PROBABILISTIC PREDICTION OF NEUROLOGICAL DISORDERS WITH A STATISTICAL ASSESSMENT OF NEUROIMAGING DATA MODALITIES.
PROBABILISTIC PREDICTION OF NEUROLOGICAL DISORDERS WITH A STATISTICAL ASSESSMENT OF NEUROIMAGING DATA MODALITIES.
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DOI：
10.1214/12-aoas562
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发表时间：
2012-12-27
期刊：
影响因子：
--
通讯作者：
Girolami M
中科院分区：
文献类型：
--
作者：
Filippone M;Marquand AF;Blain CR;Williams SC;Mourão-Miranda J;Girolami M
对于许多神经系统疾病，疾病状态的预测是重要的临床目标。神经影像学提供了有关大脑结构和功能的详细信息，可以从这些信息中统计得出这些预测。提出了一种具有高斯过程先验的多项logit模型：（i）基于全脑神经成像数据预测疾病状态;（ii）分析不同图像模态和脑区域的相对信息量。先进的马尔可夫链蒙特卡罗方法进行后验推理的模型。本文报告了一种统计评估的多种神经影像学方式适用于三个帕金森神经系统疾病的歧视，从彼此和健康对照，显示出有前途的预测性能的疾病状态相比，非概率分类的基础上，多种方式。统计分析还量化了不同的神经影像学指标和大脑区域在区分这些疾病中的相对重要性，并表明对于预测而言，获取多个神经影像学序列几乎没有好处。最后，不同的大脑区域的预测能力被发现是根据临床文献中报道的疾病的区域病理学。
For many neurological disorders, prediction of disease state is an important clinical aim. Neuroimaging provides detailed information about brain structure and function from which such predictions may be statistically derived. A multinomial logit model with Gaussian process priors is proposed to: (i) predict disease state based on whole-brain neuroimaging data and (ii) analyze the relative informativeness of different image modalities and brain regions. Advanced Markov chain Monte Carlo methods are employed to perform posterior inference over the model. This paper reports a statistical assessment of multiple neuroimaging modalities applied to the discrimination of three Parkinsonian neurological disorders from one another and healthy controls, showing promising predictive performance of disease states when compared to nonprobabilistic classifiers based on multiple modalities. The statistical analysis also quantifies the relative importance of different neuroimaging measures and brain regions in discriminating between these diseases and suggests that for prediction there is little benefit in acquiring multiple neuroimaging sequences. Finally, the predictive capability of different brain regions is found to be in accordance with the regional pathology of the diseases as reported in the clinical literature.
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