Presurgical connectome and postsurgical seizure control in temporal lobe epilepsy

Bonilha, Leonardo; Helpern, Joseph A.; Sainju, Rup K.; Nesland, Travis; Edwards, Jonathan; Glazier, Steven S.; Tabesh, Ali

doi:10.1212/01.wnl.0000435306.95271.5f

Cited by 115 publications

(150 citation statements)

References 29 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: 54%

“…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: 54%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…With respect to clinical outcome, there is convergent evidence from networks derived from diffusion MRI tractography and structural covariance that presurgical network metrics may relate to seizure freedom after surgery [55,150]. In the functional domain, a recent study furthermore showed that graph theoretical network parameters may assist in the prediction of neurocognitive outcome after surgery [151].…”

Section: What Are Other Potential Applications For Graph Theoretical mentioning

confidence: 99%

“…[55]. B) A diffusion MRI network analysis between seizure-free patients and and non-seizure-free patients indicated connectivity increases in the latter subgroup in bilateral temporal and ipsilateral supramarginal networks [150]. C) Surface maps showing regions in which graph theoretical parameters predict cognitive domain changes after surgery [151].…”

Section: Key Questions (Answered)mentioning

confidence: 99%

Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy

Bernhardt

Bonilha

Gross

2015

Epilepsy & Behavior

220

164

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“…Given the characteristics of a network‐level pathology in MTLE [Spencer, 2002], other studies have examined graph‐theory properties [Bullmore and Sporns, 2009] of both structural [Bernhardt et al, 2011; Bonilha et al, 2013] and functional data [Chiang et al, 2014; Liao et al, 2010; Vlooswijk et al, 2011]. These studies evaluated the relationships between the syndrome alterations and properties such as degree of connectivity, clustering coefficient and hub distribution, demonstrating alterations in MTLE characterized by reduced specificity and global efficiency [Bernhardt et al, 2011; Liao et al, 2010].…”

Section: Introductionmentioning

confidence: 99%

Large‐scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy

Campos

Coan

Yasuda

et al. 2016

Human Brain Mapping

103

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Mesial temporal lobe epilepsy (MTLE) with hippocampus sclerosis (HS) is associated with functional and structural alterations extending beyond the temporal regions and abnormal pattern of brain resting state networks (RSNs) connectivity. We hypothesized that the interaction of large‐scale RSNs is differently affected in patients with right‐ and left‐MTLE with HS compared to controls. We aimed to determine and characterize these alterations through the analysis of 12 RSNs, functionally parceled in 70 regions of interest (ROIs), from resting‐state functional‐MRIs of 99 subjects (52 controls, 26 right‐ and 21 left‐MTLE patients with HS). Image preprocessing and statistical analysis were performed using UF2C‐toolbox, which provided ROI‐wise results for intranetwork and internetwork connectivity. Intranetwork abnormalities were observed in the dorsal default mode network (DMN) in both groups of patients and in the posterior salience network in right‐MTLE. Both groups showed abnormal correlation between the dorsal‐DMN and the posterior salience, as well as between the dorsal‐DMN and the executive‐control network. Patients with left‐MTLE also showed reduced correlation between the dorsal‐DMN and visuospatial network and increased correlation between bilateral thalamus and the posterior salience network. The ipsilateral hippocampus stood out as a central area of abnormalities. Alterations on left‐MTLE expressed a low cluster coefficient, whereas the altered connections on right‐MTLE showed low cluster coefficient in the DMN but high in the posterior salience regions. Both right‐ and left‐MTLE patients with HS have widespread abnormal interactions of large‐scale brain networks; however, all parameters evaluated indicate that left‐MTLE has a more intricate bihemispheric dysfunction compared to right‐MTLE. Hum Brain Mapp 37:3137–3152, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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Presurgical connectome and postsurgical seizure control in temporal lobe epilepsy

Cited by 115 publications

References 29 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

Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy

Large‐scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy

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