Banerjee Monami, Okun Michael S, Vaillancourt David E, Vemuri Baba C
Department of CISE, University of Florida, Gainesville, Florida, United States of America.
Department of Neurology, University of Florida, Gainesville, Florida, United States of America.
PLoS One. 2016 Jun 9;11(6):e0155764. doi: 10.1371/journal.pone.0155764. eCollection 2016.
Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder that affects patients in all countries and of all nationalities. Magnetic resonance imaging (MRI) is currently one of the most widely used diagnostic imaging techniques utilized for detection of neurologic diseases. Changes in structural biomarkers will likely play an important future role in assessing progression of many neurological diseases inclusive of PD. In this paper, we derived structural biomarkers from diffusion MRI (dMRI), a structural modality that allows for non-invasive inference of neuronal fiber connectivity patterns. The structural biomarker we use is the ensemble average propagator (EAP), a probability density function fully characterizing the diffusion locally at a voxel level. To assess changes with respect to a normal anatomy, we construct an unbiased template brain map from the EAP fields of a control population. Use of an EAP captures both orientation and shape information of the diffusion process at each voxel in the dMRI data, and this feature can be a powerful representation to achieve enhanced PD brain mapping. This template brain map construction method is applicable to small animal models as well as to human brains. The differences between the control template brain map and novel patient data can then be assessed via a nonrigid warping algorithm that transforms the novel data into correspondence with the template brain map, thereby capturing the amount of elastic deformation needed to achieve this correspondence. We present the use of a manifold-valued feature called the Cauchy deformation tensor (CDT), which facilitates morphometric analysis and automated classification of a PD versus a control population. Finally, we present preliminary results of automated discrimination between a group of 22 controls and 46 PD patients using CDT. This method may be possibly applied to larger population sizes and other parkinsonian syndromes in the near future.
帕金森病（PD）是一种常见且使人衰弱的神经退行性疾病，影响着所有国家和所有国籍的患者。磁共振成像（MRI）是目前用于检测神经疾病的最广泛使用的诊断成像技术之一。结构生物标志物的变化可能在评估包括PD在内的许多神经疾病的进展中发挥重要的未来作用。在本文中，我们从扩散MRI（dMRI）中导出了结构生物标志物，dMRI是一种结构模态，能够对神经元纤维连接模式进行非侵入性推断。我们使用的结构生物标志物是总体平均传播子（EAP），它是一个概率密度函数，在体素水平上全面表征局部扩散情况。为了评估相对于正常解剖结构的变化，我们从对照组人群的EAP场构建了一个无偏模板脑图谱。使用EAP可以捕捉dMRI数据中每个体素处扩散过程的方向和形状信息，这一特征可以成为实现增强型PD脑图谱绘制的有力表示。这种模板脑图谱构建方法适用于小动物模型以及人类大脑。然后，可以通过非刚性配准算法评估对照模板脑图谱与新患者数据之间的差异，该算法将新数据转换为与模板脑图谱对应，从而捕捉实现这种对应所需的弹性变形量。我们展示了使用一种称为柯西变形张量（CDT）的流形值特征，它有助于对PD患者与对照组人群进行形态计量分析和自动分类。最后，我们展示了使用CDT对22名对照组和46名PD患者进行自动区分的初步结果。这种方法在不久的将来可能会应用于更大的人群规模和其他帕金森综合征。
