Cross-modality comparison between structural and metabolic networks in individual brain based on the Jensen-Shannon divergence method: a healthy Chinese population study

Li, Yulin; Zheng, Mou‐Xiong; Hua, Xu‐Yun; Gao, Xin; Wu, Jiajia; Shan, Chunlei; Zhang, Junpeng; Dong, Wei; Xu, Jian‐Guang

doi:10.1007/s00429-023-02616-z

Cited by 2 publications

(2 citation statements)

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“…In addition, the individual morphological similarity between cortical areas has been shown to be aligned with spatial expression patterns of certain important genes 32 . A newly published study explored the consistency and diversity between individual metabolic and morphological connectivity networks, and the results indicated that cross‐modality cooperation or specialization in these two networks might imply mechanisms for achieving higher order brain functions 41 . To sum up, the topological features of morphological networks may rely on specific anatomical, functional, and genetic bases, which correlate with cognitive functions and can provide effective biological markers for brain research.…”

Section: Applications In the Human Brainmentioning

confidence: 99%

“…32 A newly published study explored the consistency and diversity between individual metabolic and morphological connectivity networks, and the results indicated that cross-modality cooperation or specialization in these two networks might imply mechanisms for achieving higher order brain functions. 41 To sum up, the topological features of morphological networks may rely on specific anatomical, functional, and genetic bases, which correlate with cognitive functions and can provide effective biological markers for brain research. Currently, the individual morphological network approach has been widely applied in the research of normal brain and neuropsychiatric disorders (Table 1).…”

Section: Appli C Ati On S In the H Uman B R Ainmentioning

confidence: 99%

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Individual‐level brain morphological similarity networks: Current methodologies and applications

Cai

Wang

et al. 2023

CNS Neurosci Ther

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AimsThe human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)‐based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI‐based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual‐level morphological networks.BackgroundThere have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual‐level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders.ConclusionThis paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions.

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Section: Applications In the Human Brainmentioning

confidence: 99%

Section: Appli C Ati On S In the H Uman B R Ainmentioning

confidence: 99%