Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

Wu, Huijun; Wang, Hao; Lü, Linyuan

doi:10.1142/s0129183118400077

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: ) Graph Classificationmentioning

confidence: 99%

Hyperparameter-free and Explainable Whole Graph Embedding

Wang¹,

Deng

Lü

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Myelin probably shapes the functional activity (Fornari et al, 2007), correlates with psychiatric traits (Ziegler et al, 2019), and interacts with the functional connectome . Technically, myelin owns a putative microstructural MRI marker characterized by the ratio of T1-and T2-weighted (T1w/T2w) MRI images (Glasser & Van Essen, 2011;Sydnor et al, 2021), and a macroscale myelin-based microstructural brain network can be constructed based on the similarity of myelin contents between cortical regions (Wu et al, 2018), enabling the utility of myelin evidence to explore complex structure-function relationships in the human brain.…”

Section: Introductionmentioning

confidence: 99%

Higher-order interaction of brain microstructural and functional connectome

Wang

Liu

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Despite a relatively fixed anatomical structure, the human brain can support rich cognitive functions, triggering particular interest in investigating structure-function relationships. Myelin is a vital brain microstructure marker, yet the individual microstructure-function relationship is poorly understood. Here, we explore the brain microstructure-function relationships using a higher-order framework. Global (network-level) higher-order microstructure-function relationships negatively correlate with male participants’ personality scores and decline with aging. Nodal (node-level) higher-order microstructure-function relationships are not aligned uniformly throughout the brain, being stronger in association cortices and lower in sensory cortices, showing gender differences. Notably, higher-order microstructure-function relationships are maintained from the whole-brain to local circuits, which uncovers a compelling and straightforward principle of brain structure-function interactions. Additionally, targeted artificial attacks can disrupt these higher-order relationships, and the main results are robust against several factors. Together, our results increase the collective knowledge of higher-order structure-function interactions that may underlie cognition, individual differences, and aging.

show abstract

Cortical microstructural brain network mediates the association between personality trait of agreeableness and life satisfaction

Wu,

Fan,

Yan

et al. 2023

Cerebral Cortex

View full text Add to dashboard Cite

Personality traits are commonly regarded as relatively stable, whereas life satisfaction can fluctuate with time and circumstances, shaped by external influences and personal encounters. The correlation between personality traits and life satisfaction is well-established, yet the underlying neural mechanisms of the myelin-based microstructural brain network connecting them remain unclear. Here, we constructed individual-level whole-brain myelin microstructural networks from the MRI data of 1,043 healthy adults and performed correlation analysis to detect significant personality trait-related and life satisfaction-related subnetworks. A mediation analysis was used to verify whether the shared structural basis of personality traits and life satisfaction significantly mediated their association. The results showed that agreeableness positively correlated with life satisfaction. We identified a shared structural basis of the personality trait of agreeableness and life satisfaction. The regions comprising this overlapping network include the superior parietal lobule, inferior parietal lobule, and temporoparietal junction. Moreover, the shared microstructural connections mediate the association between the personality trait of agreeableness and life satisfaction. This large-scale neuroimaging investigation substantiates a mediation framework for understanding the microstructural connections between personality and life satisfaction, offering potential targets for assessment and interventions to promote human well-being.

show abstract

Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

Cited by 4 publications

References 32 publications

Hyperparameter-free and Explainable Whole Graph Embedding

Hyperparameter-free and Explainable Whole Graph Embedding

Higher-order interaction of brain microstructural and functional connectome

Cortical microstructural brain network mediates the association between personality trait of agreeableness and life satisfaction

Contact Info

Product

Resources

About