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

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.

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.

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.

Diagnostic Superiority of 18 F-FDG PET Over MRI in Detecting Anti-LGI1 Autoimmune Encephalitis : A Comparative Study With Insights Into Faciobrachial Dystonic Seizures Mechanisms and Recurrence Identification

OBJECTIVE

Our study aimed to investigate the utility of 18 F-FDG PET imaging in diagnosing and monitoring patients with anti-leucine-rich glioma-inactivated 1 antibody autoimmune encephalitis (anti-LGI1 AE). We also sought to understand the mechanisms of faciobrachial dystonic seizures (FBDSs).

PATIENTS AND METHODS

We analyzed 18 F-FDG PET scans from 50 patients with anti-LGI1 AE, using visual and semiquantitative methods, and compared these with 24 healthy controls. All patients tested positive for anti-LGI1 antibodies in serum or cerebrospinal fluid before PET imaging. The patients were divided into FBDS and non-FBDS groups to compare metabolic differences using voxel-based semiquantitative analysis. Finally, we separately analyzed PET images of patients with symptom recurrence.

RESULTS

The sensitivity of 18 F-FDG PET was superior to MRI (97.9% vs 63.8%, respectively; P < 0.001). Semiquantitative analysis revealed hypermetabolism in the basal ganglia, medial temporal lobe, and brainstem, and hypometabolism in most neocortical regions compared with healthy controls. The FBDS group exhibited hypometabolism in the frontal and temporal lobes compared with the non-FBDS group. Among 7 recurrent patients, 3 were confirmed as recurrence and 3 as sequelae by PET. One patient relapsed shortly after discontinuing corticosteroids when PET indicated active lesions.

CONCLUSIONS

18 F-FDG PET scans were more sensitive than MRI in detecting anti-LGI1 AE, which displayed a pattern of hypermetabolism in the basal ganglia and medial temporal lobe, as well as neocortex hypometabolism. Hypometabolism in the frontal and temporal lobes was associated with FBDS. Furthermore, 18 F-FDG PET scans can differentiate recurrence from sequelae and guide the timing of immunotherapy cessation.

Movement disorders in autoimmune encephalitis: an update

Autoimmune encephalitis (AE) is a form of encephalitis resulting from an immune response targeting central nervous system antigens, which is characterized by cognitive impairment, neuropsychiatric symptoms, seizures, movement disorders (MDs), and other encephalopathy symptoms. MDs frequently manifest throughout the progression of the disease, with recurrent involuntary movements leading to discomfort and, in some cases, necessitating admission to the intensive care unit. Prompt identification and management of MDs can aid in the diagnosis and prognosis of AE. This review synthesizes current knowledge on the characteristics, underlying mechanisms, and treatment options for MDs in the context of AE.

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.