Insomnia disorder is a significant global health concern. This research aimed to explore the pathogenesis of insomnia disorder using static and dynamic degree centrality methods at the voxel level. A total of 29 patients diagnosed with insomnia disorder and 28 healthy controls were ultimately included to examine differences in degree centrality between the two groups. Additionally, the relationship between altered degree centrality values and various clinical indicators was analyzed. The results revealed that patients with insomnia disorder exhibited higher static degree centrality in brain regions associated with sensory processing, such as the occipital gyrus, inferior temporal gyrus, and supramarginal gyrus. In contrast, lower static degree centrality was observed in the parahippocampal gyrus, amygdala, insula, and thalamus. Changes in dynamic degree centrality were identified in regions including the parahippocampal gyrus, anterior cingulum, medial superior frontal gyrus, inferior parietal gyrus, and precuneus. Notably, a negative correlation was found between dynamic degree centrality in the inferior parietal gyrus and the Pittsburgh Sleep Quality Index, while a positive correlation was observed between static degree centrality in the inferior temporal gyrus and the Hamilton Depression Scale. These findings suggest that dysfunction in centrality within the sensory processing cortex and subcortical nuclei may be associated with the sleep-wake imbalance in individuals with insomnia disorder, contributing to our understanding of hyperarousal mechanisms in insomnia. Moreover, the abnormalities observed in the default mode network and the salience network provide insights into understanding the neuropathogenesis of insomnia from both static and dynamic centrality perspectives. The clinical trial registration number: ChiCTR2200058768. Date: 2022-04-16.
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http://dx.doi.org/10.1007/s11682-024-00958-8 | DOI Listing |
Neuroimage
January 2025
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China; NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China. Electronic address:
Environmental and social changes during early school age have a profound impact on brain development. However, it remains unclear how the brains of typically-developing children adjust white matter to optimize network topology during this period. This study aims to propose the fiber length distribution as a novel nodal metric to capture the continuous maturation of brain network.
View Article and Find Full Text PDFPLoS One
January 2025
Laboratoire d'Anthropologie Sociale, Ecole des Hautes Etudes en Sciences Sociales, Paris, France.
Civic organizations, ranging from interest groups to voluntary associations, significantly influence policy formation in representative democracies. This work presents a local case study that examines the relationship between voluntary associations and local political institutions in a village with nearly two thousand residents. Traditionally, sociologists' approaches have focused on individual characteristics such as age, gender, or socio-professional status.
View Article and Find Full Text PDFJ Pain Res
January 2025
Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Purpose: To investigate whether functional radiomic features in bilateral hippocampi can identify the cognitively impaired patients from low-back-related leg pain (LBLP).
Patients And Methods: For this retrospective study, a total of 95 clinically definite LBLP patients (40 cognitively impaired patients and 45 cognitively preserved patients) were included, and all patients underwent functional MRI and clinical assessments. After calculating the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) imaging, the radiomic features (n = 819) of bilateral hippocampi were extracted from these images, respectively.
Sci Rep
January 2025
Imaging Department, Yantaishan Hospital, Yantai, China.
Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region.
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