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="https://cloudfront-jove-com.remotexs.ntu.edu.sg/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>

<ol>
	<li>Sporns, O., Tononi, G., K&#246;tter, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Human+Connectome:+A+Structural+Description+of+the+Human+Brain.">The Human Connectome: A Structural Description of the Human Brain.</a> PLoS Comput Biol. 1 (4), 42 (2005).</li><li>Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., 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=Psychophysiological+and+modulatory+interactions+in+neuroimaging.">Psychophysiological and modulatory interactions in neuroimaging.</a> NeuroImage. 6 (3), 218-229 (1997).</li><li>McLaren, D. G., Ries, M. L., Xu, G., Johnson, S. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+generalized+form+of+context-dependent+psychophysiological+interactions+(gPPI):+a+comparison+to+standard+approaches.">A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches.</a> NeuroImage. 61 (4), 1277-1286 (2012).</li><li>O'Reilly, J. X., Woolrich, M. W., Behrens, T. E. J., Smith, S. M., Johansen-Berg, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tools+of+the+trade:+psychophysiological+interactions+and+functional+connectivity.">Tools of the trade: psychophysiological interactions and functional connectivity.</a> Soc Cogn Affect Neurosci. 7 (5), 604-609 (2012).</li><li>Moody, T. D., Sasaki, M. A., 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=Functional+connectivity+for+face+processing+in+individuals+with+body+dysmorphic+disorder+and+anorexia+nervosa.">Functional connectivity for face processing in individuals with body dysmorphic disorder and anorexia nervosa.</a> Psychol Med. 45 (16), 3491-3503 (2015).</li><li>Simard, I., Luck, D., Mottron, L., Zeffiro, T. A., Souli&#232;res, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Autistic+fluid+intelligence:+Increased+reliance+on+visual+functional+connectivity+with+diminished+modulation+of+coupling+by+task+difficulty.">Autistic fluid intelligence: Increased reliance on visual functional connectivity with diminished modulation of coupling by task difficulty.</a> NeuroImage Clin. 9, 467-478 (2015).</li><li>Yan, L. -. R., Wu, Y. -. B., Zeng, X. -. H., Gao, L. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dysfunctional+putamen+modulation+during+bimanual+finger-to-thumb+movement+in+patients+with+Parkinson's+disease.">Dysfunctional putamen modulation during bimanual finger-to-thumb movement in patients with Parkinson's disease.</a> Front Hum Neurosci. 9, 516 (2015).</li><li>Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., Braver, T. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-task+connectivity+reveals+flexible+hubs+for+adaptive+task+control.">Multi-task connectivity reveals flexible hubs for adaptive task control.</a> Nat Neurosci. 16 (9), 1348-1355 (2013).</li><li>McLaren, D. G., Sperling, R. A., Atri, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flexible+modulation+of+network+connectivity+related+to+cognition+in+Alzheimer's+disease.">Flexible modulation of network connectivity related to cognition in Alzheimer's disease.</a> NeuroImage. 100, 544-557 (2014).</li><li>Morawetz, C., Bode, S., Baudewig, J., Heekeren, H. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effective+amygdala-prefrontal+connectivity+predicts+individual+differences+in+successful+emotion+regulation.">Effective amygdala-prefrontal connectivity predicts individual differences in successful emotion regulation.</a> Soc Cogn Affect Neurosci. , 169 (2016).</li><li>Takashima, A., Bakker, I., van Hell, J. G., Janzen, G., McQueen, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Richness+of+information+about+novel+words+influences+how+episodic+and+semantic+memory+networks+interact+during+lexicalization.">Richness of information about novel words influences how episodic and semantic memory networks interact during lexicalization.</a> NeuroImage. 84, 265-278 (2014).</li><li>Harrison, T. M., Burggren, A. C., Small, G. W., 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=Altered+memory-related+functional+connectivity+of+the+anterior+and+posterior+hippocampus+in+older+adults+at+increased+genetic+risk+for+Alzheimer's+disease.">Altered memory-related functional connectivity of the anterior and posterior hippocampus in older adults at increased genetic risk for Alzheimer's disease.</a> Hum Brain Mapp. 37 (1), 366-380 (2016).</li><li>Wechsler, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Wecshler Adult Intelligence Scale, 3rd Edition. , (1997).</li><li>Cauthen, N. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Verbal+fluency:+normative+data.">Verbal fluency: normative data.</a> J Clin Psychol. 34 (1), 126-129 (1978).</li><li>Goodglass, H. P., Kaplan, E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Boston Naming Test, 3rd Edition. , (2001).</li><li>Buschke, H., Fuld, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+storage,+retention,+and+retrieval+in+disordered+memory+and+learning.">Evaluating storage, retention, and retrieval in disordered memory and learning.</a> Neurol. 24 (11), 1019-1025 (1974).</li><li>Osterrieth, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Le+test+de+copie+d'une+figure+complex:+Contribution+&#224;+l'&#233;tude+de+la+perception+et+de+la+memoir.">Le test de copie d'une figure complex: Contribution &#224; l'&#233;tude de la perception et de la memoir.</a> Archives de Psychologie. 30, 286-356 (1944).</li><li>Hamilton, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+assessment+of+anxiey+states+by+rating.">The assessment of anxiey states by rating.</a> Br J Med Psychol. 32, 50-55 (1959).</li><li>Hamilton, 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+rating+scale+for+depression.">A rating scale for depression.</a> J Neurol Neurosurg Psychiatry. 23, 56-62 (1960).</li><li>Folstein, M. F., Robins, L. N., Helzer, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Mini-Mental+State+Examination.">The Mini-Mental State Examination.</a> Arch Gen Psychiatry. 40 (7), 812 (1983).</li><li>O'Brien, D., Campbell, K. A., Morken, N. W., Bair, R. J., Heath, E. 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+Nucleic+Acid+Purification+for+Large+Samples.">Automated Nucleic Acid Purification for Large Samples.</a> J Lab Autom. 6 (2), 67-70 (2001).</li><li>Lehmann, M., Ghosh, P. 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.

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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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		<tr height="42">
			<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>
		</tr>
		<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>
		</tr>
		<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...

Watch this Scientific Journal Video about Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease at JoVE.com

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 ...

generalized-psychophysiological-interaction-ppi-analysis-memory

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Neuroscience

14.8K Views.  University of California, Los Angeles. 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.

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

Preparation of Oligomeric &beta;-amyloid1-42 and Induction of Synaptic Plasticity Impairment on Hippocampal Slices

Impairment of synaptic connections is likely to underlie the subtle amnesic changes occurring at the early stages of Alzheimer s Disease (AD). &beta;-amyloid (A&beta;), a peptide produced in high amounts in AD, is known to reduce Long-Term Potentiation (LTP), a cellular correlate of learning and memory. Indeed, LTP impairment caused by A&beta; is a useful experimental paradigm for studying synaptic dysfunctions in AD models and for screening drugs capable of mitigating or reverting such synaptic impairments. Studies have shown that A&beta; produces the LTP disruption preferentially via its oligomeric form. Here we provide a detailed protocol for impairing LTP by perfusion of oligomerized synthetic A&beta;1-42 peptide onto acute hippocampal slices. In this video, we outline a step-by-step procedure for the preparation of oligomeric A&beta;1-42. Then, we follow an individual experiment in which LTP is reduced in hippocampal slices exposed to oligomerized A&beta;1-42 compared to slices in a control experiment where no A&beta;1-42 exposure had occurred.

Preparation of Oligomeric &#946;-amyloid1-42 and Induction of Synaptic Plasticity Impairment on Hippocampal Slices

One feature of Alzheimer's Disease is the elevation of A&beta;1-42 peptide. Here we provide a protocol for preparing synthetic A&beta;1-42 oligomers, which impairs hippocampal Long-Term Potentiation, a cellular correlate of memory. This procedure is useful for investigating mechanisms of A&beta;-induced pathology and drug screening.

Impairment of synaptic connections is likely to underlie the subtle amnesic changes occurring at the early stages of Alzheimer s Disease (AD). ...

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3. Specifically, it affects the ability of neurons to receive inputs from other cells2 and contributes to the propagation of action potentials4,5. The morphology of the neurites also affects how information is processed. The diversity of dendrite morphologies facilitate local and long range signaling and allow individual neurons or groups of neurons to carry out specialized functions within the neuronal network6,7. Alterations in dendrite morphology, including fragmentation of dendrites and changes in branching patterns, have been observed in a number of disease states, including Alzheimer's disease8, schizophrenia9,10, and mental retardation11. The ability to both understand the factors that shape dendrite morphologies and to identify changes in dendrite morphologies is essential in the understanding of nervous system function and dysfunction.
Neurite morphology is often analyzed by Sholl analysis and by counting the number of neurites and the number of branch tips. This analysis is generally applied to dendrites, but it can also be applied to axons. Performing this analysis by hand is both time consuming and inevitably introduces variability due to experimenter bias and inconsistency. The Bonfire program is a semi-automated approach to the analysis of dendrite and axon morphology that builds upon available open-source morphological analysis tools. Our program enables the detection of local changes in dendrite and axon branching behaviors by performing Sholl analysis on subregions of the neuritic arbor. For example, Sholl analysis is performed on both the neuron as a whole as well as on each subset of processes (primary, secondary, terminal, root, etc.) Dendrite and axon patterning is influenced by a number of intracellular and extracellular factors, many acting locally. Thus, the resulting arbor morphology is a result of specific processes acting on specific neurites, making it necessary to perform morphological analysis on a smaller scale in order to observe these local variations12.
The Bonfire program requires the use of two open-source analysis tools, the NeuronJ plugin to ImageJ and NeuronStudio. Neurons are traced in ImageJ, and NeuronStudio is used to define the connectivity between neurites. Bonfire contains a number of custom scripts written in MATLAB (MathWorks) that are used to convert the data into the appropriate format for further analysis, check for user errors, and ultimately perform Sholl analysis. Finally, data are exported into Excel for statistical analysis. A flow chart of the Bonfire program is shown in Figure 1.

We have developed a computer program to analyze neuronal morphology. In combination with two existing open source analysis tools, our program performs Sholl analysis and determines the number of neurites, branch points, and neurite tips. The analyses are performed so that local changes in neurite morphology can be observed.

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3.  Specifically, it affects the ability of neurons to ...

Detection of Neuritic Plaques in Alzheimer's Disease Mouse Model

Alzheimer's disease (AD) is the most common neurodegenerative disorder leading to dementia. Neuritic plaque formation is one of the pathological hallmarks of Alzheimer's disease. The central component of neuritic plaques is a small filamentous protein called amyloid &beta; protein (A&beta;)1, which is derived from sequential proteolytic cleavage of the beta-amyloid precursor protein (APP) by &beta;-secretase and &gamma;-secretase. The amyloid hypothesis entails that A&gamma;-containing plaques as the underlying toxic mechanism in AD pathology2. The postmortem analysis of the presence of neuritic plaque confirms the diagnosis of AD. To further our understanding of A&gamma; neurobiology in AD pathogenesis, various mouse strains expressing AD-related mutations in the human APP genes were generated. Depending on the severity of the disease, these mice will develop neuritic plaques at different ages. These mice serve as invaluable tools for studying the pathogenesis and drug development that could affect the APP processing pathway and neuritic plaque formation. In this protocol, we employ an immunohistochemical method for specific detection of neuritic plaques in AD model mice. We will specifically discuss the preparation from extracting the half brain, paraformaldehyde fixation, cryosectioning, and two methods to detect neurotic plaques in AD transgenic mice: immunohistochemical detection using the ABC and DAB method and fluorescent detection using thiofalvin S staining method.

One of the pathological characteristics of AD is the formation of Amyloid &#x03B2; protein positive neuritic plaques. In this protocol we describe two methods to detect neuritic plaques in transgenic AD model mice: immunohistochemical detection using the ABC and DAB method and fluorescent detection using thioflavin S staining method.

Alzheimer's disease (AD) is the most common neurodegenerative disorder leading to dementia. Neuritic plaque formation is one of the pathological hallmarks ...

Morris Water Maze Test for Learning and Memory Deficits in Alzheimer&#39;s Disease Model Mice

The Morris Water Maze (MWM) was first established by neuroscientist Richard G. Morris in 1981 in order to test hippocampal-dependent learning, including acquisition of spatial memoryand long-term spatial memory 1. The MWM is a relatively simple procedure typically consisting of six day trials, the main advantage being the differentiation between the spatial (hidden-platform) and non-spatial (visible platform) conditions 2-4. In addition, the MWM testing environment reduces odor trail interference 5. This has led the task to be used extensively in the study of the neurobiology and neuropharmacology of spatial learning and memory. The MWM plays an important role in the validation of rodent models for neurocognitive disorders such as Alzheimer&#x2019;s Disease 6, 7. In this protocol we discussed the typical procedure of MWM for testing learning and memory and data analysis commonly used in Alzheimer&#x2019;s disease transgenic model mice.

Morris Water Maze Test for Learning and Memory Deficits in Alzheimer's Disease Model Mice

The Morris Water Maze is a behavioral task to test hippocampal-dependent learning and memory. It has been widely used in the study of neurobiology, neuropharmacology and neurocognitive disorders in rodent models.

The Morris Water Maze (MWM) was first established by neuroscientist Richard G. Morris in 1981 in order to test hippocampal-dependent learning, including ...

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

Nervous system development requires the correct specification of neuron position and identity, followed by accurate neuron class-specific dendritic development and axonal wiring. Recently the dendritic arborization (DA) sensory neurons of the Drosophila larval peripheral nervous system (PNS) have become powerful genetic models in which to elucidate both general and class-specific mechanisms of neuron differentiation.
There are four main DA neuron classes (I-IV)1. They are named in order of increasing dendrite arbor complexity, and have class-specific differences in the genetic control of their differentiation2-10. The DA sensory system is a practical model to investigate the molecular mechanisms behind the control of dendritic morphology11-13 because: 1) it can take advantage of the powerful genetic tools available in the fruit fly, 2) the DA neuron dendrite arbor spreads out in only 2 dimensions beneath an optically clear larval cuticle making it easy to visualize with high resolution in vivo, 3) the class-specific diversity in dendritic morphology facilitates a comparative analysis to find key elements controlling the formation of simple vs. highly branched dendritic trees, and 4) dendritic arbor stereotypical shapes of different DA neurons facilitate morphometric statistical analyses.
DA neuron activity modifies the output of a larval locomotion central pattern generator14-16. The different DA neuron classes have distinct sensory modalities, and their activation elicits different behavioral responses14,16-20. Furthermore different classes send axonal projections stereotypically into the Drosophila larval central nervous system in the ventral nerve cord (VNC)21. These projections terminate with topographic representations of both DA neuron sensory modality and the position in the body wall of the dendritic field7,22,23. Hence examination of DA axonal projections can be used to elucidate mechanisms underlying topographic mapping7,22,23, as well as the wiring of a simple circuit modulating larval locomotion14-17.
We present here a practical guide to generate and analyze genetic mosaics24 marking DA neurons via MARCM (Mosaic Analysis with a Repressible Cell Marker)1,10,25 and Flp-out22,26,27 techniques (summarized in Fig. 1).

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

The dendritic arborization sensory neurons of the Drosophila larval peripheral nervous system are useful models to elucidate both general and neuron class-specific mechanisms of neuron differentiation. We present a practical guide to generate and analyze dendritic arborization neuron genetic mosaics.

Nervous system development requires the correct specification of neuron position and identity, followed by accurate neuron class-specific dendritic ...

T-maze Forced Alternation and Left-right Discrimination Tasks for Assessing Working and Reference Memory in Mice

Forced alternation and left-right discrimination tasks using the T-maze have been widely used to assess working and reference memory, respectively, in rodents. In our laboratory, we evaluated the two types of memory in more than 30 strains of genetically engineered mice using the automated version of this apparatus. Here, we present the modified T-maze apparatus operated by a computer with a video-tracking system and our protocols in a movie format. The T-maze apparatus consists of runways partitioned off by sliding doors that can automatically open downward, each with a start box, a T-shaped alley, two boxes with automatic pellet dispensers at one side of the box, and two L-shaped alleys. Each L-shaped alley is connected to the start box so that mice can return to the start box, which excludes the effects of experimenter handling on mouse behavior. This apparatus also has an advantage that in vivo microdialysis, in vivo electrophysiology, and optogenetics techniques can be performed during T-maze performance because the doors are designed to go down into the floor. In this movie article, we describe T-maze tasks using the automated apparatus and the T-maze performance of &alpha;-CaMKII+/- mice, which are reported to show working memory deficits in the eight-arm radial maze task. Our data indicated that &alpha;-CaMKII+/- mice showed a working memory deficit, but no impairment of reference memory, and are consistent with previous findings using the eight-arm radial maze task, which supports the validity of our protocol. In addition, our data indicate that mutants tended to exhibit reversal learning deficits, suggesting that &alpha;-CaMKII deficiency causes reduced behavioral flexibility. Thus, the T-maze test using the modified automatic apparatus is useful for assessing working and reference memory and behavioral flexibility in mice.

This article presents the protocol of T-maze tests using a modified automated apparatus for assessing the learning and memory functions in mice.

Forced alternation and left-right discrimination tasks using the T-maze have been widely used to assess working and reference memory, respectively, in ...

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs)1. Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue2. Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (&gt;97% spike detection efficiency), has a low rate of false positive spike detection (&lt; 0.003 spikes/sec), and a high temporal precision (about 3 msec) 3. This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo3,4.

Understanding the function of the vertebrate central nervous system requires recordings from many neurons because cortical function arises on the level of populations of neurons. Here we describe an optical method to record suprathreshold neural activity with single-cell and single-spike resolution, dithered random-access scanning. This method records somatic fluorescence calcium signals from up to 100 neurons with high temporal resolution. A maximum-likelihood algorithm deconvolves the underlying suprathreshold neural activity from the somatic fluorescence calcium signals. This method reliably detects spikes with high detection efficiency and a low rate of false positives and can be used to study neural populations in vitro and in vivo.

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also ...

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability1, 2. The worker bees spend the first half of their life inside the dark hive, where they are nursing the larvae or building the regular hexagonal combs for food (e.g. pollen or nectar) and brood3. The antennae are extraordinary multisensory feelers and play a pivotal role in various tactile mediated tasks4, including hive building5 and pattern recognition6. Later in life, each single bee leaves the hive to forage for food. Then a bee has to learn to discriminate profitable food sources, memorize their location, and communicate it to its nest mates7. Bees use different floral signals like colors or odors7, 8, but also tactile cues from the petal surface9 to form multisensory memories of the food source. Under laboratory conditions, bees can be trained in an appetitive learning paradigm to discriminate tactile object features, such as edges or grooves with their antennae10, 11, 12, 13. This learning paradigm is closely related to the classical olfactory conditioning of the proboscis extension response (PER) in harnessed bees14. The advantage of the tactile learning paradigm in the laboratory is the possibility of combining behavioral experiments on learning with various physiological measurements, including the analysis of the antennal movement pattern.

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

In this protocol we show how to condition harnessed honey bees to tactile stimuli and introduce a 2D motion capture technique for analyzing the kinematics of fine-scale antennal sampling pattern.

Honey bees (Apis mellifera L.) are eusocial insects and well known for their complex division of labor and associative learning capability1, 2. The worker ...

Whole Mount Immunolabeling of Olfactory Receptor Neurons in the Drosophila Antenna

Odorant molecules bind to their target receptors in a precise and coordinated manner. Each receptor recognizes a specific signal and relays this information to the brain. As such, determining how olfactory information is transferred to the brain, modifying both perception and behavior, merits investigation. Interestingly, there is emerging evidence that cellular transduction and transcriptional factors are involved in the diversification of olfactory receptor neuron. Here we provide a robust whole mount immunological labeling method to assay in vivo olfactory receptor neuron organization. Using this method, we identified all olfactory receptor neurons with anti-ELAV antibody, a known pan-neural marker and Or49a-mCD8::GFP, an olfactory receptor neuron specifically expressed in Nba neuron using anti-GFP antibody.

Whole Mount Immunolabeling of Olfactory Receptor Neurons in the Drosophila Antenna

Herein we describe the process of whole mount immunostaining of Drosophila antennae, which enables us to better understand the molecular mechanisms involved in the diversification of olfactory receptor neurons (ORN)s.

Odorant molecules bind to their target receptors in a precise and coordinated manner. Each receptor recognizes a specific signal and relays this ...

Stereotaxic Infusion of Oligomeric Amyloid-beta into the Mouse Hippocampus

Alzheimer&#8217;s disease is a neurodegenerative disease affecting the aging population. A key neuropathological feature of the disease is the over-production of amyloid-beta and the deposition of amyloid-beta plaques in brain regions of the afflicted individuals. Throughout the years scientists have generated numerous Alzheimer&#8217;s disease mouse models that attempt to replicate the amyloid-beta pathology. Unfortunately, the mouse models only selectively mimic the disease features. Neuronal death, a prominent effect in the brains of Alzheimer&#8217;s disease patients, is noticeably lacking in these mice. Hence, we and others have employed a method of directly infusing soluble oligomeric species of amyloid-beta - forms of amyloid-beta that have been proven to be most toxic to neurons - stereotaxically into the brain. In this report we utilize male C57BL/6J mice to document this surgical technique of increasing amyloid-beta levels in a select brain region. The infusion target is the dentate gyrus of the hippocampus because this brain structure, along with the basal forebrain that is connected by the cholinergic circuit, represents one of the areas of degeneration in the disease. The results of elevating amyloid-beta in the dentate gyrus via stereotaxic infusion reveal increases in neuron loss in the dentate gyrus within 1 week, while there is a concomitant increase in cell death and cholinergic neuron loss in the vertical limb of the diagonal band of Broca of the basal forebrain. These effects are observed up to 2 weeks. Our data suggests that the current amyloid-beta infusion model provides an alternative mouse model to address region specific neuron death in a short-term basis. The advantage of this model is that amyloid-beta can be elevated in a spatial and temporal manner.

Here, we present a protocol for direct stereotaxic brain infusion of amyloid-beta. This methodology provides an alternative in vivo mouse model to address the short-term effects of amyloid-beta on brain neurons.