Structural brain network changes in patients with neurofibromatosis type 1: A retrospective study

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

To evaluate alterations of global and local structural brain connectivity in patients with restless legs syndrome (RLS). Patients with primary RLS and healthy controls were recruited at a sleep center where they underwent diffusion tensor imaging (DTI) of the brain. We calculated the network measures of global and local structural brain connectivity based on the DTI in both groups using DSI studio program and a graph theory. A total of 69 patients with primary RLS and 51 healthy controls were included in the study. We found a significant difference in the global structural connectivity between the groups. The transitivity in the patients with RLS was lower than that in healthy controls (0.031 vs. 0.033, p = 0.035). Additionally, there were significant differences in the local structural connectivity between the groups. The characteristic path length (r = 0.283, p = 0.018), radius of graph (r = 0.260, p = 0.030), and diameter of graph (r = 0.280, p = 0.019) were all positively correlated with RLS severity, whereas the mean clustering coefficient (r = -0.327, p = 0.006), global efficiency (r = -0.272, p = 0.023), small-worldness index (r = -0.325, p = 0.006), and transitivity (r = -0.351, p = 0.003) were negatively correlated with RLS severity. We identified changes in the global structural connectivity of patients with RLS using graph theory based on DTI, which showed decreased segregation in the brain network compared to healthy controls. These changes are well correlated with RLS severity. We also found changes in local structural connectivity, especially in regions involved in sensorimotor function, which suggests that these areas play a pivotal role in RLS. These findings contribute to a better understanding of the pathophysiology of RLS symptoms.

BackgroundThe aim of this study was to investigate alterations in structural connectivity and structural co-variance network in patients with focal cortical dysplasia (FCD).MethodsWe enrolled 37 patients with FCD and 35 healthy controls. All subjects underwent brain MRI with the same scanner and with the same protocol, which included diffusion tensor imaging (DTI) and T1-weighted imaging. We analyzed the structural connectivity based on DTI, and structural co-variance network based on the structural volume with T1-weighted imaging. We created a connectivity matrix and obtained network measures from the matrix using the graph theory. We tested the difference in network measure between patients with FCD and healthy controls.ResultsIn the structural connectivity analysis, we found that the local efficiency in patients with FCD was significantly lower than in healthy controls (2.390 vs. 2.578, p = 0.031). Structural co-variance network analysis revealed that the mean clustering coefficient, global efficiency, local efficiency, and transitivity were significantly decreased in patients with FCD compared to those in healthy controls (0.527 vs. 0.635, p = 0.036; 0.545 vs. 0.648, p = 0.026; 2.699 vs. 3.801, p = 0.019; 0.791 vs. 0.954, p = 0.026, respectively).ConclusionsWe demonstrate that there are significant alterations in structural connectivity, based on DTI, and structural co-variance network, based on the structural volume, in patients with FCD compared to healthy controls. These findings suggest that focal lesions with FCD could affect the whole-brain network and that FCD is a network disease.

Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are “small world.” There were significant difference between NC and AD group in characteristic path lengths (z = −2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.

Radiation-induced brain injury (RBI) is a common complication in patients with nasopharyngeal carcinoma (NPC) who have undergone radiotherapy (RT), which is characterized by significant cognitive and psychological impairments. Although radiation-induced regional structural abnormalities have been well-reported, the effects of RT on the whole brain structural covariance networks are mostly unknown. Here, we performed a source-based morphometry (SBM) study to solve this issue. In this cross-sectional study, 131 NPC patients with pre- and post-RT were stratified into pre-RT (n=47) and post-RT (n=84) groups. The SBM method was adopted to investigate the radiation-induced alterations in structural covariance networks in patients with NPC. Compared to the pre-RT group, our SBM analyses revealed increased z-scores in the independent component 05 (IC05; mainly located in the posterior cingulate, precuneus areas, and superior parietal lobe) (P=0.040) and decreased z-scores in the temporal-occipital network (P=0.015) and cerebellar network (P=0.023) in post-RT NPC patients. Compared to the pre-RT group, voxel-based morphometry (VBM) revealed reduced gray matter volume in the left temporal lobe, cerebellum, bilateral thalamus, left insular, and occipital lobe in the post-RT group. Notably, a significant negative correlation was observed between the mean radiation doses of the right temporal lobe and the z-score of the cerebellar network (r=-0.349, P=0.027). This present study revealed radiation-induced changes in structural covariance networks and cortical volume in patients with NPC. These findings shed some light on the neural basis of symptom patterns in RBI and may support the development of new intervention strategies to prevent progression to radiation-induced brain necrosis.

Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.

Carpal tunnel syndrome (CTS) is a common peripheral entrapment neuropathy. However, CTS-related changes of brain structural covariance and structural covariance networks (SCNs) patterns have not been clearly studied. To explore CTS-related brain changes from perspectives of structural connectivity and SCNs. Brain structural magnetic resonance images were acquired from 27 CTS patients and 19 healthy controls (HCs). Structural covariance and SCNs were constructed based on gray matter volume. The global network properties including clustering coefficient (Cp), characteristic path length (Lp), small-worldness index, global efficiency (Eglob), and local efficiency (Eloc) and regional network properties including degree, betweenness centrality (BC), and Eloc of a given node were calculated with graph theoretical analysis. Compared with HCs, the strength of structural connectivity between the dorsal anterior insula and medial prefrontal thalamus decreased (P <.001) in CTS patients. There was no intergroup difference of area under the curve for Cp, Lp¸ Eglob, and Eloc (all P >.05). The real-world SCN of CTS patients showed a small-world topology ranging from 2% to 32%. CTS patients showed lower nodal degrees of the dorsal anterior insula and medial prefrontal thalamus, and higher Eloc of a given node and BC in the lateral occipital cortex (P <.001) and the dorsolateral middle temporal gyrus (P <.001) than HCs, respectively. CTS had a profound impact on brain structures from perspectives of structural connectivity and SCNs.

Structural covariance networks (SCNs) may offer unique insights into the developmental impact of childhood maltreatment (CM) because they are thought to reflect coordinated maturation of distinct gray matter regions. T1-weighted magnetic resonance images were acquired from 121 young people with emerging mental illness. Diffusion-weighted and resting-state functional imaging was also acquired from a random subset of participants (n = 62). Ten study-specific SCNs were identified using a whole-brain gray matter independent component analysis. The effects of CM and age on average gray matter density and the expression of each SCN were calculated. CM was linked to age-related decreases in gray matter density across an SCN that overlapped with the default mode network (DMN) and frontoparietal network. Resting-state functional connectivity (rsFC) and structural connectivity were calculated in the study-specific SCN and across the whole brain. Gray matter covariance was significantly correlated with rsFC across the SCN, and rsFC fully mediated the relationship between gray matter covariance and structural connectivity in the nonmaltreated group. A unique association of gray matter covariance with structural connectivity was detected among individuals with a history of CM. Perturbation of gray matter development across the DMN and frontoparietal network following CM may have significant implications for mental well-being, given the networks' roles in self-referential activity. Cross-modal comparisons suggest that reduced gray matter following CM could arise from deficient functional activity earlier in life.

BackgroundBrain structural alterations play an important role in patients with cervical spondylotic myelopathy (CSM). However, while there have been studies on regional brain structural alterations, only few studies have focused on the topological organization of the brain structural covariance network. This work aimed to describe the structural covariance network architecture alterations that are possibly linked to cortex reorganization in patients with CSM.MethodsHigh-resolution anatomical images of 31 CSM patients and 31 healthy controls (HCs) were included in the study. The images were acquired using a sagittal three-dimensional T1-weighted BRAVO sequence. Firstly, the gray matter volume of 90 brain regions of automated anatomical labeling atlas were computed using a VBM toolbox based on the DARTEL algorithm. Then, the brain structural covariance network was constructed by thresholding the gray matter volume correlation matrices. Subsequently, the network measures and nodal property were calculated based on graph theory. Finally, the differences in the network metrics and nodal property between groups were compared using a non-parametric test.ResultsPatients with CSM showed larger global efficiency and smaller local efficiency, clustering coefficient, characteristic path length, and sigma values than HCs. Patients with CSM had greater betweenness in the left superior parietal gyrus (SPG.L) and the left supplementary motor area (SMA.L) than HCs. Besides, patients with CSM had smaller betweenness in right middle occipital gyrus. The brain structural covariance networks of CSM patients exhibited equal resilience to random failure as those of HCs. However, the maximum relative size of giant connected components was approximately 10% larger in HCs than in CSM patients, upon removal of 44 nodes in targeted attack.ConclusionThese observed alternations in global network measures in CSM patients reflect that the brain structural covariance network in CSM exhibits the less optimal small-world model compared to that in HCs. Increased betweenness in SPG.L and SMA.L seems to be related to cortex reorganization to recover multiple sensory functions after spinal cord injury in CSM patients. The network resilience of patients with CSM exhibiting a relative mild vulnerability, compared to HCs, is probably attributable to the balance and interplay between cortex reorganization and ongoing degeneration.

Background: Despite accumulated evidence for adult brain plasticity, the temporal relationships between large-scale functional and structural connectivity changes in human brain networks remain unclear. Methods: By analysing a unique richly detailed 19-week longitudinal neuroimaging dataset, we tested whether macroscopic functional connectivity changes lead to the corresponding structural alterations in the adult human brain, and examined whether such time lags between functional and structural connectivity changes are affected by functional differences between different large-scale brain networks. Results: In this single-case study, we report that, compared to attention-related networks, functional connectivity changes in default-mode, fronto-parietal, and sensory-related networks occurred in advance of modulations of the corresponding structural connectivity with significantly longer time lags. In particular, the longest time lags were observed in sensory-related networks. In contrast, such significant temporal differences in connectivity change were not seen in comparisons between anatomically categorised different brain areas, such as frontal and occipital lobes. These observations survived even after multiple validation analyses using different connectivity definitions or using parts of the datasets. Conclusions: Although the current findings should be examined in independent datasets with different demographic background and by experimental manipulation, this single-case study indicates the possibility that plasticity of macroscopic brain networks could be affected by cognitive and perceptual functions implemented in the networks, and implies a hierarchy in the plasticity of functionally different brain systems.

Tinnitus is considered a neurological disorder affecting both auditory and nonauditory networks. This study aimed to investigate the structural brain covariance network in tinnitus patients and analyze its altered topological properties. Fifty three primary tinnitus patients and 67 age- and sex-matched healthy controls (HCs) were included. Gray matter volume (GMV) of each participant was extracted using voxel-based morphometry, a group-level structural covariance network (SCN) was constructed based on the GMV of each participant, and graph theoretic analyses were performed using graph analysis toolbox (GAT). The differences in the topological properties of SCN between both groups were compared and analyzed. Both groups exhibited small-world attributes. Compared with HCs, tinnitus patients had significantly higher characteristic path length, lambda, transitivity, and assortativity (p < 0.05), and significantly lower global efficiency (p < 0.05). Tinnitus patients had higher clustering coefficient and reduced gamma and modularity, but neither was remarkable. The hubs in tinnitus network focused on the temporal lobe. In addition, the tinnitus network was found to be reduced in robustness to targeted attacks compared with HCs. Besides, a significant negative correlation between Tinnitus Handicap Inventory (THI) score and GMV in the left angular gyrus (r = -0.283, p = 0.040) as well as left superior temporal pole (r = -0.282, p = 0.041) were identified. Tinnitus patients showed reduced small-world properties, altered hub nodes, and reduced ability to respond to targeted attacks in brain network. The GMV in the left angular gyrus and left superior temporal pole showed significant negative correlation with tinnitus distress (THI score), indicating potential therapeutic target.

Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adult may arise in part from coordinated cortical maturation, and that regional co-activation in functional networks may guide and refine the maturation of SCNs over childhood development.

Network characteristics of genetic generalized epilepsy: Are the syndromes distinct?

Characteristics of the cortico-striato-thalamo-cerebellar structural covariance network in Meige syndrome.

The structural covariance network (SCN) alterations in patients with temporal lobe epilepsy and comorbid sleep disorder (PWSD) remain poorly understood. This study aimed to investigate changes in SCNs using structural magnetic resonance imaging. Thirty-four PWSD patients, thirty-three patients with temporal lobe epilepsy without sleep disorder (PWoSD), and seventeen healthy controls underwent high-resolution structural MRI imaging. Subsequently, SCNs were constructed based on gray matter volume and analyzed via graph-theoretical approaches. PWSD exhibited significantly increased clustering coefficients, shortest path lengths, transitivity, and local efficiency. In addition, various distributions and numbers of SCN hubs were identified in PWSD. Furthermore, PWSD networks were less robust to random and target attacks than those of healthy controls and PWoSD patients. This study identifies aberrant SCN changes in PWSD that may be related to the susceptibility of patients with epilepsy to sleep disorders.