Covariance Network In Patients Research Articles

Aberrant individual structure covariance network in patients with mesial temporal lobe epilepsy.

Mesial temporal lobe epilepsy (mTLE) is a complex neurological disorder that has been recognized as a widespread global network disorder. The group-level structural covariance network (SCN) could reveal the structural connectivity disruption of the mTLE but could not reflect the heterogeneity at the individual level. This study adopted a recently proposed individual structural covariance network (IDSCN) method to clarify the alternated structural covariance connection mode in mTLE and to associate IDSCN features with the clinical manifestations and regional brain atrophy. We found significant IDSCN abnormalities in the ipsilesional hippocampus, ipsilesional precentral gyrus, bilateral caudate, and putamen in mTLE patients than in healthy controls. Moreover, the IDSCNs of these areas were positively correlated with the gray matter atrophy rate. Finally, we identified several connectivities with weak associations with disease duration, frequency, and surgery outcome. Our research highlights the role of hippo-thalamic-basal-cortical circuits in the pathophysiologic process of disrupted whole-brain morphological covariance networks in mTLE, and builds a bridge between brain-wide covariance network changes and regional brain atrophy.

Abnormal Topological Organization of Structural Covariance Networks in Patients with Temporal Lobe Epilepsy Comorbid Sleep Disorder.

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.

Involvement of limbic structures in patients with isolated rapid eye movement sleep behavior disorder.

This study aimed to investigate the alterations in limbic structure volumes and limbic covariance network in patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) and to compare them with healthy controls. We retrospectively enrolled 35 patients with iRBD and 35 healthy controls who underwent three-dimensional T1-weighted brain MRI. Volumetric analysis of subcortical limbic structures, including the hippocampus, amygdala, thalamus, mammillary body, hypothalamus, basal forebrain, septal nuclei, fornix, and nucleus accumbens, was performed. Furthermore, the limbic covariance network was examined using graph theory based on the limbic structure volumes. Some of the limbic structure volumes differed significantly. The right amygdala and hypothalamus volumes were lower in the patients with iRBD than in the healthy controls (0.101% vs. 0.114%, p = 0.016, and 0.027% vs. 0.030%, p = 0.045, respectively). However, there were no significant differences in the limbic covariance network between the groups. This study demonstrated that the volumes of the right amygdala and hypothalamus are lower in patients with iRBD, even without cognitive impairments, than in healthy controls. However, there were no significant differences in the limbic covariance network between the groups. The involvements of the limbic structures could be related to the conversion to neurodegenerative diseases in patients with iRBD.

Alteration in topological organization characteristics of gray matter covariance networks in patients with prediabetes.

Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes. A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T1 images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters. The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001). The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.

Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.

Alterations of limbic structure volumes and limbic covariance network in patients with cluster headache

The aim of this study was to compare the limbic structures and covariance network in patients with cluster headache to those of healthy controls. We enrolled 23 patients with newly diagnosed cluster headache and 31 healthy controls. They underwent three-dimensional T1-weighted imaging utilizing a 3.0 Tesla MRI scanner. Volumetric analysis of the subcortical limbic structures, including the hippocampus, amygdala, thalamus, mammillary body, hypothalamus, basal forebrain, septal nuclei, fornix, and nucleus accumbens, was performed. We examined the limbic covariance network using a graph theory. The volumes of the limbic structures between patients with cluster headache and healthy controls were significantly different. The volume of the left hippocampus in patients with cluster headache was significantly lower than that in healthy controls (0.256 vs 0.291 %, p = 0.002). Patients with cluster headache showed significant alterations of the limbic covariance network. The average strength, global efficiency, local efficiency, mean clustering coefficient, and transitivity were lower (5.238 vs 10.322, p = 0.030; 0.355 vs 0.608, p = 0.020; 0.547 vs 1.553, p = 0.020; 0.424 vs 0.895, p = 0.016; respectively), whereas the characteristic path length was higher (3.314 vs 1.752, p = 0.040) in patients with cluster headache than in healthy controls. We detected alterations of limbic structure volumes in patients with cluster headache compared to healthy controls, especially in the hippocampus. We also found significant alterations in the limbic covariance network in patients with cluster headache who showed decreased segregation and integration. These abnormalities could be related to the pathophysiology of cluster headache.

Hippocampal injury in patients with status epilepticus: Quantitative analysis of hippocampal volume and structural co-variance network

ObjectiveThis study aimed to evaluate the differences in hippocampal structural volumes and intra-hippocampal networks between patients with status epilepticus (SE) and healthy controls. MethodsWe enrolled 45 patients with SE and 35 age- and sex-matched healthy controls. We excluded patients with active structural lesions, which could be a direct cause of SE, but included patients with co-existing lesions. Co-existing lesions were defined as any lesions possibly related to the occurrence of SE, including encephalomalacia, cavernous malformation, dural arteriovenous fistula, and normal pressure hydrocephalus, etc. We divided 45 patients into those with co-existing lesions (n = 21) and those without co-existing lesions (n = 24). We conducted a volumetric analysis using FreeSurfer (version 7), and the intra-hippocampal structural co-variance network was analyzed with a graph theoretical analysis based on the structural volumes of the hippocampal subfields. ResultsThe structural volumes and intra-hippocampal structural co-variance networks were not different between patients with and without co-existing lesions. However, both structural volumes and intra-hippocampal structural co-variance networks were significantly different in patients with SE compared to healthy controls, and the ratio of the volume difference: [(volume of controls-volume of patients)/volume of controls] was highest in the left hippocampus (0.195), left amygdala (0.143), left thalamus (0.126), and right cortex (0.084). In addition, the global connectivity measurements including radius, diameter, eccentricity, and assortativity were significantly increased, and the small-worldness index was significantly decreased in patients with SE. Notably, structural volumes were negatively related to age but not to the duration of SE. SignificanceOur study revealed significant alterations in structural volumes and intra-hippocampal structural co-variance networks in patients with SE compared to healthy controls, even though hippocampal atrophy was not evident on visual analysis; this is likely due to the direct effect of SE itself.

Aberrant Topological Patterns of Structural Covariance Networks in Cognitively Normal Elderly Adults With Mild Behavioral Impairment.

Mild behavioral impairment (MBI), characterized by the late-life onset of sustained and meaningful neuropsychiatric symptoms, is increasingly recognized as a prodromal stage of dementia. However, the underlying neural mechanisms of MBI remain unclear. Here, we examined alterations in the topological organization of the structural covariance networks of patients with MBI (N = 32) compared with normal controls (N = 38). We found that the gray matter structural covariance networks of both the patients with MBI and controls exhibited a small-world topology evidenced by sigma value larger than one. The patients with MBI had significantly decreased clustering coefficients at several network densities and local efficiency at densities ranging from 0.05 to 0.26, indicating decreased local segregation. No significant differences in the characteristic path length, gamma value, sigma value, or global efficiency were detected. Locally, the patients with MBI showed significantly decreased nodal betweenness centrality in the left middle frontal gyrus, right inferior frontal gyrus (opercular part), and left Heschl gyrus and increased betweenness centrality in the left gyrus rectus, right insula, bilateral precuneus, and left thalamus. Moreover, the difference in the bilateral precuneus survived after correcting for multiple comparisons. In addition, a different number and distribution of hubs was identified in patients with MBI, showing more paralimbic hubs than observed in the normal controls. In conclusion, we revealed abnormal topological patterns of the structural covariance networks in patients with MBI and offer new insights into the network dysfunctional mechanisms of MBI.

Abnormalities of Cortical Morphology and Structural Covariance Network in Patients with Subacute Basal Ganglia Stroke

The direct damage caused by ischemic stroke is relatively localized, but structural reorganization of cortical regions could occur across the brain. Changes of large-scale, cortical structural brain networks after basal ganglia stroke are less well reported. We, therefore, aim to explore the abnormalities of cortical morphology and structural network topology in patients with unilateral basal ganglia stroke during the subacute period. Thirty patients with first-ever basal ganglia stroke and thirty age- and sex-matched healthy controls were recruited for our analysis. Patients underwent structural magnetic resonance imaging examinations and clinical assessment from seven days to three months post-stroke. Alterations in cortical morphology and topological properties of the cortical structural network were measured respectively using the surface-based morphology and graph-theoretical methods. We observed focal cortical atrophy, specifically in areas of frontal and temporal cortices. Moreover, the cortical thickness in the contralesional transverse temporal gyrus and superior temporal gyrus was positively correlated with cognitive function scores. Network analysis revealed that patients with basal ganglia stroke showed increased clustering coefficient, increased mean local efficiency as well as a reorganization of degree-based hubs. In addition, these patients also showed reduced robustness under a random attack compared to healthy controls. These findings indicated a unique pattern of cortical reorganization and the abnormal topological organization of cortical thickness-based structural covariance networks in patients with basal ganglia stroke, which is beneficial to understand the pathophysiological mechanisms of functional disorders at the cortical structural network level and find potential targets for induced neuromodulation.

Alterations of structural connectivity and structural co-variance network in focal cortical dysplasia

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.

Altered Topological Properties of Brain Structural Covariance Networks in Patients With Cervical Spondylotic Myelopathy.

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.

Altered Cortical Thickness-Based Individualized Structural Covariance Networks in Patients with Schizophrenia and Bipolar Disorder.

Structural covariance is described as coordinated variation in brain morphological features, such as cortical thickness and volume, among brain structures functionally or anatomically interconnected to one another. Structural covariance networks, based on graph theory, have been studied in mental disorders. This analysis can help in understanding the brain mechanisms of schizophrenia and bipolar disorder. We investigated cortical thickness-based individualized structural covariance networks in patients with schizophrenia and bipolar disorder. T1-weighted magnetic resonance images were obtained from 39 patients with schizophrenia, 37 patients with bipolar disorder type I, and 32 healthy controls, and cortical thickness was analyzed via a surface-based morphometry analysis. The structural covariance of cortical thickness was calculated at the individual level, and covariance networks were analyzed based on graph theoretical indices: strength, clustering coefficient (CC), path length (PL) and efficiency. At the global level, both patient groups showed decreased strength, CC and efficiency, and increased PL, compared to healthy controls. In bipolar disorder, we found intermediate network measures among the groups. At the nodal level, schizophrenia patients showed decreased CCs in the left suborbital sulcus and the right superior frontal sulcus, compared to bipolar disorder patients. In addition, patient groups showed decreased CCs in the right insular cortex and the left superior occipital gyrus. Global-level network indices, including strength, CCs and efficiency, positively correlated, while PL negatively correlated, with the positive symptoms of the Positive and Negative Syndrome Scale for patients with schizophrenia. The nodal-level CC of the right insular cortex positively correlated with the positive symptoms of schizophrenia, while that of the left superior occipital gyrus positively correlated with the Young Mania Rating Scale scores for bipolar disorder. Altered cortical structural networks were revealed in patients, and particularly, the prefrontal regions were more altered in schizophrenia. Furthermore, altered cortical structural networks in both patient groups correlated with core pathological symptoms, indicating that the insular cortex is more vulnerable in schizophrenia, and the superior occipital gyrus is more vulnerable in bipolar disorder. Our individualized structural covariance network indices might be promising biomarkers for the evaluation of patients with schizophrenia and bipolar disorder.

Whole-brain structural covariance network abnormality in first-episode and drug-naïve major depressive disorder

There has been a growing interest in the abnormality of networks across the brain in major depressive disorder (MDD). We aimed to investigate the structural covariance networks in patients with first-episode and drug-naïve MDD using structural imaging. A total of 77 patients with first-episode and drug-naïve MDD and 79 healthy subjects (HS) were recruited, from whom high-resolution T1-weighted images were analysed. Incident component analysis was used to calculate the brain networks based on grey matter volume covariance. There were significant differences in salience network, medial temporal lobe network, default mode network and central executive network between MDD and HS (p < 0.05). Further, the disturbance of medial temporal lobe network was significantly correlated with the severity of depressive symptoms (p < 0.05). In conclusion, we found a novel abnormality in the brain network in the medial temporal lobe primarily involving the hippocampus and parahippocampal gyrus in patients with first-episode and treatment-naïve MDD.

178. Brain Network Biomarkers of Remitted Psychotic Depression

Little is known about the neurobiological basis of major depressive disorder with psychotic features ("psychotic depression"). This preliminary case-control study addresses this knowledge gap by using structural magnetic resonance imaging to compare structural covariance networks in patients with remitted psychotic depression and healthy controls.

Editorial: Principles Underlying Post-Stroke Recovery of Upper Extremity Sensorimotor Function – A Neuroimaging Perspective

Editorial: Principles Underlying Post-Stroke Recovery of Upper Extremity Sensorimotor Function – A Neuroimaging Perspective