The mind consists of disparate neural populations that talk and work together with each other. Though fiber bundles, similarities in molecular structure, and synchronized neural exercise all mirror how mind areas doubtlessly work together with each other, a complete research of how all these interregional relationships collectively mirror mind construction and performance stays lacking. Right here, we systematically combine 7 multimodal, multiscale forms of interregional similarity (“connectivity modes”) derived from gene expression, neurotransmitter receptor density, mobile morphology, glucose metabolism, haemodynamic exercise, and electrophysiology in people. We first present that for all connectivity modes, function similarity decreases with distance and will increase when areas are structurally linked. Subsequent, we present that connectivity modes exhibit distinctive and numerous connection patterns, hub profiles, spatial gradients, and modular group. All through, we observe a constant primacy of molecular connectivity modes—particularly correlated gene expression and receptor similarity—that map onto a number of phenomena, together with the wealthy membership and patterns of irregular cortical thickness throughout 13 neurological, psychiatric, and neurodevelopmental issues. Lastly, to assemble a single multimodal wiring map of the human cortex, we fuse all 7 connectivity modes and present that the fused community maps onto main organizational options of the cortex together with structural connectivity, intrinsic practical networks, and cytoarchitectonic lessons. Altogether, this work contributes to the integrative research of interregional relationships within the human cerebral cortex.
Quotation: Hansen JY, Shafiei G, Voigt Okay, Liang EX, Cox SML, Leyton M, et al. (2023) Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in well being and illness. PLoS Biol 21(9):
Tutorial Editor: Claus C. Hilgetag, College Medical Heart Hamburg-Eppendorf: Universitatsklinikum Hamburg-Eppendorf, GERMANY
Acquired: March 21, 2023; Accepted: August 28, 2023; Printed: September 25, 2023
Copyright: © 2023 Hansen et al. That is an open entry article distributed below the phrases of the Inventive Commons Attribution License, which allows unrestricted use, distribution, and replica in any medium, supplied the unique creator and supply are credited.
Knowledge Availability: All code and knowledge used to carry out the analyses can be found on GitHub at https://github.com/netneurolab/hansen_many_networks/tree/v1.0.0 and on Zenodo at https://zenodo.org/document/8250809 (DOI: 10.5281/zenodo.8250809).
Funding: BM acknowledges assist from the Pure Sciences and Engineering Analysis Council of Canada (NSERC), Canadian Institutes of Well being Analysis (CIHR), Mind Canada Basis Future Leaders Fund, the Canada Analysis Chairs Program, the Michael J. Fox Basis, and the Wholesome Brains for Wholesome Lives initiative. JYH acknowledges assist from the Helmholtz Worldwide BigBrain Analytics & Studying Laboratory, the Pure Sciences and Engineering Analysis Council of Canada, and The Neuro Irv and Helga Cooper Basis. SDJ acknowledges assist from the Nationwide Well being and Medical Analysis Council of Australia (APP1174164). The funders had no function in research design, knowledge assortment and evaluation, choice to publish, or preparation of the manuscript.
Competing pursuits: The authors have declared that no competing pursuits exist.
attention-deficit/hyperactivity dysfunction; AHBA,
Allen Human Mind Atlas; ANTs,
Superior Normalization Instruments; ASD,
autism spectrum dysfunction; DWI,
diffusion weighted imaging; ENIGMA,
Enhancing Neuroimaging Genetics by way of Meta-Evaluation; fMRI,
practical magnetic resonance imaging; HCP,
Human Connectome Challenge; LCMV,
linearly constrained minimal variance; MEG/EEG,
magnetic resonance imaging; MT,
magnetization switch; OCD,
obsessive-compulsive dysfunction; SNF,
similarity community fusion; SSP,
Mind connectivity classically refers back to the bodily neural fibers that hyperlink disparate neuronal populations. Axonal projections might be reconstructed by imaging fluorescently labeled proteins which might be both injected into or genetically expressed by a cell, or by stacking electron microscopy photos of thinly sliced mind sections [1,2]. On the macroscale, diffusion-weighted imaging can be utilized to hint massive fiber bundles that join pairs of mind areas in vivo, which collectively represent the structural connectome [3,4]. Throughout organisms, spatial scales, and imaging strategies, the mind’s white-matter structure (“construction”) reveals hallmark options together with a prevalence of brief vary connections leading to functionally segregated modules [5,6], and a small variety of disproportionately densely interconnected hubs . In the end, finding out the mind’s structural connectome has superior our understanding of how data is transmitted [8,9], how mind construction helps operate , and the way perturbations could lead to network-defined pathology unfold .
Nonetheless, the graph illustration of the structural connectome, during which regional nodes are equivalent, doesn’t account for the molecular and physiological heterogeneity that exists within the mind. An rising illustration of connectivity is function similarity: If 2 mind areas exhibit comparable organic options, we would anticipate them to be associated to 1 one other and engaged in frequent operate [12–16]. Neuroanatomical tract-tracing research in nonhuman primates have extensively proven that organic function similarity is key to mind group [17,18]. These pioneering research demonstrated that neuronal projection patterns might be predicted primarily based on the laminar differentiation of the supply and goal areas  and has been prolonged to human prefrontal cortex and different mannequin organisms [20,21]. Moreover, native variations in laminar structure observe a gradient of receptor density [22,23] and synaptic plasticity , indicating an alignment between a number of native options and connectivity. Nonetheless, these research are at the moment restricted to qualitative measurements of cytoarchitectonic similarity, small subsets of mind areas, mannequin organisms, and to a single perspective of molecular make-up (however see ).
An alternate strategy is to amass densely sampled whole-cortex neuroimaging knowledge throughout massive populations with the purpose of setting up a connectivity matrix primarily based on function similarity. This strategy is already broadly used on the BOLD sign the place haemodynamic time programs are correlated with one another and likewise exists for time collection measures from different imaging modalities akin to magneto-/electroencephalography (MEG/EEG) and dynamic FDG-fPET (all referred to as “practical connectivity”) [26–30]. In circumstances the place a number of measures of a function exist at every mind area, akin to gene expression ranges throughout many genes, interregional similarity might be estimated with respect to a single native function [14,23,31–35]. In every case, the following area × area correlation matrix represents a type of connectivity between mind areas.
As a number of estimates of interregional similarity turn out to be out there by way of rising applied sciences and knowledge sharing efforts, it turns into potential to combine them right into a single framework and deduce how they work together with each other, and in what methods they’re distinctive or complementary. For instance, cortical construction is heterogeneously coupled to haemodynamic practical connectivity alongside the sensory-association cortical hierarchy [36,37]. Details about interregional function similarity provides extra perception on how construction helps operate and has been proven to enhance the structure-function concordance [23,38,39]. The advance in neuroimaging strategies and knowledge sharing requirements has now made it potential to review a number of types of interregional relationships collectively, spanning a spread of spatial and temporal scales. The way forward for connectomics is due to this fact now not restricted to structural connectivity, however might be approached from a multimodal, multiscale angle.
Right here, we combine 7 layers of interregional relationships, together with gene expression, receptor density, mobile composition, metabolism, electrophysiology, and temporal fingerprints, to assemble a complete, multiscale wiring blueprint of the cerebral cortex. Though they’re all successfully networks reconstructed by correlating function similarity, hereafter, we consult with them as connectivity modes. First, we set up the frequent and distinctive manners during which connectivity modes mirror cortical construction and geometry. Subsequent, we determine cross-modal hubs in addition to circuits that constantly show massive interregional similarity throughout a number of connectivity modes. We then check how completely different connectivity modes seize patterns of irregular cortical thickness throughout 13 neurological, psychiatric, and neurodevelopmental issues. Furthermore, we present that connectivity modes exhibit numerous gradient and modular decompositions. Lastly, we iteratively fuse all 7 connectivity modes right into a single multimodal community. All 7 connectivity modes are publicly out there in 3 parcellation resolutions (https://github.com/netneurolab/hansen_many_networks), in hopes of facilitating integrative, multiscale evaluation of human cortical connectivity.
For every mind function, a similarity community might be represented as a area × area matrix. Rows and columns characterize cortical areas, and parts—the perimeters of the similarity community—characterize how equally 2 areas current the precise function. This similarity can be regarded as connectedness, such that 2 areas that share comparable options are thought of strongly linked. For simplicity, we due to this fact consult with correlation-based similarity as “connectivity” and the similarity networks as “connectivity modes.” To comprehensively benchmark cortical connectivity modes, we assemble and analyze 7 completely different connectivity matrices, spanning a number of spatial and temporal scales. These embody: (1) correlated gene expression, describing transcriptional similarity throughout >8,000 genes from the Allen Human Mind Atlas (AHBA) ; (2) receptor similarity, describing how correlated pairs of cortical areas are by way of protein density of 18 neurotransmitter receptors/transporters ; (3) laminar similarity, describing how correlated pairs of cortical areas are by way of cell-staining depth profiles from the BigBrain atlas [14,41]; (4) metabolic connectivity, measured because the correlation of dynamic FDG-PET (glucose uptake) time collection [29,42]; (5) haemodynamic resting-state connectivity, measured because the correlation of practical magnetic resonance imaging (fMRI) BOLD time collection from the Human Connectome Challenge (HCP) ; (6) electrophysiological connectivity, measured as the primary principal element of resting-state magnetoencephalography (MEG) connectivity throughout 6 canonical frequency bands from the HCP [43,44]; and (7) temporal profile similarity, a complete account for dynamic similarity (above and past a Pearson’s correlation between time collection, as is the case in haemodynaimc connectivity) which is measured because the correlation between time collection options of the fMRI BOLD sign [45–47]. To facilitate comparability between networks, and to mitigate variations between knowledge sorts and processing pipelines, every community was parcellated to 400 cortical areas and edge values had been normalized utilizing Fisher’s r-to-z remodel . Networks had been additionally parcellated to another practical and anatomical cortical atlas in a number of resolutions (100 and 68 cortical areas) for the sensitivity and replication analyses (see Sensitivity and replication evaluation).
Frequent organizational patterns of connectivity modes
In Fig 1A, we visualize every normalized connectivity matrix as a heatmap the place the colorbar limits are −3 and three normal deviations of the sting weight distribution (for edge weight distributions, see S1A Fig). Cortical areas are ordered by left then proper hemisphere. Inside every hemisphere, areas are additional stratified by their membership within the 7 canonical intrinsic practical networks (Schaefer-400 parcellation [48,49]). Homotopic connections stand out, indicating that homologous cortical areas in left and proper hemispheres are constantly comparable to one another regardless of the organic function (S1B Fig [50,51]). Earlier work has hypothesized that cortical dynamics in homotopic areas are synchronized because of frequent brainstem enter [52,53]; our work opens an extra speculation that similarities in dynamics can also be associated to comparable molecular composition.
Fig 1. Frequent organizational patterns of connectivity modes.
Every connectivity mode is represented as a normalized similarity matrix, the place parts of the matrix index how equally two cortical areas current a selected function. (a) Connectivity modes are proven as heatmaps, ordered in response to the 400-region Schaefer parcellation . The colorbar limits are −3 to three normal deviations of the sting weight distribution. (b) Edge weights between each pair of cortical areas (i.e., higher triangular parts) lower with Euclidean distance throughout all 7 connectivity modes. Darker coloration represents larger density of factors. Exponential equations or Spearman correlation coefficients are proven relying on whether or not the connection is healthier match by an exponential or linear operate. Comparable relationships with geodesic distance are proven in S2 Fig. (c) Edge weight distributions are visualized individually for edges that additionally exist within the structural connectome (blue) and people that don’t (grey), in response to a group-consensus structural connectome from the HCP . Structurally linked cortical areas present larger similarity than areas that aren’t structurally linked. Boxplots characterize the primary, second (median), and third quartiles, whiskers characterize the non-outlier end-points of the distribution, and diamonds characterize outliers (>1.5 inter-quartile vary). (d) For edges that additionally exist within the structural connectome, connectivity mode edge weight will increase with the power of the structural connection. The information underlying this determine might be discovered at https://github.com/netneurolab/hansen_many_networks. HCP, Human Connectome Challenge.
Visually, every connectivity mode demonstrates nonrandom community group, which we discover in subsequent sections. Moreover, similarity between cortical areas decreases as each Euclidean and geodesic distance between cortical areas will increase (Figs 1B and S2), in step with the notion that proximal neural parts are extra just like each other [18,23,31,45,54,55]. Nonetheless, there’s variability in how function similarity decreases with distance. For instance, dynamic modes exhibit stronger exponential relationships, whereas molecular modes exhibit both weak exponential or linear (within the case of laminar similarity) suits.
We subsequent sought to narrate every connectivity mode to the mind’s underlying structural structure. We constructed a weighted structural connectome utilizing diffusion-weighted MRI knowledge from the HCP; this community represents whether or not, and the way a lot, 2 cortical areas are linked by white matter streamlines. We discover that, throughout all 7 connectivity modes, cortical areas which might be bodily linked by white matter present larger function similarity than these that aren’t linked, suggesting that biologically comparable neuronal populations are in direct communication (Fig 1C). These variations are larger than in a inhabitants of degree- and edge length-preserving surrogate structural connectomes, indicating that the impact is particularly because of wiring reasonably than spatial proximity . Notably, neuroanatomical research in mannequin organisms have discovered that cytoarchitectonic similarity predicts neuronal projections higher than distance [57–59]; we broaden on this by displaying that each one connectivity modes exhibit larger similarity for structurally linked cortical areas within the human. Lastly, for the subset of edges with a structural connection, we discover a correlation between the power of the structural connection and every connectivity mode’s edge weight (Fig 1D) [59,60]. Altogether, we discover that connectivity modes exhibit commonalities that respect distance, neuroanatomy, and anatomical connectivity, no matter imaging modality or organic mechanism.
Structural and geometric options of connectivity modes
Though connectivity modes share organizational properties, the median correlation between them is r = 0.25 (vary: r = 0.10–0.53; S3 Fig). In different phrases, connectivity modes should not redundant. To immediately evaluate edge weights throughout connectivity modes, we transformed edge weights to ranks, such that the smallest (i.e., most detrimental) edge is ranked 1 and the strongest (i.e., most optimistic) edge is ranked 79,800 (equal to the variety of edges in every community, below the 400-region Schaefer parcellation). We deal with 2 metrics to categorise edges between cortical areas: distance (mind geometry) and structural connectivity (mind construction).
Spatial proximity influences interregional similarity, such that proximal areas are inclined to share comparable organic and physiological options (Fig 1B) [56,59,61]. We due to this fact sought to analyze how distance shapes interregional function similarity in larger element and in a comparative method. We first bin all 79,800 edges into 50 equally sized bins (1,596 edges per bin). For every connectivity mode individually, we calculate the median edge rank inside every bin (Fig 2A). Median edge rank decreases as the space between cortical areas in every bin will increase, in step with our discovering in Fig 1B. We discover 2 broad patterns: receptor similarity, temporal similarity, haemodynamic connectivity, and metabolic connectivity present average lower of edge power with distance, whereas correlated gene expression, laminar similarity, and particularly electrophysiological connectivity exhibit a sharper lower of edge power with distance. In different phrases, distance performs a singular function in shaping every particular person connectivity mode, with electrophysiological connectivity, laminar similarity, and correlated gene expression being most affected by distance. That receptor similarity is grouped with predominantly dynamic modes (haemodynamic, metabolic, and temporal similarity) could mirror the affect that receptor density has on cortical dynamics.
Fig 2. Structural and geometric options of connectivity modes.
To check edge weights throughout networks, edges are rank-transformed. (a) Edges are binned into 50 equally sized bins of accelerating Euclidean distance (79,800 edges whole below the 400-region Schaefer parcellation, 1,596 edges per bin). For every connectivity mode, the median edge rank is plotted inside every bin. (b) A kernel density estimation is utilized on the rank-transformed function similarity (edge rank) distribution of edges that even have a structural connection, for every connectivity mode. (c) For a structural diploma threshold okay ∈ [5, 50], we calculate the wealthy membership coefficient ratio and present a attribute enhance in wealthy membership coefficient ratio when 30 ≤ okay ≤ 43. Circles point out structural diploma thresholds the place the wealthy membership coefficient ratio is considerably larger than a null distribution of ratios calculated utilizing a degree-preserving rewired community (1,000 repetitions). On the appropriate, we present the set of structural edges connecting areas with structural diploma ≥37. Edge shade and thickness are proportional to edge weight, and level dimension is proportional to structural diploma. The binary structural connectome is proven within the inset. (d) For every okay ∈ [5, 50] and for every connectivity mode, we calculate the median edge rank of structurally-supported edges that linked areas with structural diploma ≥ okay. Circles point out structural diploma thresholds the place the median rich-link edge rank of a connectivity mode is considerably larger than the sting rank of all different structurally supported edges (Welch’s t check, one-sided). The information underlying this determine might be discovered at https://github.com/netneurolab/hansen_many_networks.
We subsequent shift our focus to the subset of edges with an anatomical connection, in response to the structural connectome (N = 4,954 out of 79,800 edges). For every connectivity mode, we plot the distribution of rank-transformed function similarity (edge rank) for these edges that additionally exist within the structural connectome (Fig 2B). This lets us decide which connectivity modes exhibit the best coupling between excessive interregional function similarity and structural connectivity: particularly, receptor similarity and correlated gene expression. Earlier work has discovered a detailed correspondence between cytoarchitecture and neuronal projections in macaque brains [18,62]; our findings counsel a potential genetic and neuroreceptor mechanism underlying this relationship. The primacy of molecular connectivity modes is a discovering that returns within the subsequent evaluation and after we evaluate connectivity modes to illness pathology (Connectivity modes and disease-specific irregular cortical thickness).
We subsequent observe how edge power adjustments relying on the structural embedding of every cortical area. We deal with the cortex’s wealthy membership: a set of disproportionately interconnected high-degree areas that’s thought to mediate long-range data propagation and integration [7,63]. Is that this wealthy membership structure supported by particular organic and physiological options? To handle this query, for every structural diploma threshold okay∈[5,50] (the place structural diploma is outlined because the variety of structural connections made by a cortical area), we calculate the wealthy membership coefficient ratio on the binary structural connectome: the tendency for areas of diploma ≥okay to be preferentially linked to 1 one other, with respect to a inhabitants of degree-preserving surrogate networks. We discover that the wealthy membership coefficient ratio is inflated at roughly 30≤okay≤43, confirming the existence of wealthy membership group (Fig 2C). This topological wealthy membership regime denotes a level vary the place cortical areas are unexpectedly densely interconnected . Subsequent, for every connectivity mode at every okay, we calculate the median edge rank of all structurally supported edges that hyperlink 2 cortical areas with diploma ≥okay (Fig 2D). Furthermore, we ask whether or not within-set edge ranks (i.e., edges connecting areas with diploma ≥okay) are statistically larger than all different edges (Welch’s one-sampled t check).
We discover that edges within the cortex’s topological wealthy membership regime are notably dominated by molecular options (e.g., laminar similarity, correlated gene expression, and receptor similarity) . Haemodynamic and electrophysiological connectivity are particularly weak for hyperlinks between high-degree areas, and temporal similarity is unstable. Metabolic connectivity is an extra connectivity mode that demonstrates considerably elevated edge power for hyperlinks between high-degree areas, suggesting that power consumption is synchronized between structural hubs [63,65–67]. Collectively, these findings point out that the wealthy membership could mirror coordinated patterns of interregional microscale similarity throughout a number of molecular options. Alternatively, the wealthy membership will not be characterised by comparable neural dynamics, presumably associated to the practical flexibility of those areas .
Mapping hubs within the human mind has been a subject of nice curiosity within the final 15 years, however the majority of our data comes from anatomical and haemodynamic connectivity [69,70]. For a extra complete understanding of mind areas that make many robust connections, it might be vital to map their connectivity profiles at completely different ranges of group. We due to this fact ask whether or not there exist edges which might be constantly high-strength, and in that case, which cortical areas, which we name cross-modal hubs, make these connections. For each connectivity mode, we present an axial view of the 0.5% strongest edges (Fig 3A; see S4 Fig for coronal and sagittal views). Apparently, high-strength edges fluctuate throughout connectivity modes: some networks kind densely interconnected cores (i.e., electrophysiological connectivity and temporal similarity), some emphasize long-range (i.e., haemodynamic connectivity) or short-range (i.e., metabolic connectivity) connections, and others seem extra nonspecific (i.e., correlated gene expression, receptor similarity, and laminar similarity; Fig 3A). This variability can also be mirrored within the hubness profiles of every connectivity modality, the place a area’s hubness is outlined because the sum of the rank-transformed edge weights between it and all different areas (Fig 3B). The variability of hubness factors to the significance of characterizing community structure from a number of complementary views.
Fig 3. Cross-modal hubs.
(a) For every connectivity mode, we plot the 0.5% strongest edges. Darker and thicker edges point out stronger edges. Factors characterize cortical areas and are sized in response to the sum of edge weights (weighted diploma). Cortical views are axial, with anterior areas on the high of the web page (for coronal and sagittal views, see S4 Fig). (b) For every connectivity mode, regional hubness is outlined because the sum of rank reworked edge weights throughout areas. (c) For a various threshold of strongest edges (0.5%–5% in 0.5% intervals), we calculate the proportion of edges that join 2 areas inside the identical intrinsic community  (left) and cytoarchitectonic class  (proper). (d) Throughout all 7 connectivity modes, we calculate the median edge rank of every edge and plot the 0.5% strongest edges (left). Likewise, we calculate the median hubness (proven in panel b), which we discover is considerably correlated with evolutionary cortical growth (r = 0.42, pspin = 0.0001) . The information underlying this determine might be discovered at https://github.com/netneurolab/hansen_many_networks.
Are there consistencies in high-strength edges and areas? Earlier work has proven that the cortex might be organized into modules of areas which might be both functionally comparable (“intrinsic networks” ) or cellularly comparable (“cytoarchitectonic lessons” [71,72]). We wished to know whether or not connectivity modes throughout a number of scales emphasize edges that hyperlink cortical areas inside these practical and cytoarchitectonic networks, no matter whether or not the connectivity mode represents cortical operate or mobile composition. For a given community classification (e.g., intrinsic networks), we name edges that be a part of 2 cortical areas in the identical community (e.g., the visible community) intra-class edges . We then calculate how lots of the x strongest edges in a given connectivity mode overlap with intra-class edges. We let x fluctuate in increments of 0.5% from 0.5% to five% of the strongest edges in a connectivity mode.
For intrinsic networks (Fig 3C, left), the strongest edges within the haemodynamic community are virtually solely intra-class edges (90.2% for the highest 0.5% strongest edges, and 72.2% for the highest 5% edges). The strongest edges in correlated gene expression are additionally primarily intra-class edges (88.7% for the highest 0.5% strongest edges) however this ratio decreases to 52.8% at 5% of the strongest edges. In the meantime, for cytoarchitectonic lessons (Fig 3C, proper), receptor similarity, correlated gene expression, and metabolic connectivity most maximize intra-class edges. Throughout each intrinsic and cytoarchitectonic networks, temporal similarity retains the fewest intra-class edges. Nonetheless, the detrimental slopes in Fig 3C signifies that, for each connectivity mode, strongest edges are preferentially edges that join cortical areas inside the identical practical and cytoarchitectonic community (Fig 3C). Extra typically, after we contemplate the median edge rank throughout all connectivity modes, we discover that constantly high-strength edges primarily join visible, posterior parietal, and anterior temporal areas (Fig 3D, left).
Lastly, we deal with the cortical areas: given the spatial range of hub profiles (Fig 3B), are there areas that constantly present comparatively excessive weighted diploma—that’s, are constantly just like different mind areas—throughout a number of connectivity modes? We quantify cross-modal hubness because the median hubness throughout connectivity modes (i.e., the median throughout mind plots proven in Fig 3B). We discover that transmodal eulaminate areas such because the supramarginal gyrus, superior parietal cortex, precuneus, and dorsolateral prefrontal cortex are most constantly just like different cortical areas throughout 7 organic phenotypes, from molecular composition to neural dynamics (Fig 3D, proper). Apparently, the areas recognized as cross-modal hubs are generally regarded as hubs within the structural connectome; we discover that in addition they exhibit massive function similarity throughout a number of ranges of group.
Why are some cortical areas extremely just like many different areas throughout a number of spatial scales and organic mechanisms? We hypothesized that cross-modal hubs are extra cognitively versatile and in a position to assist higher-order, evolutionarily superior cognitive processes. We due to this fact correlated cross-modal hubness with a map of evolutionary cortical growth . Certainly, the recognized cross-modal core coincides with cortical areas which might be extra expanded throughout phylogeny (r = 0.43, pspin = 0.0001). In different phrases, cortical areas which might be expanded in people and due to this fact seemingly concerned in higher-order cognition share many options throughout a number of scales, suggesting they will combine indicators from a extra numerous set of neural circuits. In the end, hubs which might be outlined utilizing connectivity modes aside from the classical structural connectome present novel views on how areas take part in neural circuits.
Connectivity modes and disease-specific irregular cortical thickness
Pioneering research in postmortem tissue gave rise to the speculation that the physicochemical composition of neurons at native mind areas leads to a selective vulnerability to mind illness . Different classical research have proven that illness propagation within the cerebral cortex is said to microscale options akin to myelination . These propagation patterns have been efficiently modeled on the stage of the whole-cortex utilizing the structural connectome, and sometimes carry out higher when knowledgeable by native organic options such because the expression of a selected gene [76,77]. Current findings construct on this notion and posit that the course and expression of a number of mind ailments is mediated by shared molecular vulnerability reasonably than a single molecular perturbation [33,78]. We due to this fact examined whether or not illness propagation patterns derived from the connectivity modes might predict irregular cortical thickness patterns for 13 completely different neurological, psychiatric, and neurodevelopmental ailments and issues from the Enhancing Neuroimaging Genetics by way of Meta-Evaluation (ENIGMA) consortium (N = 21,000 sufferers, N = 26,000 controls) [33,79,80]. The disease-specific irregular cortical thickness patterns are regional z-scored case-versus-control impact sizes, representing deviation from normative cortical thickness. We refer to those regional values as “irregular cortical thickness” or just “abnormality.”
We outline the “publicity” that area i has to area j’s pathology because the product between the (i,j)-edge power (cij if cij>0) and area j’s irregular cortical thickness (dj) (Fig 4A) [33,81–83]. Then, the worldwide illness publicity to area i is the imply publicity between area i and all different areas within the community with optimistic edge power (word that we discover constant outcomes after we use all edges of the community (S5a Fig)). Lastly, we correlate irregular cortical thickness with illness publicity to find out whether or not the illness demonstrates a cortical illness profile that displays the underlying connectivity mode (Fig 4A, proper). Given a illness the place larger illness publicity leads to larger irregular cortical thickness, we’d look forward to finding a big optimistic correlation. This evaluation is repeated for every connectivity mode and every dysfunction, and correlation coefficients are visualized in Fig 4B (see S6 Fig for outcomes together with the fused community (Fusing connectivity modes)).
Fig 4. Contributions of connectivity modes to illness vulnerability.
Irregular cortical thickness profiles for 13 neurological, psychiatric, and neurodevelopmental issues had been collected from the ENIGMA consortium (cortex plots proven in panel b; N = 21,000 sufferers, N = 26,000 controls [79,80]). (a) Given a selected dysfunction and connectivity mode, dj represents the irregular cortical thickness of area j, and cij represents the sting weight (similarity) between areas i and j. For each area i, we calculate the common irregular cortical thickness of all different areas j≠i within the community, weighted by the sting power (“illness publicity”; word that we omit detrimental connections, such that Ni represents the variety of optimistic connections made by area i). Subsequent, we correlate illness publicity and regional irregular cortical thickness throughout cortical areas (scatter plot; factors characterize cortical areas). We present the connectivity profiles of two instance areas (highlighted in purple within the left mind community and orange in the appropriate mind community). (b) The analytic workflow introduced in panel (a) is repeated for every dysfunction and connectivity mode, and we visualize Spearman correlations in a heatmap. (c) This evaluation is repeated for weighted structural connectivity (the place we solely contemplate structurally linked areas), and Euclidean distance (the place we at all times contemplate all areas within the community). We additionally repeat this evaluation for the fused community (see Fusing connectivity modes), and outcomes are proven in S6 Fig. The information underlying this determine might be discovered at https://github.com/netneurolab/hansen_many_networks. ADHD, attention-deficit/hyperactivity dysfunction; ASD, autism spectrum dysfunction; ENIGMA, Enhancing Neuroimaging Genetics by way of Meta-Evaluation; OCD, obsessive-compulsive dysfunction.
We discover that correlated gene expression and receptor similarity most constantly amplify the publicity of pathology in a fashion that intently resembles the structural cortical profile of the illness. Apparently, after we repeat the evaluation utilizing solely detrimental edges (cij if cij<0), we discover reverse outcomes: Cortical areas with excessive abnormality are negatively linked (that’s, are dissimilar to) areas with low abnormality (S5B Fig). This means that dissimilarity could attenuate illness unfold. By repeating the evaluation utilizing weighted structural connectivity (during which case, we solely contemplate structurally linked areas) and Euclidean distance between cortical areas (during which case, we at all times contemplate the total community), we’re in a position to uncover circumstances the place function similarity amplifies illness publicity greater than construction or distance alone (Fig 4C). Irregular cortical thickness patterns of psychiatric issues specifically (e.g., MDD, schizophrenia, bipolar dysfunction, OCD) are higher defined by correlated gene expression and receptor similarity than construction or distance. This integrative evaluation makes it potential to hone in on the imaging modalities and organic mechanisms which may most mirror cortical pathology in a disease-general method. Moreover, it demonstrates the worth in using function similarity as a community reasonably than limiting community fashions to the structural connectome.
Gradients and modules of connectivity modes
We subsequent contemplate how every connectivity mode is intrinsically organized, each by way of axes of variation (i.e., spatial gradients) and community modules [14,22,84–86]. The principal gradient, quantified as the primary principal element of a connectivity mode, is a regional quantification of how function similarity varies throughout the cerebral cortex. They are often interpreted as a single-dimensional illustration of the connectivity mode and can spotlight the areas which might be particularly just like or dissimilar from each other. We begin by finding out an underappreciated factor of the principal element: How a lot variance is defined by (i.e., how consultant is) every principal gradient? We discover that the prominence of the primary gradient can fluctuate considerably throughout connectivity modes (Fig 5A). For instance, the temporal similarity gradient is particularly dominant (accounting for 73.8% of variance), whereas the metabolic connectivity gradient is particularly nondominant (accounting for 12.7% of variance; Fig 5B). Moreover, we discover that cortical gradients don’t all observe a uniform sensory-association axis [87–89], reasonably, the primary principal element of every connectivity mode varies significantly (median absolute correlation between gradients r = 0.36; Fig 5C).
Fig 5. Gradients and modules of connectivity modes.
(a) The primary principal element (“gradient”) of every connectivity mode is proven on the cortex. (b) The p.c variance defined for the primary 5 principal elements of every connectivity mode. (c) The Pearson’s correlation between each pair of community gradients, visualized as a heatmap. CGE, correlated gene expression; RS, receptor similarity; LS, laminar similarity; MC, metabolic connectivity; HC, haemodynamic connectivity; EC, electrophysiological connectivity; TS, temporal similarity. (d) The Louvain neighborhood detection algorithm is utilized to every connectivity mode throughout completely different decision parameters (0.1 ≤ γ ≤ 6.0, in intervals of 0.1) and the variety of ensuing communities is plotted as a operate of γ. (e) For every connectivity mode, we present a single neighborhood detection resolution for a specified γ, and we point out the variety of communities (n). The information underlying this determine might be discovered at https://github.com/netneurolab/hansen_many_networks.
An alternate perspective of intrinsic community group comes from contemplating whether or not and the way the community clusters into segregated modules . In different phrases, which subsets of cortical areas are just like each other (in response to a selected connectivity mode) and are these modules constant throughout connectivity modes? We apply the Louvain neighborhood detection algorithm to every connectivity mode to seek for teams of areas that exhibit excessive within-module similarity and excessive between-module dissimilarity [90,91]. The Louvain algorithm is unsupervised and doesn’t require a predefined variety of clusters as enter; as a substitute, the decision parameter (γ) tunes the benefit with which extra communities are detected (bigger γ leads to extra communities being recognized). To get a way of the decision of every community (i.e., the variety of communities the community would possibly naturally exhibit, if in any respect), we observe the variety of communities recognized by the Louvain neighborhood detection algorithm throughout completely different values of γ (Fig 5D). We discover that the neighborhood detection resolution for electrophysiology is very unstable, with the variety of recognized communities altering quickly with small adjustments in γ. Essentially the most secure resolution at γ = 1 merely delineates the principle cortical lobes that means that electrophysiological connectivity group is healthier described as a gradient however not as distinct modules of mind areas. Haemodynamic connectivity and temporal similarity present an analogous development, the place partitions of larger than roughly 5 networks turn out to be more and more unstable. In the meantime, correlated gene expression, laminar similarity, and receptor similarity present extra secure neighborhood options, the place bigger adjustments in γ are required for the community to separate itself into extra communities. This means that molecular connectivity modes might be described from the attitude of a small quantity (<10) of modules. We present 1 potential consensus neighborhood detection resolution for every community in Fig 5E, which demonstrates that the modular group and gradient decomposition of networks are typically intently aligned. Collectively, this reveals that every connectivity mode has a singular gradient decomposition and neighborhood construction.
Fusing connectivity modes
Every connectivity mode that now we have studied to this point represents a single scale of group describing distinct however associated interregional relationships. Provided that the mind is built-in, how do these connectivity modes layer onto each other to assist mind construction and performance? To handle this questions, we apply an unsupervised studying method, similarity community fusion (SNF), to merge all 7 connectivity modes right into a single multimodal community (Fig 6A) . SNF iteratively fuses every connectivity mode in a fashion that strengthens edges which might be constantly robust and weakens inconsistent (or constantly weak) edges, whereas giving every connectivity modality equal affect on the fusion processes. Altogether, the fused community represents a data-driven integration of every stage of cortical connectivity.
Fig 6. Community fusion.
SNF was utilized to all 7 connectivity modes to assemble a single built-in community [92,169]. (a) Toy instance of SNF. SNF iteratively combines the 7 connectivity modes in a fashion that provides extra weight to edges between observations which might be constantly high-strength throughout knowledge sorts (black edges). (b) The fused community. We present the matrix-representation of the community (left), the highest 0.5% strongest edges of the community (backside), and the weighted diploma of every mind area (proper). Observe that the sting weights within the fused community is a byproduct of the iterative multiplication and normalization steps (see Strategies for particulars) and due to this fact can turn out to be very small. Better edge magnitude represents larger similarity (no detrimental edges exist, by design). (c) Edge weight decreases exponentially with Euclidean distance. (d) Structurally linked edges have larger edge weight than edges with out an underlying structural connection, towards a level and edge-length preserving null mannequin  (left), and is correlated with structural connectivity (proper). (e) For a various threshold of strongest edges (0.5%–5% in 0.5% intervals), we calculate the proportion of edges that join 2 areas inside the identical intrinsic community (left), cytoarchitectonic class (center), and the union of intrinsic networks and cytoarchitectonic lessons (proper). The information underlying this determine might be discovered at https://github.com/netneurolab/hansen_many_networks. SNF, similarity community fusion.
The fused community’s strongest edges exist between areas inside somatomotor and visible cortex (Fig 6B, backside), seemingly reflecting the conserved molecular and dynamic composition of those phylogenetically older cortical areas. In the meantime, cortical areas with the best weighted diploma exist in anterior temporal and superior frontal cortex (Fig 6B, proper). The fused community reveals nonrandom community group together with robust homotopic connectivity and a detrimental exponential relationships with distance (Fig 6B left, c). As well as, structurally linked edges have considerably stronger edge weight than non-connected edges, towards a degree- and edge-length preserving structural null (Fig 6D). Lastly, the fused community demonstrates a larger correlation between edge weight and weighted structural connectivity than any of the person connectivity modes (r = 0.53). This reveals how combining interregional similarity throughout a number of scales higher displays anatomical connectivity than any single perspective of interregional similarity . This can be as a result of areas which might be comparable throughout a number of scales usually tend to be linked or as a result of mind connectivity offers rise to shared organic options.
We subsequent requested whether or not the strongest edges within the fused community exist between functionally and cytoarchitectonically comparable cortical areas (Fig 6E). We discover that almost all (97.7%) of the highest 0.5% strongest edges within the fused community are between areas inside the identical practical community. Actually, the fused community outperforms haemodynamic connectivity—the connectivity mode for which these intrinsic practical networks are designed and optimized. Likewise, for cytoarchitectonic lessons, we discover that the fused community retains extra intra-class edges than every other community when the variety of strongest edges thought of is ≥2.5%. For the reason that fused community represents an built-in connectivity mode, we requested whether or not the strongest edges of the fused community would possibly concurrently maximize intrinsic and cytoarchitectonic intra-class edges. Certainly, when contemplating the highest 0.5% to five.0% strongest edges, the variety of edges that exist between areas in the identical intrinsic and cytoarchitectonic lessons is constantly best for the fused community. Altogether, the fused community maps onto intrinsic networks and cytoarchitectonic lessons higher than any particular person community. This demonstrates how large-scale phenomena emerge from a confluence of a number of microscopic determinants.
Sensitivity and replication evaluation
Lastly, to make sure outcomes should not depending on the parcellation, we repeated all analyses (besides Fig 4 which depends upon the 68-region Desikan–Killiany parcellation) utilizing the 100-region Schaefer parcellation and the 68-region Desikan–Killiany parcellation [48,93,94]. We discover comparable outcomes below these various parcellations (S7 Fig). These coarser resolutions reveal dense frontal inter-connectivity within the metabolic community, which was not seen on the 400-node parcellation seemingly because of smoothing results in dynamic PET knowledge. Moreover, we share all 7 connectivity modes at these 3 parcellations (Schaefer-400, Schaefer-100, Desikan–Killany-68) in hopes of facilitating integrative connectome analyses sooner or later (https://github.com/netneurolab/hansen_many_networks).
This work integrates a number of representations of cortical connectivity to determine how numerous connectivity modes contribute to cortical construction and performance. We systematically doc the frequent organizational patterns of connectivity modes, in addition to their distinctive contributions to construction and geometry. We discover that molecular connectivity modes amplify illness publicity leading to spatial patterns of irregular cortical thickness. We present that connectivity modes exhibit numerous dominant gradients and modular construction. Lastly, we derive a multimodal, multiscale community by parsimoniously integrating a number of connectivity modes.
Connectomics—the research of relationships between neural parts throughout a number of scales—is a vital and well-liked paradigm in neuroscience [3,95,96]. Quite a few technological and analytic strategies have been developed to reconstruct interregional relationships, some targeted on bodily wiring, others on molecular similarity, and others nonetheless on coherence between regional neural exercise. Regardless of being rooted in frequent questions, these connectivity modes are sometimes studied in separate literatures. What community options are distinctive or frequent to every connectivity mode stays unknown and the follow of finding out connectivity modes individually has precluded a really built-in understanding of interregional relationships.
Detailed complete datasets alongside higher knowledge sharing practices have made multimodal, integrative approaches to finding out human mind connectivity extra possible [89,97–99]. Examples embody comparisons of dynamic FDG-PET and BOLD connectivity [15,29], BOLD connectivity and electrophysiology [28,44,100,101], structural and BOLD connectivity [60,102], and correlated gene expression and structural connectivity . Combining connectivity modes has additionally been used to higher resolve clusters of practical activation in BOLD knowledge  and inform the appliance of deep mind stimulation to psychiatric and neurological ailments [105,106]. Encouragingly, earlier work has discovered that incorporating a number of views of mind connectivity can lead to novel discoveries, together with improved generative fashions of mind connectivity , structure-function coupling [23,38], epicenters of transdiagnostic alterations [33,34], and the characterization of homophilic wiring rules .
Though these integrative approaches open thrilling new questions on mind group, an vital problem stays: How can we be sure that conclusions are rooted within the underlying biology reasonably than assumptions and idiosyncrasies of particular person knowledge modalities? We tried to mitigate this problem by repeating analyses utilizing different analytic selections, making use of conservative null fashions, normalizing every connectivity mode previous to evaluation (Fisher’s r-to-z transformation), and rank-transforming edges to facilitate comparability of edge strengths throughout knowledge sorts. This offers a stage of confidence however is under no circumstances an exhaustive verification that knowledge sorts don’t affect outcomes. Certainly, every dataset is accompanied by its personal set of limitations together with situations of false positives and negatives in diffusion tractography [109–111], nonspecific binding for some PET tracers , and heterogeneous patterns of sign to noise ratios throughout all imaging sorts. As open datasets are created and shared, it would turn out to be extra possible to find out how outcomes are influenced by processing selections and imaging modalities.
The research of connectomics has been dominated by a deal with structural and haemodynamic connectivity. This has resulted within the assumption that, on the stage of the whole-cortex, homologous , spatially proximal , and structurally linked  mind areas are typically extra comparable. By systematically integrating 7 multiscale views of cortical connectivity, we will conclude in a extra systematic and complete method that these properties are certainly elementary to cortical group however that there’s appreciable variation throughout connectivity modes. For instance, the detrimental exponential relationship with distance is nearly linear for molecular connectivity modes, particularly after we contemplate geodesic as a substitute of Euclidean distance (S2 Fig). A second assumption is that numerous mind options ought to all observe the functionally outlined unimodal-transmodal hierarchical gradient and might be organized by way of intrinsic resting-state networks [49,86,114–116]. Nonetheless, we discover that microscale connectivity modes (e.g., correlated gene expression, receptor similarity) are nicely delineated by a partition primarily based on cytoarchitectonic lessons, whereas dynamic connectivity modes (e.g., haemodynamic connectivity, electrophysiological connectivity) match higher into intrinsic practical techniques. Certainly, connectivity modes are poorly correlated with each other, suggesting that every connectivity mode offers a basically completely different however vital view of how cortical areas take part in neural circuits at completely different spatial and temporal scales .
In an effort to grasp which cortical areas are constantly central throughout many ranges of description, we determine a set of cross-modal hubs. Mind hubs are conventionally outlined as areas with a comparatively massive variety of structural connections, however this definition ignores the multiscale character of mind networks. Certainly, we discover that hub profiles should not redundant throughout organic mechanisms. As an alternative, we determine a subset of cortical areas which might be uniquely central throughout a number of ranges of description. These cross-modal hubs exist within the precuneus, supramarginal gyrus, and dorsolateral prefrontal cortex: affiliation areas that broaden in floor space and become extra differentiated eulaminate (6-layer) cortex throughout evolution [18,73,118]. This means that phylogenetic structural modifications—together with elevated mobile complexity and density —could assist integration of data throughout a number of organic scales, leading to higher-level cognition together with language, planning, and sophisticated government capabilities. Apparently, these areas are distinct from the anatomically central (e.g., limbic) areas that had been beforehand hypothesized to be integrative general-domain hubs, primarily based on a number of measures of centrality calculated on the structural connectome . Future work ought to examine extra deeply how structural centrality is aligned with organic function similarity. Altogether, cross-modal hubs open a brand new perspective on hub operate: As an alternative of being rooted solely in excessive structural connectivity, hubs might be categorized in response to their participation in numerous organic techniques .
Integrative connectomics opens the potential of benchmarking and evaluating organic mechanisms to 1 one other. For instance, we constantly determine a dichotomy between molecular (e.g., correlated gene expression, receptor similarity, laminar similarity) and dynamic (e.g., haemodynamic and electrophysiological connectivity) modes. First, molecular function similarity is considerably elevated for hyperlinks between areas of the mind’s wealthy membership: high-degree areas that present dense inter-connectivity which is believed to enhance international communication effectivity and integration . A transcriptional signature of wealthy membership connectivity was beforehand proven to be pushed by genes concerned in metabolism, supporting the speculation that the mind’s wealthy membership is energetically costly [63,121,122]. Apparently, we discover that metabolic connectivity is elevated in wealthy hyperlinks, suggesting that the wealthy membership can also be synchronized in its power consumption [66,67].
Second, molecular function similarity—notably correlated gene expression and receptor similarity—finest explains the spatial patterning of a number of cortical illness abnormalities. Current work has explored the concept a number of pathologies unfold trans-synaptically, together with misfolded proteins, aberrant neurodevelopmental indicators, and excitotoxic electrical discharge, leading to patterns of pathology that mirror the underlying structural structure of the mind [11,81]. Right here, we contemplate the chance that shared vulnerability to illness arises not simply from structural connectivity but in addition from multiscale organic attributes . We use adjustments in cortical thickness because the marker of potential pathology and discover that when illness publicity is knowledgeable by transcriptional and receptor similarity, we will reproduce the cortical profile of a number of ailments (r>0.5 for many). We additionally discover proof that molecular dissimilarity could function a mitigating issue of pathological unfold (S5B Fig), though extra work is important to find out the hyperlink between regional dissimilarity and pathology. The constant primacy of molecular connectivity modes demonstrates that mapping cortical connectivity from the attitude of underlying microscale options—gene transcription, receptor density, mobile composition—is simply as, if no more, informative than oft-studied dynamical modes akin to haemodynamic connectivity. This evaluation might be prolonged in future work by finding out the connectivity modes of affected person populations and can hopefully inspire future causal work linking molecular mechanisms to the spreading of pathological markers within the mind.
Lastly, we look at the gradient and modular group of connectivity modes. Low-dimensional topographical representations of cortical options, whether or not spatially steady (gradients) or discrete (modules), current perception on how completely different ranges of cortical group are aligned with each other. For instance, graded adjustments within the proportion of neural projections originating from higher versus decrease laminar layers has been associated to gradients of pyramidal neuron soma dimension, variety of synaptic boutons, variety of vesicles, quantity of neurotransmitter launch, and firing charge, offering a complete clarification for the laminar origin of cortico-cortical connections . Likewise , confirmed that autoradiography-derived gradients of receptor density mirror excitatory/inhibitory and ionotropic/metabotropic ratios and observe a sensory-association practical hierarchy. Right here, we view native molecular and dynamic options from the attitude of a community to review the group of cortical “connectivity,” reasonably than axes of variation of the underlying knowledge [22,23,124]. This strategy lets us apply not solely gradient decomposition but in addition neighborhood detection—hardly ever utilized in mind imaging outdoors of structural and haemodynamic connectivity—to the networks. We discover that connectivity modes have distinctive gradient and modular decomposition which suggests it isn’t enough to imagine a single spatial group for the cortex. Apparently, earlier work has discovered that fMRI-derived practical communities can themselves be numerous: they fluctuate over time, throughout duties, and all through hormonal cycles [125–127]. These non permanent adjustments in community group could mirror the various modular group of underlying molecular mechanisms. The organic origins of the variety in spatial gradients can be an vital route for future analysis [88,128].
All through this report, now we have illustrated how cortical connectivity might be prolonged from neural wiring to many complementary views of interregional relationships. We deal with densely sampled knowledge throughout the whole-cortex to make normal claims about patterns of cortical group. A lot of the knowledge employed are derived from in vivo neuroimaging in comparatively massive samples of wholesome grownup people. Nonetheless, these questions on multiscale cortical group can—and have, for many years—be requested with extra anatomical specificity utilizing strategies akin to cell staining and tract-tracing in small samples of ex vivo brains, typically from mannequin organisms [17,18,21,129]. Such research have demonstrated that neurons make projection patterns which might be tightly linked to the mobile structure of the cortex, together with the laminar differentiation of the supply and goal of a neural projection  (e.g., the Structural Mannequin, reviewed in ). Intertwined with laminar differentiation and tract-tracing projection patterns can also be the phylogenetic age of the cortical area , markers of plasticity and stability , and sure additionally receptor structure [130,131]. Moreover, the sector of developmental biology presents elementary organizational rules for the way the mind develops its spatial group and topology . Massive-scale neuroimaging connectomic research complement organic and neuroanatomical research by extending predictions to the dimensions of the entire human cortex and throughout many extra mind phenotypes. For instance, we affirm that laminar similarity is said to connectivity and the mind’s wealthy membership , however lengthen this to gene expression, receptor structure, and metabolism. The synergy between neuroanatomical and imaging fields is important to totally seize interregional relationships throughout a number of layers of description.
The current work needs to be thought of alongside some methodological issues. First, the outcomes are solely consultant of the 7 included connectivity modes; future work ought to replicate the findings in comparable connectivity modes derived from exterior datasets, in addition to lengthen this work into extra types of connectivity. One thrilling avenue can be to annotate structural connectomes with measures of myelin or axon caliber derived from quantitative MRI akin to magnetization switch (MT), T1 leisure charge (R1), or axon diameter [133–135]. Second, every connectivity matrix relies on the standard of the imaging modality, and every imaging technique operates at a singular spatial and temporal decision. Outcomes could due to this fact be influenced by variations in how the information are acquired. Along with this, the group-consensus structural community that was used all through the analyses (specifically Figs 1, 2, 4 and 6) was reconstructed from diffusion spectrum imaging and tractography, which is susceptible to false-positives and false-negatives [109,111]. We tried to mitigate this by operating in depth sensitivity analyses. Third, in an effort to make correlated gene expression similar to the opposite modes, knowledge interpolation and mirroring was carried out, doubtlessly biasing this community in direction of homotopic connections. Fourth, connectivity modes are compiled throughout completely different people of various ages, intercourse ratios, and handedness. Outcomes are due to this fact restricted to group-averages and inspire future deep phenotyping research of the mind throughout a number of scales and modalities. Fifth, the chosen functionally outlined Schaefer parcellation used for all essential analyses could higher mirror practical networks (e.g., haemodynamic connectivity, electrophysiological connectivity, temporal similarity) than molecular networks. We aimed to mitigate this limitation by repeating analyses utilizing an anatomically outlined parcellation (Desikan–Killiany; S7 Fig). Future integrative parcellations designed utilizing a number of mind phenotypes can be best for finding out multiscale, multimodal connectivity modes.
Altogether, this work combines 7 views of cortical connectivity from numerous spatial scales and imaging modalities together with gene expression, receptor density, mobile composition, metabolic consumption, haemodynamic exercise, electrophysiology, and time collection options. We exhibit each the same and complementary methods during which connectivity modes mirror cortical geometry, construction, and illness. This serves as a step in direction of the next-generation integrative, multimodal research of cortical connectivity.
We assemble cortical connectivity modes for 7 completely different mind options: gene expression, receptor density, lamination, glucose uptake, haemodynamic exercise, electrophysiological exercise, and temporal profiles. Every connectivity mode is outlined throughout 400 cortical areas, ordered in response to 7 intrinsic networks (visible, somatomotor, dorsal consideration, ventral consideration, limbic, frontoparietal, and default mode), separated by hemispheres (left, proper) . This functionally outlined parcellation scheme was chosen as a result of the parcels are roughly equal in dimension and parcel boundaries respect each practical boundaries (as decided by resting-state and task-based fMRI) in addition to histological boundaries . Nonetheless, we repeated the analyses utilizing the coarser 100-region Schaefer parcellation in addition to an anatomically outlined 68-region Desikan–Killiany parcellation and located constant outcomes (S7 Fig; parcellated connectivity modes all out there at https://github.com/netneurolab/hansen_many_networks). To facilitate comparability between connectivity modes, every connectivity mode is normalized utilizing Fisher’s r-to-z remodel (z = arctanh(r)). We describe the development of every connectivity mode intimately under.
Correlated gene expression.
Correlated gene expression represents the transcriptional similarity between pairs of cortical areas. Regional microarray expression knowledge had been obtained from 6 postmortem brains (1 feminine, ages 24.0–57.0, 42.50±13.38) supplied by the AHBA (https://human.brain-map.org ). Knowledge had been processed with the abagen toolbox (model 0.1.1; https://github.com/rmarkello/abagen ) utilizing a 400-region volumetric atlas in MNI house.
First, microarray probes had been reannotated utilizing knowledge supplied by ; probes not matched to a sound Entrez ID had been discarded. Subsequent, probes had been filtered primarily based on their expression depth relative to background noise , such that probes with depth lower than the background in ≥50% of samples throughout donors had been discarded, yielding 31,569 probes. When a number of probes listed the expression of the identical gene, we chosen and used the probe with essentially the most constant sample of regional variation throughout donors (i.e., differential stability ), 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 supplied within the ontology from the AHBA.
The MNI coordinates of tissue samples had been up to date to these generated by way of 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 . Samples had been assigned to mind areas within the supplied atlas if their MNI coordinates had been inside 2 mm of a given parcel. If a mind area was not assigned a tissue pattern primarily based on the above process, each voxel within the area was mapped to the closest tissue pattern from the donor in an effort to generate a dense, interpolated expression map. The common 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, in an effort to acquire an estimate of the parcellated expression values for the lacking area. All tissue samples not assigned to a mind area within the supplied atlas had been discarded.
Inter-subject variation was addressed by normalizing tissue pattern expression values throughout genes utilizing a strong sigmoid operate :
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 equivalent process. Samples assigned to the identical mind area had been averaged individually for every donor, yielding a regional expression matrix for every donor with 400 rows, similar to mind areas, and 15,633 columns, similar to the retained genes. A threshold of 0.1 was imposed on the differential stability of every gene, such that solely secure genes had been retained for future evaluation, leading to 8,687 retained genes.
Lastly, the area × area correlated gene expression matrix was constructed by correlating (Pearson’s r) the normalized gene expression profile at each pair of mind areas. This matrix was then normalized utilizing Fisher’s r-to-z remodel.
Receptor similarity indexes the diploma to which the receptor density profiles at 2 cortical areas are correlated. Conceptually, it may be regarded as how equally 2 cortical areas would possibly “hear” the identical neural sign. PET tracer photos for 18 neurotransmitter receptors and transporters had been obtained from  and neuromaps (v0.0.1, https://github.com/netneurolab/neuromaps ). The receptors/transporters span 9 neurotransmitter techniques together with: dopamine (D1, D2, DAT), norepinephrine (NET), serotonin (5-HT1A, 5-HT1B, 5-HT2, 5-HT4, 5-HT6, 5-HTT), acetylcholine (α4β2, M1, VAChT), glutamate (mGluR5), GABA (GABAA), histamine (H3), cannabinoid (CB1), and opioid (MOR). Tracer names and variety of contributors (with variety of females in parentheses) are listed for every receptor in S1 Desk. Every PET tracer picture was parcellated to 400 cortical areas and z-scored. A region-by-region receptor similarity matrix was constructed by correlating (Pearson’s r) receptor profiles at each pair of cortical areas. This matrix was then normalized utilizing Fisher’s r-to-z remodel.
Laminar similarity is estimated from histological knowledge and goals to uncover how comparable pairs of cortical areas are by way of mobile distributions throughout the cortical laminae. Particularly, we use knowledge from the BigBrain, a high-resolution (20 μm) histological reconstruction of a postmortem mind from a 65-year-old male [14,41]. Cell-staining depth profiles had been sampled throughout 50 equivolumetric surfaces from the pial floor to the white mater floor to estimate laminar variation in neuronal density and soma dimension. Depth profiles at numerous 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.
The information had been obtained on fsaverage floor (164k vertices) from the BigBrainWarp toolbox  and had been parcellated into 400 cortical areas in response to the Schaefer-400 atlas . The area × area laminar similarity matrix was calculated because the partial correlation (Pearson’s r) of cell intensities between pairs of cortical areas, after correcting for the imply depth throughout cortical areas. Laminar similarity was first launched in  and has additionally been known as “microstructure profile covariance.” This matrix was then normalized utilizing Fisher’s r-to-z remodel.
Metabolic connectivity indexes how equally 2 cortical areas metabolize glucose over time and due to this fact how equally 2 cortical areas eat power. Volumetric 4D PET photos of [F18]-fluordoxyglucose (FDG, a glucose analogue) tracer uptake over time had been obtained from . Particularly, 26 wholesome contributors (77% feminine, 18 to 23 years previous) had been recruited from the overall inhabitants and underwent a 95-min simultaneous MR-PET scan in a Siemens (Erlangen) Biograph 3-Tesla molecular MR scanner. Individuals had been positioned supine within the scanner bore with their head in a 16-channel radiofrequency head coil and had been instructed to lie as nonetheless as potential with eyes open and consider nothing specifically. FDG (common dose 233 MBq) was infused over the course of the scan at a charge of 36 mL/h utilizing a BodyGuard 323 MR-compatible infusion pump (Caesarea Medical Electronics, Caesarea, Israel). Infusion onset was locked to the onset of the PET scan. This knowledge has been validated and analyzed beforehand in [15,29].
PET photos had been reconstructed and preprocessed in response to . Particularly, the 5,700-second PET time collection for every topic was binned into 356 3D sinogram frames every of 16-s intervals. The attenuation for all required knowledge was corrected by way of the pseudo-CT technique . Abnormal Poisson-Ordered Subset Expectation Maximization algorithm (3 iterations, 21 subsets) with level unfold operate correction was used to reconstruct 3D volumes from the sinogram frames. The reconstructed DICOM slices had been transformed to NIFTI format with dimension 344 × 344 × 127 (voxel dimension: 2.09 × 2.09 × 2.03 mm3) for every quantity. A 5 mm FWHM Gaussian postfilter was utilized to every 3D quantity. All 3D volumes had been temporally concatenated to kind a 4D (344 × 344 × 127 × 356) NIFTI quantity. A guided movement correction technique utilizing concurrently acquired MRI was utilized to right the movement through the PET scan; 225 16-s volumes had been retained commencing for additional analyses.
Subsequent, the 225 PET volumes had been movement corrected (FSL MCFLIRT ) and the imply PET picture was mind extracted and used to masks the 4D knowledge. The fPET knowledge had been additional processed utilizing a spatiotemporal gradient filter to take away the accumulating impact of the radiotracer and different low-frequency elements of the sign . Lastly, every time level of the PET volumetric time collection had been registered to MNI152 template house utilizing Superior Normalization Instruments in Python (ANTSpy, https://github.com/ANTsX/ANTsPy), parcellated to 400 areas in response to the Schaefer atlas, and time collection at pairs of cortical areas had been correlated (Pearson’s r) to assemble a metabolic connectivity matrix for every topic. A gaggle-averaged metabolic connectome was obtained by averaging connectivity throughout topics, and lastly, the matrix was normalized utilizing Fisher’s r-to-z remodel.
Haemodynamic connectivity, generally merely known as “practical connectivity,” captures how equally pairs of cortical areas exhibit fMRI BOLD exercise at relaxation . The fMRI BOLD time collection picks up on magnetic variations between oxygenated and deoxygenated hemoglobin to measure the haemodynamic response: the oversupply of oxygen to lively mind areas . fMRI knowledge had been obtained for 326 unrelated contributors (age vary 22 to 35 years, 145 males) from the HCP (S900 launch ). All 4 resting state fMRI scans (2 scans (R/L and L/R part encoding instructions) on day 1 and a couple of scans (R/L and L/R part encoding instructions) on day 2, every about 15-min lengthy; TR = 720 ms) had been out there for all contributors. fMRI knowledge had been preprocessed utilizing HCP minimal preprocessing pipelines [43,145]. Particularly, all 3T fMRI time collection had been corrected for gradient nonlinearity, head movement utilizing a inflexible physique transformation, and geometric distortions utilizing scan pairs with reverse part encoding instructions (R/L, L/R) . Additional preprocessing steps embody co-registration of the corrected photos to the T1w structural MR photos, mind extraction, normalization of entire mind depth, high-pass filtering (>2,000 s FWHM; to right for scanner drifts), and eradicating extra noise utilizing the ICA-FIX course of [146,147]. The preprocessed time collection had been then parcellated to 400 cortical mind areas in response to the Schaefer atlas . The parcellated time collection had been used to assemble practical connectivity matrices as a Pearson correlation coefficient between pairs of regional time collection for every of the 4 scans of every participant. A gaggle-average practical connectivity matrix was constructed because the imply practical connectivity throughout all people and scans. This matrix was then normalized utilizing Fisher’s r-to-z remodel.
Electrophysiological connectivity was measured utilizing MEG recordings, which tracks the magnetic area produced by neural currents. Resting state MEG knowledge was acquired for n = 33 unrelated wholesome younger adults (age vary 22 to 35 years) from the HCP (S900 launch ). The information contains resting state scans of roughly 6-min lengthy and noise recording for all contributors. MEG anatomical knowledge and 3T structural MRI of all contributors had been additionally obtained for MEG preprocessing.
The current MEG knowledge was first processed and utilized by . Resting state MEG knowledge was preprocessed utilizing the open-source software program, Brainstorm (https://neuroimage.usc.edu/brainstorm/ ), following the web tutorial for the HCP dataset (https://neuroimage.usc.edu/brainstorm/Tutorials/HCP-MEG). MEG recordings had been registered to particular person structural MRI photos earlier than making use of the next preprocessing steps. First, notch filters had been utilized at 60, 120, 180, 240, and 300 Hz, adopted by a high-pass filter at 0.3 Hz to take away slow-wave and DC-offset artifacts. Subsequent, unhealthy channels from artifacts (together with heartbeats, eye blinks, saccades, muscle actions, and noisy segments) had been eliminated utilizing Sign-Area Projections (SSP).
Preprocessed sensor-level knowledge was used to assemble a supply estimation on HCP’s fsLR4k cortex floor for every participant. Head fashions had been computed utilizing overlapping spheres and knowledge and noise covariance matrices had been estimated from resting state MEG and noise recordings. Linearly constrained minimal variance (LCMV) beamformers was used to acquire the supply exercise for every participant. Knowledge covariance regularization was carried out and the estimated supply variance was normalized by the noise covariance matrix to scale back the impact of variable supply depth. All eigenvalues smaller than the median eigenvalue of the information covariance matrix had been changed by the median. This helps keep away from instability of knowledge covariance inversion attributable to the smallest eigenvalues and regularizes the information covariance matrix. Supply orientations had been constrained to be regular to the cortical floor at every of the 8,000 vertex areas on the cortical floor, then parcellated in response to the Schaefer-400 atlas .
After preprocessing and parcellating the information, amplitude envelope correlations had been carried out between time collection at every pair of mind areas, for six canonical frequency bands individually (delta (2 to 4 Hz), theta (5 to 7 Hz), alpha (8 to 12 Hz), beta (15 to 29 Hz), low gamma (30 to 59 Hz), and excessive gamma (60 to 90 Hz)). Amplitude envelope correlation is utilized as a substitute of immediately correlating the time collection due to the excessive sampling charge (2,034.5 Hz) of the MEG recordings. An orthogonalization course of was utilized to right for the spatial leakage impact by eradicating all shared zero-lag indicators . The composite electrophysiological connectivity matrix is the primary principal element of all 6 connectivity matrices (vectorized higher triangle) and intently resembles alpha connectivity (S8 Fig). Lastly, the matrix underwent Fisher’s r-to-z remodel.
Temporal profile similarity.
Temporal profile similarity was first launched by, and obtained from , and represents how a lot 2 cortical areas exhibit comparable temporal options, as calculated on fMRI time collection. Observe that though this connectivity mode is derived from the identical imaging modality as haemodynamic connectivity, it’s basically completely different from haemodynamic connectivity because it represents a complete account of dynamic similarity (Pearson’s r = 0.24, S3 Fig). That is in distinction to haemodynamic connectivity that measures the Pearson’s correlation between the time collection themselves. Particularly, we used the extremely comparative time collection evaluation toolbox, hctsa [46,47] to carry out a large function extraction of the parcellated fMRI time collection (see Haemodynamic connectivity) at every mind area of every participant. The hctsa package deal extracted over 7,000 native time collection options utilizing a variety of operations primarily based on time collection evaluation. The extracted options embody, however should not restricted to, distributional options, entropy and variability, autocorrelation, time-delay embeddings, and nonlinear options of a given time collection. Following the function extraction process, the outputs of the operations that produced errors had been eliminated and the remaining options (6,441 options) had been normalized throughout nodes utilizing an outlier-robust sigmoidal remodel. We used Pearson’s correlation coefficients to measure the pairwise similarity between the time collection options of all potential combos of cortical areas. Because of this, a temporal profile similarity community was constructed for every particular person and every run, representing the power of the similarity of the native temporal fingerprints of cortical areas. This matrix was then normalized utilizing Fisher’s r-to-z remodel.
Diffusion weighted imaging (DWI) knowledge had been obtained for 326 unrelated contributors (age vary 22 to 35 years, 145 males) from the HCP (S900 launch ) . DWI knowledge was preprocessed utilizing the MRtrix3 package deal  (https://www.mrtrix.org/). Extra particularly, fiber orientation distributions had been generated utilizing the multi-shell multi-tissue constrained spherical deconvolution algorithm from MRtrix [151,152]. White matter edges had been then reconstructed utilizing probabilistic streamline tractography primarily based on the generated fiber orientation distributions . The tract weights had been then optimized by estimating an acceptable cross-section multiplier for every streamline following the process proposed by  and a connectivity matrix was constructed for every participant utilizing the 400-region Schaefer parcellation . A gaggle-consensus binary community was constructed utilizing a way that preserves the density and edge-length distributions of the person connectomes [155–157]. Edges within the group-consensus community had been assigned weights by averaging the log-transformed streamline rely of nonzero edges throughout contributors. Edge weights had been then scaled to values between 0 and 1.
Patterns of cortical thickness from the ENIGMA consortium and the enigma toolbox had been out there for 13 neurological, neurodevelopmental, and psychiatric issues (https://github.com/MICA-MNI/ENIGMA [33,79,80]), together with: 22q11.2 deletion syndrome (N = 474 contributors, N = 315 controls) , attention-deficit/hyperactivity dysfunction (ADHD; N = 733 contributors, N = 539 controls) , autism spectrum dysfunction (ASD; N = 1,571 contributors, N = 1,651 controls) , idiopathic generalized (N = 367 contributors), proper temporal lobe (N = 339 contributors), and left temporal lobe (N = 415 contributors) epilepsies (N = 1,727 controls) , despair (N = 2,148 contributors, N = 7,957 controls) , obsessive-compulsive dysfunction (OCD; N = 1,905 contributors, N = 1,760 controls) , schizophrenia (N = 4,474 contributors, N = 5,098 controls) , bipolar dysfunction (N = 1,837 contributors, N = 2,582 controls) , weight problems (N = 1,223 contributors, N = 2,917 controls) , schizotypy (N = 3,004 contributors) , and Parkinson’s illness (N = 2,367 contributors, N = 1,183 controls) . The ENIGMA consortium is a data-sharing initiative that depends on standardized processing and evaluation pipelines, such that dysfunction maps are comparable . Altogether, over 21,000 contributors had been scanned throughout the 13 issues, towards virtually 26,000 controls. The evaluation was restricted to adults in all circumstances besides ASD the place the irregular cortical thickness map is simply out there aggregated throughout all ages (2 to 64 years). The values for every map are z-scored impact sizes (Cohen’s d) of cortical thickness in affected person populations versus wholesome controls. Imaging and processing protocols might be discovered at http://enigma.ini.usc.edu/protocols/. Native evaluate boards and ethics committees authorised every particular person research individually, and written knowledgeable consent was supplied in response to native necessities.
We calculate illness publicity for each illness and community, after masking the community such that each one edges with detrimental power are assigned a power of 0. For a given community and illness, illness publicity of a node i is outlined as,
the place Ni is the variety of optimistic connections made by area i, dj is the irregular cortical thickness at area j, and cij is the sting power between areas i and j. This evaluation was repeated after regressing the exponential slot in Fig 1B from every community, to make sure outcomes should not pushed by distance (S9 Fig).
For every connectivity mode, communities had been recognized utilizing the Louvain algorithm, which maximizes optimistic edge power inside communities and detrimental edge power between communities . Particularly, mind areas had been assigned to communities in a fashion that maximizes the standard operate
the place is the community with solely optimistic correlations and likewise for and detrimental correlations. The time period represents the null mannequin: the anticipated density of connections between nodes i and j, the place and . The variable σi is the neighborhood task of node i and δ(σi, σj) is the Kronecker operate and is the same as 1 when σi = σj and 0 in any other case. The decision parameter, γ, scales the relative significance of the null mannequin, making it simpler (γ>1) or tougher (γ<1) for the algorithm to uncover many communities. In different phrases, as γ will increase, more and more advantageous community partitions are recognized. We examined 60 values of γ, from γ = 0.1 to γ = 6.0, in increments of 0.1. At every γ, we repeated the algorithm 250 instances and constructed a consensus partition, following the process really useful in .
Similarity community fusion
First launched by , SNF is a technique for combining a number of measurement sorts for a similar observations (e.g., sufferers, or in our case, mind areas) right into a single similarity community the place edges between observations characterize their cross-modal similarity. For every knowledge supply, SNF constructs an impartial similarity community, defines the Okay nearest neighbors for every remark, after which iteratively combines the networks in a fashion that provides extra weight to edges between observations which might be constantly high-strength throughout knowledge sorts. We used snfpy (https://github.com/rmarkello/snfpy ), an open-source Python implementation of the unique SNF code supplied by . A quick description of the principle steps in SNF follows, tailored from its authentic presentation in .
Within the current report, the 7 knowledge sources to be fused are the 7 connectivity modes (correlated gene expression, receptor similarity, laminar similarity, metabolic connectivity, haemodynamic connectivity, electrophysiological connectivity, and temporal similarity). First, similarity networks for every connectivity mode are constructed the place edges are decided utilizing a scaled exponential similarity kernel:
the place W(i,j) is the sting weight between areas i and j, ρ(xi, xj) is the Euclidean distance between areas i and j, μ∈ℝ is a hyperparameter that’s set empirically, and
the place is the common distance between xi and all different areas within the community. Observe that μ is a scaling issue that determines the weighting of edges between areas within the similarity community and is ready to μ = 0.5 within the current report.
In different phrases, the matrix P encodes the total details about the similarity of every area to all different areas (inside a given connectivity mode), whereas S encodes solely the similarity of the Okay most comparable areas to every area. Okay is SNF’s second hyperparameter, which we set to 1 tenth the variety of areas within the community .
After every iteration, the generated matrices are renormalized as within the normalization step. Fusion stops when the matrices have converged or after a specified variety of iterations (in our case, 20). Areas xi and xj will seemingly be neighbors within the fused community if they’re neighbors in a number of similarity networks. Moreover, if xi and xj should not very comparable in a single knowledge kind, their similarity might be expressed in one other knowledge kind. Observe that the sting weights within the last fused community are a byproduct of the iterative multiplication and normalization steps, and due to this fact can turn out to be very small. Better edge magnitude represents larger similarity (no detrimental edges exist, by design).
After the fusion course of, we affirm that no single community exerts undue affect on the ultimate fused community by repeating the fusion course of whereas excluding a single community. The minimal correlation (Spearman r) between the leave-one-out fused community and the whole fused community is 0.958. Along with this, we affirm that various Okay and μ parameters wouldn’t make massive distinction to the fused community. We check Okay∈[20, 59] and μ∈[0.3, 0.8] and discover that these various fused networks are extremely correlated with the unique (minimal Spearman r = 0.924).
Spatial autocorrelation-preserving permutation exams had been used to evaluate statistical significance of associations throughout cortical areas, termed “spin exams” [170–172]. We created a surface-based illustration of the parcellation on the FreeSurfer fsaverage floor, by way of recordsdata from the Connectome Mapper toolkit (https://github.com/LTS5/cmp). We used the spherical projection of the fsaverage floor to outline spatial coordinates for every parcel by choosing the coordinates of the vertex closest to the middle of the mass of every parcel . These parcel coordinates had been then randomly rotated, and authentic parcels had been reassigned the worth of the closest rotated parcel (10,000 repetitions). Parcels for which the medial wall was closest had been assigned the worth of the following most proximal parcel as a substitute. The process was carried out on the parcel decision reasonably than the vertex decision to keep away from upsampling the information and to every hemisphere individually.
Structural networks had been randomized utilizing a process that preserves the density, edge size, diploma distributions of the empirical community [56,172]. Edges had been binned in response to Euclidean distance (10 bins). Inside every bin, pairs of edges had been chosen at random and swapped, for a complete variety of swaps equal to the variety of areas within the community multiplied by 20. This process was repeated 1,000 instances to generate 1,000 null structural networks, which had been then used to generate null distributions of network-level measures.
We thank Vincent Bazinet, Zhen-Qi Liu, Filip Milisav, Laura Suarez, and Andrea Luppi for his or her feedback and strategies on the manuscript; the Monash College Neural Programs and Behviour Lab for insightful dialogue; and all people concerned in making the employed open-source datasets out there.
Lanciego JL, Wouterlood FG. Neuroanatomical tract-tracing strategies that did go viral. Mind Struct Funct. 2020;225(4):1193–1224. pmid:32062721
Lichtman JW, Livet J, Sanes JR. A technicolour strategy to the connectome. Nat Rev Neurosci. 2008;9(6):417–422. pmid:18446160
Sporns O, Tononi G, Kötter R. The human connectome: A structural description of the human mind. PLoS Comput Biol. 2005;1(4):e42. pmid:16201007
Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008;6(7):e159. pmid:18597554
Sporns O, Betzel RF. Modular mind networks. Annu Rev Psychol. 2016;67:613–640. pmid:26393868
Rosen BQ, Halgren E. An estimation of absolutely the variety of axons signifies that human cortical areas are sparsely linked. PLoS Biol. 2022;20(3):e3001575. pmid:35286306
Van Den Heuvel MP, Sporns O. Wealthy-club group of the human connectome. J Neurosci. 2011;31(44):15775–15786. pmid:22049421
Avena-Koenigsberger A, Misic B, Sporns O. Communication dynamics in advanced mind networks. Nat Rev Neurosci. 2018;19(1):17–33.
Seguin C, Tian Y, Zalesky A. Community communication fashions enhance the behavioral and practical predictive utility of the human structural connectome. Netw Neurosci. 2020;4(4):980–1006. pmid:33195945
Suárez LE, Markello RD, Betzel RF, Misic B. Linking construction and performance in macroscale mind networks. Traits Cogn Sci. 2020. pmid:32160567
Zheng YQ, Zhang Y, Yau Y, Zeighami Y, Larcher Okay, Misic B, et al. Native vulnerability and international connectivity collectively form neurodegenerative illness propagation. PLoS Biol. 2019;17(11). pmid:31751329
Zilles Okay, Bacha-Trams M, Palomero-Gallagher N, Amunts Okay, Friederici AD. Frequent molecular foundation of the sentence comprehension community revealed by neurotransmitter receptor fingerprints. Cortex. 2015;63:79–89. pmid:25243991
Anderson KM, Krienen FM, Choi EY, Reinen JM, Yeo BT, Holmes AJ. Gene expression hyperlinks practical networks throughout cortex and striatum. Nat Commun. 2018;9(1):1428. pmid:29651138
Paquola C, De Wael RV, Wagstyl Okay, Bethlehem RA, Hong SJ, Seidlitz J, et al. Microstructural and practical gradients are more and more dissociated in transmodal cortices. PLoS Biol. 2019;17(5):e3000284. pmid:31107870
Voigt Okay, Liang EX, Misic B, Ward PGD, Egan GF, Jamadar SD. Metabolic and practical connectivity present distinctive and complementary insights into cognition-connectome relationships. Cereb Cortex. 2022.
Horwitz B, Duara R, Rapoport SI. Intercorrelations of glucose metabolic charges between mind areas: Software to wholesome males in a state of diminished sensory enter. J Cereb Blood Circulate Metab. 1984;4(4):484–499. pmid:6501442
Barbas H. Sample within the laminar origin of corticocortical connections. J Comp Neurol. 1986;252(3):415–422. pmid:3793985
García-Cabezas MÁ, Zikopoulos B, Barbas H. The structural mannequin: A idea linking connections, plasticity, pathology, improvement and evolution of the cerebral cortex. Mind Struct Funct. 2019;224(3):985–1008. pmid:30739157
Barbas H, Rempel-Clower N. Cortical construction predicts the sample of corticocortical connections. Cereb Cortex (New York, NY: 1991). 1997;7(7):635–646. pmid:9373019
Zikopoulos B, García-Cabezas MÁ, Barbas H. Parallel traits in cortical grey and white matter structure and connections in primates enable advantageous research of pathways in people and reveal community disruptions in autism. PLoS Biol. 2018;16(2):e2004559.
Goulas A, Majka P, Rosa MG, Hilgetag CC. A blueprint of mammalian cortical connectomes. PLoS Biol. 2019;17(3):e2005346. pmid:30901324
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 U S A. 2021;118(3). pmid:33452137
Hansen JY, Shafiei G, Markello RD, Good Okay, Cox SM, Nørgaard M, et al. Mapping neurotransmitter techniques to the structural and practical group of the human neocortex. Nat Neurosci. 2022:1–13.
García-Cabezas MÁ, Joyce MKP, John YJ, Zikopoulos B, Barbas H. Mirror traits of plasticity and stability indicators in primate prefrontal cortex. Eur J Neurosci. 2017;46(8):2392–2405. pmid:28921934
Hilgetag CC, Medalla M, Beul SF, Barbas H. The primate connectome in context: Ideas of connections of the cortical visible system. Neuroimage. 2016;134:685–702. pmid:27083526
Friston KJ. Purposeful and efficient connectivity: A evaluate. Mind Join. 2011;1(1):13–36. pmid:22432952
Chu CJ, Tanaka N, Diaz J, Edlow BL, Wu O, Hämäläinen M, et al. EEG practical connectivity is partially predicted by underlying white matter connectivity. Neuroimage. 2015;108:23–33. pmid:25534110
Brookes MJ, Hale JR, Zumer JM, Stevenson CM, Francis ST, Barnes GR, et al. Measuring practical connectivity utilizing MEG: Methodology and comparability with fcMRI. Neuroimage. 2011;56(3):1082–1104. pmid:21352925
Jamadar SD, Ward PG, Liang EX, Orchard ER, Chen Z, Egan GF. Metabolic and hemodynamic resting-state connectivity of the human mind: A high-temporal decision simultaneous BOLD-fMRI and FDG-fPET multimodality research. Cereb Cortex. 2021;31(6):2855–2867. pmid:33529320
Garces P, Pereda E, Hernández-Tamames JA, Del-Pozo F, Maestu F, Pineda-Pardo JÁ. Multimodal description of entire mind connectivity: A comparability of resting state MEG, fMRI, and DWI. Hum Mind Mapp. 2016;37(1):20–34. pmid:26503502
Richiardi J, Altmann A, Milazzo AC, Chang C, Chakravarty MM, Banaschewski T, et al. Correlated gene expression helps synchronous exercise in mind networks. Science. 2015;348(6240):1241–1244.
Seidlitz J, Váša F, Shinn M, Romero-Garcia R, Whitaker KJ, Vértes PE, et al. Morphometric similarity networks detect microscale cortical group and predict inter-individual cognitive variation. Neuron. 2018;97(1):231–247. pmid:29276055
Hansen JY, Shafiei G, Vogel JW, Good Okay, Bearden CE, Hoogman M, et al. Native molecular and international connectomic contributions to cross-disorder cortical abnormalities. Nat Commun. 2022;13(1):1–17.
Hettwer M, Larivière S, Park B, van den Heuvel O, Schmaal L, Andreassen O, et al. Coordinated cortical thickness alterations throughout six neurodevelopmental and psychiatric issues. Nat Commun. 2022;13(1):1–14.
Sebenius I, Seidlitz J, Warrier V, Bethlehem RA, Alexander-Bloch A, Mallard TT, et al. Strong estimation of cortical similarity networks from mind MRI. Nat Neurosci. 2023:1–11. pmid:37460809
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
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 U S A. 2020;117(1):771–778. pmid:31874926
Paquola C, Seidlitz J, Benkarim O, Royer J, Klimes P, Bethlehem RA, et al. A multi-scale cortical wiring house hyperlinks mobile structure and practical dynamics within the human mind. PLoS Biol. 2020;18(11):e3000979. pmid:33253185
Mills BD, Grayson DS, Shunmugavel A, Miranda-Dominguez O, Feczko E, Earl E, et al. Correlated gene expression and anatomical communication assist synchronized mind exercise within the mouse practical connectome. J Neurosci. 2018;38(25):5774–5787. pmid:29789379
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
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
Jamadar SD, Ward PG, Shut TG, Fornito A, Premaratne M, O’Brien Okay, et al. Simultaneous BOLD-fMRI and fixed infusion FDG-PET knowledge of the resting human mind. Sci Knowledge. 2020;7(1):1–12.
Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil Okay, et al. The WU-minn human connectome undertaking: An summary. Neuroimage. 2013;80:62–79. pmid:23684880
Shafiei G, Baillet S, Misic B. Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex. PLoS Biol. 2022;20(8):e3001735. pmid:35914002
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
Fulcher BD, Little MA, Jones NS. Extremely comparative time-series evaluation: The empirical construction of time collection and their strategies. J R Soc Interface. 2013;10(83):20130048. pmid:23554344
Fulcher BD, Jones NS. Hctsa: A computational framework for automated time-series phenotyping utilizing huge function extraction. Cell Syst. 2017;5(5):527–531. pmid:29102608
Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ, et al. Native-global parcellation of the human cerebral cortex from intrinsic practical connectivity MRI. Cereb Cortex. 2018;28(9):3095–3114. pmid:28981612
Yeo B, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The group of the human cerebral cortex estimated by intrinsic practical connectivity. J Neurophysiol. 2011;106(3):1125–1165. pmid:21653723
Barbas H, Hilgetag CC, Saha S, Dermon CR, Suski JL. Parallel group of contralateral and ipsilateral prefrontal cortical projections within the rhesus monkey. BMC Neurosci. 2005;6(1):1–17. pmid:15869709
Goulas A, Uylings HB, Hilgetag CC. Ideas of ipsilateral and contralateral cortico-cortical connectivity within the mouse. Mind Struct Funct. 2017;222:1281–1295. pmid:27497948
Uddin LQ, Mooshagian E, Zaidel E, Scheres A, Margulies DS, Kelly AC, et al. Residual practical connectivity within the split-brain revealed with resting-state fMRI. Neuroreport. 2008;19(7):703.
Mancuso L, Uddin LQ, Nani A, Costa T, Cauda F. Mind practical connectivity in people with callosotomy and agenesis of the corpus callosum: A scientific evaluate. Neurosci Biobehav Rev. 2019;105:231–248. pmid:31412269
Hilgetag CC, Beul SF, van Albada SJ, Goulas A. An architectonic kind precept integrates macroscopic cortico-cortical connections with intrinsic cortical circuits of the primate mind. Netw Neurosci. 2019;3(4):905–923. pmid:31637331
Fornito A, Arnatkevičiūtė A, Fulcher BD. Bridging the hole between connectome and transcriptome. Traits Cogn Sci. 2019;23(1):34–50. pmid:30455082
Betzel RF, Bassett DS. Specificity and robustness of long-distance connections in weighted, interareal connectomes. Proc Natl Acad Sci U S A. 2018;115(21):E4880–E4889. pmid:29739890
Beul SF, Grant S, Hilgetag CC. A predictive mannequin of the cat cortical connectome primarily based on cytoarchitecture and distance. Mind Struct Funct. 2015;220:3167–3184. pmid:25062666
Beul SF, Barbas H, Hilgetag CC. A predictive structural mannequin of the primate connectome. Sci Rep. 2017;7(1):43176. pmid:28256558
Aparicio-Rodríguez G, García-Cabezas MÁ. Comparability of the predictive energy of two fashions of cortico-cortical connections in primates: The space rule mannequin and the structural mannequin. Cereb Cortex. 2023:bhad104. pmid:37041104
Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, et al. Predicting human resting-state practical connectivity from structural connectivity. Proc Natl Acad Sci U S A. 2009;106(6):2035–2040. pmid:19188601
Stiso J, Bassett DS. Spatial embedding imposes constraints on neuronal community architectures. Traits Cogn Sci. 2018;22(12):1127–1142. pmid:30449318
Barbas H. Normal cortical and particular prefrontal connections: Ideas from construction to operate. Annu Rev Neurosci. 2015;38:269–289. pmid:25897871
Fulcher BD, Fornito A. A transcriptional signature of hub connectivity within the mouse connectome. Proc Natl Acad Sci U S A. 2016;113(5):1435–1440. pmid:26772314
Colizza V, Flammini A, Serrano MA, Vespignani A. Detecting rich-club ordering in advanced networks. Nat Phys. 2006;2(2):110–115.
Arnatkeviciute A, Fulcher BD, Oldham S, Tiego J, Paquola C, Gerring Z, et al. Genetic influences on hub connectivity of the human connectome. Nat Commun. 2021;12(1):1–14.
Liang X, Zou Q, He Y, Yang Y. Coupling of practical connectivity and regional cerebral blood circulation reveals a physiological foundation for community hubs of the human mind. Proc Natl Acad Sci U S A. 2013;110(5):1929–1934. pmid:23319644
Vaishnavi SN, Vlassenko AG, Rundle MM, Snyder AZ, Mintun MA, Raichle ME. Regional cardio glycolysis within the human mind. Proc Natl Acad Sci U S A. 2010;107(41):17757–17762. pmid:20837536
Griffa A, Van den Heuvel MP. Wealthy-club neurocircuitry: Perform, evolution, and vulnerability. Dialogues Clin Neurosci. 2018.
Sporns O, Honey CJ, Kötter R. Identification and classification of hubs in mind networks. PLoS ONE. 2007;2(10):e1049. pmid:17940613
Van den Heuvel MP, Sporns O. Community hubs within the human mind. Traits Cogn Sci. 2013;17(12):683–696. pmid:24231140
von Economo CF, Koskinas GN. Die cytoarchitektonik der hirnrinde des erwachsenen menschen. J. Springer; 1925.
Vértes P, Rittman T, Whitaker Okay, Romero-Garcia R, Váša F, Kitzbichler M, et al. Gene transcription profiles related to intra-modular and inter-modular hubs in human fMRI networks. Philos Trans R Soc Lond B Biol Sci. 2016;371:735–769.
Hill J, Inder T, Neil J, Dierker D, Harwell J, Van Essen D. Comparable patterns of cortical growth throughout human improvement and evolution. Proc Natl Acad Sci U S A. 2010;107(29):13135–13140. pmid:20624964
Klatzo I. Cécile & oskar vogt: The importance of their contributions in fashionable neuroscience. In: Mind edema XII: Proceedings of the twelfth worldwide symposium, hakone, Japan, November 10–13, 2002. Springer; 2003. p. 29–32.
Braak H, Braak E. Improvement of alzheimer-related neurofibrillary adjustments within the neocortex inversely recapitulates cortical myelogenesis. Acta Neuropathol. 1996;92:197–201. pmid:8841666
Henderson MX, Cornblath EJ, Darwich A, Zhang B, Brown H, Gathagan RJ, et al. Unfold of α-synuclein pathology by way of the mind connectome is modulated by selective vulnerability and predicted by community evaluation. Nat Neurosci. 2019;22(8):1248–1257.
Shafiei G, Bazinet V, Dadar M, Manera AL, Collins DL, Dagher A, et al. Community construction and transcriptomic vulnerability form atrophy in frontotemporal dementia. Mind. 2023;146(1):321–336. pmid:35188955
Warren JD, Rohrer JD, Schott JM, Fox NC, Hardy J, Rossor MN. Molecular nexopathies: A brand new paradigm of neurodegenerative illness. Traits Neurosci. 2013;36(10):561–569. pmid:23876425
Thompson PM, Jahanshad N, Ching CR, Salminen LE, Thomopoulos SI, Brilliant J, et al. ENIGMA and international neuroscience: A decade of large-scale research of the mind in well being and illness throughout greater than 40 nations. Transl Psychiatry. 2020;10(1):1–28.
Larivière S, Paquola C, Park B, Royer J, Wang Y, Benkarim O, et al. The ENIGMA toolbox: Multiscale neural contextualization of multisite neuroimaging datasets. Nat Strategies. 2021;18(7):698–700. pmid:34194050
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;87(8):727–735. pmid:31837746
Shafiei G, Bazinet V, Dadar M, Manera AL, Collins DL, Dagher A, et al. Community construction and transcriptomic vulnerability form atrophy in frontotemporal dementia. Mind. 2022.
Chopra S, Oldham S, Segal A, Holmes A, Sabaroedin Okay, Orchard ER, et al. Community constraints on longitudinal gray matter adjustments in first episode psychosis. medRxiv. 2022;
Hänisch B, Hansen JY, Bernhardt BC, Eickhoff SB, Dukart J, Misic B, et al. Cerebral chemoarchitecture shares organizational traits with mind construction and performance. Elife. 2023;12:e83843. pmid:37440423
Pricey R, Seidlitz J, Markello RD, Arnatkeviciute A, Anderson KM, Bethlehem RA, et al. Three transcriptional axes underpin anatomy, improvement, and issues of the human cortex. bioRxiv. 2022.
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 U S A. 2016;113(44):12574–12579. pmid:27791099
Sydnor VJ, Larsen B, Bassett DS, Alexander-Bloch A, Truthful DA, Liston C, et al. Neurodevelopment of the affiliation cortices: Patterns, mechanisms, and implications for psychopathology. Neuron. 2021;109(18):2820–2846. pmid:34270921
Huntenburg JM, Bazin PL, Margulies DS. Massive-scale gradients in human cortical group. Traits Cogn Sci. 2018;22(1):21–31. pmid:29203085
Markello RD, Hansen JY, Liu ZQ, Bazinet V, Shafiei G, Suárez LE, et al. Neuromaps: Structural and practical interpretation of mind maps. Nat Strategies. 2022;1–8.
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Quick unfolding of communities in massive networks. J Stat Mech Idea Exp. 2008;2008(10):P10008.
Bassett DS, Porter MA, Wymbs NF, Grafton ST, Carlson JM, Mucha PJ. Strong detection of dynamic neighborhood construction in networks. Chaos: An Interdisciplinary. J Nonlinear Sci. 2013;23(1):013142.
Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, et al. Similarity community fusion for aggregating knowledge sorts on a genomic scale. Nat Strategies. 2014;11(3):333–337. pmid:24464287
Cammoun L, Gigandet X, Meskaldji D, Thiran JP, Sporns O, Do KQ, et al. Mapping the human connectome at a number of scales with diffusion spectrum MRI. J Neurosci Strategies. 2012;203(2):386–397. pmid:22001222
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automatic labeling system for subdividing the human cerebral cortex on MRI scans into gyral primarily based areas of curiosity. Neuroimage. 2006;31(3):968–980. pmid:16530430
Bassett DS, Sporns O. Community neuroscience. Nat Neurosci. 2017;20(3):353–364.
Lichtman JW, Denk W. The massive and the small: Challenges of imaging the mind’s circuits. Science. 2011;334(6056):618–623. pmid:22053041
Voytek B. The information science way forward for neuroscience idea. Nat Strategies. 2022:1–2.
Dipasquale O, Selvaggi P, Veronese M, Gabay AS, Turkheimer F, Mehta MA. Receptor-enriched evaluation of practical connectivity by targets (REACT): A novel, multimodal analytical strategy knowledgeable by PET to review the pharmacodynamic response of the mind below MDMA. Neuroimage. 2019;195:252–260.
Garden T, Howarda MA, Turkheimera F, Misicb B, Decoc G, Martinsa D, et al. From neurotransmitters to networks: Transcending organisational hierarchies with molecular-informed practical imaging. PsyArXiv. 2022.
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 U S A. 2011;108(40):16783–16788. pmid:21930901
He BJ, Snyder AZ, Zempel JM, Smyth MD, Raichle ME. Electrophysiological correlates of the mind’s intrinsic large-scale practical structure. Proc Natl Acad Sci U S A. 2008;105(41):16039–16044. pmid:18843113
Van Den Heuvel MP, Mandl RC, Kahn RS, Hulshoff Pol HE. Functionally linked resting-state networks mirror the underlying structural connectivity structure of the human mind. Hum Mind Mapp. 2009;30(10):3127–3141. pmid:19235882
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
Mansour S, Seguin C, Winkler A, Noble S, Zalesky A. Topological cluster statistic (TCS): In direction of structural-connectivity-guided fMRI cluster enhancement. Res Sq. 2022.
Baldermann JC, Schüller T, Kohl S, Voon V, Li N, Hollunder B, et al. Connectomic deep mind stimulation for obsessive-compulsive dysfunction. Biol Psychiatry. 2021;90(10):678–688. pmid:34482949
Horn A, Reich M, Vorwerk J, Li N, Wenzel G, Fang Q, et al. Connectivity predicts deep mind stimulation end result in p arkinson illness. Ann Neurol. 2017;82(1):67–78. pmid:28586141
Oldham S, Fulcher BD, Aquino Okay, Arnatkevičiūtė A, Paquola C, Shishegar R, et al. Modeling spatial, developmental, physiological, and topological constraints on human mind connectivity. Sci Adv. 2022;8(22):eabm6127. pmid:35658036
Bazinet V, Hansen JY, Vos de Wael R, Bernhardt BC, van den Heuvel MP, Misic B. Assortative mixing in micro-architecturally annotated mind connectomes. Nat Commun. 2023;14(1):2850. pmid:37202416
Maier-Hein KH, Neher PF, Houde JC, Côté MA, Garyfallidis E, Zhong J, et al. The problem of mapping the human connectome primarily based on diffusion tractography. Nat Commun. 2017;8(1):1–13.
Jones DK, Knösche TR, Turner R. White matter integrity, fiber rely, and different fallacies: The do’s and don’ts of diffusion MRI. Neuroimage. 2013;73:239–254. pmid:22846632
Zalesky A, Fornito A, Cocchi L, Gollo LL, van den Heuvel MP, Breakspear M. Connectome sensitivity or specificity: Which is extra vital? Neuroimage. 2016;142:407–420. pmid:27364472
Ekelund J, Slifstein M, Narendran R, Guillin O, Belani H, Guo NN, et al. In vivo DA d 1 receptor selectivity of NNC 112 and SCH 23390. Mol Imaging Biol. 2007;9(3):117–125. pmid:17473957
Roberts JA, Perry A, Lord AR, Roberts G, Mitchell PB, Smith RE, et al. The contribution of geometry to the human connectome. Neuroimage. 2016;124:379–393. pmid:26364864
Mesulam MM. From sensation to cognition. Mind. 1998;121(6):1013–1052. pmid:9648540
Jones E, Powell T. An anatomical research of converging sensory pathways inside the cerebral cortex of the monkey. Mind. 1970;93(4):793–820. pmid:4992433
Pandya DN, Kuypers HG. Cortico-cortical connections within the rhesus monkey. Mind Res. 1969;13(1):13–36. pmid:4185124
Bazinet V, de Wael RV, Hagmann P, Bernhardt BC, Misic B. Multiscale communication in cortico-cortical networks. Neuroimage. 2021;243:118546. pmid:34478823
Xu T, Nenning KH, Schwartz E, Hong SJ, Vogelstein JT, Goulas A, et al. Cross-species practical alignment reveals evolutionary hierarchy inside the connectome. Neuroimage. 2020;223:117346. pmid:32916286
García-Cabezas MÁ, Hacker JL, Zikopoulos B. Homology of neocortical areas in rats and primates primarily based on cortical kind evaluation: An replace of the speculation on the twin origin of the neocortex. Mind Struct Funct. 2022:1–25.
Zhang J, Scholtens LH, Wei Y, Van Den Heuvel MP, Chanes L, Barrett LF. Topography impacts topology: Anatomically central areas exhibit a “high-level connector” profile within the human cortex. Cereb Cortex. 2020;30(3):1357–1365. pmid:31504277
Bullmore E, Sporns O. The financial system of mind community group. Nat Rev Neurosci. 2012;13(5):336–349. pmid:22498897
Collin G, Sporns O, Mandl RC, Van Den Heuvel MP. Structural and practical features referring to price and good thing about wealthy membership group within the human cerebral cortex. Cereb Cortex. 2014;24(9):2258–2267. pmid:23551922
Goulas A, Zilles Okay, Hilgetag CC. Cortical gradients and laminar projections in mammals. Traits Neurosci. 2018;41(11):775–788. pmid:29980393
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
Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST. Dynamic reconfiguration of human mind networks throughout studying. Proc Natl Acad Sci U S A. 2011;108(18):7641–7646. pmid:21502525
Bassett DS, Wymbs NF, Rombach MP, Porter MA, Mucha PJ, Grafton ST. Job-based core-periphery group of human mind dynamics. PLoS Comput Biol. 2013;9(9):e1003171. pmid:24086116
Mueller JM, Pritschet L, Santander T, Taylor CM, Grafton ST, Jacobs EG, et al. Dynamic neighborhood detection reveals transient reorganization of practical mind networks throughout a feminine menstrual cycle. Netw Neurosci. 2021;5(1):125–144. pmid:33688609
Bernhardt BC, Smallwood J, Keilholz S, Margulies DS. Gradients in mind group. Neuroimage. 2022;251:118987. pmid:35151850
Zilles Okay, Schleicher A, Rath M, Bauer A. Quantitative receptor autoradiography within the human mind. Histochemistry. 1988;90(2):129–137.
Palomero-Gallagher N, Zilles Okay. Cortical layers: Cyto-, myelo-, receptor-and synaptic structure in human cortical areas. Neuroimage. 2019;197:716–741. pmid:28811255
Froudist-Walsh S, Xu T, Niu M, Rapan L, Zhao L, Margulies DS, et al. Gradients of neurotransmitter receptor expression within the macaque cortex. Nat Neurosci. 2023;1–14. pmid:37336976
Nieuwenhuys R, Nieuwenhuys R. Ideas of present vertebrate neuromorphology. Mind Behav Evol. 2017;90(2):117–130. pmid:28988231
Lutti A, Dick F, Sereno MI, Weiskopf N. Utilizing high-resolution quantitative mapping of R1 as an index of cortical myelination. Neuroimage. 2014;93:176–188. pmid:23756203
Assaf Y, Blumenfeld-Katzir T, Yovel Y, Basser PJ. AxCaliber: A technique for measuring axon diameter distribution from diffusion MRI. Magn Reson Med. 2008;59(6):1347–1354. pmid:18506799
Nelson MC, Royer J, Lu WD, Leppert IR, Campbell JS, Schiavi S, et al. The human mind connectome weighted by the myelin content material and whole intra-axonal cross-sectional space of white matter tracts. Netw Neurosci. 2023:1–68.
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
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
Quackenbush J. Microarray knowledge normalization and transformation. Nat Genet. 2002;32(4):496–501. pmid:12454644
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
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
Burgos N, Cardoso MJ, Thielemans Okay, Modat M, Pedemonte S, Dickson J, et al. Attenuation correction synthesis for hybrid PET-MR scanners: Software to mind research. IEEE Trans Med Imaging. 2014;33(12):2332–2341. pmid:25055381
Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the sturdy and correct linear registration and movement correction of mind photos. Neuroimage. 2002;17(2):825–841. pmid:12377157
Fox MD, Raichle ME. Spontaneous fluctuations in mind exercise noticed with practical magnetic resonance imaging. Nat Rev Neurosci. 2007;8(9):700–711. pmid:17704812
Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, et al. Dynamic magnetic resonance imaging of human mind exercise throughout main sensory stimulation. Proc Natl Acad Sci U S A. 1992;89(12):5675–5679. pmid:1608978
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
de Wael RV, Larivière S, Caldairou B, Hong SJ, Margulies DS, Jefferies E, et al. Anatomical and microstructural determinants of hippocampal subfield practical connectome embedding. Proc Natl Acad Sci U S A. 2018;115(40):10154–10159. pmid:30249658
Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. Automated denoising of practical MRI knowledge: Combining impartial element evaluation and hierarchical fusion of classifiers. Neuroimage. 2014;90:449–468. pmid:24389422
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
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
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;202:116137. pmid:31473352
Dhollander T, Raffelt D, Connelly A. Unsupervised 3-tissue response operate estimation from single-shell or multi-shell diffusion MR knowledge with no co-registered T1 picture. In: ISMRM workshop on breaking the obstacles of diffusion MRI. ISMRM; 2016.
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
Tournier JD, Calamante F, Connelly A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. In: Proceedings of the Worldwide Society for Magnetic Resonance in Drugs. John Wiley & Sons, Inc. New Jersey, USA; 2010.
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
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
Mišić B, Betzel RF, Griffa A, De Reus MA, He Y, Zuo XN, et al. Community-based asymmetry of the human auditory system. Cereb Cortex. 2018;28(7):2655–2664. pmid:29722805
Betzel RF, Griffa A, Hagmann P, Mišić B. Distance-dependent consensus thresholds for producing group-representative structural mind networks. Netw Neurosci. 2019;3(2):475–496. pmid:30984903
Solar D, Ching CR, Lin A, Forsyth JK, Kushan L, Vajdi A, et al. Massive-scale mapping of cortical alterations in 22q11. 2 deletion syndrome: Convergence with idiopathic psychosis and results of deletion dimension. Mol Psychiatry. 2020;25(8):1822–1834. pmid:29895892
Hoogman M, Muetzel R, Guimaraes JP, Shumskaya E, Mennes M, Zwiers MP, et al. Mind imaging of the cortex in ADHD: A coordinated evaluation of large-scale scientific and population-based samples. Am J Psychiatry. 2019;176(7):531–542. pmid:31014101
Van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Busatto GF, et al. Cortical and subcortical mind morphometry variations between sufferers with autism spectrum dysfunction and wholesome people throughout the lifespan: Outcomes from the ENIGMA ASD working group. Am J Psychiatry. 2018;175(4):359–369. pmid:29145754
Whelan CD, Altmann A, Botía JA, Jahanshad N, Hibar DP, Absil J, et al. Structural mind abnormalities within the frequent epilepsies assessed in a worldwide ENIGMA research. Mind. 2018;141(2):391–408. pmid:29365066
Schmaal L, Hibar D, Sämann PG, Corridor G, Baune B, Jahanshad N, et al. Cortical abnormalities in adults and adolescents with main despair primarily based on mind scans from 20 cohorts worldwide within the ENIGMA main depressive dysfunction working group. Mol Psychiatry. 2017;22(6):900–909. pmid:27137745
Boedhoe PS, Schmaal L, Abe Y, Alonso P, Ameis SH, Anticevic A, et al. Cortical abnormalities related to pediatric and grownup obsessive-compulsive dysfunction: Findings from the ENIGMA obsessive-compulsive dysfunction working group. Am J Psychiatry. 2018;175(5):453–462. pmid:29377733
Van Erp TG, Walton E, Hibar DP, Schmaal L, Jiang W, Glahn DC, et al. Cortical mind abnormalities in 4474 people with schizophrenia and 5098 management topics by way of the enhancing neuro imaging genetics by way of meta evaluation (ENIGMA) consortium. Biol Psychiatry. 2018;84(9):644–654. pmid:29960671
Hibar D, Westlye LT, Doan NT, Jahanshad N, Cheung J, Ching CR, et al. Cortical abnormalities in bipolar dysfunction: An MRI evaluation of 6503 people from the ENIGMA bipolar dysfunction working group. Mol Psychiatry. 2018;23(4):932–942. pmid:28461699
Opel N, Thalamuthu A, Milaneschi Y, Grotegerd D, Flint C, Leenings R, et al. Mind structural abnormalities in weight problems: Relation to age, genetic threat, and customary psychiatric issues. Mol Psychiatry. 2020:1–14.
Kirschner M, Hodzic-Santor B, Antoniades M, Nenadic I, Kircher T, Krug A, et al. Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 people assessed in a worldwide ENIGMA research. Mol Psychiatry. 2022;27(2):1167–1176. pmid:34707236
Laansma MA, Brilliant JK, Al-Bachari S, Anderson TJ, Ard T, Assogna F, et al. Worldwide multicenter evaluation of mind construction throughout scientific levels of parkinson’s illness. Mov Disord. 2021. pmid:34288137
Markello RD, Shafiei G, Tremblay C, Postuma RB, Dagher A, Misic B. Multimodal phenotypic axes of parkinson’s illness. NPJ Parkinson Dis. 2021;7(1):1–12. pmid:33402689
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
Markello RD, Misic B. Evaluating spatial null fashions for mind maps. Neuroimage. 2021;118052. pmid:33857618
Vasa F, Misic B. Null fashions in community neuroscience. Nat Rev Neurosci. 2022. pmid:35641793