Can hubs of the human connectome be identified consistently with diffusion MRI?

Gajwani, Mehul; Oldham, Stuart; Pang, James C.; Arnatkevičiūtė, Aurina; Tiego, Jeggan; Bellgrove, Mark A.; Fornito, Alex

doi:10.1162/netn_a_00324

Cited by 4 publications

(8 citation statements)

References 115 publications

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“…After demonstrating that instrength gradients direct traveling waves in a 2D network model, we explored if gradients exist in the human connectome and found that instrength increased from temporal and parietal towards frontal and occipital regions. In contrast to previous studies [65][66][67] , we quantified instrength patterns statistically with spectrospatial mode analysis and found similar gradients across different cohorts and parcellations. We only studied SCs with similarsized parcels, high spatial resolution (≥400 regions), and across many subjects (≥70 subjects) to ensure high quality estimates.…”

Section: Discussioncontrasting

confidence: 65%

Human connectome topology directs cortical traveling waves and shapes frequency gradients

Koller,

Schirner,

Ritter

2024

Nat Commun

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Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While the direction of traveling waves has been linked to brain function and dysfunction, the factors that determine this direction remain elusive. We hypothesized that structural connectivity instrength gradients — defined as the gradually varying sum of incoming connection strengths across the cortex — could shape both traveling wave direction and frequency gradients. We confirm the presence of instrength gradients in the human connectome across diverse cohorts and parcellations. Using a cortical network model, we demonstrate how these instrength gradients direct traveling waves and shape frequency gradients. Our model fits resting-state MEG functional connectivity best in a regime where instrength-directed traveling waves and frequency gradients emerge. We further show how structural subnetworks of the human connectome generate opposing wave directions and frequency gradients observed in the alpha and beta bands. Our findings suggest that structural connectivity instrength gradients affect both traveling wave direction and frequency gradients.

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Section: Discussioncontrasting

confidence: 65%

Human connectome topology directs cortical traveling waves and shapes frequency gradients

Koller,

Schirner,

Ritter

2024

Nat Commun

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“…and the spatial location of high-strength hub nodes. We assess heavy-tailedness and identify hubs using the nonparametric procedure out-lined in [40, 56] (see Methods for more details). Briefly, this procedure entails identifying hubs as the right tail outliers of the strength distribution.…”

Section: Resultsmentioning

confidence: 99%

“…Second, while the datasets covered a broad range of possible processing choices, they did not allow to delineate their individual effects on null model performance. As tools for multiverse analysis in connectomics are developed to facilitate the isolation of specific processing choices across a range of pipelines [40], future work can increasingly interrogate how they affect connectomes and downstream network analysis, including null network generation.…”

Section: Discussionmentioning

confidence: 99%

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A simulated annealing algorithm for randomizing weighted networks

Milisav,

Bazinet,

Betzel

et al. 2024

Preprint

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Scientific discovery in connectomics relies on the use of network null models. To systematically evaluate the prominence of brain network features, empirical measures are compared against null statistics computed in randomized networks. Modern imaging and tracing technologies provide an increasingly rich repertoire of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here, to adapt network null models to weighted network inference, we propose a simulated annealing procedure for generating strength sequence-preserving randomized networks. This model outperforms other commonly used rewiring algorithms in preserving weighted degree (strength). We show that these results generalize to directed networks as well as a wide range of real-world networks, making them generically applicable in neuroscience and in other scientific disciplines. Furthermore, we introduce morphospace representation as a tool for the assessment of null network ensemble variability and feature preservation. Finally, we show how the choice of a network null model can yield fundamentally different inferences about established organizational features of the brain such as the rich-club phenomenon and lay out best practices for the use of rewiring algorithms in brain network inference. Collectively, this work provides a simple but powerful inferential method to meet the challenges of analyzing richly detailed next-generation connectomics datasets.

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“…Tractography exhibits well-documented biases whereby a) short-range connections are overrepresented, overshadowing longer-distance tracts; and b) larger brain regions appear disproportionately connected (i.e., connectivity is positively correlated with regional surface area). 59 To account for these issues, for both the test-retest and validation samples, we generated distance-based consensus structural connectivity matrices per the approach described in ref. 60 .…”

Section: Methodsmentioning

confidence: 99%

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

Greaves,

Novelli,

Razi

2024

Preprint

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Interregional brain communication is mediated by the brain's physical wiring (i.e., structural connectivity). Yet, it remains unclear whether models describing directed, functional interactions between latent neuronal populations—effective connectivity—benefit from incorporating macroscale structural connectivity. Here, we assess a hierarchical empirical Bayes method: structural connectivity-based priors constrain the inversion of group-level resting-state effective connectivity, using subject-level posteriors as input; subsequently, group-level posteriors serve as empirical priors for re-evaluating subject-level effective connectivity. This approach permits knowledge of the brain's structure to inform inference of (multilevel) effective connectivity. In 17 resting-state brain networks, we find that a positive, monotonic relationship between structural connectivity and the prior probability of group-level effective connectivity generalizes across sessions and samples. Providing further validation, we show that inter-network differences in the coupling between structural and effective connectivity recapitulate a well-known unimodal-transmodal hierarchy. Thus, our results provide support for the use of our method over structurally uninformed alternatives.

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Can hubs of the human connectome be identified consistently with diffusion MRI?

Cited by 4 publications

References 115 publications

Human connectome topology directs cortical traveling waves and shapes frequency gradients

Human connectome topology directs cortical traveling waves and shapes frequency gradients

A simulated annealing algorithm for randomizing weighted networks

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

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