Lorenzi Marco, Simpson Ivor J, Mendelson Alex F, Vos Sjoerd B, Cardoso M Jorge, Modat Marc, Schott Jonathan M, Ourselin Sebastien
Translational Imaging Group, CMIC, UCL, London, UK.
MRI Unit, Epilepsy Society, Chalfont St Peter, UK.
Sci Rep. 2016 Apr 11;6:22161. doi: 10.1038/srep22161.
The joint analysis of brain atrophy measured with magnetic resonance imaging (MRI) and hypometabolism measured with positron emission tomography with fluorodeoxyglucose (FDG-PET) is of primary importance in developing models of pathological changes in Alzheimer's disease (AD). Most of the current multimodal analyses in AD assume a local (spatially overlapping) relationship between MR and FDG-PET intensities. However, it is well known that atrophy and hypometabolism are prominent in different anatomical areas. The aim of this work is to describe the relationship between atrophy and hypometabolism by means of a data-driven statistical model of non-overlapping intensity correlations. For this purpose, FDG-PET and MRI signals are jointly analyzed through a computationally tractable formulation of partial least squares regression (PLSR). The PLSR model is estimated and validated on a large clinical cohort of 1049 individuals from the ADNI dataset. Results show that the proposed non-local analysis outperforms classical local approaches in terms of predictive accuracy while providing a plausible description of disease dynamics: early AD is characterised by non-overlapping temporal atrophy and temporo-parietal hypometabolism, while the later disease stages show overlapping brain atrophy and hypometabolism spread in temporal, parietal and cortical areas.
利用磁共振成像（MRI）测量脑萎缩与利用氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）测量代谢减退进行联合分析，对于建立阿尔茨海默病（AD）病理变化模型至关重要。目前AD中的大多数多模态分析假定MR和FDG-PET强度之间存在局部（空间重叠）关系。然而，众所周知，萎缩和代谢减退在不同解剖区域较为突出。这项工作的目的是通过数据驱动的非重叠强度相关性统计模型来描述萎缩与代谢减退之间的关系。为此，通过偏最小二乘回归（PLSR）的一种计算上易于处理的公式对FDG-PET和MRI信号进行联合分析。PLSR模型在来自ADNI数据集的1049名个体的大型临床队列中进行估计和验证。结果表明，所提出的非局部分析在预测准确性方面优于经典局部方法，同时对疾病动态提供了合理描述：早期AD的特征是非重叠的颞叶萎缩和颞顶叶代谢减退，而疾病后期阶段则表现为重叠性脑萎缩以及代谢减退在颞叶、顶叶和皮质区域扩散。
