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

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.

Clinical Evaluation of a Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis

OBJECTIVE

To evaluate the influence of quantitative reports (QReports) on the radiological assessment of hippocampal sclerosis (HS) from MRI of patients with epilepsy in a setting mimicking clinical reality.

METHODS

The study included 40 patients with epilepsy, among them 20 with structural abnormalities in the mesial temporal lobe (13 with HS). Six raters blinded to the diagnosis assessed the 3T MRI in two rounds, first using MRI only and later with both MRI and the QReport. Results were evaluated using inter-rater agreement (Fleiss' kappa [Formula: see text]) and comparison with a consensus of two radiological experts derived from clinical and imaging data, including 7T MRI.

RESULTS

For the primary outcome, diagnosis of HS, the mean accuracy of the raters improved from 77.5% with MRI only to 86.3% with the additional QReport (effect size [Formula: see text]). Inter-rater agreement increased from [Formula: see text] to [Formula: see text]. Five of the six raters reached higher accuracies, and all reported higher confidence when using the QReports.

CONCLUSION

In this pre-use clinical evaluation study, we demonstrated clinical feasibility and usefulness as well as the potential impact of a previously suggested imaging biomarker for radiological assessment of HS.

Advances in Neuroimaging and Multiple Post-Processing Techniques for Epileptogenic Zone Detection of Drug-Resistant Epilepsy

Among the approximately 20 million patients with drug-resistant epilepsy (DRE) worldwide, the vast majority can benefit from surgery to minimize seizure reduction and neurological impairment. Precise preoperative localization of epileptogenic zone (EZ) and complete resection of the lesions can influence the postoperative prognosis. However, precise localization of EZ is difficult, and the structural and functional alterations in the brain caused by DRE vary by etiology. Neuroimaging has emerged as an approach to identify the seizure-inducing structural and functional changes in the brain, and magnetic resonance imaging (MRI) and positron emission tomography (PET) have become routine noninvasive imaging tools for preoperative evaluation of DRE in many epilepsy treatment centers. Multimodal neuroimaging offers unique advantages in detecting EZ, especially in improving the detection rate of patients with negative MRI or PET findings. This approach can characterize the brain imaging characteristics of patients with DRE caused by different etiologies, serving as a bridge between clinical and pathological findings and providing a basis for individualized clinical treatment plans. In addition to the integration of multimodal imaging modalities and the development of special scanning sequences and image post-processing techniques for early and precise localization of EZ, the application of deep machine learning for extracting image features and deep learning-based artificial intelligence have gradually improved diagnostic efficiency and accuracy. These improvements can provide clinical assistance for precisely outlining the scope of EZ and indicating the relationship between EZ and functional brain areas, thereby enabling standardized and precise surgery and ensuring good prognosis. However, most existing studies have limitations imposed by factors such as their small sample sizes or hypothesis-based study designs. Therefore, we believe that the application of neuroimaging and post-processing techniques in DRE requires further development and that more efficient and accurate imaging techniques are urgently needed in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Distinct and shared patterns of brain plasticity during electroconvulsive therapy and treatment as usual in depression: an observational multimodal MRI-study

Electroconvulsive therapy (ECT) is a highly effective treatment for depression. Previous studies point to ECT-induced volume increase in the hippocampi and amygdalae, and to increase in cortical thickness. However, it is unclear if these neuroplastic changes are associated with treatment response. This observational study aimed to address this research question by comparing neuroplasticity between patients with depression receiving ECT and patients with depression that respond to treatment as usual (TAU-responders). Twenty ECT-patients (16 major depressive disorder (MDD), 4 depressed bipolar disorder), 20 TAU-responders (20 MDD) and 20 healthy controls (HC) were scanned twice with multimodal magnetic resonance imaging (structure: MP2RAGE; perfusion: arterial spin labeling). ECT-patients were scanned before and after an ECT-index series (ECT-group). TAU-responders were scanned during a depressive episode and following remission or treatment response. Volumes and cerebral blood flow (CBF) of the hippocampi and amygdalae, and global mean cortical thickness were compared between groups. There was a significant group × time interaction for hippocampal and amygdalar volumes, CBF in the hippocampi and global mean cortical thickness. Hippocampal and amygdalar enlargements and CBF increase in the hippocampi were observed in the ECT-group but neither in TAU-responders nor in HC. Increase in global mean cortical thickness was observed in the ECT-group and in TAU-responders but not in HC. The co-occurrence of increase in global mean cortical thickness in both TAU-responders and in ECT-patients may point to a shared mechanism of antidepressant response. This was not the case for subcortical volume and CBF increase.

Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis

Brain morphometry is usually based on non-enhanced (pre-contrast) T1-weighted MRI. However, such dedicated protocols are sometimes missing in clinical examinations. Instead, an image with a contrast agent is often available. Existing tools such as FreeSurfer yield unreliable results when applied to contrast-enhanced (CE) images. Consequently, these acquisitions are excluded from retrospective morphometry studies, which reduces the sample size. We hypothesize that deep learning (DL)-based morphometry methods can extract morphometric measures also from contrast-enhanced MRI. We have extended DL+DiReCT to cope with contrast-enhanced MRI. Training data for our DL-based model were enriched with non-enhanced and CE image pairs from the same session. The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image. A longitudinal dataset of patients with multiple sclerosis (MS), comprising relapsing remitting (RRMS) and primary progressive (PPMS) subgroups, was used for the evaluation. Global and regional cortical thickness derived from non-enhanced and CE images were contrasted to results from FreeSurfer. Correlation coefficients of global mean cortical thickness between non-enhanced and CE images were significantly larger with DL+DiReCT (r = 0.92) than with FreeSurfer (r = 0.75). When comparing the longitudinal atrophy rates between the two MS subgroups, the effect sizes between PPMS and RRMS were higher with DL+DiReCT both for non-enhanced (d = -0.304) and CE images (d = -0.169) than for FreeSurfer (non-enhanced d = -0.111, CE d = 0.085). In conclusion, brain morphometry can be derived reliably from contrast-enhanced MRI using DL-based morphometry tools, making additional cases available for analysis and potential future diagnostic morphometry tools.

Hippocampal volume in patients with bilateral and unilateral peripheral vestibular dysfunction

Previous studies have found that peripheral vestibular dysfunction is associated with altered volumes in different brain structures, especially in the hippocampus. However, published evidence is conflicting. Based on previous findings, we compared hippocampal volume, as well as supramarginal, superior temporal, and postcentral gyrus in a sample of 55 patients with different conditions of peripheral vestibular dysfunction (bilateral, chronic unilateral, acute unilateral) to 39 age- and sex-matched healthy controls. In addition, we explored deviations in gray-matter volumes in hippocampal subfields. We also analysed correlations between morphometric data and visuo-spatial performance. Patients with vestibular dysfunction did not differ in total hippocampal volume from healthy controls. However, a reduced volume in the right presubiculum of the hippocampus and the left supramarginal gyrus was observed in patients with chronic and acute unilateral vestibular dysfunction, but not in patients with bilateral vestibular dysfunction. No association of altered volumes with visuo-spatial performance was found. An asymmetric vestibular input due to unilateral vestibular dysfunction might lead to reduced central brain volumes that are involved in vestibular processing.