Disrupted topological properties of brain white matter networks in left temporal lobe epilepsy: A diffusion tensor imaging study

Xu, Yong; Qiu, Shuqing; Wang, Jinhui; Liu, Z.; Zhang, Rupu; Li, S.; Liu, Cheng; Wang, W.; Huang, Ruiwang

doi:10.1016/j.neuroscience.2014.08.040

Cited by 20 publications

(27 citation statements)

References 59 publications

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“…They are also in line with those of previous reports utilizing graph analyses to assess the DTI structural connectome. [11][12][13][14][15] We did not observe the reported paradoxical increase in clustering coefficient or local efficiency, 12,13 and there has been some variation in the reported alteration of the clustering coefficient in patients with TLE. 28 One possible explanation for this discrepancy is that the clustering coefficient depends on the stage of disease; indeed, it has been reported to increase during most of the sclerotic process and decrease in the final stages of disease.…”

Section: Discussioncontrasting

confidence: 57%

“…Several studies have analyzed DTI-based structural connectomes in TLE; the majority have reported altered connectivity to be most prominent within the ipsilateral temporal lobe. [11][12][13][14][15] In recent years, several studies have investigated the performance of machine learning algorithms, such as that of the support vector machine (SVM), for automatic localization of epileptogenic foci using MR voxel-based morphometry (VBM) 2,3 and fMRI. 5 Because graph theory metrics use a subset of numeric parameters to summarize the characteristic properties of huge and complex brain networks, they are mathematically good candidates for a machine learning approach to identify the multivariate feature combinations that best predict an outcome of interest.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

et al. 2016

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Background and Purpose: We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE).Materials and Methods: We analyzed diffusion tensor and 3-dimensional (3D) T 1 -weighted images of 44 patients with TLE (right, 15, left, 29; mean age, 33.0 « 11.6 years) and 14 age-matched controls. We constructed a whole brain structural connectome for each subject, calculated graph theoretical network measures, and used a support vector machine (SVM) for classification among 3 groups (right TLE versus controls, left TLE versus controls, and right TLE versus left TLE) following a feature reduction process with sparse linear regression.Results: In left TLE, we found a significant decrease in local efficiency and the clustering coefficient in several brain regions, including the left posterior cingulate gyrus, left cuneus, and both hippocampi. In right TLE, the right hippocampus showed reduced nodal degree, clustering coefficient, and local efficiency. With use of the leave-one-out crossvalidation strategy, the SVM classifier achieved accuracy of 75.9 to 89.7% for right TLE versus controls, 74.4 to 86.0% for left TLE versus controls, and 72.7 to 86.4% for left TLE versus right TLE.Conclusion: Machine learning of graph theoretical measures from the DTI structural connectome may give support to lateralization of the TLE focus. The present good discrimination between left and right TLE suggests that, with further refinement, the classifier should improve presurgical diagnostic confidence.

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Section: Discussioncontrasting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

et al. 2016

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“…The most commonly found changes include increased C and increased L in TLE patients as compared to controls (Bartolomei et al, 2013; Horstmann et al, 2010; Ponten et al, 2007). However, other studies have found different and even opposing changes in C and L (Liao et al, 2010; Quraan et al, 2013; Xu et al, 2014). These inconsistent results are likely due to the use of a variety of techniques for creation of the graphs (Horstmann et al, 2010), difference in frequency band for analysis (Horstmann et al, 2010; Quraan et al, 2013; Wilke et al, 2009, 2011), variability among patients, and a low sample number.…”

Section: Network Organization At the Macrocircuit Level: Applicatiomentioning

confidence: 86%

Organization and control of epileptic circuits in temporal lobe epilepsy

Alexander¹,

Maroso²,

Soltész³

2016

Progress in Brain Research

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When studying the pathological mechanisms of epilepsy, there are a seemingly endless number of approaches from the ultrastructural level—receptor expression by EM—to the behavioral level—comorbid depression in behaving animals. Epilepsy is characterized as a disorder of recurrent seizures, which are defined as “a transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain” (Fisher et al., 2005). Such abnormal activity typically does not occur in a single isolated neuron; rather, it results from pathological activity in large groups—or circuits—of neurons. Here we choose to focus on two aspects of aberrant circuits in temporal lobe epilepsy: their organization and potential mechanisms to control these pathological circuits. We also look at two scales: microcircuits, ie, the relationship between individual neurons or small groups of similar neurons, and macrocircuits, ie, the organization of large-scale brain regions. We begin by summarizing the large body of literature that describes the stereotypical anatomical changes in the temporal lobe—ie, the anatomical basis of alterations in microcircuitry. We then offer a brief introduction to graph theory and describe how this type of mathematical analysis, in combination with computational neuroscience techniques and using parameters obtained from experimental data, can be used to postulate how microcircuit alterations may lead to seizures. We then zoom out and look at the changes which are seen over large whole-brain networks in patients and animal models, and finally we look to the future.

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“…Previous studies using graph-based theoretical approaches in several brain and psychiatric disorders, such as pediatric post-traumatic stress disorder,26,27 Alzheimer’s disease,28 depression,29 epilepsy,30,31 and schizophrenia,32,33 have shown that patients have alterations in topological properties of brain function. Watts and Strogatz34 first proposed the small-world network (characterized by a short path length between brain regions and a high degree of clustering, with high global integration between different brain regions and high local specialization), which corresponds to an intermediate state between a random network and a regular network 35.…”

Section: Introductionmentioning

confidence: 99%

Disrupted small-world brain functional network topology in male patients with severe obstructive sleep apnea revealed by resting-state fMRI

Chen¹,

Fan²,

Li³

et al. 2017

NDT

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PurposeObstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can damage cognitive function. However, the functional network organization remains poorly understood. The aim of this study was to investigate the topological properties of OSA patients using a graph theoretical analysis.Patients and methodsA total of 30 male patients with untreated severe OSA and 25 male education- and age-matched good sleepers (GSs) underwent functional magnetic resonance imaging (MRI) examinations. Clinical and cognitive evaluations were conducted by an experienced psychologist. GRETNA (a toolbox for topological analysis of imaging connectomics) was used to construct the brain functional network and calculate the small-world properties (γ, λ, σ, Eglob, and Eloc). Relationships between these small-world properties and clinical and neuropsychological assessments were investigated in OSA patients.ResultsThe networks of both OSA patients and GSs exhibited efficient small-world topology over the sparsity range of 0.05–0.40. Compared with GSs, the OSA group had significantly decreased γ, but significantly increased λ and σ. The OSA group’s brain network showed significantly decreased Eglob (P<0.05) over the sparsity range of 0.09–0.15, but significantly increased Eloc over the sparsity range of 0.23–0.40. In OSA patients, γ was significantly negatively correlated with apnea–hypopnea index (AHI; r=−0.326, P=0.015) and Epworth Sleepiness Scale (ESS; r=−0.274, P=0.043), λ was significantly positively correlated with AHI (r=0.373, P=0.005) and ESS (r=0.269, P=0.047), and σ was significantly negatively correlated with AHI (r=−0.363, P=0.007) and ESS (r=−0.295, P=0.029).ConclusionOur results suggest that the high degree of local integration and integrity of the brain connections in OSA patients may be disrupted. The topological alterations of small-world properties may be the mechanism of cognitive impairment in OSA patients. In addition, σ, γ, and λ could be used as a quantitative physiological index for auxiliary clinical diagnoses.

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Disrupted topological properties of brain white matter networks in left temporal lobe epilepsy: A diffusion tensor imaging study

Cited by 20 publications

References 59 publications

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

Organization and control of epileptic circuits in temporal lobe epilepsy

Disrupted small-world brain functional network topology in male patients with severe obstructive sleep apnea revealed by resting-state fMRI

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