Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability

Li, Yinzhi; Wang, Ningkai; Wang, Hao; Lv, Yating; Zou, Qihong; Wang, Jinhui

doi:10.1016/j.neuroimage.2021.118018

Cited by 58 publications

(75 citation statements)

References 78 publications

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“…We use a common framework (Kong et al, 2015; Li et al, 2021b; Tijms et al, 2012) to map individual morphometry-based SCs based on the interregional similarity of morphological features. Considering that the distribution of morphological features of brain regions is discrete, we choose the earth mover’s distance, which is a statistical measurement of the difference between two discrete probability distributions (Elizaveta Levina, 2001; Mallows, 1972).…”

Section: Star Methodsmentioning

confidence: 99%

Structural insight into the individual variability architecture of the functional brain connectome

Sun

Zhao

Liang

et al. 2022

Preprint

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Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher prediction accuracy in primary regions and lower accuracy in heteromodal regions. We also decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

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Section: Star Methodsmentioning

confidence: 99%

Structural insight into the individual variability architecture of the functional brain connectome

Sun

Zhao

Liang

et al. 2022

Preprint

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“…For a given pair of regions within the cerebellum, morphological connectivity was estimated by calculating Jensen-Shannon divergence based similarity in the distribution of intraregional cortical thickness. Details on the calculation of morphological connectivity are described elsewhere (Li et al 2021; H. Wang et al 2016).…”

Section: Methodsmentioning

confidence: 99%

“…Details on the calculation of morphological connectivity are described elsewhere (Li et al 2021;H. Wang et al 2016).…”

Section: Morphological Connectivity Estimationmentioning

confidence: 99%

A Multisite and Multimodal Comparative Study of Cerebellar Connectome Between Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders

Yang

et al. 2022

Preprint

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The cerebellum has been increasingly recognized to play key roles in the pathology of multiple sclerosis (MS) and spectrum disorders (NMOSD), two main demyelinating diseases with similar clinical presentations. Despite accumulating evidence from neuroimaging research for cerebellar volumetric alterations in the diseases, however, there have been no network-based studies examining convergent and divergent alterations in cerebellar connectome between MS and NMOSD. This multisite and multimodal study examined common and specific alterations in within-cerebellar coordination and cerebello-cerebral communication between MS and NMOSD by retrospectively collecting structural and resting-state functional MRI data from 208 MS patients, 200 NMOSD patients and 228 healthy controls (HCs) in seven sites in China. Morphological brain networks were constructed by estimating interregional similarity in cortical thickness and functional brain networks were formed by calculating interregional temporal synchronization in functional signals. After identifying cerebellar modular architecture and based on prior cerebral cytoarchitectonic classification and functional partition, within-cerebellar and cerebello-cerebral morphological and functional connectivity were compared among the MS, NMOSD and HC groups. Five modules were identified within the cerebellum including Primary Motor A (PMA), Primary Motor B (PMB), Primary Non-Motor (PNM), Secondary Motor (SM) and Secondary Non-Motor (SNM) modules. Compared with the HCs, the MS and NMOSD patients exhibited both increases and decreases in within-cerebellar morphological connectivity that were mainly involved in the PMA, PMB and SNM. Particularly, the two patient groups showed a common altered pattern characterized by decreases between the PMA and SNM, both of which were more densely connected with the PMB. For cerebello-cerebral morphological connectivity, widespread reductions were found in both patient groups for the SM and SNM with almost all cerebral cytoarchitectonic classes and functional systems while increases were observed only in the NMOSD patients for the PMB with cerebral areas involving motor and sensory domains. With regard to cerebellar functional connectivity, fewer alterations were observed in the patients that were all characterized by reductions and were mainly involved in cerebello-cerebral interactions between cerebellar motor modules and cerebral association cortex and high-order networks, particularly in the NMOSD patients. Cerebellar connectivity-based classification achieved around 60% accuracies to distinguish the three groups to each other with morphological connectivity as predominant features for differentiating the patients from controls while functional connectivity for discriminating the two diseases. Altogether, this study characterizes common and specific circuit dysfunctions of the cerebellum between MS and NMOSD, which provide novel insights into shared and unique pathophysiologic mechanisms underlying the two diseases.

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“…Specifically, to construct the brain network, nodes were first defined based on the Schaefer atlas [50] with 400 brain regions (Schaef er2018 400P arcels 17N etworks). Second, the relationships among different nodes were calculated by the Earth Mover's Distance (EMD) (D E ) between the probability distribution function (PDF) of the brain cortical sulcal depth of any paired brain regions [51], [52]. In detail, the brain cortical sulcal depth value was extracted for each brain region.…”

Section: ) Graph Classificationmentioning

confidence: 99%

Hyperparameter-free and Explainable Whole Graph Embedding

Wang¹,

Deng

Lü

et al. 2021

Preprint

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Many real-world complex systems can be described as graphs. For a large-scale graph with low sparsity, a node's adjacency vector is a long and sparse representation, limiting the practical utilization of existing machine learning methods on nodal features. In practice, graph embedding (graph representation learning) attempts to learn a lower-dimensional representation vector for each node or the whole graph while maintaining the most basic information of graph. Since various machine learning methods can efficiently process lowerdimensional vectors, graph embedding has recently attracted a lot of attention. However, most node embedding or whole graph embedding methods suffer from the problem of having more sophisticated methodology, hyperparameter optimization, and low explainability. This paper proposes a hyperparameterfree, extensible, and explainable whole graph embedding method, combining the DHC (Degree, H-index and Coreness) theorem and Shannon Entropy (E), abbreviated as DHC-E. The new whole graph embedding scheme can obtain a trade-off between simplicity and quality under supervised classification learning tasks, using molecular, social, and brain networks. In addition, the proposed approach has a good performance in lowerdimensional graph visualization. Overall, the new methodology is simple, hyperparameter-free, extensible, and explainable for whole graph embedding with promising potential for exploring graph classification, prediction, and lower-dimensional graph visualization.

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Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability

Cited by 58 publications

References 78 publications

Structural insight into the individual variability architecture of the functional brain connectome

Structural insight into the individual variability architecture of the functional brain connectome

A Multisite and Multimodal Comparative Study of Cerebellar Connectome Between Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders

Hyperparameter-free and Explainable Whole Graph Embedding

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