Abstract
Systematic spatial variation in microarchitecture is observed across the cortex. These microarchitectural gradients are reflected in neural activity, which can be captured by neurophysiological timeseries. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical microarchitecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional timeseries profiles to a comprehensive multimodal, multiscale atlas of cortical microarchitecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple microarchitectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.
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Introduction
Signals, in the form of electrical impulses, are perpetually generated, propagated, and integrated via multiple types of neurons and neuronal populations^{1,2}. The wiring of the brain guides the propagation of signals through networks of nested polyfunctional neural circuits^{3,4}. The resulting fluctuations in membrane potentials and firing rates ultimately manifest as patterned neurophysiological activity^{5,6,7}.
A rich literature demonstrates links between cortical microarchitecture and dynamics. Numerous studies have investigated the cellular and laminar origins of cortical rhythms^{8,9,10,11,12,13}. For instance, electro and magnetoencephalography (EEG/MEG) signals appear to be more sensitive to dipoles originating from pyramidal cells of cortical layers IIIII and V^{14,15}. Moreover, specific timeseries features of neuronal electrophysiology depend on neuron type, morphology and local gene transcription, particularly genes associated with ion channel regulation^{16,17,18}. However, previous studies have mostly focused on single or small sets of featuresofinterest, often mapping single microarchitectural features to single dynamical features. Starting with the discovery of 8–12 Hz alpha rhythm in the electroencephalogram^{19}, conventional timeseries analysis in neuroscience has typically focused on canonical electrophysiological rhythms^{20,21,22,23,24}. More recently, there has also been a growing interest in studying the intrinsic timescales that display a hierarchy of temporal processing from fast fluctuating activity in unimodal cortex to slower encoding of contextual information in transmodal cortex^{25,26,27,28,29,30,31,32,33,34}. How ongoing neurophysiological dynamics arise from specific features of neural circuit microarchitecture remains a key question in neuroscience^{1,2,12}.
Recent analytic advances have opened new opportunities to perform neurophysiological timeseries phenotyping by computing comprehensive feature sets that go beyond power spectral measures, including measures of signal amplitude distribution, entropy, fractal scaling and autocorrelation^{35,36,37,38,39,40}. Concomitant advances in imaging technologies and data sharing offer new ways to measure brain structure with unprecedented detail and depth^{41,42,43}, including gene expression^{44}, myelination^{45,46}, neurotransmitter receptors^{47,48,49,50,51,52,53,54}, cytoarchitecture^{55,56,57}, laminar differentiation^{56,58}, cell type composition^{44,59,60}, metabolism^{61,62} and evolutionary expansion^{63,64}.
Here we comprehensively characterize the dynamical signature of neurophysiological activity and relate it to the underlying microarchitecture by integrating multiple, multimodal maps of human cortex. Instead of manually selecting a small number of featuresofinterest, we use extensive sets of dynamical and microarchitectural features using datadriven approaches. Specifically, we first derive cortical spontaneous neurophysiological activity using sourceresolved magnetoencephalography (MEG) from the Human Connectome Project (HCP; see ref. ^{65}). We then apply highly comparative timeseries analysis (hctsa; see refs. ^{35,36}) to estimate a comprehensive set of timeseries features for each brain region (Fig. 1). At the same time, we construct a microarchitectural atlas of the cortex that includes maps of microstructure, metabolism, neurotransmitter receptors and transporters, laminar differentiation and cell types (Fig. 2). Finally, we map these extensive microarchitectural and dynamical atlases to one another using multivariate statistical analysis.
Results
Regional neurophysiological timeseries were estimated by applying linearly constrained minimum variance (LCMV) beamforming to resting state MEG data from the Human Connectome Project (HCP; see ref. ^{65}) using Brainstorm software^{66}(see Methods for details). Highly comparative timeseries analysis (hctsa; see refs. ^{35,36}) was then used to perform massive timeseries feature extraction from regional MEG recordings. This procedure provides a featurebased representation of timeseries, where given timeseries are represented by timeseries feature vectors^{36,37}. This timeseries phenotyping analysis is a datadriven method that quantifies dynamic repertoire of neural activity using interdisciplinary metrics of temporal structure of the signal and yields a comprehensive ‘fingerprint’ of dynamical properties of each brain region. Applying timeseries phenotyping to regional MEG timeseries, we estimated 6880 timeseries features for 100 cortical regions from the Schaefer100 atlas^{67}. The hctsa library contains a vast and interdisciplinary set of features with potentially correlated values that span various conceptual timeseries characteristics. The list of timeseries features includes, but is not limited to, statistics derived from the autocorrelation function, power spectrum, amplitude distribution, and entropy estimates (Fig. 1).
To estimate a comprehensive set of multimodal microarchitectural features, we used the recentlydeveloped neuromaps toolbox^{43} as well as the BigBrainWarp toolbox^{57}, the Allen Human Brain Atlas (AHBA^{44}) and the abagen toolbox^{68} to transform and compile a set of 45 features, including measures of microstructure, metabolism, cortical expansion, receptors and transporters, layer thickness and cell typespecific gene expression (Fig. 2). Note that the microstructure maps include principal gradients of gene expression and neurotransmitter profiles as they each represent proxy measures of certain molecular properties. Specifically, the principal component of gene expression (gene expression PC1) provides a potential proxy for cell type distribution across the cortex^{44,69,70} and the principal component of neurotransmitter receptors and transporters (neurotransmitter PC1) provides a summary measure of protein densities of 18 neurotransmitter receptors and transporters^{47,71}. We also included individual neurotransmitter receptor and transporter maps as well as cell typespecific gene expression maps to assess their effects separately.
In subsequent analyses, we first assess the topographic organization of neurophysiological dynamics by quantifying the dominant patterns of variations in restingstate MEG timeseries properties. We then characterize the signature of neurophysiological dynamics with respect to microarchitectural attributes across the cortex. Finally, we perform sensitivity analyses to investigate potential effects of confounding factors on the findings, such as signaltonoise ratio and parcellation resolution (see Sensitivity analysis for details).
Topographic distribution of neurophysiological dynamics
The hctsa timeseries phenotyping procedure generated 6880 timeseries features per brain region. Since hctsa contains multiple algorithmic variants for quantifying any given timeseries property, the identified timeseries features potentially capture related dynamical behaviour and include groups of correlated properties. Hence, we first sought to identify dominant macroscopic patterns or gradients of neurophysiological dynamics using principal component analysis (PCA)^{38}. Applying PCA to the groupaverage region × feature matrix, we find evidence of a single dominant component that captures 48.7% of the variance in regional timeseries features (Fig. 3a). The dominant component or “gradient” of neurophysiological dynamics (PC1) mainly spans the posterior parietal cortex and sensorymotor cortices on one end and the anterior temporal, orbitofrontal and ventromedial cortices on the other end (Fig. 3a). Focusing on intrinsic functional networks, we find that the topographic organization of the dominant neurophysiological dynamics varies along a sensory–fugal axis from dorsal attention, somatomotor and visual networks to limbic and default mode networks^{72} (Fig. 3a).
We next investigated the toploading timeseries features on the first component, using the univariate correlations between each of the original feature maps and the PC1 map (i.e., PCA loadings). All correlations were statistically assessed using spatial autocorrelationpreserving null models ("spin tests”^{73,74}; see Methods for details). Figure 3b shows that numerous features are positively and negatively correlated with PC1; the full list of features, their correlation coefficients and pvalues are available in the online Supplementary Dataset S1. Inspection of the top loading features reveals that the majority are statistics derived from the structure of the power spectrum or closely related measures. Examples include power in different frequency bands, parameters of various model fits to the power spectrum, and related measures, such as the shape of the autocorrelation function and measures of fluctuation analysis. Figure 3b shows how the power spectrum varies across the cortex, with each line representing a brain region. Regions are coloured by their position in the putative unimodal–transmodal hierarchy^{75}; the variation visually suggests that unimodal regions display more prominent alpha (8–12 Hz) and beta (15–29 Hz) power peaks. Collectively, these results demonstrate that the traditional focus of electrophysiological timeseries analysis on statistics of the power spectrum is consistent with the dominant variations in MEG dynamics captured by the diverse library of hctsa timeseries features.
Given that the topographic organization of PC1 was closely related to power spectral features, we directly tested the link between PC1 and conventional bandlimited power spectral measures^{21,22,23}, as well as intrinsic timescale^{30} (Supplementary Figure 1). Figure 3c shows the correlations between PC1 and delta (2–4 Hz), theta (5–7 Hz), alpha (8–12 Hz), beta (15–29 Hz), logamma (30–59 Hz) and higamma (60–90 Hz) power maps, and intrinsic neural timescale^{28,30,31,32,33,34,76}. We find that PC1 is significantly correlated with intrinsic timescale (r_{s} = 0.84, p_{spin} = 0.038; FDRcorrected) and higamma (r_{s} = 0.87, p_{spin} = 0.006; FDRcorrected). The results were consistent when we used bandlimited power maps that were adjusted for the aperiodic component of the power spectrum as opposed to the total power^{21} (Supplementary Fig. 2). The fact that PC1 correlates with intrinsic timescale is consistent with the notion that both capture broad variations in the power spectrum. Given that the intrinsic timescale reflects characteristics of the aperiodic component of the power spectrum (these measures are mathematically related; see Methods for details), we also directly assessed the association between PC1 and the exponent and offset of the aperiodic component. The exponent describes the “curve” or the overall “line” or the slope of the aperiodic component and the offset describes the overall vertical shift (up and down translation) of the whole power spectrum^{21}. PC1 was significantly correlated with both measures, suggesting that timeseries features captured by PC1 also reflect properties of the aperiodic component of the power spectrum (Supplementary Fig. 3). In addition, previous reports suggest that broadband gamma activity also partly reflects the aperiodic neurophysiological activity and broadband shifts in the power spectrum^{77,78,79}. This is consistent with our findings that PC1 is associated with gamma power and intrinsic timescale, mainly capturing broad variations in power spectrum and characteristics of the aperiodic activity.
Note that we focused on PC1 because the other components (PC2 and above) accounted for 10% or less of the variance in timeseries features and were not significantly associated with hctsa timeseries features. Moreover, to verify that the apparent lowdimensionality of the data and the identified PC patterns were not driven by the smaller number of samples (i.e., brain regions) than features (i.e., timeseries features), we performed a sensitivity analysis where we randomly selected 100 timeseries features (from the original list of 6 880 features) and reran PCA (1 000 repetitions). The identified PC patterns and their corresponding amount of variance explained were consistent with the original analysis using the full set of timeseries features (Supplementary Fig. 4).
Neurophysiological signatures of microarchitecture
How do the regional neurophysiological timeseries features map onto multimodal microarchitectural features? To address this question, we implemented a multivariate partial least squares analysis (PLS; see refs. ^{80,81}) that integrates multiple multimodal brain maps into the analysis and seeks to identify linear combinations of timeseries features and linear combinations of microarchitectural features that optimally covary with one another. Figure 4a shows that the analysis identifies multiple such combinations, termed latent variables (similar results were obtained using sparse canonical correlation analysis (sCCA); Supplementary Fig. 5). Statistical significance of each latent variable was assessed using spatial autocorrelationpreserving permutation tests^{70,74}. The first latent variable was statistically significant, capturing the greatest covariance between timeseries and microarchitectural features (covariance explained = 75.4%, p_{spin} = 0.011).
Figure 4b shows the spatial topography of timeseries features and microarchitectural scores for the first latent variable. These are the weighted sums of the original input features according to the weighting identified by the latent variable. The correlation between the score maps is maximized by the analysis (r_{s} = 0.73, p_{spin} = 0.0059). We therefore sought to estimate whether the same mapping between timeseries and microarchitectural features can be observed outofsample. We adopted a distancedependent crossvalidation procedure where “seed” regions were randomly chosen and the 75% most physically proximal regions were selected as the training set, while the remaining 25% most physically distal regions were selected as the test set^{70} (see Methods for more details). For each traintest split, we fit a PLS model to the train set and project the test set onto the weights (i.e., singular vectors) derived from the train set. The resulting test set scores are then correlated to estimate an outofsample correlation coefficient. Figure 4b shows that microarchitecture and timeseries feature scores are correlated in training set (mean r_{s} = 0.75) and test set (mean r_{s} = 0.5), demonstrating consistent findings in outofsample analysis.
We next examined the corresponding timeseries and microarchitecture feature loadings and identified the most contributing features to the spatial patterns captured by the first latent variable (Fig. 4c, d). The top loading timeseries features were mainly related to measures of selfcorrelation or predictability of the MEG signal. The selfcorrelation measures mostly reflect the linear correlation structure of neurophysiological timeseries, particularly longlag autocorrelations (at lags > 15 time steps, or > 30 ms). A wide range of other highly weighted timeseries features captured other aspects of signal predictability, including measures of the shape of the autocorrelation function (e.g., the time lag at which the autocorrelation function crosses zero), how the autocorrelation structure changes after removing loworder local trends (e.g., residuals from fitting linear models to rolling 5timestep, or 10 ms, windows), scaling properties assessed using fluctuation analysis (e.g., scaling of signal variance across timescales), and measures derived from a wavelet decomposition (e.g., wavelet coefficients at different timescales). The full list of timeseries feature loadings for the first latent variable is available in the online Supplementary Dataset S2.
To illustrate the spatial distribution of highly contributing timeseries features, Fig. 4c shows three toploading features that mirror the spatial variation of the first PLS latent variable. For example, Figure 4c, left depicts the distribution of the groupaverage first zerocrossing point of the autocorrelation function. The autocorrelation function of the unimodal cortex (marked with a pink circle) crosses zero autocorrelation at a lower lag than the transmodal cortex (marked with a purple circle), suggesting faster autocorrelation decay and longer correlation length in transmodal cortex than in unimodal cortex. Another example is the linear autocorrelation of the MEG signal at longer time lags. Figure 4c, left shows autocorrelation at a lag of 48 ms (24 time steps), demonstrating lower autocorrelation in unimodal cortex and higher autocorrelation in transmodal cortex. Note that the list of toploading features includes linear autocorrelation at other time lags and autocorrelation at a lag of 48 ms was only selected as an illustrative example (Supplementary Fig. 6 depicts the full range of loadings for linear autocorrelation at all time lags included in hctsa). Finally, we examined the scaling exponent, α, estimated using detrended fluctuation analysis as the slope of a linear fit to the loglog plot of the fluctuations of the detrended signal across timescales^{82,83}. Figure 4c, right depicts this scaling exponent across the cortex, which exhibits a similar spatial pattern as the previous two examples, indicating lower selfcorrelation in unimodal cortex (pink circle) compared to transmodal cortex (purple circle). Other variations of fluctuation analysis also featured heavily in the list of toploading features, including goodness of fit of the linear fit, fitting of multiple scaling regimes, and different types of detrending and mathematical formulation of fluctuation size.
Figure 4d shows the corresponding microarchitectural loadings. The most contributing microarchitectural features to the spatial patterns captured by the first latent variable are the principal component of gene expression (gene expression PC1; a potential proxy for cell type distribution^{44,69,70}), T1w/T2w ratio (a proxy for intracortical myelin^{46}), principal component of neurotransmitter receptors and transporters (neurotransmitter PC1), and oxygen and glucose metabolism (strong positive loadings). We also find high contributions (strong negative loadings) from specific neurotransmitter receptor and transporters, in particular metabotropic serotonergic and dopaminergic receptors, as well as from cell typespecific gene expression of oligodendrocyte precursors (opc), which are involved in myelinogenesis^{84,85,86,87,88}. Consistent findings were obtained when we used univariate analysis to relate regional timeseries features and the top loading microarchitectural maps, in particular principal component of gene expression and T1w/T2w ratio, which have previously been extensively studied as archetypical microarchitectural gradients^{30,41,69,89,90} (Supplementary Fig. 7). Altogether, this analysis provides a comprehensive chart or ‘lookup table’ of how microarchitectural and timeseries feature maps are associated with one another. These results demonstrate that cortical variation in multiple microarchitectural attributes manifests as a gradient of timeseries properties of neurophysiological activity, particularly the properties that reflect the longrange selfcorrelation structure of the signal.
Sensitivity analysis
To assess the extent to which the results are affected by potential confounding factors and methodological choices, we repeated the analyses using alternative approaches. First, to ensure that the findings are not influenced by MEG signaltonoise ratio (SNR), we calculated SNR at each source location using a noise model that estimates how sensitive the sourcelevel MEG signal is to source location and orientation^{91,92}. We performed two followup analyses using the SNR map (Supplementary Fig. 8): (1) SNR was first compared with the full set of MEG timeseries features using mass univariate Pearson correlations. Timeseries features that were significantly correlated with SNR were removed from the feature set without correcting for multiple comparisons (p_{spin} < 0.05; 10,000 spatial autocorrelationpreserving permutation tests^{73,74}). Note that this is a more conservative feature selection procedure compared to conventional multiple comparisons correction, because fewer features would be removed if correction for multiple comparisons was applied. PCA was applied to the remaining set of features (Supplementary Figure 8b). The principal component of the retained 3819 features (i.e., PC1  feature subset) explained 31.6% of the variance and was significantly correlated with the original PC1 of the full set of features (r_{s} = 0.93, p_{spin} = 0.0001), reflecting similar spatial pattern as the original analysis. (2) SNR was regressed out from the full set of timeseries features using linear regression analysis. PCA was then applied to the resulting feature residuals (Supplementary Fig. 8c). The principal component of SNRregressed features (i.e., PC1  SNR regressed) explained 41.4% of the variance and reflected the same spatial pattern as the original analysis (r_{s} = 0.70, p_{spin} = 0.0004). Moreover, we assessed the effects of environmental and instrumental noise on the findings, where we applied principal component analysis to the hctsa features obtained from preprocessed emptyroom MEG recordings^{23} (see Methods for more details). PCA weights of the timeseries features of the emptyroom MEG recordings were aligned with the PCA weights of the timeseries features of the restingstate MEG recordings using the Procrustes method (see ref. ^{93}; https://github.com/satra/mapalign). The principal component of neurophysiological dynamics was then compared with the principal component of timeseries features obtained from emptyroom recordings, where no significant associations were identified (Supplementary Fig. 9; r_{s} = − 0.17, p_{spin} = 0.69). These analyses demonstrate that the timeseries features captured by the dominant axis of variation in neurophysiological dynamics are independent from measures of MEG signaltonoise ratio.
Finally, to ensure that the findings are independent from the parcellation resolution, we repeated the analyses using a higher resolution parcellation (Schaefer400 atlas with 400 cortical regions^{67}). The results were consistent with the original analysis (Supplementary Figs. 10, 11). In particular, the first principal component (PC1) accounted for 48.6% of the variance and displayed a similar spatial organization as the one originally obtained for the Schaefer100 atlas (Supplementary Fig. 10a). As before, the top loading timeseries features were mainly related to the characteristics of the power spectral density (Supplementary Fig. 10b, c). The full list of features, their loadings and pvalues are available in the online Supplementary Dataset S3. Moreover, PLS analysis identified a single significant latent variable (p_{spin} = 0.0083) that accounted for 75.7% of the covariance (Supplementary Fig. 11a). Microarchitecture and timeseries feature scores displayed similar spatial patterns to the ones obtained for the Schaefer100 atlas (Supplementary Fig. 11b). The corresponding feature loadings were also consistent with the original findings (microarchitectural loadings in Supplementary Fig. 11c and timeseries feature loadings in the online Supplementary Dataset S4.)
Discussion
In the present study, we use timeseries phenotyping analysis to comprehensively chart the dynamic fingerprint of neurophysiological activity from the restingstate MEG signal. We then map the resulting dynamical atlas to a multimodal microarchitectural atlas to identify the neurophysiological signatures of cortical microarchitecture. We demonstrate that cortical variation in neurophysiological timeseries properties mainly reflects power spectral density and is closely associated with intrinsic timescale and selfcorrelation structure of the signal. Moreover, the spatial organization of neurophysiological dynamics follows gradients of microarchitecture, such as neurotransmitter receptor and transporters, gene expression and T1w/T2w ratios, and reflects cortical metabolic demands.
Numerous studies have previously investigated neural oscillations and their relationship with neural communication patterns in the brain^{8,10,11,94}. Previous reports also suggest that neural oscillations influence behaviour and cognition^{94,95,96,97,98} and are involved in multiple neurological diseases and disorders^{97,99}. Neural oscillations manifest as the variations of power amplitude of neurophysiological signal in the frequency domain^{10,21,100,101}. Power spectral characteristics of the neurophysiological signal, such as mean power amplitude in canonical frequency bands, have previously been used to investigate the underlying mechanisms of largescale brain activity and to better understand the individual differences in brain function^{22,23,31,98,102,103}. Other timeseries properties that are related to the power spectral density have also been used to study neural dynamics, including measures of intrinsic timescale and selfaffinity or selfsimilarity of the signal (e.g., autocorrelation and fluctuation analysis)^{25,30,82,83,104,105,106}.
Applying a datadriven timeseries feature extraction analysis, we find that the topographic organization of neurophysiological timeseries signature follows a sensory–fugal axis, separating somatomotor, occipital and parietal cortices from anterior temporal, orbitofrontal and ventromedial cortices. This dynamic fingerprint of neurophysiological activity is mainly characterized by linear correlation structure of MEG signal captured by hctsa timeseries features. The linear correlation structure manifests in both power spectral properties and the autocorrelation function. This dominant spatial variation of timeseries features also resembles the spatial distribution of intrinsic timescale, another measure related to the characteristics of power spectral density^{28,30,33}. Altogether, while the findings highlight underrepresented timeseries features, they emphasize the importance of conventional methods in characterizing neurophysiological activity and the key role of linear correlation structure in MEG dynamics.
Earlier reports found that regional neural dynamics, including measures of power spectrum and intrinsic timescale, reflect the underlying circuit properties and cortical microarchitecture^{25,28,30}. The relationship between neural dynamics and cortical microarchitecture is often examined using a single, or a few microstructural features. Recent advances in data collection and integration and the increasing number of data sharing initiatives have provided a unique opportunity to comprehensively study cortical circuit properties and microarchitecture using a wide range of multimodal datasets^{43,44,47,56,57,65,107}. Here we use such datasets and compile multiple microarchitectural maps, including measures of microstructure, metabolism, cortical expansion, receptors and transporters, layer thickness and cell typespecific gene expressions, to chart the multivariate associations between neurophysiological dynamics and cortical microarchitecture.
Our findings build on previous reports by showing that neurophysiological dynamics follow the underlying cytoarchitectonic and microstructural gradients. In particular, our findings confirm that MEG intrinsic dynamics are associated with the heterogeneous distribution of gene expression and intracortical myelin^{30,89,108,109} and neurotransmitter receptors and transporters^{47}. In addition, we link the dynamic signature of ongoing neurophysiological activity with multiple metabolic attributes^{62,110}; for instance, we find that regions with greater oxygen and glucose metabolism tend to display lower temporal autocorrelation and therefore more variable momenttomoment intrinsic activity. This is consistent with previously reported high metabolic rates of oxygen and glucose consumption in the sensory cortex^{61}. We also find a prominent association with cell typespecific gene expression of oligodendrocyte precursors (opc), potentially reflecting the contribution of these cells to myelin generation by giving rise to myelinating oligodendrocytes during development^{84,85,86,87,88} and to myelin regulation and metabolic support of myelinated axons in the adult neural circuits^{87,88,111}. Finally, we find that the dominant dynamic signature of neural activity covaries with the granular cortical layer IV, consistent with the idea that layer IV receives prominent subcortical (including thalamic) feedforward projections^{112,113}. Collectively, our findings build on the emerging literature on how heterogeneous microarchitectural properties along with macroscale network embedding (e.g., corticocortical connectivity and subcortical projections) jointly shape regional neural dynamics^{38,39,40,114,115,116,117}.
The present findings must be interpreted with respect to several methodological considerations. First, we used MEG data from a subset of individuals with no familial relationships from the HCP dataset. Although all the presented analyses are performed using the grouplevel data, future work with larger sample sizes can provide more generalizable outcomes^{118,119}. Larger sample sizes will also help go beyond associative analysis and allow for predictive analysis of neural dynamics and microarchitecture in unseen datasets. Second, MEG is susceptible to low SNR and has variable sensitivity to neural activity from different regions (i.e., sources). Thus, electrophysiological recordings with higher spatial resolution, such as intracranial electroencephalography (iEEG and ECoG), may provide more precise measures of neural dynamics that can be examined with respect to cortical microarchitecture. However, a major caveat with iEEG and ECoG is that they lack whole brain coverage, limiting their practical usage in such analysis. An alternative noninvasive modality is onscalp MEG, which offers both high SNR and spatial resolution^{120,121,122,123}. Third, we note that the included microarchitectural maps are by no means direct measurements of the underlying neurobiological features. For example, the “myelin” map is estimated based on the ratio of T1weighted to T2weighted MRI scans, which is only sensitive to intracortical myelin and is not a true measure of tissue myelin content^{46,69}. The “cortical layer thickness” maps are from a deeplearning based layer segmentation of the BigBrain histological atlas and are not precise measurements of laminar differentiation of the brain^{56,57,58}. Although we aimed to select noninvasive modalities that are most sensitive to microstructure, cytoarchitecture, and cellular and molecular features, the included maps can only provide proxy, indirect assessments of such biological properties. Finally, despite the fact that we attempt to use a comprehensive list of timeseries properties and multiple microarchitectural features, neither the timeseries features nor the microarchitectural maps are exhaustive sets of measures. Moreover, microarchitectural features are groupaverage maps that are compiled from different datasets. Multimodal datasets from the same individuals are required to perform individuallevel comparisons between the dynamical and microarchitectural atlases.
Altogether, using a datadriven approach, the present findings show that neurophysiological signatures of cortical microarchitecture are hierarchically organized across the cortex, reflecting the underlying circuit properties. These findings highlight the importance of conventional measures for studying the characteristics of neurophysiological activity, while also identifying lesscommonly used timeseries features that covary with cortical microarchitecture. Collectively, this work opens new avenues for studying the anatomical basis of neurophysiological activity.
Methods
Dataset: human connectome project (HCP)
Resting state magnetoencephalography (MEG) data from a sample of healthy young adults (n = 33; age range 22–35 years; 16 female and 17 male) with no familial relationships were obtained from Human Connectome Project (HCP; S900 release^{65}; informed consent obtained). The WUMinn HCP Consortium (consortium of US and European institutions led by Washington University and the University of Minnesota) approved the study protocol. The obtained data includes resting state scans of approximately 6 minutes long (sampling rate = 2034.5 Hz; antialiasing lowpass filter at 400 Hz) and emptyroom recordings for all participants. 3T structural magnetic resonance imaging (MRI) data and MEG anatomical data (i.e., cortical sheet with 8004 vertices and transformation matrix required for coregistration of MEG sensors and MRI scans) of all participants were also obtained for MEG preprocessing.
Resting state magnetoencephalography (MEG)
Resting state MEG data was analyzed using Brainstorm software, which is documented and freely available for download online under the GNU general public license (see ref. ^{66}; http://neuroimage.usc.edu/brainstorm). For each individual, MEG sensor recordings were registered to their structural MRI scan using the anatomical transformation matrix provided by HCP for coregistration, following the procedure described in Brainstorm online tutorials for the HCP dataset (https://neuroimage.usc.edu/brainstorm/Tutorials/HCPMEG). The data were downsampled to 1/4 of the original sampling rate (i.e., 509 Hz) to facilitate processing. The preprocessing was performed by applying notch filters at 60, 120, 180, 240, and 300 Hz, and was followed by a highpass filter at 0.3 Hz to remove slowwave and DCoffset artifacts. Bad channels were marked based on the information obtained through the data management platform of HCP (ConnectomeDB; https://db.humanconnectome.org/). The artifacts (including heartbeats, eye blinks, saccades, muscle movements, and noisy segments) were then removed from the recordings using automatic procedures as proposed by Brainstorm. More specifically, electrocardiogram (ECG) and electrooculogram (EOG) recordings were used to detect heartbeats and blinks, respectively. We then used SignalSpace Projections (SSP) to automatically remove the detected artifacts. We also used SSP to remove saccades and muscle activity as lowfrequency (1–7 Hz) and highfrequency (40–240 Hz) components, respectively.
The preprocessed sensorlevel data was then used to obtain a source estimation on HCP’s fsLR4k cortical surface for each participant (i.e., 8004 vertices). Head models were computed using overlapping spheres and the data and noise covariance matrices were estimated from the resting state MEG and noise recordings. Linearly constrained minimum variance (LCMV) beamforming from Brainstorm was then used to obtain the source activity for each participant. We performed data covariance regularization to avoid the instability of data covariance matrix inversion due to the smallest eigenvalues of its eigenspectrum. Data covariance regularization was performed using the “median eigenvalue” method from Brainstorm^{66}, such that the eigenvalues of the eigenspectrum of data covariance matrix that were smaller than the median eigenvalue were replaced with the median eigenvalue itself. The estimated source variance was also normalized by the noise covariance matrix to reduce the effect of variable source depth. Source orientations were constrained to be normal to the cortical surface at each of the 8004 vertex locations on the fsLR4k surface. Sourcelevel timeseries were parcellated into 100 regions using the Schaefer100 atlas^{67} for each participant, such that a given parcel’s time series was estimated as the first principal component of its constituting sources’ time series. Finally, we estimated sourcelevel signaltonoise ratio (SNR) as follows^{91,92}:
where a is the source amplitude (i.e., typical strength of a dipole, which is 10 nAm^{5}), N is the number of sensors, b_{k} is the signal at sensor k estimated by the forward model for a source with unit amplitude, and \({s}_{k}^{2}\) is the noise variance at sensor k. Groupaverage sourcelevel SNR was parcellated using the Schaefer100 atlas.
To estimate a measure of environmental and instrumental noise, emptyroom MEG recordings of all individuals were obtained from HCP and were preprocessed using an identical procedure to the restingstate recordings. The preprocessed sourcelevel timeseries obtained from emptyroom recordings were parcellated and subjected to timeseries feature extraction analysis to estimate timeseries features from noise data for each participant (see Timeseries feature extraction using hctsa).
Power spectral analysis
Welch’s method was used to estimate power spectrum density (PSD) from the sourcelevel timeseries for each individual, using overlapping windows of length 4 seconds with 50% overlap. Average power at each frequency band was then calculated for each vertex (i.e., source) as the mean power across the frequency range of a given frequency band. Sourcelevel power data were parcellated into 100 regions using the Schaefer100 atlas^{67} at six canonical electrophysiological bands (i.e., delta (δ: 2–4 Hz), theta (θ: 5–7 Hz), alpha (α: 8–12 Hz), beta (β: 15–29 Hz), low gamma (loγ: 30–59 Hz), and high gamma (hiγ: 60–90 Hz)). We contributed the groupaverage vertexlevel power maps on the fsLR4k surface to the publicly available neuromaps toolbox^{43}.
Intrinsic timescale
The regional intrinsic timescale was estimated using spectral parameterization with the FOOOF (fitting oscillations & one over f) toolbox^{21}. Specifically, the sourcelevel power spectral density were used to extract the neural timescale at each vertex and for each individual using the procedure described in ref. ^{30}. The FOOOF algorithm decomposes the power spectra into periodic (oscillatory) and aperiodic (1/flike) components by fitting the power spectral density in the loglog space^{21} (Supplementary Fig. 2). The algorithm identifies the oscillatory peaks (periodic component), the “knee parameter” k that controls for the bend in the aperiodic component and the aperiodic “exponent” χ^{21,30}. The knee parameter k is then used to calculate the “knee frequency” as f_{k} = k^{1/χ}, which is the frequency where a knee or a bend occurs in the power spectrum density^{30}. Finally, the intrinsic timescale τ is estimated as^{30}:
We used the FOOOF algorithm to fit the power spectral density with “knee” aperiodic mode over the frequency range of 1–60 Hz. Note that since the first notch filter was applied at 60 Hz during the preprocessing analysis, we did not fit the model above 60 Hz. Following the guidelines from the FOOOF algorithm and Donoghue et al.^{21}, the rest of the parameters were defined as: peak width limits (peak_width_limits) = 1–6 Hz; maximum number of peaks (max_n_peaks) = 6; minimum peak height (min_peak_height) = 0.1; and peak threshold (peak_threshold) = 2. Intrinsic timescale τ was estimated at each vertex for each individual and was parcellated using the Schaefer100 atlas^{67}. The performance of model fits by the FOOOF algorithm was quantified as the “goodness of fit” or R^{2} for each model fitted to a given power spectrum (Supplementary Fig. 2; range of R^{2} = [0.95, 0.98]). We contributed the groupaverage vertexlevel intrinsic timescale map on the fsLR4k surface to the publicly available neuromaps toolbox^{43}.
In addition to the aperiodic component used to calculate the intrinsic timescale, the FOOOF spectral parameterization algorithm also provides the extracted peak parameters of the periodic component at each vertex for each participant. We used the oscillatory peak parameters to estimate bandlimited power maps that were adjusted for the aperiodic component as opposed to the total power maps estimated above^{21} (see Power spectral analysis). We defined the power band limits as delta (2–4 Hz), theta (5–7 Hz), alpha (8–14 Hz), and beta (15–30 Hz), based on the distribution of peak center frequencies across all vertices and participants (Supplementary Fig. 2b). Given the lack of clear oscillatory peaks in high frequencies (above 40 Hz), the FOOOF algorithm struggles with detecting consistent peaks in gamma frequencies and above^{21,22}. Thus, we did not analyze bandlimited power in gamma frequencies using spectral parameterization. For each of the 4 predefined power bands, we estimated an “oscillation score” following the procedure described by Donoghue et al.^{21}. Specifically, for each participant and frequency band, we identified the extracted peak at each vertex. If more than one peak was detected at a given vertex, the peak with maximum power was selected. The average peak power was then calculated at each vertex and frequency band across participants. The groupaverage peak power map was then normalized for each frequency band, such that the average power at each vertex was divided by the maximum average power across all vertices. Separately, we calculated a vertexlevel probability map for each frequency band as the percentage of participants with at least one detected peak at a given vertex at that frequency band. Finally, the bandlimited “oscillation score” maps were obtained by multiplying the normalized groupaverage power maps with their corresponding probability maps for each frequency band. The oscillation score maps were parcellated using the Schaefer100 atlas^{67} (Supplementary Fig. 2a).
Timeseries feature extraction using hctsa
We used the highly comparative timeseries analysis toolbox, hctsa^{35,36}, to perform a massive feature extraction of the preprocessed timeseries for each brain region for each participant. The hctsa package extracted over 7000 local timeseries features using a wide range of timeseries analysis methods^{35,36}. The extracted features include, but are not limited to, measures of data distribution, temporal dependency and correlation properties, entropy and variability, parameters of timeseries model fit, and nonlinear properties of a given timeseries^{35,37}.
The hctsa feature extraction analysis was performed on the parcellated MEG timeseries for each participant. Given that applying hctsa on the full timeseries is computationally expensive, we used 80 seconds of data for feature extraction after dropping the first 30 seconds. Previous reports suggest that relatively short segments of about 30 to 120 seconds of restingstate data are sufficient to estimate robust properties of intrinsic brain activity^{22}. Nevertheless, to ensure that we can robustly estimate timeseries features from 80 seconds of data, we calculated a subset of hctsa features using the catch22 toolbox^{124} on subsequent segments of timeseries with varying length for each participant. Specifically, we extracted timeseries features from short segments of data ranging from 5 to 125 seconds in increments of 5 s. To identify the timeseries length required to estimate robust and stable features, we calculated the Pearson correlation coefficient between features of two subsequent segments (e.g., features estimated from 10 and 5 seconds of data). The correlation coefficient between the estimated features started to stabilize at timeseries segments of around 30 s, consistent with previous reports^{22} (Supplementary Fig. 12). Following the feature extraction procedure from timeseries segments of 80 s, the outputs of the operations that produced errors were removed and the remaining features (6880 features) were normalized across nodes using an outlierrobust sigmoidal transform for each participant separately. A groupaverage region × feature matrix was generated from the normalized individuallevel features. We also applied hctsa analysis to the parcellated emptyroom recordings (80 seconds) to estimate timeseries features from noise data using an identical procedure to restingstate data, identifying 6148 features per region per participant. The timeseries features were normalized across brain regions for each participant. A groupaverage emptyroom feature set was obtained and used for further analysis.
Microarchitectural features from neuromaps
We used the neuromaps toolbox (https://github.com/netneurolab/neuromaps)^{43} to obtain microarchitectural and neurotransmitter receptor and transporter maps in the maps’ native spaces. Details about all maps and their data sources are available in^{43}. Briefly, all data that were originally available in any surface space were transformed to the fsLR32k surface space using linear interpolation to resample data and were parcellated into 100 cortical regions using the Schaefer100 atlas in fsLR32k space^{67}. All volumetric data were retained in their native MNI152 volumetric space and were parcellated into 100 cortical regions using the volumetric Schaefer atlas in MNI152 space^{67}. Microarchitectural maps included T1w/T2w as a proxy measure of cortical myelin^{46,125}, cortical thickness^{125}, principal component of gene expression^{44,68}, principal component of neurotransmitter receptors and transporters^{47}, synapse density (using [^{11}C]UCBJ PET tracer that binds to the synaptic vesicle glycoprotein 2A (SV2A))^{55,126,127,128,129,130,131,132,133,134,135,136,137}, metabolism (i.e., cerebral blood flow and volume, oxygen and glucose metabolism, glycolytic index)^{61}, evolutionary and developmental expansion^{63}, allometric scaling from Philadelphia Neurodevelopmental Cohort (PNC) and National Institutes of Health (NIH)^{64}. Neurotransmitter maps included 18 different neurotransmitter receptors and transporters across 9 different neurotransmitter systems, namely serotonin (5HT1a, 5HT1b, 5HT2a, 5HT4, 5HT6, 5HTT), histamine (H3), dopamine (D1, D2, DAT), norepinephrine (NET), acetylcholine (α4β2, M1, VAChT), cannabinoid (CB1), opioid (MOR), glutamate (mGluR5), and GABA (GABAa/bz)^{47}.
BigBrain histological data
Layer thickness data for the 6 cortical layers (IVI) were obtained from the BigBrain atlas, which is a volumetric, highresolution (20 × 20 × 20 μm) histological atlas of a postmortem human brain (65yearold male)^{56,57,58}. In the BigBrain atlas, sections of the post mortem brain are stained for cell bodies using Merker staining technique^{138}. These sections are then imaged and used to reconstruct a volumetric histological atlas of the human brain that reflects neuronal density and soma size and captures the regional differentiation of cytoarchitecture^{56,57,58,107,139}. The approximate cortical layer thickness data obtained from the BigBrainWarp toolbox^{57}, were originally generated using a convolutional neural network that automatically segments the cortical layers from the pial to white surfaces^{58}. Full description of how the cortical layer thickness was approximated is available elsewhere^{58}. The cortical layer thickness data for the 6 cortical layers were obtained on the fsaverage surface (164k vertices) from the BigBrainWarp toolbox^{57} and were parcellated into 100 cortical regions using the Schaefer100 atlas^{67}.
Cell typespecific gene expression
Regional microarray expression data were obtained from 6 postmortem brains (1 female, ages 24–57, 42.5 ± 13.4) provided by the Allen Human Brain Atlas (AHBA, https://human.brainmap.org; see ref. ^{44}). Data were processed with the abagen toolbox (version 0.1.3doc; https://github.com/rmarkello/abagen; see ref. ^{68}) using the Schaefer100 volumetric atlas in MNI space^{67}.
First, microarray probes were reannotated using data provided by^{140}; probes not matched to a valid Entrez ID were discarded. Next, probes were filtered based on their expression intensity relative to background noise^{141}, such that probes with intensity less than the background in ≥ 50.00% of samples across donors were discarded. When multiple probes indexed the expression of the same gene, we selected and used the probe with the most consistent pattern of regional variation across donors (i.e., differential stability; see ref. ^{142}), calculated with:
where ρ is Spearman’s rank correlation of the expression of a single probe, p, across regions in two donors B_{i} and B_{j}, and N is the total number of donors. Here, regions correspond to the structural designations provided in the ontology from the AHBA.
The MNI coordinates of tissue samples were updated to those generated via nonlinear registration using the Advanced Normalization Tools (ANTs; https://github.com/chrisfilo/alleninf). To increase spatial coverage, tissue samples were mirrored bilaterally across the left and right hemispheres^{143}. Samples were assigned to brain regions in the provided atlas if their MNI coordinates were within 2 mm of a given parcel. If a brain region was not assigned a tissue sample based on the above procedure, every voxel in the region was mapped to the nearest tissue sample from the donor in order to generate a dense, interpolated expression map. The average of these expression values was taken across all voxels in the region, weighted by the distance between each voxel and the sample mapped to it, in order to obtain an estimate of the parcellated expression values for the missing region. All tissue samples not assigned to a brain region in the provided atlas were discarded.
Intersubject variation was addressed by normalizing tissue sample expression values across genes using a robust sigmoid function^{35}:
where 〈x〉 is the median and IQR_{x} is the normalized interquartile range of the expression of a single tissue sample across genes. Normalized expression values were then rescaled to the unit interval:
Gene expression values were then normalized across tissue samples using an identical procedure. Samples assigned to the same brain region were averaged separately for each donor and then across donors, yielding a regional expression matrix of 15,633 genes.
Finally, cell typespecific gene expression maps were calculated using gene sets identified by a cell type deconvolution analysis^{59,60,70}. Detailed description of the analysis is available at^{59}. Briefly, cellspecific gene sets were compiled across 5 singlecell and singlenucleus RNA sequencing studies of adult human postmortem cortical samples^{144,145,146,147,148,149}. Gene expression maps of the compiled studyspecific cell types were obtained from AHBA. Unsupervised hierarchical clustering analysis was used to identify 7 canonical cell classes that included astrocytes (astro), endothelial cells (endo), microglia (micro), excitatory neurons (neuronex), inhibitory neurons (neuronin), oligodendrocytes (oligo) and oligodendrocyte precursors (opc)^{59}. We used the resulting gene sets to obtain average cell typespecific expression maps for each of these 7 cell classes from the regional expression matrix of 15,633 genes.
Partial Least Squares (PLS)
Partial least squares (PLS) analysis was used to investigate the relationship between restingstate MEG timeseries features and microarchitecture maps. PLS is a multivariate statistical technique that identifies mutually orthogonal, weighted linear combinations of the original variables in the two datasets that maximally covary with each other, namely the latent variables^{80,81}. In the present analysis, one dataset is the hctsa feature matrix (i.e., X_{n×t}) with n = 100 rows as brain regions and t = 6880 columns as timeseries features. The other dataset is the compiled set of microarchitectural maps (i.e., Y_{n×m}) with n = 100 rows (brain regions) and m = 45 columns (microarchitecture maps). To identify the latent variables, both data matrices were normalized columnwise (i.e., zscored) and a singular value decomposition was applied to the correlation matrix \({{{{{{{\bf{R}}}}}}}}={{{{{{{{\bf{X}}}}}}}}}^{{\prime} }{{{{{{{\bf{Y}}}}}}}}\) as follows:
where U_{t×m} and V_{m×m} are orthonormal matrices of left and right singular vectors and S_{m×m} is the diagonal matrix of singular values. Each column of U and V matrices corresponds to a latent variable. Each element of the diagonal of S is the corresponding singular value. The singular values are proportional to the covariance explained by latent variable and can be used to calculate effect sizes as \({\eta }_{i}={s}_{i}^{2}/\mathop{\sum }\nolimits_{j=1}^{J}{s}_{j}^{2}\) where η_{i} is the effect size for the ith latent variable (LV_{i}), s_{i} is the corresponding singular value, and J is the total number of singular values (here J = m). The left and right singular vectors U and V demonstrate the extent to which the timeseries features and microarchitectural maps contribute to latent variables, respectively. Timeseries features with positive weights covary with microarchitectural maps with positive weights, while negatively weighted timeseries features and microarchitectural maps covary together. Singular vectors can be used to estimate brain scores that demonstrate the extent to which each brain region expresses the weighted patterns identified by latent variables. Brain scores for timeseries features and microarchitectural maps are calculated by projecting the original data onto the PLSderived weights (i.e., U and V):
Loadings for timeseries features and microarchitectural maps are then computed as the Pearson correlation coefficient between the original data matrices and their corresponding brain scores. For example, timeseries feature loadings are the correlation coefficients between the original hctsa timeseries feature vectors and PLSderived brain scores for timeseries features.
The statistical significance of latent variables was assessed using 10,000 permutation tests, where the original data was randomized using spatial autocorrelationpreserving nulls (see “Null model” for more details). The PLS analysis was repeated for each permutation, resulting in a null distribution of singular values. The significance of the original singular values were then assessed against the permuted null distributions (Fig. 4a). The reliability of PLS loadings was estimated using bootstrap resampling^{150}, where rows of the original data matrices X and Y are randomly resampled with replacement 10,000 times. The PLS analysis was then repeated for each resampled data, generating a sampling distribution for each timeseries feature and microarchitectural map (i.e., generating 10,000 bootstrapresampled loadings). The bootstrapresampled loading distributions are then used to estimate 95% confidence intervals for loadings (e.g., see Fig. 4d).
Given that PLSderived brain scores are by design highly correlated, we used a distancedependent crossvalidation analysis to assess the outofsample correlations between brain scores^{70}. Specifically, 75% of the closest brain regions in Euclidean distance to a random “seed” region were selected as training set, while the 25% remaining distant regions were selected as test set. We then reran the PLS analysis on the training set (i.e., 75% of regions) and used the resulting weights (i.e., singular values) to estimated brain scores for test set. The outofsample correlation was then calculated as the Spearman’s rank correlation coefficient between test set brain scores of timeseries features and microarchitectural maps. We repeated this analysis 99 times, such that each time a random brain region was selected as the seed region, yielding distributions of training set brain scores correlations and test set (outofsample) correlations (Fig. 4b). Note that 99 is the maximum number of traintest splits here given that brain maps consist of 100 regions.
Finally, we used sparse canonical correlation analysis (sCCA; see ref. ^{151}) as an alternative multivariate analysis technique to assess whether using a different method with sparsity affects the results^{151,152}. Similar to PLS, CCA is another reducedrank regression analysis that is used to identify multivariate linear relationships between two sets of data matrices^{70,81,153,154,155}. The main difference between CCA and PLS is that in CCA the correlation matrix between the input sets is corrected for withinset correlations, ensuring that the identified link between the two input data matrices is not driven by the correlation structure within one of them^{81}. Moreover, sparse CCA (sCCA) adds a regularization parameter to the analysis to impose sparsity and avoid overfitting^{151}. The regularization parameter ranges between 0 and 1, where 0 corresponds to highest possible sparsity and 1 corresponds to lowest possibility sparsity. We used sCCA (regularization parameter = 0.7) to identify multivariate associations between neurophysiological timeseries features and microarchitectural features and found similar results to the original PLS analysis (Supplementary Fig. 5).
Null model
To make inferences about the topographic correlations between any two brain maps, we implement a null model that systematically disrupts the relationship between two topographic maps but preserves their spatial autocorrelation^{73,74,156}. We used the Schaefer100 atlas in the HCP’s fsLR32k grayordinate space^{65,67}. The spherical projection of the fsLR32k surface was used to define spatial coordinates for each parcel by selecting the vertex closest to the centerofmass of each parcel^{157,158,159}. The resulting spatial coordinates were used to generate null models by applying randomlysampled rotations and reassigning node values based on the closest resulting parcel (10,000 repetitions). The rotation was applied to one hemisphere and then mirrored to the other hemisphere. Where appropriate, the results were corrected for multiple comparisons by controlling the false discovery rate (FDR correction^{160}).
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
All data used in the reported analyses are openly available at https://github.com/netneurolab/shafiei_megdynamics. Source data to generate the figures are provided with this paper. The original data used in this study were obtained from the Human Connectome Project (HCP; S900 release) and are publicly available at https://db.humanconnectome.org/. The original HCP data can be accessed following the HCP data use terms. The microarchitectural data is openly available in neuromaps at https://netneurolab.github.io/neuromaps/. The cortical layer thickness data is openly available in BigBrainWarp at https://bigbrainwarp.readthedocs.io/en/latest/. The Allen Human Brain Atlas (AHBA) data is openly available at https://human.brainmap.org. The Schaefer parcellations (i.e., Shcaefer100 and Schaefer400 atlases) are openly available at https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal. Source data are provided with this paper.
Code availability
Code used to process and analyze data is available on GitHub (https://github.com/netneurolab/shafiei_megdynamics) and on Zenodo (https://doi.org/10.5281/zenodo.8258832^{161}). All analyses were conducted using Python 3.7.9, MATLAB R2020a, netneurotools v0.2.3, and other standard Python packages (e.g., Matplotlib, Mayavi, NiBabel, NumPy, Pandas, Scikitlearn, SciPy, Seaborn). MEG data were processed using the open software toolbox Brainstorm v220420 (MATLAB). The open source python toolbox neuromaps v0.0.3 was used to compile the microarchitectural feature maps (Python). Allen Human Brain Atlas (AHBA) data was processed using the abagen toolbox v0.1.3 (Python). BigBrainWarp toolbox was used to obtain the cortical layer thickness data (Python). Timeseries analysis was performed using the highly comparative timeseries analysis (hctsa) toolbox v1.07 (Matlab). Spectral parameterization of MEG power was performed using the FOOOF toolbox v1.0.0 (Python). PLS analysis was performed using the pyls package (https://github.com/rmarkello/pyls) (Python).
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Acknowledgements
We thank Justine Hansen, Estefany Suarez, Filip Milisav, Andrea Luppi, Vincent Bazinet, ZhenQi Liu for their comments on the manuscript. B.M. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research (CIHR), Brain Canada Foundation Future Leaders Fund, the Canada Research Chairs Program, the Michael J. Fox Foundation, and the Healthy Brains for Healthy Lives initiative. S.B. acknowledges support from the NIH (R01 EB026299), a Discovery grant from the Natural Science and Engineering Research Council of Canada (NSERC 43635513), the CIHR Canada research Chair in Neural Dynamics of Brain Systems, the Brain Canada Foundation with support from Health Canada, and the Innovative Ideas program from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative. B.V. acknowledges support from NIH National Institute of General Medical Sciences grant (R01GM134363). G.S. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds de recherche du Québec  Nature et Technologies (FRQNT).
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Conceptualization: G.S. and B.M.; Methodology: G.S., B.D.F, and B.M.; Formal Analysis: G.S.; Data Curation: G.S.; Writing  Original Draft: G.S. and B.M.; Writing  Review & Editing: G.S., B.D.F, B.V., T.D.S., S.B., and B.M; Visualization: G.S.; Supervision: B.M.
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Shafiei, G., Fulcher, B.D., Voytek, B. et al. Neurophysiological signatures of cortical microarchitecture. Nat Commun 14, 6000 (2023). https://doi.org/10.1038/s41467023416896
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DOI: https://doi.org/10.1038/s41467023416896
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