The author is grateful for NIH grant support:
NIH/NIBIB 5R01EB006204-03 Multivariate approaches to neuroimaging analysis
NIH/NIA 5R01AG026114-02 Early AD Detection with ASL MRI &amp; Covariance Analysis
ADNI: Imaging data was provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (NIH U01AG024904). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics ,Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., and Wyeth, as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health(<a href="http://www.fnih.org">http://www.fnih.org</a>). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.

<ol> <li>To give a conceptual overview of multivariate analysis we can picture a very simple situation: a hypothetical data set for 50 human participants, where only three regions, denoted as voxels (=3-dimensional pixels in Figure 1) in the brain were measured. (Insert Figure 1 here, read caption as voice over.)</li> <li>The general aim of multivariate analysis is to identify the major sources of variance in the data, and then describing the major effects of interest in the data in terms of these sources of variance. Figure 2 shows a simplistic example. (Insert Figure 2 here, read caption as voice over.)</li> <li>We now apply both univariate and multivariate analysis to a clinical data set. We downloaded FDG-PET resting scans for 95 early Alzheimer's patients and 102 age-matched controls from the website of the Alzheimer's Disease Neuroimaging Initiative (http://www.loni.ucla.edu/ADNI/). We randomly picked 20 scans of both patients and controls and designated them as our derivation sample. The remaining 75 and 82 scans, respectively, constitute our replication sample. Univariate and multivariate Alzheimer's disease (AD) markers will now be derived in the derivation sample, and their diagnostic efficacy tested in the replication sample.</li> <li>For the univariate marker, we contrast the 20 AD scans with the 20 controls scans in the derivation sample and pick the brain location that shows the largest decrease in PET signal in the AD patients as shown by a T-test. To test the diagnostic efficacy of this region, we check the data in the replication sample at this location and plot its PET signal as a function of disease status.</li> <li>For the multivariate marker, we first perform a PCA on the combined 40 scans in the derivation sample, and then construct a covariance pattern from the first 5 Principal Components whose subject scaling factor shows a maximal mean difference between AD patients and healthy controls. (Details can be found in these representative papers 2.) The diagnostic covariance pattern obtained form the derivation sample is then prospectively applied to the replication sample. The resulting subject scaling factors are plotted as a function of disease status.</li> <li>To provide a more general comparison of both univariate and multivariate approaches from step 4 and 5, we perform a "split sample" simulation and repeat both steps 1,000 times on resampled data, each time forming a 20/20 derivation sample and a 75/82 replication of AD patients and healthy controls afresh. Univariate and multivariate disease markers are computed from the derivation sample and the decision threshold is set such that at most 1 healthy control is misclassified as AD (= specificity 95%). The disease markers with their specific decision thresholds are then prospectively applied to the replication samples. The classification error rates in the replication sample are recorded for all resampling iterations.</li> </ol> Representative Results Univariate performance The results can be seen in detail in Figure 3. The area of the largest AD-related FDG deficit was found in the super temporal gyrus, Brodmann area 38. The area under the ROC-curve achieved was AUC=0.90. The generalization of this contrast to the replication sample was quite good with an area under the ROC curve of AUC=0.84. Multivariate performance The results can be seen in detail in Figure 4. Areas with positive loadings, hinting at a relative preservation of signal in the face of disease were found in the cerebellum, while associated signal loss was found the parietotemporal and frontal areas, and the posterior cingulate gyrus. The areas under ROC-curves in both derivation and replication samples were slightly better than the univariate marker at 0.96 and 0.88, respectively. Split-sample simulations The results can be seen in detail in Figure 5. The figure shows that the multivariate marker gives better replication of diagnostic performance than the univariate marker. The mean total error rate for the multivariate marker is 0.203, whereas for the univariate marker it is 0.307. <img src="/files/ftp_upload/1988/1988fig1.jpg" alt="Figure 1" /> Figure 1. This simple figure describes the difference between univariate and multivariate analytic strategies: a hypothetical 3-dimensional data set is displayed in this illustration. On the left side, there is no correlation between the 3 variables plotted. On the right side in contrast, one can see a major source of variance indicating a positive correlation between all three voxels. A univariate analysis that just considered mean values on a voxel-by-voxel basis could not tell any difference between these two scenarios. Multivariate analysis, in contrast, identifies the major sources of variance in the data (red arrow) before proceeding to construct neural activation patterns form these sources. <img src="/files/ftp_upload/1988/1988fig2.jpg" alt="Figure 2" /> Figure 2. This slide shows in a simplified form the basic accomplishment of any multivariate analysis in neuroimaging data. The data array Y(s,x), which depends on a subject index s, and a voxel index x, indicating the location of the voxel in the brain, is decomposed into a sum of several terms. First, a product of a purely subject-dependent factor score, ssf(s), and a purely voxel-dependent covariance pattern, v(x). Second, activation that cannot be accounted for by the covariance pattern is captured in a subject- and voxel-dependent noise term, e(s,x). The two graphics below the equation give an example of the subject scaling factor and the covariance pattern. Every participant manifests the covariance pattern, just to a different degree as shown by the subject factor score. Rather than having to keep track of every voxel's behavior separately, the covariance pattern and its subject expression provide a parsimonious summary of the major source of variance. As the subject scaling factor increases in magnitude, the areas denoted in blue in the covariance pattern decrease their associated activation, while the areas indicated in red simultaneously increase their associated activation. The subject factor score can be correlated with external variables of interest like subject age or behavioral performance in a cognitive task, and no correction for multiple comparisons has to be applied to this correlation. Several techniques for such decomposition exist, but the most common one is Principal Components Analysis (PCA). This is the technique of choice for us. Note that subject scaling factors can be obtained by projecting the covariance pattern into any data set of equal dimensionality, not just the data set that produced the covariance pattern in the first place. This makes covariance patterns suitable for testing whether brain-behavioral relationships that were observed in one data set can be replicated in a different data set. <img src="/files/ftp_upload/1988/1988fig3.jpg" alt="Figure 3" /> Figure 3. This figure shows the result of the univariate analysis. In the lower left panel, the FDG signal values are plotted for the area that shows the largest AD-related deficit in the derivation sample. Its MNI coordinates are X=2 mm ,Y= -48 mm , Z= 30mm (Precuneus/PCG, Brodmann Area 31). The lower right panel shows the FDG signal at this very location in the replication sample. One can appreciate that the FDG differences between AD patients and controls in the replication sample, while still significant overall, are reduced with more overlap between the groups. <img src="/files/ftp_upload/1988/1988fig4.jpg" alt="Figure 4" /> Figure 4. This figure shows the results of the multivariate analysis. In the top panel, we display several axial slices that show significantly positively and negatively weighted areas (p&lt;0.001) in the covariance pattern in red and blue, respectively. Note that we scaled every scan by its global mean value, so red and blue color indicate relative rather and absolute increases and decreases of PET signal with disease severity. Red areas thus hint at relative preservation in the face of the disease, while blue indicates a loss of signal as a consequence of the disease. Red areas are mainly found in the cerebellum, while blue areas appear in the posterior cingulate gyrus, parietotemporal and frontal regions. Lower left panel: the subject factor scores of the AD-related covariance pattern are displayed in the derivation sample. Higher subject scores are found for the AD patients. Lower right panel: the subject factor scores resulting from the prospective application of the AD-related covariance pattern to the replication sample are plotted here. One can appreciate a slight worsening of the diagnostic contrast with increased overlap in the replication sample, but the generalization of the diagnostic efficacy is noticeably better than in the univariate case. <img src="/files/ftp_upload/1988/1988fig5.jpg" alt="Figure 5" /> Figure 5. This figure shows the results of the 1,000 split-sample simulations. Listed are means and standard deviations of the univariate and multivariate diagnostic error rates in the replication samples. One can appreciate that the multivariate marker's generalization of performance is considerably better, although somewhat more variable than the univariate marker's.

<ol>
	<li>Moeller, J. R., Strother, S. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+regional+covariance+approach+to+the+analysis+of+functional+patterns+in+positron+emission+tomographic+data.">A regional covariance approach to the analysis of functional patterns in positron emission tomographic data.</a> J Cereb Blood Flow Metab. 11 (2), A121-A121 (1991).</li><li>Scarmeas, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Covariance+PET+patterns+in+early+Alzheimer's+disease+and+subjects+with+cognitive+impairment+but+no+dementia:+utility+in+group+discrimination+and+correlations+with+functional+performance.">Covariance PET patterns in early Alzheimer's disease and subjects with cognitive impairment but no dementia: utility in group discrimination and correlations with functional performance.</a> Neuroimage. 23 (1), 35-35 (2004).</li><li>Siedlecki, K. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Examining+the+multifactorial+nature+of+cognitive+aging+with+covariance+analysis+of+positron+emission+tomography+data.">Examining the multifactorial nature of cognitive aging with covariance analysis of positron emission tomography data.</a> J Int Neuropsychol Soc. 15 (6), 973-973 (2009).</li><li>Burnham, K. P., Anderson, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Model selection and multimodel inference a practical information-theoretic approach. , (2002).</li><li>Moeller, J. R., Strother, S. C., Sidtis, J. J., Rottenberg, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scaled+subprofile+model:+a+statistical+approach+to+the+analysis+of+functional+patterns+in+positron+emission+tomographic+data.">Scaled subprofile model: a statistical approach to the analysis of functional patterns in positron emission tomographic data.</a> J Cereb Blood Flow Metab. 7 (5), 649-649 (1987).</li><li>Habeck, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multivariate+and+univariate+neuroimaging+biomarkers+of+Alzheimer's+disease.">Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease.</a> Neuroimage. 40 (4), 1503-1503 (2008).</li><li>Habeck, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+approach+to+spatial+covariance+modeling+of+functional+brain+imaging+data:+ordinal+trend+analysis.">A new approach to spatial covariance modeling of functional brain imaging data: ordinal trend analysis.</a> Neural Comput. 17 (7), 1602-1602 (2005).</li><li>McIntosh, A. R., Bookstein, F. L., Haxby, J. V., Grady, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+pattern+analysis+of+functional+brain+images+using+partial+least+squares.">Spatial pattern analysis of functional brain images using partial least squares.</a> Neuroimage. 3 Pt 1, 143-143 (1996).</li><li>McIntosh, A. R., Lobaugh, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Partial+least+squares+analysis+of+neuroimaging+data:+applications+and+advances.">Partial least squares analysis of neuroimaging data: applications and advances.</a> Neuroimage. 23, S250-S250 (2004).</li></ol>

No conflicts of interest declared.

We hope to have given the viewer a flavor of the basics of multivariate analysis; interested viewers are encouraged to check out our website. A few choices for parameters in the multivariate analysis were made that can be subject debate to considerable debate. We spared the discussion of these issues in this article to avoid distraction from the major issues. First, we chose the first 6 Principal Components to construct our AD-related covariance pattern. There are theoretical reasons for this choice that we did not discuss 4. The particular choice of 6 Principal Components though is not critical for our argument: one can chose in the range from 2 to 20 PCs and still obtain superior generalization performance of the multivariate marker in the split-sample simulations. The results are similarly robust with respect to the choice of numbers of subjects in derivation and replication samples. We chose 20 subjects for both groups in the replication sample, but this was purely for mathematical convenience to speed up the computations. Our results about the relative merits of both techniques would hold similarly if the numbers of subjects in the derivation samples were increased. Second, we only presented the most basic kind of multivariate analysis. Considerable complication with techniques borrowed from the Machine-Learning literature, linear and non-linear transformations prior to the PCA, and various other wrinkles are feasible that could boost the generalization performance even more. For simplicity we have not touched on these possibilities in this article.

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.

Watch this Scientific Journal Video about Basics of Multivariate Analysis in Neuroimaging Data at JoVE.com

Basics of Multivariate Analysis in Neuroimaging Data

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 ...

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16.8K Views.  Columbia University. 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.

Video: Basics of Multivariate Analysis in Neuroimaging Data

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

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

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

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

Cross-Modal Multivariate Pattern Analysis

Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6.
Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog?
In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices.

Classical multivariate pattern analysis predicts sensory stimuli a subject perceives from neural activity in the corresponding cortices (e.g. visual stimuli from activity in visual cortex). Here, we apply pattern analysis cross-modally and show that sound- and touch-implying visual stimuli can be predicted from activity in auditory and somatosensory cortices, respectively.

Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the ...

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

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

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

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

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.

Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain ...

TIRFM and pH-sensitive GFP-probes to Evaluate Neurotransmitter Vesicle Dynamics in SH-SY5Y Neuroblastoma Cells: Cell Imaging and Data Analysis

Synaptic vesicles release neurotransmitters at chemical synapses through a dynamic cycle of fusion and retrieval. Monitoring synaptic activity in real time and dissecting the different steps of exo-endocytosis at the single-vesicle level are crucial for understanding synaptic functions in health and disease.
Genetically-encoded pH-sensitive probes directly targeted to synaptic vesicles and Total Internal Reflection Fluorescence Microscopy (TIRFM) provide the spatio-temporal resolution necessary to follow vesicle dynamics. The evanescent field generated by total internal reflection can only excite fluorophores placed in a thin layer (&#60;150 nm) above the glass cover on which cells adhere, exactly where the processes of exo-endocytosis take place. The resulting high-contrast images are ideally suited for vesicles tracking and quantitative analysis of fusion events.
In this protocol, SH-SY5Y human neuroblastoma cells are proposed as a valuable model for studying neurotransmitter release at the single-vesicle level by TIRFM, because of their flat surface and the presence of dispersed vesicles. The methods for growing SH-SY5Y as adherent cells and for transfecting them with synapto-pHluorin are provided, as well as the technique&#160;to perform TIRFM and imaging. Finally, a strategy aiming to select, count, and analyze fusion events at whole-cell and single-vesicle levels is presented.
To validate the imaging procedure and data analysis approach, the dynamics of pHluorin-tagged vesicles are&#160;analyzed under resting and stimulated (depolarizing potassium concentrations) conditions. Membrane depolarization increases the frequency of fusion events and causes a parallel raise of the net fluorescence signal recorded in whole cell. Single-vesicle analysis reveals modifications of fusion-event behavior (increased peak height and width). These data suggest that potassium depolarization not only induces a massive neurotransmitter release but also modifies the mechanism of vesicle fusion and recycling.
With the appropriate fluorescent probe, this technique can be employed in different cellular systems to dissect the mechanisms of constitutive and stimulated secretion.

This paper provides a method for investigating neurotransmitter vesicle dynamics in neuroblastoma cells, using a synaptobrevin2-pHluorin construct and Total Internal Reflection Fluorescence Microscopy. The strategy developed for image processing and data analysis is also reported.

Synaptic vesicles release neurotransmitters at chemical synapses through a dynamic cycle of fusion and retrieval. Monitoring synaptic activity in real ...

Analyzing Synaptic Modulation of Drosophila melanogaster Photoreceptors after Exposure to Prolonged Light

The nervous system has the remarkable ability to adapt and respond to various stimuli. This neural adjustment is largely achieved through plasticity at the synaptic level. The Active Zone (AZ) is the region at the presynaptic membrane that mediates neurotransmitter release and is composed of a dense collection of scaffold proteins. AZs of Drosophila melanogaster (Drosophila) photoreceptors undergo molecular remodeling after prolonged exposure to natural ambient light. Thus the level of neuronal activity can rearrange the molecular composition of the AZ and contribute to the regulation of the functional output.
Starting from the light exposure set-up preparation to the immunohistochemistry, this protocol details how to quantify the number, the spatial distribution, and the delocalization level of synaptic molecules at AZs in Drosophila photoreceptors. Using image analysis software, clusters of the GFP-fused AZ component Bruchpilot were identified for each R8 photoreceptor (R8) axon terminal. Detected Bruchpilot spots were automatically assigned to individual R8 axons. To calculate the distribution of spot frequency along the axon, we implemented a customized software plugin. Each axon&#39;s start-point and end-point were manually defined and the position of each Bruchpilot spot was projected onto the connecting line between start and end-point. Besides the number of Bruchpilot clusters, we also quantified the delocalization level of Bruchpilot-GFP within the clusters. These measurements reflect in detail the spatially resolved synaptic dynamics in a single neuron under different environmental conditions to stimuli.

Analyzing Synaptic Modulation of Drosophila melanogaster Photoreceptors after Exposure to Prolonged Light

Here we show how to quantify the number and spatial distribution of synaptic active zones in Drosophila melanogaster&#160;photoreceptors, highlighted with a genetically encoded molecular marker, and their modulation after prolonged exposure to light.

The nervous system has the remarkable ability to adapt and respond to various stimuli. This neural adjustment is largely achieved through plasticity at ...

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

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

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

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

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

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Brainstem evoked response audiometry (BERA) is of central relevance in the clinical neurophysiology. As other evoked potential (EP) techniques, such as visually evoked potentials (VEPs) or somatosensory evoked potentials (SEPs), the auditory evoked potentials (AEPs) are triggered by the repetitive presentation of identical stimuli, the electroencephalographic (EEG) response of which is subsequently averaged resulting in distinct positive (p) and negative (n) deflections. In humans, both the amplitude and the latency of individual peaks can be used to characterize alterations in synchronization and conduction velocity in the underlying neuronal circuitries. Importantly, AEPs are also applied in basic and preclinical science to identify and characterize the auditory function in pharmacological and genetic animal models. Even more, animal models in combination with pharmacological testing are utilized to investigate for potential benefits in the treatment of sensorineural hearing loss (e.g., age- or noise-induced hearing deficits). Here we provide a detailed and integrative description of how to record auditory brainstem-evoked responses (ABRs) in mice using click and tone-burst application. A specific focus of this protocol is on pre-experimental animal housing, anesthesia, ABR recording, ABR filtering processes, automated wavelet-based amplitude growth function analysis, and latency detection.

Brainstem evoked response audiometry is an important tool in clinical neurophysiology. Nowadays, brainstem evoked response audiometry is also applied in the basic science and preclinical studies involving both pharmacological and genetic animal models. Here we provide a detailed description of how auditory brainstem responses can be successfully recorded and analyzed in mice.

Brainstem evoked response audiometry (BERA) is of central relevance in the clinical neurophysiology. As other evoked potential (EP) techniques, such as ...

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.

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).

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

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children&#8217;s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.