Isolated dystonia is a neurological disorder of heterogeneous pathophysiology, which causes involuntary muscle contractions leading to abnormal movements and postures. Its diagnosis is remarkably challenging due to the absence of a biomarker or gold standard diagnostic test. This leads to a low agreement between clinicians, with up to 50% of cases being misdiagnosed and diagnostic delays extending up to 10.1 y. We developed a deep learning algorithmic platform, DystoniaNet, to automatically identify and validate a microstructural neural network biomarker for dystonia diagnosis from raw structural brain MRIs of 612 subjects, including 392 patients with three different forms of isolated focal dystonia and 220 healthy controls. DystoniaNet identified clusters in corpus callosum, anterior and posterior thalamic radiations, inferior fronto-occipital fasciculus, and inferior temporal and superior orbital gyri as the biomarker components. These regions are known to contribute to abnormal interhemispheric information transfer, heteromodal sensorimotor processing, and executive control of motor commands in dystonia pathophysiology. The DystoniaNet-based biomarker showed an overall accuracy of 98.8% in diagnosing dystonia, with a referral of 3.5% of cases due to diagnostic uncertainty. The diagnostic decision by DystoniaNet was computed in 0.36 s per subject. DystoniaNet significantly outperformed shallow machine-learning algorithms in benchmark comparisons, showing nearly a 20% increase in its diagnostic performance. Importantly, the microstructural neural network biomarker and its DystoniaNet platform showed substantial improvement over the current 34% agreement on dystonia diagnosis between clinicians. The translational potential of this biomarker is in its highly accurate, interpretable, and generalizable performance for enhanced clinical decision-making.

孤立性肌张力障碍是一种神经病理学异常的病理生理学疾病，可引起不自主的肌肉收缩，导致异常的运动和姿势。由于缺乏生物标志物或金标准诊断测试，其诊断非常具有挑战性。这导致临床医生之间的共识度低，多达50％的病例被误诊，诊断延迟延长至10.1年。我们开发了深度学习算法平台DystoniaNet，可从612位受试者的原始结构脑MRI中自动识别和验证用于肌张力障碍诊断的微结构神经网络生物标记物，其中包括392例具有三种不同形式的孤立性局灶性肌张力障碍的患者和220位健康对照。DystoniaNet确定了identified体，丘脑前部和后部放射线中的簇，下额枕筋膜，以及颞下和眶上回作为生物标志物的组成部分。已知这些区域有助于异常的半球间信息传递，异质感觉运动处理以及肌张力障碍病理生理中运动命令的执行控制。基于DystoniaNet的生物标记物在诊断肌张力障碍中显示出98.8％的总体准确性，由于诊断不确定性，转诊病例的比例为3.5％。DystoniaNet的诊断决定是每个受试者0.36 s。在基准测试中，DystoniaNet的性能明显优于浅层的机器学习算法，其诊断性能提高了近20％。重要的，显微结构神经网络生物标记物及其DystoniaNet平台相对于目前临床医生之间关于肌张力障碍诊断的当前34％协议显示出显着改善。该生物标志物的翻译潜力在于其高度准确，可解释和可推广的性能，可增强临床决策水平。