Analysis Of Structural Brain Networks Research Articles

Structural brain network analysis in occipital lobe epilepsy

BackgroundThis study aimed to analyze the structural brain network in patients with occipital lobe epilepsy (OLE) and investigate the differences in structural brain networks between patients with OLE and healthy controls.MethodsPatients with OLE and healthy controls with normal brain MRI findings were enrolled. They underwent diffusion tensor imaging using a 3.0T MRI scanner, and we computed the network measures of global and local structural networks in patients with OLE and healthy controls using the DSI studio program. We compared network measures between the groups.ResultsWe enrolled 23 patients with OLE and 42 healthy controls. There were significant differences in the global structural network between patients with OLE and healthy controls. The assortativity coefficient (-0.0864 vs. -0.0814, p = 0.0214), mean clustering coefficient (0.0061 vs. 0.0064, p = 0.0203), global efficiency (0.0315 vs. 0.0353, p = 0.0086), and small-worldness index (0.0001 vs. 0.0001, p = 0.0175) were lower, whereas the characteristic path length (59.2724 vs. 53.4684, p = 0.0120) was higher in patients with OLE than those in the healthy controls. There were several nodes beyond the occipital lobe that showed significant differences in the local structural network between the groups. In addition, the assortativity coefficient was negatively correlated with the duration of epilepsy (r=-0.676, p = 0.001).

Systematic validation of structural brain networks in cerebral small vessel disease

Cerebral small vessel disease (SVD) is considered a disconnection syndrome, which can be quantified using structural brain network analysis obtained from diffusion MRI. Network analysis is a demanding analysis approach and the added benefit over simpler diffusion MRI analysis is largely unexplored in SVD. In this pre-registered study, we assessed the clinical and technical validity of network analysis in two non-overlapping samples of SVD patients from the RUN DMC study (n = 52 for exploration and longitudinal analysis and n = 105 for validation). We compared two connectome pipelines utilizing single-shell or multi-shell diffusion MRI, while also systematically comparing different node and edge definitions. For clinical validation, we assessed the added benefit of network analysis in explaining processing speed and in detecting short-term disease progression. For technical validation, we determined test-retest repeatability. Our findings in clinical validation show that structural brain networks provide only a small added benefit over simpler global white matter diffusion metrics and do not capture short-term disease progression. Test-retest reliability was excellent for most brain networks. Our findings question the added value of brain network analysis in clinical applications in SVD and highlight the utility of simpler diffusion MRI based markers.

Alteration of brain structural organization after sepsis with Fatigue - a structural brain network analysis

In our recent cross-sectional investigation, we found in sepsis survivors with persistent cognitive impairment a high number of patients who still suffer from Fatigue. This finding is of importance because Fatigue is highlighted as an associated long-term sequela after sepsis and therefore these patients require an appropriate rehabilitation therapy. The aim of this study was to verify whether sepsis survivors with both cognitive impairment and Fatigue show any alteration in brain structure. 19 survivors of severe sepsis (longer than 2 years post sepsis) with persistent cognitive deficits ascertained with a battery of neuropsychological tests with cognitive and motor Fatigue symptoms (according to two German Fatigue scales) were investigated with a high-resolution. T1 weighted image of the brain at a 3.0 Tesla MRI scanner. The Voxel-based morphometry (VBM) was performed using VBM8 toolbox. 19 age- and sex-matched healthy control subjects were also scanned with MRI. VBM analysis revealed significant gray matter volume reduction in sepsis survivors particularly in the lateral frontal operculum and anterior cingulate cortex. These regions are part of the cingulo-opercular network which maintains alertness. Gray matter volume loss of the orbitofrontal cortex is functionally associated with Fatigue. These findings emphasize that networks of structural brain organization can be altered with corresponding clinical symptoms and neuropsychological deficits after sepsis.

Effect of Fixed-Density Thresholding on Structural Brain Networks: A Demonstration in Cerebral Small Vessel Disease.

A popular solution to control for edge density variability in structural brain network analysis is to threshold the networks to a fixed density across all subjects. However, it remains unclear how this type of thresholding affects the basic network architecture in terms of edge weights, hub location, and hub connectivity and, especially, how it affects the sensitivity to detect disease-related abnormalities. We investigated these two questions in a cohort of patients with cerebral small vessel disease and age-matched controls. Brain networks were reconstructed from diffusion magnetic resonance imaging data using deterministic fiber tractography. Networks were thresholded to a fixed density by removing edges with the lowest number of streamlines. We compared edge length (mm), fractional anisotropy (FA), proportion of hub connections, and hub location between the unthresholded and the thresholded networks of each subject. Moreover, we compared weighted graph measures of global and local connectivity obtained from the (un)thresholded networks between patients and controls. We performed these analyses over a range of densities (2-20%). Results indicate that fixed-density thresholding disproportionally removes edges composed of long streamlines, but is independent of FA. The edges removed were not preferentially connected to hub or nonhub nodes. Over half of the original hubs were reproducible when networks were thresholded to a density ≥10%. Furthermore, the between-group differences in graph measures observed in the unthresholded network remained present after thresholding, irrespective of the chosen density. We therefore conclude that moderate fixed-density thresholds can successfully be applied to control for the effects of density in structural brain network analysis.

Identification of Tendency to Alcohol Misuse From the Structural Brain Networks.

The propensity to engage in risky behaviors including excessive alcohol consumption may impose increased medical, emotional, and psychosocial burdens. Personality and behavioral traits of individuals may contribute in part to the involvement in risky behaviors, and therefore the classification of one’s traits may help identify those who are at risk for future onset of the addictive disorder and related behavioral issues such as alcohol misuse. Personality and behavioral characteristics including impulsivity, anger, reward sensitivity, and avoidance were assessed in a large sample of healthy young adults (n = 475). Participants also underwent diffusion tensor imaging for the analysis of structural brain networks. A data-driven clustering using personality and behavioral traits of the participants identified four subtypes. As compared with individuals clustered into the neutral type, individuals with a high level of impulsivity (A subtype) and those with high levels of reward sensitivity, impulsivity, anger, and avoidance (B subtype) showed significant associations with problem drinking. In contrast, individuals with high levels of impulsivity, anger, and avoidance but not reward sensitivity (C subtype) showed a pattern of social drinking that was similar to those of the neutral subtype. Furthermore, logistic regression analysis with ridge estimators was applied to demonstrate the neurobiological relevance for the identified subtypes according to distinct patterns of structural brain connectivity within the addiction circuitry [neutral vs. A subtype, the area under the receiver operator characteristic curve (AUC) = 0.74, 95% CI = 0.67–0.81; neutral vs. B subtype, AUC = 0.74, 95% CI = 0.66–0.82; neutral vs. C subtype, AUC = 0.77, 95% CI = 0.70–0.84]. The current findings enable the characterization of individuals according to subtypes based on personality and behavioral traits that are also corroborated by neuroimaging data and may provide a platform to better predict individual risks for addictive disorders.

Test-Retest Reliability of Graph Theoretic Metrics in Adolescent Brains.

Graph theory analysis of structural brain networks derived from diffusion tensor imaging (DTI) has become a popular analytical method in neuroscience, enabling advanced investigations of neurological and psychiatric disorders. The purpose of this study was to investigate (1) the effects of edge weighting schemes and (2) the effects of varying interscan periods on graph metrics within the adolescent brain. We compared a binary (B) network definition with three weighting schemes: fractional anisotropy (FA), streamline count, and streamline count with density and length correction (SDL). Two commonly used global and two local graph metrics were examined. The analysis was conducted with two groups of adolescent volunteers who received DTI scans either 12 weeks apart (16.62 ± 1.10 years) or within the same scanning session (30 min apart) (16.65 ± 1.14 years). The intraclass correlation coefficient was used to assess test-retest reliability and the coefficient of variation (CV) was used to assess precision. On average, each edge scheme produced reliable results at both time intervals. Weighted measures outperformed binary measures, with SDL weights producing the most reliable metrics. All edge schemes except FA displayed high CV values, leaving FA as the only edge scheme that consistently showed high precision while also producing reliable results. Overall findings suggest that FA weights are more suited for DTI connectome studies in adolescents.

A structural brain network analysis for rapid eye movement sleep behavior disorder (RBD) patients

A structural brain network analysis for rapid eye movement sleep behavior disorder (RBD) patients

Structural brain network analysis of children with localization-related epilepsy

IntroductionEpilepsy is considered to arise from dysfunction in neural networks. Recent advances in neuroimaging and its analysis have made it possible to investigate both functional and structural connectivity in the brain. The aim of this study was to elucidate alterations in the structural connectivity in children with localization-related epilepsy using the mathematical method of graph theoretical analysis. MethodologyFifteen children with localization-related epilepsy (8 female subjects; mean age, 8.5±3.5years) as an epilepsy group and 23 children without a history of seizure (12 female subjects; mean age, 8.9±3.7years) as a control group underwent three-dimensional T1-weighted brain magnetic resonance imaging (MRI). Gray matter images segmented and spatially normalized from the MRIs of both groups were analyzed using statistical parametric mapping with the Graph Analysis Toolbox. We compared global networks (global efficiency, clustering coefficient and network strength) and regional networks (betweenness centrality and clustering) between patients and controls. ResultsThe global efficiency tended to be increased (p=0.081) and the global modularity was significantly increased (p=0.017) in the epilepsy group as compared with the control group. The epilepsy group showed locally decreased betweenness centrality mainly in the bilateral cingulate gyri, right perisylvian area, and bilateral precentral gyri, and locally increased clustering in the bilateral cingulate gyri, right perisylvian area, and medial frontal lobes as compared with the control group. The epilepsy group showed higher network resilience to random attack and targeted attack than the control group. Voxel-based morphometry did not show any difference between the two groups. ConclusionsWe observed globally increased structural connectivity along with excessive network robustness in patients with localization-related epilepsy. Local abnormality of connectivity was observed mainly in the cingulate gyrus, perisylvian area, and precentral gyrus. This alteration in the structural connectivity without any morphometric changes may be related to the underlying epileptogenicity.

Altered structural connectivity of pain-related brain network in burning mouth syndrome-investigation by graph analysis of probabilistic tractography.

Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome featuring idiopathic oral pain and burning discomfort despite clinically normal oral mucosa. The etiology of chronic pain syndrome is unclear, but preliminary neuroimaging research has suggested the alteration of volume, metabolism, blood flow, and diffusion at multiple brain regions. According to the neuromatrix theory of Melzack, pain sense is generated in the brain by the network of multiple pain-related brain regions. Therefore, the alteration of pain-related network is also assumed as an etiology of chronic pain. In this study, we investigated the brain network of BMS brain by using probabilistic tractography and graph analysis. Fourteen BMS patients and 14 age-matched healthy controls underwent 1.5T MRI. Structural connectivity was calculated in 83 anatomically defined regions with probabilistic tractography of 60-axis diffusion tensor imaging and 3D T1-weighted imaging. Graph theory network analysis was used to evaluate the brain network at local and global connectivity. In BMS brain, a significant difference of local brain connectivity was recognized at the bilateral rostral anterior cingulate cortex, right medial orbitofrontal cortex, and left pars orbitalis which belong to the medial pain system; however, no significant difference was recognized at the lateral system including the somatic sensory cortex. A strengthened connection of the anterior cingulate cortex and medial prefrontal cortex with the basal ganglia, thalamus, and brain stem was revealed. Structural brain network analysis revealed the alteration of the medial system of the pain-related brain network in chronic pain syndrome.

Graph analysis of structural brain networks in Alzheimer’s disease: beyond small world properties

Changes in brain connectivity in patients with early Alzheimer's disease (AD) have been investigated using graph analysis. However, these studies were based on small data sets, explored a limited range of network parameters, and did not focus on more restricted sub-networks, where neurodegenerative processes may introduce more prominent alterations. In this study, we constructed structural brain networks out of 87 regions using data from 135 healthy elders and 100 early AD patients selected from the Open Access Series of Imaging Studies (OASIS) database. We evaluated the graph properties of these networks by investigating metrics of network efficiency, small world properties, segregation, product measures of complexity, and entropy. Because degenerative processes take place at different rates in different brain areas, analysis restricted to sub-networks may reveal changes otherwise undetected. Therefore, we first analyzed the graph properties of a network encompassing all brain areas considered together, and then repeated the analysis after dividing the brain areas into two sub-networks constructed by applying a clustering algorithm. At the level of large scale network, the analysis did not reveal differences between AD patients and controls. In contrast, the same analysis performed on the two sub-networks revealed that small worldness diminished with AD only in the sub-network containing the areas of medial temporal lobe known to be heaviest and earliest affected. The second sub-network, which did not present significant AD-induced modifications of 'classical' small world parameters, nonetheless showed a trend towards an increase in small world propensity, a novel metric that unbiasedly quantifies small world structure. Beyond small world properties, complexity and entropy measures indicated that the intricacy of connection patterns and structural diversity decreased in both sub-networks. These results show that neurodegenerative processes impact volumetric networks in a non-global fashion. Our findings provide new quantitative insights into topological principles of structural brain networks and their modifications during early stages of Alzheimer's disease.

Structural brain network analysis in families multiply affected with bipolar I disorder

Disrupted structural connectivity is associated with psychiatric illnesses including bipolar disorder (BP). Here we use structural brain network analysis to investigate connectivity abnormalities in multiply affected BP type I families, to assess the utility of dysconnectivity as a biomarker and its endophenotypic potential. Magnetic resonance diffusion images for 19 BP type I patients in remission, 21 of their first degree unaffected relatives, and 18 unrelated healthy controls underwent tractography. With the automated anatomical labelling atlas being used to define nodes, a connectivity matrix was generated for each subject. Network metrics were extracted with the Brain Connectivity Toolbox and then analysed for group differences, accounting for potential confounding effects of age, gender and familial association. Whole brain analysis revealed no differences between groups. Analysis of specific mainly frontal regions, previously implicated as potentially endophenotypic by functional magnetic resonance imaging analysis of the same cohort, revealed a significant effect of group in the right medial superior frontal gyrus and left middle frontal gyrus driven by reduced organisation in patients compared with controls. The organisation of whole brain networks of those affected with BP I does not differ from their unaffected relatives or healthy controls. In discreet frontal regions, however, anatomical connectivity is disrupted in patients but not in their unaffected relatives.