Bernasconi Andrea, Bernasconi Neda
Neuroimaging of Epilepsy Laboratory [NOEL] and Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
Eur Neurol. 2022;85(5):333-341. doi: 10.1159/000525262. Epub 2022 Jun 15.
Epilepsy is a prevalent chronic condition affecting about 50 million people worldwide. A third of patients with focal epilepsy suffer from seizures unresponsive to medication. Uncontrolled seizures damage the brain, are associated with cognitive decline, and have negative impact on well-being. For these patients, the surgical resection of the brain region that gives rise to seizures is the most effective treatment.
Magnetic resonance imaging (MRI) plays a central role in detecting epileptogenic brain lesions. In this review, we critically discuss advances in neuroimaging acquisition, analytical post-acquisition techniques, and machine leaning methods for the detection of epileptogenic lesions, prediction of clinical outcomes, and identification of disease subtypes.
MRI is a mandatory investigation for diagnosis and treatment of epilepsy, particularly when surgery is being considered. Continuous progress in imaging techniques, combined with machine learning, will continue to push the boundaries of lesion visibility and provide increasingly precise predictors of clinical outcomes. Current efforts aiming at strengthening the competences of epileptologists in neuroimaging will ultimately reduce the need for invasive diagnostics.
癫痫是一种常见的慢性病,全球约有5000万人受其影响。三分之一的局灶性癫痫患者的癫痫发作对药物治疗无反应。不受控制的癫痫发作会损害大脑,与认知能力下降有关,并对幸福感产生负面影响。对于这些患者,手术切除引发癫痫发作的脑区是最有效的治疗方法。
磁共振成像(MRI)在检测致痫性脑病变中起着核心作用。在本综述中,我们批判性地讨论了神经影像学采集、采集后分析技术以及用于检测致痫性病变、预测临床结果和识别疾病亚型的机器学习方法的进展。
MRI是癫痫诊断和治疗的必要检查,尤其是在考虑手术时。成像技术的不断进步与机器学习相结合,将继续拓展病变可视性的边界,并提供越来越精确的临床结果预测指标。目前旨在增强癫痫学家神经影像学能力的努力最终将减少侵入性诊断的需求。