使用贝叶斯空间模型预测大脑活动。
Predicting brain activity using a Bayesian spatial model.
机构信息
Center for Biomedical Imaging Statistics, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
出版信息
Stat Methods Med Res. 2013 Aug;22(4):382-97. doi: 10.1177/0962280212448972. Epub 2012 Jun 28.
Abstract
Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our approach attempts to overcome some shortcomings of the modeling methods used in other neuroimaging settings, by borrowing strength from the spatial correlations present in the data. Our proposed methodology is applicable to data from various imaging modalities including functional magnetic resonance imaging and positron emission tomography, and we provide an illustration here using positron emission tomography data from a study of Alzheimer's disease to predict disease progression.
摘要
提高功能神经影像学技术的临床适用性是一个新兴的目标，例如，用于诊断和治疗目的。我们提出了一种新的贝叶斯空间层次框架，用于根据个体的基线功能神经影像学数据预测后续的神经活动。我们的方法试图通过利用数据中存在的空间相关性来克服其他神经影像学环境中使用的建模方法的一些缺点。我们提出的方法适用于各种成像模式的数据，包括功能磁共振成像和正电子发射断层扫描，我们在这里使用阿尔茨海默病研究中的正电子发射断层扫描数据来说明如何预测疾病进展。
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