基于多模态磁共振信息的早期帕金森病影像标记及计算机辅助诊断研究
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摘要
研究背景
帕金森病(Parkinson disease, PD)于1817年由英国神经内科医生Parkinson首先发现并描述。该病在全球60岁以上人群的发病率约为1%,据统计,我国55岁以上人群中有约170万PD患者,每年大约有10万人被确诊患上帕金森病,已占到全球患者总人数的一半。PD的三大临床表现为静止性震颤、肌强直和运动迟缓,主要病理改变是黑质纹状体细胞的进行性损失和细胞内路易斯小体的聚集。
PD早期诊断一直是一个世界性的难题。在临床上可以根据英国帕金森病协会脑库标准对PD的进行诊断,诊断依赖于两个或两个以上的基本运动症状(静止性震颤,运动迟缓或强直)的出现。但是,在对患者运动症状进行评估时,PD的主要症状同药物导致帕金森症、进行性核上性麻痹、多发性硬化、路易斯小体痴呆等疾病相似；能够根据临床症状诊断为帕金森病时,往往已经有80%的纹状体多巴胺减少。因此,寻找有效提示PD病变存在的生物标记,发展PD早期诊断技术或方法,是目前急需解决的问题。
近年来,很多客观指标被开发出来希望用于PD早期诊断,其中发展最快的是神经影像技术。目前应用得比较多的是单光子发射计算机体层扫描(SPECT)及正电子发射计算机体层扫描(PET)技术。PD早期的主要病理改变是黑质-纹体通路的退化,而原发性震颤患者、血管性帕金森症患者、药物导致帕金森症患者和AD患者的黑质-纹体通路没有退化,因此可以通过PET或SPECT将PD和上述疾病加以区分,使得早期诊断成为可能。
但是由于SPECT和PET定位精度不高而且具有放射性,因此研究者将目光转向到非侵入性的磁共振成像(MRI)上来。MRI是一种可以很精细提供各脑区结构、功能和代谢情况的成像技术,它不仅可以得到组织的解剖影像,更重要的是可以得到组织的功能影像。多项使用MR结构成像技术的研究发现PD患者脑结构早期即出现改变,比如PD患者壳核体积在早期及中晚期均较正常人明显减小,并与Hoehn&Yahr分期呈负相关；早期的患者黑质纹状体纤维的各向异性分数(FA)显著降低,FA值的下降在黑质尾侧更加显著,降低程度远高于黑质嘴侧。这些研究提示PD的脑结构变化有可能能够为神经影像学诊断提供帮助。MRI功能成像研究也发现PD患者脑功能存在改变,比如有认知缺陷的早期PD患者患者比无认知缺陷的患者在执行记忆任务时前额叶和尾状核的激活有明显降低,这提示BOLD脑成像在PD诊断中的潜在价值。
然而,在基于MR结构的研究中,大多数研究都是发现结构模式的改变,而没有形成影像学指标。在基于MR功能成像的研究中,由于功能图像数据信噪比很低,必须从多个不同层面提取特征(比如区域一致性、低频振幅和脑网络指标),其中研究脑网络有两个模型：小世界模型和连接度模型,到底哪种模型所产生的指标更适合识别PD仍然未知。虽然上面各个模态都有自己的优势,但是他们也有着各自的局限性。比如BOLD-fMRI主要考察脑灰质皮层的功能状态,却无法评价脑白质的功能改变。DTI可以对脑白质的微观功能进行评价,却不能观察其在宏观上的改变。而神经精神疾病的神经学机制较为复杂,需要从不同角度进行评价。因此近年来,神经影像学的研究经历了从结构成像到功能成像,从单模态成像到多模态成像的发展过程,形成了所谓的“多模态MRI”。但是如何将多模态特征转换成特异性指标仍然是一个需要认真探讨的课题。
本文希望利用MRI技术提取PD患者结构和功能信息,分别验证他们在识别PD方面的有效性,最后使用模式识别的方法将多模态信息转变成能够进行辅助诊断的指标。
方法
实验一：我们验证结构模式变化可以转变成特异性的影像学标记。有36位早期PD患者和46位正常对照参加本研究,每个受试者都采集3D结构像。受试者图像通过SPM8进行偏移校正,分割,标准化,重采样(3mm),调制。然后对预处理过后的灰质图像进行体素筛选,最后对被筛选出来的体素进行聚类分析,生成能代表两组差异的感兴趣区(ROI),然后使用这些脑区的体积值进行分类。
实验二：验证两个脑网络模型的参数到底哪个更适合作为区分PD的指标。共17位早期PD患者和19位正常对照参加本实验。任务编码采用组块设计,受试者执行对指任务。图像采集包括高分辨解剖T1加权像和血氧水平依赖(BOLD)平面回波T2加权像。fMRI数据分析采用SPM8软件。选用的复杂网络是采用seed-voxel法构造SMA相关网络。分别使用小世界模型和连接度模型计算各项指标,其中小世界模型计算六个全局参数(聚类系数,最短路径长度,标准化的聚类系数,标准化的特征的路径长度,全局效率,局部效率)和三个节点参数(节点的度,节点效率,节点的中心性)；连接度模型仅仅计算各节点的连接度。最后具有统计学差异的参数被用于分类。
实验三：提取受试者结构信息和功能信息,通过模式识别算法将这些信息转变成一个辅助诊断指标(变异指数)。共19位早期PD患者和27位正常对照参加本实验,每位受试者采集静息态fMRI图像和结构图像。结构图像和功能图像都采用SPM8进行预处理,利用标准模板分别提取结构特征和功能特征,提取三个不同层面的功能特征：ReHo、ALFF和脑网络指标；结构特征提取的是灰质、白质和脑脊液结构图像的特征。然后进行特征选择,特征融合；将挑选出来的特征放入到支持向量机中进行训练,生成一个分类器。最后将每个人的特征重新输入到分类器中,得到一个新的指标,即变异指数。
结果
实验一：共找到10个具有显著结构差异的ROI,分布在颞叶、枕叶、额叶等部位。其中右尾状核(ROI6)的分类准确性为76.83%,ROC曲线下面积为0.8207；额上回(ROI9)分类的准确性75.61%,ROC曲线下面积为0.814。
实验二：无论是正常人SMA还是PD患者的SMA相关网络都具有小世界属性。相对正常人,PD网络中右枕中回,右顶下回,右颞中回的度要大些,右枕中回和右颞中回的节点效率要高一些,右后扣带回的节点中心性显著下降；使用这些有显著差异的指标进行分类,准确性都低于60%。对于连接度模型,右SMA和右枕中回的连接度具有显著差异,使用这些有显著差异的指标进行分类,右SMA的分类准确性为69.44%,右枕中回的分类准确性为63.89%。
实验三：本文多模态方法分类准确性达到86.96%,敏感性是78.95%,特异性是92.59%。这个结果好于任何一种单模态分类结果。使用每个受试者的变异指数作为阈值,可以生成ROC曲线,曲线下面积为0.951,这体现了一个不错的分类性能。
结论
实验结果显示早期PD患者大脑结构确实已经发生改变,某些脑区的结构变化是有效识别PD的影像学指标；在两个脑网络模型中,小世界模型的指标更适合做定性研究,连接度则有潜力成为识别早期PD的指标。最后生成的基于多模态数据的变异系数能够有效区分PD和正常人,取得了不错的分类性能(准确性=86.96%)。这个结果提示我们综合结构和功能特征的变异系数能够为临床早期帕金森病的诊断提供有价值的信息。
Parkinson's disease (PD) was first discovered and described by the British neurologist Parkinson in1817. According to statistics, the incidence of the disease in the world's population over the age of60is about1%. There are1.7million PD patients in the population over the age of55in our country, which has accounted for half of the total number of patients worldwide, and about10million patients are newly diagnosed every year. There are three major clinical symptoms of PD:resting tremor, rigidity and bradykinesia, and its main pathological change is characterized as nigrostriatal cell loss and presence of intracellular a-synuclein-positive inclusions called Lewy bodies.
The early diagnosis of PD is very difficult. Currently, clinicians use the UK Parkinson's Disease Society Brain Bank criteria to diagnose PD, which depends on the appearing of two or more basic motor symptoms (resting tremor, rigidity or bradykinesia). But, the main features of motor symptoms of PD are shared or at least partly shared by several other disorders, like drug induced parkinsonism, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and dementia with Lewy bodies. When the evidence is sufficient to diagnose PD, the decrement of striatal dopamine is almost up to80%, already in advanced stage pathologically. So finding effective biomarkers and developing diagnosis techniques or methods for early diagnosis of PD, is the problem urgently needs to be resolved.
A variety of objective indicators have been used for the PD early diagnosis, among which the fastest growing are neuroimaging techniques. Currently, the main techniques of functional imaging are single photon emission computed tomography (SPECT) and positron emission tomography (PET). Early pathological changes in PD is nigrostriatal pathway degradation, the other Parkinsonism diseases have not this change. So we can distinguish between PD and the disease by PET or SPECT, making early diagnosis possible.
However, due to the low positioning accuracy and radioactivity of SPECT and PET, researchers now turn more attention to a non-invasive technique:magnetic resonance imaging (MRI). Providing subtle status of each region of the brain, the measurment of MRI is not only the organization of the anatomical images, more important is the organization of the various functional imaging. Many studies using MR structural imaging techniques have found that the brain structure of PD patients appears to change in early stage. For example, comparing with normal subjects, the putamen volume in all early, middle and late PD patients was significantly reduced, and was negatively correlated with Hoehn&Yahr stage. The fractional anisotropy (FA) values of nigrostriatal fibers in early patients was significantly reduced, more in the substantia nigra caudal than in rostral. These studies suggest that brain structure changes in PD may be able to help neuroimaging diagnosis. MRI functional imaging (fMRI) studies have also found that the change in PD patients with brain function exists, such as significantly reduced activation of prefrontal cortex and caudate nucleus in early PD patients with cognitive deficits when performing memory tasks, compared with patients without cognitive deficits, suggesting the potential value of the BOLD brain imaging in the diagnosis of PD.
However, based on the study of the MRI structure, most of the research found that the structural change in the mode, without forming the image indicators. Due to the low Signal-to-noise ratio, it is necessary to extract features from different level (e.g. regional homogeneity, low-frequency amplitude and parameters of brain network) on MR functional imaging study. There are two models in the study of brain networks:the small-world model and connectivity model, which one is more suitable to identify PD is still unknown. Although the above mode has its own advantages, they also have limitations. Such as BOLD-fMRI mainly detects functional status of gray matter cortex, but can not evaluate the functional changes of white matter. Neurological mechanisms of neuropsychiatric diseases are more complex and require evaluation from different perspectives. Therefore, in recent years, neuroimaging studies experienced structural imaging to functional imaging, single mode imaging to the development of multi-modal imaging process, the formation of the so-called "multi-modal MRI. How to convert the multi-modal characteristics to specific markers is still unknown.
So, this study hopes to extract multi-modal informations (functional and structural) from PD patients, and use pattern recognition technique to convert it into index for identifying patients with early PD.
Materials andMethods
1. We first verified whether the brain areas which have different structure in two groups were imaging markers to identify patients with PD. Thirty-six right-handed patients and forty-six normal volunteers participated in this study after signing an informed consent form. The age and gender differences between the two groups were tested using a two-sample t-test and a χ2test, respectively, and no significant differences were observed between the groups (table1). The study was approved by the Medical Ethics Committee of the hospitals. All patients were diagnosed at an early stage (H&Y Ⅰ-Ⅱ). All data was preprocessed using SPM8.The process included:bias correction, segmentation, normalization, re-sample and modulation. Through the preprocessing, each subject has three images:gray matter (GM) image, white matter (WM) image and cerebrospinal fluid (CSF) image. Only the GM image was used in this study. The next step is to identify region of interests. After using RMRD (reliability mapping of regional differences) method to obtain the "reliable" voxels, cluster analysis was performed to identify the ROIs and then classify the PD and NC using the value of volume.
2. We vertify which parameters in two network models are more suitable as indicators to distinguish PD. Twenty-one patients with PD and twenty-two normal volunteers participated in this study. All the participants were asked to perform the automatic finger-thumb opposition task. The right pre-SMA (pre-supplementary motor area) was defined as seed region according to the result of between-group activation map, and then the SMA-related networks were constructed. Parameters of small-world model and connectivity model were calculated. At last, parameters with statistically significant difference were used for classification.
3. A new index (abnormality index-score) based on multimodal imaging was generated by the pattern recognition algorithm. Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels:ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method.
Results:
1. Through clustering analysis, we finally have chosen ten clusters as ROIs. The classification accuracy of the right caudate nucleus (ROI6) was76.83%and the area under the ROC curve was0.8207. The classification accuracy of the right frontal gyrus (ROI9) was75.61%and the area under the ROC curve was0.814.
2. For the nodal parameters of small-world model, two sample t-test revealed a significant class effect (P<0.05) on the right middle occipital gyrus, right inferior parietal gyrus, right middle temporal gyrus and right posterior cingulate gyrus. The classification accuracy of these node parameters was all below60%.For the connective degree of nodes, two sample t-test revealed a significant class effect (P<0.05) on right SMA and right middle occipital gyrus. The classification accuracy of the right SMA was69.44%and the classification accuracy of the right middle occipital gyrus was63.894%.
3. Using the proposed methods to classify the data set, good results (accuracy=86.96%, sensitivity=78.95%, specificity=92.59%) were obtained.
Conclusions:
Structural changes can be used as specific biomarkers to identify PD. Parameters of small-world model are more suitable for qualitative research and parameters of connectivity model have the potential to identify the early PD.The abnormality index-score based on a variety of imaging modalities demonstrates a promising diagnosis performance, and it shows potential for improving the clinical diagnosis of early PD.
帕金森病(Parkinson disease, PD)于1817年由英国神经内科医生Parkinson首先发现并描述。该病在全球60岁以上人群的发病率约为1%,据统计,我国55岁以上人群中有约170万PD患者,每年大约有10万人被确诊患上帕金森病,已占到全球患者总人数的一半。PD的三大临床表现为静止性震颤、肌强直和运动迟缓,主要病理改变是黑质纹状体细胞的进行性损失和细胞内路易斯小体的聚集。
PD早期诊断一直是一个世界性的难题。在临床上可以根据英国帕金森病协会脑库标准对PD的进行诊断,诊断依赖于两个或两个以上的基本运动症状(静止性震颤,运动迟缓或强直)的出现。但是,在对患者运动症状进行评估时,PD的主要症状同药物导致帕金森症、进行性核上性麻痹、多发性硬化、路易斯小体痴呆等疾病相似；能够根据临床症状诊断为帕金森病时,往往已经有80%的纹状体多巴胺减少。因此,寻找有效提示PD病变存在的生物标记,发展PD早期诊断技术或方法,是目前急需解决的问题。
近年来,很多客观指标被开发出来希望用于PD早期诊断,其中发展最快的是神经影像技术。目前应用得比较多的是单光子发射计算机体层扫描(SPECT)及正电子发射计算机体层扫描(PET)技术。PD早期的主要病理改变是黑质-纹体通路的退化,而原发性震颤患者、血管性帕金森症患者、药物导致帕金森症患者和AD患者的黑质-纹体通路没有退化,因此可以通过PET或SPECT将PD和上述疾病加以区分,使得早期诊断成为可能。
但是由于SPECT和PET定位精度不高而且具有放射性,因此研究者将目光转向到非侵入性的磁共振成像(MRI)上来。MRI是一种可以很精细提供各脑区结构、功能和代谢情况的成像技术,它不仅可以得到组织的解剖影像,更重要的是可以得到组织的功能影像。多项使用MR结构成像技术的研究发现PD患者脑结构早期即出现改变,比如PD患者壳核体积在早期及中晚期均较正常人明显减小,并与Hoehn&Yahr分期呈负相关；早期的患者黑质纹状体纤维的各向异性分数(FA)显著降低,FA值的下降在黑质尾侧更加显著,降低程度远高于黑质嘴侧。这些研究提示PD的脑结构变化有可能能够为神经影像学诊断提供帮助。MRI功能成像研究也发现PD患者脑功能存在改变,比如有认知缺陷的早期PD患者患者比无认知缺陷的患者在执行记忆任务时前额叶和尾状核的激活有明显降低,这提示BOLD脑成像在PD诊断中的潜在价值。
然而,在基于MR结构的研究中,大多数研究都是发现结构模式的改变,而没有形成影像学指标。在基于MR功能成像的研究中,由于功能图像数据信噪比很低,必须从多个不同层面提取特征(比如区域一致性、低频振幅和脑网络指标),其中研究脑网络有两个模型：小世界模型和连接度模型,到底哪种模型所产生的指标更适合识别PD仍然未知。虽然上面各个模态都有自己的优势,但是他们也有着各自的局限性。比如BOLD-fMRI主要考察脑灰质皮层的功能状态,却无法评价脑白质的功能改变。DTI可以对脑白质的微观功能进行评价,却不能观察其在宏观上的改变。而神经精神疾病的神经学机制较为复杂,需要从不同角度进行评价。因此近年来,神经影像学的研究经历了从结构成像到功能成像,从单模态成像到多模态成像的发展过程,形成了所谓的“多模态MRI”。但是如何将多模态特征转换成特异性指标仍然是一个需要认真探讨的课题。
本文希望利用MRI技术提取PD患者结构和功能信息,分别验证他们在识别PD方面的有效性,最后使用模式识别的方法将多模态信息转变成能够进行辅助诊断的指标。
方法
实验一：我们验证结构模式变化可以转变成特异性的影像学标记。有36位早期PD患者和46位正常对照参加本研究,每个受试者都采集3D结构像。受试者图像通过SPM8进行偏移校正,分割,标准化,重采样(3mm),调制。然后对预处理过后的灰质图像进行体素筛选,最后对被筛选出来的体素进行聚类分析,生成能代表两组差异的感兴趣区(ROI),然后使用这些脑区的体积值进行分类。
实验二：验证两个脑网络模型的参数到底哪个更适合作为区分PD的指标。共17位早期PD患者和19位正常对照参加本实验。任务编码采用组块设计,受试者执行对指任务。图像采集包括高分辨解剖T1加权像和血氧水平依赖(BOLD)平面回波T2加权像。fMRI数据分析采用SPM8软件。选用的复杂网络是采用seed-voxel法构造SMA相关网络。分别使用小世界模型和连接度模型计算各项指标,其中小世界模型计算六个全局参数(聚类系数,最短路径长度,标准化的聚类系数,标准化的特征的路径长度,全局效率,局部效率)和三个节点参数(节点的度,节点效率,节点的中心性)；连接度模型仅仅计算各节点的连接度。最后具有统计学差异的参数被用于分类。
实验三：提取受试者结构信息和功能信息,通过模式识别算法将这些信息转变成一个辅助诊断指标(变异指数)。共19位早期PD患者和27位正常对照参加本实验,每位受试者采集静息态fMRI图像和结构图像。结构图像和功能图像都采用SPM8进行预处理,利用标准模板分别提取结构特征和功能特征,提取三个不同层面的功能特征：ReHo、ALFF和脑网络指标；结构特征提取的是灰质、白质和脑脊液结构图像的特征。然后进行特征选择,特征融合；将挑选出来的特征放入到支持向量机中进行训练,生成一个分类器。最后将每个人的特征重新输入到分类器中,得到一个新的指标,即变异指数。
结果
实验一：共找到10个具有显著结构差异的ROI,分布在颞叶、枕叶、额叶等部位。其中右尾状核(ROI6)的分类准确性为76.83%,ROC曲线下面积为0.8207；额上回(ROI9)分类的准确性75.61%,ROC曲线下面积为0.814。
实验二：无论是正常人SMA还是PD患者的SMA相关网络都具有小世界属性。相对正常人,PD网络中右枕中回,右顶下回,右颞中回的度要大些,右枕中回和右颞中回的节点效率要高一些,右后扣带回的节点中心性显著下降；使用这些有显著差异的指标进行分类,准确性都低于60%。对于连接度模型,右SMA和右枕中回的连接度具有显著差异,使用这些有显著差异的指标进行分类,右SMA的分类准确性为69.44%,右枕中回的分类准确性为63.89%。
实验三：本文多模态方法分类准确性达到86.96%,敏感性是78.95%,特异性是92.59%。这个结果好于任何一种单模态分类结果。使用每个受试者的变异指数作为阈值,可以生成ROC曲线,曲线下面积为0.951,这体现了一个不错的分类性能。
结论
实验结果显示早期PD患者大脑结构确实已经发生改变,某些脑区的结构变化是有效识别PD的影像学指标；在两个脑网络模型中,小世界模型的指标更适合做定性研究,连接度则有潜力成为识别早期PD的指标。最后生成的基于多模态数据的变异系数能够有效区分PD和正常人,取得了不错的分类性能(准确性=86.96%)。这个结果提示我们综合结构和功能特征的变异系数能够为临床早期帕金森病的诊断提供有价值的信息。
Parkinson's disease (PD) was first discovered and described by the British neurologist Parkinson in1817. According to statistics, the incidence of the disease in the world's population over the age of60is about1%. There are1.7million PD patients in the population over the age of55in our country, which has accounted for half of the total number of patients worldwide, and about10million patients are newly diagnosed every year. There are three major clinical symptoms of PD:resting tremor, rigidity and bradykinesia, and its main pathological change is characterized as nigrostriatal cell loss and presence of intracellular a-synuclein-positive inclusions called Lewy bodies.
The early diagnosis of PD is very difficult. Currently, clinicians use the UK Parkinson's Disease Society Brain Bank criteria to diagnose PD, which depends on the appearing of two or more basic motor symptoms (resting tremor, rigidity or bradykinesia). But, the main features of motor symptoms of PD are shared or at least partly shared by several other disorders, like drug induced parkinsonism, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and dementia with Lewy bodies. When the evidence is sufficient to diagnose PD, the decrement of striatal dopamine is almost up to80%, already in advanced stage pathologically. So finding effective biomarkers and developing diagnosis techniques or methods for early diagnosis of PD, is the problem urgently needs to be resolved.
A variety of objective indicators have been used for the PD early diagnosis, among which the fastest growing are neuroimaging techniques. Currently, the main techniques of functional imaging are single photon emission computed tomography (SPECT) and positron emission tomography (PET). Early pathological changes in PD is nigrostriatal pathway degradation, the other Parkinsonism diseases have not this change. So we can distinguish between PD and the disease by PET or SPECT, making early diagnosis possible.
However, due to the low positioning accuracy and radioactivity of SPECT and PET, researchers now turn more attention to a non-invasive technique:magnetic resonance imaging (MRI). Providing subtle status of each region of the brain, the measurment of MRI is not only the organization of the anatomical images, more important is the organization of the various functional imaging. Many studies using MR structural imaging techniques have found that the brain structure of PD patients appears to change in early stage. For example, comparing with normal subjects, the putamen volume in all early, middle and late PD patients was significantly reduced, and was negatively correlated with Hoehn&Yahr stage. The fractional anisotropy (FA) values of nigrostriatal fibers in early patients was significantly reduced, more in the substantia nigra caudal than in rostral. These studies suggest that brain structure changes in PD may be able to help neuroimaging diagnosis. MRI functional imaging (fMRI) studies have also found that the change in PD patients with brain function exists, such as significantly reduced activation of prefrontal cortex and caudate nucleus in early PD patients with cognitive deficits when performing memory tasks, compared with patients without cognitive deficits, suggesting the potential value of the BOLD brain imaging in the diagnosis of PD.
However, based on the study of the MRI structure, most of the research found that the structural change in the mode, without forming the image indicators. Due to the low Signal-to-noise ratio, it is necessary to extract features from different level (e.g. regional homogeneity, low-frequency amplitude and parameters of brain network) on MR functional imaging study. There are two models in the study of brain networks:the small-world model and connectivity model, which one is more suitable to identify PD is still unknown. Although the above mode has its own advantages, they also have limitations. Such as BOLD-fMRI mainly detects functional status of gray matter cortex, but can not evaluate the functional changes of white matter. Neurological mechanisms of neuropsychiatric diseases are more complex and require evaluation from different perspectives. Therefore, in recent years, neuroimaging studies experienced structural imaging to functional imaging, single mode imaging to the development of multi-modal imaging process, the formation of the so-called "multi-modal MRI. How to convert the multi-modal characteristics to specific markers is still unknown.
So, this study hopes to extract multi-modal informations (functional and structural) from PD patients, and use pattern recognition technique to convert it into index for identifying patients with early PD.
Materials andMethods
1. We first verified whether the brain areas which have different structure in two groups were imaging markers to identify patients with PD. Thirty-six right-handed patients and forty-six normal volunteers participated in this study after signing an informed consent form. The age and gender differences between the two groups were tested using a two-sample t-test and a χ2test, respectively, and no significant differences were observed between the groups (table1). The study was approved by the Medical Ethics Committee of the hospitals. All patients were diagnosed at an early stage (H&Y Ⅰ-Ⅱ). All data was preprocessed using SPM8.The process included:bias correction, segmentation, normalization, re-sample and modulation. Through the preprocessing, each subject has three images:gray matter (GM) image, white matter (WM) image and cerebrospinal fluid (CSF) image. Only the GM image was used in this study. The next step is to identify region of interests. After using RMRD (reliability mapping of regional differences) method to obtain the "reliable" voxels, cluster analysis was performed to identify the ROIs and then classify the PD and NC using the value of volume.
2. We vertify which parameters in two network models are more suitable as indicators to distinguish PD. Twenty-one patients with PD and twenty-two normal volunteers participated in this study. All the participants were asked to perform the automatic finger-thumb opposition task. The right pre-SMA (pre-supplementary motor area) was defined as seed region according to the result of between-group activation map, and then the SMA-related networks were constructed. Parameters of small-world model and connectivity model were calculated. At last, parameters with statistically significant difference were used for classification.
3. A new index (abnormality index-score) based on multimodal imaging was generated by the pattern recognition algorithm. Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels:ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method.
Results:
1. Through clustering analysis, we finally have chosen ten clusters as ROIs. The classification accuracy of the right caudate nucleus (ROI6) was76.83%and the area under the ROC curve was0.8207. The classification accuracy of the right frontal gyrus (ROI9) was75.61%and the area under the ROC curve was0.814.
2. For the nodal parameters of small-world model, two sample t-test revealed a significant class effect (P<0.05) on the right middle occipital gyrus, right inferior parietal gyrus, right middle temporal gyrus and right posterior cingulate gyrus. The classification accuracy of these node parameters was all below60%.For the connective degree of nodes, two sample t-test revealed a significant class effect (P<0.05) on right SMA and right middle occipital gyrus. The classification accuracy of the right SMA was69.44%and the classification accuracy of the right middle occipital gyrus was63.894%.
3. Using the proposed methods to classify the data set, good results (accuracy=86.96%, sensitivity=78.95%, specificity=92.59%) were obtained.
Conclusions:
Structural changes can be used as specific biomarkers to identify PD. Parameters of small-world model are more suitable for qualitative research and parameters of connectivity model have the potential to identify the early PD.The abnormality index-score based on a variety of imaging modalities demonstrates a promising diagnosis performance, and it shows potential for improving the clinical diagnosis of early PD.
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