Name: cross-modal multivariate pattern analysis
Uploaded: 2011-11-09T13:00:00
Duration: 13 min 51 s
Description: Watch this Scientific Journal Video about Cross-Modal Multivariate Pattern Analysis at JoVE.com

This work was supported by grants by the Mathers Foundation and the National Institutes of Health (grant number 5P50NS019632-27) to Antonio and Hanna Damasio.

<ol>
	<li>Haynes, J. -. D., Rees, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decoding+mental+states+from+brain+activity+in+humans.">Decoding mental states from brain activity in humans.</a> Nat. Rev. Neurosci. 7, 523-534 (2006).</li><li>Norman, K. A., Polyn, S. M., Detre, G. J., Haxby, J. V. <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, 424-430 (2006).</li><li>Kriegeskorte, N., Bandettini, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+for+information,+not+activation,+to+exploit+high-resolution+fMRI.">Analyzing for information, not activation, to exploit high-resolution fMRI.</a> NeuroImage. 38, 649-662 (2007).</li><li>Mur, M., Bandettini, P. A., Kriegeskorte, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Revealing+representational+content+with+pattern-information+fMRI+-+an+introductory+guide.">Revealing representational content with pattern-information fMRI - an introductory guide.</a> Soc. Cogn. Affect. Neurosci. 4, 101-109 (2009).</li><li>Kamitani, Y., Tong, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decoding+the+visual+and+subjective+contents+of+the+human+brain.">Decoding the visual and subjective contents of the human brain.</a> Nat. Neurosci. 8, 679-685 (2005).</li><li>Formisano, E., De Martino, F., Bonte, M., Goebel, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term="Who"+is+saying+"what"?+Brain-based+decoding+of+human+voice+and+speech?.">"Who" is saying "what"? Brain-based decoding of human voice and speech?.</a> Science. 322, 970-973 (2008).</li><li>Meyer, K., Kaplan, J. T., Essex, R., Webber, C., Damasio, H., Damasio, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+visual+stimuli+on+the+basis+of+activity+in+auditory+cortices.">Predicting visual stimuli on the basis of activity in auditory cortices.</a> Nat. Neurosci. 13, 667-668 (2010).</li><li>Meyer, K., Kaplan, J. T., Essex, R., Damasio, H., Damasio, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Seeing+touch+is+correlated+with+content-specific+activity+in+primary+somatosensory+cortex.">Seeing touch is correlated with content-specific activity in primary somatosensory cortex.</a> Cereb. Cortex. 21, 2113-2121 (2011).</li><li>Damasio, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Time-locked+multiregional+retroactivation:+a+systems-level+proposal+for+the+neural+substrates+of+recall+and+recognition.">Time-locked multiregional retroactivation: a systems-level proposal for the neural substrates of recall and recognition.</a> Cognition. 33, 25-62 (1989).</li><li>Meyer, K., Damasio, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Convergence+and+divergence+in+a+neural+architecture+for+recognition+and+memory.">Convergence and divergence in a neural architecture for recognition and memory.</a> Trends. Neurosci. 32, 376-382 (2009).</li><li>Friston, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modalities,+modes,+and+models+in+functional+neuroimaging.">Modalities, modes, and models in functional neuroimaging.</a> Science. 326, 399-403 (2009).</li><li>Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., Pollmann, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PyMVPA:+a+Python+toolbox+for+multivariate+pattern+analysis+of+fMRI+data.">PyMVPA: a Python toolbox for multivariate pattern analysis of fMRI data.</a> Neuroinformatics. 7, 37-53 (2009).</li><li>Harrison, S. A., Tong, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decoding+reveals+the+contents+of+visual+working+memory+in+early+visual+areas.">Decoding reveals the contents of visual working memory in early visual areas.</a> Nature. 458, 632-635 (2009).</li><li>Serences, J. T., Ester, E. F., Vogel, E. K., Awh, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stimulus-specific+delay+activity+in+human+primary+visual+cortex.">Stimulus-specific delay activity in human primary visual cortex.</a> Psych Sci. 20, 207-214 (2009).</li></ol>

No conflicts of interest declared.

The findings of our previous studies demonstrate that cross-modal MVPA is a useful tool to study the neural correlates of mental images experienced in the "mind's ear" and the "mind's touch". Specifically, the results show that the content of such images is correlated with neural activity in the early auditory and somatosensory cortices, respectively, providing direct empirical support for Damasio's framework of convergence-divergence zones. 
The basic paradigm we describe can be extended in a number of ways. Most obviously, similar studies can be carried out using different combinations of sensory modalities. In this respect, it is possible that establishing certain cross-modal associations prior to the experiment may increase the chances of success. For instance, to study cross-modal representations in olfactory cortices, one could prime subjects by exposing them simultaneously to the sight and the smell of a number of food items. Shortly after, inside the fMRI scanner, the olfactory memories of the smells would be triggered by visual cues, and MVPA could be used in an attempt to assign the trials to the correct food items based solely on activity in olfactory cortices. 
Another question of interest is whether the cross-modally induced patterns of activity in the early sensory cortices bear similarities with the patterns that are induced when sound or touch is actually experienced; in other words, does seeing the glass vase shatter invoke a similar pattern of neural activity in early auditory cortices as actually hearing the same event? This question, again, can be addressed through MVPA: is a classifier that has been trained on data recorded while subjects heard certain sounds able to correctly discriminate data recorded while the subjects watched corresponding video clips? In our auditory study, we did attempt such a classification, but the results were marginal (see Fig. 3 in ref. 7). In that study, however, the auditory associations of the participants were not controlled in any way; in other words, we do not know how similar the auditory images each subject associated with videos were to the audio tracks used to train the classifier. Again, it might be interesting to address the same question after specific cross-modal associations have been primed in the subjects, as described above for a visuo-olfactory association. This would permit to control more reliably the mental experience of the subjects during the video trials and thus might increase prediction performance of the classifier.
To conclude, we have introduced an extension to the classical MVPA paradigm by showing that stimuli can be predicted not only within, but also across sensory modalities. Thus, we show that the use of MVPA is not limited to investigating the correlates of perceptual representations induced directly by external sensory stimulation. Rather, MVPA can also assess the neural substrate of mental images that are triggered internally: in accordance with Damasio's convergence-divergence zone framework, our findings suggest that the conscious experience of mental images that are reconstructed based on memories is correlated with content-specific neural representations in the early sensory cortices.

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.

Watch this Scientific Journal Video about Cross-Modal Multivariate Pattern Analysis at JoVE.com

Cross-Modal Multivariate Pattern Analysis

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 ...

cross-modal-multivariate-pattern-analysis

Research

JoVE Journal

Neuroscience

Basics of Multivariate Analysis in Neuroimaging Data

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

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

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

Brain Imaging Investigation of the Impairing Effect of Emotion on Cognition

Emotions can impact cognition by exerting both enhancing (e.g., better memory for emotional events) and impairing (e.g., increased emotional distractibility) effects (reviewed in 1). Complementing our recent protocol 2 describing a method that allows investigation of the neural correlates of the memory-enhancing effect of emotion (see also 1, 3-5), here we present a protocol that allows investigation of the neural correlates of the detrimental impact of emotion on cognition. The main feature of this method is that it allows identification of reciprocal modulations between activity in a ventral neural system, involved in 'hot' emotion processing (HotEmo system), and a dorsal system, involved in higher-level 'cold' cognitive/executive processing (ColdEx system), which are linked to cognitive performance and to individual variations in behavior (reviewed in 1). Since its initial introduction 6, this design has proven particularly versatile and influential in the elucidation of various aspects concerning the neural correlates of the detrimental impact of emotional distraction on cognition, with a focus on working memory (WM), and of coping with such distraction 7,11, in both healthy 8-11 and clinical participants 12-14.

We present a protocol that allows investigation of the neural mechanisms mediating the detrimental impact of emotion on cognition, using functional magnetic resonance imaging. This protocol can be used with both healthy and clinical participants.

Emotions can impact cognition by exerting both enhancing (e.g., better memory for emotional events) and impairing (e.g., increased emotional ...

Transcriptome Analysis of Single Cells

Many gene expression analysis techniques rely on material isolated from heterogeneous populations of cells from tissue homogenates or cells in culture.1,2,3 In the case of the brain, regions such as the hippocampus contain a complex arrangement of different cell types, each with distinct mRNA profiles. The ability to harvest single cells allows for a more in depth investigation into the molecular differences between and within cell populations. We describe a simple and rapid method for harvesting cells for further processing. Pipettes often used in electrophysiology are utilized to isolate (using aspiration) a cell of interest and conveniently deposit it into an Eppendorf tube for further processing with any number of molecular biology techniques. Our protocol can be modified for the harvest of dendrites from cell culture or even individual cells from acute slices.
We also describe the aRNA amplification method as a major downstream application of single cell isolations. This method was developed previously by our lab as an alternative to other gene expression analysis techniques such as reverse-transcription or real-time polymerase chain reaction (PCR).4,5,6,7,8 This technique provides for linear amplification of the polyadenylated RNA beginning with only femtograms of material and resulting in microgram amounts of antisense RNA. The linearly amplified material provides a more accurate estimation than PCR exponential amplification of the relative abundance of components of the transcriptome of the isolated cell. The basic procedure consists of two rounds of amplification. Briefly, a T7 RNA polymerase promoter site is incorporated into double stranded cDNA created from the mRNA transcripts. An overnight in vitro transcription (IVT) reaction is then performed in which T7 RNA polymerase produces many antisense transcripts from the double stranded cDNA. The second round repeats this process but with some technical differences since the starting material is antisense RNA. It is standard to repeat the second round, resulting in three rounds of amplification. Often, the third round in vitro transcription reaction is performed using biotinylated nucleoside triphosphates so that the antisense RNA produced can be hybridized and detected on a microarray.7,8

In this article we describe a simple method for the harvesting of single cells from rat primary neuronal cultures and subsequent transcriptome analysis using aRNA amplification. This approach is generalizable to any cell type.

Many gene expression analysis techniques rely on material isolated from heterogeneous populations of cells from tissue homogenates or cells in ...

T-maze Forced Alternation and Left-right Discrimination Tasks for Assessing Working and Reference Memory in Mice

Forced alternation and left-right discrimination tasks using the T-maze have been widely used to assess working and reference memory, respectively, in rodents. In our laboratory, we evaluated the two types of memory in more than 30 strains of genetically engineered mice using the automated version of this apparatus. Here, we present the modified T-maze apparatus operated by a computer with a video-tracking system and our protocols in a movie format. The T-maze apparatus consists of runways partitioned off by sliding doors that can automatically open downward, each with a start box, a T-shaped alley, two boxes with automatic pellet dispensers at one side of the box, and two L-shaped alleys. Each L-shaped alley is connected to the start box so that mice can return to the start box, which excludes the effects of experimenter handling on mouse behavior. This apparatus also has an advantage that in vivo microdialysis, in vivo electrophysiology, and optogenetics techniques can be performed during T-maze performance because the doors are designed to go down into the floor. In this movie article, we describe T-maze tasks using the automated apparatus and the T-maze performance of &alpha;-CaMKII+/- mice, which are reported to show working memory deficits in the eight-arm radial maze task. Our data indicated that &alpha;-CaMKII+/- mice showed a working memory deficit, but no impairment of reference memory, and are consistent with previous findings using the eight-arm radial maze task, which supports the validity of our protocol. In addition, our data indicate that mutants tended to exhibit reversal learning deficits, suggesting that &alpha;-CaMKII deficiency causes reduced behavioral flexibility. Thus, the T-maze test using the modified automatic apparatus is useful for assessing working and reference memory and behavioral flexibility in mice.

This article presents the protocol of T-maze tests using a modified automated apparatus for assessing the learning and memory functions in mice.

Forced alternation and left-right discrimination tasks using the T-maze have been widely used to assess working and reference memory, respectively, in ...

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties1. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties2.
Many innovative and useful methods currently exist for creating novel objects and object categories3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings.
First, shape variations are generally imposed by the experimenter5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints.
Second, the existing methods have difficulty capturing the shape complexity of natural objects11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases.
Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms.
Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis14. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection9,12,13. Objects and object categories created by these simulations can be further manipulated by various morphing methods to generate systematic variations of shape characteristics15,16. The VP and morphing methods can also be applied, in principle, to novel virtual objects other than digital embryos, or to virtual versions of real-world objects9,13. Virtual objects created in this fashion can be rendered as visual images using a conventional graphical toolkit, with desired manipulations of surface texture, illumination, size, viewpoint and background. The virtual objects can also be 'printed' as haptic objects using a conventional 3-D prototyper.
We also describe some implementations of these computational algorithms to help illustrate the potential utility of the algorithms. It is important to distinguish the algorithms from their implementations. The implementations are demonstrations offered solely as a 'proof of principle' of the underlying algorithms. It is important to note that, in general, an implementation of a computational algorithm often has limitations that the algorithm itself does not have.
Together, these methods represent a set of powerful and flexible tools for studying object recognition and perceptual learning by biological and computational systems alike. With appropriate extensions, these methods may also prove useful in the study of morphogenesis and phylogenesis.

We describe a novel methodology for creating naturalistic 3-D objects and object categories with precisely defined feature variations. We use simulations of the biological processes of morphogenesis and phylogenesis to create novel, naturalistic virtual 3-D objects and object categories that can then be rendered as visual images or haptic objects.

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object ...

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 ...

The Swimmeret System of Crayfish: A Practical Guide for the Dissection of the Nerve Cord and Extracellular Recordings of the Motor Pattern

Here we demonstrate the dissection of the crayfish abdominal nerve cord. The preparation comprises the last two thoracic ganglia (T4, T5) and the chain of abdominal ganglia (A1 to A6). This chain of ganglia includes the part of the central nervous system (CNS) that drives coordinated locomotion of the pleopods (swimmerets): the swimmeret system. It is known for over five decades that in crayfish each swimmeret is driven by its own independent pattern generating kernel that generates rhythmic alternating activity 1-3. The motor neurons innervating the musculature of each swimmeret comprise two anatomically and functionally distinct populations 4. One is responsible for the retraction (power stroke, PS) of the swimmeret. The other drives the protraction (return stroke, RS) of the swimmeret. Motor neurons of the swimmeret system are able to produce spontaneously a fictive motor pattern, which is identical to the pattern recorded in vivo 1.
The aim of this report is to introduce an interesting and convenient model system for studying rhythm generating networks and coordination of independent microcircuits for students&#8217; practical laboratory courses. The protocol provided includes step-by-step instructions for the dissection of the crayfish&#8217;s abdominal nerve cord, pinning of the isolated chain of ganglia, desheathing the ganglia and recording the swimmerets fictive motor pattern extracellularly from the isolated nervous system.
Additionally, we can monitor the activity of swimmeret neurons recorded intracellularly from dendrites. Here we also describe briefly these techniques and provide some examples. Furthermore, the morphology of swimmeret neurons can be assessed using various staining techniques. Here we provide examples of intracellular (by iontophoresis) dye filled neurons and backfills of pools of swimmeret motor neurons. In our lab we use this preparation to study basic functions of fictive locomotion, the effect of sensory feedback on the activity of the CNS, and coordination between microcircuits on a cellular level.

Here we describe the dissection of the crayfish abdominal nerve cord. We also demonstrate an electrophysiological technique to record fictive locomotion from swimmeret motor neurons.

Here we demonstrate the dissection of the crayfish abdominal nerve cord. The preparation comprises the last two thoracic ganglia (T4, T5) and the chain of ...

Peering at Brain Polysomes with Atomic Force Microscopy

The translational machinery, i.e., the polysome or polyribosome, is one of the biggest and most complex cytoplasmic machineries in cells. Polysomes, formed by ribosomes, mRNAs, several proteins and non-coding RNAs, represent integrated platforms where translational controls take place. However, while the ribosome has been widely studied, the organization of polysomes is still lacking comprehensive understanding. Thus much effort is required in order to elucidate polysome organization and any novel mechanism of translational control that may be embedded. Atomic force microscopy (AFM) is a type of scanning probe microscopy that allows the acquisition of 3D images at nanoscale resolution. Compared to electron microscopy (EM) techniques, one of the main advantages of AFM is that it can acquire thousands of images both in air and in solution, enabling the sample to be maintained under near physiological conditions without any need for staining and fixing procedures. Here, a detailed protocol for the accurate purification of polysomes from mouse brain and their deposition on mica substrates is described. This protocol enables polysome imaging in air and liquid with AFM and their reconstruction as three-dimensional objects. Complementary to cryo-electron microscopy (cryo-EM), the proposed method can be conveniently used for systematically analyzing polysomes and studying their organization.

While ribosome structure has been extensively characterized, the organization of polysomes is still understudied. To overcome this lack of knowledge, we present here a detailed preparation protocol for accurate imaging of mammalian polysomes by atomic force microscopy (AFM) in air and liquid.

The translational machinery, i.e., the polysome or polyribosome, is one of the biggest and most complex cytoplasmic machineries in cells. Polysomes, ...

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 ...

Visualization of the Axonal Projection Pattern of Embryonic Motor Neurons in Drosophila

The establishment of functional neuromuscular circuits relies on precise connections between developing motor axons and target muscles. Motor neurons extend growth cones to navigate along specific pathways by responding to a large number of axon guidance cues that emanate from the surrounding extracellular environment. Growth cone target recognition also plays a critical role in neuromuscular specificity. This work presents a standard immunohistochemistry protocol to visualize motor neuron projections of late stage-16 Drosophila melanogaster embryos. This protocol includes a few key steps, including a genotyping procedure, to sort the desired mutant embryos; an immunostaining procedure, to tag embryos with fasciclin II (FasII) antibody; and a dissection procedure, to generate filleted preparations from fixed embryos. Motor axon projections and muscle patterns in the periphery are much better visualized in flat preparations of filleted embryos than in whole-mount embryos. Therefore, the filleted preparation of fixed embryos stained with FasII antibody provides a powerful tool to characterize the genes required for motor axon pathfinding and target recognition, and it can also be applied to both loss-of-function and gain-of-function genetic screens.

Visualization of the Axonal Projection Pattern of Embryonic Motor Neurons in Drosophila

This work details a standard immunohistochemistry method to visualize motor neuron projections of late stage-16 Drosophila melanogaster embryos. The filleted preparation of fixed embryos stained with FasII antibody provides a powerful tool to characterize the genes required for motor axon pathfinding and target recognition during neural development.

The establishment of functional neuromuscular circuits relies on precise connections between developing motor axons and target muscles. Motor neurons ...