This work was supported by Miguel Servet Research Contract MS14/00041 and Research Project PI14/00292 from the Plan Nacional de I+D+i 2013&#8211;2016, the Instituto de Salud Carlos III-Subdirecci&#243;n General de Evaluaci&#243;n y Fomento de la Investigaci&#243;n, the European Regional Development Fund (FEDER), and by PFIS Predoctoral Contract FI16/00311. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

1. Installation of SDM-PSI
<ol>
	<li>Go to <a href="https://www.sdmproject.com/software/" target="_blank">https://www.sdmproject.com/software/</a> to download the version of SDM-PSI for the operating system of the computer as a ZIP file.</li>
	<li>Decompress the ZIP file. To avoid problems, decompress it within a local folder without blank spaces in its path.</li>
	<li>Click the file SdmPsiGui to execute the graphical interface of SDM-PSI, and close the About splash window that will automatically open.</li>
	<li>If SdmPsiGui does not find all required paths, it will automatically offer to display the preferences window. Press Yes.
	<ol>
		<li>If MRICron is not installed in the computer go to <a href="https://www.nitrc.org/frs/?group_id=152" target="_blank">https://www.nitrc.org/frs/?group_id=152</a> to download the version for the operating system of the computer as a ZIP file, and decompress the ZIP file.</li>
		<li>In the Brain viewer tab of the preferences window, ensure that Brain viewer is set to MRICron, and click the folder icon next to Brain viewer executable to find the MRICron executable.</li>
		<li>Ensure that all remaining paths in the different tabs have blue marks, which indicate that the paths are correct.</li>
	</ol>
	</li>
	<li>In case that SdmPsiGui has not automatically displayed the preferences window, go to the Tools menu and click Preferences.</li>
	<li>In the Multithreading tab, specify the number of concurrent threads to use in the calculations. Some SDM-PSI calculations take a very long time (from hours to days) and consume a large amount of RAM memory (from hundreds of megabytes to gigabytes). The use of multiple threads (parallel processing) substantially decreases the time but increases the memory used.</li>
	<li>Close the preferences window and SdmPsiGui.</li>
</ol>
2. Meta-analysis plan
<ol>
	<li>Specify a precise question. For instance, &#34;do patients with OCD have gray matter volume regional abnormalities?&#34;</li>
	<li>Write clear inclusion criteria that allow a systematic inclusion of the studies. For instance, &#34;all studies that performed whole-brain voxel-based comparison of gray matter volume between individuals with OCD and healthy controls&#34;.</li>
	<li>Write clear exclusion criteria that allow a systematic exclusion of those studies that cannot or should not be included for specific reasons. For instance, &#34;studies with less than 10 patients, duplicated datasets, or studies from which the required information cannot be retrieved&#34;.</li>
	<li>Write down the data to extract from each study. The following list includes the recommended data (some of them are not strictly required, but their absence would impoverish the meta-analysis):
	<ul>
		<li>An identification of the study.</li>
		<li>The sample sizes.</li>
		<li>The statistical significance level, which is the t-value, z-value or p-value used in the study to determine which voxels were statistically significant.</li>
		<li>The software and stereotactic space. See Table 1 for the list of software packages and stereotactic spaces understood by SDM-PSI.</li>
		<li>The coordinates and height of the peaks. The height of a peak is its t-value or z-value, but a p-value is also useful.</li>
		<li>Variables that will be used to describe the samples or to conduct subgroup analyses or meta-regressions.</li>
	</ul>
	</li>
	<li>To increase the quality of the review, consider following the &#34;Ten simple rules for neuroimaging meta-analysis&#34;20 and the PRISMA checklist21.</li>
	<li>To increase the transparency of the review, consider registering the protocol beforehand on a publicly available database such as PROSPERO (<a href="https://www.crd.york.ac.uk/PROSPERO/" target="_blank">https://www.crd.york.ac.uk/PROSPERO/</a>).</li>
</ol>
3. Exhaustive search
<ol>
	<li>Select a set of keywords that allow finding any study that might meet the inclusion criteria. For instance, the keywords might be &#34;obsessive&#8211;compulsive disorder&#34; plus &#34;morphometry&#34;, &#34;voxel-based&#34; or &#34;voxelwise&#34;.</li>
	<li>Conduct the search on databases such as PubMed and Web of Science:
	<ol>
		<li>Go to database website, e.g. <a href="https://www.ncbi.nlm.nih.gov/pubmed/" target="_blank">https://www.ncbi.nlm.nih.gov/pubmed/</a> for PubMed.</li>
		<li>Type the search query. In the example meta-analysis, the query might be &#34;obsessive&#8211;compulsive disorder&#34; AND (&#34;morphometry&#34; OR &#34;voxel-based&#34; OR &#34;voxelwise&#34;). In this query, the operator &#34;AND&#34; means that the studies must have all keywords, the operator &#34;OR&#34; means that the studies must have at least one of the keywords, and the parentheses indicate the order of these logical operations. Therefore, the retrieved studies must have the keyword &#34;obsessive&#8211;compulsive disorder&#34; and at least one of the keywords &#34;morphometry&#34;, &#34;voxel-based&#34; or &#34;voxelwise&#34;. Note that other strategies are possible.</li>
	</ol>
	</li>
	<li>Apply the inclusion/exclusion criteria. For instance, from the results provided by the databases, select only the articles that analyze differences in gray matter volume between patients with obsessive-compulsive disorder and controls performing whole-brain voxel-based morphometry studies, and discard studies including less than 10 patients and studies that re-analyzed previously published data.</li>
	<li>To increase the exhaustiveness of the search, consider conducting a manual search over the referenced works in the selected studies.</li>
	<li>To maximize the inclusion of studies and avoid uncertainty in the collection of data, consider contacting the corresponding authors to ask for any missing or unclear data.</li>
	<li>Record the number of studies retrieved and the number of studies excluded for each reason. Consider creating a PRISMA flow diagram21 with these numbers.</li>
</ol>
4. Collection of the data
<ol>
	<li>For each included study, read the manuscript to find the specific data to extract.</li>
	<li>Save the data from the studies systematically, e.g., typing the data in pre-formatted spreadsheet files. To minimize typing errors, consider copying and pasting the numbers of interest and double-checking the saved data.</li>
	<li>When the statistical significance level is unclear, consider following these recommendations:
	<ol>
		<li>If the manuscript reports peaks obtained using two whole-brain statistical significance levels, e.g. p-value &#60; 0.001 without correction for multiple comparisons (from now on, &#34;uncorrected threshold&#34;) and familywise error rate (FWER) &#60; 0.05 (from now on &#34;corrected threshold&#34;), select the uncorrected threshold, and include all peaks obtained using this uncorrected threshold. The reason to prefer the uncorrected threshold is that studies usually obtain more peaks applying an uncorrected threshold, and SDM estimates the maps more accurately if it has information from more peaks.</li>
		<li>If the manuscript reports peaks obtained using an uncorrected threshold for increases and a corrected threshold for decreases (or vice versa), select the uncorrected threshold but include only the peaks obtained using the corrected threshold. This is a conservative approximation because it might discard some peaks obtained using the uncorrected threshold. An example of this situation might be when a manuscript states something such as &#34;we detected FWER-corrected larger gray matter volumes in some regions, while we did not detect smaller gray matter volume in any region even using uncorrected p-value &#60; 0.001&#34;.</li>
		<li>If the authors applied cluster-based statistics, use the cluster-forming height threshold. This is a conservative approximation because some voxels might have had t-values higher than the threshold, but the authors discarded them because their clusters were not large enough.</li>
		<li>If the manuscript does not specify a threshold, use a slightly smaller value than the t-value of the smallest peak. The reason to use this t-value is that if the authors had applied this statistical significance threshold without requiring a minimum size for the clusters, they would have found the same peaks.</li>
	</ol>
	</li>
	<li>When recording the peak information do the following:
	<ol>
		<li>Exclude peaks obtained using a statistical significance threshold that is more liberal than the threshold selected for the rest of the brain. An example of this situation is when the authors applied more liberal thresholds or small volume corrections to some a priori brain regions.</li>
		<li>Convert z-values and p-values into t-values. Click the button Convert peaks in the SDM-PSI software to convert them easily. Alternatively, convert them in the same spreadsheet file (e.g., &#34;=T.INV(1-0.001,34)&#34; for p-value = 0.001 and 34 degrees of freedom; the degrees of freedom are the sum of the sample sizes minus the number of parameters, which in a two-sample comparison are two plus the number of covariates used in the original comparison).</li>
		<li>Use positive t-values for peaks of increase (e.g., activation) and negative t-values for peaks of decrease (e.g., deactivation). See Table 2 for guidance on how to decide the sign of the t-values. 
		NOTE: We obtained information from studies &#34;Heuvel&#34; and &#34;Soriano-Mas&#34; after personal communication.</li>
	</ol>
	</li>
</ol>
5. Introduction of data into SDM-PSI
<ol>
	<li>Open SdmPsiGui and close the About splash window (avoid having any key pressed while closing it).</li>
	<li>Click the Change meta-analysis button at the upper-left part of the graphical interface to select a directory for the meta-analysis (any new empty directory of choice will do).</li>
	<li>Click the button SDM table editor to input general information from the studies, including their identification (column &#34;study&#34;), their sample sizes (columns &#34;n1&#34; and &#34;n2&#34;), the t-value that they used as statistical thresholds (column &#34;t_thr&#34;), and other potential variables to conduct subgroup analyses or meta-regressions.</li>
	<li>Within the selected directory, create a text file for each study with the coordinates and t-value of each peak:
	<ol>
		<li>Open a text editor to create a text file named as [the identification of study] + &#34;.&#34; + [the software] + &#34;_&#34; + [the stereotactic space] + &#34;.txt&#34;. For instance, for the study &#34;Carmona&#34; that was conducted with SPM and reports the coordinates in MNI space, the name of the text file must be &#34;Carmona.spm_mni.txt&#34;. If the study has no peaks, the software and stereotactic space can be replaced by &#34;no_peaks&#34;.</li>
		<li>Write the coordinates and t-value of each peak in a different row. For instance, the first rows of the text file &#34;Carmona.spm_mni.txt&#34; should be: 
		40,39,21,-5.14 
		53,27,21,-3.77 
		56,23,20,-3.63</li>
	</ol>
	</li>
</ol>
6. Pre-processing
<ol>
	<li>Click in the Preprocessing button at the left menu bar, select the modality of the studies at the list box labeled &#34;Modality&#34; and press OK. In the example meta-analysis, the modality is &#34;VBM &#8211; gray matter&#34;.</li>
	<li>Wait (some minutes) while SDM-PSI calculates the maps of the lower and upper bounds of potential effect sizes. SdmPsiGui will show four progress bars that display the status of the execution and the expected remaining time for the current process. During the calculations, the color of the circle next to &#34;Processing status&#34; will be yellow, and will change to green if the execution ends successfully, or to red if it fails.</li>
</ol>
7. Main analysis
<ol>
	<li>Click the Mean button at the left menu bar and press OK.</li>
	<li>Wait (some minutes) while SDM-PSI conducts the multiple imputation and meta-analysis (Figure 1).</li>
	<li>Click the Threshold button in the left menu toolbox, select the uncorrected p-values of the main analysis (&#34;MyTest_uncorrp&#34; by default) and press OK. SDM-PSI will automatically open both MRICron to visualize the results and a webpage with a detailed report of them.</li>
	<li>Press the FWE correction button at the left menu toolbox, select the main analysis in the list box (&#34;MyTest&#34; by default) and press OK.</li>
	<li>Wait (some hours or even days) while SDM-PSI conducts the permutation test.</li>
	<li>Click the Threshold button in the left menu toolbox, select the TFCE-correction of the main analysis (&#34;MyTest_corrp_tfce&#34; by default) and press OK. SDM-PSI will automatically open both MRICron to visualize the results and a webpage with a detailed report of them.</li>
</ol>
8. Heterogeneity, publication bias and grading
<ol>
	<li>Click the Extract button on the left menu toolbox, select a peak from the main analysis (&#34;MyTest_z_p_0.05000_10_neg_peak1&#34; by default) and press OK. SDM-PSI will automatically open a webpage with statistics of this peak. Write down the heterogeneity I2 statistic.</li>
	<li>Click the Bias Test button on the left menu toolbox, select a peak from the main analysis (&#34;MyTest_z_p_0.05000_10_neg_peak1&#34; by default) and press OK. SDM-PSI will automatically open a webpage with a funnel plot and the results of a test for small-study effect and a test for excess significance. The former tests whether there is asymmetry in the funnel plot (i.e., larger effect size in small studies), which could indicate that small studies are only published if they find large effect sizes or other sources of bias. The latter tests whether the number of studies with statistically significant results is larger than expected, which could indicate that studies are only published if they find statistically significant results or other sources of bias.</li>
	<li>Press the Evidence grading button from the top toolbox, select the main analysis (&#34;MyTest&#34; by default) from the list box and press OK. After some seconds, SDM-PSI will automatically open MRICron to show the classes of evidence.</li>
</ol>

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The authors have nothing to disclosure.

As introduced earlier, most voxel-based meta-analytic methods use a test for convergence of peaks that has some limitations, and then conduct a binary classification of the evidence exclusively based on p-values.
In this protocol, we detailed how to conduct a voxel-based meta-analysis using SDM-PSI, which has a number of positive features including a standard permutation test to assess the statistical significance of the effects. In addition, we show how the strength of the evidence can be graded using a set of criteria that go beyond a binary classification that solely relies on one statistical significance level.
To facilitate the replication of the example meta-analysis, we provide the data already extracted from the manuscripts from a previous meta-analysis. Interestingly, in the manuscript of that meta-analysis, the evidence &ldquo;seemed&rdquo; stronger than the evidence that we found with the updated methods. We therefore suggest that unsystematic evaluations of the evidence in previous voxel-based meta-analyses are taken with caution.
We hope that following this protocol, neuroimaging meta-analyses provide a richer and more granulate description of the evidence of neuroimaging findings.

Since the introduction of magnetic resonance imaging, the neuroimaging community has published thousands of studies of the neural substrates of psychological functions and neuropsychiatric disorders. To summarize these findings, several methods have been developed1,2,3,4,5,6. Original voxel-based neuroimaging studies report the coordinates of the peaks of statistical significance (e.g., in a comparison of gray matter volume between patients and controls), and meta-analytic methods commonly assess whether there is convergence of peaks in certain brain regions.
However, we have previously shown that these tests for convergence of peaks rely on delicate assumptions that might influence the patterns of meta-analysis results and their statistical significance7. Specifically, these tests assume that voxels are independent and that they have the same probability of a "false" peak, while in real gray matter, voxels correlate with their neighbors and the probability that a voxel has a "false" peak depends on its tissue composition. In addition, they also encompass paradoxes such as that the statistical power increases in the presence of few true effects, and decreases when there are multiple true effects.
To overcome these problems, we developed a method that imputes the brain maps of statistical effects for each study and then conducts a standard random-effects meta-analysis to formally test whether the effects are different from zero. This method is called "Seed-based d Mapping with Permutation of Subject Images" (SDM-PSI)8 and its main features include:
<ul>
	<li>Accounting for both increases and decreases of the outcome of interest (e.g., activation and deactivation) so that contradictory findings cancel each other4;</li>
	<li>Use of effect size estimates with random-effects modeling, which increases reliability and performance9;</li>
	<li>Potential simultaneous inclusion of available 3D statistical images (i.e., maps of t-test values)10;</li>
	<li>Subject-based permutation tests identical to those of FSL "randomize" tool11;</li>
	<li>Use of threshold-free cluster enhancement (TFCE) statistics12.</li>
</ul>
We have detailed and fully validated the SDM methods elsewhere4,8,10,13,14.
In addition, we suggest not relying on a binary classification of the voxels based on the level of statistical significance (significant vs. not significant) but, conversely, assessing the strength of the evidence using a set of criteria22. The binary statistical significance reductionism leads to poor control of the false positive and false negative rates15, whereas the criteria use ranges of statistical significance levels and take into account the amount of data or potential biases. The SDM-PSI software returns the necessary elements to conduct such a classification8 and thus they can be employed to afford a more granular classification of the strength of the evidence.
Here we show how to conduct a meta-analysis of voxel-based neuroimaging studies using SDM-PSI. To exemplify the protocol, we use data from a published meta-analysis of voxel-based morphometry studies that investigated gray matter abnormalities in patients with obsessive-compulsive disorder (OCD)4. However, we will not use the methods employed in that early meta-analysis, but the aforementioned state-of-the-art procedures. The reader can download the software and these data from our website (<a href="http://www.sdmproject.com/" target="_blank">http://www.sdmproject.com/</a>) to replicate the analysis.
All researchers who aim to conduct a meta-analysis of voxel-based neuroimaging studies can follow this protocol. The method can be used with functional magnetic resonance imaging (fMRI, e.g., BOLD response to a stimulus)16, voxel-based morphometry (VBM, e.g., gray matter volume)17, diffusion tensor imaging (DTI, e.g., fractional anisotropy)18, position emission tomography (PET, e.g., receptor occupancy)19 and surface-based morphometry (SBM, e.g. cortical thickness) studies/datasets.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer</td>
			<td></td>
			<td></td>
			<td>32- or 64-bit Windows, 64-bit Mac OSX, or 64-bit Linux</td>
		</tr>
	</tbody>
</table>

meta-analysis of voxel-based neuroimaging studies using seed-based d mapping with permutation of subject images (sdm-psi)

Most methods for conducting meta-analysis of voxel-based neuroimaging studies do not assess whether effects are not null, but whether there is a convergence of peaks of statistical significance, and reduce the assessment of the evidence to a binary classification exclusively based on p-values (i.e., voxels can only be &ldquo;statistically significant&rdquo; or &ldquo;non-statistically significant&rdquo;). Here, we detail how to conduct a meta-analysis using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), a novel method that uses a standard permutation test to assess whether effects are not null. We also show how to grade the strength of the evidence according to a set of criteria that considers a range of statistical significance levels (from more liberal to more conservative), the amount of data or the detection of potential biases (e.g., small-study effect and excess of significance). To exemplify the procedure, we detail the conduction of a meta-analysis of voxel-based morphometry studies in obsessive-compulsive disorder, and we provide all the data already extracted from the manuscripts to allow the reader to replicate the meta-analysis easily. SDM-PSI can also be used for meta-analyses of functional magnetic resonance imaging, diffusion tensor imaging, position emission tomography and surface-based morphometry studies.

As shown in the map opened in MRICron when thresholding of the main analysis (Figure 2, step 7.6), patients with OCD had statistically significantly smaller gray matter volume in dorsal anterior cingulate/medial frontal cortex. The accompanying webpage details that the cluster is moderately small (143 voxels) and mainly located at Brodmann area 32, and that the peak of the cluster is at MNI [2, 32, 32], has a z-value of -4.97 and a FWER-corrected p-value of 0.01.
In the webpages obtained in steps 8.1 and 8.2, the low I2 statistic (1.5%) indicates very small heterogeneity, the funnel plot does not show asymmetries (Figure 3), and both the test for small-study effect and the test for excess of significance are negative. However, the evidence of smaller gray matter volume in dorsal anterior cingulate cortex is moderately weak, as shown in the map open in MRICron when grading evidence, especially due to limited amount of data.
When the main analysis was thresholded using a less stringent statistical significance level (step 7.3), patients also showed statistically significantly larger gray matter volume in the striatum and superior parietal gyrus (uncorrected p-values = 0.00006 and 0.0002 respectively), but the evidence of these abnormalities should be considered weaker.

<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59841/59841fig01.jpg" /> 
Figure 1: Main window of the SDP-PSI graphical user interface during a mean execution. <a href="https://www.jove.com/files/ftp_upload/59841/59841fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59841/59841fig02.jpg" /> 
Figure 2: Regions of statistically significantly smaller gray matter volume in patients with obsessive-compulsive disorder as compared to matched healthy controls. 
The cluster of statistical significance covers 143 voxels, has its peak at MNI [2,32,32], and includes mostly dorsal anterior cingulate/medial frontal cortex, Brodmann area 32. <a href="https://www.jove.com/files/ftp_upload/59841/59841fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59841/59841fig03.jpg" /> 
Figure 3: Funnel plot for the peak of the cluster of statistically significantly smaller gray matter volume in dorsal anterior cingulate cortex. <a href="https://www.jove.com/files/ftp_upload/59841/59841fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Software packages</td>
			<td>Coding in SDM</td>
		</tr>
		<tr>
			<td>Statistical Parametric Mapping (SPM)</td>
			<td>spm</td>
		</tr>
		<tr>
			<td>FMRIB Software Library (FSL)</td>
			<td>fsl</td>
		</tr>
		<tr>
			<td>Other packages</td>
			<td>other</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Stereotactic space</td>
			<td>Coding in SDM</td>
		</tr>
		<tr>
			<td>Montreal Neurological Institute (MNI)</td>
			<td>mni</td>
		</tr>
		<tr>
			<td>Raw Talairach</td>
			<td>tal</td>
		</tr>
		<tr>
			<td>MNI converted to Talairach using Brett transform</td>
			<td>brett</td>
		</tr>
	</tbody>
</table>
Table 1: List of software packages and stereotactic spaces understood by SDM-PSI.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>t-values must be positive when:</td>
			<td>t-values must be negative when:</td>
		</tr>
		<tr>
			<td>One-sample fMRI studies</td>
			<td>task &#62; baseline (activation)</td>
			<td>task &#60; baseline (deactivation)</td>
		</tr>
		<tr>
			<td>Two-sample fMRI studies</td>
			<td>patients &#62; controls in task &#62; baseline (hyper-activation)</td>
			<td>patients &#60; controls in task &#62; baseline (hypo-activation)</td>
		</tr>
		<tr>
			<td></td>
			<td>patients &#60; controls in task &#60; baseline (failure of deactivation)</td>
			<td>patients &#62; controls in task &#60; baseline (hyper-deactivation)</td>
		</tr>
		<tr>
			<td>Two-sample VBM / FA studies</td>
			<td>patients &#62; controls (larger volume / FA)</td>
			<td>patients &#60; controls (smaller volume / FA)</td>
		</tr>
	</tbody>
</table>
Table 2: Sign of the t-values of the peaks.

Watch this Scientific Journal Video about Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI at JoVE.com

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

We detail how to conduct a meta-analysis of voxel-based neuroimaging studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI).

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)

Most methods for conducting meta-analysis of voxel-based neuroimaging studies do not assess whether effects are not null, but whether there is a ...

meta-analysis-voxel-based-neuroimaging-studies-using-seed-based-d

Research

JoVE Journal

Neuroscience

68.3K Views.  Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS). We detail how to conduct a meta-analysis of voxel-based neuroimaging studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI).

Video: Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Functional Mapping with Simultaneous MEG and EEG

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the processing of simple sensory stimuli. We will use somatosensory stimuli to locate the hand somatosensory areas, auditory stimuli to locate the auditory cortices, visual stimuli in four quadrants of the visual field to locate the early visual areas. These type of experiments are used for functional mapping in epileptic and brain tumor patients to locate eloquent cortices. In basic neuroscience similar experimental protocols are used to study the orchestration of cortical activity. The acquisition protocol includes quality assurance procedures, subject preparation for the combined MEG/EEG study, and acquisition of evoked-response data with somatosensory, auditory, and visual stimuli. We also demonstrate analysis of the data using the equivalent current dipole model and cortically-constrained minimum-norm estimates. Anatomical MRI data are employed in the analysis for visualization and for deriving boundaries of tissue boundaries for forward modeling and cortical location and orientation constraints for the minimum-norm estimates.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to map brain areas involved in the processing of simple sensory stimuli.

We use magnetoencephalography (MEG) and electroencephalography (EEG) to locate and determine the temporal evolution in brain areas involved in the ...

Basics of Multivariate Analysis in Neuroimaging Data

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.

The current article describes the basics of multivariate analysis and contrasts it to the more commonly used voxel-wise univariate analysis. Both types of analysis are applied to a clinical-neuroscience data set. Supplementary split-half simulations show better replication of the multivariate results in independent data sets.

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be ...

Mapping Inhibitory Neuronal Circuits by Laser Scanning Photostimulation

Inhibitory neurons are crucial to cortical function. They comprise about 20% of the entire cortical neuronal population and can be further subdivided into diverse subtypes based on their immunochemical, morphological, and physiological properties1-4. Although previous research has revealed much about intrinsic properties of individual types of inhibitory neurons, knowledge about their local circuit connections is still relatively limited3,5,6. Given that each individual neuron's function is shaped by its excitatory and inhibitory synaptic input within cortical circuits, we have been using laser scanning photostimulation (LSPS) to map local circuit connections to specific inhibitory cell types. Compared to conventional electrical stimulation or glutamate puff stimulation, LSPS has unique advantages allowing for extensive mapping and quantitative analysis of local functional inputs to individually recorded neurons3,7-9. Laser photostimulation via glutamate uncaging selectively activates neurons perisomatically, without activating axons of passage or distal dendrites, which ensures a sub-laminar mapping resolution. The sensitivity and efficiency of LSPS for mapping inputs from many stimulation sites over a large region are well suited for cortical circuit analysis.
Here we introduce the technique of LSPS combined with whole-cell patch clamping for local inhibitory circuit mapping. Targeted recordings of specific inhibitory cell types are facilitated by use of transgenic mice expressing green fluorescent proteins (GFP) in limited inhibitory neuron populations in the cortex3,10, which enables consistent sampling of the targeted cell types and unambiguous identification of the cell types recorded. As for LSPS mapping, we outline the system instrumentation, describe the experimental procedure and data acquisition, and present examples of circuit mapping in mouse primary somatosensory cortex. As illustrated in our experiments, caged glutamate is activated in a spatially restricted region of the brain slice by UV laser photolysis; simultaneous voltage-clamp recordings allow detection of photostimulation-evoked synaptic responses. Maps of either excitatory or inhibitory synaptic input to the targeted neuron are generated by scanning the laser beam to stimulate hundreds of potential presynaptic sites. Thus, LSPS enables the construction of detailed maps of synaptic inputs impinging onto specific types of inhibitory neurons through repeated experiments. Taken together, the photostimulation-based technique offers neuroscientists a powerful tool for determining the functional organization of local cortical circuits.

This paper introduces an approach of combining laser scanning photostimulation with whole cell recordings in transgenic mice expressing GFP in limited inhibitory neuron populations. The technique allows for extensive mapping and quantitative analysis of local synaptic circuits of specific inhibitory cortical neurons.

Inhibitory neurons are crucial to cortical function. They comprise about 20% of the entire cortical neuronal population and can be further subdivided into ...

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains technically challenging. One approach, Activation-Induced Manganese-enhanced MRI (AIM MRI), has been used successfully to map neuronal activity in rodents 1-5. In AIM MRI, Mn2+ acts a calcium analog and accumulates in depolarized neurons 6,7. Because Mn2+ shortens the T1 tissue property, regions of elevated neuronal activity will enhance in MRI. Furthermore, Mn2+ clears slowly from the activated regions; therefore, stimulation can be performed outside the magnet prior to imaging, enabling greater experimental flexibility. However, because Mn2+ does not readily cross the blood-brain barrier (BBB), the need to open the BBB has limited the use of AIM MRI, especially in mice.
One tool for opening the BBB is ultrasound. Though potentially damaging, if ultrasound is administered in combination with gas-filled microbubbles (i.e., ultrasound contrast agents), the acoustic pressure required for BBB opening is considerably lower. This combination of ultrasound and microbubbles can be used to reliably open the BBB without causing tissue damage 8-11.
Here, a method is presented for performing AIM MRI by using microbubbles and ultrasound to open the BBB. After an intravenous injection of perflutren microbubbles, an unfocused pulsed ultrasound beam is applied to the shaved mouse head for 3 minutes. For simplicity, we refer to this technique of BBB Opening with Microbubbles and UltraSound as BOMUS 12. Using BOMUS to open the BBB throughout both cerebral hemispheres, manganese is administered to the whole mouse brain. After experimental stimulation of the lightly sedated mice, AIM MRI is used to map the neuronal response.
To demonstrate this approach, herein BOMUS and AIM MRI are used to map unilateral mechanical stimulation of the vibrissae in lightly sedated mice 13. Because BOMUS can open the BBB throughout both hemispheres, the unstimulated side of the brain is used to control for nonspecific background stimulation. The resultant 3D activation map agrees well with published representations of the vibrissae regions of the barrel field cortex 14. The ultrasonic opening of the BBB is fast, noninvasive, and reversible; and thus this approach is suitable for high-throughput and/or longitudinal studies in awake mice.

A technique is described for broadly opening the blood-brain barrier in the mouse using microbubbles and ultrasound. Using this technique, manganese can be administered to the mouse brain. Because manganese is an MRI contrast agent that accumulates in depolarized neurons, this approach enables imaging of neuronal activity.

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains ...

3-D Imaging and Analysis of Neurons Infected In Vivo with Toxoplasma gondii

Toxoplasma gondii is an obligate, intracellular parasite with a broad host range, including humans and rodents. In both humans and rodents, Toxoplasma establishes a lifelong persistent infection in the brain. While this brain infection is asymptomatic in most immunocompetent people, in the developing fetus or immunocompromised individuals such as acquired immune deficiency syndrome (AIDS) patients, this predilection for and persistence in the brain can lead to devastating neurologic disease. Thus, it is clear that the brain-Toxoplasma interaction is critical to the symptomatic disease produced by Toxoplasma, yet we have little understanding of the cellular or molecular interaction between cells of the central nervous system (CNS) and the parasite. In the mouse model of CNS toxoplasmosis it has been known for over 30 years that neurons are the cells in which the parasite persists, but little information is available about which part of the neuron is generally infected (soma, dendrite, axon) and if this cellular relationship changes between strains. In part, this lack is secondary to the difficulty of imaging and visualizing whole infected neurons from an animal. Such images would typically require serial sectioning and stitching of tissue imaged by electron microscopy or confocal microscopy after immunostaining. By combining several techniques, the method described here enables the use of thick sections (160 &#181;m) to identify and image whole cells that contain cysts, allowing three-dimensional visualization and analysis of individual, chronically infected neurons without the need for immunostaining, electron microscopy, or serial sectioning and stitching. Using this technique, we can begin to understand the cellular relationship between the parasite and the infected neuron.

3-D Imaging and Analysis of Neurons Infected In Vivo with Toxoplasma gondii

Using this protocol, we were able to image 160 &#181;m thick brain sections from mice infected with the parasite Toxoplasma gondii, which enables visualization and analysis of the spatial relationship between the encysting parasite and the infected neuron.

Toxoplasma gondii is an obligate, intracellular parasite with a broad host range, including humans and rodents. In both humans and rodents, Toxoplasma ...

Preparation of Acute Brain Slices Using an Optimized N-Methyl-D-glucamine Protective Recovery Method

This protocol is a practical guide to the N-methyl-D-glucamine (NMDG) protective recovery method of brain slice preparation. Numerous recent studies have validated the utility of this method for enhancing neuronal preservation and overall brain slice viability. The implementation of this technique by early adopters has facilitated detailed investigations into brain function using diverse experimental applications and spanning a wide range of animal ages, brain regions, and cell types. Steps are outlined for carrying out the protective recovery brain slice technique using an optimized NMDG artificial cerebrospinal fluid (aCSF) media formulation and enhanced procedure to reliably obtain healthy brain slices for patch clamp electrophysiology. With this updated approach, a substantial improvement is observed in the speed and reliability of gigaohm seal formation during targeted patch clamp recording experiments while maintaining excellent neuronal preservation, thereby facilitating challenging experimental applications. Representative results are provided from multi-neuron patch clamp recording experiments to assay synaptic connectivity in neocortical brain slices prepared from young adult transgenic mice and mature adult human neurosurgical specimens. Furthermore, the optimized NMDG protective recovery method of brain slicing is compatible with both juvenile and adult animals, thus resolving a limitation of the original methodology. In summary, a single media formulation and brain slicing procedure can be implemented across various species and ages to achieve excellent viability and tissue preservation.

Preparation of Acute Brain Slices Using an Optimized N-Methyl-D-glucamine Protective Recovery Method

This protocol demonstrates the implementation of an optimized N-methyl-D-glucamine (NMDG) protective recovery method of brain slice preparation. A single media formulation is used to reliably obtain healthy brain slices from animals of any age and for diverse experimental applications.

This protocol is a practical guide to the N-methyl-D-glucamine (NMDG) protective recovery method of brain slice preparation. Numerous recent studies have ...

The Use of Trace Eyeblink Classical Conditioning to Assess Hippocampal Dysfunction in a Rat Model of Fetal Alcohol Spectrum Disorders

Neonatal rats were administered a relatively high concentration of ethyl alcohol (11.9% v/v) during postnatal days 4-9, a time when the fetal brain undergoes rapid organizational change and is similar to accelerated brain changes that occur during the third trimester in humans. This model of fetal alcohol spectrum disorders (FASDs) produces severe brain damage, mimicking the amount and pattern of binge-drinking that occurs in some pregnant alcoholic mothers. We describe the use of trace eyeblink classical conditioning (ECC), a higher-order variant of associative learning, to assess long-term hippocampal dysfunction that is typically seen in alcohol-exposed adult offspring. At 90 days of age, rodents were surgically prepared with recording and stimulating electrodes, which measured electromyographic (EMG) blink activity from the left eyelid muscle and delivered mild shock posterior to the left eye, respectively. After a 5 day recovery period, they underwent 6 sessions of trace ECC to determine associative learning differences between alcohol-exposed and control rats. Trace ECC is one of many possible ECC procedures that can be easily modified using the same equipment and software, so that different neural systems can be assessed. ECC procedures in general, can be used as diagnostic tools for detecting neural pathology in different brain systems and different conditions that insult the brain.

Trace eyeblink classical conditioning (ECC) was used to assess hippocampal-dependent associative learning in adult rats that were administered a high concentration (11.9% v/v) of alcohol during early neonatal brain development. In general, ECC procedures are sound diagnostic tools for detecting brain dysfunction across many psychological and biomedical settings.

Neonatal rats were administered a relatively high concentration of ethyl alcohol (11.9% v/v) during postnatal days 4-9, a time when the fetal brain ...

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Type 2 diabetes mellitus (T2DM) is a global health crisis which is characterized by insulin signaling impairment and chronic inflammation in peripheral tissues. The hypothalamus in the central nervous system (CNS) is the control center for energy and insulin signal response regulation. Chronic inflammation in peripheral tissues and imbalances of certain chemokines (such as CCL5, TNF&#945;, and IL-6) contribute to diabetes and obesity. However, the functional mechanism(s) connecting chemokines and hypothalamic insulin signal regulation still remain unclear.
In vitro primary neuron culture models are convenient and simple models which can be used to investigate insulin signal regulation in hypothalamic neurons. In this study, we introduced exogeneous GLUT4 protein conjugated with GFP (GFP-GLUT4) into primary hypothalamic neurons to track GLUT4 membrane translocation upon insulin stimulation. Time-lapse images of GFP-GLUT4 protein trafficking were recorded by deconvolution microscopy, which allowed users to generate high-speed, high-resolution images without damaging the neurons significantly while conducting the experiment. The contribution of CCR5 in insulin regulated GLUT4 translocation was observed in CCR5 deficient hypothalamic neurons, which were isolated and cultured from CCR5 knockout mice. Our results demonstrated that the GLUT4 membrane translocation efficiency was reduced in CCR5 deficient hypothalamic neurons after insulin stimulation.

This protocol describes a technique for observation of real-time Green Fluorescence Protein (GFP) tagged Glucose Transporter 4 (GLUT4) protein trafficking upon insulin stimulation and characterization of the biological role of CCR5 in the insulin&#8211;GLUT4 signaling pathway with Deconvolution Microscopy.

Type 2 diabetes mellitus (T2DM) is a global health crisis which is characterized by insulin signaling impairment and chronic inflammation in peripheral ...

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy (NIRS) Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Portable neuroimaging approaches provide new advances to the study of brain function and brain development with previously inaccessible populations and in remote locations. This paper shows the development of field functional Near Infrared Spectroscopy (fNIRS) imaging to the study of child language, reading, and cognitive development in a rural village setting of C&#244;te d&#39;Ivoire. Innovation in methods and the development of culturally appropriate neuroimaging protocols allow&#160;a first-time look into the brain&#39;s development and children&#39;s learning outcomes in understudied environments. This paper demonstrates protocols for transporting and setting up a mobile laboratory, discusses considerations for field versus laboratory neuroimaging, and presents a guide for developing neuroimaging consent procedures and building meaningful long-term collaborations with local government and science partners. Portable neuroimaging methods can be used to study complex child development contexts, including the impact of significant poverty and adversity on brain development. The protocol presented here has been developed for use in C&#244;te d&#39;Ivoire, the world&#39;s primary source of cocoa, and where reports of child labor in the cocoa sector are common. Yet, little is known about the impact of child labor on brain development and learning. Field neuroimaging methods have the potential to yield new insights into such urgent issues, and the development of children globally.

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy NIRS Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Portable neuroimaging approaches (functional Near Infrared Spectroscopy) provide advances to the study of the brain in previously inaccessible regions; here, rural C&#244;te d&#39;Ivoire. Innovation in methods and development of culturally-appropriate neuroimaging protocols permits novel study of the brain&#39;s development and children&#39;s learning outcomes in environments with significant poverty and adversity.

Portable neuroimaging approaches provide new advances to the study of brain function and brain development with previously inaccessible populations and in ...

Head Implants for the Neuroimaging of Awake, Head-Fixed Rats

Anesthetics, commonly used in preclinical and fundamental scientific research, have a depressive influence on the metabolic, neuronal, and vascular functions of the brain and can adversely influence neurophysiological results. The use of awake animals for research studies is advantageous but poses the major challenge of keeping the animals calm and stationary to minimize motion artifacts throughout data acquisition. Awake imaging in smaller-sized rodents (e.g., mice) is very common but remains scant in rats as rats are bigger, stronger, and have a greater tendency to oppose movement restraints and head fixation over the long durations required for imaging. A new model of neuroimaging of awake, head-fixed rats using customized hand-sewn slings, 3D-printed head implants, head caps, and a headframe is described. The results acquired following a single trial of single-whisker stimulation suggest an increase in the intensity of the evoked functional response. The acquisition of the evoked functional response from awake, head-fixed rats is faster than that from anesthetized rats, reliable, reproducible, and can be used for repeated longitudinal studies.

A detailed new procedure for functional imaging of awake, head-fixed rats is described.

Anesthetics, commonly used in preclinical and fundamental scientific research, have a depressive influence on the metabolic, neuronal, and vascular ...