Name: meta-analysis of voxel-based neuroimaging studies using seed-based d mapping with permutation of subject images (sdm-psi)
Uploaded: 2019-11-27T12:30:00.000+00:00
Duration: 6 min 26 s
Description: 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

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><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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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

Nanyang Technological University

Research

JoVE Journal

Neuroscience

69.6K Views.  Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS). Seed-based d mapping is een methode voor het zetten van de meta-analyse neuroimaging studies die tegenstrijdig lijken. Bijvoorbeeld, de resultaten van studies van grijze stof afwijkingen in OCS. Eerdere meta-analytische methoden hebben vaak beoordeeld of studies meer statistische pieken in de ene of in een andere reden ondersteunen, terwijl SDM-PSI beoordeelt of er een effect is in een bepaalde reden.Onderzoekers hebben meestal gebruikt SDM om de neurale substraten van psychologische f...

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

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 ...

Topographical Estimation of Visual Population Receptive Fields by fMRI

Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic resonance imaging allows us to estimate voxel-based population receptive fields (pRF), i.e., the part of the visual field that activates the cells within each voxel. Prior, direct, pRF estimation methods1 suffer from certain limitations: 1) the pRF model is chosen a-priori and may not fully capture the actual pRF shape, and 2) pRF centers are prone to mislocalization near the border of the stimulus space. Here a new topographical pRF estimation method2 is proposed that largely circumvents these limitations. A linear model is used to predict the Blood Oxygen Level-Dependent (BOLD) signal by convolving the linear response of the pRF to the visual stimulus with the canonical hemodynamic response function. PRF topography is represented as a weight vector whose components represent the strength of the aggregate response of voxel neurons to stimuli presented at different visual field locations. The resulting linear equations can be solved for the pRF weight vector using ridge regression3, yielding the pRF topography. A pRF model that is matched to the estimated topography can then be chosen post-hoc, thereby improving the estimates of pRF parameters such as pRF-center location, pRF orientation, size, etc. Having the pRF topography available also allows the visual verification of pRF parameter estimates allowing the extraction of various pRF properties without having to make a-priori assumptions about the pRF structure. This approach promises to be particularly useful for investigating the pRF organization of patients with disorders of the visual system.

It is important to obtain unbiased estimates of visual population receptive fields (pRFs) by functional magnetic resonance imaging. We use mild regularization constraints to estimate pRF topography without a-priori assumptions about pRF shape, allowing us to choose specific pRF models post-hoc. This is particularly advantageous in subjects with visual-pathway lesions.

Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic ...

Behavior

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when studying the brain, as the functional role that a brain area fulfills may be largely defined by its connections to other brain areas. In this report, we describe a novel, non-invasive approach for relating brain structure and function using magnetic resonance imaging (MRI). This approach, a combination of structural imaging of long-range fiber connections and functional imaging data, is illustrated in two distinct cognitive domains, visual attention and face perception. Structural imaging is performed with diffusion-weighted imaging (DWI) and fiber tractography, which track the diffusion of water molecules along white-matter fiber tracts in the brain (Figure 1). By visualizing these fiber tracts, we are able to investigate the long-range connective architecture of the brain. The results compare favorably with one of the most widely-used techniques in DWI, diffusion tensor imaging (DTI). DTI is unable to resolve complex configurations of fiber tracts, limiting its utility for constructing detailed, anatomically-informed models of brain function. In contrast, our analyses reproduce known neuroanatomy with precision and accuracy. This advantage is partly due to data acquisition procedures: while many DTI protocols measure diffusion in a small number of directions (e.g., 6 or 12), we employ a diffusion spectrum imaging (DSI)1, 2 protocol which assesses diffusion in 257 directions and at a range of magnetic gradient strengths. Moreover, DSI data allow us to use more sophisticated methods for reconstructing acquired data. In two experiments (visual attention and face perception), tractography reveals that co-active areas of the human brain are anatomically connected, supporting extant hypotheses that they form functional networks. DWI allows us to create a "circuit diagram" and reproduce it on an individual-subject basis, for the purpose of monitoring task-relevant brain activity in networks of interest.

We describe a novel approach for simultaneous analysis of brain function and structure using magnetic resonance imaging (MRI). We assess brain structure with high-resolution diffusion-weighted imaging and white-matter fiber tractography. Unlike standard structural MRI, these techniques allow us to directly relate anatomical connectivity to functional properties of brain networks.

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when ...

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

Medicine

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.

We use magneto- and electroencephalography (MEG/EEG), combined with anatomical information captured by magnetic resonance imaging (MRI), to map the dynamics of the cortical network associated with auditory attention.

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the ...

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.

The Default Mode Network (DMN) in Temporal Lobe Epilepsy (TLE) is analyzed in the resting state of the brain using seed-based functional connectivity MRI (fcMRI).

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal ...

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.

Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain ...

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations over time in the blood-oxygenation level dependent signal. rs-fcMRI has been applied extensively to identify abnormalities in brain connectivity in different neurologic and psychiatric diseases. However, the relationship among rs-fcMRI connectivity abnormalities, brain electrophysiology and disease state is unknown, in part because the causal significance of alterations in functional connectivity in disease pathophysiology has not been established. Transcranial Magnetic Stimulation (TMS) is a technique that uses electromagnetic induction to noninvasively produce focal changes in cortical activity. When combined with electroencephalography (EEG), TMS can be used to assess the brain's response to external perturbations. Here we provide a protocol for combining rs-fcMRI, TMS and EEG to assess the physiologic significance of alterations in functional connectivity in patients with neuropsychiatric disease. We provide representative results from a previously published study in which rs-fcMRI was used to identify regions with abnormal connectivity in patients with epilepsy due to a malformation of cortical development, periventricular nodular heterotopia (PNH). Stimulation in patients with epilepsy resulted in abnormal TMS-evoked EEG activity relative to stimulation of the same sites in matched healthy control patients, with an abnormal increase in the late component of the TMS-evoked potential, consistent with cortical hyperexcitability. This abnormality was specific to regions with abnormal resting-state functional connectivity. Electrical source analysis in a subject with previously recorded seizures demonstrated that the origin of the abnormal TMS-evoked activity co-localized with the seizure-onset zone, suggesting the presence of an epileptogenic circuit. These results demonstrate how rs-fcMRI, TMS and EEG can be utilized together to identify and understand the physiological significance of abnormal brain connectivity in human diseases.

Resting-state functional-connectivity MRI has identified abnormalities in patients with a wide range of neuropsychiatric disorders, including epilepsy due to malformations of cortical development. Transcranial Magnetic Stimulation in combination with EEG can demonstrate that patients with epilepsy have cortical hyperexcitability in regions with abnormal connectivity.

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations ...

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.

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.

In neuroimaging, functional magnetic resonance imaging (fMRI) measures the blood-oxygenation-level dependent (BOLD) signal in the brain. The degree of ...

Mapping Regional Homogeneity and Functional Connectivity of the Visual Cortex in Resting-State fMRI

A combined regional homogeneity (ReHo) and functional connectivity (FC) method, a type of noninvasive functional magnetic resonance imaging (fMRI) method, has been used to evaluate synchronous neuronal activity changes in retinitis pigmentosa (RP). The purpose of this study is to describe our method for analysis of intra- and interregional synchronizations of changes in neuronal activity in RP patients. The advantages of the combined ReHo and FC method are that it is both noninvasive and sufficiently sensitive to investigate changes in cerebral synchronous neuronal activity changes in vivo. Here, 16 RP patients and 14 healthy controls closely matched in age, sex, and education underwent resting-state fMRI scans. Two sample t-tests were conducted to compare ReHo and FC across groups. Our results showed that visual network disconnection and reorganization of the retino-thalamocortical pathway and dorsal visual stream occurred in the RP patients. Here, we describe the details of this method, its use, and the impact of its key parameters in a step-by-step manner.

We present a protocol for analyzing functional magnetic resonance imaging data to investigate spontaneous neural activity alterations in retinitis pigmentosa patient using a combined regional homogeneity and functional connectivity method.

A combined regional homogeneity (ReHo) and functional connectivity (FC) method, a type of noninvasive functional magnetic resonance imaging (fMRI) method, ...

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

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