The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional Change Card Sort task, participants switch from sorting cards one way, such as shape, to sorting them a different way, such as color. Adjusting behavior in this way exacts a small performance cost, or switch cost, such that responses are typically slower and more error-prone on switch trials in which the sorting rule changes as compared to repeat trials in which the sorting rule remains the same. The ability to flexibly adjust behavior is often said to develop gradually, in part because behavioral costs such as switch costs typically decrease with increasing age. Why aspects of higher-order cognition, such as behavioral flexibility, develop so gradually remains an open question. One hypothesis is that these changes occur in association with functional changes in broad-scale cognitive control networks. On this view, complex mental operations, such as switching, involve rapid interactions between several distributed brain regions, including those that update and maintain task rules, re-orient attention, and select behaviors. With development, functional connections between these regions strengthen, leading to faster and more efficient switching operations. The current video describes a method of testing this hypothesis through the collection and multivariate analysis of fMRI data from participants of different ages.
23 Related JoVE Articles!
In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries
Institutions: University of Sydney, University of Wollongong, Australian Synchrotron, Australian Nuclear Science and Technology Organisation, University of Wollongong, University of New South Wales.
Li-ion batteries are widely used in portable electronic devices and are considered as promising candidates for higher-energy applications such as electric vehicles.1,2
However, many challenges, such as energy density and battery lifetimes, need to be overcome before this particular battery technology can be widely implemented in such applications.3
This research is challenging, and we outline a method to address these challenges using in situ
NPD to probe the crystal structure of electrodes undergoing electrochemical cycling (charge/discharge) in a battery. NPD data help determine the underlying structural mechanism responsible for a range of electrode properties, and this information can direct the development of better electrodes and batteries.
We briefly review six types of battery designs custom-made for NPD experiments and detail the method to construct the ‘roll-over’ cell that we have successfully used on the high-intensity NPD instrument, WOMBAT, at the Australian Nuclear Science and Technology Organisation (ANSTO). The design considerations and materials used for cell construction are discussed in conjunction with aspects of the actual in situ
NPD experiment and initial directions are presented on how to analyze such complex in situ
Physics, Issue 93, In operando, structure-property relationships, electrochemical cycling, electrochemical cells, crystallography, battery performance
Using an Automated 3D-tracking System to Record Individual and Shoals of Adult Zebrafish
Like many aquatic animals, zebrafish (Danio rerio
) moves in a 3D space. It is thus preferable to use a 3D recording system to study its behavior. The presented automatic video tracking system accomplishes this by using a mirror system and a calibration procedure that corrects for the considerable error introduced by the transition of light from water to air. With this system it is possible to record both single and groups of adult zebrafish. Before use, the system has to be calibrated. The system consists of three modules: Recording, Path Reconstruction, and Data Processing. The step-by-step protocols for calibration and using the three modules are presented. Depending on the experimental setup, the system can be used for testing neophobia, white aversion, social cohesion, motor impairments, novel object exploration etc
. It is especially promising as a first-step tool to study the effects of drugs or mutations on basic behavioral patterns. The system provides information about vertical and horizontal distribution of the zebrafish, about the xyz-components of kinematic parameters (such as locomotion, velocity, acceleration, and turning angle) and it provides the data necessary to calculate parameters for social cohesions when testing shoals.
Behavior, Issue 82, neuroscience, Zebrafish, Danio rerio, anxiety, Shoaling, Pharmacology, 3D-tracking, MK801
Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
Institutions: University of Northern Colorado, Arizona State University, Iowa State University.
The purpose of this study was two-fold: 1) demonstrate a technique that can be used to directly estimate the inertial properties of a below-knee prosthesis, and 2) contrast the effects of the proposed technique and that of using intact limb inertial properties on joint kinetic estimates during walking in unilateral, transtibial amputees. An oscillation and reaction board system was validated and shown to be reliable when measuring inertial properties of known geometrical solids. When direct measurements of inertial properties of the prosthesis were used in inverse dynamics modeling of the lower extremity compared with inertial estimates based on an intact shank and foot, joint kinetics at the hip and knee were significantly lower during the swing phase of walking. Differences in joint kinetics during stance, however, were smaller than those observed during swing. Therefore, researchers focusing on the swing phase of walking should consider the impact of prosthesis inertia property estimates on study outcomes. For stance, either one of the two inertial models investigated in our study would likely lead to similar outcomes with an inverse dynamics assessment.
Bioengineering, Issue 87, prosthesis inertia, amputee locomotion, below-knee prosthesis, transtibial amputee
A Proboscis Extension Response Protocol for Investigating Behavioral Plasticity in Insects: Application to Basic, Biomedical, and Agricultural Research
Institutions: Arizona State University.
Insects modify their responses to stimuli through experience of associating those stimuli with events important for survival (e.g.
, food, mates, threats). There are several behavioral mechanisms through which an insect learns salient associations and relates them to these events. It is important to understand this behavioral plasticity for programs aimed toward assisting insects that are beneficial for agriculture. This understanding can also be used for discovering solutions to biomedical and agricultural problems created by insects that act as disease vectors and pests. The Proboscis Extension Response (PER) conditioning protocol was developed for honey bees (Apis mellifera
) over 50 years ago to study how they perceive and learn about floral odors, which signal the nectar and pollen resources a colony needs for survival. The PER procedure provides a robust and easy-to-employ framework for studying several different ecologically relevant mechanisms of behavioral plasticity. It is easily adaptable for use with several other insect species and other behavioral reflexes. These protocols can be readily employed in conjunction with various means for monitoring neural activity in the CNS via electrophysiology or bioimaging, or for manipulating targeted neuromodulatory pathways. It is a robust assay for rapidly detecting sub-lethal effects on behavior caused by environmental stressors, toxins or pesticides.
We show how the PER protocol is straightforward to implement using two procedures. One is suitable as a laboratory exercise for students or for quick assays of the effect of an experimental treatment. The other provides more thorough control of variables, which is important for studies of behavioral conditioning. We show how several measures for the behavioral response ranging from binary yes/no to more continuous variable like latency and duration of proboscis extension can be used to test hypotheses. And, we discuss some pitfalls that researchers commonly encounter when they use the procedure for the first time.
Neuroscience, Issue 91, PER, conditioning, honey bee, olfaction, olfactory processing, learning, memory, toxin assay
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
Institutions: University of Pennsylvania.
It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.
Neuroscience, Issue 89, fMRI, MVPA, connectivity, informational connectivity, functional connectivity, networks, multi-voxel pattern analysis, decoding, classification, method, multivariate
Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
Institutions: University of California Merced, University of California Merced.
Spring-like materials are ubiquitous in nature and of interest in nanotechnology for energy harvesting, hydrogen storage, and biological sensing applications. For predictive simulations, it has become increasingly important to be able to model the structure of nanohelices accurately. To study the effect of local structure on the properties of these complex geometries one must develop realistic models. To date, software packages are rather limited in creating atomistic helical models. This work focuses on producing atomistic models of silica glass (SiO2
) nanoribbons and nanosprings for molecular dynamics (MD) simulations. Using an MD model of “bulk” silica glass, two computational procedures to precisely create the shape of nanoribbons and nanosprings are presented. The first method employs the AWK programming language and open-source software to effectively carve various shapes of silica nanoribbons from the initial bulk model, using desired dimensions and parametric equations to define a helix. With this method, accurate atomistic silica nanoribbons can be generated for a range of pitch values and dimensions. The second method involves a more robust code which allows flexibility in modeling nanohelical structures. This approach utilizes a C++ code particularly written to implement pre-screening methods as well as the mathematical equations for a helix, resulting in greater precision and efficiency when creating nanospring models. Using these codes, well-defined and scalable nanoribbons and nanosprings suited for atomistic simulations can be effectively created. An added value in both open-source codes is that they can be adapted to reproduce different helical structures, independent of material. In addition, a MATLAB graphical user interface (GUI) is used to enhance learning through visualization and interaction for a general user with the atomistic helical structures. One application of these methods is the recent study of nanohelices via MD simulations for mechanical energy harvesting purposes.
Physics, Issue 93, Helical atomistic models; open-source coding; graphical user interface; visualization software; molecular dynamics simulations; graphical processing unit accelerated simulations.
Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
Institutions: Rutgers University, Rutgers University, Rutgers University, Rutgers University, Rutgers University.
Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are deliberately planned, while others spontaneously occur below conscious awareness. The deliberate component of this dichotomy has been studied extensively in several contexts, while the spontaneous component remains largely under-explored. Moreover, how perceptual processes modulate these movement classes is still unclear. In particular, a currently debated issue is whether the visuomotor system is governed by the spatial percept produced by a visual illusion or whether it is not affected by the illusion and is governed instead by the veridical percept. Bistable percepts such as 3D depth inversion illusions (DIIs) provide an excellent context to study such interactions and balance, particularly when used in combination with reach-to-grasp movements. In this study, a methodology is developed that uses a DII to clarify the role of top-down processes on motor action, particularly exploring how reaches toward a target on a DII are affected in both deliberate and spontaneous movement domains.
Behavior, Issue 86, vision for action, vision for perception, motor control, reach, grasp, visuomotor, ventral stream, dorsal stream, illusion, space perception, depth inversion
Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
Institutions: University of Houston.
The behavior of confined colloidal suspensions with attractive interparticle interactions is critical to the rational design of materials for directed assembly1-3
, drug delivery4
, improved hydrocarbon recovery5-7
, and flowable electrodes for energy storage8
. Suspensions containing fluorescent colloids and non-adsorbing polymers are appealing model systems, as the ratio of the polymer radius of gyration to the particle radius and concentration of polymer control the range and strength of the interparticle attraction, respectively. By tuning the polymer properties and the volume fraction of the colloids, colloid fluids, fluids of clusters, gels, crystals, and glasses can be obtained9
. Confocal microscopy, a variant of fluorescence microscopy, allows an optically transparent and fluorescent sample to be imaged with high spatial and temporal resolution in three dimensions. In this technique, a small pinhole or slit blocks the emitted fluorescent light from regions of the sample that are outside the focal volume of the microscope optical system. As a result, only a thin section of the sample in the focal plane is imaged. This technique is particularly well suited to probe the structure and dynamics in dense colloidal suspensions at the single-particle scale: the particles are large enough to be resolved using visible light and diffuse slowly enough to be captured at typical scan speeds of commercial confocal systems10
. Improvements in scan speeds and analysis algorithms have also enabled quantitative confocal imaging of flowing suspensions11-16,37
. In this paper, we demonstrate confocal microscopy experiments to probe the confined phase behavior and flow properties of colloid-polymer mixtures. We first prepare colloid-polymer mixtures that are density- and refractive-index matched. Next, we report a standard protocol for imaging quiescent dense colloid-polymer mixtures under varying confinement in thin wedge-shaped cells. Finally, we demonstrate a protocol for imaging colloid-polymer mixtures during microchannel flow.
Chemistry, Issue 87, confocal microscopy, particle tracking, colloids, suspensions, confinement, gelation, microfluidics, image correlation, dynamics, suspension flow
Cortical Source Analysis of High-Density EEG Recordings in Children
Institutions: UCL Institute of Child Health, University College London.
EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1
. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2
, because the composition and spatial configuration of head tissues changes dramatically over development3
In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.
Behavior, Issue 88, EEG, electroencephalogram, development, source analysis, pediatric, minimum-norm estimation, cognitive neuroscience, event-related potentials
Quantification of Orofacial Phenotypes in Xenopus
Institutions: Virginia Commonwealth University.
has become an important tool for dissecting the mechanisms governing craniofacial development and defects. A method to quantify orofacial development will allow for more rigorous analysis of orofacial phenotypes upon abrogation with substances that can genetically or molecularly manipulate gene expression or protein function. Using two dimensional images of the embryonic heads, traditional size dimensions-such as orofacial width, height and area- are measured. In addition, a roundness measure of the embryonic mouth opening is used to describe the shape of the mouth. Geometric morphometrics of these two dimensional images is also performed to provide a more sophisticated view of changes in the shape of the orofacial region. Landmarks are assigned to specific points in the orofacial region and coordinates are created. A principle component analysis is used to reduce landmark coordinates to principle components that then discriminate the treatment groups. These results are displayed as a scatter plot in which individuals with similar orofacial shapes cluster together. It is also useful to perform a discriminant function analysis, which statistically compares the positions of the landmarks between two treatment groups. This analysis is displayed on a transformation grid where changes in landmark position are viewed as vectors. A grid is superimposed on these vectors so that a warping pattern is displayed to show where significant landmark positions have changed. Shape changes in the discriminant function analysis are based on a statistical measure, and therefore can be evaluated by a p-value. This analysis is simple and accessible, requiring only a stereoscope and freeware software, and thus will be a valuable research and teaching resource.
Developmental Biology, Issue 93, Orofacial quantification, geometric morphometrics, Xenopus, orofacial development, orofacial defects, shape changes, facial dimensions
Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
Institutions: University of Maine.
Localization-based super resolution microscopy can be applied to obtain a spatial map (image) of the distribution of individual fluorescently labeled single molecules within a sample with a spatial resolution of tens of nanometers. Using either photoactivatable (PAFP) or photoswitchable (PSFP) fluorescent proteins fused to proteins of interest, or organic dyes conjugated to antibodies or other molecules of interest, fluorescence photoactivation localization microscopy (FPALM) can simultaneously image multiple species of molecules within single cells. By using the following approach, populations of large numbers (thousands to hundreds of thousands) of individual molecules are imaged in single cells and localized with a precision of ~10-30 nm. Data obtained can be applied to understanding the nanoscale spatial distributions of multiple protein types within a cell. One primary advantage of this technique is the dramatic increase in spatial resolution: while diffraction limits resolution to ~200-250 nm in conventional light microscopy, FPALM can image length scales more than an order of magnitude smaller. As many biological hypotheses concern the spatial relationships among different biomolecules, the improved resolution of FPALM can provide insight into questions of cellular organization which have previously been inaccessible to conventional fluorescence microscopy. In addition to detailing the methods for sample preparation and data acquisition, we here describe the optical setup for FPALM. One additional consideration for researchers wishing to do super-resolution microscopy is cost: in-house setups are significantly cheaper than most commercially available imaging machines. Limitations of this technique include the need for optimizing the labeling of molecules of interest within cell samples, and the need for post-processing software to visualize results. We here describe the use of PAFP and PSFP expression to image two protein species in fixed cells. Extension of the technique to living cells is also described.
Basic Protocol, Issue 82, Microscopy, Super-resolution imaging, Multicolor, single molecule, FPALM, Localization microscopy, fluorescent proteins
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Institutions: Princeton University.
The aim of de novo
protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo
protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (https://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.
Genetics, Issue 77, Molecular Biology, Bioengineering, Biochemistry, Biomedical Engineering, Chemical Engineering, Computational Biology, Genomics, Proteomics, Protein, Protein Binding, Computational Biology, Drug Design, optimization (mathematics), Amino Acids, Peptides, and Proteins, De novo protein and peptide design, Drug design, In silico sequence selection, Optimization, Fold specificity, Binding affinity, sequencing
One Dimensional Turing-Like Handshake Test for Motor Intelligence
Institutions: Ben-Gurion University.
In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human. However, this test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement, and the movement of the human hand is a sophisticated demonstration of this function. Therefore, we propose a Turing-like handshake test, for machine motor intelligence. We administer the test through a telerobotic system in which the interrogator is engaged in a task of holding a robotic stylus and interacting with another party (human or artificial). Instead of asking the interrogator whether the other party is a person or a computer program, we employ a two-alternative forced choice method and ask which of two systems is more human-like. We extract a quantitative grade for each model according to its resemblance to the human handshake motion and name it "Model Human-Likeness Grade" (MHLG). We present three methods to estimate the MHLG. (i) By calculating the proportion of subjects' answers that the model is more human-like than the human; (ii) By comparing two weighted sums of human and model handshakes we fit a psychometric curve and extract the point of subjective equality (PSE); (iii) By comparing a given model with a weighted sum of human and random signal, we fit a psychometric curve to the answers of the interrogator and extract the PSE for the weight of the human in the weighted sum. Altogether, we provide a protocol to test computational models of the human handshake. We believe that building a model is a necessary step in understanding any phenomenon and, in this case, in understanding the neural mechanisms responsible for the generation of the human handshake.
Neuroscience, Issue 46, Turing test, Human Machine Interface, Haptics, Teleoperation, Motor Control, Motor Behavior, Diagnostics, Perception, handshake, telepresence
A Protocol for Computer-Based Protein Structure and Function Prediction
Institutions: University of Michigan , University of Kansas.
Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.
Biochemistry, Issue 57, On-line server, I-TASSER, protein structure prediction, function prediction
C. elegans Tracking and Behavioral Measurement
Institutions: University of Toronto, Vrije Universiteit, Okinawa Institute of Science and Technology, University of Toronto.
We have developed instrumentation, image processing, and data analysis techniques to quantify the locomotory behavior of C. elegans
as it crawls on the surface of an agar plate. For the study of the genetic, biochemical, and neuronal basis of behavior, C. elegans
is an ideal organism because it is genetically tractable, amenable to microscopy, and shows a number of complex behaviors, including taxis, learning, and social interaction1,2
. Behavioral analysis based on tracking the movements of worms as they crawl on agar plates have been particularly useful in the study of sensory behavior3
, and general mutational phenotyping5
. Our system works by moving the camera and illumination system as the worms crawls on a stationary agar plate, which ensures no mechanical stimulus is transmitted to the worm. Our tracking system is easy to use and includes a semi-automatic calibration feature. A challenge of all video tracking systems is that it generates an enormous amount of data that is intrinsically high dimensional. Our image processing and data analysis programs deal with this challenge by reducing the worms shape into a set of independent components, which comprehensively reconstruct the worms behavior as a function of only 3-4 dimensions6,7
. As an example of the process we show that the worm enters and exits its reversal state in a phase specific manner.
Neuroscience, Issue 69, Physics, Biophysics, Anatomy, Microscopy, Ethology, Behavior, Machine Vision, C. elegans, animal model
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
Institutions: University of Zurich.
Mori's Uncanny Valley Hypothesis1,2
proposes that the perception of humanlike characters such as robots and, by extension, avatars (computer-generated characters) can evoke negative or positive affect (valence) depending on the object's degree of visual and behavioral realism along a dimension of human likeness
) (Figure 1
). But studies of affective valence of subjective responses to variously realistic non-human characters have produced inconsistent findings 3, 4, 5, 6
. One of a number of reasons for this is that human likeness is not perceived as the hypothesis assumes. While the DHL can be defined following Mori's description as a smooth linear change in the degree of physical humanlike similarity, subjective perception of objects along the DHL can be understood in terms of the psychological effects of categorical perception (CP) 7
. Further behavioral and neuroimaging investigations of category processing and CP along the DHL and of the potential influence of the dimension's underlying category structure on affective experience are needed. This protocol therefore focuses on the DHL and allows examination of CP. Based on the protocol presented in the video as an example, issues surrounding the methodology in the protocol and the use in "uncanny" research of stimuli drawn from morph continua to represent the DHL are discussed in the article that accompanies the video. The use of neuroimaging and morph stimuli to represent the DHL in order to disentangle brain regions neurally responsive to physical human-like similarity from those responsive to category change and category processing is briefly illustrated.
Behavior, Issue 76, Neuroscience, Neurobiology, Molecular Biology, Psychology, Neuropsychology, uncanny valley, functional magnetic resonance imaging, fMRI, categorical perception, virtual reality, avatar, human likeness, Mori, uncanny valley hypothesis, perception, magnetic resonance imaging, MRI, imaging, clinical techniques
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.
Neuroscience, Issue 76, Neurobiology, Anatomy, Physiology, Medicine, Biomedical Engineering, Electroencephalography, EEG, electroencephalogram, Multiscale entropy, sample entropy, MEG, neuroimaging, variability, noise, timescale, non-linear, brain signal, information theory, brain, imaging
Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments
Institutions: Argonne National Laboratory, Argonne National Laboratory, MassThink LLC.
In materials science and engineering it is often necessary to obtain quantitative measurements of surface topography with micrometer lateral resolution. From the measured surface, 3D topographic maps can be subsequently analyzed using a variety of software packages to extract the information that is needed.
In this article we describe how white light interferometry, and optical profilometry (OP) in general, combined with generic surface analysis software, can be used for materials science and engineering tasks. In this article, a number of applications of white light interferometry for investigation of surface modifications in mass spectrometry, and wear phenomena in tribology and lubrication are demonstrated. We characterize the products of the interaction of semiconductors and metals with energetic ions (sputtering), and laser irradiation (ablation), as well as ex situ
measurements of wear of tribological test specimens.
Specifically, we will discuss:
Aspects of traditional ion sputtering-based mass spectrometry such as sputtering rates/yields measurements on Si and Cu and subsequent time-to-depth conversion.
Results of quantitative characterization of the interaction of femtosecond laser irradiation with a semiconductor surface. These results are important for applications such as ablation mass spectrometry, where the quantities of evaporated material can be studied and controlled via pulse duration and energy per pulse. Thus, by determining the crater geometry one can define depth and lateral resolution versus experimental setup conditions.
Measurements of surface roughness parameters in two dimensions, and quantitative measurements of the surface wear that occur as a result of friction and wear tests.
Some inherent drawbacks, possible artifacts, and uncertainty assessments of the white light interferometry approach will be discussed and explained.
Materials Science, Issue 72, Physics, Ion Beams (nuclear interactions), Light Reflection, Optical Properties, Semiconductor Materials, White Light Interferometry, Ion Sputtering, Laser Ablation, Femtosecond Lasers, Depth Profiling, Time-of-flight Mass Spectrometry, Tribology, Wear Analysis, Optical Profilometry, wear, friction, atomic force microscopy, AFM, scanning electron microscopy, SEM, imaging, visualization
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Institutions: The Feinstein Institute for Medical Research.
The scaled subprofile model (SSM)1-4
is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1
). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6
. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8
. Using logistic regression analysis of subject scores (i.e.
pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e.
composite networks with improved discrimination of patients from healthy control subjects5,6
. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9
. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10
. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11
. These standardized values can in turn be used to assist in differential diagnosis12,13
and to assess disease progression and treatment effects at the network level7,14-16
. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
Medicine, Issue 76, Neurobiology, Neuroscience, Anatomy, Physiology, Molecular Biology, Basal Ganglia Diseases, Parkinsonian Disorders, Parkinson Disease, Movement Disorders, Neurodegenerative Diseases, PCA, SSM, PET, imaging biomarkers, functional brain imaging, multivariate spatial covariance analysis, global normalization, differential diagnosis, PD, brain, imaging, clinical techniques
State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
Institutions: Beth Israel Deaconess Medical Center, Aalto University School of Science and Technology.
Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.1,2
Within the field of TMS, the term state dependency
refers to the initial, baseline condition of the particular neural region targeted for stimulation. As can be inferred, the effects of TMS can (and do) vary according to this primary susceptibility and responsiveness of the targeted cortical area.3,4,5
In this experiment, we will examine this concept of state dependency through the elicitation and subjective experience of motive phosphenes. Phosphenes are visually perceived flashes of small lights triggered by electromagnetic pulses to the visual cortex. These small lights can assume varied characteristics depending upon which type of visual cortex is being stimulated. In this particular study, we will be targeting motive phosphenes as elicited through the stimulation of V1/V2 and the V5/MT+ complex visual regions.6
Neuroscience, Issue 46, Transcranial Magnetic Stimulation, state dependency, motive phosphenes, visual priming, V1/V2, V5/MT+
Basics of Multivariate Analysis in Neuroimaging Data
Institutions: Columbia University.
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.
JoVE Neuroscience, Issue 41, fMRI, PET, multivariate analysis, cognitive neuroscience, clinical neuroscience
Cross-Modal Multivariate Pattern Analysis
Institutions: University of Southern California.
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.
Neuroscience, Issue 57, perception, sensory, cross-modal, top-down, mental imagery, fMRI, MRI, neuroimaging, multivariate pattern analysis, MVPA
Recordings of Neural Circuit Activation in Freely Behaving Animals
Institutions: University of Maryland.
The relationship between patterns of neural activity and corresponding behavioral expression is difficult to establish in unrestrained animals. Traditional non-invasive methods require at least partially restrained research subjects, and they only allow identification of large numbers of simultaneously activated neurons. On the other hand, small ensembles of neurons or individual neurons can only be measured using single-cell recordings obtained from largely reduced preparations. Since the expression of natural behavior is limited in restrained and dissected animals, the underlying neural mechanisms that control such behavior are difficult to identify.
Here, I present a non-invasive physiological technique that allows measuring neural circuit activation in freely behaving animals. Using a pair of wire electrodes inside a water-filled chamber, the bath electrodes record neural and muscular field potentials generated by juvenile crayfish during natural or experimentally evoked escape responses. The primary escape responses of crayfish are mediated by three different types of tail-flips which move the animals away from the point of stimulation. Each type of tail-flip is controlled by its own neural circuit; the two fastest and most powerful escape responses require activation of different sets of large “command” neurons. In combination with behavioral observations, the bath electrode recordings allow unambiguous identification of these neurons and the associated neural circuits. Thus activity of neural circuitry underlying naturally occurring behavior can be measured in unrestrained animals and in different behavioral contexts.
Neuroscience, Issue 29, Electrophysiology, bath electrodes, neurons, behavior