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.
22 Related JoVE Articles!
An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings
Institutions: University of KwaZulu-Natal, Durban, South Africa, Jembi Health Systems, University of Amsterdam, Stanford Medical School.
HIV-1 drug resistance has the potential to seriously compromise the effectiveness and impact of antiretroviral therapy (ART). As ART programs in sub-Saharan Africa continue to expand, individuals on ART should be closely monitored for the emergence of drug resistance. Surveillance of transmitted drug resistance to track transmission of viral strains already resistant to ART is also critical. Unfortunately, drug resistance testing is still not readily accessible in resource limited settings, because genotyping is expensive and requires sophisticated laboratory and data management infrastructure. An open access genotypic drug resistance monitoring method to manage individuals and assess transmitted drug resistance is described. The method uses free open source software for the interpretation of drug resistance patterns and the generation of individual patient reports. The genotyping protocol has an amplification rate of greater than 95% for plasma samples with a viral load >1,000 HIV-1 RNA copies/ml. The sensitivity decreases significantly for viral loads <1,000 HIV-1 RNA copies/ml. The method described here was validated against a method of HIV-1 drug resistance testing approved by the United States Food and Drug Administration (FDA), the Viroseq genotyping method. Limitations of the method described here include the fact that it is not automated and that it also failed to amplify the circulating recombinant form CRF02_AG from a validation panel of samples, although it amplified subtypes A and B from the same panel.
Medicine, Issue 85, Biomedical Technology, HIV-1, HIV Infections, Viremia, Nucleic Acids, genetics, antiretroviral therapy, drug resistance, genotyping, affordable
Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells
Institutions: Massachusetts General Hospital and Harvard Medical School.
A basic necessity for researchers studying adaptive immunity with in vivo
experimental models is an ability to identify T cells based on their T cell antigen receptor (TCR) specificity. Many indirect methods are available in which a bulk population of T cells is stimulated in vitro
with a specific antigen and epitope-specific T cells are identified through the measurement of a functional response such as proliferation, cytokine production, or expression of activation markers1
. However, these methods only identify epitope-specific T cells exhibiting one of many possible functions, and they are not sensitive enough to detect epitope-specific T cells at naive precursor frequencies. A popular alternative is the TCR transgenic adoptive transfer model, in which monoclonal T cells from a TCR transgenic mouse are seeded into histocompatible hosts to create a large precursor population of epitope-specific T cells that can be easily tracked with the use of a congenic marker antibody2,3
. While powerful, this method suffers from experimental artifacts associated with the unphysiological frequency of T cells with specificity for a single epitope4,5
. Moreover, this system cannot be used to investigate the functional heterogeneity of epitope-specific T cell clones within a polyclonal population.
The ideal way to study adaptive immunity should involve the direct detection of epitope-specific T cells from the endogenous T cell repertoire using a method that distinguishes TCR specificity solely by its binding to cognate peptide:MHC (pMHC) complexes. The use of pMHC tetramers and flow cytometry accomplishes this6
, but is limited to the detection of high frequency populations of epitope-specific T cells only found following antigen-induced clonal expansion. In this protocol, we describe a method that coordinates the use of pMHC tetramers and magnetic cell enrichment technology to enable detection of extremely low frequency epitope-specific T cells from mouse lymphoid tissues3,7
. With this technique, one can comprehensively track entire epitope-specific populations of endogenous T cells in mice at all stages of the immune response.
Immunology, Issue 68, Cellular Biology, Molecular Biology, T cell, T cell receptor, tetramer, flow cytometry, antigen-specific, immunology, immune response, magnetic, enrichment, in vivo
Generation of Human Alloantigen-specific T Cells from Peripheral Blood
Institutions: University of California, San Diego.
The study of human T lymphocyte biology often involves examination of responses to activating ligands. T cells recognize and respond to processed peptide antigens presented by MHC (human ortholog HLA) molecules through the T cell receptor (TCR) in a highly sensitive and specific manner. While the primary function of T cells is to mediate protective immune responses to foreign antigens presented by self-MHC, T cells respond robustly to antigenic differences in allogeneic tissues. T cell responses to alloantigens can be described as either direct or indirect alloreactivity. In alloreactivity, the T cell responds through highly specific recognition of both the presented peptide and the MHC molecule. The robust oligoclonal response of T cells to allogeneic stimulation reflects the large number of potentially stimulatory alloantigens present in allogeneic tissues. While the breadth of alloreactive T cell responses is an important factor in initiating and mediating the pathology associated with biologically-relevant alloreactive responses such as graft versus host disease and allograft rejection, it can preclude analysis of T cell responses to allogeneic ligands. To this end, this protocol describes a method for generating alloreactive T cells from naive human peripheral blood leukocytes (PBL) that respond to known peptide-MHC (pMHC) alloantigens. The protocol applies pMHC multimer labeling, magnetic bead enrichment and flow cytometry to single cell in vitro
culture methods for the generation of alloantigen-specific T cell clones. This enables studies of the biochemistry and function of T cells responding to allogeneic stimulation.
Immunology, Issue 93, T cell, immunology, human cell culture, transplantation, flow cytometry, alloreactivity
Adenoviral Transduction of Naive CD4 T Cells to Study Treg Differentiation
Institutions: Helmholtz Zentrum München.
Regulatory T cells (Tregs) are essential to provide immune tolerance to self as well as to certain foreign antigens. Tregs can be generated from naive CD4 T cells in vitro
with TCR- and co-stimulation in the presence of TGFβ and IL-2. This bears enormous potential for future therapies, however, the molecules and signaling pathways that control differentiation are largely unknown.
Primary T cells can be manipulated through ectopic gene expression, but common methods fail to target the most important naive state of the T cell prior to primary antigen recognition. Here, we provide a protocol to express ectopic genes in naive CD4 T cells in vitro
before inducing Treg differentiation. It applies transduction with the replication-deficient adenovirus and explains its generation and production. The adenovirus can take up large inserts (up to 7 kb) and can be equipped with promoters to achieve high and transient overexpression in T cells. It effectively transduces naive mouse T cells if they express a transgenic Coxsackie adenovirus receptor (CAR). Importantly, after infection the T cells remain naive (CD44low
) and resting (CD25-
) and can be activated and differentiated into Tregs similar to non-infected cells. Thus, this method enables manipulation of CD4 T cell differentiation from its very beginning. It ensures that ectopic gene expression is already in place when early signaling events of the initial TCR stimulation induces cellular changes that eventually lead into Treg differentiation.
Immunology, Issue 78, Cellular Biology, Molecular Biology, Medicine, Biomedical Engineering, Bioengineering, Infection, Genetics, Microbiology, Virology, T-Lymphocytes, Regulatory, CD4-Positive T-Lymphocytes, Regulatory, Adenoviruses, Human, MicroRNAs, Antigens, Differentiation, T-Lymphocyte, Gene Transfer Techniques, Transduction, Genetic, Transfection, Adenovirus, gene transfer, microRNA, overexpression, knock down, CD4 T cells, in vitro differentiation, regulatory T cell, virus, cell, flow cytometry
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Institutions: Yale School of Medicine, Yale School of Medicine, New York University , New York University , New York University .
Most of the choices we make have uncertain consequences. In some cases the probabilities for different possible outcomes are precisely known, a condition termed "risky". In other cases when probabilities cannot be estimated, this is a condition described as "ambiguous". While most people are averse to both risk and ambiguity1,2
, the degree of those aversions vary substantially across individuals, such that the subjective value
of the same risky or ambiguous option can be very different for different individuals. We combine functional MRI (fMRI) with an experimental economics-based method3
to assess the neural representation of the subjective values of risky and ambiguous options4
. This technique can be now used to study these neural representations in different populations, such as different age groups and different patient populations.
In our experiment, subjects make consequential choices between two alternatives while their neural activation is tracked using fMRI. On each trial subjects choose between lotteries that vary in their monetary amount and in either the probability of winning that amount or the ambiguity level associated with winning. Our parametric design allows us to use each individual's choice behavior to estimate their attitudes towards risk and ambiguity, and thus to estimate the subjective values that each option held for them. Another important feature of the design is that the outcome of the chosen lottery is not revealed during the experiment, so that no learning can take place, and thus the ambiguous options remain ambiguous and risk attitudes are stable. Instead, at the end of the scanning session one or few trials are randomly selected and played for real money. Since subjects do not know beforehand which trials will be selected, they must treat each and every trial as if it and it alone was the one trial on which they will be paid. This design ensures that we can estimate the true subjective value of each option to each subject. We then look for areas in the brain whose activation is correlated with the subjective value of risky options and for areas whose activation is correlated with the subjective value of ambiguous options.
Neuroscience, Issue 67, Medicine, Molecular Biology, fMRI, magnetic resonance imaging, decision-making, value, uncertainty, risk, ambiguity
A Tactile Automated Passive-Finger Stimulator (TAPS)
Institutions: Duquesne University, McMaster University.
Although tactile spatial acuity tests are used in both neuroscience research and clinical assessment, few automated devices exist for delivering controlled spatially structured stimuli to the skin. Consequently, investigators often apply tactile stimuli manually. Manual stimulus application is time consuming, requires great care and concentration on the part of the investigator, and leaves many stimulus parameters uncontrolled. We describe here a computer-controlled tactile stimulus system, the Tactile Automated Passive-finger Stimulator (TAPS), that applies spatially structured stimuli to the skin, controlling for onset velocity, contact force, and contact duration. TAPS is a versatile, programmable system, capable of efficiently conducting a variety of psychophysical procedures. We describe the components of TAPS, and show how TAPS is used to administer a two-interval forced-choice tactile grating orientation test.
Corresponding Author: Daniel Goldreich
Medicine, Neuroscience, Issue 28, tactile, somatosensory, touch, cutaneous, acuity, psychophysics, Bayesian, grating orientation, sensory neuroscience, spatial discrimination
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Institutions: University of Calgary , University of Calgary .
We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion.
Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via
quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.
Medicine, Issue 78, Anatomy, Physiology, Cancer Biology, angular spread, architectural distortion, breast cancer, Computer-Assisted Diagnosis, computer-aided diagnosis (CAD), entropy, fractional Brownian motion, fractal dimension, Gabor filters, Image Processing, Medical Informatics, node map, oriented texture, Pattern Recognition, phase portraits, prior mammograms, spectral analysis
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
Institutions: University of Western Ontario.
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.
Behavior, Issue 87, Neurosciences, fMRI, Cognitive Control, Development, Functional Connectivity
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
Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies
Institutions: Research Centre Jülich GmbH, Research Centre Jülich GmbH.
As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm (the visual oddball task) with a long history of behavioral and electroencephalography (EEG) experiments to a functional magnetic resonance imaging (fMRI) experiment is considered. The aims of this paper are to briefly describe fMRI and when its use is appropriate in cognitive neuroscience; illustrate how task design can influence the results of an fMRI experiment, particularly when that task is borrowed from another imaging modality; explain the practical aspects of performing an fMRI experiment. It is demonstrated that manipulating the task demands in the visual oddball task results in different patterns of blood oxygen level dependent (BOLD) activation. The nature of the fMRI BOLD measure means that many brain regions are found to be active in a particular task. Determining the functions of these areas of activation is very much dependent on task design and analysis. The complex nature of many fMRI tasks means that the details of the task and its requirements need careful consideration when interpreting data. The data show that this is particularly important in those tasks relying on a motor response as well as cognitive elements and that covert and
overt responses should be considered where possible. Furthermore, the data show that transferring an EEG paradigm to an fMRI experiment needs careful consideration and it cannot be assumed that the same paradigm will work equally well across imaging modalities. It is therefore recommended that the design of an fMRI study is pilot tested behaviorally to establish the effects of interest and then pilot tested in the fMRI environment to ensure appropriate design, implementation and analysis for the effects of interest.
Behavior, Issue 91, fMRI, task design, data interpretation, cognitive neuroscience, visual oddball task, target detection
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
Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
Institutions: Yale University, Yale University, Yale University, Yale University, Massachusetts General Hospital, University of California, Irvine.
We describe experimental and statistical steps for creating dopamine movies of the brain from dynamic PET data. The movies represent minute-to-minute fluctuations of dopamine induced by smoking a cigarette. The smoker is imaged during a natural smoking experience while other possible confounding effects (such as head motion, expectation, novelty, or aversion to smoking repeatedly) are minimized.
We present the details of our unique analysis. Conventional methods for PET analysis estimate time-invariant kinetic model parameters which cannot capture short-term fluctuations in neurotransmitter release. Our analysis - yielding a dopamine movie - is based on our work with kinetic models and other decomposition techniques that allow for time-varying parameters 1-7
. This aspect of the analysis - temporal-variation - is key to our work. Because our model is also linear in parameters, it is practical, computationally, to apply at the voxel level. The analysis technique is comprised of five main steps: pre-processing, modeling, statistical comparison, masking and visualization. Preprocessing is applied to the PET data with a unique 'HYPR' spatial filter 8
that reduces spatial noise but preserves critical temporal information. Modeling identifies the time-varying function that best describes the dopamine effect on 11
C-raclopride uptake. The statistical step compares the fit of our (lp-ntPET) model 7
to a conventional model 9
. Masking restricts treatment to those voxels best described by the new model. Visualization maps the dopamine function at each voxel to a color scale and produces a dopamine movie. Interim results and sample dopamine movies of cigarette smoking are presented.
Behavior, Issue 78, Neuroscience, Neurobiology, Molecular Biology, Biomedical Engineering, Medicine, Anatomy, Physiology, Image Processing, Computer-Assisted, Receptors, Dopamine, Dopamine, Functional Neuroimaging, Binding, Competitive, mathematical modeling (systems analysis), Neurotransmission, transient, dopamine release, PET, modeling, linear, time-invariant, smoking, F-test, ventral-striatum, clinical techniques
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Institutions: University of Ulm.
Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo
. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls.
DTI data analysis is performed in a variate fashion, i.e.
voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e.
differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.
Medicine, Issue 77, Neuroscience, Neurobiology, Molecular Biology, Biomedical Engineering, Anatomy, Physiology, Neurodegenerative Diseases, nuclear magnetic resonance, NMR, MR, MRI, diffusion tensor imaging, fiber tracking, group level comparison, neurodegenerative diseases, brain, imaging, clinical techniques
Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
Institutions: Rutgers University, Koç University, New York University, Fairfield University.
We describe a high-throughput, high-volume, fully automated, live-in 24/7 behavioral testing system for assessing the effects of genetic and pharmacological manipulations on basic mechanisms of cognition and learning in mice. A standard polypropylene mouse housing tub is connected through an acrylic tube to a standard commercial mouse test box. The test box has 3 hoppers, 2 of which are connected to pellet feeders. All are internally illuminable with an LED and monitored for head entries by infrared (IR) beams. Mice live in the environment, which eliminates handling during screening. They obtain their food during two or more daily feeding periods by performing in operant (instrumental) and Pavlovian (classical) protocols, for which we have written protocol-control software and quasi-real-time data analysis and graphing software. The data analysis and graphing routines are written in a MATLAB-based language created to simplify greatly the analysis of large time-stamped behavioral and physiological event records and to preserve a full data trail from raw data through all intermediate analyses to the published graphs and statistics within a single data structure. The data-analysis code harvests the data several times a day and subjects it to statistical and graphical analyses, which are automatically stored in the "cloud" and on in-lab computers. Thus, the progress of individual mice is visualized and quantified daily. The data-analysis code talks to the protocol-control code, permitting the automated advance from protocol to protocol of individual subjects. The behavioral protocols implemented are matching, autoshaping, timed hopper-switching, risk assessment in timed hopper-switching, impulsivity measurement, and the circadian anticipation of food availability. Open-source protocol-control and data-analysis code makes the addition of new protocols simple. Eight test environments fit in a 48 in x 24 in x 78 in cabinet; two such cabinets (16 environments) may be controlled by one computer.
Behavior, Issue 84, genetics, cognitive mechanisms, behavioral screening, learning, memory, timing
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
Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions
Institutions: University of Alberta, University of Illinois, University of Alberta, University of Alberta, University of Alberta, University of Illinois at Urbana-Champaign, University of Illinois at Urbana-Champaign.
The ability to gauge social interactions is crucial in the assessment of others’ intentions. Factors such as facial expressions and body language affect our decisions in personal and professional life alike 1
. These "friend or foe
" judgements are often based on first impressions, which in turn may affect our decisions to "approach or avoid
". Previous studies investigating the neural correlates of social cognition tended to use static facial stimuli 2
. Here, we illustrate an experimental design in which whole-body animated characters were used in conjunction with functional magnetic resonance imaging (fMRI) recordings. Fifteen participants were presented with short movie-clips of guest-host interactions in a business setting, while fMRI data were recorded; at the end of each movie, participants also provided ratings of the host behaviour. This design mimics more closely real-life situations, and hence may contribute to better understanding of the neural mechanisms of social interactions in healthy behaviour, and to gaining insight into possible causes of deficits in social behaviour in such clinical conditions as social anxiety and autism 3
Neuroscience, Issue 53, Social Perception, Social Knowledge, Social Cognition Network, Non-Verbal Communication, Decision-Making, Event-Related fMRI
Monitoring Acupuncture Effects on Human Brain by fMRI
Institutions: Massachusetts General Hospital and Harvard Medical School, William Beaumont Hospital.
Functional MRI is used to study the effects of acupuncture on the BOLD response and the functional connectivity of the human brain. Results demonstrate that acupuncture mobilizes a limbic-paralimbic-neocortical network and its anti-correlated sensorimotor/paralimbic network at multiple levels of the brain and that the hemodynamic response is influenced by the psychophysical response. Physiological monitoring may be performed to explore the peripheral response of the autonomic nerve function. This video describes the studies performed at LI4 (hegu), ST36 (zusanli) and LV3 (taichong), classical acupoints that are commonly used for modulatory and pain-reducing actions. Some issues that require attention in the applications of fMRI to acupuncture investigation are noted.
Neuroscience, Issue 38, acupuncture, BOLD fMRI, limbic-paralimbic-neocortical system, psychophysical response, physiological monitoring
Real-time fMRI Biofeedback Targeting the Orbitofrontal Cortex for Contamination Anxiety
Institutions: Yale University School of Medicine , Yale University School of Medicine , Yale University School of Medicine , Yale University School of Medicine .
We present a method for training subjects to control activity in a region of their orbitofrontal cortex associated with contamination anxiety using biofeedback of real-time functional magnetic resonance imaging (rt-fMRI) data. Increased activity of this region is seen in relationship with contamination anxiety both in control subjects1
and in individuals with obsessive-compulsive disorder (OCD),2
a relatively common and often debilitating psychiatric disorder involving contamination anxiety. Although many brain regions have been implicated in OCD, abnormality in the orbitofrontal cortex (OFC) is one of the most consistent findings.3, 4
Furthermore, hyperactivity in the OFC has been found to correlate with OCD symptom severity5
and decreases in hyperactivity in this region have been reported to correlate with decreased symptom severity.6
Therefore, the ability to control this brain area may translate into clinical improvements in obsessive-compulsive symptoms including contamination anxiety. Biofeedback of rt-fMRI data is a new technique in which the temporal pattern of activity in a specific region (or associated with a specific distributed pattern of brain activity) in a subject's brain is provided as a feedback signal to the subject. Recent reports indicate that people are able to develop control over the activity of specific brain areas when provided with rt-fMRI biofeedback.7-12
In particular, several studies using this technique to target brain areas involved in emotion processing have reported success in training subjects to control these regions.13-18
In several cases, rt-fMRI biofeedback training has been reported to induce cognitive, emotional, or clinical changes in subjects.8, 9, 13, 19
Here we illustrate this technique as applied to the treatment of contamination anxiety in healthy subjects. This biofeedback intervention will be a valuable basic research tool: it allows researchers to perturb brain function, measure the resulting changes in brain dynamics and relate those to changes in contamination anxiety or other behavioral measures. In addition, the establishment of this method serves as a first step towards the investigation of fMRI-based biofeedback as a therapeutic intervention for OCD. Given that approximately a quarter of patients with OCD receive little benefit from the currently available forms of treatment,20-22
and that those who do benefit rarely recover completely, new approaches for treating this population are urgently needed.
Medicine, Issue 59, Real-time fMRI, rt-fMRI, neurofeedback, biofeedback, orbitofrontal cortex, OFC, obsessive-compulsive disorder, OCD, contamination anxiety, resting connectivity
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Institutions: The University of Texas at Austin.
Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1
. Since, T2
* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE
~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1
-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2
. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3
. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1
. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.
Neuroscience, Issue 63, fMRI, midbrain, brainstem, colliculus, BOLD, brain, Magentic Resonance Imaging, MRI
Functional Imaging with Reinforcement, Eyetracking, and Physiological Monitoring
Institutions: Columbia University, Columbia University, Columbia University.
We use functional brain imaging (fMRI) to study neural circuits that underlie decision-making. To understand how outcomes affect decision processes, simple perceptual tasks are combined with appetitive and aversive reinforcement. However, the use of reinforcers such as juice and airpuffs can create challenges for fMRI. Reinforcer delivery can cause head movement, which creates artifacts in the fMRI signal. Reinforcement can also lead to changes in heart rate and respiration that are mediated by autonomic pathways. Changes in heart rate and respiration can directly affect the fMRI (BOLD) signal in the brain and can be confounded with signal changes that are due to neural activity. In this presentation, we demonstrate methods for administering reinforcers in a controlled manner, for stabilizing the head, and for measuring pulse and respiration.
Medicine, Issue 21, Neuroscience, Psychiatry, fMRI, Decision Making, Reward, Punishment, Pulse, Respiration, Eye Tracking, Psychology
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
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