Abnormal Topological Organization of Human Brain Connectome in Primary Dysmenorrhea Patients Using Graph Theoretical Analysis.

Background: Accumulating studies have revealed altered brain function and structure in regions linked to sensory, pain and emotion in individuals with primary dysmenorrhea (PD). However, the changes in the topological properties of the brain's functional connectome in patients with PD experiencing chronic pain remain poorly understood. Purpose: Our study aimed to explore the mechanism of functional brain network impairment in individuals withPD through a graph-theoretic analysis. Material and Methods: This study was a randomized controlled trial that included individuals with PD and healthy controls (HC) from June 2021 to June 2022. The experiment took place in the magnetic resonance imaging facility at Jiangxi Provincial People's Hospital. Static MRI scans were conducted on 23 female patients with PD and 23 healthy female controls. A two-sample t-test was conducted to compare the global and nodal indices between the two groups, while the Network-Based Statistics (NBS) method was utilized to explore the functional connectivity alterations between the groups. Results: In the global index, The PD group exhibited decreased Sigma (p = 0.0432) and Gamma (p = 0.0470) compared to the HC group among the small-world network properties.(p<0.05) In the nodal index, the PD group displayed reduced betweenness centrality and increased degree centrality in the default mode network (DMN), along with decreased nodal efficiency and increased degree centrality in the visual network (VN). (P < 0.05, Bonferroni-corrected) Furthermore, in the connection analysis, PD patients showed altered functional connectivity in the basal ganglia network (BGN), VN, and DMN.(NBS corrected). Conclusion: Our results indicate that individuals with PD showed abnormal brain network efficiency and abnormal connection within DMN, VN and BGN related to pain matrix. These findings have important references for understanding the neural mechanism of pain in PD.

Topological Organization of the Brain Network in Patients with Primary Angle-closure Glaucoma Through Graph Theory Analysis.

Primary angle-closure glaucoma (PACG) is a sight-threatening eye condition that leads to irreversible blindness. While past neuroimaging research has identified abnormal brain function in PACG patients, the relationship between PACG and alterations in brain functional networks has yet to be explored. This study seeks to examine the influence of PACG on brain networks, aiming to advance knowledge of its neurobiological processes for better diagnostic and therapeutic approaches utilizing graph theory analysis. A cohort of 44 primary angle-closure glaucoma (PACG) patients and 44 healthy controls participated in this study. Functional brain networks were constructed using fMRI data and the Automated Anatomical Labeling 90 template. Subsequently, graph theory analysis was employed to evaluate global metrics, nodal metrics, modular organization, and network-based statistics (NBS), enabling a comparative analysis between PACG patients and the control group. The analysis of global metrics, including small-worldness and network efficiency, did not exhibit significant differences between the two groups. However, PACG patients displayed elevated nodal metrics, such as centrality and efficiency, in the left frontal superior medial, right frontal superior medial, and right posterior central brain regions, along with reduced values in the right temporal superior gyrus region compared to healthy controls. Furthermore, Module 5 showed notable disparities in intra-module connectivity, while Module 1 demonstrated substantial differences in inter-module connectivity with both Module 7 and Module 8. Noteworthy, the NBS analysis unveiled a significantly altered network when comparing the PACG and healthy control groups. The study proposes that PACG patients demonstrate variations in nodal metrics and modularity within functional brain networks, particularly affecting the prefrontal, occipital, and temporal lobes, along with cerebellar regions. However, an analysis of global metrics suggests that the overall connectivity patterns of the entire brain network remain unaltered in PACG patients. These results have the potential to serve as early diagnostic and differential markers for PACG, and interventions focusing on brain regions with high degree centrality and nodal efficiency could aid in optimizing therapeutic approaches.

Machine learning analysis reveals abnormal functional network hubs in the primary angle-closure glaucoma patients.

Background: Primary angle-closure glaucoma (PACG) is a serious and irreversible blinding eye disease. Growing studies demonstrated that PACG patients were accompanied by vision and vision-related brain region changes. However, whether the whole-brain functional network hub changes occur in PACG patients remains unknown. Purpose: The purpose of the study was to investigate the brain function network hub changes in PACG patients using the voxel-wise degree centrality (DC) method. Materials and methods: Thirty-one PACG patients (21 male and 10 female) and 31 healthy controls (HCs) (21 male and 10 female) closely matched in age, sex, and education were enrolled in the study. The DC method was applied to investigate the brain function network hub changes in PACG patients. Moreover, the support vector machine (SVM) method was applied to distinguish PACG patients from HC patients. Results: Compared with HC, PACG patients had significantly higher DC values in the right fusiform, left middle temporal gyrus, and left cerebelum_4_5. Meanwhile, PACG patients had significantly lower DC values in the right calcarine, right postcentral gyrus, left precuneus gyrus, and left postcentral gyrus. Furthermore, the SVM classification reaches a total accuracy of 72.58%, and the ROC curve of the SVM classifier has an AUC value of 0.85 (r = 0.25). Conclusion: Our results showed that PACG patients showed widespread brain functional network hub dysfunction relative to the visual network, auditory network, default mode network, and cerebellum network, which might shed new light on the neural mechanism of optic atrophy in PACG patients.

Altered Temporal Dynamics of the Amplitude of Low-Frequency Fluctuations in Comitant Exotropia Patients.

Purpose: Growing evidence reported that patients with comitant exotropia (CE) were accompanied by static cerebral neural activity changes. However, whether the dynamic time-varying of neural activity changes in patients with CE remains unknown. Methods: A total of 36 patients with CE (25 men and 11 women) and 36 well-matched healthy controls are enrolled in the study. The dynamic amplitude of low-frequency fluctuation (dALFF) combined with the sliding window method was used to assess the dynamic neural activity changes in patients with CE. Results: Compared with HCs, patients with CE had decreased dALFF values in the right superior parietal lobule (SPL) and right precuneus gyrus (PreCUN). Moreover, we found that the dALFF maps showed an accuracy of 48.61% and an area under the curve of.54 for distinguishing the patients with CE from HCs. Conclusion: Our study demonstrated that patients with CE showed altered dynamic neural activity changes in the right SPL and right PreCUN, which might indicate the neuropathological mechanism of stereoscopic dysfunction in patients with CE.

The Effect of Long-Term Menstrual Pain on Large-Scale Brain Network in Primary Dysmenorrhea Patients.

Purpose: Primary dysmenorrhea (PD) is a common gynecological disease, characterized by crampy and suprapubic pain occurring with menses. Growing evidences demonstrated that PD patients were associated with abnormalities in brain function and structure. However, little is known regarding whether the large-scale brain network changes in PD patients. The purpose of this study was to investigate the effect of long-term menstrual pain on large-scale brain network in PD patients using independent component analysis (ICA) method. Methods: Twenty-eight PD patients (female, mean age, 24.25±1.00 years) and twenty-eight healthy controls (HCs) (mean age, 24.46±1.31 years), closely matched for age, sex, and education, underwent resting-state magnetic resonance imaging scans. ICA was applied to extract the resting-state networks (RSNs) in two groups. Then, two-sample t-tests were conducted to investigate different intranetwork FCs within RSNs and interactions among RSNs between two groups. Results: Compared to the HC group, PD patients showed significant increased intra-network FCs within the auditory network (AN), sensorimotor network (SMN), right executive control network (RECN). However, PD patients showed significant decreased intra-network FCs within ventral default mode network (vDMN) and salience network (SN). Moreover, FNC analysis showed increased VN-AN and decreased VN-SMN functional connectivity between two groups. Conclusion: Our study highlighted that PD patients had abnormal brain networks related to auditory, sensorimotor and higher cognitive network. Our results offer important insights into the altered large-scale brain network neural mechanisms of pain in PD patients.

The predictive value of local to remote functional connectivity changes in comitant exotropia patients.

BACKGROUND: Comitant exotropia (CE) is a common eye disease with abnormal eye movement, whereas altered synchronous neural activity in CE patients is poorly understood. The purpose of our study was to investigate local to remote functional connectivity of blood oxygen level-dependent (BOLD) signals changes in CE patients. MATERIAL AND METHODS: Thirty-four patients and thirty-four healthy controls (HCs) underwent resting-state MRI scans. The ReHo and FC method was applied to investigate the local to remote functional connectivity changes in CE patients. RESULTS: Compared to the HC group, CE patients showed significant increased ReHo values in the left cerebellar_crus2 and left middle frontal gyrus. Meanwhile, CE patients showed significant decreased ReHo values in the right middle temporal gyrus, left postcentral gyrus and right angular. Moreover, CE patients showed an increased FC between the cerebellar network, sensorimotor network (SMN) and default-mode network (DMN). The support vector machine (SVM) classification was up to a total accuracy of 94.12%. The AUC of the classification model was 0.99 on the basis of ReHo map. CONCLUSION: Our result highlights that CE patients had abnormal local to remote functional connectivity in the cerebellar network, SMN, DMN, which might indicate the neural mechanism of eye movements and stereo vision dysfunction in CE patients. Moreover, the SVM algorithm reveals ReHo maps as a potential biomarker for predicting clinical outcomes in CE patients.

Effect of Impaired Stereoscopic Vision on Large-Scale Resting-State Functional Network Connectivity in Comitant Exotropia Patients.

Background: Comitant exotropia (CE) is a common eye movement disorder, characterized by impaired eye movements and stereoscopic vision. CE patients reportedly exhibit changes in the central nervous system. However, it remains unclear whether large-scale brain network changes occur in CE patients. Purpose: This study investigated the effects of exotropia and stereoscopic vision dysfunction on large-scale brain networks in CE patients via independent component analysis (ICA). Methods: Twenty-eight CE patients (mean age, 15.80 ± 2.46 years) and 27 healthy controls (HCs; mean age, 16.00 ± 2.68 years; closely matched for age, sex, and education) underwent resting-state magnetic resonance imaging. ICA was applied to extract resting-state networks (RSNs) in both groups. Two-sample's t-tests were conducted to investigate intranetwork functional connectivity (FC) within RSNs and interactions among RSNs between the two groups. Results: Compared with the HC group, the CE group showed increased intranetwork FC in the bilateral postcentral gyrus of the sensorimotor network (SMN). The CE group also showed decreased intranetwork FC in the right cerebellum_8 of the cerebellum network (CER), the right superior temporal gyrus of the auditory network (AN), and the right middle occipital gyrus of the visual network (VN). Moreover, functional network connectivity (FNC) analysis showed that CER-AN, SMN-VN, SN-DMN, and DMN-VN connections were significantly altered between the two groups. Conclusion: Comitant exotropia patients had abnormal brain networks related to the CER, SMN, AN, and VN. Our results offer important insights into the neural mechanisms of eye movements and stereoscopic vision dysfunction in CE patients.

Machine Learning Analysis Reveals Abnormal Static and Dynamic Low-Frequency Oscillations Indicative of Long-Term Menstrual Pain in Primary Dysmenorrhea Patients.

BACKGROUND: Previous neuroimaging studies demonstrated that patients with primary dysmenorrhea (PD) exhibited dysfunctional resting-state brain activity. However, alterations of dynamic brain activity in PD patients have not been fully characterized. PURPOSE: Our study aimed to assess the effect of long-term menstrual pain on changes in static and dynamic neural activity in PD patients. MATERIAL AND METHODS: Twenty-eight PD patients and 28 healthy controls (HCs) underwent resting-state magnetic resonance imaging scans. The amplitude of low-frequency fluctuations (ALFF) and dynamic ALFF was used as classification features in a machine learning approach involving a support vector machine (SVM) classifier. RESULTS: Compared with the HC group, PD patients showed significantly increased ALFF values in the right cerebellum_crus2, right rectus, left supplementary motor area, right superior frontal gyrus, right supplementary motor area, and left superior frontal medial gyrus. Additionally, PD patients showed significantly decreased ALFF values in the right middle temporal gyrus and left thalamus. PD patients also showed significantly increased dALFF values in the right fusiform, Vermis_10, right middle temporal gyrus, right putamen, right insula, left thalamus, right precentral gyrus, and right postcentral gyrus. Based on ALFF and dALFF values, the SVM classifier achieved respective overall accuracies of 96.36% and 85.45% and respective areas under the curve of 1.0 and 0.95. CONCLUSION: PD patients demonstrated abnormal static and dynamic brain activities that involved the default mode network, sensorimotor network, and pain-related subcortical nuclei. Moreover, ALFF and dALFF may offer sensitive biomarkers for distinguishing patients with PD from HCs.