在阿尔茨海默病和轻度认知功能损害的定向功能连接格兰杰因果关系分析中的应用

摘要

我们基于静息态功能磁共振成像与格兰杰因果分析法，研究了定向功能连接后扣带皮层与整个大脑在阿尔茨海默病 (AD) 患者、 患者轻度认知功能损害 (MCI) 和健康对照组之间改建。

摘要

受损功能连接在默认模式网络 (DMN) 可能参与阿尔茨海默病 (AD) 的进展。后扣带皮层 (PCC) 是一个潜在的成像标记为监测进展的广告。以前的研究没有集中的功能连接 PCC 和节点以外 DMN，地区之间，但我们的研究是努力探索这些被忽视的功能连接。收集数据，我们使用功能磁共振成像 (fMRI) 和格兰杰因果关系分析 (GCA)。功能磁共振成像提供研究大脑不同区域之间的动态相互作用的非侵入性方法。GCA 是确定一次性系列是否有用在预测另一个统计假设检验。简单来说，就判断比较"已知的最后一刻，在这次的 X 的概率分布上的所有信息"和"都已知的最后时刻，只 Y，此时 X 的概率分布上的所有信息"，以确定是否 Y 和 X 之间的因果关系。这个定义基于完整的信息源和固定的顺序。这一分析的主要步骤是使用 X 和 Y 建立的回归方程，并画一种因果关系的假设检验。因为一般配合力效应可以衡量因果效应，我们用它来调查功能连通性的各向异性和探讨 PCC 的枢纽作用。在这里，我们筛选出 116 参与者进行 MRI 扫描，和经过预处理从神经影像学获得的数据，我们用于 GCA 派生的每个节点的因果关系。最后，我们总结的定向的连接是轻度认知功能损害 (MCI) 和广告组，对全脑 PCC 和 PCC 的整个大脑之间显著不同。

引言

广告是，可以使用组织病理、 电生理和神经影像学¹诊断中枢神经系统退行性疾病。与内存相关 DMN 是重要的系统的相互作用的大脑区域，与广告，关联和其异常的作用的特点是 AD^2，3。PCC 是传统默认网络在静息状态的重要区域，起着关键作用，情景记忆、 空间注意、 自我评价和其他认知功能^{4，5，，67}。此外，它可能是成像监测指标的 AD 进展。使用 GCA，廖等人发现 PCC 是一个区域的多个 cytoarchitectonics 具有多个连接和脑功能结构⁸中扮演重要的角色。钟等人报告说，PCC 相互作用收到多数其他区域内 DMN³辐合中心。此外，庙等人证明，DMN 枢纽地区，PCC 与其他节点⁹的最大的因果关系。在一起，所有这一证据表明 PCC 的定向的连接是有价值的广告研究和 PCC 有待进一步深入作为 DMN 至关重要的地区。

以往的研究仅仅局限在 PCC 和内 DMN; 其他区域之间的连接然而，定向功能连接 PCC 和大脑区域外 DMN，以及他们对广告的影响之间的变化尚未探索的¹⁰。我们进一步研究了这未知的功能连接在正常对照组、 MCI，患者和 AD 患者。通过观察 PCC 和整个大脑区域之间的直接的连接，我们旨在澄清与 AD 进展，相关的脑功能变化，从而建立了新型的客观依据，评估疾病的严重程度。

功能连接是指在脑血液氧水平依赖 (BOLD) 功能磁共振成像信号区域间的互动，可以通过同步低频波动 (LFFs) 表示。因此，为观察 PCC 和其它脑区之间的功能连接，我们分析了 PCC 和全脑网络之间的功能连接的功能磁共振成像使用 GCA，pcc 作为区域的利息率 (ROI)。这项技术直接推导出使用从神经影像学¹¹获得的数据的每个节点的基本关系。最近，GCA 已被应用于脑电图 (EEG) 和功能磁共振成像研究，以揭示大脑区域¹²之间的因果效应。所有这些研究都表明 GCA 技术可能是最优检测在大脑中的每个节点之间的因果关系。

研究方案

This study was approved by the Ethics Committee of Zhejiang Provincial People's Hospital. Every enrolled subject signed a written informed consent.

1. Sample Classification and Screening

1. Diagnose and divide 116 patients into AD and MCI groups.
   NOTE: Use the 2011 National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) diagnostic criteria and the Mini-Mental State Examination (MMSE) criteria for identification and classification of MCI, which is described in detail in Yu et al.¹⁰
2. Select participants in the healthy control group.
   NOTE: The age, gender, and education level of the control group were matched with patients in the MCI and AD groups.
3. Assess all subjects by MMSE¹⁰.
4. Exclude the subjects who did not fulfill the inclusion criteria. For all subjects, the exclusion criteria are mentioned in Yu et al.¹⁰
5. Use MRI to scan subjects; exclude subjects with unusable data due to head movements.
   NOTE: Ultimately, we screened 26 patients with MCI, 32 patients with AD, and 58 controls.

2. Acquisition of Neuroimaging

1. Remove metal and magnetic objects before entering the 3.0 T MRI Laboratory.
2. Install an MRI receiving coil at the magnetic resonance scanning bed. The receiving coil is an 8 channel circularly polarized brain phased-array coil.
3. Instruct the participant to lie on the bed, and to remain supine with the head advanced and the long axis of the body along the long axis of the bed. Place the participant's head on the bracket of the coil, and make sure that the orbitomeatal line is perpendicular to bed.
4. Tell the participant to place the upper limbs to the sides of the body, keep eyes closed, not think of anything in particular, and move as little as possible during the scan. Place foam pads on the head in the bilateral temporal area to prevent head movement and provide headphones to reduce scanner noise for the participant.
   NOTE: Dimensions of the foam pad are: long diameter = 13 cm, short diameter = 10 cm, the thickest thickness = 7 cm, the thinnest thickness = 3 cm, average thickness = 5 cm.
5. Adjust the position of the head through the positioning light so that the sagittal positioning cursor is in the midline of the face, and the axis positioning cursor is parallel to the lateral canthus. Then move the bed to keep the axis positioning cursor on the eyebrows or 2 cm below it.
6. Move the head to the center of the magnet. Perform an fMRI brain scan, including gradient Echo-Planar Imaging (EPI-BOLD) and 3D-T1-MPRAGE imaging.
   NOTE: Use the following parameters:
   EPI-BOLD: TR/TE = 2,000/30 ms, layer thickness/layer pitch = 3.2/0.8 mm, 31 slices, matrix = 64 x 64, FOV = 220 x 220 mm, voxel size = 3 x 3 x 4 mm, flip angle = 90 °, scanning time of 484 s, and a total of 240 scanning images.
   3D-T1-MPRAGE imaging: TR/TE = 8.5/3.2 ms, flip angle = 15 °, field of view = 250 x 250 mm, matrix = 256 x 256, slice number = 176, slice thickness/gap = 1/0 mm, scanning time of 353 s, and a total of 192 scanning images.
7. Keep the patient safe when they are leaving the bed at the end of the scan.

3. Data Preprocessing

NOTE: Analyze the raw data for resting-state brain functions by using the Resting-State fMRI (rs-fMRI) Data Analysis Toolkit plus (RESTplus).

1. Open RESTplus through MATLAB and left click on Pipeline.
2. Import the relevant files into RESTplus. Select the work directory and then the starting EPI and T1 directories.
3. Convert DICOM files to NIFTI. Check off the DICOM to NIFTI box in preprocessing and check off the EPI DICOM to NIFTU and the T1 DICOM to NIFTI parameters.
4. Remove the first 10 time points by checking off the Remove first n time points and setting the n parameter as 10.
5. Set the slice timing according to rs-fMRI parameters. Check off the Slice timing box. Set the slice number according to the rs-fMRI parameters of the study. Enter the slice order.
   NOTE: The acquisition of data of each layer in the brain scan is not at the same time point, and thus, it needs to be calibrated to the same time point.
6. Correct the time and head motion. Check off Realign.
   NOTE: The exclusion criterion for excessive head motion was >2.0 mm translation or >2.0 ° rotation in any direction. In the RESTplus this is a default parameter (left click on the option of 'Realign').
7. Perform spatial normalization by using T1 image unified segmentation and all heads standardized to the same space. Check off Normalize and leave the default parameters at the bottom. Select the Normalize by using T1 image unified segmentation and European parameters.
   NOTE: Resample the rs-fMRI images with voxels of 3 × 3 × 3 mm, and other parameters in the RESTplus are default, just left click on the option of 'Normalize by using T1 image unified segmentation'.
8. Perform spatial smoothing using an isotropic Gaussian kernel with a full-width at half maximum (FWHM) of 6 mm. Check off Smooth.
9. Remove the linear trend by checking off Detrend.
10. Regress out signals from nuisance regressors (WM, CSF, Global) to increase signal-to-noise ratio. Check off Nuisance covariates regression and the following parameters: 6 head motion parameters, global mean signal, white matter signal, and cerebrospinal fluid signal.
    NOTE: During this step, set the 'Polynomial trend' as 1 as default, and choose the '6 head motion parameters', the 'Nuisance regressors (WM, CSF, Global)' and the 'add mean back' as default.
11. Use band-pass filtering to retain signals between 0.01 - 0.08 Hz. Remove high-frequency physiological noise, and low-frequency drift. Check off Filter.

4. Directed Connectivity Analysis

NOTE: Perform GCA combined with the BOLD signals for each voxel in the whole brain after extracting the average BOLD signal intensity in the seed area.

1. Perform the voxel-wise GCA by using the REST-GCA in the REST toolbox. In the post-processing box, check off GCA.
2. Set the 'order' as 1 as default. Select the parameters in the input.
3. Define ROI and identify seed points of interest in the PCC. Select Define ROI and choose the Spherical ROI. Select Next. Set the center coordinates and radius of the seed ROI based on the known data and select OK.
   NOTE: An ROI for the DMN was placed at the PCC (centering at x = 0, y = -53, z = 26 with radius = 6 mm), as in a previous study¹³.
4. Select Run and OK to run the program.
5. Find folders named ZGCA and GCA after processing of relevant file data. Sort out the files of ZGCA and classify them into four subfolders, xx, xy, yx, yy accordingly.
   NOTE: Later, mainly use the xy and yx subfolders. The three sets of file data ('AD' 'MCI' 'NC') are all processed and sorted according to steps 3.1 - 4.5 above.
6. Open RESTplus through MATLAB and left click on Statistical Analysis. Left click on REST Two-Sample T-Test.
7. Name the output result as T1xy and set the output directory. Left click on Add Group Images to open the xy subfolder in the AD Results folder and the xy subfolder in the NC Results folder.
8. In the option of Mask File, left click to open the BrainMask_05_61*73*61 subfile in the 'mask' folder.
9. Select Compute to run the program.
10. Name the output result as T2xy and set the output directory. Left click on Add Group Images to open the xy subfolder in the AD Results folder and the xy subfolder in the MCI Results folder. Repeat steps 4.8 - 4.9.
11. Name the output result as T3xy and set the output directory. Left click on Add Group Images to open the xy subfolder in the MCI Results folder and the xy subfolder in the NC Results folder. Repeat steps 4.8 - 4.9.
12. Name the output result as T1yx and set the output directory. Left click on Add Group Images to open the yx subfolder in the AD Results folder and the yx subfolder in the NC Results folder. Repeat steps 4.8 - 4.9.
13. Name the output result as T2yx and set the output directory. Left click on Add Group Images to open the yx subfolder in the AD Results folder and the yx subfolder in the MCI Results folder. Repeat steps 4.8 - 4.9.
14. Name the output result as T3yx and set the output directory. Left click on Add Group Images to open the yx subfolder in the MCI Results folder and the yx subfolder in the NC Results folder. Repeat steps 4.8 - 4.9.
15. Finally, obtain the six result files by following steps 4.6 - 4.14 and left click on viewer of RESTplus to view the result. Import the template named ch2 in Underlay.
16. Find the six result files in the output directory and fill in the Overlay one by one. Obtain the final result graph, and the six result files that correspond to the six graphs.
17. Use Statistical Product and Service Solutions (SPSS) to process the data obtained from the previous step.
    1. Present Continuous variables as means and Standard Deviations (SD).
    2. Present categorical variables as numbers and percentages, then use the chi-square test.
       NOTE: All p-values of <0.05 were considered statistically significant.

结果

Demographic information

Table 1 presents the characteristics of the subjects. All the subjects had an education level of junior school or above. Age, gender, and education level were similar between the three groups (P >0.05), while the MMSE scores were significantly different (p <0.01).

Directed brain functional connectivity

讨论

本报告介绍了过程比较了定向功能 PCC 给整个大脑，从整个大脑与连接之间广告，PCC MCI 和对照组。此外，这一进程的关键一步是样本的分类和筛选实验前。因此，分类和筛选标准至关重要，因为如果他们是错误的可以影响结果的准确性。在议定书 》 中所列，我们用于 2011 NINCDS ADRDA 诊断标准和 MMSE 和标准的 MCI; 鉴定和分类我们筛选标准也述上述协议。我们排除了那些不适合审判，，然后，准确分...

材料

Name	Company	Catalog Number	Comments
116 patients	Zhejiang Provincial People’s hospital	-	This study was approved by the ethics committee of Zhejiang Provincial People’s hospital. Every enrolled subject signed a written informed consent form.
Siemens Trio 3.0 T MRI scanner	Siemens, Erlangen, Germany	20571	Equipped with AudioComfort that reduces acoustic noise up to 90%; Provides high performance at a low noise level; Ultra light-weight coil; Unique MRI sequence design; Supports up to 400 pounds without restrictions.
RESTplus	Hangzhou Normal University, Hangzhou, Zhejiang, China	20160122	RESTplus evolved from REST (Resting-State fMRI Data Analysis Toolkit), a convenient toolkit to calculate Functional Connectivity (FC), Regional Homogeneity(ReHo), Amplitude of Low-Frequency Fluctuation (ALFF), Fractional ALFF (fALFF), Gragner causality, degree centrality, voxel-mirrored homotopic connectivity (VMHC) and perform statistical analysis.
DPARSF	Hangzhou Normal University, Hangzhou, Zhejiang, China	130615	Data Processing Assistant for Resting-State fMRI (DPARSF) is a convenient plug-in software within DPABI, which is based on SPM. You just need to arrange your DICOM files, and click a few buttons to set parameters, DPARSF will then give all the preprocessed data, functional connectivity, ReHo, ALFF/fALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results.
SPSS	SPSS Inc., Chicago, IL, USA	-	SPSS offers detailed analysis options to look deeper into your data and spot trends that you might not have noticed.