Structural connectivity differences in left and right temporal lobe epilepsy

Besson, Pierre; Dinkelacker, Vera; Valabrègue, Romain; Thivard, Lionel; Leclerc, Xavier; Baulac, Michel; Sammler, Daniela; Colliot, Olivier; Lehericy, Stéphane; Samson, Séverine; Dupont, Sophie

doi:10.1016/j.neuroimage.2014.04.071

Cited by 189 publications

(178 citation statements)

References 73 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: 59%

“…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. 29 As for differences between left and right TLE, most studies have reported a stronger impact of left TLE on network function as assessed by DTI 7,15 and restingstate fMRI, 27 as in this study. Although the reason for the preferential vulnerability of the left hemisphere is unclear, one plausible explanation is that it is related to interference by the disease with for- mation of the language network.…”

Section: Discussionmentioning

confidence: 60%

“…Although the reason for the preferential vulnerability of the left hemisphere is unclear, one plausible explanation is that it is related to interference by the disease with for- mation of the language network. 15 It has been suggested that 3 factors will determine the practical utility of a connectome-based classifier-accurate and robust prediction, clinically informative outcome that cannot be predicted using other means, and predictive accuracy superior to that of other simpler and less expensive measures. 10 With regard to the first factor, the ROC analyses indicated moderate to high accuracy for prediction of TLE laterality.…”

Section: Discussionmentioning

confidence: 99%

“…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

Kamiya

Amemiya

Suzuki

et al. 2016

MRMS

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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: 59%

Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kamiya

Amemiya

Suzuki

et al. 2016

MRMS

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“…As mentioned before, it is well‐known that patients with left MTLE have worse performance in memory tasks and distinct structural damage of cerebral gray and white matter than patients with right MTLE [Besson et al, 2014; Keller et al, 2002; Riederer et al, 2008]. These data put together implies that right and left MTLE could have different pathological mechanisms.…”

Section: Introductionmentioning

confidence: 81%

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

Campos

Coan

Yasuda

et al. 2016

Human Brain Mapping

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102

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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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Magnetic resonance fingerprinting of temporal lobe white matter in mesial temporal lobe epilepsy

Wang

Cao

et al. 2019

Ann Clin Transl Neurol

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Objective Mesial temporal lobe epilepsy (MTLE) is a network disorder. We aimed to quantify the white matter alterations in the temporal lobe of MTLE patients with hippocampal sclerosis (MTLE‐HS) by using magnetic resonance fingerprinting (MRF), a novel imaging technique, which allows simultaneous measurements of multiple parameters with a single acquisition. Methods We consecutively recruited 27 unilateral MTLE‐HS patients and 22 healthy controls. Measurements including T1, T2, and PD values in the temporopolar white matter and temporal stem were recorded and analyzed. Results We found increased T2 value in both sides, and increased T1 value in the ipsilateral temporopolar white matter of MTLE‐HS patients, as compared with healthy controls. The T1 and T2 values were higher in the ipsilateral than the contralateral side. In the temporal stem, increased T1 and T2 values in the ipsilateral side of the MTLE‐HS patients were also observed. Only increased T2 values were observed in the contralateral temporal stem. No significant differences in PD values were observed in either the temporopolar white matter or temporal stem of the MTLE‐HS patients. Correlation analysis revealed that T1 and T2 values in the ipsilateral temporopolar white matter were negatively correlated with the age at epilepsy onset. Interpretation By using MRF, we were able to assess the alterations of T1 and T2 in the temporal lobe white matter of MTLE‐HS patients. MRF could be a promising imaging technique in identifying mild changes in MTLE patients, which might optimize the pre‐surgical evaluation and therapeutic interventions in these patients.

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Structural connectivity differences in left and right temporal lobe epilepsy

Cited by 189 publications

References 73 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

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

Magnetic resonance fingerprinting of temporal lobe white matter in mesial temporal lobe epilepsy

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