Name: Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimers Disease and Mild Cognitive Impairment (Video) | JoVE Journal
Uploaded: 2017-08-07T15:00:00.000+00:00
Duration: 8 min 43 s
Description: Learn Granger Causality Analysis to study directed functional connectivity in Alzheimer's disease using fMRI data. This method reveals causal brain network dynamics between the PCC and whole brain regions.

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5 minutes

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 (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>

<ol><li>Delbeuck, X., Van der Linden, M., Collette, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Alzheimer%27s+disease+as+a+disconnection+syndrome%3F%5BTitle%5D)+AND+%22Neuropsychol+Rev%22%5BJournal%5D)">Alzheimer's disease as a disconnection syndrome?</a> Neuropsychol Rev. 13 (2), 79-92 (2003).</li>
<li>Wang, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Altered+functional+connectivity+in+early+Alzheimer%27s+disease%3A+a+resting-state+fMRI+study%5BTitle%5D)+AND+%22Hum+Brain+Mapp%22%5BJournal%5D)">Altered functional connectivity in early Alzheimer's disease: a resting-state fMRI study.</a> Hum Brain Mapp. 28 (10), 967-978 (2007).</li>
<li>Zhong, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Altered+effective+connectivity+patterns+of+the+default+mode+network+in+Alzheimer%27s+disease%3A+an+fMRI+study%5BTitle%5D)+AND+%22Neurosci+Lett%22%5BJournal%5D)">Altered effective connectivity patterns of the default mode network in Alzheimer's disease: an fMRI study.</a> Neurosci Lett. 578, 171-175 (2014).</li>
<li>Gusnard, D. A., Raichle, M. E., Raichle, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Searching+for+a+baseline%3A+functional+imaging+and+the+resting+human+brain%5BTitle%5D)+AND+%22Nat+Rev+Neurosci%22%5BJournal%5D)">Searching for a baseline: functional imaging and the resting human brain.</a> Nat Rev Neurosci. 2 (10), 685-694 (2001).</li>
<li>Greicius, M. D., Krasnow, B., Reiss, A. L., Menon, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Functional+connectivity+in+the+resting+brain%3A+a+network+analysis+of+the+default+mode+hypothesis%5BTitle%5D)+AND+%22Proc+Natl+Acad+Sci+U+S+A%22%5BJournal%5D)">Functional connectivity in the resting brain: a network analysis of the default mode hypothesis.</a> Proc Natl Acad Sci U S A. 100 (1), 253-258 (2003).</li>
<li>Ries, M. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Task-dependent+posterior+cingulate+activation+in+mild+cognitive+impairment%5BTitle%5D)+AND+%22NeuroImage%22%5BJournal%5D)">Task-dependent posterior cingulate activation in mild cognitive impairment.</a> NeuroImage. 29 (2), 485-492 (2006).</li>
<li>Braak, H., Braak, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Neuropathological+stageing+of+Alzheimer-related+changes%5BTitle%5D)+AND+%22Acta+Neuropathol%22%5BJournal%5D)">Neuropathological stageing of Alzheimer-related changes.</a> Acta Neuropathol. 82 (4), 239-259 (1991).</li>
<li>Liao, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evaluating+the+effective+connectivity+of+resting+state+networks+using+conditional+Granger+causality%5BTitle%5D)+AND+%22Biol+Cybern%22%5BJournal%5D)">Evaluating the effective connectivity of resting state networks using conditional Granger causality.</a> Biol Cybern. 102 (1), 57-69 (2010).</li>
<li>Miao, X., Wu, X., Li, R., Chen, K., Yao, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Altered+connectivity+pattern+of+hubs+in+default-mode+network+with+Alzheimer%27s+disease%3A+an+Granger+causality+modeling+approach%5BTitle%5D)+AND+%22PloS+one%22%5BJournal%5D)">Altered connectivity pattern of hubs in default-mode network with Alzheimer's disease: an Granger causality modeling approach.</a> PloS one. 6 (10), e25546(2011).</li>
<li>Yu, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Directed+functional+connectivity+of+posterior+cingulate+cortex+and+whole+brain+in+Alzheimer%27s+disease+and+mild+cognitive+impairment%5BTitle%5D)+AND+%22Curr+Alzheimer+Res%22%5BJournal%5D)">Directed functional connectivity of posterior cingulate cortex and whole brain in Alzheimer's disease and mild cognitive impairment.</a> Curr Alzheimer Res. , (2016).</li>
<li>Kaminski, M., Ding, M., Truccolo, W. A., Bressler, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evaluating+causal+relations+in+neural+systems%3A+granger+causality%2C+directed+transfer+function+and+statistical+assessment+of+significance%5BTitle%5D)+AND+%22Biol+Cybern%22%5BJournal%5D)">Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance.</a> Biol Cybern. 85 (2), 145-157 (2001).</li>
<li>Zang, Z. X., Yan, C. G., Dong, Z. Y., Huang, J., Zang, Y. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Granger+causality+analysis+implementation+on+MATLAB%3A+a+graphic+user+interface+toolkit+for+fMRI+data+processing%5BTitle%5D)+AND+%22J+Neurosci+Methods%22%5BJournal%5D)">Granger causality analysis implementation on MATLAB: a graphic user interface toolkit for fMRI data processing.</a> J Neurosci Methods. 203 (2), 418-426 (2012).</li>
<li>Hedden, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Disruption+of+functional+connectivity+in+clinically+normal+older+adults+harboring+amyloid+burden%5BTitle%5D)+AND+%22J+Neurosci%22%5BJournal%5D)">Disruption of functional connectivity in clinically normal older adults harboring amyloid burden.</a> J Neurosci. 29 (40), 12686-12694 (2009).</li>
<li>Liao, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Small-world+directed+networks+in+the+human+brain%3A+multivariate+Granger+causality+analysis+of+resting-state+fMRI%5BTitle%5D)+AND+%22NeuroImage%22%5BJournal%5D)">Small-world directed networks in the human brain: multivariate Granger causality analysis of resting-state fMRI.</a> NeuroImage. 54 (4), 2683-2694 (2011).</li>
<li>Liao, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evaluating+the+effective+connectivity+of+resting+state+networks+using+conditional+Granger+causality%5BTitle%5D)+AND+%22Biol+Cybern%22%5BJournal%5D)">Evaluating the effective connectivity of resting state networks using conditional Granger causality.</a> Biol Cybern. 102 (1), 57-69 (2010).</li>
<li>Zhang, H. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Detection+of+PCC+functional+connectivity+characteristics+in+resting-state+fMRI+in+mild+Alzheimer%27s+disease%5BTitle%5D)+AND+%22Behav+Brain+Res%22%5BJournal%5D)">Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer's disease.</a> Behav Brain Res. 197 (1), 103-108 (2009).</li>
<li>Deshpande, G., Hu, X., Stilla, R., Sathian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Effective+connectivity+during+haptic+perception%3A+a+study+using+Granger+causality+analysis+of+functional+magnetic+resonance+imaging+data%5BTitle%5D)+AND+%22NeuroImage%22%5BJournal%5D)">Effective connectivity during haptic perception: a study using Granger causality analysis of functional magnetic resonance imaging data.</a> NeuroImage. 40 (4), 1807-1814 (2008).</li>
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</ol>

The authors declare that they do not have any competing financial interests.

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><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

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 &gt;0.05), while the MMSE scores were significantly different (p &lt;0.01).
Directed brain functional connectivity
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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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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.

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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.

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

This article describes the 4 Mountains Test (4MT), a hippocampus-dependent test of working allocentric spatial memory. The hippocampus is affected early in Alzheimer's disease (AD) and this article outlines the 4MT methodology and results of patient testing, which demonstrates the value of the 4MT in the diagnosis of pre-dementia AD.

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia 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.

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

The retina shares prominent similarities with the brain and thus represents a unique window to study vasculature and neuronal structure in the brain non-invasively. This protocol describes a method to study dementia using retinal imaging techniques. This method can potentially aid in diagnosis and risk assessment of dementia.

Using Retinal Imaging to Study Dementia

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.

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

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.

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

This paper presents an integrative approach to investigating the functional network for spatial navigation in the human brain. This approach incorporates a large-scale neuroimaging meta-analytic database, resting-state functional magnetic resonance imaging, and network modeling and graph-theoretical techniques.

Modeling the Functional Network for Spatial Navigation in the Human Brain

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.

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

DeepOmicsAE is a workflow centered on the application of a deep learning method (i.e., an autoencoder) to reduce the dimensionality of multi-omics data, providing a foundation for predictive models and signaling modules representing multiple layers of omics data.

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Learn Granger Causality Analysis to study directed functional connectivity in Alzheimer's disease using fMRI data. This method reveals causal brain network dynamics between the PCC and whole brain regions.

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimers Disease and Mild Cognitive Impairment (Video) | JoVE Journal

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

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