Neuroimaging Phenotyping and Assessment of Structural-Metabolic-Electrophysiological Alterations in the Temporal Neocortex of Focal Cortical Dysplasia IIIa

Neural network mapping of gelastic behavior in children with hypothalamus hamartoma

BACKGROUND

Hypothalamus hamartomas (HHs) are rare, congenital, tumor-like, and nonprogressive malformations resulting in drug-resistant epilepsy, mainly affecting children. Gelastic seizures (GS) are an early hallmark of epilepsy with HH. The aim of this study was to explore the disease progression and the underlying physiopathological mechanisms of pathological laughter in HH.

METHODS

We obtained clinical information and metabolic images of 56 HH patients and utilized ictal semiology evaluation to stratify the specimens into GS-only, GS-plus, and no-GS subgroups and then applied contrasted trajectories inference (cTI) to calculate the pseudotime value and evaluate GS progression. Ordinal logistic regression was performed to identify neuroimaging-clinical predictors of GS, and then voxelwise lesion network-symptom mapping (LNSM) was applied to explore GS-associated brain regions.

RESULTS

cTI inferred the specific metabolism trajectories of GS progression and revealed increased complexity from GS to other seizure types. This was further validated via actual disease duration (Pearson R = 0.532, P = 0.028). Male sex [odds ratio (OR) = 2.611, P = 0.013], low age at seizure onset (OR = 0.361, P = 0.005), high normalized HH metabolism (OR =  - 1.971, P = 0.037) and severe seizure burden (OR =  - 0.006, P = 0.032) were significant neuroimaging clinical predictors. LNSM revealed that the dysfunctional cortico-subcortico-cerebellar network of GS and the somatosensory cortex (S1) represented a negative correlation.

CONCLUSIONS

This study sheds light on the clinical characteristics and progression of GS in children with HH. We identified distinct subtypes of GS and demonstrated the involvement of specific brain regions at the cortical-subcortical-cerebellar level. These valuable results contribute to our understanding of the neural correlates of GS.

Artificial intelligence in epilepsy - applications and pathways to the clinic

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

Deep Learning Approaches for Imaging-Based Automated Segmentation of Tuberous Sclerosis Complex

The present study presents a novel approach for identifying epileptogenic tubers in patients with tuberous sclerosis complex (TSC) and automating tuber segmentation using a three-dimensional convolutional neural network (3D CNN). The study retrospectively included 31 TSC patients whose lesions were manually annotated from multiparametric neuroimaging data. Epileptogenic tubers were determined via presurgical evaluation and stereoelectroencephalography recording. Neuroimaging metrics were extracted and compared between epileptogenic and non-epileptogenic tubers. Additionally, five datasets with different preprocessing strategies were used to construct and train 3D CNNs for automated tuber segmentation. The normalized positron emission tomography (PET) metabolic value was significantly lower in epileptogenic tubers defined via presurgical evaluation (p = 0.001). The CNNs showed high performance for localizing tubers, with an accuracy between 0.992 and 0.994 across the five datasets. The automated segmentations were highly correlated with clinician-based features. The neuroimaging characteristics for epileptogenic tubers were demonstrated, increasing surgical confidence in clinical practice. The validated deep learning detection algorithm yielded a high performance in determining tubers with an excellent agreement with reference clinician-based segmentation. Collectively, when coupled with our investigation of minimal input requirements, the approach outlined in this study represents a clinically invaluable tool for the management of TSC.

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.

Multimodal imaging-based diagnostic approach for MRI-negative posterior cortex epilepsy

Background

Posterior cortex epilepsy (PCE) primarily comprises seizures originating from the occipital, parietal, and/or posterior edge of the temporal lobe. Electroclinical dissociation and subtle imaging representation render the diagnosis of PCE challenging. Improved methods for accurately identifying patients with PCE are necessary.

Objectives

To develop a novel voxel-based image postprocessing method for better visual identification of the neuroimaging abnormalities associated with PCE.

Design

Multicenter, retrospective study.

Methods

Clinical and imaging features of 165 patients with PCE were retrospectively reviewed and collected from five epilepsy centers. A total of 37 patients (32.4% female, 20.2 ± 8.9 years old) with magnetic resonance imaging (MRI)-negative PCE were finally included for analysis. Image postprocessing features were calculated over a neighborhood for each voxel in the multimodality data. The postprocessed maps comprised structural deformation, hyperintense signal, and hypometabolism. Five raters from three different centers were blinded to the clinical diagnosis and determined the neuroimaging abnormalities in the postprocessed maps.

Results

The average accuracy of correct identification was 55.7% (range from 43.2 to 62.2%) and correct lateralization was 74.1% (range from 64.9 to 81.1%). The Cronbach's alpha was 0.766 for the correct identification and 0.683 for the correct lateralization with similar results of the interclass correlation coefficient, thus indicating reliable agreement between the raters.

Conclusion

The image postprocessing method developed in this study can potentially improve the visual detection of MRI-negative PCE. The technique could lead to an increase in the number of patients with PCE who could benefit from the surgery.

Neural underpinnings of default mode network on empathy revealed by intracranial stereoelectroencephalography

BACKGROUND

Empathy is the ability to understand and share the feelings of others. It is fundamental to emotional intelligence and social iterations. Neuroimaging studies have demonstrated that empathy activates brain regions associated with the social cognition network.

AIM

To explore the neural underpinnings of empathy revealed by stereoelectroencephalography utilizing recurrence quantification analysis (RQA).

METHODS

This retrospective cohort included 38 epilepsy patients with stereoelectroencephalography implantation. RQA metrics were applied to parameterize the network organization of default mode network (DMN) brain regions. The relationships between DMN, seizure burden activity, and empathy, as measured using the Interpersonal Reactivity Index, were examined using partial least-square regression and mediation analysis.

RESULTS

RQA metrics with DMN (R²  = 0.75, P_Bonferroni  < 0.001) and its subsystems (medial temporal subsystem: R²  = 0.53, P_Bonferroni  < 0.001; core subsystem: R²  = 0.70, P_Bonferroni  < 0.001; dorsal medial subsystem: R²  = 0.48, P_Bonferroni  < 0.001) were positively correlated with empathy scores. Of 13 RQA metrics, the mean diagonal line length, entropy of the diagonal line lengths, trapping time, maximal vertical line length, and recurrence time of second type were found to be statistically higher in patient cohorts with reportedly high empathy. Furthermore, DMN characteristics (b path: F = 3.69, P = 0.04), rather than seizure burdens (direct effect: t = 0.33, P = 0.74, c' = - 0.007), mediated empathy status.

CONCLUSION

The present study used various RQA metrics to parameterize the network organization of DMN and determine the neural underpinning of DMN for empathy modulation.

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.