Dorian Pustina

Early and Late Age of Seizure Onset have a Differential Impact on Brain Resting-State Organization in Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) is associated with abnormalities which extend into the entire brain. While the age of seizure onset (SO) has a large impact on brain plasticity, its effect on brain connectivity at rest remains unclear, especially, in interaction with factors such as the presence of mesial temporal sclerosis (MTS). In this context, we investigated whole-brain and regional functional connectivity (FC) organization in 50 TLE patients who underwent a resting-state fMRI scan, in comparison to healthy controls, using graph-theory measures. We first classified TLE patients according to the presence of MTS or not. Then, we categorized the patients based on their age of SO into two subgroups (early or late age of SO). Results revealed whole-brain differences with both reduced functional segregation and increased integration in the patients, regardless of the age of SO and MTS, relative to the controls. At a local level, we revealed that the connectivity of the ictal hippocampus remains the most impaired for an early SO, even in the absence of MTS. Importantly, we showed that the impact of age of SO on whole-brain and regional resting-state FC depends on the presence of MTS. Overall, our results highlight the importance of investigating the effect of age of SO when examining resting-state activity in TLE, as this factor leads different perturbations of network modularity and connectivity at the global and local level, with different implications for regional plasticity and adaptive organization.

Resting-State Functional Connectivity Predicts the Strength of Hemispheric Lateralization for Language Processing in Temporal Lobe Epilepsy and Normals

In temporal lobe epilepsy (TLE), determining the hemispheric specialization for language before surgery is critical to preserving a patients cognitive abilities post‐surgery. To date, the major techniques utilized are limited by the capacity of patients to efficiently realize the task. We determined whether resting‐state functional connectivity (rsFC) is a reliable predictor of language hemispheric dominance in right and left TLE patients, relative to controls. We chose three subregions of the inferior frontal cortex (pars orbitalis, pars triangularis, and pars opercularis) as the seed regions. All participants performed both a verb generation task and a resting‐state fMRI procedure. Based on the language task, we computed a laterality index (LI) for the resulting network. This revealed that 96% of the participants were left‐hemisphere dominant, although there remained a large degree of variability in the strength of left lateralization. We tested whether LI correlated with rsFC values emerging from each seed. We revealed a set of regions that was specific to each group. Unique correlations involving the epileptic mesial temporal lobe were revealed for the right and left TLE patients, but not for the controls. Importantly, for both TLE groups, the rsFC emerging from a contralateral seed was the most predictive of LI. Overall, our data depict the broad patterns of rsFC that support strong versus weak left hemisphere language laterality. This project provides the first evidence that rsFC data may potentially be used on its own to verify the strength of hemispheric dominance for language in impaired or pathologic populations. Hum Brain Mapp, 36:288–303, 2015.

Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis

The gold standard for identifying stroke lesions is manual tracing, a method that is known to be observer dependent and time consuming, thus impractical for big data studies. We propose LINDA (Lesion Identification with Neighborhood Data Analysis), an automated segmentation algorithm capable of learning the relationship between existing manual segmentations and a single T1‐weighted MRI. A dataset of 60 left hemispheric chronic stroke patients is used to build the method and test it with k‐fold and leave‐one‐out procedures. With respect to manual tracings, predicted lesion maps showed a mean dice overlap of 0.696 ± 0.16, Hausdorff distance of 17.9 ± 9.8 mm, and average displacement of 2.54 ± 1.38 mm. The manual and predicted lesion volumes correlated at r = 0.961. An additional dataset of 45 patients was utilized to test LINDA with independent data, achieving high accuracy rates and confirming its cross‐institutional applicability. To investigate the cost of moving from manual tracings to automated segmentation, we performed comparative lesion‐to‐symptom mapping (LSM) on five behavioral scores. Predicted and manual lesions produced similar neuro‐cognitive maps, albeit with some discussed discrepancies. Of note, region‐wise LSM was more robust to the prediction error than voxel‐wise LSM. Our results show that, while several limitations exist, our current results compete with or exceed the state‐of‐the‐art, producing consistent predictions, very low failure rates, and transferable knowledge between labs. This work also establishes a new viewpoint on evaluating automated methods not only with segmentation accuracy but also with brain–behavior relationships. LINDA is made available online with trained models from over 100 patients. Hum Brain Mapp 37:1405‐1421, 2016.

Presurgical thalamic “hubness” predicts surgical outcome in temporal lobe epilepsy

Objective: To characterize the presurgical brain functional architecture presented in patients with temporal lobe epilepsy (TLE) using graph theoretical measures of resting-state fMRI data and to test its association with surgical outcome. Methods: Fifty-six unilateral patients with TLE, who subsequently underwent anterior temporal lobectomy and were classified as obtaining a seizure-free (Engel class I, n = 35) vs not seizure-free (Engel classes II–IV, n = 21) outcome at 1 year after surgery, and 28 matched healthy controls were enrolled. On the basis of their presurgical resting-state functional connectivity, network properties, including nodal hubness (importance of a node to the network; degree, betweenness, and eigenvector centralities) and integration (global efficiency), were estimated and compared across our experimental groups. Cross-validations with support vector machine (SVM) were used to examine whether selective nodal hubness exceeded standard clinical characteristics in outcome prediction. Results: Compared to the seizure-free patients and healthy controls, the not seizure-free patients displayed a specific increase in nodal hubness (degree and eigenvector centralities) involving both the ipsilateral and contralateral thalami, contributed by an increase in the number of connections to regions distributed mostly in the contralateral hemisphere. Simulating removal of thalamus reduced network integration more dramatically in not seizure-free patients. Lastly, SVM models built on these thalamic hubness measures produced 76% prediction accuracy, while models built with standard clinical variables yielded only 58% accuracy (both were cross-validated). Conclusions: A thalamic network associated with seizure recurrence may already be established presurgically. Thalamic hubness can serve as a potential biomarker of surgical outcome, outperforming the clinical characteristics commonly used in epilepsy surgery centers.

Increased microstructural white matter correlations in left, but not right, temporal lobe epilepsy

Microstructural white matter tract correlations have been shown to reflect known patterns of phylogenetic development and functional specialization in healthy subjects. The aim of this study was to establish intertract correlations in a group of controls and to examine potential deviations from normality in temporal lobe epilepsy (TLE). We investigated intertract correlations in 28 healthy controls, 21 left TLE (LTLE) and 23 right TLE (RTLE). Nine tracts were investigated, comprising the parahippocampal fasciculi, the uncinate fasciculi, the arcuate fasciculi, the frontoparietal tracts, and the fornix. An abnormal increase in tract correlations was observed in LTLE, while RTLE showed intertract correlations similar to controls. In the control group, tract correlations increased with increasing fractional anisotropy (FA), while in the TLE groups tract correlations increased with decreasing FA. Cluster analyses revealed agglomeration of bilateral pairs of homologous tracts in healthy subjects, with such pairs separated in our LTLE and RTLE groups. Discriminant analyses aimed at distinguishing LTLE from RTLE, revealing that tract correlations produce higher rates of accurate group classification than FA values. Our results confirm and extend previous work by showing that LTLE compared to RTLE patients display not only more extensive losses in microstructural orientation but also more aberrant intertract correlations. Aberrant correlations may be related to pathologic processes (i.e., seizure spread) or to adaptive processes aimed at preserving key cognitive functions. Our data suggest that tract correlations may have predictive value in distinguishing LTLE from RTLE, potentially moving diffusion imaging to a place of greater prominence in clinical practice. Hum Brain Mapp, 36:85–98, 2015.

Distinct Types of White Matter Changes Are Observed after Anterior Temporal Lobectomy in Epilepsy

Anterior temporal lobectomy (ATL) is commonly adopted to control medically intractable temporal lobe epilepsy (TLE). Depending on the side of resection, the degree to which Wallerian degeneration and adaptive plasticity occur after ATL has important implications for understanding cognitive and clinical outcome. We obtained diffusion tensor imaging from 24 TLE patients (12 left) before and after surgery, and 12 matched controls at comparable time intervals. Voxel-based analyses were performed on fractional anisotropy (FA) before and after surgery. Areas with postoperative FA increase were further investigated to distinguish between genuine plasticity and processes related to the degeneration of crossing fibers. Before surgery, both patient groups showed bilateral reduced FA in numerous tracts, but left TLE patients showed more extensive effects, including language tracts in the contralateral hemisphere (superior longitudinal fasciculus and uncinate). After surgery, FA decreased ipsilaterally in both ATL groups, affecting the fornix, uncinate, stria terminalis, and corpus callosum. FA increased ipsilaterally along the superior corona radiata in both left and right ATL groups, exceeding normal FA values. In these clusters, the mode of anisotropy increased as well, confirming fiber degeneration in an area with crossing fibers. In left ATL patients, pre-existing low FA values in right superior longitudinal and uncinate fasciculi normalized after surgery, while MO values did not change. Preoperative verbal fluency correlated with FA values in all areas that later increased FA in left TLE patients, but postoperative verbal fluency correlated only with FA of the right superior longitudinal fasciculus. Our results demonstrate that genuine reorganization occurs in non-dominant language tracts after dominant hemisphere resection, a process that may help implement the inter-hemispheric shift of language activation found in fMRI studies. The results indicate that left TLE patients, despite showing more initial white matter damage, have the potential for greater adaptive changes postoperatively than right TLE patients.

Temporal Lobe Epilepsy and Surgery Selectively Alter the Dorsal, Not the Ventral, Default-Mode Network

The default-mode network (DMN) is a major resting-state network. It can be divided in two distinct networks: one is composed of dorsal and anterior regions [referred to as the dorsal DMN (dDMN)], while the other involves the more posterior regions [referred to as the ventral DMN (vDMN)]. To date, no studies have investigated the potentially distinct impact of temporal lobe epilepsy (TLE) on these networks. In this context, we explored the effect of TLE and anterior temporal lobectomy (ATL) on the dDMN and vDMN. We utilized two resting-state fMRI sessions from left, right TLE patients (pre-/post-surgery) and normal controls (sessions 1/2). Using independent component analysis, we identified the two networks. We then evaluated for differences in spatial extent for each network between the groups, and across the scanning sessions. The results revealed that, pre-surgery, the dDMN showed larger differences between the three groups than the vDMN, and more particularly between right and left TLE than between the TLE patients and controls. In terms of change post-surgery, in both TLE groups, the dDMN also demonstrated larger changes than the vDMN. For the vDMN, the only changes involved the resected temporal lobe for each ATL group. For the dDMN, the left ATL group showed post-surgical increases in several regions outside the ictal temporal lobe. In contrast, the right ATL group displayed a large reduction in the frontal cortex. The results highlight that the two DMNs are not impacted by TLE and ATL in an equivalent fashion. Importantly, the dDMN was the more affected, with right ATL having a more deleterious effects than left ATL. We are the first to highlight that the dDMN more strongly bears the negative impact of TLE than the vDMN, suggesting there is an interaction between the side of pathology and DM sub-network activity. Our findings have implications for understanding the impact TLE and subsequent ATL on the functions implemented by the distinct DMNs.

Contralateral interictal spikes are related to tapetum damage in left temporal lobe epilepsy

In temporal lobe epilepsy (TLE), the epileptogenic focus is focal and unilateral in the majority of patients. A key characteristic of focal TLE is the presence of subclinical epileptiform activity in both the ictal and contralateral “healthy” hemisphere. Such interictal activity is clinically important, as it may reflect the spread of pathology, potentially leading to secondary epileptogenesis. The role played by white matter pathways in this process is unknown.

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

ABSTRACT Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain‐behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass‐univariate approach. Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass‐univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results ‐ higher dice overlap and smaller average displacement ‐ compared to VLSM. This advantage persisted at different sample sizes (N = 20–131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation‐based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross‐validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions showed a systematic advantage of SCCAN over VLSM; under no circumstance could VLSM exceed the accuracy obtained with SCCAN. When considering functional units composed of multiple brain areas VLSM identified fewer areas than SCCAN. The investigation of real scores of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately identify known language‐critical areas, while VLSM either produced diffuse maps (FDR correction) or few scattered voxels (FWER correction). Overall, this study shows that a multivariate method, such as, SCCAN, outperforms VLSM in a number of scenarios, including functional dependency on single or multiple areas, different sample sizes, different multi‐area combinations, and different thresholding mechanisms (FWER, Bonferroni, FDR). These results support previous claims that multivariate methods are in general more accurate than mass‐univariate approaches, and should be preferred over traditional VLSM approaches. All the methods described in this study are available in the newly developed LESYMAP package for R. HIGHLIGHTSVoxel‐based lesion to symptom mapping (LSM) suffers from poor accuracy.We propose a novel multivariate LSM method based on canonical correlations.Univariate and multivariate methods were compared in simulated and real analyses.The multivariate method exceeded systematically the accuracy of traditional VLSM.The new method is implemented in the LESYMAP package for R.

Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study.

Pre-surgical evaluation of patients with temporal lobe epilepsy (TLE) relies on information obtained from multiple neuroimaging modalities. The relationship between modalities and their combined power in predicting the seizure focus is currently unknown. We investigated asymmetries from three different modalities, PET (glucose metabolism), MRI (cortical thickness), and diffusion tensor imaging (DTI; white matter anisotropy) in 28 left and 30 right TLE patients (LTLE and RTLE). Stepwise logistic regression models were built from each modality separately and from all three combined, while bootstrapped methods and split-sample validation verified the robustness of predictions. Among all multimodal asymmetries, three PET asymmetries formed the best predictive model (100% success in full sample, >95% success in split-sample validation). The combinations of PET with other modalities did not perform better than PET alone. Probabilistic classifications were obtained for new clinical cases, which showed correct lateralization for 7/7 new TLE patients (100%) and for 4/5 operated patients with discordant or non-informative PET reports (80%). Metabolism showed closer relationship with white matter in LTLE and closer relationship with gray matter in RTLE. Our data suggest that metabolism is a powerful modality that can predict seizure laterality with high accuracy, and offers high value for automated predictive models. The side of epileptogenic focus can affect the relationship of metabolism with brain structure. The data and tools necessary to obtain classifications for new TLE patients are made publicly available.