We assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE) to identify correlates of postsurgical seizure ...recurrences.
We examined data from 51 patients with TLE who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the preoperative structural, diffusion, and postoperative structural MRI, we generated 2 networks: presurgery network and surgically spared network. Standardizing these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient into a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.
Patients with more abnormal nodes had a lower chance of complete seizure freedom at 1 year and, even if seizure-free at 1 year, were more likely to relapse within 5 years. The number of abnormal nodes was greater and their locations more widespread in the surgically spared networks of patients with poor outcome than in patients with good outcome. We achieved an area under the curve of 0.84 ± 0.06 and specificity of 0.89 ± 0.09 in predicting unsuccessful seizure outcomes (International League Against Epilepsy ILAE 3-5) as opposed to complete seizure freedom (ILAE 1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year 1 and associated with relapses up to 5 years after surgery.
Node abnormality offers a personalized, noninvasive marker that can be combined with clinical data to better estimate the chances of seizure freedom at 1 year and subsequent relapse up to 5 years after ATLR.
This study provides Class II evidence that node abnormality predicts postsurgical seizure recurrence.
During the last decade, diffusion tensor imaging (DTI) has been used extensively to investigate microstructural properties of white matter fiber pathways. In many of these DTI-based studies, fiber ...tractography has been used to infer relationships between bundle-specific mean DTI metrics and measures-of-interest (e.g., when studying diffusion changes related to age, cognitive performance, etc.) or to assess potential differences between populations (e.g., comparing males vs. females, healthy vs. diseased subjects, etc.). As partial volume effects (PVEs) are known to affect tractography and, subsequently, the estimated DTI measures sampled along these reconstructed tracts in an adverse way, it is important to gain insight into potential confounding factors that may modulate this PVE. For instance, for thicker fiber bundles, the contribution of PVE-contaminated voxels to the mean metric for the entire fiber bundle will be smaller, and vice-versa — which means that the extent of PVE-contamination will vary from bundle to bundle. With the growing popularity of tractography-based methods in both fundamental research and clinical applications, it is of paramount importance to examine the presence of PVE-related covariates, such as thickness, orientation, curvature, and shape of a fiber bundle, and to investigate the extent to which these hidden confounds affect diffusion measures. To test the hypothesis that these PVE-related covariates modulate DTI metrics depending on the shape of a bundle, we performed simulations with synthetic diffusion phantoms and analyzed bundle-specific DTI measures of the cingulum and the corpus callosum in 55 healthy subjects. Our results indicate that the estimated bundle-specific mean values of diffusion metrics, including the frequently used fractional anisotropy and mean diffusivity, were indeed modulated by fiber bundle thickness, orientation, and curvature. Correlation analyses between gender and diffusion measures yield different results when volume is included as a covariate. This indicates that incorporating these PVE-related factors in DTI analyses is imperative to disentangle changes in “true microstructural” tissue properties from these hidden covariates.
► In fiber tract simulations, DTI measures (e.g., FA, MD) correlate with bundle volume. ► Confirming these simulations, FA was correlated with bundle volume in the cingulum. ► Correlations of FA with gender change when bundle volume is included as a covariate. ► We suggest to include tract volume as a covariate to improve DTI analysis specificity.
In diffusion tensor magnetic resonance imaging (DT-MRI), limitations concerning complex fiber architecture (when an image voxel contains fiber populations with more than one dominant orientation) are ...well-known. Fractional anisotropy (FA) values are lower in such areas because of a lower directionality of diffusion on the voxel-scale, which makes the interpretation of FA less straightforward. Moreover, the interpretation of the axial and radial diffusivities is far from trivial when there is more than one dominant fiber orientation within a voxel. In this work, using (i) theoretical considerations, (ii) simulations, and (iii) experimental data, it is demonstrated that the mean diffusivity (or the trace of the diffusion tensor) is lower in complex white matter configurations, compared with tissue where there is a single dominant fiber orientation within the voxel. We show that the magnitude of this reduction depends on various factors, including configurational and microstructural properties (e.g., the relative contributions of different fiber populations) and acquisition settings (e.g., the b-value). These results increase our understanding of the quantitative metrics obtained from DT-MRI and, in particular, the effect of the microstructural architecture on the mean diffusivity. More importantly, they reinforce the growing awareness that differences in DT-MRI metrics need to be interpreted cautiously.
► The mean diffusivity (MD) in diffusion tensor MRI is affected by crossing fibers. ► MD values are lower in complex fiber architecture than in single fiber voxels. ► This is shown using theoretical considerations, simulations and in vivo experiments. ► In vivo, mean diffusivity values decrease when fibers cross at larger angles.
Sudden unexpected death in epilepsy is a major cause of premature death in people with epilepsy. We aimed to assess whether structural changes potentially attributable to sudden death pathogenesis ...were present on magnetic resonance imaging in people who subsequently died of sudden unexpected death in epilepsy. In a retrospective, voxel-based analysis of T1 volume scans, we compared grey matter volumes in 12 cases of sudden unexpected death in epilepsy (two definite, 10 probable; eight males), acquired 2 years median, interquartile range (IQR) 2.8 before death median (IQR) age at scanning 33.5 (22) years, with 34 people at high risk age 30.5 (12); 19 males, 19 at low risk age 30 (7.5); 12 males of sudden death, and 15 healthy controls age 37 (16); seven males. At-risk subjects were defined based on risk factors of sudden unexpected death in epilepsy identified in a recent combined risk factor analysis. We identified increased grey matter volume in the right anterior hippocampus/amygdala and parahippocampus in sudden death cases and people at high risk, when compared to those at low risk and controls. Compared to controls, posterior thalamic grey matter volume, an area mediating oxygen regulation, was reduced in cases of sudden unexpected death in epilepsy and subjects at high risk. The extent of reduction correlated with disease duration in all subjects with epilepsy. Increased amygdalo-hippocampal grey matter volume with right-sided changes is consistent with histo-pathological findings reported in sudden infant death syndrome. We speculate that the right-sided predominance reflects asymmetric central influences on autonomic outflow, contributing to cardiac arrhythmia. Pulvinar damage may impair hypoxia regulation. The imaging findings in sudden unexpected death in epilepsy and people at high risk may be useful as a biomarker for risk-stratification in future studies.
There is accumulating evidence that at current acquisition resolutions for diffusion-weighted (DW) MRI, the vast majority of white matter voxels contains “crossing fibers”, referring to complex fiber ...configurations in which multiple and distinctly differently oriented fiber populations exist. Spherical deconvolution based techniques are appealing to characterize this DW intra-voxel signal heterogeneity, as they provide a balanced trade-off between constraints on the required hardware performance and acquisition time on the one hand, and the reliability of the reconstructed fiber orientation distribution function (fODF) on the other hand. Recent findings, however, suggest that an inaccurate calibration of the response function (RF), which represents the DW signal profile of a single fiber orientation, can lead to the detection of spurious fODF peaks which, in turn, can have a severe impact on tractography results. Currently, the computation of this RF is either model-based or estimated from selected voxels that have a fractional anisotropy (FA) value above a predefined threshold. For both approaches, however, there are user-defined settings that affect the RF and, consequently, fODF estimation and tractography. Moreover, these settings still rely on the second-rank diffusion tensor, which may not be the appropriate model, especially at high b-values. In this work, we circumvent these issues for RF calibration by excluding “crossing fibers” voxels in a recursive framework. Our approach is evaluated with simulations and applied to in vivo and ex vivo data sets with different acquisition settings. The results demonstrate that with the proposed method the RF can be calibrated in a robust and automated way without needing to define ad-hoc FA threshold settings. Our framework facilitates the use of spherical deconvolution approaches in data sets in which it is not straightforward to define RF settings a priori.
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•We propose robust response function estimation for spherical deconvolution.•This recursive framework is completely independent of the diffusion tensor model.•Method excludes crossing fiber voxels recursively using an fODF peak ratio threshold.•It is robust towards the threshold and less dependent on underlying data properties.•It can be applied to data sets with different acquisition settings and properties.
Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white ...matter network, and how the network changes relate to seizure outcome.
We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks.
Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole.
Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.
•Connection change pipeline predicts impact of epilepsy surgery on connectome.•Surgery leads to patient-specific reductions in efficiency, strength, region volumes.•Machine learning classifier retrospectively predicts outcome using network changes.•Pipeline could be used prospectively for intended surgery in future.
Individuals with temporal lobe epilepsy (TLE) may have significant language deficits. Language capabilities may further decline following temporal lobe resections. The language network, comprising ...dispersed gray matter regions interconnected with white matter fibers, may be atypical in individuals with TLE. This review explores the structural changes to the language network and the functional reorganization of language abilities in TLE. We discuss the importance of detailed reporting of patient's characteristics, such as, left‐ and right‐sided focal epilepsies as well as lesional and nonlesional pathological subtypes. These factors can affect the healthy functioning of gray and/or white matter. Dysfunction of white matter and displacement of gray matter function could concurrently impact their ability, in turn, producing an interactive effect on typical language organization and function. Surgical intervention can result in impairment of function if the resection includes parts of this structure‐function network that are critical to language. In addition, impairment may occur if language function has been reorganized and is included in a resection. Conversely, resection of an epileptogenic zone may be associated with recovery of cortical function and thus improvement in language function. We explore the abnormality of functional regions in a clinically applicable framework and highlight the differences in the underlying language network. Avoidance of language decline following surgical intervention may depend on tailored resections to avoid critical areas of gray matter and their white matter connections. Further work is required to elucidate the plasticity of the language network in TLE and to identify sub‐types of language representation, both of which will be useful in planning surgery to spare language function.
Objective
Our objective was to identify whether the whole‐brain structural network alterations in patients with temporal lobe epilepsy (TLE) and focal to bilateral tonic–clonic seizures (FBTCS) ...differ from alterations in patients without FBTCS.
Methods
We dichotomized a cohort of 83 drug‐resistant patients with TLE into those with and without FBTCS and compared each group to 29 healthy controls. For each subject, we used diffusion‐weighted magnetic resonance imaging to construct whole‐brain structural networks. First, we measured the extent of alterations by performing FBTCS‐negative (FBTCS−) versus control and FBTCS‐positive (FBTCS+) versus control comparisons, thereby delineating altered subnetworks of the whole‐brain structural network. Second, by standardizing each patient's networks using control networks, we measured the subject‐specific abnormality at every brain region in the network, thereby quantifying the spatial localization and the amount of abnormality in every patient.
Results
Both FBTCS+ and FBTCS− patient groups had altered subnetworks with reduced fractional anisotropy and increased mean diffusivity compared to controls. The altered subnetwork in FBTCS+ patients was more widespread than in FBTCS− patients (441 connections altered at t > 3, p < .001 in FBTCS+ compared to 21 connections altered at t > 3, p = .01 in FBTCS−). Significantly greater abnormalities—aggregated over the entire brain network as well as assessed at the resolution of individual brain areas—were present in FBTCS+ patients (p < .001, d = .82, 95% confidence interval = .32–1.3). In contrast, the fewer abnormalities present in FBTCS− patients were mainly localized to the temporal and frontal areas.
Significance
The whole‐brain structural network is altered to a greater and more widespread extent in patients with TLE and FBTCS. We suggest that these abnormal networks may serve as an underlying structural basis or consequence of the greater seizure spread observed in FBTCS.
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