Aims: This study aimed to characterize the topological alterations and classification performance of high-order functional connectivity (HOFC) networks in cognitively preserved patients with Parkinson's disease (PD), relative to low-order FC (LOFC) networks.
Methods: The topological metrics of the constructed networks (LOFC and HOFC) obtained from fifty-one cognitively normal patients with PD and 60 matched healthy control subjects were analyzed. The discriminative abilities were evaluated using machine learning approach.
Results: The HOFC networks in the PD group showed decreased segregation and integration. The normalized clustering coefficient and small-worldness in the HOFC networks were correlated to motor performance. The altered nodal centralities (distributed in the precuneus, putamen, lingual gyrus, supramarginal gyrus, motor area, postcentral gyrus and inferior occipital gyrus) and intermodular FC (frontoparietal and visual networks, sensorimotor and subcortical networks) were specific to HOFC networks. Several highly connected nodes (thalamus, paracentral lobule, calcarine fissure and precuneus) and improved classification performance were found based on HOFC profiles.
Conclusion: This study identified disrupted topology of functional interactions at a high level with extensive alterations in topological properties and improved differentiation ability in patients with PD prior to clinical symptoms of cognitive impairment, providing complementary insights into complex neurodegeneration in PD.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873517 | PMC |
| http://dx.doi.org/10.1111/cns.14037 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Distinct homotopic functional connectivity patterns of the amygdalar sub-regions as biomarkers in major depressive disorder.
J Affect Disord
November 2024
Division of Diagnostic Imaging, Sheba Medical Center, Tel-Hashomer, Israel; Department of Imaging, Faculty of Medical & Health Sciences, Tel-Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. Electronic address:
Article Synopsis
- The study investigated how major depressive disorder (MDD) affects the functional connectivity between the left and right amygdala, particularly looking at the medial sub-region in MDD patients compared to healthy controls.
- Findings revealed that MDD patients showed reduced inter-hemispheric connectivity in the medial amygdala, which correlated with the severity of depressive symptoms and emotional processing abilities.
- Additionally, the medial amygdala's connectivity levels could predict treatment responses to SSRIs in over 65% of cases, highlighting the importance of this brain region in understanding depression.
Topological disruption of low- and high-order functional networks in presbycusis.
Brain Commun
April 2024
Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China.
Article Synopsis
- * It found that HOFC networks provided better classification accuracy, sensitivity, and specificity in distinguishing patients with presbycusis from healthy controls compared to LOFC networks.
- * Significant topological differences were identified in the brain networks of both groups, particularly a specific subnetwork in the HOFC networks of patients with presbycusis, indicating potential brain network abnormalities that could aid in diagnosis.
Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.
IEEE Trans Med Imaging
September 2024
Article Synopsis
- Identifying Alzheimer's disease progression stages through machine learning is complicated by class imbalances and disease variability.
- Recent applications of graph convolutional networks (GCNs) have shown promise in AD classification, yet they overlook the class imbalance and heterogeneity of the disease.
- The proposed method, CSWCL-GCN, employs a cost-sensitive weighted contrastive learning strategy to improve classification accuracy, particularly for minority classes, and it has demonstrated superior performance on a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Low and high-order topological disruption of functional networks in multiple system atrophy with freezing of gait: A resting-state study.
Neurobiol Dis
June 2024
Department of radiology, the first hospital of China medical University,Shenyang, 155 Nanjing North Street, Shenyang 110001, Liaoning, PR China. Electronic address:
Article Synopsis
- The study focuses on freezing of gait (FOG) in patients with multiple system atrophy (MSA), a rapidly progressing and serious condition, aiming to understand the topological organization of brain networks in those with and without FOG.
- Researchers analyzed low-order and high-order functional connectivity in 24 MSA patients without FOG, 20 with FOG, and 25 healthy controls using graph theory, discovering significant alterations in the brain networks of patients with FOG.
- The findings revealed disrupted functional interactions and identified specific brain regions with altered connectivity that are linked to the severity of FOG, highlighting potential new neuroimaging biomarkers for understanding this gait impairment.
The resting-state functional magnetic resonance imaging (rs-fMRI) faithfully reflects the brain activities and thus provides a promising tool for autism spectrum disorder (ASD) classification. Up to now, graph convolutional networks (GCNs) have been successfully applied in rs-fMRI based ASD classification. However, most of these methods were developed based on functional connectivities (FCs) that only reflect low-level correlation between brain regions, without integrating both high-level discriminative knowledge and phenotypic information into classification.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!