Progressive supranuclear palsy (PSP) is a rare, rapidly progressive neurodegenerative disease. Richardson’s syndrome (PSP-RS) and predominant parkinsonism (PSP-P) are characterized by wide range of cognitive and behavioural disturbances, but these variants show similar cognitive pattern of alterations, leading difficult differential diagnosis. For this reason, we explored with an Artificial Intelligence approach, whether cognitive impairment could differentiate the phenotypes. Forty Parkinson's disease (PD) patients, 25 PSP-P, 40 PSP-RS, and 34 controls were enrolled following the consensus criteria diagnosis. Participants were evaluated with neuropsychological battery for cognitive domains. Random Forest models were used for exploring the discriminant power of the cognitive tests in distinguishing among the four groups. The classifiers for distinguishing diseases from controls reached high accuracies (86% for PD, 95% for PSP-P, 99% for PSP-RS). Regarding the differential diagnosis, PD was discriminated from PSP-P with 91% (important variables: HAMA, MMSE, JLO, RAVLT_I, BDI-II) and from PSP-RS with 92% (important variables: COWAT, JLO, FAB). PSP-P was distinguished from PSP-RS with 84% (important variables: JLO, WCFST, RAVLT_I, Digit span_F). This study revealed that PSP-P, PSP-RS and PD had peculiar cognitive deficits compared with healthy subjects, from which they were discriminated with optimal accuracies. Moreover, high accuracies were reached also in differential diagnosis. Most importantly, Machine Learning resulted to be useful to the clinical neuropsychologist in choosing the most appropriate neuropsychological tests for the cognitive evaluation of PSP patients.

中文翻译：

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神经心理学评估可以区分进行性核上性麻痹的不同临床表型：机器学习方法

进行性核上性麻痹 (PSP) 是一种罕见的、快速进展的神经退行性疾病。理查森综合征 (PSP-RS) 和主要帕金森综合征 (PSP-P) 以广泛的认知和行为障碍为特征，但这些变异表现出相似的认知改变模式，导致鉴别诊断困难。出于这个原因，我们用人工智能方法探索了认知障碍是否可以区分表型。根据共识标准诊断，招募了 40 名帕金森病 (PD) 患者、25 名 PSP-P、40 名 PSP-RS 和 34 名对照。参与者接受了认知领域的神经心理学电池评估。随机森林模型用于探索认知测试在区分四组时的判别力。用于区分疾病与对照的分类器达到了很高的准确度（PD 为 86%，PSP-P 为 95%，PSP-RS 为 99%）。关于鉴别诊断，PD 与 PSP-P 的鉴别率为 91%（重要变量：HAMA、MMSE、JLO、RAVLT_I、BDI-II）和 PSP-RS 的鉴别率为 92%（重要变量：COWAT、JLO、FAB）。PSP-P 与 PSP-RS 有 84% 的区别（重要变量：JLO、WCFST、RAVLT_I、Digit span_F）。该研究表明，与健康受试者相比，PSP-P、PSP-RS 和 PD 具有特殊的认知缺陷，并以最佳准确度对其进行区分。此外，在鉴别诊断中也达到了高精度。最重要的是，