O
    7. Blondel M
    8. Prettenhofer P
    9. Weiss R
    10. Dubourg V

    (2011)

    Scikit-learn: machine learning in Python

    Journal of Machine Learning Research 12:2825–2830.

    • Google Scholar
89. 1. Poldrack RA
    2. Kittur A
    3. Kalar D
    4. Miller E
    5. Seppa C
    6. Gil Y
    7. Parker DS
    8. Sabb FW
    9. Bilder RM

    (2011) The cognitive atlas: toward a knowledge foundation for cognitive neuroscience

    Frontiers in Neuroinformatics 5:17.

    https://doi.org/10.3389/fninf.2011.00017

    • PubMed
    • Google Scholar
90. 1. Preti MG
    2. Bolton TA
    3. Van De Ville D

    (2017) The dynamic functional connectome: state-of-the-art and perspectives

    NeuroImage 160:41–54.

    https://doi.org/10.1016/j.neuroimage.2016.12.061

    • PubMed
    • Google Scholar
91. 1. Quackenbush J

    (2002) Microarray data normalization and transformation

    Nature Genetics 32 Suppl:496–501.

    https://doi.org/10.1038/ng1032

    • PubMed
    • Google Scholar
92. 1. Raut RV
    2. Snyder AZ
    3. Raichle ME

    (2020) Hierarchical dynamics as a macroscopic organizing principle of the human brain

    PNAS 117:20890–20897.

    https://doi.org/10.1073/pnas.2003383117

    • PubMed
    • Google Scholar
93. 1. Richiardi J
    2. Altmann A
    3. Milazzo AC
    4. Chang C
    5. Chakravarty MM
    6. Banaschewski T
    7. Barker GJ
    8. Bokde AL
    9. Bromberg U
    10. Büchel C
    11. Conrod P
    12. Fauth-Bühler M
    13. Flor H
    14. Frouin V
    15. Gallinat J
    16. Garavan H
    17. Gowland P
    18. Heinz A
    19. Lemaître H
    20. Mann KF
    21. Martinot JL
    22. Nees F
    23. Paus T
    24. Pausova Z
    25. Rietschel M
    26. Robbins TW
    27. Smolka MN
    28. Spanagel R
    29. Ströhle A
    30. Schumann G
    31. Hawrylycz M
    32. Poline JB
    33. Greicius MD
    34. IMAGEN consortium

    (2015) BRAIN NETWORKS. correlated gene expression supports synchronous activity in brain networks

    Science 348:1241–1244.

    https://doi.org/10.1126/science.1255905

    • PubMed
    • Google Scholar
94. 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
95. 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
96. 1. Salimi-Khorshidi G
    2. Douaud G
    3. Beckmann CF
    4. Glasser MF
    5. Griffanti L
    6. Smith SM

    (2014) Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers

    NeuroImage 90:449–468.

    https://doi.org/10.1016/j.neuroimage.2013.11.046

    • PubMed
    • Google Scholar
97. 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
98. 1. Scholtens LH
    2. Schmidt R
    3. de Reus MA
    4. van den Heuvel MP

    (2014) Linking macroscale graph analytical organization to microscale neuroarchitectonics in the macaque connectome

    Journal of Neuroscience 34:12192–12205.

    https://doi.org/10.1523/JNEUROSCI.0752-14.2014

    • PubMed
    • Google Scholar
99. 1. Sethi SS
    2. Zerbi V
    3. Wenderoth N
    4. Fornito A
    5. Fulcher BD

    (2017) Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

    Chaos: An Interdisciplinary Journal of Nonlinear Science 27:047405.

    https://doi.org/10.1063/1.4979281

    • PubMed
    • Google Scholar
100. 1. Shafiei G
     2. Zeighami Y
     3. Clark CA
     4. Coull JT
     5. Nagano-Saito A
     6. Leyton M
     7. Dagher A
     8. Mišic B

     (2019) Dopamine signaling modulates the stability and integration of intrinsic brain networks

     Cerebral Cortex 29:397–409.

     https://doi.org/10.1093/cercor/bhy264

     • PubMed
     • Google Scholar
101. 1. Shafiei G
     2. Markello RD
     3. Makowski C
     4. Talpalaru A
     5. Kirschner M
     6. Devenyi GA
     7. Guma E
     8. Hagmann P
     9. Cashman NR
     10. Lepage M
     11. Chakravarty MM
     12. Dagher A
     13. Mišić B

     (2020) Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture

     Biological Psychiatry 87:727–735.

     https://doi.org/10.1016/j.biopsych.2019.09.031

     • PubMed
     • Google Scholar
102. 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
103. 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
104. 1. Sporns O
     2. Tononi G
     3. Kötter R

     (2005) The human connectome: a structural description of the human brain

     PLOS Computational Biology 1:e42.

     https://doi.org/10.1371/journal.pcbi.0010042

     • PubMed
     • Google Scholar
105. 1. Straub I
     2. Witter L
     3. Eshra A
     4. Hoidis M
     5. Byczkowicz N
     6. Maas S
     7. Delvendahl I
     8. Dorgans K
     9. Savier E
     10. Bechmann I
     11. Krueger M
     12. Isope P
     13. Hallermann S

     (2020) Gradients in the mammalian cerebellar cortex enable Fourier-like transformation and improve storing capacity

     eLife 9:e51771.

     https://doi.org/10.7554/eLife.51771

     • PubMed
     • Google Scholar
106. 1. Suárez LE
     2. Markello RD
     3. Betzel RF
     4. Misic B

     (2020) Linking structure and function in macroscale brain networks

     Trends in Cognitive Sciences 24:302–315.

     https://doi.org/10.1016/j.tics.2020.01.008

     • PubMed
     • Google Scholar
107. Conference

     1. Tournier JD
     2. Calamante F
     3. Connelly A

     (2010)

     Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions

     Proceedings of the International Society for Magnetic Resonance in Medicine.

     • Google Scholar
108. 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
109. 1. Uddin LQ

     (2020) Bring the noise: reconceptualizing spontaneous neural activity

     Trends in Cognitive Sciences 24:734–746.

     https://doi.org/10.1016/j.tics.2020.06.003

     • PubMed
     • Google Scholar
110. 1. Van Essen DC
     2. Smith SM
     3. Barch DM
     4. Behrens TE
     5. Yacoub E
     6. Ugurbil K
     7. WU-Minn HCP Consortium

     (2013) The WU-Minn human connectome project: an overview

     NeuroImage 80:62–79.

     https://doi.org/10.1016/j.neuroimage.2013.05.041

     • PubMed
     • Google Scholar
111. 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
112. 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
113. 1. Vézquez-Rodríguez B
     2. Liu ZQ
     3. Hagmann P
     4. Misic B

     (2020) Signal propagation via cortical hierarchies

     Network Neuroscience 4:1072–1090.

     https://doi.org/10.1162/netn_a_00153

     • PubMed
     • Google Scholar
114. Book

     1. von Economo CF
     2. Koskinas GN
     3. Triarhou LC

     (2008)

     Atlas of Cytoarchitectonics of the Adult Human Cerebral Cortex, 10

     Karger Basel.

     • Google Scholar
115. Book

     1. von Economo CF
     2. Koskinas GN

     (1925)

     Die cytoarchitektonik der hirnrinde des erwachsenen menschen

     J. Springer.

     • Google Scholar
116. 1. Vos de Wael R
     2. Larivière S
     3. Caldairou B
     4. Hong SJ
     5. Margulies DS
     6. Jefferies E
     7. Bernasconi A
     8. Smallwood J
     9. Bernasconi N
     10. Bernhardt BC

     (2018) Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding

     PNAS 115:10154–10159.

     https://doi.org/10.1073/pnas.1803667115

     • PubMed
     • Google Scholar
117. 1. Wagstyl K
     2. Ronan L
     3. Goodyer IM
     4. Fletcher PC

     (2015) Cortical thickness gradients in structural hierarchies

     NeuroImage 111:241–250.

     https://doi.org/10.1016/j.neuroimage.2015.02.036

     • PubMed
     • Google Scholar
118. 1. Wagstyl K
     2. Larocque S
     3. Cucurull G
     4. Lepage C
     5. Cohen JP
     6. Bludau S
     7. Palomero-Gallagher N
     8. Lewis LB
     9. Funck T
     10. Spitzer H
     11. Dickscheid T
     12. Fletcher PC
     13. Romero A
     14. Zilles K
     15. Amunts K
     16. Bengio Y
     17. Evans AC

     (2020) BigBrain 3D atlas of cortical layers: cortical and laminar thickness gradients diverge in sensory and motor cortices

     PLOS Biology 18:e3000678.

     https://doi.org/10.1371/journal.pbio.3000678

     • PubMed
     • Google Scholar
119. 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
120. 1. Wang XJ

     (2020) Macroscopic gradients of synaptic excitation and inhibition in the neocortex

     Nature Reviews Neuroscience 21:169–178.

     https://doi.org/10.1038/s41583-020-0262-x

     • PubMed
     • Google Scholar
121. 1. Watanabe T
     2. Rees G
     3. Masuda N

     (2019) Atypical intrinsic neural timescale in autism

     eLife 8:e42256.

     https://doi.org/10.7554/eLife.42256

     • PubMed
     • Google Scholar
122. 1. Wei Y
     2. Scholtens LH
     3. Turk E
     4. van den Heuvel MP

     (2019) Multiscale examination of cytoarchitectonic similarity and human brain connectivity

     Network Neuroscience 3:124–137.

     https://doi.org/10.1162/netn_a_00057

     • PubMed
     • Google Scholar
123. 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
124. 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.

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

     • PubMed
     • Google Scholar
125. 1. Zheng YQ
     2. Zhang Y
     3. Yau Y
     4. Zeighami Y
     5. Larcher K
     6. Misic B
     7. Dagher A

     (2019) Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation

     PLOS Biology 17:e3000495.

     https://doi.org/10.1371/journal.pbio.3000495

     • PubMed
     • Google Scholar

Author details

1. Golia Shafiei

   McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   golia.shafiei@mail.mcgill.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2036-5571

2. Ross D Markello

   McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada

   Contribution

   Data curation, Software, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1057-1336

3. Reinder Vos de Wael

   McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada

   Contribution

   Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Boris C Bernhardt

   McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada

   Contribution

   Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Ben D Fulcher

   School of Physics, The University of Sydney, Sydney, Australia

   Contribution

   Resources, Software, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3003-4055

6. Bratislav Misic

   McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada

   Contribution

   Conceptualization, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   bratislav.misic@mcgill.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0307-2862

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