Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis

Disrupted topological organization of brain connectome in patients with chronic low back related leg pain and clinical correlations

Chronic pain is associated with persistent alterations in brain structure and function. However, existing research has not fully explored the relationship between brain network topological properties and clinical symptoms in patients with chronic low back-related leg pain (cLBLP). In this study, we collected resting-state functional and structural magnetic resonance imaging data, along with clinical symptom evaluation data, from 32 cLBLP patients and 31 healthy controls. A large-scale complex network analysis was conducted to evaluate the global and nodal topological properties of functional and structural brain networks. Statistical analyses were performed to determine the associations between network properties and clinical variables. The results showed significant alterations in both global and nodal topological properties of functional and structural brain networks in cLBLP patients compared to healthy controls. Additionally, a direct correlation was found between structural network properties and spatial discrimination ability, measured by two-point tactile discrimination values, while no significant association was observed between functional connectivity and spatial discrimination. This study demonstrates that cLBLP patients exbibit a decreased local efficiency of functional connectivity network and increased compensatory global efficiency of structural connectivity network. Notably, alterations in the structural connectome, rather than the functional connectome, play a more significant role in deterioration of foot tactile spatial acuity in cLBLP patients. Trial registration: This trial was registered in the Chinese Clinical Trial Registry with the registration number ChiCTR2200055321 on 2022-01-06.

Abnormal topological structure of structural covariance networks based on fractal dimension in noise induced hearing loss

The topological attributes of structural covariance networks (SCNs) based on fractal dimension (FD) and changes in brain network connectivity were investigated using graph theory and network-based statistics (NBS) in patients with noise-induced hearing loss (NIHL). High-resolution 3D T1 images of 40 patients with NIHL and 38 healthy controls (HCs) were analyzed. FD-based Pearson correlation coefficients were calculated and converted to Fisher's Z to construct the SCNs. Topological attributes and network hubs were calculated using the graph theory. Topological measures between groups were compared using nonparametric permutation tests. Abnormal connection networks were identified using NBS analysis. The NIHL group showed a significantly increased normalized clustering coefficient, normalized characteristic path length, and decreased nodal efficiency of the right medial orbitofrontal gyrus. Additionally, the network hubs based on betweenness centrality and degree centrality were both the right transverse temporal gyrus and left parahippocampal gyrus in the NIHL group. The NBS analysis revealed two subnetworks with abnormal connections. The subnetwork with enhanced connections was mainly distributed in the default mode, frontoparietal, dorsal attention, and somatomotor networks, whereas the subnetwork with reduced connections was mainly distributed in the limbic, visual, default mode, and auditory networks. These findings demonstrate the abnormal topological structure of FD-based SCNs in patients with NIHL, which may contribute to understand the complex mechanisms of brain damage at the network level, providing a new theoretical basis for neuropathological mechanisms.

Disrupted gray matter connectome in vestibular migraine: a combined machine learning and individual-level morphological brain network analysis

BACKGROUND

Although gray matter (GM) volume alterations have been extensively documented in previous voxel-based morphometry studies on vestibular migraine (VM), little is known about the impact of this disease on the topological organization of GM morphological networks. This study investigated the altered network patterns of the GM connectome in patients with VM.

METHODS

In this study, 55 patients with VM and 57 healthy controls (HCs) underwent structural T1-weighted MRI. GM morphological networks were constructed by estimating interregional similarity in the distributions of regional GM volume based on the Kullback-Leibler divergence measure. Graph-theoretical metrics and interregional morphological connectivity were computed and compared between the two groups. Partial correlation analyses were performed between significant GM connectome features and clinical parameters. Logistic regression (LR), support vector machine (SVM), and random forest (RF) classifiers were used to examine the performance of significant GM connectome features in distinguishing patients with VM from HCs.

RESULTS

Compared with HCs, patients with VM exhibited increased clustering coefficient and local efficiency, as well as reduced nodal degree and nodal efficiency in the left superior temporal gyrus (STG). Furthermore, we identified one connected component with decreased morphological connectivity strength, and the involved regions were mainly located in the STG, temporal pole, prefrontal cortex, supplementary motor area, cingulum, fusiform gyrus, and cerebellum. In the VM group, several connections in the identified connected component were correlated with clinical measures (i.e., symptoms and emotional scales); however, these correlations did not survive multiple comparison corrections. A combination of significant graph- and connectivity-based features allowed single-subject classification of VM versus HC with significant accuracy of 77.68%, 77.68%, and 72.32% for the LR, SVM, and RF models, respectively.

CONCLUSION

Patients with VM had aberrant GM connectomes in terms of topological properties and network connections, reflecting potential dizziness, pain, and emotional dysfunctions. The identified features could serve as individualized neuroimaging markers of VM.

Abnormal large-scale brain functional network dynamics in social anxiety disorder

AIMS

Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD.

METHODS

We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored.

RESULTS

Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing "widely weaker" FNC, but lower in States 2 and 4, representing "locally stronger" and "widely stronger" FNC, respectively. In State 1, representing "widely moderate" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration.

CONCLUSION

These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.

Alterations in cortical thickness of frontoparietal regions in patients with social anxiety disorder

Although functional changes of the frontal and (para)limbic area for emotional hyper-reactivity and emotional dysregulation are well documented in social anxiety disorder (SAD), prior studies on structural changes have shown mixed results. This study aimed to identify differences in cortical thickness between SAD and healthy controls (CON). Thirty-five patients with SAD and forty-two matched CON underwent structural magnetic resonance imaging. A vertex-based whole brain and regional analyses were conducted for between-group comparison. The whole-brain analysis revealed increased cortical thickness in the left insula, left superior parietal lobule, left superior temporal gyrus, and left frontopolar cortex in patients with SAD compared to CON, as well as decreased thickness in the left superior/middle frontal gyrus and left fusiform gyrus in patients (after multiple-correction). The results from the ROI analysis did not align with these findings at the statistically significant level after multiple corrections. Changes in cortical thickness were not correlated with social anxiety symptoms. While consistent results were not obtained from different analysis methods, the results from the whole-brain analysis suggest that patients with SAD exhibit distinct neural deficits in areas involved in salience, attention, and socioemotional processing.

Toward individualized connectomes of brain morphology

The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders.