Aberrant functional brain network dynamics in patients with functional constipation

Altered static and dynamic functional brain network in knee osteoarthritis: A resting-state functional magnetic resonance imaging study: Static and dynamic FNC in KOA

This study aimed to investigate altered static and dynamic functional network connectivity (FNC) and its correlation with clinical symptoms in patients with knee osteoarthritis (KOA). One hundred and fifty-nine patients with KOA and 73 age- and gender-matched healthy subjects (HS) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and clinical evaluations. Group independent component analysis (GICA) was applied, and seven resting-state networks were identified. Patients with KOA had decreased static FNC within the default mode network (DM), visual network (VS), and cerebellar network (CB) and increased static FNC between the subcortical network (SC) and VS (p < 0.05, FDR corrected). Four reoccurring FNC states were identified using k-means clustering analysis. Although abnormalities in dynamic FNCs of KOA patients have been found using the common window size (22 TR, 44 s), but the results of the clustering analysis were inconsistent when using different window sizes, suggesting dynamic FNCs might be an unstable method to compare brain function between KOA patients and HS. These recent findings illustrate that patients with KOA have a wide range of abnormalities in the static and dynamic FNCs, which provided a reference for the identification of potential central nervous therapeutic targets for KOA treatment and might shed light on the other musculoskeletal pain neuroimaging studies.

Imaging and clinical predictors of acute constipation in patients with acute ischemic stroke

Background

Constipation symptoms are highly prevalent in acute ischemic stroke, but the clinical and neuroimaging predictors are unknown. This study aimed to identify lesions and clinical features associated with acute constipation.

Methods

Data from patients with acute ischemic stroke registered in a hospital-based stroke registry between January 2018 and December 2019 were analyzed. Clinical, laboratory, and imaging features were examined for associations with acute constipation. Using the topographic lesion on diffusion-weighted images, multivariate support vector regression-based lesion-symptom mapping (SVR-LSM) was conducted and compared between the non-constipation and acute constipation groups.

Results

A total of 256 patients (mean age 67 years, men: 64%) were included. Acute constipation was noted in 81 patients (32%). Initial stroke severity, represented by initial National Institutes of Health and Stroke Scale (NIHSS) scores, was associated with acute constipation. Laboratory parameters, including fibrin degradation products (FDP), fibrinogen, D-dimer, lipoprotein (a), and free fatty acid levels, also showed statistically significant differences between the non-constipation and constipation groups. FDP, D-dimer, and free fatty acid levels were independently associated with acute constipation in the logistic regression model after adjusting for initial NIHSS scores and potassium levels. SVR-LSM revealed that bilateral lesions in the precentral gyrus, insula, opercular part of the inferior frontal gyrus, the inferior parietal lobule, and lesions in the right middle frontal gyrus were significantly associated with acute constipation. The results were consistent after controlling for the initial NIHSS scores and poststroke potassium levels. When cardioembolic stroke subjects were excluded, the right insular and prefrontal cortex lesions lost their association with acute constipation.

Conclusion

Acute constipation symptoms after acute ischemic stroke are mainly related to bilateral lesions in the insula, precentral gyrus, postcentral gyrus, and inferior parietal lobule. Clinically important predictors of acute constipation include initial neurological severity and thromboembolic markers of stroke.

Aberrant resting-state functional connectivity and topological properties of the subcortical network in functional dyspepsia patients

Functional dyspepsia (FD) is a disorder of gut-brain interaction. Previous studies have demonstrated a wide range of abnormalities in functional brain activity and connectivity patterns in FD. However, the connectivity pattern of the subcortical network (SCN), which is a hub of visceral information transmission and processing, remains unclear in FD patients. The study compared the resting-state functional connectivity (rsFC) and the global and nodal topological properties of SCN between 109 FD patients and 98 healthy controls, and then explored the correlations between the connectivity metrics and clinical symptoms in FD patients. The results demonstrated that FD patients manifested the increased rsFC in seventeen edges among the SCN, decreased small-worldness and local efficiency in SCN, as well as increased nodal efficiency and nodal degree centrality in the anterior thalamus than healthy controls (p &lt; 0.05, false discovery rate corrected). Moreover, the rsFC of the right anterior thalamus-left nucleus accumbens edge was significantly correlated with the NDSI scores (r = 0.255, p = 0.008, uncorrected) and NDLQI scores (r = −0.241, p = 0.013, uncorrected), the nodal efficiency of right anterior thalamus was significantly correlated with NDLQI scores (r = 0.204, p = 0.036, uncorrected) in FD patients. This study indicated the abnormal rsFC pattern, as well as global and nodal topological properties of the SCN, especially the bilateral anterior thalamus in FD patients, which enhanced our understanding of the central pathophysiology of FD and will lay the foundation for the objective diagnosis of FD and the development of new therapies.

The Role of Medial Prefrontal Cortex in Acupuncture Treatment for Functional Dyspepsia

Acupuncture is an effective therapy for functional dyspepsia (FD). However, the efficacy of acupuncture in the treatment of FD varies among individuals in clinical practice. This study aimed to reveal the brain response patterns in acupuncture higher response/lower response FD patients. Firstly, we performed a within-group comparison of brain function activity before and after acupuncture treatment in 115 FD patients and analyzed the correlation between brain function activity changes and clinical improvements. Secondly, 115 subjects were divided into the acupuncture higher response group or the lower response group based on the median clinical improvement values. The changes in functional brain activity after acupuncture treatment were investigated in these two groups, respectively. Finally, the identified brain regions associated with the clinical improvements were set as regions of interest (ROI), and the ROI-to-voxel functional connectivity comparisons were also performed in both groups, respectively. The results demonstrated that the functional activities of the left cerebellum inferior, right middle temporal gyrus, and right medial prefrontal cortex (mPFC) were increased, and the left Heschl and right middle cingulate cortex were decreased in 115 FD patients after acupuncture treatment. The functional connectivity changes of mPFC were correlated with improving the Nepean Dyspepsia Symptom Index. The significant increase in mPFC functional activity was also found in acupuncture higher response FD patients but not in lower response FD patients. The functional connectivity between the mPFC and default mode network (DMN) was significantly diminished in the higher response group but not in the lower response group. In conclusion, this study suggested that modulating the functional activity of the mPFC and its connectivity to the DMN may be one of the important mechanisms of acupuncture for treating FD with a higher response.

Abnormal Static and Dynamic Functional Connectivity in Left and Right Temporal Lobe Epilepsy

Objective

Temporal lobe epilepsy (TLE) can be conceptualized as a network disease. However, the network characteristics in lateralization remain controversial.

Methods

In this study, resting-state functional MRI scans were acquired from 53 TLE patients [22 with left-side TLE (LTLE) and 31 with right-side TLE (RTLE)] and 37 matched healthy controls. We focused on the characteristics of static and dynamic functional connectivity, including static connectivity patterns and topological properties, as well as temporal properties of the dynamic connectivity state and the variability of the dynamic connectivity and network topological organization. Correlation analyses were conducted between abnormal static and dynamic properties and cognitive performances.

Results

The static functional connectivity analysis presented a significantly decreased cortical-cortical connectivity pattern and increased subcortical-cortical connectivity pattern in RTLE. The global-level network in RTLE showed a significant decrease in global efficiency. The dynamic functional connectivity analysis revealed that RTLE patients exhibited aberrant connectivity states, as well as increased variability in the subcortical-cortical connectivity. The global-level network in RTLE revealed increased variance in global efficiency and local efficiency. The static or dynamic functional connectivity in LTLE did not show any significant abnormalities. The altered dynamic properties were associated with worsening cognitive performance in language and conceptual thinking by the TLE patients.

Conclusion

Our findings demonstrated the presence of abnormalities in the static and dynamic functional connectivity of TLE patients. RTLE patients exhibited more pronounced aberrant connectivity patterns and topological properties, which might represent a mechanism for reconfiguration of brain networks in RTLE patients. These observations extended our understanding of the pathophysiological network mechanisms of TLE.

Subcortical-Cortical Functional Connectivity as a Potential Biomarker for Identifying Patients with Functional Dyspepsia

The diagnosis of functional dyspepsia (FD) presently relies on the self-reported symptoms. This study aimed to determine the potential of functional brain network features as biomarkers for the identification of FD patients. Firstly, the functional brain Magnetic Resonance Imaging data were collected from 100 FD patients and 100 healthy subjects, and the functional brain network features were extracted by the independent component analysis. Then, a support vector machine classifier was established based on these functional brain network features to discriminate FD patients from healthy subjects. Features that contributed substantially to the classification were finally identified as the classifying features. The results demonstrated that the classifier performed pretty well in discriminating FD patients. Namely, the accuracy of classification was 0.84 ± 0.03 in cross-validation set and 0.80 ± 0.07 in independent test set, respectively. A total of 15 connections between the subcortical nucleus (the thalamus and caudate) and sensorimotor cortex, parahippocampus, orbitofrontal cortex were finally determined as the classifying features. Furthermore, the results of cross-brain atlas validation showed that these classifying features were quite robust in the identification of FD patients. In summary, the current findings suggested the potential of using machine learning method and functional brain network biomarkers to identify FD patients.