Name: using informational connectivity to measure the synchronous emergence of fmri multi-voxel information across time
Uploaded: 2014-07-01T13:00:00
Duration: 7 min 12 s
Description: Watch this Scientific Journal Video about Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time at JoVE.com

We thank Jim Haxby and colleagues for making their data available for further analyses. Marc N. Coutanche was funded by a fellowship from the Howard Hughes Medical Institute. This work was supported by NIH grants R0I-DC009209 and R01-EY02171701 awarded to Sharon L. Thompson-Schill.

1. Prepare the fMRI Data
Note: After conducting an fMRI scan, pre-process the collected data using the tools available in most fMRI software packages prior to starting this protocol (although spatial smoothing should be avoided or minimized to preserve multi-voxel patterns). An example of a suitable dataset is described in a previous application of the method7.
<ol>
	<li>Remove motion and mean white matter signals from the time series of pre-processed fMRI data by creating a regression model with predictors for motion parameters (roll, pitch, yaw, x, y, z) and mean white matter signal. Conduct the analyses below on the resulting residuals (i.e. the remaining variance).</li>
	<li>Import the generated residuals into an analysis package (e.g., MATLAB, Python). The open source Informational Connectivity Toolbox (http://www.informationalconnectivity.org) can import fMRI data into MATLAB.</li>
	<li>Z-score each voxel&#39;s time series.</li>
	<li>Separate the dataset&#39;s timepoints into independent sets (&#39;folds&#39;), such as different scanner runs. Note: Using scanner runs ensures independence between folds, which can otherwise be difficult to guarantee (for example, dependencies could be created between a run&#39;s timepoints during pre-processing). Runs could be grouped together to reduce the number of folds (e.g. ,even and odd runs2), although using single runs will give more training data.</li>
	<li>Create a record of the condition labels associated with timepoints by generating a vector of condition labels that is N timepoints long.</li>
	<li>Shift the condition labels forward in each run by a number of times-to-repetition (TRs) equivalent to 5 sec, in order to account for the hemodynamic lag between events and recorded fMRI signals.</li>
</ol>
2. Select and Analyze a Seed Region
<ol>
	<li>Select a seed region by isolating an anatomical area, functionally localized region or top-performing &#39;information brain mapping&#39; searchlights10. 
	Note: Steps 2.2 to 4.2 below can be performed by the open-source Informational Connectivity MATLAB Toolbox (<a href="http://www.informationalconnectivity.org">http://www.informationalconnectivity.org</a>).</li>
	<li>Compare the MVP of each time-point to a prototypical MVP for every condition (Note: this is the same approach as used in the popular correlation-based nearest neighbor classifier2). Figure 1 (top) gives an example from real data collected as participants viewed blocks of four types of man made objects.
	<ol>
		<li>Calculate a prototypical (mean) MVP for every condition by averaging the timepoints of each condition in all-but-one fold. This is the &#39;training&#39; data for each fold (e.g., for fold 2 of 5, mean-MVPs are calculated from timepoints in folds 1, 3, 4, and 5).</li>
		<li>Correlate every timepoint&#39;s MVP with the mean-MVP of each condition from the training data. This will give every timepoint one correlation value for each condition (Note: the condition with the highest correlation here would be the prediction of the popular correlation based MVPA classifier2)</li>
		<li>Fisher-transform the r-values to z-scores.</li>
	</ol>
	</li>
	<li>Quantify &#39;MVP discriminability&#39; for each timepoint: First identify the correlation from 2.2.3 that represents the relationship between the timepoint&#39;s MVP and the mean-MVP of that timepoint&#39;s condition, and then subtract the highest of the remaining correlations (i.e. &#39;correlation with correct condition&#39; minus &#39;maximum correlation with an incorrect condition&#39;). The result is that timepoint&#39;s MVP discriminability. An alternative (and valid) approach would be to subtract the average correlation of the incorrect conditions. 
	Note: The suggested approach has the intuitive advantage that timepoints with positive discriminability values are correctly classified by the correlation based classifier, while timepoints with negative values are not correctly predicted. An example of the resulting values is shown in Figure 1 (bottom). The steps up to this point are captured in the formulae below. 
	<img alt="2.3 equations" fo:content-width="4.5in" src="/files/ftp_upload/51226/51226_2.3_equations.jpg" /> 
	x&#773; is a normalized 1-by-m row vector with m voxel activation values at time-point n, y&#773; is a normalized 1-by-m row vector of the mean training pattern for correct (c) or incorrect (i) conditions for time-point n. The artanh function applies the Fisher z transform.</li>
</ol>
3. Calculate a Time Series of MVP Discriminability for Each Searchlight
<ol>
	<li>Conduct a searchlight analysis10: Place a three-dimensional cluster around each voxel in turn (a &#39;searchlight&#39;).</li>
	<li>Repeat steps 2.2 and 2.3 for each searchlight, so that every searchlight has a time series of MVP discriminability values (one per timepoint).</li>
</ol>
4. Calculate Informational Connectivity Between the Seed and Searchlights
<ol>
	<li>Correlate the seed&#39;s MVP discriminability time series (from 2.3) with each searchlight&#39;s discriminability time series (from 3.2) using Spearman&#39;s Rank correlation. The resulting rs value is the IC between the seed and searchlight.</li>
	<li>Assign each searchlight&#39;s IC value to the searchlight&#39;s central voxel and write out the resulting individual&#39;s brain map.</li>
</ol>
5. Calculate Group Statistical Map
<ol>
	<li>If the data are not already in standardized space (e.g., Talairach or MNI), transform participants&#39; IC maps into the same space.</li>
	<li>Optionally smooth the individuals&#39; searchlight maps.</li>
	<li>Create a group statistical map using a one way t-test for whether each searchlight&#39;s IC value is significantly greater than zero.</li>
</ol>
6. Test Significance
Note: Numerous approaches exist for determining statistical significance of fMRI group maps. As a permutation test can determine significance with minimal assumptions, while accounting for the dataset&#39;s level of smoothing (as each permuted group map undergoes the same processing), this option is outlined below.
<ol>
	<li>For each of 1,000 permutations, randomly shuffle the seed&#39;s MVP-discriminability values across the time series. Keep adjacent timepoints with temporal autocorrelations (such as timepoints within the same block) together (for example, by shuffling blocks rather than adjacent TRs).</li>
	<li>Calculate individuals&#39; IC maps for each permutation (step 4 above).</li>
	<li>Generate 1,000 permuted group maps: Randomly select one permuted IC map from each participant and conduct a group test on this random set (step 5 above).</li>
	<li>Threshold every permuted group map at a desired threshold (e.g., p &#60; 0.001) and extract the maximum cluster size from the map.</li>
	<li>Sort the resulting 1,000 maximum-cluster volumes and identify the cluster size at the 95th percentile (e.g., the 50th largest for 1,000 permutations).</li>
	<li>Apply the threshold used in 6.4 (e.g., p &#60; 0.001) and the minimum cluster size from 6.5 to the real (non-permuted) IC group map in order to make it cluster-corrected to p &#60; 0.05. Because every permuted map draws on the same MVP discriminability values (in a different order), this significance map highlights regions with more synchronous discriminability values than expected by chance.</li>
</ol>

<ol>
	<li>Norman, K. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Beyond+mind-reading:+multi-voxel+pattern+analysis+of+fMRI+data.">Beyond mind-reading: multi-voxel pattern analysis of fMRI data.</a> Trends Cogn Sci. 10 (9), 424-430 (2006).</li><li>Haxby, J. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distributed+and+overlapping+representations+of+faces+and+objects+in+ventral+temporal+cortex.">Distributed and overlapping representations of faces and objects in ventral temporal cortex.</a> Science. 293 (5539), 2425-2430 (2001).</li><li>Tong, F., Pratte, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decoding+patterns+of+human+brain+activity.">Decoding patterns of human brain activity.</a> Annu Rev Psychol. 63, 483-509 (2012).</li><li>Coutanche, M. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distinguishing+multi-voxel+patterns+and+mean+activation:+Why,+how,+and+what+does+it+tell+us.">Distinguishing multi-voxel patterns and mean activation: Why, how, and what does it tell us.</a> Cogn Affect Behav Neurosci. 13 (3), (2013).</li><li>Biswal, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+connectivity+in+the+motor+cortex+of+resting+human+brain+using+echo-planar+mri.">Functional connectivity in the motor cortex of resting human brain using echo-planar mri.</a> Magn Res Med. 34 (4), 537-541 (1995).</li><li>Friston, K. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Psychophysiological+and+modulatory+interactions+in+neuroimaging.">Psychophysiological and modulatory interactions in neuroimaging.</a> Neuroimage. 6 (3), 218-229 (1997).</li><li>Coutanche, M. N., Thompson-Schill, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Informational+Connectivity:+Identifying+synchronized+discriminability+of+multi-voxel+patterns+across+the+brain.">Informational Connectivity: Identifying synchronized discriminability of multi-voxel patterns across the brain.</a> Front Hum Neurosci. 7 (15), 1-14 (2013).</li><li>Chiu, Y. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracking+cognitive+fluctuations+with+multivoxel+pattern+time+course+(MVPTC)+analysis.">Tracking cognitive fluctuations with multivoxel pattern time course (MVPTC) analysis.</a> Neuropsychologia. 50 (4), 479-486 (2012).</li><li>Nelissen, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frontal+and+parietal+cortical+interactions+with+distributed+visual+representations+during+selective+attention+and+action+selection.">Frontal and parietal cortical interactions with distributed visual representations during selective attention and action selection.</a> J Neurosci. 33 (42), 16443-16458 (2013).</li><li>Kriegeskorte, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Information-based+functional+brain+mapping.">Information-based functional brain mapping.</a> Proc Natl Acad Sci U S A. 103 (10), 3863-3868 (2006).</li><li>Cox, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AFNI:+software+for+analysis+and+visualization+of+functional+magnetic+resonance+neuroimages.">AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.</a> Comput Biomed Res. 29 (3), 162-173 (1996).</li><li>Fischl, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+II:+Inflation,+flattening,+and+a+surface-based+coordinate+system.">Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system.</a> Neuroimage. 9 (2), 195-207 (1999).</li><li>Jimura, K., Poldrack, R. 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The authors declare that they have no competing financial interests.

Informational connectivity has MVPA&#39;s sensitivity to distributed pattern information, and gives an ability to study between-region interactions through a connectivity approach. MVPA and standard univariate analyses can each reveal the involvement of distinct regions, sometimes with little overlap between their results13. As expected for a method that draws on these analysis approaches, IC and FC also give complementary results7. The decision of whether to employ IC will ultimately depend on the conditions under investigation and the theoretical questions being posed. Design considerations that impact whether MVPA is conducted on a dataset will also affect whether IC is used. Studies designed with IC explicitly in mind will want to follow recommendations for MVPA14, while also ensuring that trial-level data can be extracted from across the scan&#39;s timecourse.
When examining and reporting IC results, it is important that searchlights overlapping with the seed are removed, to avoid circularity. Additionally, if directly comparing IC and FC results, it is recommended to also compare an FC analysis based on the mean activation of searchlights, rather than just voxels. This additional analysis can ensure that any differences between results are not because of differences in the levels of signal-to-noise in searchlights versus voxels.
The procedure described here focuses primarily on an exploratory analysis employing searchlights. It is worth noting that by replacing searchlights with regions-of-interest, IC can also compare regions that are selected a priori. The current discriminability metric &#8211; comparing a MVP&#39;s correlation for the &#39;true&#39; condition to the correlation for the maximum alternate condition &#8211; is also modifiable. Many machine learning classifiers have prediction weights for different classes, which could easily replace the correlation comparisons performed here (e.g., to track the &#39;confidence&#39; of a classifier over time). IC has a variety of potential uses. As well as being a primary analysis to investigate informational networks, IC can be a secondary followup analysis to an MVPA searchlight. MVPA searchlight maps are valuable for understanding which regions can distinguish different conditions, but are not typically broken down into different networks. The IC approach can help here, by revealing which sets of searchlights have synchronous discriminability. Finally, IC maps from different tasks can be compared in order to understand task networks, and patients can be compared to controls to better understand how multi-voxel differences15 are manifested at the network level.

The goal of the analysis method described here is to measure connectivity between brain regions based on fluctuations in their multi-voxel information. Advances in functional magnetic resonance imaging (fMRI) analysis techniques have revealed that a large amount of information can be contained within blood-oxygenation-level-dependent (BOLD) activity patterns that are distributed across multiple voxels1-3. A set of techniques that are sensitive to multivariate information - known as multi-voxel pattern analysis (MVPA) - has been used to show that conditions can have distinguishable MVPs despite having indistinguishable univariate responses1,2,4. Standard analyses, which compare univariate responses, can be insensitive to this multi-voxel information.
Multiple brain regions are engaged when humans process stimuli and perform cognitive operations. Functional connectivity (FC) is a method commonly employed to investigate such functional networks5,6. In its most basic form, FC quantifies co-activation, or synchrony, between different voxels or regions. FC has been used to identify functionally connected brain networks with much success. For many regions and conditions, however, univariate responses do not reflect all the available information within BOLD activity. FC techniques that track dynamically changing univariate response levels may lack sensitivity to common fluctuations in multi-voxel information. The analysis method presented here, informational connectivity (IC; first described in a recent paper7), bridges a gap between MVPA and FC, by measuring connectivity with a metric that is sensitive to multi-voxel information across time. While FC tracks dynamically changing univariate activation, IC tracks dynamically changing MVP discriminability - a measure of how well a MVP&#39;s true condition can be distinguished from (incorrect) alternatives. Importantly, in the same way that different regions can show similar levels of univariate responses to a condition despite performing distinct computations (e.g., visual processing or action planning when a person views man made objects), distinct regions can also have similar (and synchronized) levels of MVP discriminability while they process conditions differently. A recent investigation demonstrated that IC can reveal inter-regional connectivity that is not detectable with a standard FC approach7. Investigators can therefore use IC to probe interactions between brain regions as participants respond to conditions or stimuli that have characteristic distributed patterns. IC is distinct from several recent connectivity applications that examined fluctuations in univariate activation in relation to classification results8,9. Unlike these approaches, IC detects synchronous multi-voxel pattern discriminability between regions.

<table border="0" cellpadding="0" cellspacing="0" width="585">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">MATLAB software package</td>
			<td style="width:83px;">MathWorks</td>
			<td style="width:119px;"></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">AFNI software package</td>
			<td style="width:83px;">NIMH</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

The IC results can now be displayed using the investigator&#8217;s preferred fMRI analysis software package. Figure 2 shows IC results, calculated from blocks of visually presented man made objects (full details in the associated publication7).
The IC analysis is particularly valuable for conditions known to have associated MVPs: Conditions with characteristic MVPs, but without differences in univariate responses, are more likely to have distinctions between IC and FC (illustrated with data that was recorded as participants viewed different types of man made objects in Figure 3). Figure 4 shows that searchlights with significant multi-voxel information can have high IC, but are less well represented in FC results.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/51226/51226fig1highres.jpg" width="500" /> 
Figure 1. Examples of pattern discriminability over time. Top: The substrates of MVP discriminability calculated from a subject in Haxby et al. (2001)2, as analyzed in Coutanche &#38; Thompson-Schill (2013)7. The blue line shows the z-scored correlation between time-points&#8217; MVPs and the mean (&#8216;training&#8217;) pattern of the correct class. The green lines represent the MVPs&#8217; correlations with three incorrect classes. Bottom: Pattern discriminability is the difference between correlations for the correct class and highest incorrect class. Time-points with positive pattern discriminability values would be correctly classified by a correlation-based classifier. Figure originally published in Coutanche &#38; Thompson-Schill (2013)7. <a href="https://www.jove.com/files/ftp_upload/51226/51226fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" fo:content-width="5in" src="/files/ftp_upload/51226/51226fig2highres.jpg" width="500" /> 
Figure 2. Example connectivity maps. Each row shows regions significantly connected to a seed (shown in blue). Significance is determined by a group t-test (p &#60; 0.001) with minimum cluster size from permutation testing. The IC results are displayed using AFNI11 on surface maps produced with FreeSurfer12. Figure is modified from Coutanche &#38; Thompson-Schill (2013)7. <a href="https://www.jove.com/files/ftp_upload/51226/51226fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" fo:content-width="5in" src="/files/ftp_upload/51226/51226fig3highres.jpg" width="500" /> 
Figure 3. Synchronized MVP discriminability compared to mean activation. Examples of MVP discriminability in two regions with synchronous MVP discriminability (i.e. informational connectivity) without synchronous mean activation (i.e. functional connectivity); data comes from a subject from Haxby et al. (2001)2, as analyzed in Coutanche &#38; Thompson-Schill (2013)7. These data points were collected while the subject viewed visual presentations of man made objects, which are distinguishable by multi-voxel patterns, but not mean responses. <a href="https://www.jove.com/files/ftp_upload/51226/51226fig3highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" fo:content-width="5in" src="/files/ftp_upload/51226/51226fig4highres.jpg" width="500" /> 
Figure 4. Example IC and FC values between a seed in the left fusiform gyrus and searchlights across the brain. Informational and functional connectivity strengths (z-axes) are shown between a seed and searchlights, with respect to each searchlight&#8217;s mean response (x-axis) and MVPA classification accuracy (y-axis) to four types of man made objects (chance = 25%). Searchlights sharing voxels with the seed region were removed. The IC graph includes examples of searchlights with strong connectivity that have high classification performance but low mean response levels, which are not picked up in a typical FC approach (seen by the gap in the top-left octant of the right graph). Figure originally published in Coutanche &#38; Thompson-Schill (2013)7. <a href="https://www.jove.com/files/ftp_upload/51226/51226fig4highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time at JoVE.com

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

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 ...

using-informational-connectivity-to-measure-synchronous-emergence

Research

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Neuroscience

Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful rest."1,2,3 It is thought the default mode network corresponds to self-referential or "internal mentation".2,3
It has been hypothesized that, in humans, activity within the default mode network is correlated with certain pathologies (for instance, hyper-activation has been linked to schizophrenia 4,5,6 and autism spectrum disorders 7 whilst hypo-activation of the network has been linked to Alzheimer's and other neurodegenerative diseases 8). As such, noninvasive modulation of this network may represent a potential therapeutic intervention for a number of neurological and psychiatric pathologies linked to abnormal network activation. One possible tool to effect this modulation is Transcranial Magnetic Stimulation: a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.9
In order to explore the default mode network's propensity towards and tolerance of modulation, we will be combining TMS (to the left inferior parietal lobe) with functional magnetic resonance imaging (fMRI). Through this article, we will examine the protocol and considerations necessary to successfully combine these two neuroscientific tools.

In this article, we examine the methodology and considerations relevant to the combination of TMS and fMRI to examine the effects of brain stimulation on the default network.

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful ...

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar tasks. Participants' behavior during task performance in an fMRI scanner can then be correlated to the brain activity using the blood-oxygen-level-dependent signal. We measure behavior to be able to sort correct trials, where the subject performed the task correctly and then be able to examine the brain signals related to correct performance. Conversely, if subjects do not perform the task correctly, and these trials are included in the same analysis with the correct trials we would introduce trials that were not only for correct performance. Thus, in many cases these errors can be used themselves to then correlate brain activity to them. We describe two complementary tasks that are used in our lab to examine the brain during suppression of an automatic responses: the stroop1 and anti-saccade tasks. The emotional stroop paradigm instructs participants to either report the superimposed emotional 'word' across the affective faces or the facial 'expressions' of the face stimuli1,2. When the word and the facial expression refer to different emotions, a conflict between what must be said and what is automatically read occurs. The participant has to resolve the conflict between two simultaneously competing processes of word reading and facial expression. Our urge to read out a word leads to strong 'stimulus-response (SR)' associations; hence inhibiting these strong SR's is difficult and participants are prone to making errors. Overcoming this conflict and directing attention away from the face or the word requires the subject to inhibit bottom up processes which typically directs attention to the more salient stimulus. Similarly, in the anti-saccade task3,4,5,6, where an instruction cue is used to direct only attention to a peripheral stimulus location but then the eye movement is made to the mirror opposite position. Yet again we measure behavior by recording the eye movements of participants which allows for the sorting of the behavioral responses into correct and error trials7 which then can be correlated to brain activity. Neuroimaging now allows researchers to measure different behaviors of correct and error trials that are indicative of different cognitive processes and pinpoint the different neural networks involved.

The goal of our research is to correlate behavior to brain activity. Accurate behavioral measures and imaging techniques allow us to elucidate brain-behavior relationships.

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar ...

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 ...

A Simple Way to Measure Alterations in Reward-seeking Behavior Using Drosophila melanogaster

We describe a protocol for measuring ethanol self-administration in fruit flies (Drosophila melanogaster) as a proxy for changes in reward states. We demonstrate a simple way to tap into the fly reward system, modify experiences related to natural reward, and use voluntary ethanol consumption as a measure for changes in reward states. The approach serves as a relevant tool to study the neurons and genes that play a role in experience-mediated changes of internal state. The protocol is composed of two discrete parts: exposing the flies to rewarding and nonrewarding experiences, and assaying voluntary ethanol consumption as a measure of the motivation to obtain a drug reward. The two parts can be used independently to induce the modulation of experience as an initial step for further downstream assays or as an independent two-choice feeding assay, respectively. The protocol does not require a complicated setup and can therefore be applied in any laboratory with basic fly culture tools.

A Simple Way to Measure Alterations in Reward-seeking Behavior Using Drosophila melanogaster

We describe a protocol for inducing rewarding and nonrewarding experiences in fruit flies (Drosophila melanogaster) using voluntary ethanol consumption as a measure for changes in reward states.

We describe a protocol for measuring ethanol self-administration in fruit flies (Drosophila melanogaster) as a proxy for changes in reward states. We ...

Analyzing Dendritic Morphology in Columns and Layers

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and ...

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control neuroplastic processes and correct abnormal function, or even shift functions from damaged tissue to physiologically healthy brain regions, hold the potential to dramatically improve overall health. Of the current neuroplastic interventions in development, neurofeedback training (NFT) from functional Magnetic Resonance Imaging (fMRI) has the advantages of being completely non-invasive, non-pharmacologic, and spatially localized to target brain regions, as well as having no known side effects. Furthermore, NFT techniques, initially developed using fMRI, can often be translated to exercises that can be performed outside of the scanner without the aid of medical professionals or sophisticated medical equipment. In fMRI NFT, the fMRI signal is measured from specific regions of the brain, processed, and presented to the participant in real-time. Through training, self-directed mental processing techniques, that regulate this signal and its underlying neurophysiologic correlates, are developed. FMRI NFT has been used to train volitional control over a wide range of brain regions with implications for several different cognitive, behavioral, and motor systems. Additionally, fMRI NFT has shown promise in a broad range of applications such as the treatment of neurologic disorders and the augmentation of baseline human performance. In this article, we present an fMRI NFT protocol developed at our institution for modulation of both healthy and abnormal brain function, as well as examples of using the method to target both cognitive and auditory regions of the brain.

The ability to induce and/or control neural plasticity may be critical in future treatments for neurologic disorders and the recovery from brain injury. In this paper, we present a protocol on the use of neurofeedback training with functional magnetic resonance imaging to modulate human brain function.

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control ...

Assay to Measure Nucleocytoplasmic Transport in Real Time within Motor Neuron-like NSC-34 Cells

Nucleocytoplasmic transport refers to the import and export of large molecules from the cell nucleus. Recently, a number of studies have shown a connection between amyotrophic lateral sclerosis (ALS) and impairments in the nucleocytoplasmic pathway. ALS is a neurodegenerative disease affecting the motor neurons and resulting in paralysis and ultimately in death, within 2-5 years on average. Most cases of ALS are sporadic, lacking any apparent genetic linkage, but 10% are inherited in a dominant manner. Recently, hexanucleotide repeat expansions (HREs) in the chromosome 9 open reading frame 72 (C9orf72) gene were identified as a genetic cause of ALS and frontotemporal dementia (FTD). Importantly, different groups have recently proposed that these mutants affect nucleocytoplasmic transport. These studies have mostly shown the final outcome and manifestations caused by HREs on nucleocytoplasmic transport, but they do not demonstrate nuclear transport dysfunction in real time. As a result, only severe nucleocytoplasmic transport deficiency can be determined, mostly due to high overexpression or exogenous protein insertion. 
This protocol describes a new and very sensitive assay to evaluate and quantify nucleocytoplasmic transport dysfunction in real time. The rate of import of a NLS-NES-GFP protein (shuttle-GFP) can be quantified in real time using fluorescent microscopy. This is performed by using an exportin inhibitor, thus allowing the shuttle GFP only to enter the nucleus. To validate the assay, the C9orf72 HRE translated dipeptide repeats, poly(GR) and poly(PR), which have been previously shown to disrupt nucleocytoplasmic transport, were used. Using the described assay, a 50% decrease in the nuclear import rate was observed compared to the control. Using this system, minute changes in nucleocytoplasmic transport can be examined and the ability of different factors to rescue (even partially) a nucleocytoplasmic transport defect can be determined.

This protocol describes a method to sensitively measure the nucleocytoplasmic transport rate within motor neuron-like NSC-34 cells by quantifying the real-time change in the nuclear import of a NLS-NES-GFP protein.

Nucleocytoplasmic transport refers to the import and export of large molecules from the cell nucleus. Recently, a number of studies have shown a ...

Quadruple Immunostaining of the Olfactory Bulb for Visualization of Olfactory Sensory Axon Molecular Identity Codes

The mouse olfactory system is often used to study mechanisms of neural circuit formation&#12288;because of its simple anatomical structure. An Olfactory Sensory Neuron (OSN) is a bipolar cell with a single dendrite and a single unbranched axon. An OSN expresses only one Olfactory Receptor (OR) gene, OSNs expressing a given type of OR converge their axons to a few sets of invariant glomeruli in the Olfactory Bulb (OB). A remarkable feature of OSN projection is that the expressed ORs play instructive roles in axonal projection. ORs regulate the expression of multiple axon-sorting molecules and generate the combinatorial molecular code of axon-sorting molecules at the OSN axon termini. Thus, to understand the molecular mechanisms of OR-specific axon guidance mechanisms, it is vital to characterize their expression profiles at the OSN axon termini within the same glomerulus. The aim of this article was to introduce methods for collecting as many glomeruli as possible on a single OB section and for performing immunostaining using multiple antibodies. This would allow the comparison and analysis of the expression patterns of axon-sorting molecules without staining variation between OB sections.

Olfactory sensory neurons express a wide variety of axon-sorting molecules to establish proper neural circuitry. This protocol describes an immunohistochemical staining method to visualize combinatorial expressions of axon-sorting molecules at the axon termini of olfactory sensory neurons.

The mouse olfactory system is often used to study mechanisms of neural circuit formation&#12288;because of its simple anatomical structure. An Olfactory ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, and less resource-intensive than imaging. Further, the functional data produced have excellent temporal resolution, which is not achievable with structural imaging.
Current applications of electroencephalograms (EEG) are limited by data processing methods. Standard analysis techniques using raw time series data at individual channels are very limited methods of interrogating nervous system activity. More detailed information about cortical function can be achieved by examining relationships between channels and deriving statistical models of how areas are interacting, allowing visualization of connectivity between networks.
This manuscript describes a method for deriving statistical models of cortical network activity by recording EEG in a standard manner, then examining the interelectrode coherence measures to assess relationships between the recorded areas. Higher order interactions can be further examined by assessing the covariance between the coherence pairs, producing high-dimensional &#34;maps&#34; of network interactions. These data constructs can be examined to assess cortical network function and its relationship to pathology in ways not achievable with traditional techniques.
This approach offers greater sensitivity to network level interactions than is achievable with raw time series analysis. It is, however, limited by the complexity of drawing specific mechanistic conclusions about the underlying neural populations and the high volumes of data generated, requiring more advanced statistical techniques for evaluation, including dimensionality reduction and classifier-based approaches.

Standard EEG analysis techniques offer limited insight into nervous system function. Deriving statistical models of cortical connectivity offers far greater ability to investigate underlying network dynamics. Improved functional assessment opens new possibilities for diagnosis, prognostication, and outcome prediction in nervous system diseases.

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, ...