Name: a psychophysics paradigm for the collection and analysis of similarity judgments
Uploaded: 2022-03-01T13:45:00
Description: Watch this Scientific Journal Video about A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments at JoVE.com

The work is supported by funding from the National Institutes of Health (NIH), grant EY07977. The authors would also like to thank Usman Ayyaz for his assistance in testing the software, and Muhammad Naeem Ayyaz for his comments on the manuscript.

Prior to beginning the experiments, all subjects provide informed consent in accordance with institutional guidelines and the Declaration of Helsinki. In the case of this study, the protocol was approved by the institutional review board of Weill Cornell Medical College.
1. Installation and set-up
<ol>
	<li>Download the code from the GitHub repository, similarities (https://github.com/jvlab/similarities). In the command line, run:&#160;git clone https://github.com/jvlab/...

<ol>
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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Representational+geometry:+integrating+cognition,+computation,+and+the+brain.">Representational geometry: integrating cognition, computation, and the brain.</a> Trends in Cognitive Sciences. 17 (8), 401-412 (2013).</li><li>Hebart, M. N., Zheng, C. Y., Pereira, F., Baker, C. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Revealing+the+multidimensional+mental+representations+of+natural+objects+underlying+human+similarity+judgements.">Revealing the multidimensional mental representations of natural objects underlying human similarity judgements.</a> Nature Human Behaviour. 4 (11), 1173-1185 (2020).</li><li>Deng, W. S., Sloutsky, V. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+development+of+categorization:+Effects+of+classification+and+inference+training+on+category+representation.">The development of categorization: Effects of classification and inference training on category representation.</a> Developmental Psychology. 51 (3), 392-405 (2015).</li><li>Shepard, R. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stimulus+and+response+generalization:+tests+of+a+model+relating+generalization+to+distance+in+psychological+space.">Stimulus and response generalization: tests of a model relating generalization to distance in psychological space.</a> Journal of Experimental Psychology. 55 (6), 509-523 (1958).</li><li>Coombs, C. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+the+study+of+interstimulus+similarity.">A method for the study of interstimulus similarity.</a> Psychometrika. 19 (3), 183-194 (1954).</li><li>Ga&#776;rdenfors, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Conceptual Spaces: The Geometry of Thought. , (2000).</li><li>Zaidi, Q., 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=Perceptual+spaces:+mathematical+structures+to+neural+mechanisms.">Perceptual spaces: mathematical structures to neural mechanisms.</a> The Journal of Neuroscience The Official Journal of the Society for Neuroscience. 33 (45), 17597-17602 (2013).</li><li>Krishnaiah, P. R., Kanal, L. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Handbook of Statistics 2. , (1982).</li><li>Tsogo, L., Masson, M. H., Bardot, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multidimensional+Scaling+Methods+for+Many-Object+Sets:+A+Review.">Multidimensional Scaling Methods for Many-Object Sets: A Review.</a> Multivariate Behavioral Research. 35 (3), 307-319 (2000).</li><li>Kriegeskorte, N., Mur, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inverse+MDS:+Inferring+dissimilarity+structure+from+multiple+item+arrangements.">Inverse MDS: Inferring dissimilarity structure from multiple item arrangements.</a> Frontiers in Psychology. 3, 245 (2012).</li><li>Rao, V. R., Katz, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alternative+Multidimensional+Scaling+Methods+for+Large+Stimulus+Sets.">Alternative Multidimensional Scaling Methods for Large Stimulus Sets.</a> Journal of Marketing Research. 8 (4), 488-494 (1971).</li><li>Hoffman, J. I. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hypergeometric+Distribution.">Hypergeometric Distribution.</a> Biostatistics for Medical and Biomedical Practitioners. , 179-182 (2015).</li><li>Victor, J. D., Rizvi, S. M., Conte, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two+representations+of+a+high-dimensional+perceptual+space.">Two representations of a high-dimensional perceptual space.</a> Vision Research. 137, 1-23 (2017).</li><li>Knoblauch, K., Maloney, L. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+classification+images+with+generalized+linear+and+additive+models.">Estimating classification images with generalized linear and additive models.</a> Journal of Vision. 8 (16), 1-19 (2008).</li><li>Maloney, L. T., Yang, J. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Maximum+likelihood+difference+scaling.">Maximum likelihood difference scaling.</a> Journal of Vision. 3 (8), 573-585 (2003).</li><li>Logvinenko, A. D., Maloney, L. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+proximity+structure+of+achromatic+surface+colors+and+the+impossibility+of+asymmetric+lightness+matching.">The proximity structure of achromatic surface colors and the impossibility of asymmetric lightness matching.</a> Perception &amp; Psychophysics. 68 (1), 76-83 (2006).</li><li>Zhou, Y., Smith, B. H., Sharpee, T. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hyperbolic+geometry+of+the+olfactory+space.">Hyperbolic geometry of the olfactory space.</a> Science Advances. 4 (8), (2018).</li><li>Goldstone, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+efficient+method+for+obtaining+similarity+data.">An efficient method for obtaining similarity data.</a> Behavior Research Methods, Instruments, &amp; Computers. 26 (4), 381-386 (1994).</li><li>Townsend, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Theoretical+analysis+of+an+alphabetic+confusion+matrix.">Theoretical analysis of an alphabetic confusion matrix.</a> Perception &amp; Psychophysics. 9, 40-50 (1971).</li></ol>

The protocol outlined here is effective for obtaining and analyzing similarity judgments for stimuli that can be presented visually. The experimental paradigm, the analysis, and possible extensions are discussed first, and later the advantages and disadvantages of the method.
Experimental paradigm: The proposed method is demonstrated using a domain of 37 animal names, and a sample dataset of perceptual judgments is provided so that one can follow the analysis in step 5 and rep...

Humans mentally process and represent incoming sensory information to perform a wide range of tasks, such as object recognition, navigation, making inferences about the environment, and many others. Similarity judgments are commonly used to probe these mental representations1. Understanding the structure of mental representations can provide insight into the organization of conceptual knowledge2. It is also possible to gain insight into neural computations, by relating similarity judgments to brain activation patterns3. Additionally, similarity judgments reveal features that are salient in perception4. Studying how mental representations change during development can shed light on how they are learned5. Thus, similarity judgments provide valuable insight into information processing in the brain.
A common&#160;model of mental representations using similarities is a geometric space model6,7,8. Applied to sensory domains, this kind of model is often referred to as a perceptual space9. Points in the space represent stimuli and distances between points correspond to the perceived dissimilarity between them. From similarity judgments, one can obtain quantitative estimates of dissimilarities. These pairwise dissimilarities (or perceptual distances) can then be used to model the perceptual space via multidimensional scaling10.
There are many methods for collecting similarity judgments, each with its advantages and disadvantages. The most straightforward way of obtaining quantitative measures of dissimilarity is to ask subjects to rate on a scale the degree of dissimilarity between each pair of stimuli. While this is relatively quick, estimates tend to be unstable across long sessions as subjects cannot go back to previous judgments, and context effects, if present, cannot be detected. (Here, a context effect is defined as a change in the judged similarity between two stimuli, based on the presence of other stimuli that are not being compared.) Alternatively, subjects can be asked to compare all pairs of stimuli to all other pairs of stimuli. While this would yield a more reliable rank ordering of dissimilarities, the number of comparisons required scales with the fourth power of the number of stimuli, making it feasible for only small stimulus sets. Quicker alternatives, like sorting into a predefined number of clusters11&#160;or free sorting have their own limitations. Free sorting (into any number of piles) is intuitive, but it forces the subject to categorize the stimuli, even if the stimuli do not easily lend themselves to categorization. The more recent multi-arrangement method, inverse MDS, circumvents many of these limitations and is very efficient12. However, this method requires subjects to project their mental representations onto a 2D Euclidean plane and to consider similarities in a specific geometric manner, making the assumption that similarity structure can be recovered from Euclidean distances on a plane. Thus, there remains a need for an efficient method to collect large amounts of similarity judgments, without making assumptions about the geometry underlying the judgments.
Described here is a method that is both reasonably efficient and also avoids the above potential pitfalls. By asking subjects to rank stimuli in order of similarity to a central reference in each trial13, relative similarity can be probed directly, without assuming anything about the geometric structure of the subjects&#39; responses. The paradigm repeats a subset of comparisons with both identical and different contexts, allowing for direct assessment of context effects as well as the acquisition of graded responses in terms of choice probabilities. The analysis procedure decomposes these rank judgments into multiple pairwise comparisons and uses them to build and search for Euclidean models of perceptual spaces that explain the judgments. The method is suitable for describing in detail the representation of stimulus sets of moderate sizes (e.g., 19 to 49).
To demonstrate the feasibility of the approach, an experiment was conducted, using a set of 37 animals as stimuli. Data was collected over the course of 10 one-hour sessions and then analyzed separately for each subject. Analysis revealed consistency across subjects and negligible context effects. It also assessed consistency of perceived dissimilarities between stimuli with Euclidean models of their perceptual spaces. The paradigm and analysis procedures outlined in this paper are flexible and are expected to be of use to researchers interested in characterizing the geometric properties of a range of perceptual spaces.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer Workstation</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>OS: Windows/ MacOS 10 or higher/ Linux; 3.1 GHz Dual-Core Intel Core i5 or similar; 8GB or more memory; User permissions for writing and executing files</td>
		</tr>
		<tr>
			<td>conda</td>
			<td></td>
			<td>Version 4.11</td>
			<td>OS: Windows/ MacOS 10 or higher/ Linux</td>
		</tr>
		<tr>
			<td>Microsoft Excel</td>
			<td>Microsoft</td>
			<td>Any</td>
			<td>To open and shuffle rows and columns in trial conditions files.</td>
		</tr>
		<tr>
			<td>PsychoPy</td>
			<td>N/A</td>
			<td>Version 2021.2</td>
			<td>Framework for running psychophysical studies</td>
		</tr>
		<tr>
			<td>Python 3</td>
			<td>Python Software Foundation</td>
			<td>Python Version 3.8</td>
			<td>Python3 and associated built-in libraries</td>
		</tr>
		<tr>
			<td>Required Python Libraries</td>
			<td>N/A</td>
			<td>numpy version: 1.17.2 or higher; matplotlib version 3.4.3 or higher; scipy version 1.3.1 or higher; pandas version 0.25.3 or higher; seaborn version 0.9.0 or higher; scikit_learn version 0.23.1 or higher; yaml version 6.0 or higher&#160;</td>
			<td>numpy, scipy and scikit_learn are computing modules with in-built functions for optimization and vector operations. matplotlib and seaborn are plotting libraries. pandas is used to reading in and edit data from csv files.</td>
		</tr>
	</tbody>
</table>

a psychophysics paradigm for the collection and analysis of similarity judgments

Similarity judgments are commonly used to study mental representations and their neural correlates. This approach has been used to characterize perceptual spaces in many domains: colors, objects, images, words, and sounds. Ideally, one might want to compare estimates of perceived similarity between all pairs of stimuli, but this is often impractical. For example, if one asks a subject to compare the similarity of two items with the similarity of two other items, the number of comparisons grows with the fourth power of the stimulus set size. An alternative strategy is to ask a subject to rate similarities of isolated pairs, e.g., on a Likert scale. This is much more efficient (the number of ratings grows quadratically with set size rather than quartically), but these ratings tend to be unstable and have limited resolution, and the approach also assumes that there are no context effects.
Here, a novel ranking paradigm for efficient collection of similarity judgments is presented, along with an analysis pipeline (software provided) that tests whether Euclidean distance models account for the data. Typical trials consist of eight stimuli around a central reference stimulus: the subject ranks stimuli in order of their similarity to the reference. By judicious selection of combinations of stimuli used in each trial, the approach has internal controls for consistency and context effects. The approach was validated for stimuli drawn from Euclidean spaces of up to five dimensions. 
The approach is illustrated with an experiment measuring similarities among 37 words. Each trial yields the results of 28 pairwise comparisons of the form, "Was A more similar to the reference than B was to the reference?" While directly comparing all pairs of pairs of stimuli would have required 221445 trials, this design enables reconstruction of the perceptual space from 5994 such comparisons obtained from 222 trials.

Figure 1A shows part of a conditions file generated by the script in step 3.3, for the word experiment. Each row corresponds to a trial. The stimulus in the ref column appears in the center of the display. The column names stim1 to stim8 correspond to eight positions along a circle, running counterclockwise, starting from the position to the right of the central reference. A sample trial from the word experiment is shown in Figure 1B.
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Watch this Scientific Journal Video about A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments at JoVE.com

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

The protocol presents an experimental psychophysics paradigm to obtain large quantities of similarity judgments, and an accompanying analysis workflow. The paradigm probes context effects and enables modeling of similarity data in terms of Euclidean spaces of at least five dimensions.

Similarity judgments are commonly used to study mental representations and their neural correlates. This approach has been used to characterize perceptual ...

This is an efficient method for collecting and analyzing similarity judgments, and it doesn't assume anything about the geometric properties of subjects underlying mental representations. The main advantages of the method are its flexibility and minimization of assumptions about the nature of the perceptual representation. The kinds of stimuli and the complexity of trials can be varied, and a wide range of geometric models can be fit to the data.This method could be useful to researchers interested in characterizing the mental representations of low and high-level aspects of visual stimuli. Select an experiment to run. Navigate to the word experiment by clicking similarities, then experiments, then word_exp, or to the image experiment by clicking similarities, then experiments, then image_exp.Finalize the experimental stimuli. If the word experiment is being run, prepare a list of words. And for the image experiment, make a new directory and place all the stimulus images in it.In the experiments directory, find the configuration file called config. yaml by clicking similarities, then experiments, and then config.yaml. Open the file in a source code editor and update the value of the file variable to the path to the directory containing the stimulus set.This is where PsychoPy will look for the image stimuli. Create trial configurations by opening the config. yaml file in the analysis directory, then set the value of the path_2_stimulus_list parameter to the path to stimuli.txt.From the similarities directory, run the script by executing the commands displayed in the window one after the other. This creates a file called trial_conditions. csv in similarities in which each row contains the names of the stimuli appearing in a trial along with their positions.Break the full set of 222 trials generated into sessions and randomize the trial order by executing the commands displayed in the window. In the typical design, sessions comprise 111 trials, each of which requires approximately one hour to run. When prompted, enter the displayed input parameters.Rename and save each of the generated files as conditions. csv in its own directory. Copy the conditions.csv file and paste it into the current directory containing the psyexp file. Open PsychoPy and open the psyexp or py file in the relevant experiments directory. In PsychoPy, click on the green play button to run the experiment.In the modal pop-up, enter the subject name or ID and session number and click OK to start. Instructions will be displayed at the start of each session. Allow the subject about one hour to complete the task and as the task is self-paced, encourage the subjects to take breaks if needed.After all the sessions are completed, combine the raw data files and reformat them into a single JSON file for further processing by running preprocess. py in the terminal using the commands visible on the screen. When prompted, enter the requested input parameters including the path to the subject data directory, subject IDs to pre-process the data and the experiment name used to name the output file, then press Enter.This will create a JSON file in the output directory that combines responses across repeats for each trial. To determine the pair-wise choice probabilities from rank order judgments, go to similarities, then analysis and run describe_data. py in the command line.When prompted, enter the path to subject data and the list of subjects to run the analysis. This will create three kinds of plots. Generate low-dimensional Euclidean models of the perceptual spaces using the choice probabilities by running model_fitting.py using the command line displayed on the screen. When prompted, provide the inputs for the directory to the subject-data/preprocessed, the number of stimuli, which will be 37 by default, the number of iterations, the output directory, and the amount of Gaussian noise, which will be 0.18 by default. Visualize the log likelihood of the obtained models and assess their fit by running similarities, analysis, model_fitting_figure.py.When prompted, input the needed path to the CSV files containing log likelihoods. Visualize the perceptual spaces for each subject and generate scatter plots showing the points from the 5D model projected onto the first two principle components by running the displayed commands. When prompted, enter the input parameters and the path to the NPY file containing the 5D points.After the script is executed, exit the virtual environment. In the file generated for the word experiment, the first row corresponds to a trial in which eight stimuli appear around the reference stimulus monkey. The rank judgments were decomposed into pair-wise choices.The distribution of choice probabilities was highly consistent across the subjects. The data clustering near the diagonal in each panel indicates a great deal of consistency in the choice probabilities between the subjects and for the judgements that are not at the extremes. The dominant diagonal indicates that the choice probabilities in the two contexts, including the intermediate choice probabilities between zero and one, are close to identical for each subject.The log likelihoods are shown relative to the log likelihood of the best model, that is a model that assigns the observed choice probability to each comparison without constraining these probabilities by any geometric consideration. Principle component analysis was performed on the points from the 5D model of the perceptual space where the animals perceived as similar were denoted by points near each other. It is crucial to decide on all parameters when designing an experiment and to set them in the config files before beginning step two.Keeping careful track of each subject's data is also very important. The method provides a large number of similarity judgment, so a number of analyses could be applied, such as cluster analysis, a focus on context, or modeling with different geometric spaces.

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Research

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Neuroscience

Organotypic Cerebellar Cultures: Apoptotic Challenges and Detection

Organotypic cultures of neuronal tissue were first introduced by Hogue in 1947 1,2 and have constituted a major breakthrough in the field of neuroscience. Since then, the technique was developed further and currently there are many different ways to prepare organotypic cultures. The method presented here was adapted from the one described by Stoppini et al. for the preparation of the slices and from Gogolla et al. for the staining procedure 3,4.
A unique feature of this technique is that it allows you to study different parts of the brain such as hippocampus or cerebellum in their original structure, providing a big advantage over dissociated cultures in which all the cellular organization and neuronal networks are disrupted. In the case of the cerebellum it is even more advantageous because it allows the study of Purkinje cells, extremely difficult to obtain as dissociated primary culture. This method can be used to study certain developmental features of the cerebellum in vitro, as well as for electrophysiological and pharmacological experiments in both wild type and mutant mice.
The method described here was designed to study the effect of apoptotic stimuli such as Fas ligand in the developing cerebellum, using TUNEL staining to measure apoptotic cell death. If TUNEL staining is combined with cell type specific markers, such as Calbindin for Purkinje cells, it is possible to evaluate cell death in a cell population specific manner. The Calbindin staining also serves the purpose of evaluating the quality of the cerebellar cultures.

This method describes the generation of organotypic cerebellar cultures and the effect of certain apoptotic stimuli on the viability of different cerebellar cell types.

Organotypic cultures of neuronal tissue were first introduced by Hogue in 1947 1,2 and have constituted a major breakthrough in the field of neuroscience. ...

Reproducible Mouse Sciatic Nerve Crush and Subsequent Assessment of Regeneration by Whole Mount Muscle Analysis

Regeneration in the peripheral nervous system (PNS) is widely studied both for its relevance to human disease and to understand the robust regenerative response mounted by PNS neurons thereby possibly illuminating the failures of CNS regeneration1. Sciatic nerve crush (axonotmesis) is one of the most common models of peripheral nerve injury in rodents2. Crushing interrupts all axons but Schwann cell basal laminae are preserved so that regeneration is optimal3,4. This allows the investigator to study precisely the ability of a growing axon to interact with both the Schwann cell and basal laminae4. Rats have generally been the preferred animal models for experimental nerve crush. They are widely available and their lesioned sciatic nerve provides a reasonable approximation of human nerve lesions5,4. Though smaller in size than rat nerve, the mouse nerve has many similar qualities. Most importantly though, mouse models are increasingly valuable because of the wide availability of transgenic lines now allows for a detailed dissection of the individual molecules critical for nerve regeneration6, 7. Prior investigators have used multiple methods to produce a nerve crush or injury including simple angled forceps, chilled forceps, hemostatic forceps, vascular clamps, and investigator-designed clamps8,9,10,11,12. Investigators have also used various methods of marking the injury site including suture, carbon particles and fluorescent beads13,14,1. We describe our method to obtain a reproducibly complete sciatic nerve crush with accurate and persistent marking of the crush-site using a fine hemostatic forceps and subsequent carbon crush-site marking. As part of our description of the sciatic nerve crush procedure we have also included a relatively simple method of muscle whole mount we use to subsequently quantify regeneration.

In this report we describe a method to crush mouse sciatic nerve. This method uses readily available hemostatic forceps and easily and reproducibly produces complete sciatic nerve crush. In addition, we describe a method to prepare muscle whole mounts suitable for analysis of nerve regeneration after sciatic nerve crush.

Regeneration in the peripheral nervous system (PNS) is widely studied both for its relevance to human disease and to understand the robust regenerative ...

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 ...

Visualization and Genetic Manipulation of Dendrites and Spines in the Mouse Cerebral Cortex and Hippocampus using In utero Electroporation

In utero electroporation (IUE) has become a powerful technique to study the development of different regions of the embryonic nervous system 1-5. To date this tool has been widely used to study the regulation of cellular proliferation, differentiation and neuronal migration especially in the developing cerebral cortex 6-8. Here we detail our protocol to electroporate in utero the cerebral cortex and the hippocampus and provide evidence that this approach can be used to study dendrites and spines in these two cerebral regions.
Visualization and manipulation of neurons in primary cultures have contributed to a better understanding of the processes involved in dendrite, spine and synapse development. However neurons growing in vitro are not exposed to all the physiological cues that can affect dendrite and/or spine formation and maintenance during normal development. Our knowledge of dendrite and spine structures in vivo in wild-type or mutant mice comes mostly from observations using the Golgi-Cox method 9. However, Golgi staining is considered to be unpredictable. Indeed, groups of nerve cells and fiber tracts are labeled randomly, with particular areas often appearing completely stained while adjacent areas are devoid of staining. Recent studies have shown that IUE of fluorescent constructs represents an attractive alternative method to study dendrites, spines as well as synapses in mutant / wild-type mice 10-11 (Figure 1A). Moreover in comparison to the generation of mouse knockouts, IUE represents a rapid approach to perform gain and loss of function studies in specific population of cells during a specific time window. In addition, IUE has been successfully used with inducible gene expression or inducible RNAi approaches to refine the temporal control over the expression of a gene or shRNA 12. These advantages of IUE have thus opened new dimensions to study the effect of gene expression/suppression on dendrites and spines not only in specific cerebral structures (Figure 1B) but also at a specific time point of development (Figure 1C).
Finally, IUE provides a useful tool to identify functional interactions between genes involved in dendrite, spine and/or synapse development. Indeed, in contrast to other gene transfer methods such as virus, it is straightforward to combine multiple RNAi or transgenes in the same population of cells. 
In summary, IUE is a powerful method that has already contributed to the characterization of molecular mechanisms underlying brain function and disease and it should also be useful in the study of dendrites and spines.

Visualization and Genetic Manipulation of Dendrites and Spines in the Mouse Cerebral Cortex and Hippocampus using In utero Electroporation

This article describes in detail a protocol to electroporate in utero the cerebral cortex and the hippocampus at E14.5 in mice. We also show that this is a valuable method to study dendrites and spines in these two cerebral regions.

In utero electroporation (IUE) has become a powerful technique to study the development of different regions of the embryonic nervous system 1-5. To date ...

A Visual Description of the Dissection of the Cerebral Surface Vasculature and Associated Meninges and the Choroid Plexus from Rat Brain

This video presentation was created to show a method of harvesting the two most important highly vascular structures, not residing within the brain proper, that support forebrain function. They are the cerebral surface (superficial) vasculature along with associated meninges (MAV) and the choroid plexus which are necessary for cerebral blood flow and cerebrospinal fluid (CSF) homeostasis. The tissue harvested is suitable for biochemical and physiological analysis, and the MAV has been shown to be sensitive to damage produced by amphetamine and hyperthermia 1,2. As well, the major and minor cerebral vasculatures harvested in MAV are of potentially high interest when investigating concussive types of head trauma. The MAV dissected in this presentation consists of the pial and some of the arachnoid membrane (less dura) of the meninges and the major and minor cerebral surface vasculature. The choroid plexus dissected is the structure that resides in the lateral ventricles as described by Oldfield and McKinley3,4,5,6. The methods used for harvesting these two tissues also facilitate the harvesting of regional cortical tissue devoid of meninges and larger cerebral surface vasculature, and is compatible with harvesting other brain tissues such as striatum, hypothalamus, hippocampus, etc. The dissection of the two tissues takes from 5 to 10 min total. The gene expression levels for the dissected MAV and choroid plexus, as shown and described in this presentation can be found at GSE23093 (MAV) and GSE29733 (choroid plexus) at the NCBI GEO repository. This data has been, and is being, used to help further understand the functioning of the MAV and choroid plexus and how neurotoxic events such as severe hyperthermia and AMPH adversely affect their function.

This video presentation shows a method of harvesting the two most important highly vascular structures that support forebrain function. They are the cerebral surface (superficial) vasculature along with associated meninges (MAV) and the choroid plexus which are necessary for cerebral blood flow and cerebrospinal fluid (CSF) homeostasis.

This video presentation was created to show a method of harvesting the two most important highly vascular structures, not residing within the brain ...

A Novel Light Damage Paradigm for Use in Retinal Regeneration Studies in Adult Zebrafish

Light-induced retinal degeneration (LIRD) is commonly used in both rodents and zebrafish to damage rod and cone photoreceptors. In adult zebrafish, photoreceptor degeneration triggers M&#252;ller glial cells to re-enter the cell cycle and produce transient-amplifying progenitors. These progenitors continue to proliferate as they migrate to the damaged area, where they ultimately give rise to new photoreceptors. Currently, there are two widely-used LIRD paradigms, each of which results in varying degrees of photoreceptor loss and corresponding differences in the regeneration response. As more genetic and pharmacological tools are available to test the role of individual genes of interest during regeneration, there is a need to develop a robust LIRD paradigm. Here we describe a LIRD protocol that results in widespread and consistent loss of both rod and cone photoreceptors in which we have combined the use of two previously established LIRD techniques. Furthermore, this protocol can be extended for use in pigmented animals, which eliminates the need to maintain transgenic lines of interest on the albino background for LIRD studies.

Multiple light damage protocols have been described to damage photoreceptors and consequently induce a retinal regeneration response in adult zebrafish. This protocol describes an improved method that can be used in pigmented animals and that damages the vast majority of rod and cone photoreceptors across the entire retina.

Light-induced retinal degeneration (LIRD) is commonly used in both rodents and zebrafish to damage rod and cone photoreceptors. In adult zebrafish, ...

High-resolution Quantification of Odor-guided Behavior in Drosophila melanogaster Using the Flywalk Paradigm

In their natural environment, insects such as the vinegar fly Drosophila melanogaster are bombarded with a huge amount of chemically distinct odorants. To complicate matters even further, the odors detected by the insect nervous system usually are not single compounds but mixtures whose composition and concentration ratios vary. This leads to an almost infinite amount of different olfactory stimuli which have to be evaluated by the nervous system.
To understand which aspects of an odor stimulus determine its evaluation by the fly, it is therefore desirable to efficiently examine odor-guided behavior towards many odorants and odor mixtures. To directly correlate behavior to neuronal activity, behavior should be quantified in a comparable time frame and under identical stimulus conditions as in neurophysiological experiments. However, many currently used olfactory bioassays in Drosophila neuroethology are rather specialized either towards efficiency or towards resolution.
Flywalk, an automated odor delivery and tracking system, bridges the gap between efficiency and resolution. It allows the determination of exactly when an odor packet stimulated a freely walking fly, and to determine the animal&#180;s dynamic behavioral reaction.

High-resolution Quantification of Odor-guided Behavior in Drosophila melanogaster Using the Flywalk Paradigm

The automated tracking system Flywalk is used for high-resolution quantification of odor-guided behavior in Drosophila melanogaster.

In their natural environment, insects such as the vinegar fly Drosophila melanogaster are bombarded with a huge amount of chemically distinct odorants. To ...

Chronic Implantation of Whole-cortical Electrocorticographic Array in the Common Marmoset

Electrocorticography (ECoG) allows the monitoring of electrical field potentials from the cerebral cortex with high spatiotemporal resolution. Recent development of thin, flexible ECoG electrodes has enabled conduction of stable recordings of large-scale cortical activity. We have developed a whole-cortical ECoG array for the common marmoset. The array continuously covers almost the entire lateral surface of cortical hemisphere, from the occipital pole to the temporal and frontal poles, and it captures whole-cortical neural activity in one shot. This protocol describes a chronic implantation procedure of the array in the epidural space of the marmoset brain. Marmosets have two advantages regarding ECoG recordings, one being the homologous organization of anatomical structures in humans and macaques, including frontal, parietal, and temporal complexes. The other advantage is that the marmoset brain is lissencephalic and contains a large number of complexes, which are more difficult to access in macaques with ECoG, that are exposed to the brain surface.These features allow direct access to most cortical areas beneath the surface of the brain. This system provides an opportunity to investigate global cortical information processing with high resolutions at a sub-millisecond order in time and millimeter order in space.

We have developed a whole-cortical electrocorticographic array for the common marmoset that continuously covers almost the entire lateral surface of cortex, from the occipital pole to the temporal and frontal poles. This protocol describes a chronic implantation procedure of the array in the epidural space of the marmoset brain.

Electrocorticography (ECoG) allows the monitoring of electrical field potentials from the cerebral cortex with high spatiotemporal resolution. Recent ...

3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of sciatic nerve injury rodent models. In this protocol, we describe a novel kinematic analysis method that uses a three-dimensional (3D) motion capture apparatus for functional evaluations using a rat sciatic nerve crush injury model. First, the rat is familiarized with treadmill walking. Markers are then attached to the designated bone landmarks and the rat is made to walk on the treadmill at the desired speed. Meanwhile, the posterior limb movements of the rat are recorded using four cameras. Depending on the software used, marker tracings are created using both automatic and manual modes and the desired data are produced after subtle adjustments. This method of kinematic analysis, which uses a 3D motion capture apparatus, offers numerous advantages, including superior precision and accuracy. Many more parameters can be investigated during the comprehensive functional evaluations. This method has several shortcomings that require consideration: The system is expensive, can be complicated to operate, and may produce data deviations due to skin shifting. Nevertheless, kinematic analysis using a 3D motion capture apparatus is useful for performing functional anterior and posterior limb evaluations. In the future, this method may become increasingly useful for generating accurate assessments of various traumas and diseases.

We introduce a kinematic analysis method that uses a three-dimensional motion capture apparatus containing four cameras and data processing software for performing functional evaluations during fundamental research involving rodent models.

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of ...

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, and size are processed and integrated within numerous brain areas, then ultimately relayed to the motor periphery. In some instances, a reaction is needed as fast as possible. These fast visuomotor transformations, and their underlying neurological substrates, are poorly understood in humans as they have lacked a reliable biomarker. Stimulus-locked responses (SLRs) are short latency (&#60;100 ms) bursts of electromyographic (EMG) activity representing the first wave of muscle recruitment influenced by visual stimulus presentation. SLRs provide a quantifiable output of rapid visuomotor transformations, but SLRs have not been consistently observed in all subjects in past studies. Here we describe a new, behavioral paradigm featuring the sudden emergence of a moving target below an obstacle that consistently evokes robust SLRs. Human participants generated visually guided reaches toward or away from the emerging target using a robotic manipulandum while surface electrodes recorded EMG activity from the pectoralis major muscle. In comparison to previous studies that investigated SLRs using static stimuli, the SLRs evoked with this emerging target paradigm were larger, evolved earlier, and were present in all participants. Reach reaction times (RTs) were also expedited in the emerging target paradigm. This paradigm affords numerous opportunities for modification that could permit systematic study of the impact of various sensory, cognitive, and motor manipulations on fast visuomotor responses. Overall, our results demonstrate that an emerging target paradigm is capable of consistently and robustly evoking activity within a fast visuomotor system.

Presented here is a behavioral paradigm that elicits robust fast visuomotor responses on human upper limb muscles during visually guided reaches.

To reach towards a seen object, visual information has to be transformed into motor commands. Visual information such as the object&#8217;s color, shape, ...