Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning.
Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning.
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DOI：
10.1093/braincomms/fcad048
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发表时间：
2023
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
为了更好地理解进行性超跨性麻痹的病理和表型异质性以及两者之间的联系，我们将一种新型的无监督的机器学习算法（亚型和阶段推理）应用于迄今为止最大的MRI数据集（迄今包括进行性上闭核麻痹 - 里希尔森和变体进行性上腹部麻痹综合征）。
我们的队列由426个进行性超麻痹病例组成，其中367例至少进行了一项随访扫描和290个对照。 palsy–cortical variant (progressive supranulear palsy–frontal, progressive supranulear palsy–speech/language, or progressive supranulear palsy–corticobasal), and 17 with a progressive supranulear palsy–subcortical variant (progressive supranulear palsy–parkinsonism or progressive supranulear palsy–progressive gait冻结）。亚型和阶段分配的一致性。
数据支持两种亚型，每个亚型都有明显的萎缩进展：一种“皮层”亚型，其中早期萎缩在脑干中最突出，腹侧腹膜，高级小脑花梗和齿状核核，以及“皮质”子类型，在额叶的爱中，早期的萎缩与脑干萎缩范围内的岛屿疾病之间存在着强烈的关联。分配给皮层亚型的理查森病例和分配给皮层亚型的82％的进行性闭核性皮皮质病例。皮质亚型'（进行性超启动性麻痹评分量表和统一的帕金森氏病评级量表）表明，验证实验是纵向稳定的（在随访时分配了95％的扫描），并且个人分期是漫长的。 ％在同一阶段保持或在随访中延长到以后的阶段。
总而言之，我们将亚型和阶段推理应用于结构MRI数据，并在经验上确定了在渐进性超促性麻痹中的两个不同的亚型亚型在基线上准确地亚型和阶段进行性超导剂性麻痹患者对筛查患者进入临床试验以及追踪疾病进展具有重要意义。
Scotton等人。在PSP中。
To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy–Richardson and variant progressive supranuclear palsy syndromes). Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy–Richardson, 52 with a progressive supranuclear palsy–cortical variant (progressive supranuclear palsy–frontal, progressive supranuclear palsy–speech/language, or progressive supranuclear palsy–corticobasal), and 17 with a progressive supranuclear palsy–subcortical variant (progressive supranuclear palsy–parkinsonism or progressive supranuclear palsy–progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation. The data supported two subtypes, each with a distinct progression of atrophy: a ‘subcortical’ subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a ‘cortical’ subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy–subcortical cases and 81% of progressive supranuclear palsy–Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy–cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the ‘subcortical’ subtype was associated with worse clinical severity scores compared to the ‘cortical subtype’ (progressive supranuclear palsy rating scale and Unified Parkinson’s Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up. In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression. Scotton et al. apply Subtype and Stage Inference, a machine learning algorithm, to a cross-sectional MRI data to identify two distinct and stable spatiotemporal subtypes of atrophy progression in progressive supranuclear palsy (PSP). These subtypes provide unique insights into disease progression in PSP.
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影响因子：
16.6
作者：
Eshaghi A;Young AL;Wijeratne PA;Prados F;Arnold DL;Narayanan S;Guttmann CRG;Barkhof F;Alexander DC;Thompson AJ;Chard D;Ciccarelli O
通讯作者：
Ciccarelli O
影响因子：
4.8
作者：
Franzmeier, Nicolai;Hoeglinger, Gunter U.
通讯作者：
Hoeglinger, Gunter U.
影响因子：
48
作者：
Boxer, Adam L.;Lang, Anthony E.;Grossman, Murray;Knopman, David S.;Miller, Bruce L.;Schneider, Lon S.;Doody, Rachelle S.;Lees, Andrew;Golbe, Lawrence I.;Williams, David R.;Corvol, Jean-Cristophe;Ludolph, Albert;Burn, David;Lorenzl, Stefan;Litvan, Irene;Roberson, Erik D.;Hoeglinger, Guenter U.;Koestler, Mary;Jack, Clifford R., Jr.;Van Deerlin, Viviana;Randolph, Christopher;Lobach, Iryna V.;Heuer, Hilary W.;Gozes, Illana;Parker, Lesley;Whitaker, Steve;Hirman, Joe;Stewart, Alistair J.;Gold, Michael;Morimoto, Bruce H.
通讯作者：
Morimoto, Bruce H.
影响因子：
82.9
作者：
Dam, Tien;Boxer, Adam L.;Haeberlein, Samantha Budd
通讯作者：
Haeberlein, Samantha Budd
影响因子：
3.3
作者：
Fiorenzato, Eleonora;Weis, Luca;Biundo, Roberta
通讯作者：
Biundo, Roberta