Name: application of granger causality analysis of the directed functional connection in alzheimer's disease and mild cognitive impairment
Uploaded: 2017-08-07T15:00:00
Description: 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

The authors thank Gongjun JI for computer software support. This research was partially supported by the National Natural Science Foundation of China (no. 81201156, 81271517); the Zhejiang Provincial Natural Science Foundation of China (no. LY16H180007, LY13H180016, 2013C33G1360236), and the Science Foundation from the Health Commission of Zhejiang Province (no. 2013RCA001, 201522257).

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...

<ol>
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This report presents a process for comparing the directed functional connectivity from the PCC to the whole brain and from the whole brain to the PCC between AD, MCI and control groups. Moreover, a key step in this process is the classification and screening of sample before the experiment. Thus, the classification and screening criteria are crucial because the accuracy of the results can be affected if they are erroneous. As listed in the protocol, we used 2011 NINCDS-ADRDA diagnostic criteria and MMSE, and the criteria...

AD is a degenerative disease of the central nervous system that can be diagnosed using histopathology, electrophysiology, and neuroimaging1. The memory-related DMN is a vital system of the interacting brain regions associated with AD, and its abnormal function is characteristic of AD2,3. The PCC is an important region of the traditional default network in the resting state and plays pivotal roles in episodic memory, spatial attention, self-evaluation, and other cognitive functions4,5,6,7. In addition, it might be an imaging marker for monitoring AD progression. Using GCA, Liao et al. found that the PCC is a region of multiple cytoarchitectonics with multiple connections and plays an important role in functional brain structure8. Zhong et al. reported that the PCC was a convergence center that received interactions from most of the other regions within the DMN3. Furthermore, Miao et al. demonstrated that in the DMN hub regions, the PCC has the greatest causal effect relationship with other nodes9. Together, all this evidence indicates thatthe directed connection of the PCC is valuable in AD research and the PCC needs to be further studied in-depth as a vital region of the DMN.
The previous studies were confined to the connectivity between the PCC and other regions within the DMN; however, the changes in directed functional connectivity between the PCC and brain regions outside the DMN, as well as their influence on AD have not yet been explored10. Our study further investigated this unexplored functional connectivity in normal healthy controls, patients with MCI, and patients with AD. By observing the directed connectivity between the PCC and whole brain regions, we aimed to elucidate the functional changes in the brain related to AD progression, and thereby establish a novel objective basis for assessing the severity of the disease.
Functional connectivity refers to an interregional interaction that can be represented by synchronous Low Frequency Fluctuations (LFFs) in the cerebral Blood Oxygen Level Dependent (BOLD) fMRI signal. Therefore, in order to observe the functional connectivity between the PCC and other brain regions, we analyzed the functional connectivity between the PCC and the whole brain network by fMRI using GCA, with the PCC as the Region of Interest (ROI). This technique directly derives the fundamental relationship of each node using data obtained from neuroimaging11. Recently, GCA has been applied to electroencephalogram (EEG) and fMRI studies to reveal the causal effects among brain regions12. All these studies indicated that the GCA technique might be optimal for detecting the causal relationship of each node in the brain.

<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

Impaired functional connectivity in the Default Mode Network (DMN) may be involved in the progression of Alzheimer&#39;s Disease (AD). The Posterior Cingulate Cortex (PCC) is a potential imaging marker for monitoring the progression of AD. Previous studies did not focus on the functional connectivity between the PCC and nodes in regions outside the DMN, but our study is an effort to explore these overlooked functional connections. For collecting data, we used functional Magnetic Resonance Imaging (fMRI) and Granger Causality Analysis (GCA). fMRI provides a non-invasive method for studying the dynamic interactions between the different brain regions. GCA is a statistical hypothesis test for determining whether one-time series is useful in forecasting another. In simple terms, it is judged by comparing the &#34;Known all the information on the last moment, the distribution of the probability of X at this time&#34; and the &#34;Known all the information on the last moment except Y, the distribution of the probability of X at this time&#34;, to determine whether there is a causal relationship between Y and X. This definition is based on the complete information source and stationary chronological sequence. The main step of this analysis is to use X and Y to establish the regression equation and draw a causal relationship by a hypothetical test. Since GCA can measure causal effects, we used it to investigate the anisotropy of the functional connectivity and explore the hub function of the PCC. Here, we screened 116 participants for MRI scanning, and after preprocessing the data obtained from neuroimaging, we used GCA to derive the causal relationship of each node. Finally, we concluded that the directed connection is significantly different between the Mild Cognitive Impairment (MCI) and AD groups, both from the PCC to the whole brain and from the whole brain to the PCC.

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
<p...

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

Based on resting-state functional magnetic resonance imaging with Granger causality analysis, we investigated the alterations in the directed functional connectivity between the posterior cingulate cortex and whole brain in patients with Alzheimer&#39;s Disease (AD), patients with Mild Cognitive Impairment (MCI), and healthy controls.

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

The overall goal of this Granger Causality Analysis is to study directed functional connectivity in the brain related to Alzheimer's disease progression, thereby establishing a novel objective basis for assessing the severity of the disease. This method can help answer key question in the Alzheimer's disease field, such as the directed connectivity between the PCC and whole brain regions. The main advantage of this technique is that it measures the causal effects of FMRI time series and it shows the dynamics and the directions of poor signal.The implication of this technique extend towards the diagnosis of progression of Alzheimer's disease because it compares the directed functional connectivity between AD, MCI and the control groups. Through this method can provide insight into scaling directed functional connectivity of Alzheimer's, it can also be applied to other system such as electroying cephalogram studies. Generally, individual who are new to this method will struggle if they're unfamiliar with the software's operation.We first have add here for the semester, and then we rate the prior literatures about Granger Causality Analysis. While having a real-world demonstration of this method, it's critical because there are multiples to be noted beyond what can be written in the text. Begin by opening RESTplus through the software and left click on Pipeline.Import the relevant files into RESTplus. Select the work directory and then the starting EPI and T1 directories. Next, to convert Dicom files to Nifti check off the DicomToNifti box in Preprocessing.And check off the EPI Dicom to Nifiti and the T1 to Nifiti parameters. Remove the first 10 time points by checking off the RemoveFirstTimePoints and setting the end parameter as 10. Then, check off the Slice timing box.Set the slice number according to the RS FMRI parameters of the study and enter the slice order. Check Realign to correct the time and head motion. Then, perform spacial normalization by checking off Normalize and leave the default parameters at the bottom.Use the T1 image unified segmentation and all heads standardized to the same space by selecting the parameters. Normalize by using T1 image unified segmentation and European. Next select smooth to perform spacial smoothing using an isotropic Gaussian kernal with a full width at half maximum of 6 mm.Remove the linear trend by checking off Detrend. Select nuisance covariates regression in the following, six head motion parameters, global mean signal, white matter signal, and cerebrospinal fluid signal in order to increase the signal to noise ratio. Finally select filter to retain signals between 0.01 to 0.08 hertz.Remove high frequency physiological noise as well as remove low frequency drift. Begin by performing the voxel-wise Granger Causality Analysis, or GCA, by using the REST GCA in the REST toolbox. In the Postprocessing box, check off GCA.Set the order as one in the default. Select Define ROI and choose the spherical ROI to define the region of interest. Select next.Identify seed points of interest in the posterior singular cortex, or PCC, by setting the center coordinates and radius of the seed ROI based on the known data and select OK.Then select Run and OK to run the program. Next, 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.Within the software, open RESTplus and left click on Statistical Analysis. Left click on REST Two-Sample T-Test. 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. Next, left click to open the BrainMask subfile in the mask folder. Then select Compute to run the program.Name the output results 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 computation of the brain mask file to obtain outputs for T3XY, T1YX, T2YX, and T3YX for a total of six files. Left click on Viewer of RESTplus to view the resulting files. Import the template named Ch2 in Underlay.Finally, find the six resulting files in the output directory and fill in the overlay one by one. Create the final graph using the six output files. After the active nodes in the whole brain were first identified, GCA technology was used to determine 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. 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 meta frontal gyrus belonging to the DMN.While masters, this technique can be done in 14 hours if it is performed properly. While attempting this procedure, it's important to remember that all file and the photo names must be in English and can not have spaces. After it's development this technique paved the way for reserachers in the field of FMRI to explore the directed factional connectivity in the progression of Alzheimer's disease.After watching this video, you should have a good understanding of how to start it the directed factional connectivity in the brain related to AD progression. Thereby establish a narrow objective basis for assessing the severity of the disease.

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