作者感谢宫俊吉对计算机软件支持。这项研究部分受中国国家自然科学基金会 (第 81201156 号，81271517);浙江省的自然科学基金 (no。LY16H180007，LY13H180016，2013C33G1360236)，从浙江省 (号 2013RCA001，201522257)，健康委员会的科学基础。

This study was approved by the Ethics Committee of Zhejiang Provincial People&#39;s Hospital. Every enrolled subject signed a written informed consent.
1. Sample Classification and Screening
<ol>
	<li>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&#39;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</li>
	<li>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.</li>
	<li>Assess all subjects by MMSE10.</li>
	<li>Exclude the subjects who did not fulfill the inclusion criteria. For all subjects, the exclusion criteria are mentioned in Yu et al.10</li>
	<li>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.</li>
</ol>
2. Acquisition of Neuroimaging
<ol>
	<li>Remove metal and magnetic objects before entering the 3.0 T MRI Laboratory.</li>
	<li>Install an MRI receiving coil at the magnetic resonance scanning bed. The receiving coil is an 8 channel circularly polarized brain phased-array coil.</li>
	<li>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&#39;s head on the bracket of the coil, and make sure that the orbitomeatal line is perpendicular to bed.</li>
	<li>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.</li>
	<li>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.</li>
	<li>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 &#176;, 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 &#176;, 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.</li>
	<li>Keep the patient safe when they are leaving the bed at the end of the scan.</li>
</ol>
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).
<ol>
	<li>Open RESTplus through MATLAB and left click on Pipeline.</li>
	<li>Import the relevant files into RESTplus. Select the work directory and then the starting EPI and T1 directories.</li>
	<li>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.</li>
	<li>Remove the first 10 time points by checking off the Remove first n time points and setting the n parameter as 10.</li>
	<li>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.</li>
	<li>Correct the time and head motion. Check off Realign. 
	NOTE: The exclusion criterion for excessive head motion was &#62;2.0 mm translation or &#62;2.0 &#176; rotation in any direction. In the RESTplus this is a default parameter (left click on the option of &#39;Realign&#39;).</li>
	<li>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 &#215; 3 &#215; 3 mm, and other parameters in the RESTplus are default, just left click on the option of &#39;Normalize by using T1 image unified segmentation&#39;.</li>
	<li>Perform spatial smoothing using an isotropic Gaussian kernel with a full-width at half maximum (FWHM) of 6 mm. Check off Smooth.</li>
	<li>Remove the linear trend by checking off Detrend.</li>
	<li>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 &#39;Polynomial trend&#39; as 1 as default, and choose the &#39;6 head motion parameters&#39;, the &#39;Nuisance regressors (WM, CSF, Global)&#39; and the &#39;add mean back&#39; as default.</li>
	<li>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.</li>
</ol>
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.
<ol>
	<li>Perform the voxel-wise GCA by using the REST-GCA in the REST toolbox. In the post-processing box, check off GCA.</li>
	<li>Set the &#39;order&#39; as 1 as default. Select the parameters in the input.</li>
	<li>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.</li>
	<li>Select Run and OK to run the program.</li>
	<li>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 (&#39;AD&#39; &#39;MCI&#39; &#39;NC&#39;) are all processed and sorted according to steps 3.1 - 4.5 above.</li>
	<li>Open RESTplus through MATLAB and left click on Statistical Analysis. Left click on REST Two-Sample T-Test.</li>
	<li>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.</li>
	<li>In the option of Mask File, left click to open the BrainMask_05_61*73*61 subfile in the &#39;mask&#39; folder.</li>
	<li>Select Compute to run the program.</li>
	<li>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.</li>
	<li>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.</li>
	<li>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.</li>
	<li>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.</li>
	<li>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.</li>
	<li>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.</li>
	<li>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.</li>
	<li>Use Statistical Product and Service Solutions (SPSS) to process the data obtained from the previous step.
	<ol>
		<li>Present Continuous variables as means and Standard Deviations (SD).</li>
		<li>Present categorical variables as numbers and percentages, then use the chi-square test. 
		NOTE: All p-values of &#60;0.05 were considered statistically significant.</li>
	</ol>
	</li>
</ol>

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作者宣称，他们并没有任何经济利益的竞争。

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，准确分类的其余的病人，这是为后面的实验根本的病人。关键的一般配合力分析来自定向功能连接 PCC 给整个大脑，从全脑到之间的广告，MCI，PCC 和对照组使用的数据所得影像。我们在本议定书中提出一般配合力分析详细的信息。这种技术发现在定向连接组之间的显著差异，好的解释，AD 进展与功能变化的相关性。
在我们的这项研究的数据分析，差异发生之间个人 （不包括年龄、 性别和教育水平，因为这些是类似之间 3 组，并且因此，很难作出客观的评价的结论）。要解决此问题，我们分析的数据组，而不是单个的数据。每个组中的变量表示为均值和 SD 和连续变量作为数字和百分比，使用卡方检验。通过这种定量的评估，我们可以客观地评价 PCC 和整个大脑区域之间的直接的连接和澄清与大脑中的 AD 进展相关的功能变化。
虽然线性相关性和独立分量分析 (ICA) 已被广泛用于研究的功能连通性，这些结果有没有方向性。不仅是测量功能磁共振成像时间序列的因果效应，也是要显示的动态和方向的 rs-功能磁共振成像14，15日得到的大胆的信号，可以利用一般配合力。
我们分析了 PCC 和 GCA 用 PCC 作为投资回报率和定向连接发现的差异的广告，MCI，全脑网络之间的直接的连接组和对照组。因此，我们得出了 PCC，作为重要枢纽的 DMN 的大脑区域，具有重大的影响对 AD 进展。PCC 不仅可以显示出异常在接收信息，也在传递信息中显示异常。此外，这项研究显示在所有的脑区与异常连接信息的传输是定向的除了个别节点 （左额叶中回和左楔前叶），因为这些脑区的异常是单方面。这项研究的另一个有趣的发现是，这些连接异常似乎主要发生在大脑左半球。这可能是因为优势半球 （左脑） 是比右半球，导致早期的新陈代谢下降和萎缩16更易受损伤。
然而，有一些在我们的研究的限制。GCA 技术，当采样率达到 2 s，不同的血流动力学延迟很难准确地获得17，和加粗的慢动态信号在 2 s 可以导致一些快速的因果关系损失18。因此，这种限制可以导致实验数据的偏差。因为测试样本大小不是够大够高的附加的示例是必须验证结果。
目前，一些研究已用于多元 GCA 技术描述多个脑区之间的因果关系。理论上讲，多元 GCA 是一种改进的技术，可以揭示的脑部; 定向连接的复杂性然而，它具有更多的技术挑战比二元 GCA 血流动力学延迟随区域12。我们未来的目标是要解决多元 GCA 的难题，并将其应用于研究更好地展现复杂的定向连接大脑。

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 AD2，3。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。

<table border="0" cellpadding="0" cellspacing="0" width="909">
	<tbody>
		<tr height="120">
			<td height="120" style="height:120px;width:192px;">116 patients</td>
			<td style="width:169px;">Zhejiang Provincial People&#8217;s hospital</td>
			<td style="width:207px;">-</td>
			<td style="width:341px;">This study was approved by the ethics committee of Zhejiang Provincial People&#8217;s hospital. Every enrolled subject signed a written informed consent form.</td>
		</tr>
		<tr height="153">
			<td height="153" style="height:153px;width:192px;">Siemens Trio 3.0 T MRI scanner</td>
			<td style="width:169px;">Siemens, Erlangen, Germany</td>
			<td style="width:207px;">20571</td>
			<td style="width:341px;">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.</td>
		</tr>
		<tr height="201">
			<td height="201" style="height:201px;width:192px;">RESTplus</td>
			<td style="width:169px;">Hangzhou Normal University,Hangzhou,Zhejiang,China</td>
			<td style="width:207px;">20160122</td>
			<td style="width:341px;">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.</td>
		</tr>
		<tr height="220">
			<td height="220" style="height:220px;width:192px;">DPARSF</td>
			<td style="width:169px;">Hangzhou Normal University,Hangzhou,Zhejiang,China</td>
			<td style="width:207px;">130615</td>
			<td style="width:341px;">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.</td>
		</tr>
		<tr height="111">
			<td height="111" style="height:111px;width:192px;">SPSS</td>
			<td style="width:169px;">SPSS Inc., Chicago, IL, USA</td>
			<td style="width:207px;">-</td>
			<td style="width:341px;">SPSS offers detailed analysis options to look deeper into your data and spot trends that you might not have noticed.</td>
		</tr>
	</tbody>
</table>

application of granger causality analysis of the directed functional connection in alzheimer's disease and mild cognitive impairment

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。

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 &#62;0.05), while the MMSE scores were significantly different (p &#60;0.01).
Directed brain functional connectivity
The active nodes in the whole brain were first identified. Using the GCA technology, the directed functional connectivity from the PCC to the whole brain and from the whole brain to the PCC in the AD, MCI, and control groups were determined (Figure 1, Figure 2; Tables 2, 3).
The directed connectivity from the whole brain to the PCC was enhanced in the AD group as compared to the normal control group, and primarily focused in the bilateral cerebellar region outside the DMN. The directed connectivity from the PCC to the whole brain was significantly reduced in the AD group as compared to the controls, with the main regions, such as the right precuneus and left middle frontal gyrus, belonging to the DMN.
The directed functional connectivity from the PCC to the whole brain in the bilateral precuneus and postcentral gyrus regions showed a significantly enhanced causal effect in the MCI group as compared to the controls. The directed functional connectivity from the whole brain to the PCC was not significantly enhanced in the MCI group compared to the controls; however, it was significantly reduced in the regions outside the DMN including the caudate nucleus, putamen, and parietal lobule.
The directed connections were significantly different between the MCI and AD groups, both from the PCC to the whole brain and from the whole brain to the PCC. Furthermore, these differences were predominantly observed in the left hemisphere (Tables 2, 3), probably indicating asymmetry. The abnormally directed connectivity in the left hemisphere was primarily located in the DMN. With regard to the differences in the directed connections between the MCI and AD groups, more left hemisphere regions (with a significant causal effect) were involved in the connectivity from the PCC to the whole brain than the regions involved in the connectivity from the whole brain to the PCC.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Items</td>
			<td>MCI</td>
			<td>AD</td>
			<td>NC</td>
			<td>X2 or F</td>
			<td>P-value</td>
		</tr>
		<tr>
			<td>Enrolled cases (n)</td>
			<td>26</td>
			<td>32</td>
			<td>58</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>Gender (male/female)</td>
			<td>10/16</td>
			<td>13/19</td>
			<td>23/35</td>
			<td>0.028a</td>
			<td>0.986</td>
		</tr>
		<tr>
			<td>Age (years)</td>
			<td>71.64&#177;6.03</td>
			<td>71.62&#177;5.85</td>
			<td>69.87&#177;6.04</td>
			<td>1.25b</td>
			<td>0.290</td>
		</tr>
		<tr>
			<td>Years of education (years)</td>
			<td>9.82&#177;2.03</td>
			<td>9.63&#177;1.96</td>
			<td>9.76&#177;1.87</td>
			<td>0.08b</td>
			<td>0.926</td>
		</tr>
		<tr>
			<td>MMSE score (points)</td>
			<td>24.32&#177;1.74</td>
			<td>18.58&#177;1.86</td>
			<td>29.0&#177;1.58</td>
			<td>392.31b</td>
			<td>&#60;0.001</td>
		</tr>
		<tr>
			<td colspan="6">MMSE: Mini-Mental State Examination, NC: Normal Control. a: X2 value; b: F value.</td>
		</tr>
	</tbody>
</table>
Table 1: Characteristics of the MCI, AD, and Control Groups. 
The characteristics of the subjects: age, gender, and education level were similar between the 3 groups, while the MMSE scores were significantly different.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Brain region</td>
			<td>MNI space</td>
			<td>Brodmann</td>
			<td>Peak T</td>
			<td rowspan="2">Cluster size (voxels)</td>
		</tr>
		<tr>
			<td>(X Y Z)</td>
			<td>area</td>
			<td>score</td>
		</tr>
		<tr>
			<td>AD vs. NC</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Left middle frontal gyrus</td>
			<td>-30,45,0</td>
			<td>10/11</td>
			<td>-7.86</td>
			<td>204</td>
		</tr>
		<tr>
			<td>Right inferior frontal gyrus</td>
			<td>57,39,6</td>
			<td>46</td>
			<td>6.65</td>
			<td>93</td>
		</tr>
		<tr>
			<td>Right precuneus</td>
			<td>3,-54,45</td>
			<td>7/31</td>
			<td>-7.3</td>
			<td>223</td>
		</tr>
		<tr>
			<td>MCI vs. NC</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Right cerebellum</td>
			<td>30,-63,-48</td>
			<td>NaN</td>
			<td>-8.52</td>
			<td>92</td>
		</tr>
		<tr>
			<td>Right superior frontal gyrus</td>
			<td>30,63,9</td>
			<td>10/46</td>
			<td>-6.8</td>
			<td>86</td>
		</tr>
		<tr>
			<td>Left precuneus/postcentral</td>
			<td>-15,-36,78</td>
			<td>3/7</td>
			<td>6.38</td>
			<td>82</td>
		</tr>
		<tr>
			<td>Right precuneus/postcentral</td>
			<td>27,-27,72</td>
			<td>4/3</td>
			<td>7.03</td>
			<td>81</td>
		</tr>
		<tr>
			<td>AD vs.&#160;MCI</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Left cerebellum</td>
			<td>-30,-39,-42</td>
			<td>NaN</td>
			<td>8.55</td>
			<td>136</td>
		</tr>
		<tr>
			<td>Left middle temporal</td>
			<td>-33,-15,-27</td>
			<td>20/21</td>
			<td>8.09</td>
			<td>216</td>
		</tr>
		<tr>
			<td>Left caudate/lentiform nucleus</td>
			<td>-9,15,6</td>
			<td>NaN</td>
			<td>-7.02</td>
			<td>110</td>
		</tr>
		<tr>
			<td>Right precuneus/calcarine</td>
			<td>15,-75,15</td>
			<td>18/31</td>
			<td>-8.03</td>
			<td>127</td>
		</tr>
		<tr>
			<td>Left precuneus</td>
			<td>-12,-57,27</td>
			<td>31</td>
			<td>7.32</td>
			<td>127</td>
		</tr>
		<tr>
			<td>Left middle frontal gyrus</td>
			<td>-30,12,63</td>
			<td>6/8</td>
			<td>8.23</td>
			<td>86</td>
		</tr>
		<tr>
			<td>Left precentral</td>
			<td>-21,-36,72</td>
			<td>3/7</td>
			<td>-8.18</td>
			<td>87</td>
		</tr>
		<tr>
			<td colspan="5"></td>
		</tr>
	</tbody>
</table>
Table 2: PCC to the Whole Brain. 
(a) The directed connection from the PCC to the whole brain: the directed connections were significantly different between the MCI and AD groups, from the PCC to the whole brain. (b) These differences were predominantly observed in the left hemisphere, possibly indicating asymmetry.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Brain region</td>
			<td>MNI space</td>
			<td rowspan="2">Brodmann area</td>
			<td>Peak Z</td>
			<td rowspan="2">Cluster size (voxels)</td>
		</tr>
		<tr>
			<td>(X Y Z)</td>
			<td>score</td>
		</tr>
		<tr>
			<td>AD vs.&#160;NC</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Right cerebellum</td>
			<td>51,-75,-27</td>
			<td>NaN</td>
			<td>7.03</td>
			<td>88</td>
		</tr>
		<tr>
			<td>Left cerebellum</td>
			<td>-24,-87,-30</td>
			<td>NaN</td>
			<td>10</td>
			<td>138</td>
		</tr>
		<tr>
			<td>Left middle frontal gyrus</td>
			<td>-30,45,0</td>
			<td>NaN</td>
			<td>8.3</td>
			<td>261</td>
		</tr>
		<tr>
			<td>Left angular convolution</td>
			<td>-39,-63,36</td>
			<td>39/40</td>
			<td>-6.69</td>
			<td>98</td>
		</tr>
		<tr>
			<td>MCI vs.&#160;NC</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Left putamen</td>
			<td>-24,6,-6</td>
			<td>NaN</td>
			<td>-7.69</td>
			<td>77</td>
		</tr>
		<tr>
			<td>Left caudate nucleus</td>
			<td>12,9,6</td>
			<td>NaN</td>
			<td>-6.42</td>
			<td>122</td>
		</tr>
		<tr>
			<td>Left superior parietal lobule</td>
			<td>-33,-63,51</td>
			<td>7</td>
			<td>-9.31</td>
			<td>154</td>
		</tr>
		<tr>
			<td>AD vs.&#160;MCI</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Left putamen</td>
			<td>-27,9,-9</td>
			<td>NaN</td>
			<td>4.2</td>
			<td>85</td>
		</tr>
		<tr>
			<td>Right putamen</td>
			<td>21,12,-6</td>
			<td>NaN</td>
			<td>4.93</td>
			<td>100</td>
		</tr>
		<tr>
			<td>Left precuneus</td>
			<td>-18,-63,27</td>
			<td>31</td>
			<td>-4.12</td>
			<td>41</td>
		</tr>
		<tr>
			<td>Left superior parietal lobule</td>
			<td>-21,-60,48</td>
			<td>7</td>
			<td>4.7</td>
			<td>42</td>
		</tr>
		<tr>
			<td colspan="5"></td>
		</tr>
	</tbody>
</table>
Table 3: Whole Brain to the PCC. 
(a) The directed connection from PCC to the whole brain: the directed connections were significantly different between the MCI and AD groups, from the whole brain to the PCC. (b) These differences were predominantly observed in the left hemisphere, possibly indicating asymmetry.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56015/56015fig1.jpg" /> 
Figure 1: PCC to the Whole Brain. 
The directed connection from the PCC to the whole brain. The active nodes in the whole brain were first identified, and the directed functional connectivity from the PCC to the whole brain in AD, MCI, and control groups was determined. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56015/56015fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56015/56015fig2.jpg" /> 
Figure 2: Whole Brain to the PCC. 
The directed connection from whole brain to the PCC. The active nodes in the whole brain were first identified, and the directed functional connectivity from the whole brain to the PCC in AD, MCI, and control groups was determined. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56015/56015fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment at JoVE.com

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

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

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。

Impaired functional connectivity in the Default Mode Network (DMN) may be involved in the progression of Alzheimer&#39;s Disease (AD). The Posterior ...

application-granger-causality-analysis-directed-functional-connection

Nanyang Technological University

Research

JoVE Journal

Medicine

7.8K 次观看.  Zhejiang Chinese Medical University. 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。

视频: Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

An Investigation of the Effects of Sports-related Concussion in Youth Using Functional Magnetic Resonance Imaging and the Head Impact Telemetry System

One of the most commonly reported injuries in children who participate in sports is concussion or mild traumatic brain injury (mTBI)1. Children and youth involved in organized sports such as competitive hockey are nearly six times more likely to suffer a severe concussion compared to children involved in other leisure physical activities2. While the most common cognitive sequelae of mTBI appear similar for children and adults, the recovery profile and breadth of consequences in children remains largely unknown2, as does the influence of pre-injury characteristics (e.g. gender) and injury details (e.g. magnitude and direction of impact) on long-term outcomes. Competitive sports, such as hockey, allow the rare opportunity to utilize a pre-post design to obtain pre-injury data before concussion occurs on youth characteristics and functioning and to relate this to outcome following injury. Our primary goals are to refine pediatric concussion diagnosis and management based on research evidence that is specific to children and youth. To do this we use new, multi-modal and integrative approaches that will:
 1.Evaluate the immediate effects of head trauma in youth 
2.Monitor the resolution of post-concussion symptoms (PCS) and cognitive performance during recovery 
3.Utilize new methods to verify brain injury and recovery
To achieve our goals, we have implemented the Head Impact Telemetry (HIT) System. (Simbex; Lebanon, NH, USA). This system equips commercially available Easton S9 hockey helmets (Easton-Bell Sports; Van Nuys, CA, USA) with single-axis accelerometers designed to measure real-time head accelerations during contact sport participation 3 - 5. By using telemetric technology, the magnitude of acceleration and location of all head impacts during sport participation can be objectively detected and recorded. We also use functional magnetic resonance imaging (fMRI) to localize and assess changes in neural activity specifically in the medial temporal and frontal lobes during the performance of cognitive tasks, since those are the cerebral regions most sensitive to concussive head injury 6. Finally, we are acquiring structural imaging data sensitive to damage in brain white matter.

This article provides an overview of a multi-modal approach to mild traumatic brain injury diagnosis and recovery in youth. This approach combines neuropsychological testing with functional magnetic resonance imaging and the Head Impact Telemetry System to monitor the relationship between head impacts and brain activity during cognitive testing.

在使用功能性磁共振成像和头部撞击遥测系统的青年与体育有关的震荡影响的调查

One of the most commonly reported injuries in children who participate in sports is concussion or mild traumatic brain injury (mTBI)1. Children and youth ...

科研

医学

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease

Our laboratory uses event-related EEG potentials (ERPs) to understand and support behavioral investigations of episodic memory in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Whereas behavioral data inform us about the patients' performance, ERPs allow us to record discrete changes in brain activity. Further, ERPs can give us insight into the onset, duration, and interaction of independent cognitive processes associated with memory retrieval. In patient populations, these types of studies are used to examine which aspects of memory are impaired and which remain relatively intact compared to a control population. The methodology for collecting ERP data from a vulnerable patient population while these participants perform a recognition memory task is reviewed. This protocol includes participant preparation, quality assurance, data acquisition, and data analysis. In addition to basic setup and acquisition, we will also demonstrate localization techniques to obtain greater spatial resolution and source localization using high-density (128 channel) electrode arrays.

The methodology for collecting high-density event-related potential data while patients with Alzheimer's disease perform a recognition memory task is reviewed. This protocol will include subject preparation, quality assurance, data acquisition, and data analysis.

情节记忆的特点，采用事件相关电位研究在阿尔茨海默氏症患者

Our laboratory uses event-related EEG potentials (ERPs) to understand and support behavioral investigations of episodic memory in patients with amnestic ...

The Use of Primary Human Fibroblasts for Monitoring Mitochondrial Phenotypes in the Field of Parkinson's Disease

Parkinson's disease (PD) is the second most common movement disorder and affects 1% of people over the age of 60 1. Because ageing is the most important risk factor, cases of PD will increase during the next decades 2. Next to pathological protein folding and impaired protein degradation pathways, alterations of mitochondrial function and morphology were pointed out as further hallmark of neurodegeneration in PD 3-11.
After years of research in murine and human cancer cells as in vitro models to dissect molecular pathways of Parkinsonism, the use of human fibroblasts from patients and appropriate controls as ex vivo models has become a valuable research tool, if potential caveats are considered. Other than immortalized, rather artificial cell models, primary fibroblasts from patients carrying disease-associated mutations apparently reflect important pathological features of the human disease.
Here we delineate the procedure of taking skin biopsies, culturing human fibroblasts and using detailed protocols for essential microscopic techniques to define mitochondrial phenotypes. These were used to investigate different features associated with PD that are relevant to mitochondrial function and dynamics. Ex vivo, mitochondria can be analyzed in terms of their function, morphology, colocalization with lysosomes (the organelles degrading dysfunctional mitochondria) and degradation via the lysosomal pathway. These phenotypes are highly relevant for the identification of early signs of PD and may precede clinical motor symptoms in human disease-gene carriers. Hence, the assays presented here can be utilized as valuable tools to identify pathological features of neurodegeneration and help to define new therapeutic strategies in PD.

Fibroblasts from patients carrying mutations in Parkinson's disease-causing genes represent an easily accessible ex vivo model to study disease-associated phenotypes. Live cell imaging gives the opportunity to study morphological and functional parameters in living cells. Here we describe the preparation of human fibroblasts and subsequent monitoring of mitochondrial phenotypes.

利用人类纤维原细胞线粒体表型的监测领域的帕金森氏病

Parkinson's disease (PD) is the second most  common movement disorder and affects 1% of people over the age of 60 1. Because ageing is  the most important ...

Quantitative Analysis of Chromatin Proteomes in Disease

In the nucleus reside the proteomes whose functions are most intimately linked with gene regulation. Adult mammalian cardiomyocyte nuclei are unique due to the high percentage of binucleated cells,1 the predominantly heterochromatic state of the DNA, and the non-dividing nature of the cardiomyocyte which renders adult nuclei in a permanent state of interphase.2 Transcriptional regulation during development and disease have been well studied in this organ,3-5 but what remains relatively unexplored is the role played by the nuclear proteins responsible for DNA packaging and expression, and how these proteins control changes in transcriptional programs that occur during disease.6 In the developed world, heart disease is the number one cause of mortality for both men and women.7 Insight on how nuclear proteins cooperate to regulate the progression of this disease is critical for advancing the current treatment options.
Mass spectrometry is the ideal tool for addressing these questions as it allows for an unbiased annotation of the nuclear proteome and relative quantification for how the abundance of these proteins changes with disease. While there have been several proteomic studies for mammalian nuclear protein complexes,8-13 until recently14 there has been only one study examining the cardiac nuclear proteome, and it considered the entire nucleus, rather than exploring the proteome at the level of nuclear sub compartments.15 In large part, this shortage of work is due to the difficulty of isolating cardiac nuclei. Cardiac nuclei occur within a rigid and dense actin-myosin apparatus to which they are connected via multiple extensions from the endoplasmic reticulum, to the extent that myocyte contraction alters their overall shape.16 Additionally, cardiomyocytes are 40% mitochondria by volume17 which necessitates enrichment of the nucleus apart from the other organelles. Here we describe a protocol for cardiac nuclear enrichment and further fractionation into biologically-relevant compartments. Furthermore, we detail methods for label-free quantitative mass spectrometric dissection of these fractions-techniques amenable to in vivo experimentation in various animal models and organ systems where metabolic labeling is not feasible.

Advances in mass spectrometry have allowed the high throughput analysis of protein expression and modification in a host of tissues. Combined with subcellular fractionation and disease models, quantitative mass spectrometry and bioinformatics can reveal new properties in biological systems. The method described herein analyzes chromatin-associated proteins in the setting of heart disease and is readily applicable to other in vivo models of human disease.

在疾病的染色质蛋白质组的定量分析

In the nucleus reside the proteomes whose functions are most intimately linked with gene regulation. Adult mammalian cardiomyocyte nuclei are unique due ...

Collecting Saliva and Measuring Salivary Cortisol and Alpha-amylase in Frail Community Residing Older Adults via Family Caregivers

Salivary measures have emerged in bio-behavioral research that are easy-to-collect, minimally invasive, and relatively inexpensive biologic markers of stress. This article we present the steps for collection and analysis of two salivary assays in research with frail, community residing older adults-salivary cortisol and salivary alpha amylase. The field of salivary bioscience is rapidly advancing and the purpose of this presentation is to provide an update on the developments for investigators interested in integrating these measures into research on aging. Strategies are presented for instructing family caregivers in collecting saliva in the home, and for conducting laboratory analyses of salivary analytes that have demonstrated feasibility, high compliance, and yield quality specimens. The protocol for sample collection includes: (1) consistent use of collection materials; (2) standardized methods that promote adherence and minimize subject burden; and (3) procedures for controlling certain confounding agents. We also provide strategies for laboratory analyses include: (1) saliva handling and processing; (2) salivary cortisol and salivary alpha amylase assay procedures; and (3) analytic considerations.

We demonstrate: (1) procedures for collection of salivary samples in cognitive impaired older adults by family caregivers in the home setting, (2) procedures for measuring stress activity via salivary cortisol and alpha amylase, and (3) representative profiles. Protocols that allow researchers to study stress-linked processes advance our understanding of biological sensitivity and susceptibility.

Salivary measures have emerged in bio-behavioral research  that are easy-to-collect, minimally invasive, and relatively inexpensive  biologic markers of ...

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs.
LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors.
While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures.

Lesion Explorer (LE) is a semi-automatic, image-processing pipeline developed to obtain regional brain tissue and subcortical hyperintensity lesion volumetrics from structural MRI of Alzheimer's disease and normal elderly. To ensure a high level of accuracy and reliability, the following is a video-guided, standardized protocol for LE's manual procedures.

病变资源管理器:一个视频引导，标准化为老年痴呆症准确，可靠的MRI衍生Volumetrics和正常老年人协议

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are ...

Quantitative 3D In Silico Modeling (q3DISM) of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease

Neuroinflammation is now recognized as a major etiological factor in neurodegenerative disease. Mononuclear phagocytes are innate immune cells responsible for phagocytosis and clearance of debris and detritus. These cells include CNS-resident macrophages known as microglia, and mononuclear phagocytes infiltrating from the periphery. Light microscopy has generally been used to visualize phagocytosis in rodent or human brain specimens. However, qualitative methods have not provided definitive evidence of in vivo phagocytosis. Here, we describe quantitative 3D in silico modeling (q3DISM), a robust method allowing for true 3D quantitation of amyloid-&#946; (A&#946;) phagocytosis by mononuclear phagocytes in rodent Alzheimer&#39;s Disease (AD) models. The method involves fluorescently visualizing A&#946; encapsulated within phagolysosomes in rodent brain sections. Large z-dimensional confocal datasets are then 3D reconstructed for quantitation of A&#946; spatially colocalized within the phagolysosome. We demonstrate the successful application of q3DISM to mouse and rat brains, but this methodology can be extended to virtually any phagocytic event in any tissue.

Quantitative 3D In Silico Modeling q3DISM of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease

We developed a methodology for quantitative 3D in silico modeling (q3DISM) of cerebral amyloid-&#946; (A&#946;) phagocytosis by mononuclear phagocytes in rodent models of Alzheimer&#39;s disease. This method can be generalized for the quantitation of virtually any phagocytic event in vivo.

定量3D<em&gt;在硅片</em&gt;建模阿尔茨海默病的啮齿动物模型脑淀粉样蛋白-β吞噬（q3DISM）

Neuroinflammation is now recognized as a major etiological factor in neurodegenerative disease. Mononuclear phagocytes are innate immune cells responsible ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy begins many years prior to the motor onset (during the &#34;premanifest&#34; period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, &#34;MRICloud&#34;, which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the &#34;change-point&#34;, with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

This paper describes a statistical model for volumetric MRI data analysis, which identifies the &#34;change-point&#34; when brain atrophy begins in premanifest Huntington&#39;s disease. Whole-brain mapping of the change-points is achieved based on brain volumes obtained using an atlas-based segmentation pipeline of T1-weighted images.

解剖性脑 MRI 的全脑分割及变化点分析--Premanifest 亨廷顿病的应用

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

Multi-Modal Home Sleep Monitoring in Older Adults

The gold standard for sleep monitoring is attended in-lab polysomnography; however, this method may be cost-prohibitive and inconvenient for patients and research participants. Home sleep testing has gained momentum in the field of sleep medicine due to its convenience and lower cost, as well as being more naturalistic. The accuracy and quality of home sleep testing, however, may be variable because studies are not monitored by sleep technologists. There has been some success in improving the accuracy of home sleep studies by having trained sleep technicians assist participants inside their homes with putting on the devices, but this can be intrusive and time-consuming for those involved. In this protocol, participants undergo at-home sleep monitoring with multiple devices: 1) a single-channel EEG device; 2) a home sleep test for sleep-disordered breathing and periodic limb movements; 3) actigraphy; and 4) sleep logs. A major challenge of this study is obtaining high-quality sleep monitoring data on the first attempt in order to minimize participant burden. This protocol describes the implementation of educational manuals with step-by-step instructions and photos. The goal is to improve the quality of home sleep testing.

Here, we present a protocol to improve the quality of data from home sleep testing by providing a method to enhance instructions through a structured participant visit. This protocol includes the implementation of a step-by-step educational manual with photos to ensure proper placement of equipment.

老年人多式家庭睡眠监测

The gold standard for sleep monitoring is attended in-lab polysomnography; however, this method may be cost-prohibitive and inconvenient for patients and ...

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

An increasing amount of evidence shows that cognitive deficits and movement dysfunctions are not separated. Patients with mild cognitive impairment (MCI) can manifest fine motor disorders of the upper extremities. Handwriting is a complex and unique human activity involving both motor and cognitive coordination. Researchers from western countries have discovered that patients with MCI have abnormal handwriting features. However, no relevant studies have been conducted in the Chinese population. Owing to the cross-culture phenomenon of handwriting, the aim of this study is to find new handwriting tasks to demonstrate the differences in handwriting features between elderly patients with MCI and age-matched healthy individuals.

This study proposed a digital handwriting analysis of characters in individuals with mild cognitive impairment to find more information than is revealed by traditional pencil&#8211;paper handwriting analysis.

中国轻度认知障碍患者人物数字笔迹分析

An increasing amount of evidence shows that cognitive deficits and movement dysfunctions are not separated. Patients with mild cognitive impairment (MCI) ...