This work was supported by the National Institute of Aging (grant number R01AG013308 to SYB, F31AG047041 to TMH). The authors used computational and storage services associated with the Hoffman2 Shared Cluster provided by UCLA Institute for Digital Research and Education's Research Technology Group.

The present study was performed in compliance with the UCLA Institutional Review Board (IRB) protocols and approved by the UCLA Human Subjects Protection Committee. All participants gave written informed consent in order to enroll in this study.
1. Participant Selection
<ol>
	<li>Obtain IRB approval to perform the study.</li>
	<li>Screen individuals aged 55 and older for cognitive decline using a standardized neuropsychological battery. Include tests of General Intelligence (Subtests of the WAIS-III)13, Fluency (Fruits and Vegetables)14, Attention (Digits Forward and Backward)13, Language (Boston Naming Test)15, Verbal Memory (Buschke-Fuld Selective Reminding Task)16, WMS-III Logical Memory and Verbal Paired Associates learning13, and Visual Memory (Rey-Osterrieth Figure test)17.
	<ol>
		<li>Have the participants complete mood questionnaires such as the Hamilton Depression and Anxiety Inventories18,19 as well as the Mini Mental State Exam (MMSE)20.</li>
	</ol>
	</li>
	<li>Include participants that score 26 or above on the MMSE and perform better than two standard deviations below normal for their age on cognitive tests. Exclude participants with clinical anxiety, depression or any other neuropsychiatric or neurological illness. Exclude participants who do not meet MRI safety criteria or who do not consent to a blood draw. 
	NOTE: In the present study, 93 participants met these criteria (mean age = 67.4 years, 31M/49F).</li>
</ol>
2. Genotyping
<ol>
	<li>Have a trained phlebotomist or other medical professional draw blood from each participant.</li>
	<li>Isolate 200 &#181;g genomic DNA from 10 mL of the sample as described21.</li>
	<li>Carry out single nucleotide polymorphism (SNP) genotyping using real-time PCR at two loci, rs429358 and rs7412 to discriminate APOE alleles22.
	<ol>
		<li>Incorporate reporter dyes for rs429358 and rs7412 into a SNP genotyping assay. After each PCR amplification cycle is completed, plot fluorescent signals on a graph showing distribution of reporter and quencher dyes. Perform the experiment in duplicate to confirm results.</li>
	</ol>
	</li>
	<li>Analyze the SNP genotyping data using a software package developed for the real-time PCR procedure output23. 
	NOTE: The program used in the present study calculates the affinity of the sample to one of the two reporter dyes that, in turn, represents one APOE SNP over the other. In the present study, 34 carriers of the AD risk allele, APOE&#949;4 (heterozygous &#949;3/&#949;4) and 46 non-carriers (homozygous &#949;3/&#949;3) were enrolled for a total of 80 study participants. Exclude carriers of the APOE&#949;2 allele because there is evidence that this allele may have a protective effect related to AD.</li>
</ol>
3. Functional and Structural Imaging Data Collection
<ol>
	<li>Use a 3 Tesla (3T) MRI system to acquire whole-brain imaging data.
	<ol>
		<li>For functional imaging, collect axial slices using an echo planar imaging (EPI) sequence. To facilitate registration of the functional images, acquire axial slices of T2-weighted, co-planar structural images. For high-resolution structural imaging, collect axial slices using a 3D T1-weighted sequence. 
		NOTE: In the present study, a 3T magnet was used with a 12-channel head coil. The parameters below were designed for a specific scanner and coil. See Table of Materials for more information.
		<ol>
			<li>Acquire functional imaging data using the following sequence parameters: repetition time (TR) = 2,500 ms, echo time (TE) = 21 ms, field of view (FOV) = 200 mm x 200 mm, flip angle = 75&#176;, matrix = 64 x 64, 33 slices, slice thickness = 3 mm, interslice gap = 0.75 mm, voxel size = 3.125 x 3.125 x 3.75 mm.</li>
			<li>Trigger the unrelated words associative memory task to begin with the third volume of the functional imaging sequence. To account for steady-state equilibrium, exclude the first two volumes of each functional scan from analyses. 
			NOTE: The unrelated words associative memory task has been described elsewhere12,24. Briefly, it is a block-design functional task with encoding and retrieval blocks. Participants are instructed to learn pairs of unrelated words.</li>
			<li>Acquire T2-weighted, co-planar structural imaging data using the following sequence parameters: TR = 5,000 ms, TE = 34 ms, FOV = 200 mm x 200 mm, flip angle = 90&#176;, matrix = 128 x 128, 28 slices, slice thickness = 3 mm, interslice gap = 1 mm and voxel size = 1.56 x 1.56 x 4 mm.</li>
			<li>Acquire high-resolution structural (anatomical) imaging using the following Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence parameters: TR = 1,900 ms, TE = 2.26 ms, TI = 900 ms, FOV = 250 mm x 218 mm, flip angle = 9&#176;, matrix = 256 x 215, 176 slices, slice thickness = 1 mm, zero-filled to a matrix of 256 x 224 resulting in a voxel size = 1 x 0.976 x 0.976 mm.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. fMRI BOLD Data Preprocessing
<ol>
	<li>Preprocess the functional data using Functional MRI of the Brain (FMRIB) Software Library (FSL) version 6.0 (http://fsl.fmrib.ox.ac.ul) as follows:
	<ol>
		<li>For each participant&#39;s dataset, remove head motion artifact from the data using the Motion Correction FMRIB&#39;s Linear Image Registration Tool (MCFLIRT)25.</li>
		<li>Remove non-brain tissue from the images using brain extraction tool (BET) with the optional -F flag26.</li>
		<li>Use the FSL Motion Outliers tool to identify any volumes in the functional data where there is excessive motion based on frame displacement between volumes. Flag volumes where motion is measured as an outlier (above the 75th percentile + 1.5 times the inter-quartile range) compared to the rest of the scan and use the output of this program to downweight those volumes in analyses. 
		NOTE: Before running group comparisons, check that average motion, as measured by FSL Motion Outliers, does not differ across the two groups. This will help ensure that findings are not driven by group-related differences in motion.</li>
	</ol>
	</li>
	<li>Set up the preprocessing and first-level general linear model (GLM) using the graphical user interface (GUI) for FSL fMRI Expert Analysis Tool (FEAT) for the first participant. 
	NOTE: Repeat this step for each study participant. To save time, after setting up one run for one participant, write a script to run preprocessing for the remaining study participants&#39; data by altering the &#34;design.fsf&#34; file (FSL FEAT output) for each participant to reference that participant&#39;s specific data.
	<ol>
		<li>In the data tab, click on &#34;add 4D data&#34; and navigate to the motion-corrected and brain-extracted file. Set the TR to 2.5 s (corresponding to the TR of the functional sequence acquired). Use the default high pass filter (set to 100 s). 
		NOTE: High pass filtering will remove low frequency signals of no interest.</li>
		<li>In the pre-stats tab, click &#34;none&#34; under &#34;motion correction&#34; (as it was already performed in step 4.1). Uncheck &#34;BET brain extraction&#34; (as it was already completed in step 4.1). Type &#34;5&#34; in the box to set 5 mm full-width half-maximum (FWHM) Gaussian kernel for spatial smoothing. 
		NOTE: The FWHM for the smoothing kernel should generally be set at about twice the size of the functional scan voxel size.</li>
		<li>Use the output (6 columns, rows = # of TRs in the scan) of MCFLIRT to create 6 single-column text files that describe the motion correction performed at each volume within the dataset. These will be added to the model as regressors in the next step.
		<ol>
			<li>In the stats tab under &#34;full model setup&#34;, add the 6 motion parameters and their temporal derivatives as regressors or explanatory variables (EVs) in the GLM. For each motion EV choose &#34;custom&#34; (1 entry per volume) for basic shape, &#34;none&#34; for convolution and check &#34;apply temporal filtering.&#34; 
			NOTE: Motion parameters do not need to be convolved by any function because they reference the realignment performed at each functional volume during motion correction and thus do not need to be adjusted.</li>
		</ol>
		</li>
		<li>In the stats tab, select the output of FSL Motion Outliers from step 4.1 under the &#34;add additional confound EVs&#34;. 
		NOTE: This output is a matrix denoting each volume that was flagged for excessive motion and, by adding the confound file, will be deweighted in the GLM.</li>
		<li>In the stats tab, click &#34;full model set-up&#34;. Create the task timing text files denoting the onset and offset of different task phases and add these as EVs in the GLM by choosing 1 column format and navigating to the relevant text file (include one for the encoding phase of the task and one for the retrieval phase). For &#34;convolution&#34; choose the &#34;double-gamma HRF&#34; option from the drop down list for both of them. Do not model the baseline or non-active portions of the task in the GLM. 
		NOTE: HRF stands for hemodynamic response function. Convolving the task EV by the HRF shifts the timing of the task EV to be more consistent with expected task-induced BOLD signal changes in the brain.</li>
		<li>In the registration tab, check &#34;expanded functional image&#34; and &#34;main structural image&#34; for a two-step registration.
		<ol>
			<li>Select the participant&#39;s co-planar T2-weighted structural scan for the first step, in which functional data is registered to the co-planar structural data. Choose 6 degrees of freedom (DOF) for this step by clicking on the second drop down box under this step and choosing &#34;6 DOF&#34;.</li>
			<li>For the next step, in which the T2-weighted image is registered to the high resolution T1-weighted MPRAGE, select boundary based registration (BBR) from the drop down box27. 
			NOTE: BBR uses intensity differences between white matter and gray matter to register structural and functional scans and has been shown to perform better than FLIRT and other alternative methods.</li>
			<li>For the final step, in which the high-resolution structural data is registered to the standard MNI152 template, choose 12 degrees of freedom and a linear transformation by choosing &#34;12 DOF&#34;. 
			NOTE: When all the steps in section 4 are complete the functional data are preprocessed and ready for further analysis.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
5. Hippocampal Seeds
<ol>
	<li>Generate a mask of the left hippocampus in each participant&#39;s high resolution structural space using FSL&#39;s FMRIB Integrated Registration and Segmentation Tool (FIRST) segmentation algorithm28. 
	NOTE: Other regions, including right hippocampus, would be interesting and valid seeds for further analyses.</li>
	<li>Using a statistical software platform, write code in to calculate the length of the anterior and posterior thirds of the structure29. Specifically, use the length of the volumetric hippocampal mask in the anterior-posterior plane to find the coordinates demarking the anterior and posterior thirds of this plane. 
	NOTE: A recently published method of segmenting the hippocampus along the longitudinal axis might be an alternative seed creation approach30.</li>
	<li>Based on these coordinates, create anterior and posterior hippocampal mask images. Register the anterior and posterior hippocampal masks into native functional space using the &#34;example_func2highres&#34; matrix in the registration directory of the FEAT output. 
	NOTE: Using the anterior and posterior thirds prevented signal blurring across the two hippocampal seeds after registration to functional space. There is evidence of functional specialization along the longitudinal axis of the hippocampus31,32,33,34. Anterior regions are input regions and associated with encoding, while the posterior hippocampus is an output region associated with memory retrieval and consolidation35,36,37. Thus, using these regions allows assessment of functional involvement of anterior versus posterior hippocampus in encoding versus retrieval phases of the memory task.</li>
	<li>Use FSL mean timeseries (fslmeants) to extract the denoised average timeseries from the anterior and posterior hippocampal seeds (Figure 1). Follow the program instructions and use either the anterior or posterior hippocampal seed as the mask and the denoised, preprocessed functional data as the main image.</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55394/55394fig1.jpg" /> 
Figure 1: Hippocampal Seeds. In native space, a single participant&#39;s anterior hippocampus seed is shown in yellow. The posterior hippocampus seed for the same participant is shown in pink. Seeds are defined in each participant&#39;s unique structural image and then registered to their functional scan. Seeds are never in a standardized space, which improves the accuracy of the hippocampal segmentation. This figure has been reprinted with permission12. <a href="http://cloudfront.jove.com/files/ftp_upload/55394/55394fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
6. PPI Model
<ol>
	<li>Use the GUI for FSL FEAT to load the preprocessed functional data.
	<ol>
		<li>In the data tab, choose the &#34;filtered_func_data&#34; denoised image (output from the steps completed in section 4) as the input file. In the pre-stats tab, set motion correction and brain extraction to &#34;none.&#34; Unclick boxes to perform temporal filtering and spatial smoothing.</li>
	</ol>
	</li>
	<li>PPI Model Set-Up (Table 1).
	<ol>
		<li>In the stats tab, select &#34;full model set-up&#34;. In the EVs tab, add all the EVs from the first level model: 6 motion correction EVs, confound EV matrix from FSL Motion Outliers and task timing EVs. Click the up arrow to add EVs. Include in this model an EV for the physiological timecourse from the seed (the text file output of fslmeants in step 5.4) as a covariate of no interest by clicking on the up arrow.</li>
		<li>Create the PPI terms.
		<ol>
			<li>Choose &#34;interaction&#34; in the basic shape menu and select the seed timecourse EV and one task EV. For the &#34;make zero&#34; option, choose &#34;mean&#34; for the seed timecourse EV and &#34;centre&#34; for the task EV. Repeat this procedure for the other task phase(s). Run a separate model for each seed region. 
			NOTE: These new EVs are the PPI terms for the phase of the task selected (psycho) and the seed (physio). In the present study, a PPI term for the encoding phase and a second PPI term for the retrieval phase were included in each PPI model. The &#34;centre&#34; option ensures that the &#34;on&#34; and &#34;off&#34; phases of the block design task are treated equally. The &#34;mean&#34; option is always applied to the seed timecourse and results in the mean being subtracted from this regressor.</li>
		</ol>
		</li>
		<li>In the contrasts and F-tests tab, model the following specific effects by entering &#34;1&#34; in the corresponding EV cell: psych_enc (encoding task phase), psych_ret (retrieval task phase), phys (seed timecourse), PPI_enc (PPI of seed and encoding), PPI_ret (PPI of seed and retrieval). Lastly, enter a &#34;-1&#34; to model negative PPIs for each task phase.</li>
	</ol>
	</li>
</ol>
<img alt="Table 1" src="/files/ftp_upload/55394/55394table1.jpg" />
Table 1: gPPI model set-up.
7. Group Comparisons
<ol>
	<li>Select &#34;higher-level analysis&#34; in FSL FEAT to run a simple group model comparing APOE&#949;4 carriers to non-carriers for each task-seed combination. 
	NOTE: These comparisons are run to generate the relevant group 4D residuals images (&#34;res4d&#34;) needed to estimate the smoothness of the dataset. Statistically significant results from this group comparison are valid, but in the steps below another thresholding approach using AFNI and SPM8 to set a significant cluster minimum based on Monte Carlo simulations is described.</li>
	<li>Use Analysis of Functional Neuroimaging (AFNI)
	<ol>
		<li>Use AFNI&#39;s 3dFWHMx (any version after December 2015) at the command line to estimate the smoothness of the group 4D residuals images generated using FSL. 
		NOTE: A bug was discovered in AFNI&#39;s 3dClustSim and corrected in May 2015. In December 2015, AFNI&#39;s 3dFWHMx was updated to more accurately model auto-correlations. Thus, versions of these tools released in December 2015 or later should be used.</li>
		<li>Use AFNI&#39;s 3dClustSim (any version after December 2015) to determine cluster extent minimums reaching significance at different voxel-level thresholds. Include the smoothness estimates from the previous step in the command line invocation of 3dClustSim. From the table generated by 3dClustSim, based on the study hypotheses regarding the expected effects&#39; height and extent, choose a voxel-level threshold and corresponding cluster minimum size. 
		NOTE: In general, larger clusters minimize false positives.</li>
	</ol>
	</li>
	<li>Use Statistical Parametric Mapping (SPM8)
	<ol>
		<li>Using the SPM8 GUI, select &#34;specify 2nd-level&#34;. The batch editor will open. Select &#34;two sample t-test&#34; under design. Navigate to the directory with the parameter estimate images for group 1 (APOE&#949;4 carriers) and select by clicking on them. Next, add group 2 (APOE&#949;4 non-carriers) images. Run this comparison by clicking on the green play button.</li>
		<li>Return to the SPM GUI, select &#34;estimate&#34;, and navigate to the SPM.mat file created in the previous step to run the model estimation process.</li>
		<li>Select &#34;results&#34; and run group comparison contrasts: APOE&#949;4 carriers &#62; APOE&#949;4 non-carriers, APOE&#949;4 non-carriers &#62; APOE&#949;4 carriers.
		<ol>
			<li>Click on &#34;define a new contrast&#34;, choose &#34;T-contrast&#34; under &#34;type&#34; and enter &#34;1 -1&#34; in the &#34;contrast&#34; box for APOE&#949;4 carriers &#62; APOE&#949;4 non-carriers. Click &#34;done&#34;. Choose &#34;none&#34; for Apply Masking, and manually set the voxel-level threshold and the cluster size minimum according to the determination made in step 7.2.2.Enter &#34;-1 1&#34; for APOE&#949;4 non-carriers &#62; APOE&#949;4 carriers. 
			Note: In the present study, a voxelwise threshold of p &#60;0.005 was used and clusters thresholded at alpha &#60;0.05.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

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M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Greater+medial+temporal+hypometabolism+and+lower+cortical+amyloid+burden+in+ApoE4-positive+AD+patients.">Greater medial temporal hypometabolism and lower cortical amyloid burden in ApoE4-positive AD patients.</a> J Neurol Neurosurg Psychiatry. 85 (3), 266-273 (2014).</li><li>. TaqMan&#174; SNP Genotyping Assays User Guide Available from: <a target="_blank" href="https://tools.thermofisher.com/content/sfs/manuals/TaqMan_SNP_Genotyping_Assays_man.pdf">https://tools.thermofisher.com/content/sfs/manuals/TaqMan_SNP_Genotyping_Assays_man.pdf</a> (2014)</li><li>Bookheimer, S. Y., Strojwas, M. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Patterns+of+brain+activation+in+people+at+risk+for+Alzheimer's+disease.">Patterns of brain activation in people at risk for Alzheimer's disease.</a> N Engl J Med. 343 (7), 450-456 (2000).</li><li>Jenkinson, M., Bannister, P., Brady, M., Smith, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+optimization+for+the+robust+and+accurate+linear+registration+and+motion+correction+of+brain+images.">Improved optimization for the robust and accurate linear registration and motion correction of brain images.</a> NeuroImage. 17 (2), 825-841 (2002).</li><li>Smith, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+robust+automated+brain+extraction.">Fast robust automated brain extraction.</a> Hum Brain Mapp. 17 (3), 143-155 (2002).</li><li>Greve, D. N., Fischl, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accurate+and+robust+brain+image+alignment+using+boundary-based+registration.">Accurate and robust brain image alignment using boundary-based registration.</a> NeuroImage. 48 (1), 63-72 (2009).</li><li>Patenaude, B., Smith, S. M., Kennedy, D. N., Jenkinson, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Bayesian+model+of+shape+and+appearance+for+subcortical+brain+segmentation.">A Bayesian model of shape and appearance for subcortical brain segmentation.</a> NeuroImage. 56 (3), 907-922 (2011).</li><li>. Learn MATLAB Basics Available from: <a target="_blank" href="https://www.mathworks.com/support/learn-with-matlab-tutorials.html?s_tid=hp_ff_I_tutorials">https://www.mathworks.com/support/learn-with-matlab-tutorials.html?s_tid=hp_ff_I_tutorials</a> (2017)</li><li>Lerma-Usabiaga, G., Iglesias, J. E., Insausti, R., Greve, D. N., Paz-Alonso, P. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+segmentation+of+the+human+hippocampus+along+its+longitudinal+axis.">Automated segmentation of the human hippocampus along its longitudinal axis.</a> Hum Brain Mapp. 37 (9), 3353-3367 (2016).</li><li>Salami, A., Eriksson, J., Nyberg, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Opposing+effects+of+aging+on+large-scale+brain+systems+for+memory+encoding+and+cognitive+control.">Opposing effects of aging on large-scale brain systems for memory encoding and cognitive control.</a> J Neurosci. 32 (31), 10749-10757 (2012).</li><li>Schacter, D. L., Wagner, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Medial+temporal+lobe+activations+in+fMRI+and+PET+studies+of+episodic+encoding+and+retrieval.">Medial temporal lobe activations in fMRI and PET studies of episodic encoding and retrieval.</a> Hippocampus. 9 (1), 7-24 (1999).</li><li>Strange, B., Dolan, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+segregation+within+the+human+hippocampus.">Functional segregation within the human hippocampus.</a> Mol Psychiatry. 4 (6), 508-511 (1999).</li><li>Strange, B. A., Fletcher, P. C., Henson, R. N., Friston, K. J., Dolan, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segregating+the+functions+of+human+hippocampus.">Segregating the functions of human hippocampus.</a> Proc Natl Acad Sci U S A. 96 (7), 4034-4039 (1999).</li><li>Eldridge, L. L., Engel, S. A., Zeineh, M. M., Bookheimer, S. Y., Knowlton, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+dissociation+of+encoding+and+retrieval+processes+in+the+human+hippocampus.">A dissociation of encoding and retrieval processes in the human hippocampus.</a> J Neurosci. 25 (13), 3280-3286 (2005).</li><li>Strange, B. A., Witter, M. P., Lein, E. S., Moser, E. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+organization+of+the+hippocampal+longitudinal+axis.">Functional organization of the hippocampal longitudinal axis.</a> Nat Rev Neurosci. 15 (10), 655-669 (2014).</li><li>Zeineh, M. M., Engel, S. A., Thompson, P. M., Bookheimer, S. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamics+of+the+hippocampus+during+encoding+and+retrieval+of+face-name+pairs.">Dynamics of the hippocampus during encoding and retrieval of face-name pairs.</a> Science. 299 (5606), 577-580 (2003).</li><li>Nieuwenhuis, S., Forstmann, B. U., Wagenmakers, E. -. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Erroneous+analyses+of+interactions+in+neuroscience:+a+problem+of+significance.">Erroneous analyses of interactions in neuroscience: a problem of significance.</a> Nat Neurosci. 14 (9), 1105-1107 (2011).</li><li>Cisler, J. M., Bush, K., Steele, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+statistical+methods+for+detecting+context-modulated+functional+connectivity+in+fMRI.">A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI.</a> NeuroImage. 84, 1042-1052 (2014).</li><li>Friston, K. J., Harrison, L., Penny, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+causal+modelling.">Dynamic causal modelling.</a> NeuroImage. 19 (4), 1273-1302 (2003).</li></ol>

DGM is an employee of Biospective, Inc. Biospective, Inc. did not process any of the data presented.

Early task-based fMRI studies were designed to uncover statistical relationships between particular cognitive processes or demands and changes in the BOLD signal relative to a baseline measurement. This traditional approach is useful for identifying specific regions in the brain where activity is modulated by an experimental task. In contrast, a PPI analysis is chiefly concerned with the modulation of functional connectivity, or synchrony of activity, that results from a task-induced cognitive process. PPI measures conte...

The term &#34;connectome&#34; was coined in 2005 marking a paradigm shift in neuroscience that continues to this day1. The brain is increasingly described in terms of functional networks, connectivity and interactions between and among regions on a large scale. Nevertheless, the delineation of regional functional specialization and associations between fMRI-measured activity and task demands are still valid and useful approaches. In light of the growing interest in connectomics, functional connectivity approaches to task fMRI analysis are growing in popularity. One approach to measuring functional connectivity changes dependent on task demands makes use of the concept of PPI. A PPI is the interaction of an active task phase or particular task demand (&#34;psycho&#34;) with the functional connectivity (&#34;physio&#34;) of a region of interest or &#34;seed&#34; in the brain. PPI differs from bivariate, correlation-based analysis of functional connectivity, which generally measures the degree of correlation between the activity in two regions without any constraints related to task demands.
The concept and framework of a PPI analysis was originally described by Friston and colleagues in 19972. The authors asserted that their approach was important because it would allow the investigation of connectivity to be more functionally specific and allow for inferences that activity in a distal seed could be modulating activity resulting from a task demand. In 2012, McLaren and colleagues added to this original framework and described a gPPI approach in which all task phases and their interactions are included in a single model3. This approach leads to results that are more sensitive and specific to the task phase and interaction being investigated. It is this updated gPPI approach that we employ in the present study (see step 6.2.2 in Protocol). The gPPI approach has now been cited in over 200 studies. For clarity, hereafter we use &#39;PPI&#39; to describe common features of both the standard and generalized version. &#39;gPPI&#39; will be used to discuss specific advances associated with the newer framework.
The overall goal of a PPI analysis is to understand how the demands of a cognitive task influence or modulate the functional connectivity of a seed region. A PPI analysis requires a strong a priori hypothesis. Activity in the seed region must be modulated by the task in order for the PPI approach to work effectively4. For example, in the present study, we based our seed selection on the strong evidence that hippocampal activity is modulated by the cognitive demands of a memory task. Using PPI, regions that are significantly more or less functionally connected to the hippocampus during specific task phases can be identified. In short, we ask the question, &#34;in which regions is activity more correlated with the seed during context A as compared with baseline?&#34; We can also ask the logical opposite (as it is important to understand the difference): &#34;in which regions is activity less correlated with the seed during context A as compared to baseline?&#34; When interpreting group differences in PPI effects, it is important to examine the data and whether positive or negative change in functional connectivity, or both, is driving group differences.
The PPI approach has been used to study dynamic task control hubs in healthy controls, how modulation of functional connectivity is related to cognitive performance in Alzheimer&#39;s disease (AD), intelligence in individuals with autism, motor network connectivity in individuals with Parkinson&#39;s disease, face processing in individuals with body dysmorphic disorder and anorexia, emotion regulation, memory, and many other specific questions related to connectivity5,6,7,8,9,10,11. In the present study, we compare changes in functional connectivity of subregions of the hippocampus during memory encoding and retrieval between a group of individuals at increased genetic risk for AD to a group without the risk factor12. The following describes the protocol that we used, applying the gPPI approach, to allow us to test if task-elicited changes in functional connectivity differ in association with the presence of APOE&#949;4, a genetic risk factor for AD.

<table><tbody><tr><td>3T manetic resonance imaging scanner</td><td>Siemens Medical Solutions</td><td>MAGNETOM Trio, A Tim System</td><td>3T MRI Scanner</td></tr><tr><td>FSL (FMRIB Software Library)</td><td>Oxford University</td><td>Version 6.0</td><td>Functional Imaging Processing Software</td></tr><tr><td>AFNI (Analysis of Functional Neuroimaging)</td><td>National Institute of Mental Health, National Institutes of Health</td><td>Any version after May 2015</td><td>Functional Imaging Processing Software</td></tr><tr><td>SPM8 (Statistical Parametric Mapping)</td><td>University College of London</td><td>SPM8</td><td>Functional Imaging Processing Software</td></tr><tr><td>Matlab Software</td><td>The Mathworks, Inc</td><td>Version R2012a</td><td>Computing Software</td></tr><tr><td>SDS Software</td><td>Applied Biosystems, Inc</td><td>7900HT Fast Real-Time PCR System</td><td>Real Time PCR</td></tr><tr><td>Taqman Assays</td><td>ThermoFisher Scientific</td><td>Specific to SNP</td><td>SNP Genotyping</td></tr></tbody></table>

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.

With two different active task phases (encoding and retrieval) and two seed regions (anterior and posterior hippocampus) there are four conditions to report results for each group. The within-group task activation maps (not shown here, see Harrison et al., 201612) show that the occipital lobe, auditory cortex, large regions of parietal lobe, frontal language areas, superior temporal gyrus, and caudate (more pronounced during retrieval) have significant BOL...

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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.

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 ...

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Research

JoVE Journal

Neuroscience

14.8K Views.  University of California, Los Angeles. Dit manuscript wordt beschreven hoe de uitvoering van een analyse van de psychofysiologische interactie te onthullen taak-afhankelijke wijzigingen in functionele connectiviteit tussen een geselecteerde zaad regio en voxels in andere delen van de hersenen. Psychofysiologische interactie analyse is een populaire methode om taak effecten op hersenen connectiviteit, onderscheiden van traditionele univariate activering effecten te analyseren.

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

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

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

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

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

Medicine

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

This protocol describes the administration of the 4 Mountains Test (4MT), a short test of spatial memory, in which memory for the topographical layout of four mountains within a computer-generated landscape is tested using a delayed match-to-sample paradigm. Allocentric spatial memory is assessed by altering the viewpoint, colors and textures between the initially presented and target images.
Allocentric spatial memory is a key function of the hippocampus, one of the earliest brain regions to be affected in Alzheimer&#39;s disease (AD) and impairment of hippocampal function predates the onset of dementia. It was hypothesized that performance on the 4MT would aid the diagnosis of predementia AD, which manifests clinically as Mild Cognitive Impairment (MCI).
The 4MT was applied to patients with MCI, stratified further based on cerebrospinal fluid (CSF) AD biomarker status (10 MCI biomarker positive, 9 MCI biomarker negative), and with mild AD dementia, as well as healthy controls. Comparator tests included tests of episodic memory and attention widely accepted as sensitive measures of early AD. Behavioral data were correlated with quantitative MRI measures of the hippocampus, precuneus and posterior cingulate gyrus.
4MT scores were significantly different between the two MCI groups (p = 0.001), with a test score of &#8804;8/15 associated with 100% sensitivity and 78% specificity for the classification of MCI with positive AD biomarkers, i.e., predementia AD. 4MT test scores correlated with hippocampal volume (r = 0.42) and cortical thickness of the precuneus (r = 0.55).
In conclusion, the 4MT is effective in identifying the early stages of AD. The short duration, easy application and scoring, and favorable psychometric properties of the 4MT fulfil the need for a simple but accurate diagnostic test for predementia AD.

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.

This protocol describes the administration of the 4 Mountains Test (4MT), a short test of spatial memory, in which memory for the topographical layout of ...

Behavior

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Genome-wide association studies (GWAS) have successfully identified hundreds of genomic loci that are associated with human traits and disease. However, because the majority of the genome-wide significant (GWS) loci fall onto the non-coding genome, the functional impact of many remain unknown. Three-dimensional chromatin interactions identified by Hi-C or its derivatives can provide useful tools to annotate these loci by linking non-coding variants to their actionable genes. Here, we outline a protocol to map GWAS non-coding variants to their putative genes using Alzheimer&#39;s disease (AD) GWAS and Hi-C datasets from human adult brain tissue. Putative causal single-nucleotide polymorphisms (SNPs) are identified by application of fine-mapping algorithms. SNPs are then mapped to their putative target genes using enhancer-promoter interactions based on Hi-C. The resulting gene set represents AD risk genes, as they are potentially regulated by AD risk variants. To garner further biological insights into molecular mechanisms underlying AD, we characterize AD risk genes using developmental brain expression data and brain single-cell expression profiles. This protocol can be expanded to any GWAS and Hi-C datasets to identify putative target genes and molecular mechanisms underlying various human traits and diseases.

We present a protocol to identify functional implications of non-coding variants identified by genome-wide association studies (GWAS) using three-dimensional chromatin interactions.

Genome-wide association studies (GWAS) have successfully identified hundreds of genomic loci that are associated with human traits and disease. However, ...

Genetics

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive decline, such as Alzheimer&#39;s disease (AD), have become a promising tool to elucidate the underlying mechanisms and promote the therapeutic advances. An important step is the validation and characterization of expected behavioral abnormality in the models, in the case of AD, cognitive decline. The long-term behavioral investigations of laboratory animals to study the effect of aging demand substantial efforts from researchers. The IntelliCage system is a high-throughput and cost-effective test battery for mice that eliminates the need for daily human handling. Here, we describe how the system is utilized in the long-term phenotyping of a genetic Alzheimer&#39;s disease model, specifically focusing on the cognitive functions. The experiment employs repeated battery of tests that assess spatial learning and executive functions. This cost-effective age-dependent phenotyping allows us to identify the transient and/or permanent effects of genes on various cognitive aspects.

This paper describes a protocol for cognitive assessments for genetic models of the Alzheimer&#39;s disease using the IntelliCage system, which is a high throughput automated behavioral monitoring system with operant conditioning.

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive ...

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.

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

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD).
Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g., working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions.
Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.

This protocol describes the complementary neuroimaging techniques of resting state structural connectivity, task-induced deactivation, and structural connectivity analyses to examine the default network in post-traumatic stress disorder. The use of synergistic methods could potentially lead to improved diagnostics and assessments of severity, outcome, and other relevant clinical factors.

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of ...

Motor and Hippocampal Dependent Spatial Learning and Reference Memory Assessment in a Transgenic Rat Model of Alzheimer's Disease with Stroke

Alzheimer&#39;s disease (AD) is a debilitating neurodegenerative disease that results in neurodegeneration and memory loss. While age is a major risk factor for AD, stroke has also been implicated as a risk factor and an exacerbating factor. The co-morbidity of stroke and AD results in worsened stroke-related motor control and AD-related cognitive deficits when compared to each condition alone. To model the combined condition of stroke and AD, a novel transgenic rat model of AD, with a mutated form of amyloid precursor protein (a key protein involved in the development of AD) incorporated into its DNA, is given a small unilateral striatal stroke.
For a model with the combination of both stroke and AD, behavioral tests that assess stroke-related motor control, locomotion and AD-related cognitive function must be implemented. The cylinder task involves a cost-efficient, multipurpose apparatus that assesses spontaneous forelimb motor use. In this task, a rat is placed in a cylindrical apparatus, where the rat will spontaneously rear and contact the wall of the cylinder with its forelimbs. These contacts are considered forelimb motor use and quantified during video analysis after testing. Another cost-efficient motor task implemented is the beam-walk task, which assesses forelimb control, hindlimb control and locomotion. This task involves a rat walking across a wooden beam allowing for the assessment of limb motor control through analysis of forelimb slips, hindlimb slips and falls. Assessment of learning and memory is completed with Morris water maze for this behavioral paradigm. The protocol starts with spatial learning, whereby the rat locates a stationary hidden platform. After spatial learning, the platform is removed and both short-term and long-term spatial reference memory is assessed. All three of these tasks are sensitive to behavioral differences and completed within 28 days for this model, making this paradigm time-efficient and cost-efficient.

To investigate the co-morbid Alzheimer&#39;s disease (AD) and stroke condition in a novel model, three behavior tasks are described that assess both motor control and cognitive behaviors. These tasks include the beam-walk task, cylinder task and Morris water maze.

Alzheimer&#39;s disease (AD) is a debilitating neurodegenerative disease that results in neurodegeneration and memory loss. While age is a major risk ...

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.

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 ...

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

In a constantly aging population, the prevalence of neurodegenerative disorders is expected to rise. Understanding disease mechanisms is the key to find preventive and curative measures. The most effective way to achieve this is through direct examination of diseased and healthy brain tissue. The authors present a protocol to obtain, process, characterize and store good quality brain tissue donated by individuals registered in an antemortem brain donation program. The donation program includes a face-to-face empathic approach to people, a collection of complementary clinical, biological, social and lifestyle information and serial multi-dimensional assessments over time to track individual trajectories of normal aging and cognitive decline. Since many neurological diseases are asymmetrical, our brain bank offers a unique protocol for slicing fresh specimens. Brain sections of both hemispheres are alternately frozen (at -80 &#176;C) or fixed in formalin; a fixed slice on one hemisphere corresponds to a frozen one on the other hemisphere. With this approach, a complete histological characterization of all frozen material can be obtained, and omics studies can be performed on histologically well-defined tissues from both hemispheres thus offering a more complete assessment of neurodegenerative disease mechanisms. Correct and definite diagnosis of these diseases can only be achieved by combining the clinical syndrome with the neuropathological evaluation, which often adds important etiological clues necessary to interpret the pathogenesis. This method can be time consuming, expensive and limited as it only covers a limited geographical area. Regardless of its limitations, the high degree of characterization it provides can be rewarding. Our ultimate goal is to establish the first Italian Brain Bank, all the while emphasizing the importance of neuropathologically verified epidemiological studies.

This protocol describes a method to track individual brain-aging trajectories through a brain donation program and proper characterization of brains. Brain donors are involved in a long-term longitudinal study including serial multi-dimensional assessments. The protocol contains a detailed description of brain processing and an accurate diagnostic methodology.

In a constantly aging population, the prevalence of neurodegenerative disorders is expected to rise. Understanding disease mechanisms is the key to find ...

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

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