Rich-club organization is key to efficient global neuronal signaling and integration of information. Alterations interfere with higher-order cognitive processes, and are common to several psychiatric and neurological conditions. A few studies examining the structural connectome in obsessive-compulsive disorder (OCD) suggest lower efficiency of information transfer across the brain. However, it remains unclear whether this is due to alterations in rich-club organization. In the current study, the structural connectome of 28 unmedicated OCD patients, 8 of their unaffected siblings and 28 healthy controls was reconstructed by means of diffusion-weighted imaging and probabilistic tractography. Topological and weighted measures of rich-club organization and connectivity were computed, alongside global and nodal measures of network integration and segregation. The relationship between clinical scores and network properties was explored. Compared to healthy controls, OCD patients displayed significantly lower topological and weighted rich-club organization, allocating a smaller fraction of all connection weights to the rich-club core. Global clustering coefficient, local efficiency, and clustering of nonrich club nodes were significantly higher in OCD patients. Significant three-group differences emerged, with siblings displaying highest and lowest values in different measures. No significant correlation with any clinical score was found. Our results suggest weaker structural connectivity between rich-club nodes in OCD patients, possibly resulting in lower network integration in favor of higher network segregation. We highlight the need of looking at network-based alterations in brain organization and function when investigating the neurobiological basis of this disorder, and stimulate further research into potential familial protective factors against the development of OCD.
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http://dx.doi.org/10.1002/hbm.25984 | DOI Listing |
Comput Biol Med
December 2024
Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China. Electronic address:
Background: Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood.
View Article and Find Full Text PDFEpilepsy Behav
December 2024
Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China. Electronic address:
Background: The fundamental pathophysiologic understanding of different seizure types in Temporal lobe epilepsy (TLE) remains unclear. This study aimed to assess the distinct alterations of structural network in TLE patients with different seizure types and their relationships with cognitive and psychiatric symptoms.
Methods: Seventy-three patients with unilateral TLE, including 25 with uncontrolled focal to bilateral tonic-clonic seizures (FBTCS), 25 with controlled FBTCS and 23 with focal impaired awareness seizures (FIAS), as well as 26 healthy controls (HC), underwent the diffusion tensor imaging (DTI) scan.
Med Phys
December 2024
Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Purposes: Positron emission tomography (PET) imaging is widely used to detect focal lesions or diseases and to study metabolic abnormalities between organs. However, analyzing organ correlations alone does not fully capture the characteristics of the metabolic network. Our work proposes a graph-based analysis method for quantifying the topological properties of the network, both globally and at the nodal level, to detect systemic or single-organ metabolic abnormalities caused by diseases such as lung cancer.
View Article and Find Full Text PDFFront Aging Neurosci
November 2024
Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Front Netw Physiol
October 2024
Brain and Cognition, KU Leuven, Leuven, Belgium.
The nervous system, especially the human brain, is characterized by its highly complex network topology. The neurodevelopment of some of its features has been described in terms of dynamic optimization rules. We discuss the principle of adaptive rewiring, i.
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