Monday, October 3, 2022
HomeBiologyHuman electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge...

Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex


Summary

Entire-brain neural communication is usually estimated from statistical associations amongst electromagnetic or haemodynamic time-series. The connection between purposeful community architectures recovered from these 2 sorts of neural exercise stays unknown. Right here, we map electromagnetic networks (measured utilizing magnetoencephalography (MEG)) to haemodynamic networks (measured utilizing purposeful magnetic resonance imaging (fMRI)). We discover that the connection between the two modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with shut correspondence in unimodal cortex and poor correspondence in transmodal cortex. Comparability with the BigBrain histological atlas reveals that electromagnetic–haemodynamic coupling is pushed by laminar differentiation and neuron density, suggesting that the mapping between the two modalities will be defined by cytoarchitectural variation. Importantly, haemodynamic connectivity can’t be defined by electromagnetic exercise in a single frequency band, however quite arises from the blending of a number of neurophysiological rhythms. Correspondence between the 2 is essentially pushed by MEG purposeful connectivity on the beta (15 to 29 Hz) frequency band. Collectively, these findings display extremely organized however solely partly overlapping patterns of connectivity in MEG and fMRI purposeful networks, opening essentially new avenues for finding out the connection between cortical microarchitecture and multimodal connectivity patterns.

Introduction

The structural wiring of the mind imparts a definite signature on neuronal coactivation patterns. Interregional projections promote signaling and synchrony amongst distant neuronal populations, giving rise to coherent neural dynamics, measured as regional time sequence of electromagnetic or hemodynamic neural exercise [1]. Systematic coactivation amongst pairs of areas can be utilized to map purposeful connectivity (FC) networks. Over the previous decade, these dynamics are more and more recorded with out job instruction or stimulation; the ensuing “intrinsic” FC is assumed to mirror spontaneous neural exercise.

The macroscale purposeful structure of the mind is often inferred from electromagnetic or haemodynamic exercise. The previous will be measured utilizing electroencephalography (EEG) or magnetoencephalography (MEG), whereas the latter is measured utilizing purposeful magnetic resonance imaging (fMRI). Quite a few research—utilizing each MEG and fMRI—have reported proof of intrinsic purposeful patterns which can be extremely organized [29], reproducible [1013], and akin to task-driven coactivation patterns [12,14,15].

How do electromagnetic and haemodynamic networks relate to 1 one other? Though each modalities try and seize the identical underlying organic course of (neural exercise), they’re delicate to completely different physiological mechanisms and finally mirror neural exercise at essentially completely different timescales [1620]. Rising theories emphasize a hierarchy of time scales of intrinsic fluctuations throughout the cortex [2124], the place unimodal cortex is extra delicate to speedy adjustments within the sensory setting, whereas transmodal cortex is extra delicate to prior context [2530]. This raises the likelihood that the alignment between the comparatively slower purposeful structure captured by fMRI and quicker purposeful structure captured by MEG could systematically range throughout the cortex.

Earlier studies have discovered some, however not full, international overlap between the two modalities. A number of MEG- and fMRI-independent parts—representing spatiotemporal signatures of resting-state intrinsic networks—present comparable spatial topography, notably the visible, somatomotor, and default mode parts [68,31]. The spatial overlap between large-scale networks has additionally been reported in task-based research and with networks recovered from different modalities, resembling EEG and intracranial EEG [3236]. Furthermore, fMRI and MEG/EEG yield comparable fingerprinting accuracy, suggesting that they encode widespread info [3740]. Lastly, international edge-wise comparisons between fMRI networks and electrocorticography (ECoG) [41], EEG [4244], and MEG [4547] additionally yield reasonable correlations. Though international comparisons are extra widespread when completely different modalities are studied, regional and network-level relationships have additionally been explored utilizing electrophysiological and intracranial EGG recordings [36,48,49] in addition to EEG and MEG recordings [45,50,51]. Regional comparisons of electrophysiological and fMRI recordings additionally counsel that the connection between the 2 could also be affected by distinct cytoarchitecture and laminar construction of mind areas, notably in visible and frontal cortex [5259]. How the coupling between fMRI and MEG connectivity profiles varies from area to area, and the way this coupling displays cytoarchitecture, continues to be not absolutely understood. Moreover, earlier research have largely assessed the affiliation between haemodynamic and electromagnetic networks for separate frequency bands, investigating impartial contributions of particular person rhythms to haemodynamic connectivity. This successfully precludes the likelihood that superposition and mixing of elementary electromagnetic rhythms manifests as patterns of haemodynamic connectivity [45,47,61].

How regional connectivity profiles of MEG and fMRI purposeful networks are related throughout the cortex, and the way their correspondence pertains to the underlying cytoarchitecture, stays an thrilling open query. Right here, we use a linear multifactor mannequin that permits to characterize the haemodynamic FC profile of a given mind area as a linear mixture of its electromagnetic FC in a number of frequency bands. We then discover how the two modalities align throughout the neocortex and examine the contribution of cytoarchitectonic variations to their alignment.

Outcomes

Information had been derived utilizing task-free MEG and fMRI recordings in the identical unrelated individuals from the Human Connectome Undertaking (HCP; [62]; n = 33). We first develop a easy regression-based mannequin to map regional MEG connectivity to regional fMRI connectivity utilizing group-average knowledge. We then examine how regionally heterogeneous the correspondence between the 2 is, and the way completely different rhythms contribute to this regional heterogeneity. Lastly, we conduct in depth sensitivity testing to display that the outcomes are strong to a number of methodological decisions.

Relating haemodynamic and electromagnetic connectivity

To narrate fMRI and MEG FC patterns, we apply a multilinear regression mannequin [68] (Fig 1). The mannequin is specified for every mind area individually, trying to foretell a area’s haemodynamic connectivity profile from its electromagnetic connectivity profile. The dependent variable is a row of the fMRI FC matrix and the impartial variables are the corresponding rows of MEG FC matrices for six canonical electrophysiological bands, estimated utilizing amplitude envelope correlation (AEC; [60]) with spatial leakage correction (see “Strategies” for extra particulars). For a mannequin fitted for a given node i, the observations within the mannequin are the connections of node i to the opposite ji areas (Fig 1A). The mannequin predicts the fMRI FC profile of node i (i.e., i-th row) from a linear mixture of MEG FC profiles of node i within the 6 frequency bands (i.e., i-th rows of MEG FC matrices). Collectively, the mannequin embodies the concept that a number of rhythms may very well be superimposed to present rise to regionally heterogeneous haemodynamic connectivity.

thumbnail

Fig 1. Relating haemodynamic and electromagnetic connectivity.

(a) A multilinear regression mannequin was utilized to foretell resting state fMRI connectivity patterns from band-limited MEG FC (AEC; [60]). The mannequin is specified for every mind area individually, trying to foretell a area’s haemodynamic connectivity profile from its electromagnetic connectivity profile. (b) The general relationship between fMRI and MEG FC is estimated by correlating the higher triangle of fMRI FC (i.e., above diagonal) with the higher triangles of band-limited MEG FC, suggesting reasonable relationship between the 2 throughout frequency bands. (c) Regional multilinear mannequin proven in panel (a) is used to foretell fMRI FC from band-limited MEG FC for every mind area (i.e., row) individually. The empirical and predicted fMRI FC are depicted facet by facet for the regional mannequin. The entire-brain edge-wise relationship between the empirical and predicted values is proven within the scatter plot. Every grey dot represents an edge (pairwise purposeful connection) from the higher triangles of empirical and predicted fMRI FC matrices. (d) A world multilinear mannequin is used to foretell your complete higher triangle of fMRI FC from the higher triangles of the MEG FC matrices. The empirical and predicted fMRI FC are depicted facet by facet for the worldwide mannequin. The entire-brain edge-wise relationship between the empirical and predicted values is proven within the scatter plot. Every grey dot represents en edge from the higher triangles of empirical and predicted fMRI FC matrices. (e) The distribution of regional mannequin match quantified by R2 is proven for regional mannequin (grey histogram plot). The worldwide mannequin match can also be depicted for comparability (pink line). The info and code wanted to generate this determine will be present in https://github.com/netneurolab/shafiei_megfmrimapping and https://zenodo.org/file/6728338. AEC, amplitude envelope correlation; EEG, electroencephalography; FC, purposeful connectivity; fMRI, purposeful magnetic resonance imaging; MEG, magnetoencephalography.


https://doi.org/10.1371/journal.pbio.3001735.g001

Certainly, we discover that the connection between haemodynamic and electromagnetic connectivity is very heterogeneous. Band-limited MEG connectivity matrices are reasonably correlated with fMRI connectivity, starting from r = −0.06 to r = 0.36 (Fig 1B; r denotes Pearson correlation coefficient). The regional multilinear mannequin suits vary from adjusted-R2 = −0.002 to adjusted-R2 = 0.72 (R2 denotes coefficient of willpower; hereafter we confer with adjusted-R2 as R2), suggesting an in depth correspondence in some areas and poor correspondence in others (Fig 1C and 1E). Band-specific regional mannequin suits are depicted in S1 Fig, the place every band-specific MEG connectivity is individually used as a single predictor within the mannequin. For comparability, a single international mannequin is fitted to the info, predicting your complete higher triangle of the fMRI FC matrix (i.e., all values above the diagonal) from a linear mixture of the higher triangles of 6 MEG FC matrices (i.e., all values above the diagonal) (see “Strategies” for extra element). The worldwide mannequin, which concurrently relates whole-brain fMRI FC to the whole-brain MEG FC, yields an R2 = 0.15 (Fig 1D and 1E). Importantly, the worldwide mannequin clearly obscures the big selection of correspondences, which will be significantly larger or smaller for particular person areas.

Hierarchical group of cross-modal correspondence

We subsequent think about the spatial group of fMRI-MEG correspondence. Fig 2A exhibits the spatial distribution of regional R2 values, representing areas with low or excessive correspondence. Areas with robust cross-modal correspondence embrace the visible, somatomotor, and auditory cortex. Areas with low cross-modal correspondence embrace the posterior cingulate, lateral temporal, and medial prefrontal cortex.

thumbnail

Fig 2. Regional mannequin match.

(a) Spatial group of fMRI-MEG correspondence is depicted for the regional mannequin match (95% interval). The cross-modal correspondence of connectivity profiles of mind areas is distributed heterogeneously throughout the cortex, representing areas with low or excessive correspondence. Robust cross-modal correspondence is noticed in sensory areas, whereas poor correspondence is noticed for greater order areas. (b) Spatial group of the cross-modal correspondence is in contrast with the purposeful hierarchical group of cerebral cortex [63]. The 2 are considerably anticorrelated, confirming poor fMRI-MEG correspondence in connectivity profile of higher-order, transmodal areas in comparison with robust correspondence for sensory, unimodal areas. (c) Areas are stratified by their affiliation with macroscale intrinsic networks [2]. The distribution of R2 is depicted for every community, displaying a scientific gradient of cross-modal correspondence with the best correspondence within the visible community and lowest correspondence within the default mode community. (d) The mannequin match is expounded to the cytoarchitectural variation of the cortex, estimated from the cell staining depth profiles at varied cortical depths obtained from the BigBrain histological atlas [64,65]. Larger circles denote statistically vital associations after correction for a number of comparisons by controlling the FDR at 5% alpha [66]. The height affiliation between cross-modal correspondence and cytoarchitecture is noticed roughly at cortical layer IV that has excessive density of granule cells. Staining depth profiles are depicted throughout the cortex for probably the most pial, the center, and the white matter surfaces. (e) Microarray gene expression of vasoconstrictive NPY1R was estimated from the AHBA [67]. The MEG-fMRI cross-modal correspondence R2 map (i.e., regional mannequin match) is in contrast with NPY1R gene expression. rs denotes Spearman rank correlation. Intrinsic networks: vis = visible; sm = somatomotor; da = dorsal consideration; va = ventral consideration; lim = limbic; fp = frontoparietal; dmn = default mode. The info and code wanted to generate this determine will be present in https://github.com/netneurolab/shafiei_megfmrimapping and https://zenodo.org/file/6728338. AHBA, Allen Human Mind Atlas; FDR, false discovery price; fMRI, purposeful magnetic resonance imaging; MEG, magnetoencephalography; NPY1R, Neuropeptide Y Receptor Y1.


https://doi.org/10.1371/journal.pbio.3001735.g002

Collectively, the spatial format of cross-modal correspondence bears a resemblance to the unimodal–transmodal cortical hierarchy noticed in large-scale purposeful and microstructural group of the cerebral cortex [28]. To evaluate this speculation, we first in contrast the cross-modal R2 map with the principal purposeful hierarchical group of the cortex, estimated utilizing diffusion map embedding [63,71] (Fig 2B; see “Strategies” for extra particulars). The 2 are considerably anticorrelated (Spearman rank correlation coefficient rs = −0.69, pspin = 0.0001), suggesting robust cross-modal correspondence in unimodal sensory cortex and poor correspondence in transmodal cortex. We then stratify areas by their affiliation with macroscale intrinsic networks and computed the imply R2 in every community [2] (Fig 2C). Right here, we additionally observe a scientific gradient of cross-modal correspondence, with the strongest correspondence within the visible community and poorest correspondence within the default mode community.

We relate the cross-modal R2 map to the cytoarchitectural variation of the cortex (Fig 2D). We use the BigBrain histological atlas to estimate granular cell density at a number of cortical depths [64,65]. Cell-staining depth profiles had been sampled throughout 50 equivolumetric surfaces from the pial floor to the white matter floor to estimate laminar variation in neuronal density and soma measurement. Fig 2D exhibits the correlation between MEG-fMRI correspondence and cell density (y axis) at completely different cortical depths (x axis). Apparently, the mannequin match is related to cytoarchitectural variation of the cortex, with the height affiliation noticed roughly at cortical layer IV that has excessive density of granular cells and separates supra- and infragranular layers [7274]. Layer IV predominately receives feedforward projections and has excessive vascular density [7577]. We additional assess the connection between MEG-fMRI cross-modal correspondence and vascular attributes. We acquire the microarray gene expression of the vasoconstrictive NPY1R (Neuropeptide Y Receptor Y1) from Allen Human Mind Atlas (AHBA; [67]; see “Strategies” for extra particulars), given earlier studies that the BOLD response is related to the vasoconstrictive mechanism of Neuropeptide Y (NPY) performing on Y1 receptors [78]. We then evaluate the cross-modal affiliation map with the expression of NPY1R and determine a major affiliation between the 2 (Fig 2E; rs = −0.60, pspin = 0.0023). This demonstrates that areas with low cross-modal correspondence are enriched for NPY1R, whereas areas with excessive cross-modal associations have much less NPY-dependent vasoconstriction. Altogether, the outcomes counsel that the larger coupling in unimodal cortex could also be pushed by the underlying cytoarchitecture, reflecting greater density of granular cells and distinct vascularization of cortical layer IV.

We additionally relate cross-modal R2 map to the variation of construction–operate coupling throughout the cortex, which has additionally been proven to observe the unimodal–transmodal hierarchy [68,7982]. We estimate construction–operate coupling because the Spearman rank correlation between regional structural and purposeful connectivity profiles [80] (S2 Fig; see “Strategies” for extra particulars). We then correlate the recognized map with the regional mannequin match, figuring out a major affiliation between the 2 (S2 Fig; rs = 0.40, pspin = 0.0025). That is per the notion that each haemodynamic and electromagnetic neural exercise are constrained by the anatomical pathways and the underlying structural group [8385].

Heterogeneous contributions of a number of rhythms

How do completely different rhythms contribute to regional patterns of cross-modal correspondence? To handle this query and to evaluate the results of cross-correlation between MEG connectivity at completely different frequency bands (S5 Fig), we carry out a dominance evaluation for each regional multilinear mannequin [69,70]. Particularly, dominance evaluation is used to look at the separate results of every band-limited MEG FC, in addition to the results of all different doable combos of band-limited MEG FC, on the regional mannequin match. This method estimates the relative significance of predictors by developing all doable combos of predictors and refitting the multilinear mannequin for every mixture. The doable combos of predictors embrace units of single predictors, all doable pairs of predictors, all doable combos with 3 predictors, and so forth. To evaluate the affect of every band on the mannequin match, dominance evaluation refits the mannequin for every mixture and quantifies the relative contribution of every predictor as the rise in variance defined after including that predictor to the fashions (i.e., acquire in adjusted-R2). Fig 3A exhibits the worldwide dominance of every frequency band, the place dominance is quantified as “p.c relative significance” or “contribution proportion” of every band. General, we observe the best contributions from MEG connectivity at beta band, adopted by theta and alpha bands, and smallest contributions from high and low gamma bands.

thumbnail

Fig 3. Dominance evaluation.

Dominance evaluation is carried out for every regional multilinear mannequin to quantify how MEG connectivity at completely different rhythms contribute to regional patterns of cross-modal correspondence [69,70]. (a) The general contribution of every frequency band is depicted for the regional mannequin (field plots). Beta band connectivity, adopted by theta and alpha bands, contribute probably the most to the mannequin match whereas high and low gamma bands contribute the least. (b) The imply contribution of various rhythms is estimated for the intrinsic networks. In keeping with the general contributions depicted in panel (a), the best contribution is related to beta band connectivity. (c) Essentially the most dominant predictor (frequency band) is depicted for every mind area, confirming total greater contributions from beta band throughout the cortex. (d) Frequency band contribution to the regional cross-modal correspondence is proven individually for various rhythms throughout the cortex (95% intervals). The info and code wanted to generate this determine will be present in https://github.com/netneurolab/shafiei_megfmrimapping and https://zenodo.org/file/6728338. MEG, magnetoencephalography.


https://doi.org/10.1371/journal.pbio.3001735.g003

Zooming in on particular person areas and intrinsic networks, we discover that the dominance sample can also be regionally heterogeneous. Particularly, the make-up and contribution of particular MEG frequencies to a area’s fMRI connectivity profile varies from area to area. Fig 3B exhibits the dominance of particular rhythms in every intrinsic community. Fig 3C exhibits probably the most dominant predictor for each mind area. We discover that beta band contribution is highest in occipital and lateral frontal cortices. Sensorimotor cortex has excessive contributions from combos of beta, alpha, and theta bands. Parietal and temporal areas are largely dominated by delta and theta bands in addition to some contribution from alpha band. Medial frontal cortex exhibits contributions from the alpha band, whereas high and low gamma bands contribute to posterior cingulate cortex and precuneus. Fig 3D exhibits the dominance of particular rhythms individually for every area. General, we observe that beta connectivity has the best contribution proportion (95% confidence interval: [2% 66%]), largely contributing to mannequin prediction throughout the cortex. These findings are per earlier studies, demonstrating that haemodynamic connectivity is expounded to the superposition of band-limited electromagnetic connectivity and that band contributions range throughout the cortex [45,47].

Lastly, we used evaluation of variance (ANOVA) to quantitatively assess the variations in band-specific contributions to the cross-modal correspondence map (S1 Desk). Particularly, we assessed the importance and impact measurement of variations in band-specific contributions for all doable pairs of frequency bands. We determine 2 primary findings (for full outcomes, see S1 Desk): (1) General, the variability of band-specific contributions is considerably bigger between teams (i.e., bands) in comparison with the variability inside teams (F(5, 2394) = 117.31; p < 0.0001). (2) Band-specific contributions are considerably completely different from one another and are ranked in the identical order as depicted in Fig 3A. Particularly, contribution of beta band is considerably bigger than contribution of alpha band (distinction of the means = 8.65, t-value = 9.46, p-value < 0.0001, Cohen’s d = 0.69) and theta band (distinction of the means = 7.56, t-value = 8.27, p-value < 0.0001, Cohen’s d = 0.58). Additionally, the contribution from the delta band is considerably decrease than beta (distinction of the means = 12.37, t-value = 13.53, p-value < 0.0001, Cohen’s d = 0.96), alpha (distinction of the means = 3.72, t-value = 4.07, p-value = 0.0007, Cohen’s d = 0.29), and theta (distinction of the means = 4.81, t-value = 5.26, p-value < 0.0001, Cohen’s d = 0.37). Notice that though the distinction between alpha and theta band contributions just isn’t vital, each their contributions are considerably decrease than beta band and bigger than delta band. Furthermore, delta band contribution is considerably bigger than contribution of lo-gamma (distinction of the means = 3.78, t-value = 4.14, p-value = 0.0005, Cohen’s d = 0.29) and lo-gamma contribution is considerably bigger than hi-gamma (distinction of the means = 3.72, t-value = 4.07, p-value = 0.0007, Cohen’s d = 0.29). Notice that the values reported listed below are absolutely the values for distinction of the means, t-values, p-values and Cohen’s d (impact measurement). All p-values are corrected for a number of comparisons utilizing Bonferroni correction.

Sensitivity evaluation

Lastly, we word that the current report goes by way of a number of choice factors which have equally justified alternate options. Right here, we discover the opposite choices. First, quite than framing the report from an explanatory perspective (specializing in mannequin match), we as a substitute derive an equal set of outcomes utilizing a predictive perspective (specializing in out-of-sample prediction). We carry out cross-validation at each the area and topic degree (Fig 4A and 4B). For region-level cross-validation, we pseudorandomly break up the connectivity profile of a given area into prepare and check units based mostly on spatial separation (interregional Euclidean distance), such that 75% of the closest areas to a random area are chosen because the prepare set and the remaining 25% of the areas are chosen as check set (399 repetitions; see “Strategies” for extra particulars) [90]. We then prepare the multilinear mannequin utilizing the prepare set and predict the connection power of the check set for every area and every break up. The imply regional mannequin efficiency throughout splits is constant for prepare and check units (Fig 4A; r = 0.78, pspin = 0.0001). For subject-level cross-validation, we use leave-one-out-cross validation, whereby we prepare the regional multilinear fashions utilizing knowledge from n−1 topics and check each on the held-out topic. The imply regional mannequin efficiency is constant for prepare and check units (Fig 4B; r = 0.90, pspin = 0.0001). Altogether, each analyses give comparable, extremely concordant outcomes with the less complicated mannequin fit-based evaluation, figuring out robust cross-modal correspondence in unimodal sensory areas and poor correspondence in transmodal areas.

thumbnail

Fig 4. Sensitivity evaluation.

(a) A regional cross-validation was carried out by pseudorandomly splitting the connectivity profile of a given area into prepare and check units based mostly on spatial separation (see “Strategies” for extra particulars). The multilinear mannequin is then fitted on the prepare set and is used to foretell the connection power of the check set for every area and every break up. The imply regional mannequin efficiency throughout splits is depicted for prepare and check units, displaying constant outcomes between the 2 (scatter plot). The out-of-sample mannequin efficiency is stronger within the sensory, unimodal areas in comparison with transmodal areas, per authentic findings (Fig 2). (b) A subject-level cross-validation was carried out utilizing a leave-one-out strategy. The regional multilinear mannequin is educated utilizing knowledge from n−1 topics and is examined on the held-out topic for every area individually. The imply regional mannequin efficiency is proven for prepare and check units, displaying constant outcomes between the 2 (scatter plot). The out-of-sample mannequin efficiency is stronger within the sensory, unimodal areas in comparison with transmodal areas, per authentic findings (Fig 2). The evaluation can also be repeated for varied processing decisions: (c) after regressing out interregional Euclidean distance from connectivity matrices; (d) utilizing MEG connectivity knowledge with out spatial leakage correction; (e) utilizing one other MEG supply reconstruction technique (sLoreta; [86]); (f) utilizing a phase-based MEG connectivity measure (PLV; [87,88]); and (g) at a low-resolution parcellation (Schaefer-200 atlas; [89]). The outcomes are constant throughout all management analyses, figuring out comparable cross-modal correspondence maps as the unique evaluation (Fig 2A). All mind maps are proven at 95% intervals. rs denotes Spearman rank correlation. The info and code wanted to generate this determine will be present in https://github.com/netneurolab/shafiei_megfmrimapping and https://zenodo.org/file/6728338. MEG, magnetoencephalography; PLV, phase-locking worth; sLoreta, standardized low-resolution mind electromagnetic tomography.


https://doi.org/10.1371/journal.pbio.3001735.g004

To contemplate the impact of spatial proximity on the findings, we take away the exponential interregional Euclidean distance development from all connectivity matrices earlier than becoming any mannequin. The outcomes are per and with out distance correction (Fig 4C; correlation with purposeful hierarchy: rs = −0.53, pspin = 0.0001; correlation with authentic R2: rs = 0.67, pspin = 0.0001). We additionally acquire constant findings after we repeat the evaluation with out accounting for spatial leakage impact in estimating MEG connectivity with AEC (Fig 4D; correlation with purposeful hierarchy: rs = −0.60, pspin = 0.0001; correlation with authentic R2: rs = 0.84, pspin = 0.0001). Subsequent, we use one other supply reconstruction technique (standardized low-resolution mind electromagnetic tomography (sLoreta); [86]) as a substitute of linearly constrained minimal variance (LCMV) beamformers, as earlier studies counsel that sLoreta improves supply localization accuracy [91,92]. We then estimate MEG connectivity with AEC and repeat the multilinear mannequin evaluation, figuring out comparable outcomes as earlier than (Fig 4E; correlation with purposeful hierarchy: rs = −0.80, pspin = 0.0001; correlation with authentic R2: rs = 0.85, pspin = 0.0002). Subsequent, we compute MEG connectivity utilizing another, phase-based connectivity measure (phase-locking worth (PLV); [87,88]), quite than the AEC. The two FC measures yield comparable cross-modal correspondence maps (Fig 4F; correlation with purposeful hierarchy: rs = −0.53, pspin = 0.0022; correlation with authentic R2: rs = 0.66, pspin = 0.0001). We additionally repeat the evaluation utilizing a low-resolution parcellation (Schaefer-200 atlas; [89]) to make sure that the findings are impartial from the selection of parcellation. As earlier than, the outcomes display comparable cross-modal correspondence map (Fig 4G; correlation with purposeful hierarchy: rs = −0.70, pspin = 0.0001). To evaluate the extent to which the outcomes are influenced by MEG supply localization error, we evaluate the cross-modal correspondence sample to peak localization error estimated utilizing cross-talk operate (CTF) [9195]. No vital affiliation is noticed between R2 sample and CTF for LCMV (S3A Fig; rs = −0.14, pspin = 0.6) and sLoreta (S3B Fig; rs = −0.04, pspin = 0.9) supply reconstruction options. Lastly, to verify that the cross-modal correspondence sample is impartial from signal-to-noise ratio (SNR), we evaluate the regional mannequin match with the SNR map of the reconstructed sources, figuring out no vital affiliation between the 2 (S4 Fig; rs = 0.32, pspin = 0.25) (see “Strategies” for extra particulars).

Dialogue

Within the current report, we map electromagnetic purposeful networks to haemodynamic purposeful networks within the human mind. We discover 2 principal outcomes. First, the connection between the two modalities is regionally heterogeneous however systematic, reflecting the unimodal–transmodal cortical hierarchy and cytoarchitectural variation. Second, haemodynamic connectivity can’t be defined by electromagnetic connectivity in a single band, however quite displays mixing and superposition of a number of rhythms.

The truth that the affiliation between the two modalities follows a gradient from unimodal to transmodal cortex resonates with rising work on cortical hierarchies [28,63,96]. Certainly, comparable spatial variations are noticed for a number of microarchitectural options, resembling gene expression [90,97,98], T1w/T2w ratio [99], laminar differentiation [74], and neurotransmitter receptor profiles [100102]. Collectively, these research level to a pure axis of cortical group that encompasses variations in each construction and performance throughout micro-, meso-, and macroscopic spatial scales.

Apparently, we discover the closest correspondence between fMRI and MEG FC in unimodal cortex (together with the visible and somatomotor networks) and the poorest correspondence in transmodal cortex (default mode, limbic, frontoparietal, and ventral consideration networks). In different phrases, the purposeful architectures of the two modalities are constant early within the cortical hierarchy, presumably reflecting exercise associated to instantaneous adjustments within the exterior setting. Conversely, as we transfer up the hierarchy, there’s a gradual separation between the two architectures, suggesting that they’re in another way modulated by endogenous inputs and contextual info. How the two sorts of FC are associated to ongoing job demand is an thrilling query for future analysis.

Why is there systematic divergence between the two modalities? Our findings counsel that topographic variation in MEG-fMRI coupling is because of variation in cytoarchitecture and neurovascular coupling. First, we observe larger MEG-fMRI coupling in areas with outstanding granular layer IV. This consequence could mirror variation of microvascular density at completely different cortical layers [59,77,103]. Particularly, cortical layer IV is probably the most vascularized, and that is notably outstanding in main sensory areas [77]. The BOLD response primarily displays native area potentials arising from synaptic currents of feedforward enter alerts to cortical layer IV [75,76]; because of this, the BOLD response is extra delicate to cortical layer IV with excessive vascular density [104]. Subsequently, electromagnetic neuronal exercise originating from layer IV ought to be accompanied by a quicker and extra outstanding BOLD response. That is per our discovering that mind areas with extra outstanding granular layer IV (i.e., unimodal cortex) have larger correspondence between electromagnetic and haemodynamic purposeful architectures. In different phrases, heterogeneous cortical patterning of MEG-fMRI coupling could mirror heterogeneous patterning of underlying neurovascular coupling.

Second, we observe outstanding anticorrelations between vasoconstrictive NPY1R-expressing neurons and MEG-fMRI coupling. A number of research of vasodilator and vasoconstrictor mechanisms concerned in neural signaling have demonstrated hyperlinks between microvasculature and the BOLD sign [78,103]. For instance, an optogenetic and 2-photon mouse imaging examine discovered that task-related damaging BOLD sign is especially related to vasoconstrictive mechanism of NPY performing on Y1 receptors, suggesting that neurovascular coupling is cell particular [78]. Apparently, by evaluating the cortical expression of NPY1R within the human mind with MEG-fMRI correspondence sample recognized right here, we discover that areas with low cross-modal correspondence are enriched for NPY1R, whereas areas with excessive cross-modal associations have much less NPY-dependent vasoconstriction. Collectively, these outcomes counsel that MEG-fMRI correspondence is at the very least partly attributable to regional variation in cytoarchitecture and neurovascular coupling.

Extra typically, quite a few research have investigated the laminar origin of cortical rhythms. For instance, animal electrophysiological recordings demonstrated that visible and frontal cortex gamma exercise will be localized to superficial cortical layers (supragranular layers I to III and granular layer IV), whereas alpha and beta exercise are localized to deep infragranular layers (layers V to VI) [5256,58]. Comparable findings have been reported in people utilizing EEG and laminar-resolved BOLD recordings, demonstrating that gamma and beta band EEG energy are related to superficial and deep layer BOLD response, respectively, whereas alpha band EEG energy is related to BOLD response in each superficial and deep layers [57]. Laminar specificity of cortical rhythms is more and more emphasised in modern accounts of predictive processing [105]. Within the predictive coding framework, transmodal areas generate predictive alerts that modulate the exercise of sensory unimodal areas relying on context [106]. These top-down alerts are comparatively sluggish, as they evolve with the context of exogenous (stimulation) inputs. The consequence on unimodal areas is a lift of their encoding acquire, mirrored in stronger, quicker exercise that tracks incoming stimuli. They in flip generate error alerts which can be slower and mirror the discrepancy between the predictions obtained and the precise exterior enter. These slower error alerts are then registered by higher-order transmodal areas. Particular cortical layers and rhythms contribute to this predictive coding [105]. For instance, an unfamiliar, unpredicted stimulus is related to elevated gamma energy that’s fed ahead up the cortical hierarchy (i.e., bottom-up from sensory to affiliation cortices) by way of the superficial layers to switch the prediction errors. This in flip leads to low top-down, suggestions predictions by way of deep cortical layers through alpha and beta rhythms. Conversely, predicted stimuli are related to stronger suggestions alpha and beta rhythms through deep layers, inhibiting the gamma exercise for anticipated exogenous inputs [105]. This hierarchical predictive processing framework can also be thought to underlie acutely aware notion by top-down switch of perceptual predictions through alpha and beta rhythms by way of deep layers and bottom-up switch of prediction errors through gamma rhythm by way of superficial layers, minimizing predictions errors [105,107,108]. Our outcomes, linking cytoarchitecture with rhythm-specific connectivity, could assist to additional refine and develop this rising framework.

Altogether, our findings counsel that the systemic divergence between MEG and fMRI connectivity patterns could mirror variations in cortical cytoarchitecture and vascular density of cortical layers. Nonetheless, word that because of the low spatial decision of fMRI and MEG knowledge, haemodynamic and electromagnetic connectivity just isn’t resolved on the degree of cortical layers. Fairly, comparisons with cytoarchitecture are made through proxy datasets, such because the BigBrain histological atlas [64] and the AHBA [67]. Future work is required to evaluate the laminar specificity of the cross-modal affiliation in a extra direct and complete method [109112].

All through the current report, we discover that fMRI networks are finest defined as arising from the superposition of a number of band-limited MEG networks. Though earlier work has targeted on instantly correlating fMRI with MEG/EEG networks in particular bands, we present that synchronized oscillations in a number of bands may probably mix to present rise to the well-studied fMRI purposeful networks. Certainly, and as anticipated, the correlation between any particular person band-specific MEG community and fMRI is considerably smaller than the multilinear mannequin that takes under consideration all bands concurrently. Earlier work on cross-frequency interactions [113] and on multilayer MEG community group [114] has sought to characterize the participation of particular person mind areas inside and between a number of frequency networks. Our findings construct on this literature, exhibiting that the superimposed illustration might also assist to unlock the hyperlink between MEG and fMRI networks.

It’s noteworthy that the best contributions to the hyperlink between the two modalities got here from beta band connectivity. A number of authors have reported that—because it captures sluggish haemodynamic coactivation—fMRI community connectivity can be primarily pushed by slower rhythms [6,20,35,42,61,113]. Our findings display that though all frequency bands contribute to the emergence of fMRI networks, the best contributions come from beta band connectivity, adopted by theta and alpha connectivity.

The current outcomes elevate 2 vital questions for future work. First, how does structural connectivity form fMRI and MEG purposeful networks [43,81,83]? We discover that cross-modal correspondence between MEG and fMRI purposeful networks is related to construction–operate coupling measured from MRI purposeful and structural connectivity networks, suggesting that the cross-modal map could also be constrained by structural connectivity. Earlier studies display that unimodal, sensory areas have decrease neural flexibility in comparison with transmodal, affiliation areas and are extra secure throughout improvement and evolution [24,115,116]. This means that the underlying anatomical community constrains neural exercise and purposeful flexibility in a nonuniform method throughout the cortex, leading to greater levels of freedom in construction–operate coupling in areas associated to extremely versatile cognitive processes. Nonetheless, on condition that MEG and fMRI seize distinct neurophysiological mechanisms, it’s doable that haemodynamic and electromagnetic architectures have a distinct relationship with structural connectivity, and this might probably clarify why they systematically diverge by way of the cortical hierarchy [68,7982]. Second, the current outcomes present how the two modalities are associated in a task-free resting state, however what’s the relationship between fMRI and MEG connectivity throughout cognitive duties [117]? On condition that the two modalities grow to be much less correlated in transmodal cortex within the resting state, the connection between them throughout job could rely on demand and cognitive capabilities required to finish the duty.

Lastly, the current outcomes ought to be interpreted in mild of a number of methodological concerns. First, though we conduct in depth sensitivity testing, together with a number of methods of defining FC, there exist many extra methods within the literature to estimate each fMRI and MEG connectivity [118,119]. Second, to make sure that the analyses had been carried out in the identical individuals utilizing each resting state fMRI and MEG and that the individuals don’t have any familial relationships, we utilized a decreased model of the HCP pattern. Third, as a way to instantly evaluate the contributions of a number of frequency bands, all had been entered into the identical mannequin. In consequence, nonetheless, the observations within the linear fashions (community edges) should not impartial, violating a primary assumption of those statistical fashions. For that reason, we solely use mannequin suits and dominance values to check the correspondence of fMRI and MEG throughout a set of nodes, every of which is estimated beneath the identical situations. Lastly, to make sure that the findings are impartial from sensitivity of MEG to neural exercise from completely different areas, we in contrast the cross-modal correspondence map with MEG SNR and supply localization error, the place no vital associations had been recognized. Nonetheless, MEG continues to be vulnerable to such artifacts on condition that areas with decrease SNR (e.g., Sylvian fissure) are those the place supply reconstruction options have greater supply localization errors [120,121].

Regardless of complementary strengths to picture spatiotemporal mind dynamics, the hyperlinks between MEG and fMRI should not absolutely understood and the two fields have diverged. The current report bridges the two disciplines by comprehensively mapping haemodynamic and electromagnetic community architectures. By contemplating the contributions of the canonical frequency bands concurrently, we present that the superposition and mixing of MEG neurophysiological rhythms manifests as extremely structured patterns of fMRI FC. Systematic convergence and divergence among the many 2 modalities in numerous mind areas opens essentially new questions concerning the relationship between cortical hierarchies and multimodal purposeful networks.

Strategies

Dataset: Human Connectome Undertaking (HCP)

Resting state MEG knowledge of a set of wholesome younger adults (n = 33; age vary 22 to 35 years) with no familial relationships had been obtained from HCP (S900 launch; [62]). The info embrace resting state scans of about 6 minutes lengthy (sampling price = 2,034.5 Hz; anti-aliasing filter low-pass filter at 400 Hz) and noise recordings for all individuals. MEG anatomical knowledge and 3T structural magnetic resonance imaging (MRI) knowledge of all individuals had been additionally obtained for MEG preprocessing. Lastly, we obtained purposeful MRI knowledge of the identical n = 33 people from HCP dataset. All 4 resting state fMRI scans (2 scans with R/L and L/R section encoding instructions on day 1 and day 2, every about quarter-hour lengthy; TR = 720 ms) had been accessible for all individuals.

HCP knowledge processing

Resting state magnetoencephalography (MEG).

Resting state MEG knowledge had been analyzed utilizing Brainstorm software program, which is documented and freely accessible for obtain on-line beneath the GNU basic public license ([122]; http://neuroimage.usc.edu/brainstorm). The MEG recordings had been registered to the structural MRI scan of every particular person utilizing the anatomical transformation matrix offered by HCP for coregistration, following the process described in Brainstorm’s on-line tutorials for the HCP dataset (https://neuroimage.usc.edu/brainstorm/Tutorials/HCP-MEG). The preprocessing was carried out by making use of notch filters at 60, 120, 180, 240, and 300 Hz, and was adopted by a high-pass filter at 0.3 Hz to take away slow-wave and DC-offset artifacts. Unhealthy channels had been marked based mostly on the knowledge obtained by way of the info administration platform of HCP for MEG knowledge (ConnectomeDB; https://db.humanconnectome.org/). The artifacts (together with heartbeats, eye blinks, saccades, muscle actions, and noisy segments) had been then faraway from the recordings utilizing computerized procedures as proposed by Brainstorm. Extra particularly, electrocardiogram (ECG) and electrooculogram (EOG) recordings had been used to detect heartbeats and blinks, respectively. We then used Sign–Area Projections (SSPs) to routinely take away the detected artifacts. We additionally used SSP to take away saccades and muscle exercise as low-frequency (1 to 7 Hz) and high-frequency (40 to 240 Hz) parts, respectively.

The preprocessed sensor-level knowledge had been then used to acquire a supply estimation on HCP’s fsLR4k cortex floor for every participant. Head fashions had been computed utilizing overlapping spheres, and the info and noise covariance matrices had been estimated from the resting state MEG and noise recordings. LCMV beamformers technique from Brainstorm was then used to acquire the supply exercise for every participant. We carried out knowledge covariance regularization and normalized the estimated supply variance by the noise covariance matrix to scale back the impact of variable supply depth. The L2 matrix norm (i.e., regularization parameter) of knowledge covariance matrix is normally outlined as the biggest eigenvalue of its eigenspectrum. Nonetheless, the eigenspectrum of MEG knowledge covariance will be ill-conditioned, such that the eigenvalues could span many a long time the place bigger eigenvalues are overestimated and smaller eigenvalues are underestimated. In different phrases, the L2 norm of the info covariance matrix will be many instances bigger than the vast majority of eigenvalues, making it tough to pick a standard regularization parameter. Following pointers from Brainstorm [122], we used the “median eigenvalue” technique to regularize the info covariance matrix, the place the eigenvalues smaller than the median eigenvalue are changed with the median eigenvalue itself (i.e., flattening the tail of eigenvalues spectrum to the median). The covariance matrix is then reconstructed utilizing the modified eigenspectrum. This helps to keep away from the instability of knowledge covariance inversion brought on by the smallest eigenvalues and regularizes the info covariance matrix. Supply orientations had been constrained to be regular to the cortical floor at every of the 8,000 vertex places on the fsLR4k floor. Supply-level time-series had been then parcellated into 400 areas utilizing the Schaefer-400 atlas [89], such {that a} given parcel’s time sequence was estimated as the primary principal element of its constituting sources’ time sequence.

Parcellated time-series had been then used to estimate FC with an amplitude-based connectivity measure from Brainstorm (AEC; [60]). An orthogonalization course of was utilized to appropriate for the spatial leakage impact by eradicating all shared zero-lag alerts [123]. AEC FC had been derived for every participant at 6 canonical electrophysiological bands (i.e., delta (δ: 2 to 4 Hz), theta (θ: 5 to 7 Hz), alpha (α: 8 to 12 Hz), beta (β: 15 to 29 Hz), low gamma (lo-γ: 30 to 59 Hz), and excessive gamma (hi-γ: 60 to 90Hz)). Group-average MEG FC matrices had been constructed because the imply FC throughout all people for every frequency band. For comparability, band-limited group-average AEC matrices had been additionally estimated with out correcting for spatial leakage impact.

We additionally processed the MEG knowledge utilizing further methodological decisions. First, the LCMV supply reconstructed and parcellated time-series had been used to estimate FC with another, phase-based connectivity measure (PLV; [87,88]) for every frequency band. Second, one other supply reconstruction technique (sLoreta; [86]) was used as a substitute of LCMV beamformers to acquire source-level time-series, on condition that earlier studies counsel that sLoreta improves supply localization accuracy [91,92]. Supply-level time-series, obtained by sLoreta, had been then parcellated into 400 areas and had been used to estimate AEC matrices with spatial leakage correction for the 6 frequency bands. Third, to make sure that the findings are impartial from selection of parcellation, a low-resolution atlas (Schaefer-200; [89]) was used to parcellate the unique LCMV source-level time-series to 200 cortical areas and procure spatial leakage corrected AEC connectivity matrices. Lastly, we estimated MEG supply localization errors for LCMV and sLoreta supply reconstruction options utilizing CTFs [9195,121]. CTF of a given supply i is a measure of how exercise from all different sources contributes to the exercise estimated for the i-th supply. Following pointers from Brainstorm [122] and MNE-Python software program packages [124], we used CTF to calculate peak localization error of a given supply i because the Euclidean distance between the height location estimated for supply i and the true supply location i on the floor mannequin [92,95]. Supply-level CTF was then parcellated utilizing the Schaefer-400 atlas. We additionally estimated source-level SNR for LCMV supply reconstruction answer as follows [120,125]:
(1)
the place a is the supply amplitude (i.e., typical power of a dipole, which is 10 nAm; [
126]), N is the variety of sensors, bok is the sign at sensor ok estimated by the ahead mannequin for a supply with unit amplitude, and is the noise variance at sensor ok. SNR was first calculated on the supply degree and was then parcellated utilizing the Schaefer-400 atlas.

Resting state purposeful MRI.

The purposeful MRI knowledge had been preprocessed utilizing HCP minimal preprocessing pipelines [62,127]. Detailed info concerning knowledge acquisition and preprocessing is obtainable elsewhere [62,127]. Briefly, all 3T purposeful MRI time-series (voxel decision of two mm isotropic) had been corrected for gradient nonlinearity, head movement utilizing a inflexible physique transformation, and geometric distortions utilizing scan pairs with reverse section encoding instructions (R/L, L/R) [128]. Additional preprocessing steps embrace coregistration of the corrected pictures to the T1w structural MR pictures, mind extraction, normalization of complete mind depth, high-pass filtering (>2,000s FWHM; to appropriate for scanner drifts), and eradicating further noise utilizing the ICA-FIX course of [128,129]. The preprocessed time-series had been then parcellated into 400 cortical areas utilizing Schaefer-400 parcellation [89]. The parcellated time-series had been used to assemble FC matrices as Pearson correlation coefficients between pairs of regional time-series for every of the 4 scans and every participant. A gaggle-average FC matrix was constructed because the imply FC throughout all people and scans.

Diffusion weighted imaging (DWI).

Diffusion weighted imaging (DWI) knowledge had been obtained for a similar people from the HCP dataset. MRtrix3 bundle [130] (https://www.mrtrix.org/) was used to preprocess the DWI knowledge as described elsewhere [24]. Briefly, multishell multitissue constrained spherical deconvolution algorithm from MRtrix was utilized to generate fiber orientation distributions [131,132]. Probabilistic streamline tractography based mostly on the generated fiber orientation distributions was used to reconstruct white matter edges [133]. The tract weights had been optimized by estimating an acceptable cross-section multiplier for every streamline following the process proposed by Smith and colleagues [134]. Structural connectivity matrices had been then reconstructed for every participant utilizing the Schaefer-400 atlas [89]. Lastly, a binary group-level structural connectivity matrix was constructed utilizing a consensus strategy that preserves the sting size distribution in particular person individuals [135,136]. The binary consensus structural connectivity matrix was weighted by the typical structural connectivity throughout people to acquire a weighted structural connectivity matrix.

BigBrain histological knowledge

To characterize the cytoarchitectural variation throughout the cortex, cell-staining depth profile knowledge had been obtained from the BigBrain atlas [64,65]. The BigBrain is a high-resolution (20 μm) histological atlas of a postmortem human mind and contains cell-staining intensities which can be sampled at every vertex throughout 50 equivolumetric surfaces from the pial to the white matter floor utilizing the Merker staining approach [64,137]. The staining depth profile knowledge characterize neuronal density and soma measurement at various cortical depths, capturing the regional differentiation of cytoarchitecture [64,65,72,74,138]. Depth profiles at varied cortical depths can be utilized to roughly determine boundaries of cortical layers that separate supragranular (cortical layers I to III), granular (cortical layer IV), and infragranular (cortical layers V to VI) layers [65,74,138]. The info had been obtained on fsaverage floor (164k vertices) from the BigBrainWarp toolbox [65] and had been parcellated into 400 cortical areas utilizing the Schaefer-400 atlas [89].

The cross-modal correspondence map, estimated as adjusted-R2 (see “Multilinear mannequin” for extra particulars), was then in contrast with the parcellated cell-staining depth knowledge. Particularly, the regional mannequin match was correlated with cell-staining profiles at every cortical depth utilizing Spearman rank correlation (rs). 10,000 spatial-autocorrelation preserving nulls had been used to assemble a null distribution of correlation at every cortical depth (see “Null mannequin” for extra particulars on spatial-autocorrelation preserving nulls). Significance of the associations had been estimated by evaluating the empirical Spearman rank correlation with the distribution of null correlations at every cortical depth, figuring out the variety of null correlations that had been equal to or larger than the empirical correlation (two-tailed check). Lastly, Benjamini–Hochberg process [66] was used to appropriate for a number of comparisons by controlling the false discovery price (FDR) at 5% throughout all 50 comparisons.

Allen Human Mind Atlas (AHBA)

Regional microarray expression knowledge had been obtained from 6 postmortem brains (1 feminine, ages 24.0 to 57.0, 42.50 ± 13.38) offered by the AHBA (https://human.brain-map.org; [67]). Information had been processed with the abagen toolbox (model 0.1.3-doc; https://github.com/rmarkello/abagen; [139]) utilizing the Schaefer-400 volumetric atlas in MNI house [89].

First, microarray probes had been reannotated utilizing knowledge offered by [140]; probes not matched to a legitimate Entrez ID had been discarded. Subsequent, probes had been filtered based mostly on their expression depth relative to background noise [141], such that probes with depth lower than the background in ≥50.00% of samples throughout donors had been discarded. When a number of probes listed the expression of the identical gene, we chosen and used the probe with probably the most constant sample of regional variation throughout donors (i.e., differential stability; [142]), calculated with

the place ρ is Spearman’s rank correlation of the expression of a single probe, p, throughout areas in 2 donors Bi and Bj, and N is the whole variety of donors. Right here, areas correspond to the structural designations offered within the ontology from the AHBA.

The MNI coordinates of tissue samples had been up to date to these generated through nonlinear registration utilizing the Superior Normalization Instruments (ANTs; https://github.com/chrisfilo/alleninf). To extend spatial protection, tissue samples had been mirrored bilaterally throughout the left and proper hemispheres [143]. Samples had been assigned to mind areas within the offered atlas if their MNI coordinates had been inside 2 mm of a given parcel. If a mind area was not assigned a tissue pattern based mostly on the above process, each voxel within the area was mapped to the closest tissue pattern from the donor as a way to generate a dense, interpolated expression map. The typical of those expression values was taken throughout all voxels within the area, weighted by the space between every voxel and the pattern mapped to it, as a way to acquire an estimate of the parcellated expression values for the lacking area. All tissue samples not assigned to a mind area within the offered atlas had been discarded.

Intersubject variation was addressed by normalizing tissue pattern expression values throughout genes utilizing a strong sigmoid operate [144]:

the place 〈x〉 is the median and IQRx is the normalized interquartile vary of the expression of a single tissue pattern throughout genes. Normalized expression values had been then rescaled to the unit interval:

Gene expression values had been then normalized throughout tissue samples utilizing an similar process. Samples assigned to the identical mind area had been averaged individually for every donor after which throughout donors, yielding a regional expression matrix of 15,633 genes. Expression of NPY1R was extracted from the regional expression matrix and was associated to the cross-modal correspondence map, estimated as adjusted-R2 (see “Multilinear mannequin” for extra particulars), utilizing 10,000 spatial-autocorrelation preserving nulls (see “Null fashions” for extra particulars).

Multilinear mannequin

Area-level cross-validation.

Area-level cross-validation was carried out to evaluate out-of-sample mannequin efficiency. Given the spatial autocorrelation inherent to the info, random splits of mind areas into prepare and check units could end in out-of-sample correlations which can be inflated attributable to spatial proximity [145]. To take this under consideration, we used a distance-dependent cross-validation strategy the place we pseudorandomly break up the connectivity profile of a given area (e.g., node i) into prepare and check units based mostly on spatial separation [90]. We used interregional Euclidean distance to pick 75% of the closest areas to a randomly chosen supply area because the prepare set and the remaining 25% of the areas as check set. The random supply area will be any of the 399 areas related to node i; therefore, the connectivity profile of node i is break up into 399 distinctive prepare and check units. We then prepare the multilinear mannequin utilizing the prepare set and predict FC of the check set for every area and every break up. Lastly, the mannequin efficiency is quantified utilizing Pearson correlation coefficient between empirical and predicted values. The cross-validated regional mannequin efficiency is then estimated because the imply correlation coefficient between empirical and predicted values throughout splits for every mind area.

Diffusion map embedding

Diffusion map embedding was used to determine the principal axis of variation in purposeful group of the cortex (diffusion map embedding and alignment bundle; https://github.com/satra/mapalign) [63,71]. Diffusion map embedding is a nonlinear dimensionality discount approach that generates a low-dimensional illustration of high-dimensional knowledge by projecting it into an embedding house, such that the areas with comparable connectivity profiles can be nearer in distance within the new widespread house in comparison with the areas with dissimilar connectivity profiles [63,71,146]. Briefly, following the process described by Margulies and colleagues [63], every row of the group-average fMRI FC was thresholded at 90%, such that solely the highest 10% of purposeful connections was retained within the matrix. Subsequent, a cosine-similarity matrix was estimated based mostly on the remaining purposeful connections, the place the ensuing pairwise cosine distances characterize the similarity between the connectivity profiles of cortical areas in line with their strongest connections. Lastly, the diffusion map embedding was utilized to the ensuing optimistic affinity matrix. This identifies the principal axis of variation in FC, alongside which cortical areas are ordered based mostly on the similarity of their connectivity profiles. The recognized purposeful gradient or hierarchy spans the unimodal–transmodal axis, separating main sensory-motor cortices from affiliation cortex. The purposeful gradient map can also be accessible as a part of the neuromaps toolbox [147]. The purposeful gradient was used as a metric of hierarchical group of the cortex and was in contrast with the regional mannequin match (Fig 2).

Construction–operate coupling

Construction–operate coupling was estimated following the process described by Baum and colleagues [80]. Structural and purposeful connectivity profiles of every mind area (i.e., every row of the connectivity matrices) had been extracted from the weighted group-level structural and purposeful connectivity matrices. Construction–operate coupling of a given area was then estimated because the Spearman rank correlation between nonzero values of that area’s structural and purposeful connectivity profiles. Lastly, the ensuing whole-brain construction–operate coupling map was in contrast with the cross-modal correspondence map (i.e., R2 map from the regional mannequin). Significance of the affiliation between the two maps was assessed utilizing 10,000 spatial-autocorrelation preserving nulls (see “Null mannequin” for extra particulars).

Dominance evaluation

Dominance evaluation was used to quantify the distinct contributions of resting state MEG connectivity at completely different frequency bands to the prediction of resting state fMRI connectivity within the multilinear mannequin [69,70] (https://github.com/dominance-analysis/dominance-analysis). Dominance evaluation estimates the relative significance of predictors by developing all doable combos of predictors and refitting the multilinear mannequin for every mixture (a mannequin with p predictors can have 2p−1 fashions for all doable combos of predictors). The relative contribution of every predictor is then quantified as enhance in variance defined by including that predictor to the fashions (i.e., acquire in adjusted-R2). Right here, we first constructed a a number of linear regression mannequin for every area with MEG connectivity profile of that area at 6 frequency bands as impartial variables (predictors) and fMRI connectivity of the area because the dependent variable to quantify the distinct contribution of every issue utilizing dominance evaluation. The relative significance of every issue is estimated as “p.c relative significance,” which is a abstract measure that quantifies the p.c worth of the extra contribution of that predictor to all subset fashions.

Null mannequin

To make inferences concerning the topographic correlations between any 2 mind maps, we implement a null mannequin that systematically disrupts the connection between 2 topographic maps however preserves their spatial autocorrelation [145,148]. We used the Schaefer-400 atlas within the HCP’s fsLR32k grayordinate house [62,89]. The spherical projection of the fsLR32k floor was used to outline spatial coordinates for every parcel by choosing the vertex closest to the center-of-mass of every parcel [68,149,150]. The ensuing spatial coordinates had been used to generate null fashions by making use of randomly sampled rotations and reassigning node values based mostly on the closest ensuing parcel (10,000 repetitions). The rotation was utilized to 1 hemisphere after which mirrored to the opposite hemisphere.

Supporting info

S3 Fig. Supply localization error.

MEG supply localization error is estimated for (a) LCMV and (b) sLoreta supply reconstruction options utilizing CTFs [9195]. CTF is used to calculate peak localization error of a given supply i because the Euclidean distance between the height location estimated for supply i and the true supply location i on the floor mannequin [92,95]. No vital affiliation is noticed between the cross-modal correspondence R2 map and peak localization error for LCMV and sLoreta. The info and code wanted to generate this determine will be present in https://github.com/netneurolab/shafiei_megfmrimapping and https://zenodo.org/file/6728338. CTF, cross-talk operate; LCMV, linearly constrained minimal variance; MEG, magnetoencephalography; sLoreta, standardized low-resolution mind electromagnetic tomography.

https://doi.org/10.1371/journal.pbio.3001735.s004

(TIFF)

References

  1. 1.
    Fries P. A mechanism for cognitive dynamics: neuronal communication by way of neuronal coherence. Tendencies Cogn Sci. 2005;9(10):474–480. pmid:16150631
  2. 2.
    Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The group of the human cerebral cortex estimated by intrinsic purposeful connectivity. J Neurophysiol. 2011;106(3):1125. pmid:21653723
  3. 3.
    Energy JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Practical community group of the human mind. Neuron. 2011;72(4):665–678. pmid:22099467
  4. 4.
    Bellec P, Perlbarg V, Jbabdi S, Pélégrini-Issac M, Anton JL, Doyon J, et al. Identification of large-scale networks within the mind utilizing fMRI. Neuroimage. 2006;29(4):1231–1243. pmid:16246590
  5. 5.
    De Pasquale F, Della Penna S, Snyder AZ, Lewis C, Mantini D, Marzetti L, et al. Temporal dynamics of spontaneous MEG exercise in mind networks. Proc Natl Acad Sci. 2010;107(13):6040–6045. pmid:20304792
  6. 6.
    Brookes MJ, Woolrich M, Luckhoo H, Worth D, Hale JR, Stephenson MC, et al. Investigating the electrophysiological foundation of resting state networks utilizing magnetoencephalography. Proc Natl Acad Sci. 2011;108(40):16783–16788. pmid:21930901
  7. 7.
    Brookes MJ, Hale JR, Zumer JM, Stevenson CM, Francis ST, Barnes GR, et al. Measuring purposeful connectivity utilizing MEG: methodology and comparability with fcMRI. Neuroimage. 2011;56(3):1082–1104. pmid:21352925
  8. 8.
    Baker AP, Brookes MJ, Rezek IA, Smith SM, Behrens T, Smith PJP, et al. Quick transient networks in spontaneous human mind exercise. Elife. 2014;3:e01867. pmid:24668169
  9. 9.
    Tewarie P, Hillebrand A, van Dellen E, Schoonheim MM, Barkhof F, Polman C, et al. Structural diploma predicts purposeful community connectivity: a multimodal resting-state fMRI and MEG examine. Neuroimage. 2014;97:296–307. pmid:24769185
  10. 10.
    Noble S, Scheinost D, Constable RT. A decade of test-retest reliability of purposeful connectivity: A scientific evaluate and meta-analysis. Neuroimage. 2019;203:116157. pmid:31494250
  11. 11.
    Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, Berg JJ, et al. Precision purposeful mapping of particular person human brains. Neuron. 2017;95(4):791–807. pmid:28757305
  12. 12.
    Brookes MJ, Liddle EB, Hale JR, Woolrich MW, Luckhoo H, Liddle PF, et al. Process induced modulation of neural oscillations in electrophysiological mind networks. Neuroimage. 2012;63(4):1918–1930. pmid:22906787
  13. 13.
    Colclough GL, Woolrich MW, Tewarie P, Brookes MJ, Quinn AJ, Smith SM. How dependable are MEG resting-state connectivity metrics? Neuroimage. 2016;138:284–293. pmid:27262239
  14. 14.
    Cole MW, Bassett DS, Energy JD, Braver TS, Petersen SE. Intrinsic and task-evoked community architectures of the human mind. Neuron. 2014;83(1):238–251. pmid:24991964
  15. 15.
    Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, et al. Correspondence of the mind’s purposeful structure throughout activation and relaxation. Proc Natl Acad Sci. 2009;106(31):13040–13045. pmid:19620724
  16. 16.
    Corridor EL, Robson SE, Morris PG, Brookes MJ. The connection between MEG and fMRI. Neuroimage. 2014;102:80–91. pmid:24239589
  17. 17.
    Hari R, Parkkonen L. The mind timewise: how timing shapes and helps mind operate. Philos Trans R Soc Lond B Biol Sci. 2015;370(1668):20140170. pmid:25823867
  18. 18.
    Baillet S. Magnetoencephalography for mind electrophysiology and imaging. Nat Neurosci. 2017;20(3):327–339. pmid:28230841
  19. 19.
    Sadaghiani S, Wirsich J. Intrinsic connectome group throughout temporal scales: New insights from cross-modal approaches. Netw Neurosci. 2020;4(1):1–29. pmid:32043042
  20. 20.
    Sadaghiani S, Brookes MJ, Baillet S. Connectomics of human electrophysiology. Neuroimage. 2022;247:118788. pmid:34906715
  21. 21.
    Murray JD, Bernacchia A, Freedman DJ, Romo R, Wallis JD, Cai X, et al. A hierarchy of intrinsic timescales throughout primate cortex. Nat Neurosci. 2014;17(12):1661. pmid:25383900
  22. 22.
    Gao R, van den Brink RL, Pfeffer T, Voytek B. Neuronal timescales are functionally dynamic and formed by cortical microarchitecture. Elife. 2020;9:e61277. pmid:33226336
  23. 23.
    Raut RV, Snyder AZ, Raichle ME. Hierarchical dynamics as a macroscopic organizing precept of the human mind. Proc Natl Acad Sci. 2020;117(34):20890–20897. pmid:32817467
  24. 24.
    Shafiei G, Markello RD, De Wael RV, Bernhardt BC, Fulcher BD, Misic B. Topographic gradients of intrinsic dynamics throughout neocortex. Elife. 2020;9:e62116. pmid:33331819
  25. 25.
    Hasson U, Yang E, Vallines I, Heeger DJ, Rubin N. A hierarchy of temporal receptive home windows in human cortex. J Neurosci. 2008;28(10):2539–2550. pmid:18322098
  26. 26.
    Honey CJ, Thesen T, Donner TH, Silbert LJ, Carlson CE, Devinsky O, et al. Gradual cortical dynamics and the buildup of knowledge over lengthy timescales. Neuron. 2012;76(2):423–434. pmid:23083743
  27. 27.
    Baldassano C, Chen J, Zadbood A, Pillow JW, Hasson U, Norman KA. Discovering occasion construction in steady narrative notion and reminiscence. Neuron. 2017;95(3):709–721. pmid:28772125
  28. 28.
    Huntenburg JM, Bazin PL, Margulies DS. Massive-scale gradients in human cortical group. Tendencies Cogn Sci. 2018;22(1):21–31. pmid:29203085
  29. 29.
    Chaudhuri R, Knoblauch Okay, Gariel MA, Kennedy H, Wang XJ. A big-scale circuit mechanism for hierarchical dynamical processing within the primate cortex. Neuron. 2015;88(2):419–431. pmid:26439530
  30. 30.
    Chien HYS, Honey CJ. Establishing and forgetting temporal context within the human cerebral cortex. Neuron. 2020. pmid:32164874
  31. 31.
    Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK. Massive-scale cortical correlation construction of spontaneous oscillatory exercise. Nat Neurosci. 2012;15(6):884–890. pmid:22561454
  32. 32.
    Menon V, Ford JM, Lim KO, Glover GH, Pfefferbaum A. Mixed event-related fMRI and EEG proof for temporal–parietal cortex activation throughout goal detection. Neuroreport. 1997;8(14):3029–3037. pmid:9331910
  33. 33.
    Freeman WJ, Ahlfors SP, Menon V. Combining fMRI with EEG and MEG as a way to relate patterns of mind exercise to cognition. Int J Psychophysiol. 2009;73(1):43–52. pmid:19233235
  34. 34.
    Musso F, Brinkmeyer J, Mobascher A, Warbrick T, Winterer G. Spontaneous mind exercise and EEG microstates. A novel EEG/fMRI evaluation strategy to discover resting-state networks. Neuroimage. 2010;52(4):1149–1161. pmid:20139014
  35. 35.
    Liljeström M, Stevenson C, Kujala J, Salmelin R. Process-and stimulus-related cortical networks in language manufacturing: Exploring similarity of MEG-and fMRI-derived purposeful connectivity. Neuroimage. 2015;120:75–87. pmid:26169324
  36. 36.
    Das A, de Los AC, Menon V. Electrophysiological foundations of the human default-mode community revealed by intracranial-EEG recordings throughout resting-state and cognition. Neuroimage. 2022:118927. pmid:35074503
  37. 37.
    Sareen E, Zahar S, Ville DVD, Gupta A, Griffa A, Amico E. Exploring MEG mind fingerprints: Analysis, pitfalls, and interpretations. Neuroimage. 2021;240:118331. pmid:34237444
  38. 38.
    da Silva CJ, Orozco Perez HD, Misic B, Baillet S. Transient segments of neurophysiological exercise allow particular person differentiation. Nat Commun. 2021;12(1):1–11.
  39. 39.
    Demuru M, Fraschini M. EEG fingerprinting: Topic-specific signature based mostly on the aperiodic element of energy spectrum. Comput Biol Med. 2020;120:103748. pmid:32421651
  40. 40.
    Fraschini M, Pani SM, Didaci L, Marcialis GL. Robustness of purposeful connectivity metrics for EEG-based private identification over task-induced intra-class and inter-class variations. Sample Recognit Lett. 2019;125:49–54.
  41. 41.
    Betzel RF, Medaglia JD, Kahn AE, Soffer J, Schonhaut DR, Bassett DS. Structural, geometric and genetic elements predict interregional mind connectivity patterns probed by electrocorticography. Nat Biomed Eng. 2019;3(11):902–916. pmid:31133741
  42. 42.
    Deligianni F, Centeno M, Carmichael DW, Clayden JD. Relating resting-state fMRI and EEG whole-brain connectomes throughout frequency bands. Entrance Neurosci. 2014;8:258. pmid:25221467
  43. 43.
    Wirsich J, Ridley B, Besson P, Jirsa V, Bénar C, Ranjeva JP, et al. Complementary contributions of concurrent EEG and fMRI connectivity for predicting structural connectivity. Neuroimage. 2017;161:251–260. pmid:28842386
  44. 44.
    Wirsich J, Jorge J, Iannotti GR, Shamshiri EA, Grouiller F, Abreu R, et al. The connection between EEG and fMRI connectomes is reproducible throughout simultaneous EEG-fMRI research from 1.5 T to 7T. Neuroimage. 2021;231:117864. pmid:33592241
  45. 45.
    Hipp JF, Siegel M. BOLD fMRI correlation displays frequency-specific neuronal correlation. Curr Biol. 2015;25(10):1368–1374. pmid:25936551
  46. 46.
    Garcés P, Pereda E, Hernández-Tamames JA, Del-Pozo F, Maestú F, Ángel P-PJ. Multimodal description of complete mind connectivity: A comparability of resting state MEG, fMRI, and DWI. Hum Mind Mapp. 2016;37(1):20–34. pmid:26503502
  47. 47.
    Tewarie P, Vibrant MG, Hillebrand A, Robson SE, Gascoyne LE, Morris PG, et al. Predicting haemodynamic networks utilizing electrophysiology: The position of non-linear and cross-frequency interactions. Neuroimage. 2016;130:273–292. pmid:26827811
  48. 48.
    Logothetis NK. The underpinnings of the BOLD purposeful magnetic resonance imaging sign. J Neurosci. 2003;23(10):3963–3971. pmid:12764080
  49. 49.
    Mukamel R, Gelbard H, Arieli A, Hasson U, Fried I, Malach R. Coupling between neuronal firing, area potentials, and FMRI in human auditory cortex. Science. 2005;309(5736):951–954. pmid:16081741
  50. 50.
    Stevenson CM, Wang F, Brookes MJ, Zumer JM, Francis ST, Morris PG. Paired pulse despair within the somatosensory cortex: associations between MEG and BOLD fMRI. Neuroimage. 2012;59(3):2722–2732. pmid:22036680
  51. 51.
    Singh KD. Which “neural exercise do you imply? fMRI, MEG, oscillations and neurotransmitters. Neuroimage. 2012;62(2):1121–1130. pmid:22248578
  52. 52.
    Maier A, Adams GK, Aura C, Leopold DA. Distinct superficial and deep laminar domains of exercise within the visible cortex throughout relaxation and stimulation. Entrance Syst Neurosci. 2010;4:31. pmid:20802856
  53. 53.
    Maier A, Aura CJ, Leopold DA. Infragranular sources of sustained native area potential responses in macaque main visible cortex. J Neurosci. 2011;31(6):1971–1980. pmid:21307235
  54. 54.
    Buffalo EA, Fries P, Landman R, Buschman TJ, Desimone R. Laminar variations in gamma and alpha coherence within the ventral stream. Proc Natl Acad Sci. 2011;108(27):11262–11267. pmid:21690410
  55. 55.
    Smith MA, Jia X, Zandvakili A, Kohn A. Laminar dependence of neuronal correlations in visible cortex. J Neurophysiol. 2013;109(4):940–947. pmid:23197461
  56. 56.
    Bastos AM, Vezoli J, Bosman CA, Schoffelen JM, Oostenveld R, Dowdall JR, et al. Visible areas exert feedforward and suggestions influences by way of distinct frequency channels. Neuron. 2015;85(2):390–401. pmid:25556836
  57. 57.
    Scheeringa R, Koopmans PJ, van Mourik T, Jensen O, Norris DG. The connection between oscillatory EEG exercise and the laminar-specific BOLD sign. Proc Natl Acad Sci. 2016;113(24):6761–6766. pmid:27247416
  58. 58.
    Bastos AM, Loonis R, Kornblith S, Lundqvist M, Miller EK. Laminar recordings in frontal cortex counsel distinct layers for upkeep and management of working reminiscence. Proc Natl Acad Sci. 2018;115(5):1117–1122. pmid:29339471
  59. 59.
    Scheeringa R, Fries P. Cortical layers, rhythms and BOLD alerts. Neuroimage. 2019;197:689–698. pmid:29108940
  60. 60.
    Bruns A, Eckhorn R, Jokeit H, Ebner A. Amplitude envelope correlation detects coupling amongst incoherent mind alerts. Neuroreport. 2000;11(7):1509–1514. pmid:10841367
  61. 61.
    Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M. Electrophysiological signatures of resting state networks within the human mind. Proc Natl Acad Sci. 2007;104(32):13170–13175. pmid:17670949
  62. 62.
    Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil Okay, et al. The WU-Minn human connectome mission: an summary. Neuroimage. 2013;80:62–79. pmid:23684880
  63. 63.
    Margulies DS, Ghosh SS, Goulas A, Falkiewicz M, Huntenburg JM, Langs G, et al. Situating the default-mode community alongside a principal gradient of macroscale cortical group. Proc Natl Acad Sci. 2016;113(44):12574–12579. pmid:27791099
  64. 64.
    Amunts Okay, Lepage C, Borgeat L, Mohlberg H, Dickscheid T, Rousseau MÉ, et al. BigBrain: an ultrahigh-resolution 3D human mind mannequin. Science. 2013;340(6139):1472–1475. pmid:23788795
  65. 65.
    Paquola C, Royer J, Lewis LB, Lepage C, Glatard T, Wagstyl Okay, et al. The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. Elife. 2021;10:e70119. pmid:34431476
  66. 66.
    Benjamini Y, Hochberg Y. Controlling the false discovery price: a sensible and highly effective strategy to a number of testing. J R Stat Soc B Methodol. 1995;57(1):289–300.
  67. 67.
    Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically complete atlas of the grownup human mind transcriptome. Nature. 2012;489(7416):391. pmid:22996553
  68. 68.
    Vázquez-Rodríguez B, Suárez LE, Markello RD, Shafiei G, Paquola C, Hagmann P, et al. Gradients of construction–operate tethering throughout neocortex. Proc Natl Acad Sci U S A. 2019;116(42):21219–21227. pmid:31570622
  69. 69.
    Budescu DV. Dominance evaluation: a brand new strategy to the issue of relative significance of predictors in a number of regression. Psychol Bull. 1993;114(3):542.
  70. 70.
    Azen R, Budescu DV. The dominance evaluation strategy for evaluating predictors in a number of regression. Psychol Strategies. 2003;8(2):129. pmid:12924811
  71. 71.
    Langs G, Golland P, Ghosh SS. Predicting activation throughout people with resting-state purposeful connectivity based mostly multi-atlas label fusion. Worldwide Convention on Medical Picture Computing and Pc-Assisted Intervention. Springer; 2015. p. 313–320.
  72. 72.
    Paquola C, De Wael RV, Wagstyl Okay, Bethlehem RA, Hong SJ, Seidlitz J, et al. Microstructural and purposeful gradients are more and more dissociated in transmodal cortices. PLoS Biol. 2019;17(5):e3000284. pmid:31107870
  73. 73.
    Paquola C, Benkarim O, DeKraker J, Lariviere S, Frässle S, Royer J, et al. Convergence of cortical varieties and purposeful motifs within the human mesiotemporal lobe. Elife. 2020;9:e60673. pmid:33146610
  74. 74.
    Wagstyl Okay, Larocque S, Cucurull G, Lepage C, Cohen JP, Bludau S, et al. BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices. PLoS Biol. 2020;18(4):e3000678. pmid:32243449
  75. 75.
    Douglas RJ, Martin KA. Neuronal circuits of the neocortex. Annu Rev Neurosci. 2004;27:419–451. pmid:15217339
  76. 76.
    Harel N, Lin J, Moeller S, Ugurbil Okay, Yacoub E. Mixed imaging–histological examine of cortical laminar specificity of fMRI alerts. Neuroimage. 2006;29(3):879–887. pmid:16194614
  77. 77.
    Schmid F, Barrett MJ, Jenny P, Weber B. Vascular density and distribution in neocortex. Neuroimage. 2019;197:792–805. pmid:28669910
  78. 78.
    Uhlirova H, Kılıç Okay, Tian P, Thunemann M, Desjardins M. Saisan PA, et al. Cell kind specificity of neurovascular coupling in cerebral cortex. Elife. 2016;5:e14315. pmid:27244241
  79. 79.
    Preti MG, Van De Ville D. Decoupling of mind operate from construction reveals regional behavioral specialization in people. Nat Commun. 2019;10(1):1–7.
  80. 80.
    Baum GL, Cui Z, Roalf DR, Ciric R, Betzel RF, Larsen B, et al. Improvement of construction–operate coupling in human mind networks throughout youth. Proc Natl Acad Sci. 2020;117(1):771–778. pmid:31874926
  81. 81.
    Suárez LE, Markello RD, Betzel RF, Misic B. Linking construction and performance in macroscale mind networks. Tendencies Cogn Sci. 2020. pmid:32160567
  82. 82.
    Zamani Esfahlani F, Faskowitz J, Slack J, Mišić B, Betzel RF. Native structure-function relationships in human mind networks throughout the lifespan. Nat Commun. 2022;13(1):1–16.
  83. 83.
    Cabral J, Luckhoo H, Woolrich M, Joensson M, Mohseni H, Baker A, et al. Exploring mechanisms of spontaneous purposeful connectivity in MEG: how delayed community interactions result in structured amplitude envelopes of band-pass filtered oscillations. Neuroimage. 2014;90:423–435. pmid:24321555
  84. 84.
    Sorrentino P, Seguin C, Rucco R, Liparoti M, Lopez ET, Bonavita S, et al. The structural connectome constrains quick mind dynamics. Elife. 2021;10:e67400. pmid:34240702
  85. 85.
    Sarwar T, Tian Y, Yeo BT, Ramamohanarao Okay, Zalesky A. Construction-function coupling within the human connectome: A machine studying strategy. Neuroimage. 2021;226:117609. pmid:33271268
  86. 86.
    Pascual-Marqui RD. Standardized low-resolution mind electromagnetic tomography (sLORETA): technical particulars. Strategies Discover Exp Clin Pharmacol. 2002;24(Suppl D):5–12. pmid:12575463
  87. 87.
    Lachaux JP, Rodriguez E, Martinerie J, Varela FJ. Measuring section synchrony in mind alerts. Hum Mind Mapp. 1999;8(4):194–208. pmid:10619414
  88. 88.
    Mormann F, Lehnertz Okay, David P, Elger CE. Imply section coherence as a measure for section synchronization and its utility to the EEG of epilepsy sufferers. Physica D: Nonlinear Phenomena. 2000;144(3–4):358–369.
  89. 89.
    Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ, et al. Native-global parcellation of the human cerebral cortex from intrinsic purposeful connectivity MRI. Cereb Cortex. 2018;28(9):3095–3114. pmid:28981612
  90. 90.
    Hansen JY, Markello RD, Vogel JW, Seidlitz J, Bzdok D, Misic B. Mapping gene transcription and neurocognition throughout human neocortex. Nat Hum Behav. 2021:1–11.
  91. 91.
    Hauk O, Wakeman DG, Henson R. Comparability of noise-normalized minimal norm estimates for MEG evaluation utilizing a number of decision metrics. Neuroimage. 2011;54(3):1966–1974. pmid:20884360
  92. 92.
    Hauk O, Stenroos M, Treder M. EEG/MEG supply estimation and spatial filtering: the linear toolkit. Magnetoencephalography: From Indicators to Dynamic Cortical Networks. 2019; p. 167–203.
  93. 93.
    Liu AK, Dale AM, Belliveau JW. Monte Carlo simulation research of EEG and MEG localization accuracy. Hum Mind Mapp. 2002;16(1):47–62. pmid:11870926
  94. 94.
    Hauk O, Stenroos M. A framework for the design of versatile cross-talk capabilities for spatial filtering of EEG/MEG knowledge: DeFleCT. Hum Mind Mapp. 2014;35(4):1642–1653. pmid:23616402
  95. 95.
    Molins A, Stufflebeam SM, Brown EN, Hämäläinen MS. Quantification of the profit from integrating MEG and EEG knowledge in minimal l2-norm estimation. Neuroimage. 2008;42(3):1069–1077. pmid:18602485
  96. 96.
    Mesulam MM. From sensation to cognition. Mind. 1998;121(6):1013–1052. pmid:9648540
  97. 97.
    Burt JB, Demirtaş M, Eckner WJ, Navejar NM, Ji JL, Martin WJ, et al. Hierarchy of transcriptomic specialization throughout human cortex captured by structural neuroimaging topography. Nat Neurosci. 2018;21(9):1251–1259. pmid:30082915
  98. 98.
    Fulcher BD, Murray JD, Zerbi V, Wang XJ. Multimodal gradients throughout mouse cortex. Proc Natl Acad Sci U S A. 2019;116(10):4689–4695. pmid:30782826
  99. 99.
    Huntenburg JM, Bazin PL, Goulas A, Tardif CL, Villringer A, Margulies DS. A scientific relationship between purposeful connectivity and intracortical myelin within the human cerebral cortex. Cereb Cortex. 2017;27(2):981–997. pmid:28184415
  100. 100.
    Goulas A, Changeux JP, Wagstyl Okay, Amunts Okay, Palomero-Gallagher N, Hilgetag CC. The pure axis of transmitter receptor distribution within the human cerebral cortex. Proc Natl Acad Sci. 2021;118(3). pmid:33452137
  101. 101.
    Froudist-Walsh S, Xu T, Niu M, Rapan L, Margulies DS, Zilles Okay, et al. Gradients of receptor expression within the macaque cortex. bioRxiv. 2021.
  102. 102.
    Hansen JY, Shafiei G, Markello RD, Sensible Okay, Cox SM, Wu Y, et al. Mapping neurotransmitter techniques to the structural and purposeful group of the human neocortex. bioRxiv. 2021.
  103. 103.
    Drew PJ. Vascular and neural foundation of the BOLD sign. Curr Opin Neurobiol. 2019;58:61–69. pmid:31336326
  104. 104.
    Uludağ Okay, Blinder P. Linking mind vascular physiology to hemodynamic response in ultra-high area MRI. Neuroimage. 2018;168:279–295. pmid:28254456
  105. 105.
    Bastos AM, Lundqvist M, Waite AS, Kopell N, Miller EK. Layer and rhythm specificity for predictive routing. Proc Natl Acad Sci. 2020;117(49):31459–31469. pmid:33229572
  106. 106.
    Donhauser PW, Baillet S. Two distinct neural timescales for predictive speech processing. Neuron. 2020;105(2):385–393. pmid:31806493
  107. 107.
    Safron A. An Built-in World Modeling Principle (IWMT) of consciousness: combining built-in info and international neuronal workspace theories with the free vitality precept and energetic inference framework; towards fixing the laborious downside and characterizing agentic causation. Entrance Artif Intell. 2020;3:30. pmid:33733149
  108. 108.
    Seth AK, Bayne T. Theories of consciousness. Nat Rev Neurosci. 2022:1–14.
  109. 109.
    Huber L, Handwerker DA, Jangraw DC, Chen G, Corridor A, Stüber C, et al. Excessive-resolution CBV-fMRI permits mapping of laminar exercise and connectivity of cortical enter and output in human M1. Neuron 2017;96(6):1253–1263. pmid:29224727
  110. 110.
    Finn ES, Huber L, Jangraw DC, Molfese PJ, Bandettini PA. Layer-dependent exercise in human prefrontal cortex throughout working reminiscence. Nat Neurosci. 2019;22(10):1687–1695. pmid:31551596
  111. 111.
    Finn ES, Huber L, Bandettini PA. Larger and deeper: Bringing layer fMRI to affiliation cortex. Prog Neurobiol. 2021;207:101930. pmid:33091541
  112. 112.
    Huber L, Finn ES, Chai Y, Goebel R, Stirnberg R, Stöcker T, et al. Layer-dependent purposeful connectivity strategies. Prog Neurobiol. 2021;207:101835. pmid:32512115
  113. 113.
    Florin E, Baillet S. The mind’s resting-state exercise is formed by synchronized cross-frequency coupling of neural oscillations. Neuroimage. 2015;111:26–35. pmid:25680519
  114. 114.
    Brookes MJ, Tewarie PK, Hunt BA, Robson SE, Gascoyne LE, Liddle EB, et al. A multi-layer community strategy to MEG connectivity evaluation. Neuroimage. 2016;132:425–438. pmid:26908313
  115. 115.
    Yin W, Li T, Hung SC, Zhang H, Wang L, Shen D, et al. The emergence of a functionally versatile mind throughout early infancy. Proc Natl Acad Sci. 2020;117(38):23904–23913. pmid:32868436
  116. 116.
    Safron A, Klimaj V, Hipólito I. On the significance of being versatile: dynamic mind networks and their potential purposeful significances. Entrance Syst Neurosci. 2022:149. pmid:35126062
  117. 117.
    Kujala J, Sudre G, Vartiainen J, Liljeström M, Mitchell T, Salmelin R. Multivariate evaluation of correlation between electrophysiological and hemodynamic responses throughout cognitive processing. Neuroimage. 2014;92:207–216. pmid:24518260
  118. 118.
    Vinck M, Oostenveld R, Van Wingerden M, Battaglia F, Pennartz CM. An improved index of phase-synchronization for electrophysiological knowledge within the presence of volume-conduction, noise and sample-size bias. Neuroimage. 2011;55(4):1548–1565. pmid:21276857
  119. 119.
    Niso G, Bruña R, Pereda E, Gutiérrez R, Bajo R, Maestú F, et al. HERMES: in direction of an built-in toolbox to characterize purposeful and efficient mind connectivity. Neuroinformatics. 2013;11(4):405–434. pmid:23812847
  120. 120.
    Goldenholz DM, Ahlfors SP, Hämäläinen MS, Sharon D, Ishitobi M, Vaina LM, et al. Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography. Hum Mind Mapp. 2009;30(4):1077–1086. pmid:18465745
  121. 121.
    Hauk O, Stenroos M, Treder M. In the direction of an Goal Analysis of EEG/MEG Supply Estimation Strategies-The Linear Method. Neuroimage. 2022:119177. pmid:35390459
  122. 122.
    Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: a user-friendly utility for MEG/EEG evaluation. Comput Intell Neurosci. 2011;2011. pmid:21584256
  123. 123.
    Colclough GL, Brookes MJ, Smith SM, Woolrich MW. A symmetric multivariate leakage correction for MEG connectomes. Neuroimage. 2015;117:439–448. pmid:25862259
  124. 124.
    Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, et al. MEG and EEG knowledge evaluation with MNE-Python. Entrance Neurosci. 2013;7:267. pmid:24431986
  125. 125.
    Piastra MC, Nüßing A, Vorwerk J, Clerc M, Engwer C, Wolters CH. A complete examine on electroencephalography and magnetoencephalography sensitivity to cortical and subcortical sources. Hum Mind Mapp. 2021;42(4):978–992. pmid:33156569
  126. 126.
    Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. Magnetoencephalography–idea, instrumentation, and functions to noninvasive research of the working human mind. Rev Mod Phys. 1993;65(2):413.
  127. 127.
    Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, et al. The minimal preprocessing pipelines for the Human Connectome Undertaking. Neuroimage. 2013;80:105–124. pmid:23668970
  128. 128.
    de Wael RV, Larivière S, Caldairou B, Hong SJ, Margulies DS, Jefferies E, et al. Anatomical and microstructural determinants of hippocampal subfield purposeful connectome embedding. Proc Natl Acad Sci. 2018;115(40):10154–10159. pmid:30249658
  129. 129.
    Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. Automated denoising of purposeful MRI knowledge: combining impartial element evaluation and hierarchical fusion of classifiers. Neuroimage. 2014;90:449–468. pmid:24389422
  130. 130.
    Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: A quick, versatile and open software program framework for medical picture processing and visualisation. Neuroimage. 2019:116137. pmid:31473352
  131. 131.
    Dhollander T, Raffelt D, Connelly A. Unsupervised 3-tissue response operate estimation from single-shell or multi-shell diffusion MR knowledge with out a co-registered T1 picture. ISMRM Workshop on Breaking the Obstacles of Diffusion MRI. vol. 5; 2016.
  132. 132.
    Jeurissen B, Tournier JD, Dhollander T, Connelly A, Sijbers J. Multi-tissue constrained spherical deconvolution for improved evaluation of multi-shell diffusion MRI knowledge. Neuroimage. 2014;103:411–426. pmid:25109526
  133. 133.
    Tournier JD, Calamante F, Connelly A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proceedings of the worldwide society for magnetic resonance in drugs. vol. 18; 2010. p. 1670.
  134. 134.
    Smith RE, Tournier JD, Calamante F, Connelly A. SIFT2: Enabling dense quantitative evaluation of mind white matter connectivity utilizing streamlines tractography. Neuroimage. 2015;119:338–351. pmid:26163802
  135. 135.
    Mišić B, Betzel RF, Nematzadeh A, Goni J, Griffa A, Hagmann P, et al. Cooperative and aggressive spreading dynamics on the human connectome. Neuron. 2015;86(6):1518–1529. pmid:26087168
  136. 136.
    Betzel RF, Griffa A, Hagmann P, Mišić B. Distance-dependent consensus thresholds for producing group-representative structural mind networks. Netw Neurosci. 2018:1–22.
  137. 137.
    Merker B. Silver staining of cell our bodies by way of bodily improvement. J Neurosci Strategies. 1983;9(3):235–241. pmid:6198563
  138. 138.
    Wagstyl Okay, Lepage C, Bludau S, Zilles Okay, Fletcher PC, Amunts Okay, et al. Mapping cortical laminar construction within the 3D BigBrain. Cereb Cortex. 2018;28(7):2551–2562. pmid:29901791
  139. 139.
    Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. Elife. 2021;10:e72129. pmid:34783653
  140. 140.
    Arnatkevičiūtė A, Fulcher BD, Fornito A. A sensible information to linking brain-wide gene expression and neuroimaging knowledge. Neuroimage. 2019;189:353–367. pmid:30648605
  141. 141.
    Quackenbush J. Microarray knowledge normalization and transformation. Nat Genet. 2002;32(4):496–501. pmid:12454644
  142. 142.
    Hawrylycz M, Miller JA, Menon V, Feng D, Dolbeare T, Guillozet-Bongaarts AL, et al. Canonical genetic signatures of the grownup human mind. Nat Neurosci. 2015;18(12):1832. pmid:26571460
  143. 143.
    Romero-Garcia R, Whitaker KJ, Váša F, Seidlitz J, Shinn M, Fonagy P, et al. Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex. Neuroimage. 2018;171:256–267. pmid:29274746
  144. 144.
    Fulcher BD, Little MA, Jones NS. Extremely comparative time-series evaluation: the empirical construction of time sequence and their strategies. J R Soc Interface. 2013;10(83):20130048. pmid:23554344
  145. 145.
    Markello RD, Misic B. Evaluating spatial null fashions for mind maps. Neuroimage. 2021;236:118052. pmid:33857618
  146. 146.
    Coifman RR, Lafon S, Lee AB, Maggioni M, Nadler B, Warner F, et al. Geometric diffusions as a software for harmonic evaluation and construction definition of knowledge: Diffusion maps. Proc Natl Acad Sci U S A. 2005;102(21):7426–7431. pmid:15899970
  147. 147.
    Markello RD, Hansen JY, Liu ZQ, Bazinet V, Shafiei G, Suarez LE, et al. Neuromaps: structural and purposeful interpretation of mind maps. bioRxiv. 2022.
  148. 148.
    Alexander-Bloch AF, Shou H, Liu S, Satterthwaite TD, Glahn DC, Shinohara RT, et al. On testing for spatial correspondence between maps of human mind construction and performance. Neuroimage 2018;178:540–551. pmid:29860082
  149. 149.
    Shafiei G, Markello RD, Makowski C, Talpalaru A, Kirschner M, Devenyi GA, et al. Spatial patterning of tissue quantity loss in schizophrenia displays mind community structure. Biol Psychiatry. 2020. pmid:31837746
  150. 150.
    Vazquez-Rodriguez B, Liu ZQ, Hagmann P, Misic B. Sign propagation through cortical hierarchies. Web Neurosci. 2020.
RELATED ARTICLES

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Most Popular

Recent Comments