Omnipresence of the sensorimotor-association axis topography in the human connectome

Nenning, Karl-Heinz; Xu, Ting; Franco, Alexandre R.; Swallow, Khena M.; Tambini, Arielle; Margulies, Daniel S.; Smallwood, Jonathan; Colcombe, Stanley J.; Milham, Michael P.

doi:10.1016/j.neuroimage.2023.120059

Cited by 8 publications

(3 citation statements)

References 95 publications

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“…It has been shown that, similar to our results, thresholding weak connections (up to ∼ 90%) has no effect on the topological properties of the structural networks [22]. Connectivity gradients, a low-dimensional representation of functional interactions, are robust against the full range of connectivity strength thresholds [71]. Furthermore, thresholding enhances network-age association, by potentially removing spurious connections [14].…”

Section: Discussionsupporting

confidence: 78%

Binary Brains: How Excitable Dynamics Simplify Neural Connectomes

Messé,

Hütt,

Hilgetag

2024

Preprint

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Fiber networks connecting different brain regions are the structural foundation of brain dynamics and function. Recent studies have provided detailed characterizations of neural connectomes with weighted connections. However, the topological analysis of weighted networks still has conceptual and practical challenges. Consequently, many investigations of neural networks are performed on binarized networks, and the functional impact of unweighted versus weighted networks is unclear. Here we show, for the widespread case of excitable dynamics, that the excitation patterns observed in weighted and unweighted networks are nearly identical, if an appropriate network threshold is selected. We generalize this observation to different excitable models, and formally predict the network threshold from the intrinsic model features. The network-binarizing capacity of excitable dynamics suggests that neural activity patterns may primarily depend on the strongest structural connections. Our findings have practical advantages in terms of the computational cost of representing and analyzing complex networks. There are also fundamental implications for the computational simulation of connectivity-based brain dynamics and the computational function of diverse other systems governed by excitable dynamics such as artificial neural networks.

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Section: Discussionsupporting

confidence: 78%

Binary Brains: How Excitable Dynamics Simplify Neural Connectomes

Messé,

Hütt,

Hilgetag

2024

Preprint

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“…The final networks were unthresholded, signed, and weighted. We chose to use weighted rather than binary edges to reflect variation in the strength of connectivity (Cole et al, 2012; Rubinov and Sporns, 2011; Santarnecchi et al, 2014), and included both positive and negative edges because of evidence that anticorrelations are meaningful (Chai et al, 2014; Nenning et al, 2023; Santarnecchi et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Multilayer network associations between the exposome and childhood brain development

Simpson-Kent,

Gataviņš,

Tooley

et al. 2023

Preprint

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Growing up in a high poverty neighborhood is associated with elevated risk for academic challenges and health problems. Here, we take a data-driven approach to exploring how measures of children's environments relate to the development of their brain structure and function in a community sample of children between the ages of 4 and 10 years. We constructed exposomes including measures of family socioeconomic status, children's exposure to adversity, and geocoded measures of neighborhood socioeconomic status, crime, and environmental toxins. We connected the exposome to two structural measures (cortical thickness and surface area, n = 170) and two functional measures (participation coefficient and clustering coefficient, n = 130). We found dense connections within exposome and brain layers and sparse connections between exposome and brain layers. Lower family income was associated with thinner visual cortex, consistent with the theory that accelerated development is detectable in early-developing regions. Greater neighborhood incidence of high blood lead levels was associated with greater segregation of the default mode network, consistent with evidence that toxins are deposited into the brain along the midline. Our study demonstrates the utility of multilayer network analysis to bridge environmental and neural explanatory levels to better understand the complexity of child development.

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“…The largest cluster is characterized by cofluctuations of default and salience/ventral attention networks. When projected onto nodes, this cofluctuation pattern is immediately recognizable as the default mode network [ 57 ], first principal gradient [ 58 ], sensorimotor–association axis [ 59 ], or extrinsic/intrinsic or task-positive/negative division [ 60 , 61 ]. The second cluster is characterized by opposed cofluctuations of control and dorsal attention networks.…”

Section: Edge-centric Networkmentioning

confidence: 99%

Living on the edge: network neuroscience beyond nodes

Betzel,

Faskowitz,

Sporns

2023

Trends in Cognitive Sciences

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Omnipresence of the sensorimotor-association axis topography in the human connectome

Cited by 8 publications

References 95 publications

Binary Brains: How Excitable Dynamics Simplify Neural Connectomes

Binary Brains: How Excitable Dynamics Simplify Neural Connectomes

Multilayer network associations between the exposome and childhood brain development

Living on the edge: network neuroscience beyond nodes

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