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A multi-methodological MR resting state network analysis to assess the changes in brain physiology of children with ADHD.
PUBLISHED: 01-01-2014
The purpose of this work was to highlight the neurological differences between the MR resting state networks of a group of children with ADHD (pre-treatment) and an age-matched healthy group. Results were obtained using different image analysis techniques. A sample of n?=?46 children with ages between 6 and 12 years were included in this study (23 per cohort). Resting state image analysis was performed using ReHo, ALFF and ICA techniques. ReHo and ICA represent connectivity analyses calculated with different mathematical approaches. ALFF represents an indirect measurement of brain activity. The ReHo and ICA analyses suggested differences between the two groups, while the ALFF analysis did not. The ReHo and ALFF analyses presented differences with respect to the results previously reported in the literature. ICA analysis showed that the same resting state networks that appear in healthy volunteers of adult age were obtained for both groups. In contrast, these networks were not identical when comparing the healthy and ADHD groups. These differences affected areas for all the networks except the Right Memory Function network. All techniques employed in this study were used to monitor different cerebral regions which participate in the phenomenological characterization of ADHD patients when compared to healthy controls. Results from our three analyses indicated that the cerebellum and mid-frontal lobe bilaterally for ReHo, the executive function regions in ICA, and the precuneus, cuneus and the clacarine fissure for ALFF, were the "hubs" in which the main inter-group differences were found. These results do not just help to explain the physiology underlying the disorder but open the door to future uses of these methodologies to monitor and evaluate patients with ADHD.
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.
19 Related JoVE Articles!
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Authors: Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg.
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
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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
Authors: Zulfi Haneef, Agatha Lenartowicz, Hsiang J. Yeh, Jerome Engel Jr., John M. Stern.
Institutions: Baylor College of Medicine, Michael E. DeBakey VA Medical Center, University of California, Los Angeles, University of California, Los Angeles.
Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.
Medicine, Issue 90, Default Mode Network (DMN), Temporal Lobe Epilepsy (TLE), fMRI, MRI, functional connectivity MRI (fcMRI), blood oxygenation level dependent (BOLD)
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Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
Authors: Noah S. Philip, S. Louisa Carpenter, Lawrence H. Sweet.
Institutions: Alpert Medical School, Brown University, University of Georgia.
Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD). Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g., working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions. Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.
Medicine, Issue 89, default mode network, neuroimaging, functional magnetic resonance imaging, diffusion tensor imaging, structural connectivity, functional connectivity, posttraumatic stress disorder
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Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging
Authors: Jamila Andoh, Robert J. Zatorre.
Institutions: McGill University .
Auditory cortex pertains to the processing of sound, which is at the basis of speech or music-related processing1. However, despite considerable recent progress, the functional properties and lateralization of the human auditory cortex are far from being fully understood. Transcranial Magnetic Stimulation (TMS) is a non-invasive technique that can transiently or lastingly modulate cortical excitability via the application of localized magnetic field pulses, and represents a unique method of exploring plasticity and connectivity. It has only recently begun to be applied to understand auditory cortical function 2. An important issue in using TMS is that the physiological consequences of the stimulation are difficult to establish. Although many TMS studies make the implicit assumption that the area targeted by the coil is the area affected, this need not be the case, particularly for complex cognitive functions which depend on interactions across many brain regions 3. One solution to this problem is to combine TMS with functional Magnetic resonance imaging (fMRI). The idea here is that fMRI will provide an index of changes in brain activity associated with TMS. Thus, fMRI would give an independent means of assessing which areas are affected by TMS and how they are modulated 4. In addition, fMRI allows the assessment of functional connectivity, which represents a measure of the temporal coupling between distant regions. It can thus be useful not only to measure the net activity modulation induced by TMS in given locations, but also the degree to which the network properties are affected by TMS, via any observed changes in functional connectivity. Different approaches exist to combine TMS and functional imaging according to the temporal order of the methods. Functional MRI can be applied before, during, after, or both before and after TMS. Recently, some studies interleaved TMS and fMRI in order to provide online mapping of the functional changes induced by TMS 5-7. However, this online combination has many technical problems, including the static artifacts resulting from the presence of the TMS coil in the scanner room, or the effects of TMS pulses on the process of MR image formation. But more importantly, the loud acoustic noise induced by TMS (increased compared with standard use because of the resonance of the scanner bore) and the increased TMS coil vibrations (caused by the strong mechanical forces due to the static magnetic field of the MR scanner) constitute a crucial problem when studying auditory processing. This is one reason why fMRI was carried out before and after TMS in the present study. Similar approaches have been used to target the motor cortex 8,9, premotor cortex 10, primary somatosensory cortex 11,12 and language-related areas 13, but so far no combined TMS-fMRI study has investigated the auditory cortex. The purpose of this article is to provide details concerning the protocol and considerations necessary to successfully combine these two neuroscientific tools to investigate auditory processing. Previously we showed that repetitive TMS (rTMS) at high and low frequencies (resp. 10 Hz and 1 Hz) applied over the auditory cortex modulated response time (RT) in a melody discrimination task 2. We also showed that RT modulation was correlated with functional connectivity in the auditory network assessed using fMRI: the higher the functional connectivity between left and right auditory cortices during task performance, the higher the facilitatory effect (i.e. decreased RT) observed with rTMS. However those findings were mainly correlational, as fMRI was performed before rTMS. Here, fMRI was carried out before and immediately after TMS to provide direct measures of the functional organization of the auditory cortex, and more specifically of the plastic reorganization of the auditory neural network occurring after the neural intervention provided by TMS. Combined fMRI and TMS applied over the auditory cortex should enable a better understanding of brain mechanisms of auditory processing, providing physiological information about functional effects of TMS. This knowledge could be useful for many cognitive neuroscience applications, as well as for optimizing therapeutic applications of TMS, particularly in auditory-related disorders.
Neuroscience, Issue 67, Physiology, Physics, Theta burst stimulation, functional magnetic resonance imaging, MRI, auditory cortex, frameless stereotaxy, sound, transcranial magnetic stimulation
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A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion
Authors: Nick Reed, James Murphy, Talia Dick, Katie Mah, Melissa Paniccia, Lee Verweel, Danielle Dobney, Michelle Keightley.
Institutions: Holland Bloorview Kids Rehabilitation Hospital, University of Toronto, University of Toronto.
Concussion is one of the most commonly reported injuries amongst children and youth involved in sport participation. Following a concussion, youth can experience a range of short and long term neurobehavioral symptoms (somatic, cognitive and emotional/behavioral) that can have a significant impact on one’s participation in daily activities and pursuits of interest (e.g., school, sports, work, family/social life, etc.). Despite this, there remains a paucity in clinically driven research aimed specifically at exploring concussion within the youth sport population, and more specifically, multi-modal approaches to measuring recovery. This article provides an overview of a novel and multi-modal approach to measuring recovery amongst youth athletes following concussion. The presented approach involves the use of both pre-injury/baseline testing and post-injury/follow-up testing to assess performance across a wide variety of domains (post-concussion symptoms, cognition, balance, strength, agility/motor skills and resting state heart rate variability). The goal of this research is to gain a more objective and accurate understanding of recovery following concussion in youth athletes (ages 10-18 years). Findings from this research can help to inform the development and use of improved approaches to concussion management and rehabilitation specific to the youth sport community.
Medicine, Issue 91, concussion, children, youth, athletes, assessment, management, rehabilitation
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Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
Authors: Evan D. Morris, Su Jin Kim, Jenna M. Sullivan, Shuo Wang, Marc D. Normandin, Cristian C. Constantinescu, Kelly P. Cosgrove.
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 11C-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
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
Authors: Marc N. Coutanche, Sharon L. Thompson-Schill.
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
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The Use of Pharmacological-challenge fMRI in Pre-clinical Research: Application to the 5-HT System
Authors: Anne Klomp, Jordi L. Tremoleda, Anouk Schrantee, Willy Gsell, Liesbeth Reneman.
Institutions: Academic Medical Center Amsterdam, Imperial College London .
Pharmacological MRI (phMRI) is a new and promising method to study the effects of substances on brain function that can ultimately be used to unravel underlying neurobiological mechanisms behind drug action and neurotransmitter-related disorders, such as depression and ADHD. Like most of the imaging methods (PET, SPECT, CT) it represents a progress in the investigation of brain disorders and the related function of neurotransmitter pathways in a non-invasive way with respect of the overall neuronal connectivity. Moreover it also provides the ideal tool for translation to clinical investigations. MRI, while still behind in molecular imaging strategies compared to PET and SPECT, has the great advantage to have a high spatial resolution and no need for the injection of a contrast-agent or radio-labeled molecules, thereby avoiding the repetitive exposure to ionizing radiations. Functional MRI (fMRI) is extensively used in research and clinical setting, where it is generally combined with a psycho-motor task. phMRI is an adaptation of fMRI enabling the investigation of a specific neurotransmitter system, such as serotonin (5-HT), under physiological or pathological conditions following activation via administration of a specific challenging drug. The aim of the method described here is to assess brain 5-HT function in free-breathing animals. By challenging the 5-HT system while simultaneously acquiring functional MR images over time, the response of the brain to this challenge can be visualized. Several studies in animals have already demonstrated that drug-induced increases in extracellular levels of e.g. 5-HT (releasing agents, selective re-uptake blockers, etc) evoke region-specific changes in blood oxygenation level dependent (BOLD) MRI signals (signal due to a change of the oxygenated/deoxygenated hemoglobin levels occurring during brain activation through an increase of the blood supply to supply the oxygen and glucose to the demanding neurons) providing an index of neurotransmitter function. It has also been shown that these effects can be reversed by treatments that decrease 5-HT availability16,13,18,7. In adult rats, BOLD signal changes following acute SSRI administration have been described in several 5-HT related brain regions, i.e. cortical areas, hippocampus, hypothalamus and thalamus9,16,15. Stimulation of the 5-HT system and its response to this challenge can be thus used as a measure of its function in both animals and humans2,11.
Medicine, Issue 62, Pharmacological MRI, Neuroscience, rat, 5-HT, BOLD, translational imaging, brain, fMRI
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Training Synesthetic Letter-color Associations by Reading in Color
Authors: Olympia Colizoli, Jaap M. J. Murre, Romke Rouw.
Institutions: University of Amsterdam.
Synesthesia is a rare condition in which a stimulus from one modality automatically and consistently triggers unusual sensations in the same and/or other modalities. A relatively common and well-studied type is grapheme-color synesthesia, defined as the consistent experience of color when viewing, hearing and thinking about letters, words and numbers. We describe our method for investigating to what extent synesthetic associations between letters and colors can be learned by reading in color in nonsynesthetes. Reading in color is a special method for training associations in the sense that the associations are learned implicitly while the reader reads text as he or she normally would and it does not require explicit computer-directed training methods. In this protocol, participants are given specially prepared books to read in which four high-frequency letters are paired with four high-frequency colors. Participants receive unique sets of letter-color pairs based on their pre-existing preferences for colored letters. A modified Stroop task is administered before and after reading in order to test for learned letter-color associations and changes in brain activation. In addition to objective testing, a reading experience questionnaire is administered that is designed to probe for differences in subjective experience. A subset of questions may predict how well an individual learned the associations from reading in color. Importantly, we are not claiming that this method will cause each individual to develop grapheme-color synesthesia, only that it is possible for certain individuals to form letter-color associations by reading in color and these associations are similar in some aspects to those seen in developmental grapheme-color synesthetes. The method is quite flexible and can be used to investigate different aspects and outcomes of training synesthetic associations, including learning-induced changes in brain function and structure.
Behavior, Issue 84, synesthesia, training, learning, reading, vision, memory, cognition
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Community-based Adapted Tango Dancing for Individuals with Parkinson's Disease and Older Adults
Authors: Madeleine E. Hackney, Kathleen McKee.
Institutions: Emory University School of Medicine, Brigham and Woman‘s Hospital and Massachusetts General Hospital.
Adapted tango dancing improves mobility and balance in older adults and additional populations with balance impairments. It is composed of very simple step elements. Adapted tango involves movement initiation and cessation, multi-directional perturbations, varied speeds and rhythms. Focus on foot placement, whole body coordination, and attention to partner, path of movement, and aesthetics likely underlie adapted tango’s demonstrated efficacy for improving mobility and balance. In this paper, we describe the methodology to disseminate the adapted tango teaching methods to dance instructor trainees and to implement the adapted tango by the trainees in the community for older adults and individuals with Parkinson’s Disease (PD). Efficacy in improving mobility (measured with the Timed Up and Go, Tandem stance, Berg Balance Scale, Gait Speed and 30 sec chair stand), safety and fidelity of the program is maximized through targeted instructor and volunteer training and a structured detailed syllabus outlining class practices and progression.
Behavior, Issue 94, Dance, tango, balance, pedagogy, dissemination, exercise, older adults, Parkinson's Disease, mobility impairments, falls
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Transcranial Direct Current Stimulation and Simultaneous Functional Magnetic Resonance Imaging
Authors: Marcus Meinzer, Robert Lindenberg, Robert Darkow, Lena Ulm, David Copland, Agnes Flöel.
Institutions: University of Queensland, Charité Universitätsmedizin.
Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that uses weak electrical currents administered to the scalp to manipulate cortical excitability and, consequently, behavior and brain function. In the last decade, numerous studies have addressed short-term and long-term effects of tDCS on different measures of behavioral performance during motor and cognitive tasks, both in healthy individuals and in a number of different patient populations. So far, however, little is known about the neural underpinnings of tDCS-action in humans with regard to large-scale brain networks. This issue can be addressed by combining tDCS with functional brain imaging techniques like functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). In particular, fMRI is the most widely used brain imaging technique to investigate the neural mechanisms underlying cognition and motor functions. Application of tDCS during fMRI allows analysis of the neural mechanisms underlying behavioral tDCS effects with high spatial resolution across the entire brain. Recent studies using this technique identified stimulation induced changes in task-related functional brain activity at the stimulation site and also in more distant brain regions, which were associated with behavioral improvement. In addition, tDCS administered during resting-state fMRI allowed identification of widespread changes in whole brain functional connectivity. Future studies using this combined protocol should yield new insights into the mechanisms of tDCS action in health and disease and new options for more targeted application of tDCS in research and clinical settings. The present manuscript describes this novel technique in a step-by-step fashion, with a focus on technical aspects of tDCS administered during fMRI.
Behavior, Issue 86, noninvasive brain stimulation, transcranial direct current stimulation (tDCS), anodal stimulation (atDCS), cathodal stimulation (ctDCS), neuromodulation, task-related fMRI, resting-state fMRI, functional magnetic resonance imaging (fMRI), electroencephalography (EEG), inferior frontal gyrus (IFG)
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Authors: Hans-Peter Müller, Jan Kassubek.
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
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Cortical Source Analysis of High-Density EEG Recordings in Children
Authors: Joe Bathelt, Helen O'Reilly, Michelle de Haan.
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 
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Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
Authors: Eva Wagner, Sören Brandenburg, Tobias Kohl, Stephan E. Lehnart.
Institutions: Heart Research Center Goettingen, University Medical Center Goettingen, German Center for Cardiovascular Research (DZHK) partner site Goettingen, University of Maryland School of Medicine.
In cardiac myocytes a complex network of membrane tubules - the transverse-axial tubule system (TATS) - controls deep intracellular signaling functions. While the outer surface membrane and associated TATS membrane components appear to be continuous, there are substantial differences in lipid and protein content. In ventricular myocytes (VMs), certain TATS components are highly abundant contributing to rectilinear tubule networks and regular branching 3D architectures. It is thought that peripheral TATS components propagate action potentials from the cell surface to thousands of remote intracellular sarcoendoplasmic reticulum (SER) membrane contact domains, thereby activating intracellular Ca2+ release units (CRUs). In contrast to VMs, the organization and functional role of TATS membranes in atrial myocytes (AMs) is significantly different and much less understood. Taken together, quantitative structural characterization of TATS membrane networks in healthy and diseased myocytes is an essential prerequisite towards better understanding of functional plasticity and pathophysiological reorganization. Here, we present a strategic combination of protocols for direct quantitative analysis of TATS membrane networks in living VMs and AMs. For this, we accompany primary cell isolations of mouse VMs and/or AMs with critical quality control steps and direct membrane staining protocols for fluorescence imaging of TATS membranes. Using an optimized workflow for confocal or superresolution TATS image processing, binarized and skeletonized data are generated for quantitative analysis of the TATS network and its components. Unlike previously published indirect regional aggregate image analysis strategies, our protocols enable direct characterization of specific components and derive complex physiological properties of TATS membrane networks in living myocytes with high throughput and open access software tools. In summary, the combined protocol strategy can be readily applied for quantitative TATS network studies during physiological myocyte adaptation or disease changes, comparison of different cardiac or skeletal muscle cell types, phenotyping of transgenic models, and pharmacological or therapeutic interventions.
Bioengineering, Issue 92, cardiac myocyte, atria, ventricle, heart, primary cell isolation, fluorescence microscopy, membrane tubule, transverse-axial tubule system, image analysis, image processing, T-tubule, collagenase
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Brain Imaging Investigation of the Neural Correlates of Emotional Autobiographical Recollection
Authors: Ekaterina Denkova, Trisha Chakrabarty, Sanda Dolcos, Florin Dolcos.
Institutions: University of Alberta, Edmonton, University of Illinois, Urbana-Champaign, University of Illinois, Urbana-Champaign, University of Illinois, Urbana-Champaign.
Recollection of emotional autobiographical memories (AMs) is important to healthy cognitive and affective functioning 1 - remembering positive AMs is associated with increased personal well-being and self-esteem 2, whereas remembering and ruminating on negative AMs may lead to affective disorders 3. Although significant progress has been made in understanding the brain mechanisms underlying AM retrieval in general (reviewed in 4, 5), less is known about the effect of emotion on the subjective re-experience of AMs and the associated neural correlates. This is in part due to the fact that, unlike the investigations of the emotion effect on memory for laboratory-based microevents (reviewed in 6, 7-9), often times AM studies do not have a clear focus on the emotional aspects of remembering personal events (but see 10). Here, we present a protocol that allows investigation of the neural correlates of recollecting emotional AMs using functional magnetic resonance imaging (fMRI). Cues for these memories are collected prior to scanning by means of an autobiographical memory questionnaire (AMQ), therefore allowing for proper selection of emotional AMs based on their phenomenological properties (i.e., intensity, vividness, personal significance). This protocol can be used in healthy and clinical populations alike.
Neuroscience, Issue 54, Personal Memories, Retrieval Focus, Cognitive Distraction, Emotion Regulation, Neuroimaging
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Brain Imaging Investigation of the Impairing Effect of Emotion on Cognition
Authors: Gloria Wong, Sanda Dolcos, Ekaterina Denkova, Rajendra Morey, Lihong Wang, Gregory McCarthy, Florin Dolcos.
Institutions: University of Alberta, University of Alberta, University of Illinois, Duke University , Duke University , VA Medical Center, Yale University, University of Illinois, University of Illinois.
Emotions can impact cognition by exerting both enhancing (e.g., better memory for emotional events) and impairing (e.g., increased emotional distractibility) effects (reviewed in 1). Complementing our recent protocol 2 describing a method that allows investigation of the neural correlates of the memory-enhancing effect of emotion (see also 1, 3-5), here we present a protocol that allows investigation of the neural correlates of the detrimental impact of emotion on cognition. The main feature of this method is that it allows identification of reciprocal modulations between activity in a ventral neural system, involved in 'hot' emotion processing (HotEmo system), and a dorsal system, involved in higher-level 'cold' cognitive/executive processing (ColdEx system), which are linked to cognitive performance and to individual variations in behavior (reviewed in 1). Since its initial introduction 6, this design has proven particularly versatile and influential in the elucidation of various aspects concerning the neural correlates of the detrimental impact of emotional distraction on cognition, with a focus on working memory (WM), and of coping with such distraction 7,11, in both healthy 8-11 and clinical participants 12-14.
Neuroscience, Issue 60, Emotion-Cognition Interaction, Cognitive/Emotional Interference, Task-Irrelevant Distraction, Neuroimaging, fMRI, MRI
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Real-time fMRI Biofeedback Targeting the Orbitofrontal Cortex for Contamination Anxiety
Authors: Michelle Hampson, Teodora Stoica, John Saksa, Dustin Scheinost, Maolin Qiu, Jitendra Bhawnani, Christopher Pittenger, Xenophon Papademetris, Todd Constable.
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
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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
Authors: Rajesh K. Kana, Donna L. Murdaugh, Lauren E. Libero, Mark R. Pennick, Heather M. Wadsworth, Rishi Deshpande, Christi P. Hu.
Institutions: University of Alabama at Birmingham.
Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral symptoms displayed by individuals with autism. While structural neuroimaging findings have provided some insights into brain abnormalities in autism, the consistency of such findings is questionable. Functional neuroimaging, on the other hand, has been more fruitful in this regard because autism is a disorder of dynamic processing and allows examination of communication between cortical networks, which appears to be where the underlying problem occurs in autism. Functional connectivity is defined as the temporal correlation of spatially separate neurological events1. Findings from a number of recent fMRI studies have supported the idea that there is weaker coordination between different parts of the brain that should be working together to accomplish complex social or language problems2,3,4,5,6. One of the mysteries of autism is the coexistence of deficits in several domains along with relatively intact, sometimes enhanced, abilities. Such complex manifestation of autism calls for a global and comprehensive examination of the disorder at the neural level. A compelling recent account of the brain functioning in autism, the cortical underconnectivity theory,2,7 provides an integrating framework for the neurobiological bases of autism. The cortical underconnectivity theory of autism suggests that any language, social, or psychological function that is dependent on the integration of multiple brain regions is susceptible to disruption as the processing demand increases. In autism, the underfunctioning of integrative circuitry in the brain may cause widespread underconnectivity. In other words, people with autism may interpret information in a piecemeal fashion at the expense of the whole. Since cortical underconnectivity among brain regions, especially the frontal cortex and more posterior areas 3,6, has now been relatively well established, we can begin to further understand brain connectivity as a critical component of autism symptomatology. A logical next step in this direction is to examine the anatomical connections that may mediate the functional connections mentioned above. Diffusion Tensor Imaging (DTI) is a relatively novel neuroimaging technique that helps probe the diffusion of water in the brain to infer the integrity of white matter fibers. In this technique, water diffusion in the brain is examined in several directions using diffusion gradients. While functional connectivity provides information about the synchronization of brain activation across different brain areas during a task or during rest, DTI helps in understanding the underlying axonal organization which may facilitate the cross-talk among brain areas. This paper will describe these techniques as valuable tools in understanding the brain in autism and the challenges involved in this line of research.
Medicine, Issue 55, Functional magnetic resonance imaging (fMRI), MRI, Diffusion tensor imaging (DTI), Functional Connectivity, Neuroscience, Developmental disorders, Autism, Fractional Anisotropy
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Basics of Multivariate Analysis in Neuroimaging Data
Authors: Christian Georg Habeck.
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
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.