Background: Cerebral small vessel disease (CSVD) includes vascular disorders characterized by heterogeneous pathomechanisms and different neuropathological clinical manifestations. Cognitive dysfunction in CSVD is associated with reductions in structural covariance networks (SCNs). A majority of research conducted on SCNs focused on group-level analysis. However, it is crucial to investigate the individualized variations in order to gain a better understanding of heterogeneous disorders such as CSVD. Therefore, this study aimed to utilize individualized differential structural covariance network (IDSCN) analysis to detect individualized structural covariance aberration.
Methods: A total of 35 healthy controls and 33 CSVD patients with cognitive impairment participated in this investigation. Using the regional gray matter volume in their T1 images, the IDSCN was constructed for each participant. Finally, the differential structural covariance edges between the two groups were determined by comparing their IDSCN using paired-sample t-tests. On the basis of these differential edges, the two subtypes of cognitively impaired CSVD patients were identified.
Results: The findings revealed that the differential structural covariance edges in CSVD patients with cognitive impairment showed a highly heterogeneous distribution, with the edges primarily cross-distributed between the occipital lobe (specifically inferior occipital gyrus and cuneus), temporal lobe (specifically superior temporal gyrus), and the cerebellum. To varying degrees, the inferior frontal gyrus and the superior parietal gyrus were also distributed. Subsequently, a correlation analysis was performed between the resulting differential edges and the cognitive scale scores. A significant negative association was observed between the cognitive scores and the differential edges distributed in the inferior frontal gyrus and inferior occipital gyrus, the superior temporal gyrus and inferior occipital gyrus, and within the temporal lobe. Particularly in the cognitive domain of attention, the two subtypes separated by differential edges exhibited differences in cognitive scale scores [Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)]. The differential edges of the subtype 1, characterized by lower cognitive level, were mainly cross-distributed in the limbic lobe (specifically the cingulate gyrus and hippocampus), the parietal lobe (including the superior parietal gyrus and precuneus), and the cerebellum. In contrast, the differential edges of the subtype 2 with a relatively high level of cognition were distributed between the cuneus and the cerebellum.
Conclusions: The differential structural covariance was investigated between the healthy controls and the CSVD patients with cognitive impairment, showing that differential structural covariance existed between the two groups. The edge distributions in certain parts of the brain, such as cerebellum and occipital and temporal lobes, verified this. Significant associations were seen between cognitive scale scores and some of those differential edges .The two subtypes that differed in both differential edges and cognitive levels were also identified. The differential edges of subtype 1 with relatively lower cognitive levels were more distributed in the cingulate gyrus, hippocampus, superior parietal gyrus, and precuneus. This could potentially offer significant benefits in terms of accurate diagnosis and targeted treatment of heterogeneous disorders such as CSVD.
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http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2024.107829 | DOI Listing |
Sci Rep
January 2025
Faculty of Natural Sciences and Engineering, University of Ljubljana, Aškerčeva cesta 12, 1000, Ljubljana, Slovenia.
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Max Planck Institute for Intelligent Systems, Tübingen 72076, Germany.
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Laboratory of Tissue Biology and Therapeutic Engineering, UMR5305 CNRS, University Lyon 1, Lyon Cedex 07, France.
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January 2025
Air Force Engineering University, Xi'an, 710038, Shaanxi, China.
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Gansu Key Laboratory of Herbivorous Animal Biotechnology, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China.
Wool growth and fineness regulation is influenced by some factors such as genetics and environment. At the same time, lncRNA participates in numerous biological processes in animal production. In this research, we conducted a thorough analysis and characterization of the microstructure of wool, along with long non-coding RNAs (lncRNAs), their target genes, associated pathways, and Gene Ontology terms pertinent to the wool fineness development.
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