Volumetric and shape analysis of the hippocampus in temporal lobe epilepsy with GAD65 antibodies compared with non-immune epilepsy

Autoimmune encephalitis-associated epilepsy

Autoimmune encephalitis (AE), defined by clinical criteria and its frequent association with neural autoantibodies, often manifests with seizures, which usually stop with immunotherapy. However, a subset of encephalitic conditions present with recurrent seizures that are resistant to immunotherapy. Three primary neurological constellations that fall within this subset are discussed in this Perspective: temporal lobe epilepsy with antibodies against glutamic acid decarboxylase, epilepsy in the context of high-risk paraneoplastic antibodies, and epilepsy following adequately treated surface antibody-mediated AE. These entities all share a common mechanism of structural injury and potentially epileptogenic focal neural loss, often induced by cytotoxic T cells. Recently, we have proposed conceptualizing these conditions under the term autoimmune encephalitis-associated epilepsy (AEAE). Here, we discuss the new concept of AEAE as an emerging field of study. We consider the clinical characteristics of patients who should be investigated for AEAE and highlight the need for judicious use of traditional epilepsy therapeutics alongside immunotherapeutic considerations that are of uncertain and incomplete efficacy for this group of disorders. Last, we discuss future efforts needed to diagnose individuals before structural epileptogenesis has superseded inflammation and to develop improved therapeutics that target the specific immunological or functional disturbances in this entity.

Autoimmune Encephalitis and Musicogenic Epilepsy: A Case of GAD65 Antibody-Associated Seizure

Musicogenic epilepsy (ME) is a rare form of reflex epilepsy with a prevalence of 1 in 10,000,000. Recent research suggests a potential link between ME and autoimmune encephalitis, particularly involving glutamic acid decarboxylase 65-isoform (GAD65) antibodies. A 48-year-old female presented with a one-year history of music-triggered seizures. Her episodes were characterized by an initial aura followed by unresponsiveness and oral automatisms. Electroencephalography revealed abnormalities in the left anterior temporal lobe and temporal leads. Laboratory studies showed positive anti-GAD65 antibodies. The patient was treated with a combination of antiepileptic medication (Lamotrigine) and corticosteroids and intravenous immunoglobulin. This case contributes to the growing evidence supporting an association between ME and autoimmune mechanisms, particularly GAD65 antibody-mediated autoimmunity. It highlights the importance of screening autoimmune factors in ME patients and highlights the need for further research into targeted treatment strategies.

Autoimmune encephalitis: what the radiologist needs to know

Autoimmune encephalitis is a relatively novel nosological entity characterized by an immune-mediated damage of the central nervous system. While originally described as a paraneoplastic inflammatory phenomenon affecting limbic structures, numerous instances of non-paraneoplastic pathogenesis, as well as extra-limbic involvement, have been characterized. Given the wide spectrum of insidious clinical presentations ranging from cognitive impairment to psychiatric symptoms or seizures, it is crucial to raise awareness about this disease category. In fact, an early diagnosis can be dramatically beneficial for the prognosis both to achieve an early therapeutic intervention and to detect a potential underlying malignancy. In this scenario, the radiologist can be the first to pose the hypothesis of autoimmune encephalitis and refer the patient to a comprehensive diagnostic work-up - including clinical, serological, and neurophysiological assessments.In this article, we illustrate the main radiological characteristics of autoimmune encephalitis and its subtypes, including the typical limbic presentation, the features of extra-limbic involvement, and also peculiar imaging findings. In addition, we review the most relevant alternative diagnoses that should be considered, ranging from other encephalitides to neoplasms, vascular conditions, and post-seizure alterations. Finally, we discuss the most appropriate imaging diagnostic work-up, also proposing a suggested MRI protocol.

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI

Purpose

Hippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method.

Materials and Methods

We used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM).

Results

Deep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= −4.2, right = 4.2) than with FreeSurfer (left= −3.1, right = 3.7) and FSL (left= −2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods.

Conclusion

Our findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS.