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><tbody><tr><td>116 patients</td><td>Zhejiang Provincial People&amp;rsquo;s hospital</td><td>-</td><td>This study was approved by the ethics committee of Zhejiang Provincial People&amp;rsquo;s hospital. Every enrolled subject signed a written informed consent form.</td></tr><tr><td>Siemens Trio 3.0 T MRI scanner</td><td>Siemens, Erlangen, Germany</td><td>20571</td><td>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><td>RESTplus</td><td>Hangzhou Normal University, Hangzhou, Zhejiang, China</td><td>20160122</td><td>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><td>DPARSF</td><td>Hangzhou Normal University, Hangzhou, Zhejiang, China</td><td>130615</td><td>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><td>SPSS</td><td>SPSS Inc., Chicago, IL, USA</td><td>-</td><td>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

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional Change Card Sort task, participants switch from sorting cards one way, such as shape, to sorting them a different way, such as color. Adjusting behavior in this way exacts a small performance cost, or switch cost, such that responses are typically slower and more error-prone on switch trials in which the sorting rule changes as compared to repeat trials in which the sorting rule remains the same. The ability to flexibly adjust behavior is often said to develop gradually, in part because behavioral costs such as switch costs typically decrease with increasing age. Why aspects of higher-order cognition, such as behavioral flexibility, develop so gradually remains an open question. One hypothesis is that these changes occur in association with functional changes in broad-scale cognitive control networks. On this view, complex mental operations, such as switching, involve rapid interactions between several distributed brain regions, including those that update and maintain task rules, re-orient attention, and select behaviors. With development, functional connections between these regions strengthen, leading to faster and more efficient switching operations. The current video describes a method of testing this hypothesis through the collection and multivariate analysis of fMRI data from participants of different ages.

This video presents a method of examining age-related changes in functional connectivity of cognitive control networks engaged by targeted tasks/processes. The technique is based on multi-variate analysis of fMRI data.

针对调查中的认知控制网络与关联维度变化卡片分类性能的功能连通性年龄相关性差异的方法

The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional ...

科研

行为学

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

鉴定的使用神经影像学数据的与疾病-相关的的空间协方差模式

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

医学

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 ...

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.

The Default Mode Network (DMN) in Temporal Lobe Epilepsy (TLE) is analyzed in the resting state of the brain using seed-based functional connectivity MRI (fcMRI).

使用功能性磁共振成像连接在颞叶癫痫默认模式网络的网络分析

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal ...

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

In neuroimaging, functional magnetic resonance imaging (fMRI) measures the blood-oxygenation-level dependent (BOLD) signal in the brain. The degree of correlation of the BOLD signal in spatially independent regions of the brain defines the functional connectivity of those regions. During a cognitive fMRI task, a psychophysiological interaction (PPI) analysis can be used to examine changes in the functional connectivity during specific contexts defined by the cognitive task. An example of such a task is one that engages the memory system, asking participants to learn pairs of unrelated words (encoding) and recall the second word in a pair when presented with the first word (retrieval). In the present study, we used this type of associative memory task and a generalized PPI (gPPI) analysis to compare changes in hippocampal connectivity in older adults who are carriers of the Alzheimer's disease (AD) genetic risk factor apolipoprotein-E epsilon-4 (APOE&#949;4). Specifically, we show that the functional connectivity of subregions of the hippocampus changes during encoding and retrieval, the two active phases of the associative memory task. Context-dependent changes in functional connectivity of the hippocampus were significantly different in carriers of APOE&#949;4 compared to non-carriers. PPI analyses make it possible to examine changes in functional connectivity, distinct from univariate main effects, and to compare these changes across groups. Thus, a PPI analysis may reveal complex task effects in specific cohorts that traditional univariate methods do not capture. PPI analyses cannot, however, determine directionality or causality between functionally connected regions. Nevertheless, PPI analyses provide powerful means for generating specific hypotheses regarding functional relationships, which can be tested using causal models. As the brain is increasingly described in terms of connectivity and networks, PPI is an important method for analyzing fMRI task data that is in line with the current conception of the human brain.

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

This manuscript describes how to implement a psychophysiological interaction analysis to reveal task-dependent changes in functional connectivity between a selected seed region and voxels in other regions of the brain. Psychophysiological interaction analysis is a popular method to examine task effects on brain connectivity, distinct from traditional univariate activation effects.

广义心理相互作用 (PPI) 分析阿尔茨海默病遗传危险个体的记忆相关连通性

In neuroimaging, functional magnetic resonance imaging (fMRI) measures the blood-oxygenation-level dependent (BOLD) signal in the brain. The degree of ...

神经科学

Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice

This article demonstrates complete, detailed procedures for both in vivo bilateral recording and analysis of local field potential (LFP) in the cortical areas of mice, which are useful for evaluating possible laterality deficits, as well as for assessing brain connectivity and coupling of neural network activities in rodents. The pathological mechanisms underlying Alzheimer&#39;s disease (AD), a common neurodegenerative disease, remain largely unknown. Altered brain laterality has been demonstrated in aging people, but whether or not abnormal lateralization is one of the early signs of AD has not been determined. To investigate this, we recorded bilateral LFPs in 3-5-month-old AD model mice, APP/PS1, together with littermate wild type (WT) controls. The LFPs of the left and right secondary motor cortex (M2), specifically in the gamma band, were more synchronized in APP/PS1 mice than in WT controls, suggesting a declined hemispheric asymmetry of bilateral M2 in this AD mouse model. Notably, the recording and data analysis processes are flexible and easy to carry out, and can also be applied to other brain pathways when conducting experiments that focus on neuronal circuits.

We present in vivo electrophysiological recording of the local field potential (LFP) in bilateral secondary motor cortex (M2) of mice, which can be applied to evaluate hemisphere lateralization. The study revealed altered levels of synchronization between the left and right M2 in APP/PS1 mice compared to WT controls.

小鼠二次运动皮质中双边局部场电位记录半球横向化评价

This article demonstrates complete, detailed procedures for both in vivo bilateral recording and analysis of local field potential (LFP) in the cortical ...

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Cognitive impairment is a common phenomenon in Parkinson&#8217;s disease that has implications on the prognosis. A simple, noninvasive and objective proxy measurement of cognitive function in Parkinson&#8217;s disease will be helpful in detecting early cognitive decline. As a physiological metric, eye movement parameter is not confounded by the subject&#39;s attributes and intelligence and can function as a proxy marker if it correlates with cognitive functions. To this end, this study explored the relationship between the eye movement parameters and performance in cognitive tests in multiple domains. In the experiment, a visual search task with eye tracking was set up, where subjects were asked to look for a number embedded in an array of alphabets scattered randomly on a computer screen. The differentiation between the number and the alphabet is an overlearned task such that the confounding effect of cognitive ability on the eye movement parameters is minimized. The average saccadic amplitude and fixation duration were captured and calculated during the visual search task. The cognitive assessment battery covered domains of frontal-executive functions, attention, verbal and visual memory. It was found that prolonged fixation duration was associated with poorer performance in verbal fluency, visual and verbal memory, allowing further exploration on the use of eye movement parameters as proxy markers for cognitive function in Parkinson&#8217;s disease patients. The experimental paradigm has been found to be highly tolerable in our group of Parkinson&#39;s disease patients and could be applied transdiagnostically to other disease entities for similar research questions.

Here, we present a protocol to study the relationship between the eye movement parameters and cognitive functions in non-demented Parkinson&#39;s disease patients. The experiment used an eye tracker to measure the saccadic amplitude and fixation duration in a visual search task. The correlation with performance in multi-domain cognitive tasks was subsequently measured.

描述眼动参数与无视力帕金森病患者眼动功能的关系

Cognitive impairment is a common phenomenon in Parkinson&#8217;s disease that has implications on the prognosis. A simple, noninvasive and objective proxy ...

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

General clinical cognitive assessment scales are not sensitive enough to cognitive impairment in high-functioning stroke patients. The dual-task assessment has advantages for identifying cognitive deficits in high-functioning stroke patients and has been gradually applied in clinical assessment and cognitive training. Moreover, the Stroop paradigm has higher sensitivity and specificity for attentional assessment than conventional clinical cognitive assessment scales. Therefore, this study presents the dual-task assessment based on the Stroop paradigm to identify cognitive deficits in high-functioning stroke patients. This study demonstrates a single- and dual-task evaluation based on the Stroop paradigm and confirms its feasibility through case experiments and synchronized functional near-infrared spectroscopy evaluation. The Stroop reaction time and correct rate are used as the main indicators to evaluate the cognitive level of the subjects. This study protocol aims to provide new ideas to figure out the ceiling effect in general clinical assessment failure for high-functioning stroke patients.

Clinical assessment scales are notsensitive enough to cognitive dysfunction in high-functioning stroke patients. The dual-task paradigm presents advantages and potential in the assessment and cognitive training of cognitive dysfunction. The study here proposes a dual-task Stroop paradigm to identify cognitive dysfunction in high-functioning stroke patients.

检测高功能卒中患者认知缺陷的双任务斯特鲁普范式

General clinical cognitive assessment scales are not sensitive enough to cognitive impairment in high-functioning stroke patients. The dual-task ...

革新 fNIRS 研究：一种神经耦合的新方法

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals using wavelet transform coherence (WTC) seems to ignore the directionality of the interaction. The field is currently lacking a framework that allows researchers to determine whether a high coherence value obtained using a WTC function reflects in-phase synchronization (i.e., neural activation is seen in both members of the dyad at the same time), lagged synchronization (i.e., neural activation is seen in one member of the dyad prior to the other member), or anti-phase synchronization (i.e., neural activation is increased in one member of the dyad and decreased in the other). To address this need, a complementary and more sensitive approach for analyzing the phase coherence of two neural signals is proposed in this work. The toolbox allows investigators to estimate the coupling directionality by classifying the phase angle values obtained using traditional WTC into in-phase synchronization, lagged synchronization, and anti-phase synchronization. The toolbox also allows researchers to assess how the dynamics of interactions develop and change throughout the task. Using this novel WTC approach and the toolbox will advance our understanding of complex social interactions through their uses in fNIRS hyperscanning studies.

作者聚焦：解锁 fNIRS 研究的新见解 - 脑间同步分析的新框架 

Wavelet transform coherence (WTC) is a common methodology for assessing the coupling between signals that is used in functional near-infrared spectroscopy (fNIRS) hyperscanning studies. A toolbox for assessing the directionality of the signal interaction is presented in this work.

理解功能性近红外光谱 （fNIRS） 超扫描研究中跨脑相干性的新框架

Despite the growing body of functional near-infrared spectroscopy (fNIRS) hyperscanning studies, the assessment of coupling between two neural signals ...

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

探索更多视频

此视频中的章节

针对调查中的认知控制网络与关联维度变化卡片分类性能的功能连通性年龄相关性差异的方法

鉴定的使用神经影像学数据的与疾病-相关的的空间协方差模式

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

使用功能性磁共振成像连接在颞叶癫痫默认模式网络的网络分析

广义心理相互作用 (PPI) 分析阿尔茨海默病遗传危险个体的记忆相关连通性

小鼠二次运动皮质中双边局部场电位记录半球横向化评价

描述眼动参数与无视力帕金森病患者眼动功能的关系

检测高功能卒中患者认知缺陷的双任务斯特鲁普范式

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架

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

探索更多视频

此视频中的章节

针对调查中的认知控制网络与关联维度变化卡片分类性能的功能连通性年龄相关性差异的方法

鉴定的使用神经影像学数据的与疾病-相关的的空间协方差模式

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

使用功能性磁共振成像连接在颞叶癫痫默认模式网络的网络分析

广义心理相互作用 (PPI) 分析阿尔茨海默病遗传危险个体的记忆相关连通性

小鼠二次运动皮质中双边局部场电位记录半球横向化评价

描述眼动参数与无视力帕金森病患者眼动功能的关系

检测高功能卒中患者认知缺陷的双任务斯特鲁普范式

理解功能性近红外光谱 （fNIRS） 超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱 （fNIRS） 超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架

理解功能性近红外光谱（fNIRS）超扫描研究中跨脑相干性的新框架