Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Foggia, Italy.
Cortex. 2019 Apr;113:58-66. doi: 10.1016/j.cortex.2018.11.025. Epub 2018 Dec 8.
To determine the added value of multimodal structural magnetic resonance imaging (MRI) to language assessment in the differential diagnosis of primary progressive aphasia (PPA) variants.
59 PPA patients [29 nonfluent (nfvPPA), 15 semantic (svPPA), 15 logopenic (lvPPA)] and 38 healthy controls underwent 3D T1-weighted and diffusion tensor (DT) MRI. PPA patients also performed a comprehensive language assessment. Cortical thickness measures and DT MRI indices of white matter tract integrity were obtained. A random forest analysis identified MRI features associated with each clinical variant. Using ROC curves, the discriminatory power of the language features alone ("language model") and the added contribution of multimodal MRI variables were assessed ("language + MRI model").
The 'language model' alone was able to differentiate svPPA from both nfvPPA and lvPPA patients with high accuracy (area under the curve [AUC] = .95 and .99, respectively). When left inferior parietal cortical thickness and DT MRI metrics of the genu of the corpus callosum and left frontal aslant tract were added to the "language model", the ability to discriminate between nfvPPA and lvPPA cases increased from AUC .82 ("language model" only) to .94 ("language + MRI model").
Language measures alone are able to distinguish svPPA from the other two PPA variants with the highest accuracy. Multimodal structural MRI improves the distinction of nfvPPA and lvPPA, which is challenging in the clinical practice.
确定多模态结构磁共振成像(MRI)在原发性进行性失语症(PPA)变异的鉴别诊断中对语言评估的附加价值。
59 例 PPA 患者[29 例非流利型(nfvPPA)、15 例语义性(svPPA)、15 例传导性(lvPPA)]和 38 名健康对照者接受了 3D T1 加权和弥散张量(DT)MRI 检查。PPA 患者还进行了全面的语言评估。获得皮质厚度测量值和白质束完整性的 DT MRI 指数。随机森林分析确定与每种临床变异相关的 MRI 特征。使用 ROC 曲线评估仅语言特征(“语言模型”)和多模态 MRI 变量的附加贡献(“语言+MRI 模型”)的区分能力。
仅“语言模型”就能以高准确度区分 svPPA 与 nfvPPA 和 lvPPA 患者(曲线下面积 [AUC]分别为.95 和.99)。当将左侧顶下小叶皮质厚度和胼胝体膝部及左侧额斜束的 DT MRI 指标添加到“语言模型”中时,区分 nfvPPA 和 lvPPA 病例的能力从 AUC.82(仅“语言模型”)增加到.94(“语言+MRI 模型”)。
语言测量值单独使用就能以最高的准确度区分 svPPA 与其他两种 PPA 变异。多模态结构 MRI 提高了 nfvPPA 和 lvPPA 的区分能力,这在临床实践中具有挑战性。