Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, United Kingdom.
PLoS One. 2013 Jul 15;8(7):e69237. doi: 10.1371/journal.pone.0069237. Print 2013.
Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson's disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.
进行性核上性麻痹（PSP）、多系统萎缩（MSA）和特发性帕金森病（IPD）在临床上可能无法区分，尤其是在早期阶段，尽管它们的分子病理学表现明显不同。结构神经影像学有望为在个体水平上区分这些疾病提供客观的生物标志物，但迄今为止的所有研究都报告称疾病组之间的分离并不完全。在这项研究中，我们采用多类模式识别来评估从广泛可用的结构神经影像学序列中得出的解剖模式在自动分类这些疾病中的价值。为此，我们对 17 名 PSP 患者、14 名 IPD 患者和 19 名 MSA 患者进行了结构 MRI 扫描，并招募了 19 名健康对照者（HCs）。采用先进的概率模式识别方法来评估几种预先定义的解剖模式对区分这些疾病的诊断价值，包括：（i）皮质下运动网络；（ii）其组成区域中的每一个；以及（iii）整个大脑。使用皮质下运动网络可以同时以高精度区分所有疾病组。总体上提供最准确预测的区域是中脑/脑干，它可以将所有疾病组彼此以及与 HCs 区分开来。皮质下网络产生的预测也比整个大脑及其所有组成区域更准确。从中脑/脑干、小脑和所有基底节区可以准确预测 PSP；从中脑/脑干和小脑可以预测 MSA；从中脑/脑干可以预测 IPD。这项研究表明，对结构 MRI 的自动分析可以准确预测帕金森病患者的个体诊断，并确定对于这一过程特别有用的特定区域萎缩模式。
