Quantitative assessment of structural and functional changes in temporal lobe epilepsy with hippocampal sclerosis

Surface-Based Neuroimaging Pattern of Multiple System Atrophy

RATIONALE AND OBJECTIVES

Overlapping parkinsonism, cerebellar ataxia, and pyramidal signs render challenges in the clinical diagnosis of multiple system atrophy (MSA). The neuroimaging pattern is valuable to understand its pathophysiology and improve its diagnostic effect.

MATERIALS AND METHODS

We retrospectively obtained magnetic resonance imaging and susceptibility-weighted imaging in patients with MSA (including parkinsonian type [MSA-P] and cerebellar type [MSA-C]), Parkinson's disease, and normal controls. We quantified neuroimaging features to identify the optimal threshold for diagnosis. Furthermore, we explore neuroimaging patterns of MSA by mapping the subcortical morphological alterations and constructing a diagnostic model.

RESULTS

Compared to controls, normalized putaminal volume significantly decreased in patients with MSA-P (P < .001) and normalized pontine volume significantly decreased in patients with MSA-C (P < .001). The Youden index of the threshold-based clinical prediction model was 0.871-0.928 in patients with MSA. The neuroimaging pattern in patients with MSA was jointly located in the lateral putamen, and the neuroimaging pattern prediction model achieved a classification accuracy of 83.9%-100%.

CONCLUSION

The quantitative neuroimaging features and surface-based morphologic anomalies represent the markers of MSA and open new avenues for personalized clinical diagnosis.

Brain-clinical signatures for vagus nerve stimulation response

AIM

Vagus nerve stimulation (VNS) is a valuable treatment for drug-resistant epilepsy (DRE) without the indication of surgical resection. The clinical heterogeneity of DRE has limited the optimal indication of choice and diagnosis prediction. The study aimed to explore the correlations of brain-clinical signatures with the clinical phenotype and VNS responsiveness.

METHODS

A total of 89 DRE patients, including VNS- (n = 44) and drug-treated (n = 45) patients, were retrospectively recruited. The brain-clinical signature consisted of demographic information and brain structural deformations, which were measured using deformation-based morphometry and presented as Jacobian determinant maps. The efficacy and presurgical differences between these two cohorts were compared. Then, the potential of predicting VNS response using brain-clinical signature was investigated according to the different prognosis evaluation approaches.

RESULTS

The seizure reduction was higher in the VNS-treated group (42.50%) as compared to the drug-treated group (12.09%) (p = 0.11). Abnormal imaging representation, showing encephalomalacia (p_corrected  = 0.03), was commonly observed in the VNS-treated group (p = 0.04). In the patients treated with VNS, the mild/subtle brain abnormalities indicated higher seizure frequency (p = 0.03) and worse VNS response (p = 0.04). The partial least square regression analysis showed a moderate prediction potential of brain-clinical signature for VNS response (p < 0.01). The increase in the pre-VNS seizure frequency and structural etiology could indicate a worse prognosis (higher McHugh classification).

CONCLUSION

The brain-clinical signature illustrated its clinical potential in predicting the VNS response, which might allow clinicians to personalize treatment decisions for DRE patients.

Automated Detection and Surgical Planning for Focal Cortical Dysplasia with Multicenter Validation

BACKGROUND

In patients with surgically amenable focal cortical dysplasia (FCD), subtle neuroimaging representation and the risk of open surgery lead to gaps in surgical treatment and delays in surgery.

OBJECTIVE

To construct an integrated platform that can accurately detect FCD and automatically establish trajectory planning for magnetic resonance-guided laser interstitial thermal therapy.

METHODS

This multicenter study included retrospective patients to train the automated detection model, prospective patients for model evaluation, and an additional cohort for construction of the automated trajectory planning algorithm. For automated detection, we evaluated the performance and generalization of the conventional neural network in different multicenter cohorts. For automated trajectory planning, feasibility/noninferiority and safety score were calculated to evaluate the clinical value.

RESULTS

Of the 260 patients screened for eligibility, 202 were finally included. Eighty-eight patients were selected for conventional neural network training, 88 for generalizability testing, and 26 for the establishment of an automated trajectory planning algorithm. The model trained using preprocessed and multimodal neuroimaging displayed the best performance in diagnosing FCD (figure of merit = 0.827 and accuracy range = 75.0%-91.7% across centers). None of the clinical variables had a significant effect on prediction performance. Moreover, the automated trajectory was feasible and noninferior to the manual trajectory (χ2 = 3.540, P = .060) and significantly safer (overall: test statistic = 30.423, P < .001).

CONCLUSION

The integrated platform validated based on multicenter, prospective cohorts exhibited advantages of easy implementation, high performance, and generalizability, thereby indicating its potential in the diagnosis and minimally invasive treatment of FCD.

Metabolic phenotyping of hand automatisms in mesial temporal lobe epilepsy

Purpose

Hand automatisms (HA) are common clinical manifestations in mesial temporal lobe epilepsy. However, the location of the symptomatogenic zone (EZ) in HA as well as the networks involved, are still unclear. To have a better understanding of HA underlying mechanisms, we analyzed images from interictal [¹⁸F] fluorodeoxyglucose-positron emission tomography (FDG-PET) in patients with mesial temporal lobe epilepsy (mTLE).

Methods

We retrospectively recruited 79 mTLE patients and 18 healthy people that substituted the control group for the analysis. All patients underwent anterior temporal lobectomy and were seizure-free. Based on the semiology of the HA occurrence, the patients were divided into three subgroups: patients with unilateral HA (Uni-HA), with bilateral HA (Bil-HA) and without HA (None-HA). We performed the intergroup comparison analysis of the interictal FDG-PET images and compared the functional connectivity within metabolic communities.

Results

Our analysis showed that the metabolic patterns varied among the different groups. The Uni-HA subgroup had significant differences in the extratemporal lobe brain areas, mostly in the ipsilateral supplementary motor area (SMA) and middle cingulate cortex (MCC) when compared to the healthy control group. The Bil-HA subgroup demonstrated that the bilateral SMA and MCC areas were differentially affected, whereas in the None-HA subgroup the differences were evident in limited brain areas. The metabolic network involving HA showed a constrained network embedding the SMA and MCC brain regions. Furthermore, the increased metabolic synchronization between SMA and MCC was significantly correlated with HA.

Conclusion

The metabolic pattern of HA was most conspicuous in SMA and MCC brain regions. Increased metabolic synchronization within SMA and MCC was considered as the major EZ of HA.

Metabolic pattern analysis allowed allocation of the symptomatogenic zone (EZ) and brain network of hand automatisms (HA) in mesial temporal lobe epilepsy (mTLE).

The involved network of bilateral HA was larger than the unilateral one, probably due to the occurrence of contralateral dystonic posturing.

Increased metabolic synchronization within supplementary motor area (SMA) and middle cingulate cortex (MCC) regions were engaged in the representation and modulation of HA, suggesting these regions as the EZ for HA.

Whole-brain metabolic pattern analysis in patients with anti-LGI1 encephalitis

BACKGROUND AND PURPOSE

Faciobrachial dystonic seizures (FBDS) and hyponatraemia are the distinct clinical features of autoimmune encephalitis (AE) caused by antibodies against leucine-rich glioma-inactivated 1 (LGI1). The pathophysiological pattern and neural mechanisms underlying these symptoms remain largely unexplored.

METHODS

We included 30 patients with anti-LGI1 AE and 30 controls from a retrospective observational cohort. Whole-brain metabolic pattern analysis was performed to assess the pathological network of anti-LGI1 AE, as well as the symptomatic networks of FBDS. Logistic regression was applied to explore independent predictors of FBDS. Finally, we applied multiple regression model to investigate the hyponatraemia-associated brain network and its effect on serum sodium levels.

RESULTS

The pathological network of anti-LGI1 AE involved a hypermetabolism in cerebellum, subcortical structures, and Rolandic area, as well as a hypometabolism in the medial prefrontal cortex. The symptomatic network of FBDS shown a hypometabolism in cerebellum and Rolandic area (P_FDR < 0.05). Hypometabolism in the cerebellum was an independent predictor of FBDS (P < 0.001). Hyponatraemia-associated network highlighted a negative effect on caudate nucleus, frontal and temporal white matter. Serum sodium level had the negative trend with metabolism of hypothalamus (Pearson's R = -0.180, P = 0.342) but the mediation was not detected (path c' = -7.238, 95% CI = -30.947 to 16.472).

CONCLUSIONS

Our results provide insights into the whole-brain metabolic patterns of patients with anti-LGI1 AE, including the symptomatic network FBDS and the hyponatraemia-associated brain network, which is conducive to understanding the neural mechanisms and evaluating disease progress of anti-LGI1 AE.

Neuroimaging gradient alterations and epileptogenic prediction in focal cortical dysplasia Ⅲa

INTRODUCTION

Focal cortical dysplasia Type Ⅲa (FCD Ⅲa) is a highly prevalent temporal lobe epilepsy but the seizure outcomes are not satisfactory after epilepsy surgery. Hence, quantitative neuroimaging, epileptogenic alterations, as well as their values in guiding surgery are worth exploring.

METHODS

We examined 69 patients with pathologically verified FCD Ⅲa using multimodal neuroimaging and stereoelectroencephalography (SEEG). Among them, 18 received postoperative imaging which showed the extent of surgical resection and 9 underwent SEEG implantation. We also explored neuroimaging gradient alterations along with the distance to the temporal pole. Subsequently, the machine learning regression model was employed to predict whole-brain epileptogenicity. Lastly, the correlation between neuroimaging or epileptogenicity and surgical cavities was assessed.

RESULTS

FCD Ⅲa displayed neuroimaging gradient alterations on the temporal neocortex, morphology-signal intensity decoupling, low similarity of intra-morphological features and high similarity of intra-signal intensity features. The support vector regression model was successfully applied at the whole-brain level to calculate the continuous epileptogenic value at each vertex (mean-squared error = 13.8 ± 9.8).

CONCLUSION

Our study investigated the neuroimaging gradient alterations and epileptogenicity of FCD Ⅲa, along with their potential values in guiding suitable resection range and in predicting postoperative seizure outcomes. The conclusions from this study may facilitate an accurate presurgical examination of FCD Ⅲa. However, further investigation including a larger cohort is necessary to confirm the results.

Preserved microstructural integrity of the corticospinal tract in patients with glioma-induced motor epilepsy: a study using mean apparent propagator magnetic resonance imaging

BACKGROUND

To compare the microstructural integrity of the corticospinal tract (CST) between glioma patients with motor epilepsy and without epilepsy using mean apparent propagator magnetic resonance imaging (MAP-MRI).

METHODS

A total of 26 patients with glioma adjacent to the CST pathway (10 with motor epilepsy and 16 without epilepsy) and 13 matched healthy controls underwent brain structural and diffusion MRI. The morphological characteristics of the CST (tract volume, tract number, and average length) were extracted, and diffusion parameter values including mean squared displacement (MSD), q-space inverse variance (QIV), return-to-origin probability (RTOP), return-to-axis probabilities (RTAP), return-to-plane probabilities (RTPP), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) along the CST were evaluated. The CST features were compared between healthy and affected sides and the relative CST features were compared across the three groups of participants. A receiver operating characteristic (ROC) curve was plotted to assess the performance of each relative CST characteristic for glioma-induced CST changes.

RESULTS

For patients without epilepsy, the tract number, tract volume, FA, RD, MSD, QIV, and RTAP changed significantly on the affected CST side compared with those on the healthy CST side (P=0.002, 0.002, 0.030 0.017, 0.039, 0.044, and 0.002, respectively). In contrast, for patients with motor epilepsy, no significant difference was found between the affected and healthy side in almost all CST features except RTPP (P=0.028). Compared with patients with motor epilepsy, the relative tract number, tract volume, AD, and RTAP were significantly lower (P=0.027, 0.018, 0.040, and 0.027, respectively) in patients without epilepsy, and their areas under the curve (AUCs) were 0.763, 0.781, 0.744, and 0.763, respectively. No significant difference was found between patients with motor epilepsy and matched healthy controls.

CONCLUSIONS

The MAP-MRI is a promising approach for evaluating CST changes. It provides additional information reflecting the microstructural complexity of the CST and demonstrates the preserved microstructural integrity of the CST in glioma patients with motor epilepsy.

Molecular Chaperones and miRNAs in Epilepsy: Pathogenic Implications and Therapeutic Prospects

Epilepsy is a pathologic condition with high prevalence and devastating consequences for the patient and its entourage. Means for accurate diagnosis of type, patient monitoring for predicting seizures and follow up, and efficacious treatment are desperately needed. To improve this adverse outcome, miRNAs and the chaperone system (CS) are promising targets to understand pathogenic mechanisms and for developing theranostics applications. miRNAs implicated in conditions known or suspected to favor seizures such as neuroinflammation, to promote epileptic tolerance and neuronal survival, to regulate seizures, and others showing variations in expression levels related to seizures are promising candidates as useful biomarkers for diagnosis and patient monitoring, and as targets for developing novel therapies. Components of the CS are also promising as biomarkers and as therapeutic targets, since they participate in epileptogenic pathways and in cytoprotective mechanisms in various epileptogenic brain areas, even if what they do and how is not yet clear. The data in this review should help in the identification of molecular targets among the discussed miRNAs and CS components for research aiming at understanding epileptogenic mechanisms and, subsequently, develop means for predicting/preventing seizures and treating the disease.