Regional neocortical thinning in mesial temporal lobe epilepsy

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning

Objective

Temporal lobe epilepsy (TLE) predominantly originates from the anteromedial basal region of the temporal lobe, and its prognosis is generally favorable following surgical intervention. However, TLE often appears negative in magnetic resonance imaging (MRI), making it difficult to quantitatively diagnose the condition solely based on clinical symptoms. There is a pressing need for a quantitative, automated method for detecting TLE.

Methods

This study employed MRI scans and clinical data from 51 retrospective epilepsy cases, dividing them into two groups: 34 patients in TLE group and 17 patients in non-TLE group. The criteria for defining the TLE group were successful surgical removal of the epileptogenic zone in the temporal lobe and a favorable postoperative prognosis. A standard procedure was used for normalization, brain extraction, tissue segmentation, regional brain partitioning, and cortical reconstruction of T1 structural MRI images. Morphometric features such as gray matter volume, cortical thickness, and surface area were extracted from a total of 20 temporal lobe regions in both hemispheres. Support vector machine (SVM), extreme learning machine (ELM), and cmcRVFL+ classifiers were employed for model training and validated using 10-fold cross-validation.

Results

The results demonstrated that employing ELM classifiers in conjunction with specific temporal lobe gray matter volume features led to a better identification of TLE. The classification accuracy was 92.79%, with an area under the curve (AUC) value of 0.8019.

Conclusion

The method proposed in this study can significantly assist in the preoperative identification of TLE patients. By employing this method, TLE can be included in surgical criteria, which could alleviate patient symptoms and improve prognosis, thereby bearing substantial clinical significance.

Microstructural evaluation of the brain with advanced magnetic resonance imaging techniques in cases of electrical status epilepticus during sleep (ESES)

Background/aim

The cause and treatment of electrical status epilepticus during sleep (ESES), one of the epileptic encephalopathies of childhood, is unclear. The aim of this study was to evaluate possible microstructural abnormalities in the brain using advanced magnetic resonance imaging (MRI) techniques in ESES patients with and without genetic mutations.

Materials and methods

This research comprised 12 ESES patients without structural thalamic lesions (6 with genetic abnormalities and 6 without) and 12 healthy children. Whole-exome sequencing was used for the genetic mutation analysis. Brain MRI data were evaluated using tractus-based spatial statistics, voxel-based morphometry, a local gyrification index, subcortical shape analysis, FreeSurfer volume, and cortical thickness. The data of the groups were compared.

Results

The mean age in the control group was 9.05 ± 1.85 years, whereas that in the ESES group was 9.45 ± 2.72 years. Compared to the control group, the ESES patients showed higher mean thalamus diffusivity (p < 0.05). ESES patients with genetic mutations had lower axial diffusivity in the superior longitudinal fasciculus and gray matter volume in the entorhinal region, accumbens area, caudate, putamen, cerebral white matter, and outer cerebellar areas. The superior and middle temporal cortical thickness increased in the ESES patients.

Conclusion

This study is important in terms of presenting the microstructural evaluation of the brain in ESES patients with advanced MRI analysis methods as well as comparing patients with and without genetic mutations. These findings may be associated with corticostriatal transmission, ictogenesis, epileptogenesis, neuropsychiatric symptoms, cognitive impairment, and cerebellar involvement in ESES. Expanded case-group studies may help to understand the physiology of the corticothalamic circuitry in its etiopathogenesis and develop secondary therapeutic targets for ESES.

Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy: A Proof-of-Principle Study

BACKGROUND AND OBJECTIVES

A new frontier in diagnostic radiology is the inclusion of machine-assisted support tools that facilitate the identification of subtle lesions often not visible to the human eye. Structural neuroimaging plays an essential role in the identification of lesions in patients with epilepsy, which often coincide with the seizure focus. In this study, we explored the potential for a convolutional neural network (CNN) to determine lateralization of seizure onset in patients with epilepsy using T1-weighted structural MRI scans as input.

METHODS

Using a dataset of 359 patients with temporal lobe epilepsy (TLE) from 7 surgical centers, we tested whether a CNN based on T1-weighted images could classify seizure laterality concordant with clinical team consensus. This CNN was compared with a randomized model (comparison with chance) and a hippocampal volume logistic regression (comparison with current clinically available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients.

RESULTS

Across 100 runs, the CNN model was concordant with clinician lateralization on average 78% (SD = 5.1%) of runs with the best-performing model achieving 89% concordance. The CNN outperformed the randomized model (average concordance of 51.7%) on 100% of runs with an average improvement of 26.2% and outperformed the hippocampal volume model (average concordance of 71.7%) on 85% of runs with an average improvement of 6.25%. Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification.

DISCUSSION

These extratemporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extrahippocampal regions that may require additional radiologic attention.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MRI can correctly classify seizure laterality.

Spatial patterns of gray and white matter compromise relate to age of seizure onset in temporal lobe epilepsy

OBJECTIVE

Temporal Lobe Epilepsy (TLE) is frequently a neurodevelopmental disorder, involving subcortical volume loss, cortical atrophy, and white matter (WM) disruption. However, few studies have addressed how these pathological changes in TLE relate to one another. In this study, we investigate spatial patterns of gray and white matter degeneration in TLE and evaluate the hypothesis that the relationship among these patterns varies as a function of the age at which seizures begin.

METHODS

Eighty-two patients with TLE and 59 healthy controls were enrolled. T1-weighted images were used to obtain hippocampal volumes and cortical thickness estimates. Diffusion-weighted imaging was used to obtain fractional anisotropy (FA) and mean diffusivity (MD) of the superficial WM (SWM) and deep WM tracts. Analysis of covariance was used to examine patterns of WM and gray matter alterations in TLE relative to controls, controlling for age and sex. Sliding window correlations were then performed to examine the relationships between SWM degeneration, cortical thinning, and hippocampal atrophy across ages of seizure onset.

RESULTS

Cortical thinning in TLE followed a widespread, bilateral pattern that was pronounced in posterior centroparietal regions, whereas SWM and deep WM loss occurred mostly in ipsilateral, temporolimbic regions compared to controls. Window correlations revealed a relationship between hippocampal volume loss and whole brain SWM disruption in patients who developed epilepsy during childhood. On the other hand, in patients with adult-onset TLE, co-occurring cortical and SWM alterations were observed in the medial temporal lobe ipsilateral to the seizure focus.

SIGNIFICANCE

Our results suggest that although cortical, hippocampal and WM alterations appear spatially discordant at the group level, the relationship among these features depends on the age at which seizures begin. Whereas neurodevelopmental aspects of TLE may result in co-occurring WM and hippocampal degeneration near the epileptogenic zone, the onset of seizures in adulthood may set off a cascade of SWM microstructural loss and cortical atrophy of a neurodegenerative nature.

MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer's disease, and healthy controls

BACKGROUND

Radiological identification of temporal lobe epilepsy (TLE) is crucial for diagnosis and treatment planning. TLE neuroimaging abnormalities are pervasive at the group level, but they can be subtle and difficult to identify by visual inspection of individual scans, prompting applications of artificial intelligence (AI) assisted technologies.

METHOD

We assessed the ability of a convolutional neural network (CNN) algorithm to classify TLE vs. patients with AD vs. healthy controls using T1-weighted magnetic resonance imaging (MRI) scans. We used feature visualization techniques to identify regions the CNN employed to differentiate disease types.

RESULTS

We show the following classification results: healthy control accuracy = 81.54% (SD = 1.77%), precision = 0.81 (SD = 0.02), recall = 0.85 (SD = 0.03), and F1-score = 0.83 (SD = 0.02); TLE accuracy = 90.45% (SD = 1.59%), precision = 0.86 (SD = 0.03), recall = 0.86 (SD = 0.04), and F1-score = 0.85 (SD = 0.04); and AD accuracy = 88.52% (SD = 1.27%), precision = 0.64 (SD = 0.05), recall = 0.53 (SD = 0.07), and F1 score = 0.58 (0.05). The high accuracy in identification of TLE was remarkable, considering that only 47% of the cohort had deemed to be lesional based on MRI alone. Model predictions were also considerably better than random permutation classifications (p < 0.01) and were independent of age effects.

CONCLUSIONS

AI (CNN deep learning) can classify and distinguish TLE, underscoring its potential utility for future computer-aided radiological assessments of epilepsy, especially for patients who do not exhibit easily identifiable TLE associated MRI features (e.g., hippocampal sclerosis).

Ocular motility as a measure of cerebral dysfunction in adults with focal epilepsy

OBJECTIVE

Using objective oculomotor measures, we aimed to: (1) compare oculomotor performance in patients with drug-resistant focal epilepsy to healthy controls, and (2) investigate the differential impact of epileptogenic focus laterality and location on oculomotor performance.

METHODS

We recruited 51 adults with drug-resistant focal epilepsy from the Comprehensive Epilepsy Programs of two tertiary hospitals and 31 healthy controls to perform prosaccade and antisaccade tasks. Oculomotor variables of interest were latency, visuospatial accuracy, and antisaccade error rate. Linear mixed models were performed to compare interactions between groups (epilepsy, control) and oculomotor tasks, and between epilepsy subgroups and oculomotor tasks for each oculomotor variable.

RESULTS

Compared to healthy controls, patients with drug-resistant focal epilepsy exhibited longer antisaccade latencies (mean difference = 42.8 ms, P = 0.001), poorer spatial accuracy for both prosaccade (mean difference = 0.4°, P = 0.002), and antisaccade tasks (mean difference = 2.1°, P < 0.001), and more antisaccade errors (mean difference = 12.6%, P < 0.001). In the epilepsy subgroup analysis, left-hemispheric epilepsy patients exhibited longer antisaccade latencies compared to controls (mean difference = 52.2 ms, P = 0.003), while right-hemispheric epilepsy was the most spatially inaccurate compared to controls (mean difference = 2.5°, P = 0.003). The temporal lobe epilepsy subgroup displayed longer antisaccade latencies compared to controls (mean difference = 47.6 ms, P = 0.005).

SIGNIFICANCE

Patients with drug-resistant focal epilepsy exhibit poor inhibitory control as evidenced by a high percentage of antisaccade errors, slower cognitive processing speed, and impaired visuospatial accuracy on oculomotor tasks. Patients with left-hemispheric epilepsy and temporal lobe epilepsy have markedly impaired processing speed. Overall, oculomotor tasks can be a useful tool to objectively quantify cerebral dysfunction in drug-resistant focal epilepsy.

Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection

BACKGROUND

Anterior temporal lobe resection is an effective treatment for temporal lobe epilepsy. The post-surgical structural changes could influence the follow-up treatment. Capturing post-surgical changes necessitates a well-established cortical shape correspondence between pre- and post-surgical surfaces. Yet, most cortical surface registration methods are designed for normal neuroanatomy. Surgical changes can introduce wide ranging artifacts in correspondence, for which conventional surface registration methods may not work as intended.

METHODS

In this paper, we propose a novel particle method for one-to-one dense shape correspondence between pre- and post-surgical surfaces with temporal lobe resection. The proposed method can handle partial structural abnormality involving non-rigid changes. Unlike existing particle methods using implicit particle adjacency, we consider explicit particle adjacency to establish a smooth correspondence. Moreover, we propose hierarchical optimization of particles rather than full optimization of all particles at once to avoid trappings of locally optimal particle update.

RESULTS

We evaluate the proposed method on 25 pairs of T1-MRI with pre- and post-simulated resection on the anterior temporal lobe and 25 pairs of patients with actual resection. We show improved accuracy over several cortical regions in terms of ROI boundary Hausdorff distance with 4.29 mm and Dice similarity coefficients with average value 0.841, compared to existing surface registration methods on simulated data. In 25 patients with actual resection of the anterior temporal lobe, our method shows an improved shape correspondence in qualitative and quantitative evaluation on parcellation-off ratio with average value 0.061 and cortical thickness changes. We also show better smoothness of the correspondence without self-intersection, compared with point-wise matching methods which show various degrees of self-intersection.

CONCLUSION

The proposed method establishes a promising one-to-one dense shape correspondence for temporal lobe resection. The resulting correspondence is smooth without self-intersection. The proposed hierarchical optimization strategy could accelerate optimization and improve the optimization accuracy. According to the results on the paired surfaces with temporal lobe resection, the proposed method outperforms the compared methods and is more reliable to capture cortical thickness changes.

MRI based composite parameter of multiple tissue types for improved patient-level hemispheric and regional level lateralization in pediatric epilepsy

Although voxel-based morphometry (VBM) of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) changes aid in epileptic seizure lateralization, type of T1 pulse sequence, preprocessing steps and tissue segmentation methods lead to variation in tissue classification. Here, we test the prediction accuracy of individual MRI based tissue types and a novel composite ratio parameter [(GM + WM)/CSF], sensitive to parenchymal changes and independent of tissue classification variations. Pediatric patients with partial seizures (both simple and complex), but normal and lesion-free MRI were considered (33 patients; unilateral EEG; 17 female / 16 male; age mean ± SD = 11.5 ± 5 years). MRI based seizure lateralization was performed for each patient and verified with EEG findings alone or in combination with seizure semiology. T1 weighted MRI from patients and normal control subjects was spatially transformed to the Talairach atlas and automatically segmented into GM, WM and CSF tissue types. 41 age matched normal controls (11 female / 30 male; age mean ± SD = 14.6 ± 3 years) served as the null distribution to test tissue type deviations across each epilepsy patient. When verified with the patient EEG prediction, WM, GM and CSF had a hemispheric match of 76%, 70% and 55% respectively, while the composite ratio [(GM + WM)/CSF)] showed the highest accuracy of 85%. When EEG findings and seizure semiology were combined, MRI predictions using the composite ratio improved further to 88%. To further localize the epileptic focus, regional level (frontal, temporal, parietal and occipital) MRI predictions were obtained. The composite ratio performed at 88-91% accuracy, revealing regional MRI changes, not predictable with EEG. The results show inconsistent changes in GM and WM in majority of the pediatric epilepsy patients and demonstrate the applicability of the composite ratio [(GM + WM)/CSF)] as a superior predictor, independent of tissue classification variations. Clinical EEG findings combined with seizure semiology, can overcome scalp EEG's limitations and lean towards the MRI lateralization in specific cases.

Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.

Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.

Characterization of Hippocampal-Thalamic-Cortical Morphometric Reorganization in Temporal Lobe Epilepsy

Introduction

Brain cortico-subcortical connectivity has been investigated in epilepsy using the functional MRI (MRI). Although structural images cannot demonstrate dynamic changes, they provide higher spatial resolution, which allows exploration of the organization of brain in greater detail.

Methods

We used high-resolution brain MRI to study the hippocampal-thalamic-cortical networks in temporal lobe epilepsy (TLE) using a volume-based morphometric method. We enrolled 22 right-TLE, 33 left-TLE, and 28 age/gender-matched controls retrospectively. FreeSurfer software was used for the thalamus segmentation.

Results

Among the 50 subfields, ipsilateral anterior, lateral, and parts of the intralaminar and medial nuclei, as well as the contralateral parts of lateral nuclei had significant volume loss in both TLE. The anteroventral nucleus was most vulnerable. Most thalamic subfields were susceptible to seizure burden, especially the left-TLE. SPM12 was used to conduct an analysis of the gray matter density (GMD) maps. Decreased extratemporal GMD occurred bilaterally. Both TLE demonstrated significant GMD loss over the ipsilateral inferior frontal gyrus, precentral gyrus, and medial orbital cortices.

Significance

Thalamic subfield atrophy was related to the ipsilateral inferior frontal GMD changes, which presented positively in left-TLE and negatively in right-TLE. These findings suggest prefrontal-thalamo-hippocampal network disruption in TLE.

Multimodal connectome biomarkers of cognitive and affective dysfunction in the common epilepsies

Epilepsy is one of the most common chronic neurological conditions, traditionally defined as a disorder of recurrent seizures. Cognitive and affective dysfunction are increasingly recognized as core disease dimensions and can affect patient well-being, sometimes more than the seizures themselves. Connectome-based approaches hold immense promise for revealing mechanisms that contribute to dysfunction and to identify biomarkers. Our review discusses emerging multimodal neuroimaging and connectomics studies that highlight network substrates of cognitive/affective dysfunction in the common epilepsies. We first discuss work in drug-resistant epilepsy syndromes, that is, temporal lobe epilepsy, related to mesiotemporal sclerosis (TLE), and extratemporal epilepsy (ETE), related to malformations of cortical development. While these are traditionally conceptualized as ‘focal’ epilepsies, many patients present with broad structural and functional anomalies. Moreover, the extent of distributed changes contributes to difficulties in multiple cognitive domains as well as affective-behavioral challenges. We also review work in idiopathic generalized epilepsy (IGE), a subset of generalized epilepsy syndromes that involve subcortico-cortical circuits. Overall, neuroimaging and network neuroscience studies point to both shared and syndrome-specific connectome signatures of dysfunction across TLE, ETE, and IGE. Lastly, we point to current gaps in the literature and formulate recommendations for future research.

Epilepsy is increasingly recognized as a network disorder characterized by recurrent seizures as well as broad-ranging cognitive difficulties and affective dysfunction. Our manuscript reviews recent literature highlighting brain network substrates of cognitive and affective dysfunction in common epilepsy syndromes, namely temporal lobe epilepsy secondary to mesiotemporal sclerosis, extratemporal epilepsy secondary to malformations of cortical development, and idiopathic generalized epilepsy syndromes arising from subcortico-cortical pathophysiology. We discuss prior work that has indicated both shared and distinct brain network signatures of cognitive and affective dysfunction across the epilepsy spectrum, improves our knowledge of structure-function links and interindividual heterogeneity, and ultimately aids screening and monitoring of therapeutic strategies.

Neurobehavioural comorbidities of epilepsy: towards a network-based precision taxonomy

Cognitive and behavioural comorbidities are prevalent in childhood and adult epilepsies and impose a substantial human and economic burden. Over the past century, the classic approach to understanding the aetiology and course of these comorbidities has been through the prism of the medical taxonomy of epilepsy, including its causes, course, characteristics and syndromes. Although this 'lesion model' has long served as the organizing paradigm for the field, substantial challenges to this model have accumulated from diverse sources, including neuroimaging, neuropathology, neuropsychology and network science. Advances in patient stratification and phenotyping point towards a new taxonomy for the cognitive and behavioural comorbidities of epilepsy, which reflects the heterogeneity of their clinical presentation and raises the possibility of a precision medicine approach. As we discuss in this Review, these advances are informing the development of a revised aetiological paradigm that incorporates sophisticated neurobiological measures, genomics, comorbid disease, diversity and adversity, and resilience factors. We describe modifiable risk factors that could guide early identification, treatment and, ultimately, prevention of cognitive and broader neurobehavioural comorbidities in epilepsy and propose a road map to guide future research.

Recovery of cortical atrophy in patients with temporal lobe epilepsy after successful anterior temporal lobectomy

The aims of this study were to investigate whether the cortical atrophy caused by temporal lobe epilepsy (TLE) was reversible after successful anterior temporal lobectomy (ATL) and to further observe whether possible changes are related to age at surgery and cognitive changes. Twelve patients with unilateral mesial TLE who received ATL and remained seizure free in one year follow-up were included. They underwent two MRI scans few days before and oneyear after surgery. Thirty age- and sex-matched healthy participants were recruited as controls. Group comparisons were used to test the differences in cortical thickness (CTh) between the pre-/postsurgical patients and controls. Longitudinal test was used to directly show postsurgical changes of the patients. Besides, the correlations between regional cortical volume (CVo) changes and age at surgery or cognitive changes were also tested. Compared with controls, the patients with TLE showed dispersed cortical thinning especially in the bilateral frontal lobes before surgery and no significant cortical thinning except for cortices near the resected areas after surgery. The longitudinal analysis showed CTh increment in the ipsilateral precentral and postcentral gyrus, cuneus and widespread in the contralateral cortex. In the volumetric analysis, the CVo changes in the contralateral hemisphere were negatively correlated with age at surgery and positively correlated with MoCA score changes. This study suggests that the cortical atrophy caused by TLE could recover after successful ATL. The recovery ability is greater in younger subjects and is positively related to cognitive recovery. These findings could serve as new clues that patients with TLE can benefit from timely and successful ATL.

Single-subject gray matter networks in temporal lobe epilepsy patients with hippocampal sclerosis

OBJECTIVE

Previous studies have demonstrated structural brain network abnormalities in patients with temporal lobe epilepsy (TLE) using cortical thickness or gray matter (GM) volume. However, no studies have applied single-subject GM network analysis. Here, we first applied an analysis of similarity-based single-subject GM networks to individual patients with TLE.

MATERIALS AND METHODS

We recruited 51 patients with TLE and unilateral hippocampal sclerosis (22 left, 29 right TLE) and 51 age- and gender- matched healthy controls. Single-subject structural networks were extracted from three-dimensional T1-weighted magnetic resonance images for each subject. In this method, nodes were defined as small cortical regions and edges representing connecting regions that have high statistical similarity. The constructed graphs were analyzed using the graph theoretical approach. The following global and local network properties were calculated: betweenness centrality, clustering coefficient, and characteristic path length. In addition, small world properties (normalized path length λ, normalized clustering coefficient γ, and small-world network value σ) were obtained and compared with those for the controls.

RESULTS

Although the small-world configurations were retained, impaired global clustering coefficient was observed in left and right TLE. At a regional level, patients with left TLE showed a widespread decrease of the clustering coefficient beyond the ipsilateral temporal lobe and a decreased characteristic path length in the ipsilateral temporal pole. On the other hand, patients with right TLE showed a localized decrease of the clustering coefficient in the ipsilateral temporal lobe.

CONCLUSIONS

Our findings suggest that global and local network properties disrupted and moved toward randomized networks in TLE patients in comparison to controls. This network alteration was more extensive in left TLE than in right TLE patients. Single-subject GM networks may contribute to a better understanding of the pathophysiology of TLE.

Disturbance of functional and effective connectivity of the salience network involved in attention deficits in right temporal lobe epilepsy

The salience network (SN) acts as a switch that generates transient control signals to regulate the executive control network (ECN) and the default mode network (DMN) and has been implicated in cognitive processes. Temporal lobe epilepsy (TLE) is usually accompanied by different types of cognitive deficits, but whether it is associated with dysfunctional connectivity of the SN remains unknown. To address this, thirty-six patients with right TLE (rTLE) and thirty-six healthy controls (HCs) were recruited for the present study. All of the participants were subjected to attention network test (ANT) and resting-state functional resonance imaging (rs-fMRI) scanning. The patient group showed deficits in attention performance. Moreover, the functional connectivity (FC) and effective connectivity (EC) were analyzed based on key SN hubs (the anterior cingulate cortex (ACC) and the bilateral anterior insula (AI)). When compared with those in the HC group, the ACC showed increased FC with the left middle frontal gyrus and the left precentral gyrus, and the right AI showed decreased FC with the right precuneus and the right superior occipital gyrus in the patient group. The EC analysis revealed an increased inflow of information from the left middle temporal gyrus to the ACC and the right AI and an increased outflow of information from the bilateral AI to the left middle frontal gyrus. Furthermore, in the correlation analysis, the abnormal EC from the right AI to the left middle temporal gyrus was positively correlated with the executive control effect. These findings demonstrated aberrant modulation of the SN in rTLE, which was particularly characterized by dysfunctional connectivity between the SN and key brain regions in the DMN and ECN. Elucidation of this effect may further contribute to the comprehensive understanding of the neural mechanisms of the SN in regard to attention deficits in patients with TLE.

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Auditory Temporal Ordering in Patients with Medial Temporal Lobe Epilepsy with and without Hippocampal Sclerosis

Context

Temporal lobe epilepsy can affect central auditory processing (CAP) skills. Auditory temporal ordering (ATO) is a CAP skill that can be evaluated using duration pattern test (DPT).

Aim

The aim is to evaluate ATO in patients with medial temporal lobe epilepsy (MTLE) with hippocampal sclerosis (MTLE + HS) and without hippocampal sclerosis (MTLE-HS) and in their subgroups.

Settings and Design

It was a prospective cross-sectional behavioral observational study conducted in a tertiary neuropsychiatric hospital.

Subjects and Methods

The subjects were patients with refractory MTLE (N = 100), comprising 50 "MTLE + HS" patients and 50 "MTLE-HS". Age-range matched normal healthy subjects (n = 50) formed the control group. Both groups were administered duration pattern test (DPT).

Statistical Analysis Used

Analysis of variance (ANOVA) with post hoc analysis, Dunnett's two-sided and Bonferroni, paired sample t-test, Pearson's correlation, and independent t-test.

Results

The clinical groups performed significantly poorer than the control group, and however, did not differ significantly between them. The age at onset and the duration of the seizures did not have significant relation with the test measures.

Conclusions

Patients with "MTLE + HS" as well as those with "MTLE-HS" and their respective subgroups revealed abnormal ATO indicating CAP dysfunction.

Atrophy and cognitive profiles in older adults with temporal lobe epilepsy are similar to mild cognitive impairment

Epilepsy incidence and prevalence peaks in older adults yet systematic studies of brain ageing and cognition in older adults with epilepsy remain limited. Here, we characterize patterns of cortical atrophy and cognitive impairment in 73 older adults with temporal lobe epilepsy (>55 years) and compare these patterns to those observed in 70 healthy controls and 79 patients with amnestic mild cognitive impairment, the prodromal stage of Alzheimer's disease. Patients with temporal lobe epilepsy were recruited from four tertiary epilepsy surgical centres; amnestic mild cognitive impairment and control subjects were obtained from the Alzheimer's Disease Neuroimaging Initiative database. Whole brain and region of interest analyses were conducted between patient groups and controls, as well as between temporal lobe epilepsy patients with early-onset (age of onset <50 years) and late-onset (>50 years) seizures. Older adults with temporal lobe epilepsy demonstrated a similar pattern and magnitude of medial temporal lobe atrophy to amnestic mild cognitive impairment. Region of interest analyses revealed pronounced medial temporal lobe thinning in both patient groups in bilateral entorhinal, temporal pole, and fusiform regions (all P < 0.05). Patients with temporal lobe epilepsy demonstrated thinner left entorhinal cortex compared to amnestic mild cognitive impairment (P = 0.02). Patients with late-onset temporal lobe epilepsy had a more consistent pattern of cortical thinning than patients with early-onset epilepsy, demonstrating decreased cortical thickness extending into the bilateral fusiform (both P < 0.01). Both temporal lobe epilepsy and amnestic mild cognitive impairment groups showed significant memory and language impairment relative to healthy control subjects. However, despite similar performances in language and memory encoding, patients with amnestic mild cognitive impairment demonstrated poorer delayed memory performances relative to both early and late-onset temporal lobe epilepsy. Medial temporal lobe atrophy and cognitive impairment overlap between older adults with temporal lobe epilepsy and amnestic mild cognitive impairment highlights the risks of growing old with epilepsy. Concerns regarding accelerated ageing and Alzheimer's disease co-morbidity in older adults with temporal lobe epilepsy suggests an urgent need for translational research aimed at identifying common mechanisms and/or targeting symptoms shared across a broad neurological disease spectrum.

Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that node abnormality predicts postsurgical seizure recurrence.

Remodeling of brain morphology in temporal lobe epilepsy

BACKGROUND

Mesial temporal lobe epilepsy (TLE) is one of the most widespread neurological network disorders. Computational anatomy MRI studies demonstrate a robust pattern of cortical volume loss. Most statistical analyses provide information about localization of significant focal differences in a segregationist way. Multivariate Bayesian modeling provides a framework allowing inferences about inter-regional dependencies. We adopt this approach to answer following questions: Which structures within a pattern of dynamic epilepsy-associated brain anatomy reorganization best predict TLE pathology. Do these structures differ between TLE subtypes?

METHODS

We acquire clinical and MRI data from TLE patients with and without hippocampus sclerosis (n = 128) additional to healthy volunteers (n = 120). MRI data were analyzed in the computational anatomy framework of SPM12 using classical mass-univariate analysis followed by multivariate Bayesian modeling.

RESULTS

After obtaining TLE-associated brain anatomy pattern, we estimate predictive power for disease and TLE subtypes using Bayesian model selection and comparison. We show that ipsilateral para-/hippocampal regions contribute most to disease-related differences between TLE and healthy controls independent of TLE laterality and subtype. Prefrontal cortical changes are more discriminative for left-sided TLE, whereas thalamus and temporal pole for right-sided TLE. The presence of hippocampus sclerosis was linked to stronger involvement of thalamus and temporal lobe regions; frontoparietal involvement was predominant in absence of sclerosis.

CONCLUSIONS

Our topology inferences on brain anatomy demonstrate a differential contribution of structures within limbic and extralimbic circuits linked to main effects of TLE and hippocampal sclerosis. We interpret our results as evidence for TLE-related spatial modulation of anatomical networks.

Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study

Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1021 adults with epilepsy compared to 1564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy colocalized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Negative effects of age on atrophy further revealed a strong influence of connectome architecture in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided deeper insights into the macroscale features that shape the pathophysiology of common epilepsies.

Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy

This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis. The resulting cognitive subgroups were compared in regard to sociodemographic and clinical epilepsy characteristics as well as variations in brain structure and functional connectivity. Three cognitive subgroups were identified (intact, language/memory/executive function impairment, generalized impairment) which differed significantly, in a systematic fashion, across multiple features. The generalized impairment group was characterized by an earlier age at medication initiation (P < 0.05), fewer patient (P < 0.001) and parental years of education (P < 0.05), greater racial diversity (P < 0.05), and greater number of lifetime generalized seizures (P < 0.001). The three groups also differed in an orderly manner across total intracranial (P < 0.001) and bilateral cerebellar cortex volumes (P < 0.01), and rate of bilateral hippocampal atrophy (P < 0.014), but minimally in regional measures of cortical volume or thickness. In contrast, large-scale patterns of cortical-subcortical covariance networks revealed significant differences across groups in global and local measures of community structure and distribution of hubs. Resting-state fMRI revealed stepwise anomalies as a function of cluster membership, with the most abnormal patterns of connectivity evident in the generalized impairment group and no significant differences from controls in the cognitively intact group. Overall, the distinct underlying cognitive phenotypes of temporal lobe epilepsy harbor systematic relationships with clinical, sociodemographic and neuroimaging correlates. Cognitive phenotype variations in patient and familial education and ethnicity, with linked variations in total intracranial volume, raise the question of an early and persisting socioeconomic-status related neurodevelopmental impact, with additional contributions of clinical epilepsy factors (e.g., lifetime generalized seizures). The neuroimaging features of cognitive phenotype membership are most notable for disrupted large scale cortical-subcortical networks and patterns of functional connectivity with bilateral hippocampal and cerebellar atrophy. The cognitive taxonomy of temporal lobe epilepsy appears influenced by features that reflect the combined influence of socioeconomic, neurodevelopmental and neurobiological risk factors.

The morphological characteristics of hippocampus and thalamus in mesial temporal lobe epilepsy

Background

Mesial temporal lobe epilepsy (MTLE) is the most common form of focal epilepsy, which is frequently characterized by hippocampal sclerosis (HS). Accumulating studies have suggested widespread cortico-cortical connections related to MTLE. The role of subcortical structures involved in general epilepsy has been extensively investigated, but it is still limited in MTLE. Our purpose was to determine the specific morphological correlation between sclerotic hippocampal and thalamic sub-regions, using quantitative analysis, in MTLE.

Methods

In this study, 23 MTLE patients with unilateral hippocampal sclerosis and 24 healthy controls were examined with three-dimensional T1 MRI. Volume quantitative analysis in the hippocampus and thalamus was conducted and group-related volumetric difference was assessed. Moreover, vertex analysis was further performed using automated software to delineate detailed morphological patterns of the hippocampus and thalamus. The correlation was used to examine whether there is a relationship between volume changes of two subcortical structures and clinical characteristics.

Results

The patients had a significant volume decrease in the sclerotic hippocampus (p < 0.001). Compared to controls, obvious atrophic patterns were observed in the bilateral hippocampus in MTLE (p < 0.05). Only small patches of shrinkage were noted in the bilateral thalamus (p < 0.05). Moreover, the volume change of the hippocampus had a significant positive correlation with that of the thalamus (P < 0.001). Intriguingly, volume changes of the hippocampus and thalamus were correlated with the duration of epilepsy (hippocampus: P = 0.024; thalamus: P = 0.022). However, only volume changes of thalamus possibly differentiated between two prognostic groups in patients (P = 0.026).

Conclusions

We demonstrated the morphological characteristics of the hippocampus and thalamus in MTLE, providing new insights into the interrelated mechanisms between the hippocampus and thalamus, which have potential clinical significance for refining neuromodulated targets.

Cognitive phenotypes in temporal lobe epilepsy utilizing data- and clinically driven approaches: Moving toward a new taxonomy

OBJECTIVE

To identify cognitive phenotypes in temporal lobe epilepsy (TLE) and test their reproducibility in a large, multi-site cohort of patients using both data-driven and clinically driven approaches.

METHOD

Four-hundred seven patients with TLE who underwent a comprehensive neuropsychological evaluation at one of four epilepsy centers were included. Scores on tests of verbal memory, naming, fluency, executive function, and psychomotor speed were converted into z-scores based on 151 healthy controls (HCs). For the data-driven method, cluster analysis (k-means) was used to determine the optimal number of clusters. For the clinically driven method, impairment was defined as >1.5 standard deviations below the mean of the HC, and patients were classified into groups based on the pattern of impairment.

RESULTS

Cluster analysis revealed a three-cluster solution characterized by (a) generalized impairment (29%), (b) language and memory impairment (28%), and (c) no impairment (43%). Based on the clinical criteria, the same broad categories were identified, but with a different distribution: (a) generalized impairment (37%), (b) language and memory impairment (30%), and (c) no impairment (33%). There was a 82.6% concordance rate with good agreement (κ = .716) between the methods. Forty-eight patients classified as having a normal profile based on cluster analysis were classified as having generalized impairment (n = 16) or an isolated language/memory impairment (n = 32) based on the clinical criteria. Patients with generalized impairment had a longer disease duration and patients with no impairment had more years of education. However, patients demonstrating the classic TLE profile (ie, language and memory impairment) were not more likely to have an earlier age at onset or mesial temporal sclerosis.

SIGNIFICANCE

We validate previous findings from single-site studies that have identified three unique cognitive phenotypes in TLE and offer a means of translating the patterns into a clinical diagnostic criteria, representing a novel taxonomy of neuropsychological status in TLE.

Functional connectome contractions in temporal lobe epilepsy: Microstructural underpinnings and predictors of surgical outcome

OBJECTIVE

Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Although it is commonly related to hippocampal pathology, increasing evidence suggests structural changes beyond the mesiotemporal lobe. Functional anomalies and their link to underlying structural alterations, however, remain incompletely understood.

METHODS

We studied 30 drug-resistant TLE patients and 57 healthy controls using multimodal magnetic resonance imaging (MRI) analyses. All patients had histologically verified hippocampal sclerosis and underwent postoperative imaging to outline the extent of their surgical resection. Our analysis leveraged a novel resting-state functional MRI framework that parameterizes functional connectivity distance, consolidating topological and physical properties of macroscale brain networks. Functional findings were integrated with morphological and microstructural metrics, and utility for surgical outcome prediction was assessed using machine learning techniques.

RESULTS

Compared to controls, TLE patients showed connectivity distance reductions in temporoinsular and prefrontal networks, indicating topological segregation of functional networks. Testing for morphological and microstructural associations, we observed that functional connectivity contractions occurred independently from TLE-related cortical atrophy but were mediated by microstructural changes in the underlying white matter. Following our imaging study, all patients underwent an anterior temporal lobectomy as a treatment of their seizures, and postsurgical seizure outcome was determined at a follow-up at least 1 year after surgery. Using a regularized supervised machine learning paradigm with fivefold cross-validation, we demonstrated that patient-specific functional anomalies predicted postsurgical seizure outcome with 76 ± 4% accuracy, outperforming classifiers operating on clinical and structural imaging features.

SIGNIFICANCE

Our findings suggest connectivity distance contractions as a macroscale substrate of TLE. Functional topological isolation may represent a microstructurally mediated network mechanism that tilts the balance toward epileptogenesis in affected networks and that may assist in patient-specific surgical prognostication.

Microstructural imaging in temporal lobe epilepsy: Diffusion imaging changes relate to reduced neurite density

PURPOSE

Previous imaging studies in patients with refractory temporal lobe epilepsy (TLE) have examined the spatial distribution of changes in imaging parameters such as diffusion tensor imaging (DTI) metrics and cortical thickness. Multi-compartment models offer greater specificity with parameters more directly related to known changes in TLE such as altered neuronal density and myelination. We studied the spatial distribution of conventional and novel metrics including neurite density derived from NODDI (Neurite Orientation Dispersion and Density Imaging) and myelin water fraction (MWF) derived from mcDESPOT (Multi-Compartment Driven Equilibrium Single Pulse Observation of T1/T2)] to infer the underlying neurobiology of changes in conventional metrics.

METHODS

20 patients with TLE and 20 matched controls underwent magnetic resonance imaging including a volumetric T1-weighted sequence, multi-shell diffusion from which DTI and NODDI metrics were derived and a protocol suitable for mcDESPOT fitting. Models of the grey matter-white matter and grey matter-CSF surfaces were automatically generated from the T1-weighted MRI. Conventional diffusion and novel metrics of neurite density and MWF were sampled from intracortical grey matter and subcortical white matter surfaces and cortical thickness was measured.

RESULTS

In intracortical grey matter, diffusivity was increased in the ipsilateral temporal and frontopolar cortices with more restricted areas of reduced neurite density. Diffusivity increases were largely related to reductions in neurite density, and to a lesser extent CSF partial volume effects, but not MWF. In subcortical white matter, widespread bilateral reductions in fractional anisotropy and increases in radial diffusivity were seen. These were primarily related to reduced neurite density, with an additional relationship to reduced MWF in the temporal pole and anterolateral temporal neocortex. Changes were greater with increasing epilepsy duration. Bilaterally reduced cortical thickness in the mesial temporal lobe and centroparietal cortices was unrelated to neurite density and MWF.

CONCLUSIONS

Diffusivity changes in grey and white matter are primarily related to reduced neurite density with an additional relationship to reduced MWF in the temporal pole. Neurite density may represent a more sensitive and specific biomarker of progressive neuronal damage in refractory TLE that deserves further study.

Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

Mesial temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is associated with functional and structural brain alterations. Machine learning (ML) techniques have been successfully used in discriminating mTLE from healthy controls. However, either functional or structural neuroimaging data are mostly used separately as input, and the opportunity to combine both has not been exploited yet. We conducted a multimodal ML study based on functional and structural neuroimaging measures. We enrolled 37 patients with left mTLE, 37 patients with right mTLE, and 74 healthy controls and trained a support vector ML model to distinguish them by using each measure and the combinations of the measures. For each single measure, we obtained a mean accuracy of 74% and 69% for discriminating left mTLE and right mTLE from controls, respectively, and 64% when all patients were combined. We achieved an accuracy of 78% by integrating functional data and 79% by integrating structural data for left mTLE, and the highest accuracy of 84% was obtained when all functional and structural measures were combined. These findings suggest that combining multimodal measures within a single model is a promising direction for improving the classification of individual patients with mTLE.

Cortical thickness in childhood left focal epilepsy: Thinning beyond the seizure focus

OBJECTIVE

Structural brain differences are found in adults and children with epilepsy, yet pediatric samples have been heterogeneous regarding seizure type, magnetic resonance imaging (MRI) findings, and hemisphere of seizure focus. This study examines whether cortical thickness and surface area differ between children with left-hemisphere focal epilepsy (LHE) and age-matched typically developing (TD) peers. We examined whether age differentially moderated cortical thickness between groups and if cortical thickness was associated with duration of epilepsy, seizure frequency, or neuropsychological functioning.

METHODS

Thirty-five children with LHE and 35 TD children completed neuropsychological testing and 3T MR imaging. Neuropsychological measures included general intelligence and executive functioning. All MRIs were normal. Surface-based morphometric processing and analyses were conducted using FreeSurfer 6.0. Regression analyses compared age by cortical thickness differences between groups. Correlational analyses examined associations between cortical thickness in these areas with neuropsychological functioning or epilepsy characteristics.

RESULTS

Left-hemisphere focal epilepsy displayed decreased cortical thickness bilaterally compared to TD controls across 6 brain regions but no differences in surface area. Moderation analyses revealed quadratic relationships between age and cortical thickness for left frontoparietal-cingulate and right superior frontal regions. Higher performance intelligence quotient (IQ) (PIQ) and verbal IQ (VIQ) and fewer parent reported executive function problems were associated with greater cortical thickness in TD children.

SIGNIFICANCE

Children with LHE displayed thinner cortex extending beyond the hemisphere of seizure focus. The nonlinear pattern of cortical thickness across age occurring in TD children is not evident in the same manner in children with LHE. These differences in cortical thickness patterns were greatest in children 8-12 years old. Greater cortical thickness was associated with higher IQ and fewer executive control problems in daily activities in TD children. Thus, differences in cortical thickness in the absence of differences in surface area, suggest cortical thickness may be a sensitive proxy of subtle neuroanatomical changes that are related to neuropsychological functioning.

Network connectivity separate from the hypothesized irritative zone correlates with impaired cognition and higher rates of seizure recurrence

INTRODUCTION

Surgery remains an essential option for the treatment of medically intractable temporal lobe epilepsy (TLE). However, only 66% of patients achieve postoperative seizure freedom, perhaps attributable to an incomplete understanding of brain network alterations in surgical candidates. Here, we applied a novel network modeling algorithm and measured key characteristics of epileptic networks correlated with surgical outcomes and objective measures of cognition.

METHODS

Twenty-two patients were prospectively included, and relevant demographic information was attained. Resting state functional magnetic resonance imaging (rsfMRI) and electroencephalography (EEG) data were recorded and preprocessed. Using our novel algorithm, patient-specific epileptic networks were mapped preoperatively, and geographic spread was quantified. Global functional connectivity was also determined using a volumetric functional atlas. Neuropsychological pre- and postsurgical raw and standardized scores obtained blinded to epileptic network status. Key demographic data and features of epileptic networks were then correlated with surgical outcome using Pearson's product-moment correlation.

RESULTS

At an average follow-up of 18.4 months, 15/22 (68%) patients were seizure-free. Connectivity was measured globally using a functional 3D atlas. Higher mean global connectivity correlated with worse scores in preoperative neuropsychological testing of executive functioning (Ruff Figural Fluency Test [RFFT]-ER; R = 0.943, p = 0.005). A higher ratio of highly correlated connections between regions of interest (ROIs) in the hemisphere contralateral to the seizure onset correlated with impairment in executive functioning (RFFT-ER; R = 0.943, p = 0.005). Higher numbers of highly correlated connections between ROIs in the contralateral hemisphere correlated with impairment in both short- and long-term measures of verbal memory (Rey Auditory Verbal Learning Test Trials 6, 7 [RAVLT6, RAVLT7]; R = -0.650, p = 0.020, R = -0.676, p = 0.030). Epilepsy networks were modeled in each patient, and localization of the epilepsy network in the bitemporal lobes correlated with lower scores in neuropsychological tests measuring verbal learning and short-term memory (RAVLT6; R = -0.671, p = 0.024). Higher rates of seizure recurrence correlated with localization of the epilepsy network bitemporally (R = -0.542, p = 0.014), with the stronger correlation found with localization to the contralateral temporal lobe from side of surgery (R = - 0.530, p = 0.016).

CONCLUSION

Increased connectivity contralateral to seizure onset and epilepsy network spread in the bitemporal lobes correlated with lower measures of executive functioning and verbal memory. Epilepsy network localization to the bitemporal lobes, in particular, the contralateral temporal lobe, is associated with higher rates of seizure recurrence. These findings may reflect network-level disruption that has infiltrated the contralateral hemisphere and the bitemporal lobes contributing to impaired cognition and relatively worse surgical outcomes. Further identification of network parameters that predict patient outcomes may aid in patient selection, resection planning, and ultimately the efficacy of epilepsy surgery.

Premature white matter aging in patients with right mesial temporal lobe epilepsy: A machine learning approach based on diffusion MRI data

•

A brain age prediction model was developed based on diffusion MRI data.

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Patients with right MTLE exhibited older brain age than those with left MTLE.

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Predicted age difference (PAD) was correlated with seizure frequency in right MTLE.

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Right uncinate fasciculus had highest contribution to the observed PAD in right MTLE.

Brain age prediction based on machine learning has been applied to various neurological diseases to discover its clinical values. By this innovative approach, it has been reported that the patients with refractory epilepsy had premature brain aging. Of refractory epilepsy, right and left subtypes of mesial temporal lobe epilepsy (MTLE) are the most common forms and exhibit distinct patterns in white matter alterations. So far, it is unclear whether these two subtypes of MTLE would have difference in white matter aging due to distinct white matter alterations. To address this issue, a machine learning based brain age model using diffusion MRI data was established to investigate biological age of white matter tracts. All diffusion MRI datasets were obtained from the same 3-Tesla MRI scanner. To build the brain age prediction model, diffusion MRI datasets of 300 healthy participants were processed to extract age-relevant diffusion indices from 76 major white matter tracts. The extracted diffusion indices underwent Gaussian process regression to build the prediction model for white matter brain age. The model was validated in an independent testing set (N = 40) to ensure no overfitting of the model. The model was then applied to patients with right and left MTLE and matched controls (right MTLE: N = 17, left MTLE: N = 18, controls: N = 37), and predicted age difference (PAD) was obtained by calculating the difference between each individual's predicted brain age and chronological age. The higher PAD score indicated older brain age. The results showed that right MTLE exhibited older predicted brain age than the other two groups (PAD of right MTLE = 10.9 years [p < 0.05 against left MTLE; p < 0.001 against control]; PAD of left MTLE = 2.2 years [p > 0.1 against control]; PAD of controls = 0.82 years). Patients with right and left MTLE showed strong correlations of the PAD scores with age of onset and duration of illness, but both groups showed opposite directions of correlations. In right MTLE, positive correlation of PAD with seizure frequency was found, and the right uncinate fasciculus was the most attributable tract to the increase in PAD. In conclusion, the present study found that patients with right MTLE exhibited premature white matter brain aging and their PAD scores were correlated with seizure frequency. Therefore, PAD is a potentially useful indicator of white matter impairment and disease severity in patients with right MTLE.

Neuroanatomical correlates of personality traits in temporal lobe epilepsy: Findings from the Epilepsy Connectome Project

Behavioral and personality disorders in temporal lobe epilepsy (TLE) have been a topic of interest and controversy for decades, with less attention paid to alterations in normal personality structure and traits. In this investigation, core personality traits (the Big 5) and their neurobiological correlates in TLE were explored using the Neuroticism Extraversion Openness-Five Factor Inventory (NEO-FFI) and structural magnetic resonance imaging (MRI) through the Epilepsy Connectome Project (ECP). NEO-FFI scores from 67 individuals with TLE (34.6 ± 9.5 years; 67% women) were compared to 31 healthy controls (32.8 ± 8.9 years; 41% women) to assess differences in the Big 5 traits (agreeableness, openness, conscientiousness, neuroticism, and extraversion). Individuals with TLE showed significantly higher neuroticism, with no significant differences on the other traits. Neural correlates of neuroticism were then determined in participants with TLE including cortical and subcortical volumes. Distributed reductions in cortical gray matter volumes were associated with increased neuroticism. Subcortically, hippocampal and amygdala volumes were negatively associated with neuroticism. These results offer insight into alterations in the Big 5 personality traits in TLE and their brain-related correlates.

Identifying the neural basis of a language-impaired phenotype of temporal lobe epilepsy

OBJECTIVE

To identify neuroimaging and clinical biomarkers associated with a language-impaired phenotype in refractory temporal lobe epilepsy (TLE).

METHODS

Eighty-five patients with TLE were characterized as language-impaired (TLE-LI) or non-language-impaired (TLE-NLI) based on comprehensive neuropsychological testing. Structural magnetic resonance imaging (MRI), diffusion tensor imaging, and functional MRI (fMRI) were obtained in patients and 47 healthy controls (HC). fMRI activations and cortical thickness were calculated within language regions of interest, and fractional anisotropy (FA) was calculated within deep white matter tracts associated with language. Analyses of variance were performed to test for differences among the groups in imaging measures. Receiver operator characteristic curves were used to determine how well different clinical versus imaging measures discriminated TLE-LI from TLE-NLI.

RESULTS

TLE-LI patients showed significantly less activation within left superior temporal cortex compared to HC and TLE-NLI, regardless of side of seizure onset. TLE-LI also showed decreased FA in the inferior longitudinal fasciculus and arcuate fasciculus compared to HC. Cortical thickness did not differ between groups in any region. A model that included language-related fMRI activations within the superior temporal gyrus, age at onset, and demographic variables was the most predictive of language impairment (area under the curve = 0.80).

SIGNIFICANCE

These findings demonstrate a unique imaging signature associated with a language-impaired phenotype in TLE, characterized by functional and microstructural alterations within the language network. Reduced left superior temporal activation combined with compromise to language association tracts underlies this phenotype, extending our previous work on cognitive phenotypes that could have implications for treatment-planning or cognitive progression in TLE.

Language function in childhood idiopathic epilepsy syndromes

PURPOSE

To examine the impact of diverse syndromes of focal and generalized epilepsy on language function in children with new and recent onset epilepsy. Of special interest was the degree of shared language abnormality across epilepsy syndromes and the unique effects associated with specific epilepsy syndromes.

METHODS

Participants were 136 youth with new or recent-onset (diagnosis within past 12 months) epilepsy and 107 healthy first-degree cousin controls. The participants with epilepsy included 20 with Temporal Lobe Epilepsy (TLE; M age = 12.99  years, SD = 3.11), 41 with Benign Epilepsy with Centrotemporal Spikes (BECTS; M age = 10.32, SD = 1.67), 42 with Juvenile Myoclonic Epilepsy (JME; M age = 14.85, SD = 2.75) and 33 with absence epilepsy (M age = 10.55, SD = 2.76). All children were administered a comprehensive test battery which included multiple measures of language and language-dependent abilities (i.e., verbal intelligence, vocabulary, verbal reasoning, object naming, reception word recognition, word reading, spelling, lexical and semantic fluency, verbal list learning and delayed verbal memory). Test scores were adjusted for age and gender and analyzed via MANCOVA.

RESULTS

Language abnormalities were found in all epilepsy patient groups. The most broadly affected children were those with TLE and absence epilepsy, whose performance differed significantly from controls on 8 of 11 and 9 of 11 tests respectively. Although children with JME and BECTS were less affected, significant differences from controls were found on 4 of 11 tests each. While each group had a unique profile of language deficits, commonalities were apparent across both idiopathic generalized and localization-related diagnostic categories.

DISCUSSION

The localization related and generalized idiopathic childhood epilepsies examined here were associated with impact on diverse language abilities early in the course of the disorder.

Differential sensitivity of structural, diffusion, and resting-state functional MRI for detecting brain alterations and verbal memory impairment in temporal lobe epilepsy

OBJECTIVES

Temporal lobe epilepsy (TLE) is known to affect large-scale gray and white matter networks, and these network changes likely contribute to the verbal memory impairments observed in many patients. In this study, we investigate multimodal imaging patterns of brain alterations in TLE and evaluate the sensitivity of different imaging measures to verbal memory impairment.

METHODS

Diffusion tensor imaging (DTI), volumetric magnetic resonance imaging (vMRI), and resting-state functional MRI (rs-fMRI) were evaluated in 46 patients with TLE and 33 healthy controls to measure patterns of microstructural, structural, and functional alterations, respectively. These measurements were obtained within the white matter directly beneath neocortex (ie, superficial white matter [SWM]) for DTI and across neocortex for vMRI and rs-fMRI. The degree to which imaging alterations within left medial temporal lobe/posterior cingulate (LMT/PC) and left lateral temporal regions were associated with verbal memory performance was evaluated.

RESULTS

Patients with left TLE and right TLE both demonstrated pronounced microstructural alterations (ie, decreased fractional anisotropy [FA] and increased mean diffusivity [MD]) spanning the entire frontal and temporolimbic SWM, which were highly lateralized to the ipsilateral hemisphere. Conversely, reductions in cortical thickness in vMRI and alterations in the magnitude of the rs-fMRI response were less pronounced and less lateralized than the microstructural changes. Both stepwise regression and mediation analyses further revealed that FA and MD within SWM in LMT/PC regions were the most robust predictors of verbal memory, and that these associations were independent of left hippocampal volume.

SIGNIFICANCE

These findings suggest that microstructural loss within the SWM is pronounced in patients with TLE, and injury to the SWM within the LMT/PC region plays a critical role in verbal memory impairment.

Neuroimaging and connectomics of drug-resistant epilepsy at multiple scales: From focal lesions to macroscale networks

Epilepsy is among the most common chronic neurologic disorders, with 30%-40% of patients having seizures despite antiepileptic drug treatment. The advent of brain imaging and network analyses has greatly improved the understanding of this condition. In particular, developments in magnetic resonance imaging (MRI) have provided measures for the noninvasive characterization and detection of lesions causing epilepsy. MRI techniques can probe structural and functional connectivity, and network analyses have shaped our understanding of whole-brain anomalies associated with focal epilepsies. This review considers the progress made by neuroimaging and connectomics in the study of drug-resistant epilepsies due to focal substrates, particularly temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal lobe epilepsies associated with malformations of cortical development. In these disorders, there is evidence of widespread disturbances of structural and functional connectivity that may contribute to the clinical and cognitive prognosis of individual patients. It is hoped that studying the interplay between macroscale network anomalies and lesional profiles will improve our understanding of focal epilepsies and assist treatment choices.

Alterations in Brain Metabolites in Patients with Epilepsy with Impaired Consciousness: A Case-Control Study of Interictal Multivoxel 1H-MRS Findings

BACKGROUND AND PURPOSE

Previous studies have shown perfusion abnormalities in the thalamus and upper brain stem in patients with epilepsy with impaired consciousness. We hypothesized that these areas associated with consciousness will also show metabolic abnormalities. However, metabolic abnormalities in those areas correlated with consciousness has not been characterized with multiple-voxel ¹H-MRS. In this study, we investigated the metabolic alterations in these brain regions and assessed the correlation between seizure features and metabolic alterations.

MATERIALS AND METHODS

Fifty-seven patients with epilepsy and 24 control subjects underwent routine MR imaging and 3D multiple-voxel ¹H-MRS. Patients were divided into 3 subgroups: focal impaired awareness seizures (n = 18), primary generalized tonic-clonic seizures (n = 19), and secondary generalized tonic-clonic seizures (n = 20). The measured metabolite alterations in NAA/Cr, NAA/(Cr + Cho), and Cho/Cr ratios in brain regions associated with the consciousness network were compared between the patient and control groups. ROIs were placed in the bilateral inferior frontal gyrus, supramarginal gyrus, cingulate gyrus, precuneus, thalamus, and upper brain stem. Correlations between clinical parameters (epilepsy duration and seizure frequency) and metabolite alterations were analyzed.

RESULTS

Significantly lower NAA/Cr and NAA/(Cho + Cr) ratios (P < .05 and < .01, respectively) were observed in the bilateral thalamus and upper brain stem in all experimental groups, and significantly high Cho/Cr ratios (P < .05) were observed in the right thalamus in the focal impaired awareness seizures group. There were no significant differences in metabolite ratios among the 3 patient groups (P > .05). The secondary generalized tonic-clonic seizures group showed a negative correlation between the duration of epilepsy and the NAA/(Cr + Cho) ratio in the bilateral thalamus (P < .05).

CONCLUSIONS

Metabolic alterations were observed in the brain stem and thalamus in patients with epilepsy with impaired consciousness.

Microstructure-Informed Connectomics: Enriching Large-Scale Descriptions of Healthy and Diseased Brains

Rapid advances in neuroimaging and network science have produced powerful tools and measures to appreciate human brain organization at multiple spatial and temporal scales. It is now possible to obtain increasingly meaningful representations of whole-brain structural and functional brain networks and to formally assess macroscale principles of network topology. In addition to its utility in characterizing healthy brain organization, individual variability, and life span-related changes, there is high promise of network neuroscience for the conceptualization and, ultimately, management of brain disorders. In the current review, we argue for a science of the human brain that, while strongly embracing macroscale connectomics, also recommends awareness of brain properties derived from meso- and microscale resolutions. Such features include MRI markers of tissue microstructure, local functional properties, as well as information from nonimaging domains, including cellular, genetic, or chemical data. Integrating these measures with connectome models promises to better define the individual elements that constitute large-scale networks, and clarify the notion of connection strength among them. By enriching the description of large-scale networks, this approach may improve our understanding of fundamental principles of healthy brain organization. Notably, it may also better define the substrate of prevalent brain disorders, including stroke, autism, as well as drug-resistant epilepsies that are each characterized by intriguing interactions between local anomalies and network-level perturbations.

Brain network alterations of mesial temporal lobe epilepsy with cognitive dysfunction following anterior temporal lobectomy

The aims of this study were to investigate the brain network connectivity alterations of intractable unilateral mesial temporal lobe epilepsy (MTLE) with cognitive dysfunction before and after anterior temporal lobectomy (ATL) using resting-state functional magnetic resonance imaging (rs-fMRI) study and to further observe the correlation between the brain network connectivity with cognitive performance. Fourteen patients with unilateral left MTLE before and after ATL were compared with thirty healthy controls (HCs) on functional connectivity (FC) between resting-state networks (RSNs). The correlation between the neuropsychological tests of patients and abnormal FC was further investigated. When compared with the HCs, patients before surgery showed significantly changed FC between special RSNs. No difference of FC was found between each RSN when patients were compared with the HCs after surgery. Compared with patients before surgery, patients after surgery showed significantly decreased FC between RSNs. Abnormal FC between RSNs significantly correlated with Montreal Cognitive Assessment (MoCA) scores. Our study suggested that dynamic alterations of RSN after ATL in unilateral MTLE may be closely related with seizure generating. However, unchanged FC between RSN before and after ATL may be closely related with cognitive performance. The present findings may help us understand the feature of brain network alterations in patients with left MTLE who became seizure-free following ATL.

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis

Objective

In contrast to adult cohorts, neocortical changes in epileptic children with hippocampal damage are not well characterized. Here, we mapped multimodal neocortical markers of epilepsy‐related structural compromise in a pediatric cohort of temporal lobe epilepsy and explored how they relate to clinical factors.

Methods

We measured cortical thickness, gray–white matter intensity contrast and intracortical FLAIR intensity in 22 patients with hippocampal sclerosis (HS) and 30 controls. Surface‐based linear models assessed between‐group differences in morphological and MR signal intensity markers. Structural integrity of the hippocampus was measured by quantifying atrophy and FLAIR patterns. Linear models were used to evaluate the relationships between hippocampal and neocortical MRI markers and clinical factors.

Results

In the hippocampus, patients demonstrated ipsilateral atrophy and bilateral FLAIR hyperintensity. In the neocortex, patients showed FLAIR signal hyperintensities and gray–white matter boundary blurring in the ipsilesional mesial and lateral temporal neocortex. In contrast, cortical thinning was minimal and restricted to a small area of the ipsilesional temporal pole. Furthermore, patients with a history of febrile convulsions demonstrated more pronounced FLAIR hyperintensity in the ipsilesional temporal neocortex.

Interpretation

Pediatric HS patients do not yet demonstrate the widespread cortical thinning present in adult cohorts, which may reflect consequences of a protracted disease process. However, pronounced temporal neocortical FLAIR hyperintensity and blurring of the gray–white matter boundary are already detectable, suggesting that alterations in MR signal intensities may reflect a different underlying pathophysiology that is detectable earlier in the disease and more pervasive in patients with a history of febrile convulsions.

Executive dysfunction is associated with an altered executive control network in pediatric temporal lobe epilepsy

OBJECTIVES

Children with temporal lobe epilepsy (TLE) exhibit executive dysfunction on traditional neuropsychological tests. However, there is limited evidence of neural network alterations associated with this clinical executive dysfunction. The objective of this study was to characterize working memory deficits in children with TLE via activation of the executive control network on functional magnetic resonance imaging (fMRI) and determine the relationships to fMRI behavioral findings and traditional neuropsychological tests.

EXPERIMENTAL DESIGN

Functional magnetic resonance imaging was conducted on 17 children with TLE and 18 healthy control participants (age 8-16 years) while they performed the N-back task in order to assess activation of the executive control network. N-back accuracy, N-back reaction time, and traditional neuropsychological tests (Delis-Kaplan Executive Function System [D-KEFS] color-word interference and card-sort test) were also assessed.

PRINCIPAL OBSERVATIONS

Children with TLE exhibited executive dysfunction on D-KEFS testing, reduced N-back accuracy, and increased N-back reaction time compared with healthy controls; D-KEFS and N-back behavioral findings were significantly correlated. Children with TLE also exhibited significant reduction in activation of the frontal lobe within the executive control network compared to healthy controls. These alterations were significantly correlated with N-back behavioral findings and D-KEFS testing.

CONCLUSIONS

Children with TLE exhibit executive dysfunction, which correlates with executive control network alterations. This lends validity to the theory that the executive control network contributes to working memory function. The findings also indicate that children with TLE have network alterations in nontemporal brain regions.

Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery

OBJECTIVE

We evaluated whether deep learning applied to whole-brain presurgical structural connectomes could be used to predict postoperative seizure outcome more accurately than inference from clinical variables in patients with mesial temporal lobe epilepsy (TLE).

METHODS

Fifty patients with unilateral TLE were classified either as having persistent disabling seizures (SZ) or becoming seizure-free (SZF) at least 1 year after epilepsy surgery. Their presurgical structural connectomes were reconstructed from whole-brain diffusion tensor imaging. A deep network was trained based on connectome data to classify seizure outcome using 5-fold cross-validation.

RESULTS

Classification accuracy of our trained neural network showed positive predictive value (PPV; seizure freedom) of 88 ± 7% and mean negative predictive value (NPV; seizure refractoriness) of 79 ± 8%. Conversely, a classification model based on clinical variables alone yielded <50% accuracy. The specific features that contributed to high accuracy classification of the neural network were located not only in the ipsilateral temporal and extratemporal regions, but also in the contralateral hemisphere.

SIGNIFICANCE

Deep learning demonstrated to be a powerful statistical approach capable of isolating abnormal individualized patterns from complex datasets to provide a highly accurate prediction of seizure outcomes after surgery. Features involved in this predictive model were both ipsilateral and contralateral to the clinical foci and spanned across limbic and extralimbic networks.

Regional cortical thickness changes accompanying generalized tonic-clonic seizures

Objective

Generalized tonic-clonic seizures are accompanied by cardiovascular and respiratory sequelae that threaten survival. The frequency of these seizures is a major risk factor for sudden unexpected death in epilepsy (SUDEP), a leading cause of untimely death in epilepsy. The circumstances accompanying such fatal events suggest a cardiovascular or respiratory failure induced by unknown neural processes rather than an inherent cardiac or lung deficiency. Certain cortical regions, especially the insular, cingulate, and orbitofrontal cortices, are key structures that integrate sensory input and influence diencephalic and brainstem regions regulating blood pressure, cardiac rhythm, and respiration; output from those cortical regions compromised by epilepsy-associated injury may lead to cardiorespiratory dysregulation. The aim here was to assess changes in cortical integrity, reflected as cortical thickness, relative to healthy controls. Cortical alterations in areas that influence cardiorespiratory action could contribute to SUDEP mechanisms.

Methods

High-resolution T1-weighted images were collected with a 3.0-Tesla MRI scanner from 53 patients with generalized tonic-clonic seizures (Mean age ± SD: 37.1 ± 12.6 years, 22 male) at Case Western Reserve University, University College London, and the University of California at Los Angeles. Control data included 530 healthy individuals (37.1 ± 12.6 years; 220 male) from UCLA and two open access databases (OASIS and IXI). Cortical thickness group differences were assessed at all non-cerebellar brain surface locations (P < 0.05 corrected).

Results

Increased cortical thickness appeared in post-central gyri, insula, and subgenual, anterior, posterior, and isthmus cingulate cortices. Post-central gyri increases were greater in females, while males showed more extensive cingulate increases. Frontal and temporal cortex, lateral orbitofrontal, frontal pole, and lateral parietal and occipital cortices showed thinning. The extents of thickness changes were sex- and hemisphere-dependent, with only males exhibiting right-sided and posterior cingulate thickening, while females showed only left lateral orbitofrontal thinning. Regional cortical thickness showed modest correlations with seizure frequency, but not epilepsy duration.

Significance

Cortical thickening and thinning occur in patients with generalized tonic-clonic seizures, in cardiovascular and somatosensory areas, with extent of changes sex- and hemisphere-dependent. The data show injury in key autonomic and respiratory cortical areas, which may contribute to dysfunctional cardiorespiratory patterns during seizures, as well as to longer-term SUDEP risk.

Cortical thickness analysis in operculo-insular epilepsy

Background

In temporal lobe epilepsy (TLE), advanced neuroimaging techniques reveal anomalies extending beyond the temporal lobe such as thinning of fronto-central cortices. Operculo-insular epilepsy (OIE) is an under-recognized and poorly characterized condition with the potential of mimicking TLE. In this work, we investigated insular and extra-insular cortical thickness (CT) changes in OIE.

Methods

All participants (14 patients with refractory OIE, 9 age- and sex-matched patients with refractory TLE and 26 healthy controls) underwent a T1-weighted acquisition on a 3 T MRI. Anatomical images were processed with Advanced Normalization Tools. Between-group analysis of CT was performed using a two-sided t-test (threshold of p < 0.05 after correction for multiple comparisons; cut-off threshold of 250 voxels) between (i) patients with OIE vs TLE, and (ii) patients with OIE vs healthy controls.

Results

Significant widespread thinning was observed in OIE patients as compared with healthy controls mainly in the ipsilateral insula, peri-rolandic region, orbito-frontal area, mesiotemporal structures and lateral temporal neocortex. Contralateral cortical shrinkage followed a similar albeit milder and less diffuse pattern.The CT of OIE patients was equal or reduced relative to the TLE group for every cortical region analyzed. Thinning was observed diffusely in OIE patients, predominantly inboth insulae and the ipsilateral occipito-temporal area.

Conclusion

Our results reveal structural anomalies extending beyond the operculo-insular area in OIE.

Does bilingualism increase brain or cognitive reserve in patients with temporal lobe epilepsy?

OBJECTIVE

Bilingual healthy adults have been shown to exhibit an advantage in executive functioning (EF) that is associated with microstructural changes in white matter (WM) networks. Patients with temporal lobe epilepsy (TLE) often show EF deficits that are associated with WM compromise. In this study, we investigate whether bilingualism can increase cognitive reserve and/or brain reserve in bilingual patients with TLE, mitigating EF impairment and WM compromise.

METHODS

Diffusion tensor imaging was obtained in 19 bilingual and 26 monolingual patients with TLE, 12 bilingual healthy controls (HC), and 21 monolingual HC. Fractional anisotropy (FA) and mean diffusivity (MD) were calculated for the uncinate fasciculus (Unc) and cingulum (Cing), superior frontostriatal tract (SFS), and inferior frontostriatal tract (IFS). Measures of EF included Trail Making Test-B (TMT-B) and Delis-Kaplan Executive Function System Color-Word Inhibition/Switching. Analyses of covariance were conducted to compare FA and MD of the Unc, Cing, SFS, and IFS and EF performance across groups.

RESULTS

In bilingual patients, FA was lower in the ipsilateral Cing and Unc compared to all other groups. For both patient groups, MD of the ipsilateral Unc was higher relative to HC. Despite more pronounced reductions in WM integrity, bilingual patients performed similarly to monolingual TLE and both HC groups on EF measures. By contrast, monolingual patients performed worse than HC on TMT-B. In addition, differences in group means between bilingual and monolingual patients on TMT-B approached significance when controlling for the extent of WM damage (P = .071; d = 0.62), suggesting a tendency toward higher performance for bilingual patients.

SIGNIFICANCE

Despite poorer integrity of regional frontal lobe WM, bilingual patients performed similarly to monolingual patients and HC on EF measures. These findings align with studies suggesting that bilingualism may provide a protective factor for individuals with neurological disease, potentially through reorganization of EF networks that promote greater cognitive reserve.

Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning

OBJECTIVE

Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In this study, we utilized surface-based MRI morphometry and machine learning for automated lesion detection in a mixed cohort of patients with FCD type II from 3 different epilepsy centers.

METHODS

Sixty-one patients with pharmacoresistant epilepsy and histologically proven FCD type II were included in the study. The patients had been evaluated at 3 different epilepsy centers using 3 different MRI scanners. T1-volumetric sequence was used for postprocessing. A normal database was constructed with 120 healthy controls. We also included 35 healthy test controls and 15 disease test controls with histologically confirmed hippocampal sclerosis to assess specificity. Features were calculated and incorporated into a nonlinear neural network classifier, which was trained to identify lesional cluster. We optimized the threshold of the output probability map from the classifier by performing receiver operating characteristic (ROC) analyses. Success of detection was defined by overlap between the final cluster and the manual labeling. Performance was evaluated using k-fold cross-validation.

RESULTS

The threshold of 0.9 showed optimal sensitivity of 73.7% and specificity of 90.0%. The area under the curve for the ROC analysis was 0.75, which suggests a discriminative classifier. Sensitivity and specificity were not significantly different for patients from different centers, suggesting robustness of performance. Correct detection rate was significantly lower in patients with initially normal MRI than patients with unequivocally positive MRI. Subgroup analysis showed the size of the training group and normal control database impacted classifier performance.

SIGNIFICANCE

Automated surface-based MRI morphometry equipped with machine learning showed robust performance across cohorts from different centers and scanners. The proposed method may be a valuable tool to improve FCD detection in presurgical evaluation for patients with pharmacoresistant epilepsy.

Topographic principles of cortical fluid-attenuated inversion recovery signal in temporal lobe epilepsy

OBJECTIVE

In drug-resistant temporal lobe epilepsy (TLE), relative to the large number of whole-brain morphological studies, neocortical T2 changes have not been systematically investigated. The aim of this study was to assess the anatomical principles that govern the distribution of neocortical T2-weighted fluid-attenuated inversion recovery (FLAIR) signal intensity and uncover its topographic principles.

METHODS

Using a surface-based sampling scheme, we mapped neocortical FLAIR intensity of 61 TLE patients relative to 38 healthy controls imaged at 3 T. To address topographic principles of the susceptibility to FLAIR signal changes in TLE, we assessed associations with normative data on tissue composition using 2 complementary approaches. First, we evaluated whether the degree of TLE-related FLAIR intensity changes differed across cytoarchitectonic classes as defined by Von Economo-Koskinas taxonomy. Second, as a proxy to map regions with similar intracortical composition, we carried out a FLAIR intensity covariance paradigm in controls by seeding systematically from all cortical regions, and identified those networks that were the best spatial predictors of the between-group FLAIR changes.

RESULTS

Increased intensities were observed in bilateral limbic and paralimbic cortices (hippocampus, parahippocampus, cingulate, temporopolar, insular, orbitofrontal). Effect sizes were highest in periallocortical limbic and insular classes as defined by the Von Economo-Koskinas cytoarchitectonic taxonomy. Furthermore, systematic FLAIR intensity covariance analysis in healthy controls revealed that similarity patterns characteristic of limbic cortices, most notably the hippocampus, served as sensitive predictors for the topography of FLAIR hypersignal in patients. FLAIR intensity findings were robust against correction for morphological confounds. Patients with a history of febrile convulsions showed more marked signal changes in parahippocampal and retrosplenial cortices, known to be strongly connected to the hippocampus.

SIGNIFICANCE

FLAIR intensity mapping and covariance analysis provide a model of TLE gray matter pathology based on shared vulnerability of periallocortical and limbic cortices.