Altered large‐scale individual‐based morphological brain network in spinocerebellar ataxia type 3

Su, Shu; Sha, Runhua; Qiu, Haishan; Chu, Jianping; Lin, Liping; Qian, Long; Hu, Manshi; Wu, Chao; Cheung, Gerald L.; Yang, Zhiyun; Chen, Yingqian; Zhao, Jing

doi:10.1111/cns.14332

Cited by 4 publications

(2 citation statements)

References 29 publications

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“… 13 , 14 This method has been widely used in many diseases, including attention‐deficit/hyperactivity disorder (ADHD), 15 autism spectrum disorder (ASD), 16 and spinocerebellar ataxia (SCA). 17 To the best of our knowledge, the use of individual‐level morphological network analysis to study the brain network changes in SMA has not been reported.…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, these shortcomings can be complemented by the newly proposed individual‐level morphological similarity network method by using Kullback–Leibler divergence‐based similarity (KLDs) measurement 13,14 . This method has been widely used in many diseases, including attention‐deficit/hyperactivity disorder (ADHD), 15 autism spectrum disorder (ASD), 16 and spinocerebellar ataxia (SCA) 17 . To the best of our knowledge, the use of individual‐level morphological network analysis to study the brain network changes in SMA has not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Disrupted individual‐level morphological brain network in spinal muscular atrophy types 2 and 3

Li,

Nie,

Xiang

et al. 2024

CNS Neurosci Ther

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Background and ObjectiveSpinal muscular atrophy (SMA) is one of the most common monogenic neuromuscular diseases, and the pathogenesis mechanisms, especially the brain network topological properties, remain unknown. This study aimed to use individual‐level morphological brain network analysis to explore the brain neural network mechanisms in SMA.MethodsIndividual‐level gray matter (GM) networks were constructed by estimating the interregional similarity of GM volume distribution using both Kullback–Leibler divergence‐based similarity (KLDs) and Jesen‐Shannon divergence‐based similarity (JSDs) measurements based on Automated Anatomical Labeling 116 and Hammersmith 83 atlases for 38 individuals with SMA types 2 and 3 and 38 age‐ and sex‐matched healthy controls (HCs). The topological properties were analyzed by the graph theory approach and compared between groups by a nonparametric permutation test. Additionally, correlation analysis was used to assess the associations between altered topological metrics and clinical characteristics.ResultsCompared with HCs, although global network topology remained preserved in individuals with SMA, brain regions with altered nodal properties mainly involved the right olfactory gyrus, right insula, bilateral parahippocampal gyrus, right amygdala, right thalamus, left superior temporal gyrus, left cerebellar lobule IV–V, bilateral cerebellar lobule VI, right cerebellar lobule VII, and vermis VII and IX. Further correlation analysis showed that the nodal degree of the right cerebellar lobule VII was positively correlated with the disease duration, and the right amygdala was negatively correlated with the Hammersmith Functional Motor Scale Expanded (HFMSE) scores.ConclusionsOur findings demonstrated that topological reorganization may prioritize global properties over nodal properties, and disrupted topological properties in the cortical–limbic‐cerebellum circuit in SMA may help to further understand the network pathogenesis underlying SMA.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disrupted individual‐level morphological brain network in spinal muscular atrophy types 2 and 3

Li,

Nie,

Xiang

et al. 2024

CNS Neurosci Ther

Self Cite

View full text Add to dashboard Cite

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Comparative evaluation of interpretation methods in surface-based age prediction for neonates

Wu,

Xie,

Cheng

et al. 2024

NeuroImage

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Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles

Dong,

Liu,

Huang

et al. 2024

Cerebral Cortex

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Spinocerebellar ataxia type 3 (SCA3) is primarily characterized by progressive cerebellar degeneration, including gray matter atrophy and disrupted anatomical and functional connectivity. The alterations of cerebellar white matter structural network in SCA3 and the underlying neurobiological mechanism remain unknown. Using a cohort of 20 patients with SCA3 and 20 healthy controls, we constructed cerebellar structural networks from diffusion MRI and investigated alterations of topological organization. Then, we mapped the alterations with transcriptome data from the Allen Human Brain Atlas to identify possible biological mechanisms for regional selective vulnerability to white matter damage. Compared with healthy controls, SCA3 patients exhibited reduced global and nodal efficiency, along with a widespread decrease in edge strength, particularly affecting edges connected to hub regions. The strength of inter-module connections was lower in SCA3 group and negatively correlated with the Scale for the Assessment and Rating of Ataxia score, International Cooperative Ataxia Rating Scale score, and cytosine–adenine–guanine repeat number. Moreover, the transcriptome–connectome association study identified the expression of genes involved in synapse-related and metabolic biological processes. These findings suggest a mechanism of white matter vulnerability and a potential image biomarker for the disease severity, providing insights into neurodegeneration and pathogenesis in this disease.

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Altered large‐scale individual‐based morphological brain network in spinocerebellar ataxia type 3

Cited by 4 publications

References 29 publications

Disrupted individual‐level morphological brain network in spinal muscular atrophy types 2 and 3

Disrupted individual‐level morphological brain network in spinal muscular atrophy types 2 and 3

Comparative evaluation of interpretation methods in surface-based age prediction for neonates

Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles

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