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 ...

<ol>
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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. 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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.

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		<tr height="21">
			<td height="21" style="height:21px;width:307px;">3T manetic resonance imaging scanner</td>
			<td style="width:280px;">Siemens Medical Solutions</td>
			<td style="width:237px;">MAGNETOM Trio, A Tim System</td>
			<td style="width:267px;">3T MRI Scanner</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">FSL (FMRIB Software Library)</td>
			<td style="width:280px;">Oxford University</td>
			<td style="width:237px;">Version 6.0</td>
			<td>Functional Imaging Processing Software</td>
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			<td height="42" style="height:42px;width:307px;">AFNI (Analysis of Functional Neuroimaging)</td>
			<td style="width:280px;">National Institute of Mental Health, National Institutes of Health</td>
			<td style="width:237px;">Any version after May 2015</td>
			<td>Functional Imaging Processing Software</td>
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		<tr height="21">
			<td height="21" style="height:21px;width:307px;">SPM8 (Statistical Parametric Mapping)</td>
			<td style="width:280px;">University College of London</td>
			<td style="width:237px;">SPM8</td>
			<td>Functional Imaging Processing Software</td>
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		<tr height="21">
			<td height="21" style="height:21px;width:307px;">Matlab Software</td>
			<td style="width:280px;">The Mathworks, Inc</td>
			<td style="width:237px;">Version R2012a</td>
			<td>Computing Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">SDS Software</td>
			<td style="width:280px;">Applied Biosystems, Inc</td>
			<td>7900HT Fast Real-Time PCR System</td>
			<td>Real Time PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:307px;">Taqman Assays</td>
			<td style="width:280px;">ThermoFisher Scientific</td>
			<td style="width:237px;">Specific to SNP</td>
			<td>SNP Genotyping</td>
		</tr>
	</tbody>
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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&#039;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 ...

The overall goal of this analysis is to identify memory-related context-dependent changes to functional connectivity between regions of the hippocampus and the rest of the brain. This method can help answer key questions in the field of cognitive neuroscience such as how does functional connectivity change in response to specific cognitive demands within an experimental task. The main advantage of this technique is that it allows researchers to test specific hypotheses regarding the functional connectivity of key brain regions during a cognitive task.For this experiment, include individual aged 55 and older with cognitive decline who have been genotyped for the Alzheimer's disease risk allele apolipoprotein E epsilon four prior to the experiment, screen subjects for MRI safety and obtain informed consent. Use a three-tesla MRI system to acquire whole brain imaging data. For functional imaging, collect axial slices using an echo-planar imaging sequence while running an unrelated words-associative memory task.To facilitate registration of the functional images, also acquire axial slices of T2-weighted coplanar structural images. For high-resolution structural imaging, collect axial slices using a 3D T1-weighted sequence. Once imaging is complete for all participants, set up preprocessing steps and the first level general linear model using FSL FMRI expert analysis tool, or FEAT, for the first participant.In the Data tab, click on Select 4D data and navigate to the motion corrected and brain extracted file. Set the TR to match that of the functional sequence and use the default high pass filter. Now, in the Pre-stats tab, click none under motion correction and uncheck BET brain extraction.Enter five millimeters to set the full width half maximum Gaussian kernel for spatial smoothing. Then, click Full model setup and create the task timing files denoting the onset and offset of the task phases. Add these to the GLM by choosing three-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 convolution, choose the Double-Gamma HRF option. Next, use the output of the MCFLIRT tool to create six single-column text files that describe the motion correction performed at each volume within the data set.Select Full model setup and add the parameters and their temporal derivatives as explanatory variables, or EVs, in the GLM. For each motion EV, choose Custom for basic shape, none for convolution and temporal filling. Now, navigate to the Stats tab in the software and select the output of the FSL motion outliers tool under the Add additional confound EVs option.Now, in the Registration tab, check Expanded functional image and Main structural image for a two-step registration. Select the participants coplanar T2-weighted structural scan for the first step to register the functional to the structural data. Choose six degrees of freedom in the second dropdown box.For the next step, register the T2-weighted image to the high-resolution T1-weighted MP-RAGE by selecting boundary based registration from the dropdown box. Finally, register the high-resolution structural data to the standard MNI 152 template selecting 12 degrees of freedom and a linear transformation. Before setting up the psychophysiological interaction model, first load the preprocess data in FSL FEAT software.Choose the denoised image as the input file. In the Pre-stats tabs, set motion correction and brain extraction to None. Do not perform temporal filtering or spatial smoothing.Then, in the Stats tab, select Full model setup and in the EVs tab, add all the variables from the first-level modeling including motion correction, the confound matrix from FSL motion outliers and task timing. Include an EV for the physiological time course from the seed as a covariate of no interest. Next, create the PPI terms by choosing Interaction in the Basic shape menu, and select the seed time course EV and one task EV.For the Make zero option, choose Center for the task variables and Mean for the seed time course EV.Now, in the Contrasts and F-tests tab, model the following specific effects by entering one in the corresponding EV cells.Encoding task phase, retrieval task phase, seed time course, PPI of seed and encoding and PPI of seed and retrieval. Lastly, enter negative one to model negative PPIs for each task phase. Use statistical parametric mapping software tools to run group level comparisons.Begin by selecting Specify second level, then select Two-sample T-test under Design. Navigate to the directory with the parameter estimate images for group one and select them. Then, add the images for group two and run this comparison by clicking on the Play button.Now, return to the main window. Select Estimate and navigate to the SPM. mat file created in the previous step to run the model estimation.Next, under the Results tab, select Define a new contrast. Choose T-contrast and enter one negative one in the Contrast box for APOE-4 carriers greater than APOE-4 non-carriers, then click OK.Finally, run group comparison contrasts as seen here. Choose None for Apply masking, and then manually set the voxel-level threshold and the cluster size minimum according to output from AFNI's 3dClustSim software.Enter negative one-one for APOE-4 non-carriers greater than APOE-4 carriers. Within group generalized psychophysiological interaction analyses revealed significant decreases in functional connectivity in APOE-4 carriers, green, for both task conditions and hippocampal sub-regions. In APOE-4 non-carriers, red, significant decreases in functional connectivity were only observed with posterior hippocampus during encoding.During retrievals, significant differences between APOE-4 carriers and non-carriers were found in the left supramarginal gyrus, dark blue, the right supramarginal angular junction, orange, as well in the right precuneus, purple. The peak coordinate for each cluster is reported in MNI space. Here, contrasts of parameter estimates from each cluster are plotted by group.The red lines indicate zero and highlight that carriers have decreased functional connectivity to anterior hippocampus in these regions during retrieval. The band within the boxes represents the median, while the upper and lower edges of the boxes represent the first and third quartiles respectively. After its development, this technique paved the way for functional neuroimagers to explore dynamic task-related connectivity in humans.This includes both healthy and patient cohorts as well as individuals at increased genetic risk for disease as we describe here. After watching this video, you should have a good understanding of how to use a PPI analysis to test for context-dependent functional connectivity changes between your seed region of interest and the rest of the brain.

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JoVE Journal

Neuroscience

14.6K 조회수.  University of California, Los Angeles. The overall goal of this analysis is to identify memory-related context-dependent changes to functional connectivity between regions of the hippocampus and the rest of the brain. This method can help answer key questions in the field of cognitive neuroscience such as how does functional connectivity change in response to specific cognitive demands within an experimental task. The main advantage of this technique is that it allows researchers to test specific hypotheses regarding the functiona...