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Pubmed Article
Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
PLoS ONE
PUBLISHED: 01-24-2013
Recent studies have shown that multivariate pattern analysis (MVPA) can be useful for distinguishing brain disorders into categories. Such analyses can substantially enrich and facilitate clinical diagnoses. Using MPVA methods, whole brain functional networks, especially those derived using different frequency windows, can be applied to detect brain states. We constructed whole brain functional networks for groups of vascular dementia (VaD) patients and controls using resting state BOLD-fMRI (rsfMRI) data from three frequency bands - slow-5 (0.01 ? 0.027 Hz), slow-4 (0.027?0.073 Hz), and whole-band (0.01 ? 0.073 Hz). Then we used the support vector machine (SVM), a type of MVPA classifier, to determine the patterns of functional connectivity. Our results showed that the brain functional networks derived from rsfMRI data (19 VaD patients and 20 controls) in these three frequency bands appear to reflect neurobiological changes in VaD patients. Such differences could be used to differentiate the brain states of VaD patients from those of healthy individuals. We also found that the functional connectivity patterns of the human brain in the three frequency bands differed, as did their ability to differentiate brain states. Specifically, the ability of the functional connectivity pattern to differentiate VaD brains from healthy ones was more efficient in the slow-5 (0.01 ? 0.027 Hz) band than in the other two frequency bands. Our findings suggest that the MVPA approach could be used to detect abnormalities in the functional connectivity of VaD patients in distinct frequency bands. Identifying such abnormalities may contribute to our understanding of the pathogenesis of VaD.
ABSTRACT
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.
17 Related JoVE Articles!
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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)
51442
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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
51226
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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
50319
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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)
51730
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The Use of Magnetic Resonance Spectroscopy as a Tool for the Measurement of Bi-hemispheric Transcranial Electric Stimulation Effects on Primary Motor Cortex Metabolism
Authors: Sara Tremblay, Vincent Beaulé, Sébastien Proulx, Louis-Philippe Lafleur, Julien Doyon, Małgorzata Marjańska, Hugo Théoret.
Institutions: University of Montréal, McGill University, University of Minnesota.
Transcranial direct current stimulation (tDCS) is a neuromodulation technique that has been increasingly used over the past decade in the treatment of neurological and psychiatric disorders such as stroke and depression. Yet, the mechanisms underlying its ability to modulate brain excitability to improve clinical symptoms remains poorly understood 33. To help improve this understanding, proton magnetic resonance spectroscopy (1H-MRS) can be used as it allows the in vivo quantification of brain metabolites such as γ-aminobutyric acid (GABA) and glutamate in a region-specific manner 41. In fact, a recent study demonstrated that 1H-MRS is indeed a powerful means to better understand the effects of tDCS on neurotransmitter concentration 34. This article aims to describe the complete protocol for combining tDCS (NeuroConn MR compatible stimulator) with 1H-MRS at 3 T using a MEGA-PRESS sequence. We will describe the impact of a protocol that has shown great promise for the treatment of motor dysfunctions after stroke, which consists of bilateral stimulation of primary motor cortices 27,30,31. Methodological factors to consider and possible modifications to the protocol are also discussed.
Neuroscience, Issue 93, proton magnetic resonance spectroscopy, transcranial direct current stimulation, primary motor cortex, GABA, glutamate, stroke
51631
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Consensus Brain-derived Protein, Extraction Protocol for the Study of Human and Murine Brain Proteome Using Both 2D-DIGE and Mini 2DE Immunoblotting
Authors: Francisco-Jose Fernandez-Gomez, Fanny Jumeau, Maxime Derisbourg, Sylvie Burnouf, Hélène Tran, Sabiha Eddarkaoui, Hélène Obriot, Virginie Dutoit-Lefevre, Vincent Deramecourt, Valérie Mitchell, Didier Lefranc, Malika Hamdane, David Blum, Luc Buée, Valérie Buée-Scherrer, Nicolas Sergeant.
Institutions: Inserm UMR 837, CHRU-Lille, Faculté de Médecine - Pôle Recherche, CHRU-Lille.
Two-dimensional gel electrophoresis (2DE) is a powerful tool to uncover proteome modifications potentially related to different physiological or pathological conditions. Basically, this technique is based on the separation of proteins according to their isoelectric point in a first step, and secondly according to their molecular weights by SDS polyacrylamide gel electrophoresis (SDS-PAGE). In this report an optimized sample preparation protocol for little amount of human post-mortem and mouse brain tissue is described. This method enables to perform both two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) and mini 2DE immunoblotting. The combination of these approaches allows one to not only find new proteins and/or protein modifications in their expression thanks to its compatibility with mass spectrometry detection, but also a new insight into markers validation. Thus, mini-2DE coupled to western blotting permits to identify and validate post-translational modifications, proteins catabolism and provides a qualitative comparison among different conditions and/or treatments. Herein, we provide a method to study components of protein aggregates found in AD and Lewy body dementia such as the amyloid-beta peptide and the alpha-synuclein. Our method can thus be adapted for the analysis of the proteome and insoluble proteins extract from human brain tissue and mice models too. In parallel, it may provide useful information for the study of molecular and cellular pathways involved in neurodegenerative diseases as well as potential novel biomarkers and therapeutic targets.
Neuroscience, Issue 86, proteomics, neurodegeneration, 2DE, human and mice brain tissue, fluorescence, immunoblotting. Abbreviations: 2DE (two-dimensional gel electrophoresis), 2D-DIGE (two-dimensional fluorescence difference gel electrophoresis), mini-2DE (mini 2DE immunoblotting),IPG (Immobilized pH Gradients), IEF (isoelectrofocusing), AD (Alzheimer´s disease)
51339
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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
Authors: Bianca DeBenedictis, J. Bruce Morton.
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
51003
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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
50427
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Simultaneous EEG Monitoring During Transcranial Direct Current Stimulation
Authors: Pedro Schestatsky, Leon Morales-Quezada, Felipe Fregni.
Institutions: Universidade Federal do Rio Grande do Sul, Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Harvard Medical School, De Montfort University.
Transcranial direct current stimulation (tDCS) is a technique that delivers weak electric currents through the scalp. This constant electric current induces shifts in neuronal membrane excitability, resulting in secondary changes in cortical activity. Although tDCS has most of its neuromodulatory effects on the underlying cortex, tDCS effects can also be observed in distant neural networks. Therefore, concomitant EEG monitoring of the effects of tDCS can provide valuable information on the mechanisms of tDCS. In addition, EEG findings can be an important surrogate marker for the effects of tDCS and thus can be used to optimize its parameters. This combined EEG-tDCS system can also be used for preventive treatment of neurological conditions characterized by abnormal peaks of cortical excitability, such as seizures. Such a system would be the basis of a non-invasive closed-loop device. In this article, we present a novel device that is capable of utilizing tDCS and EEG simultaneously. For that, we describe in a step-by-step fashion the main procedures of the application of this device using schematic figures, tables and video demonstrations. Additionally, we provide a literature review on clinical uses of tDCS and its cortical effects measured by EEG techniques.
Behavior, Issue 76, Medicine, Neuroscience, Neurobiology, Anatomy, Physiology, Biomedical Engineering, Psychology, electroencephalography, electroencephalogram, EEG, transcranial direct current stimulation, tDCS, noninvasive brain stimulation, neuromodulation, closed-loop system, brain, imaging, clinical techniques
50426
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Authors: Rangaraj M. Rangayyan, Shantanu Banik, J.E. Leo Desautels.
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
50341
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Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
Authors: Gabriella Garcia, Catherine Norise, Olufunsho Faseyitan, Margaret A. Naeser, Roy H. Hamilton.
Institutions: University of Pennsylvania , University of Pennsylvania , Veterans Affairs Boston Healthcare System, Boston University School of Medicine, Boston University School of Medicine.
Transcranial magnetic stimulation (TMS) has been shown to significantly improve language function in patients with non-fluent aphasia1. In this experiment, we demonstrate the administration of low-frequency repetitive TMS (rTMS) to an optimal stimulation site in the right hemisphere in patients with chronic non-fluent aphasia. A battery of standardized language measures is administered in order to assess baseline performance. Patients are subsequently randomized to either receive real rTMS or initial sham stimulation. Patients in the real stimulation undergo a site-finding phase, comprised of a series of six rTMS sessions administered over five days; stimulation is delivered to a different site in the right frontal lobe during each of these sessions. Each site-finding session consists of 600 pulses of 1 Hz rTMS, preceded and followed by a picture-naming task. By comparing the degree of transient change in naming ability elicited by stimulation of candidate sites, we are able to locate the area of optimal response for each individual patient. We then administer rTMS to this site during the treatment phase. During treatment, patients undergo a total of ten days of stimulation over the span of two weeks; each session is comprised of 20 min of 1 Hz rTMS delivered at 90% resting motor threshold. Stimulation is paired with an fMRI-naming task on the first and last days of treatment. After the treatment phase is complete, the language battery obtained at baseline is repeated two and six months following stimulation in order to identify rTMS-induced changes in performance. The fMRI-naming task is also repeated two and six months following treatment. Patients who are randomized to the sham arm of the study undergo sham site-finding, sham treatment, fMRI-naming studies, and repeat language testing two months after completing sham treatment. Sham patients then cross over into the real stimulation arm, completing real site-finding, real treatment, fMRI, and two- and six-month post-stimulation language testing.
Medicine, Issue 77, Neurobiology, Neuroscience, Anatomy, Physiology, Biomedical Engineering, Molecular Biology, Neurology, Stroke, Aphasia, Transcranial Magnetic Stimulation, TMS, language, neurorehabilitation, optimal site-finding, functional magnetic resonance imaging, fMRI, brain, stimulation, imaging, clinical techniques, clinical applications
50228
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Authors: Jennifer J. Heisz, Anthony R. McIntosh.
Institutions: Baycrest.
When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.
Neuroscience, Issue 76, Neurobiology, Anatomy, Physiology, Medicine, Biomedical Engineering, Electroencephalography, EEG, electroencephalogram, Multiscale entropy, sample entropy, MEG, neuroimaging, variability, noise, timescale, non-linear, brain signal, information theory, brain, imaging
50131
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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
3985
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Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network
Authors: Mark A. Halko, Mark C. Eldaief, Jared C. Horvath, Alvaro Pascual-Leone.
Institutions: Beth Israel Deaconess Medical Center.
The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful rest."1,2,3 It is thought the default mode network corresponds to self-referential or "internal mentation".2,3 It has been hypothesized that, in humans, activity within the default mode network is correlated with certain pathologies (for instance, hyper-activation has been linked to schizophrenia 4,5,6 and autism spectrum disorders 7 whilst hypo-activation of the network has been linked to Alzheimer's and other neurodegenerative diseases 8). As such, noninvasive modulation of this network may represent a potential therapeutic intervention for a number of neurological and psychiatric pathologies linked to abnormal network activation. One possible tool to effect this modulation is Transcranial Magnetic Stimulation: a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.9 In order to explore the default mode network's propensity towards and tolerance of modulation, we will be combining TMS (to the left inferior parietal lobe) with functional magnetic resonance imaging (fMRI). Through this article, we will examine the protocol and considerations necessary to successfully combine these two neuroscientific tools.
Neuroscience, Issue 46, Transcranial Magnetic Stimulation, rTMS, fMRI, Default Mode Network, functional connectivity, resting state
2271
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Cross-Modal Multivariate Pattern Analysis
Authors: Kaspar Meyer, Jonas T. Kaplan.
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
3307
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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
1988
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Simultaneous fMRI and Electrophysiology in the Rodent Brain
Authors: Wen-ju Pan, Garth Thompson, Matthew Magnuson, Waqas Majeed, Dieter Jaeger, Shella Keilholz.
Institutions: Emory University, Georgia Institute of Technology, Emory University.
To examine the neural basis of the blood oxygenation level dependent (BOLD) magnetic resonance imaging (MRI) signal, we have developed a rodent model in which functional MRI data and in vivo intracortical recording can be performed simultaneously. The combination of MRI and electrical recording is technically challenging because the electrodes used for recording distort the MRI images and the MRI acquisition induces noise in the electrical recording. To minimize the mutual interference of the two modalities, glass microelectrodes were used rather than metal and a noise removal algorithm was implemented for the electrophysiology data. In our studies, two microelectrodes were separately implanted in bilateral primary somatosensory cortices (SI) of the rat and fixed in place. One coronal slice covering the electrode tips was selected for functional MRI. Electrode shafts and fixation positions were not included in the image slice to avoid imaging artifacts. The removed scalp was replaced with toothpaste to reduce susceptibility mismatch and prevent Gibbs ringing artifacts in the images. The artifact structure induced in the electrical recordings by the rapidly-switching magnetic fields during image acquisition was characterized by averaging all cycles of scans for each run. The noise structure during imaging was then subtracted from original recordings. The denoised time courses were then used for further analysis in combination with the fMRI data. As an example, the simultaneous acquisition was used to determine the relationship between spontaneous fMRI BOLD signals and band-limited intracortical electrical activity. Simultaneous fMRI and electrophysiological recording in the rodent will provide a platform for many exciting applications in neuroscience in addition to elucidating the relationship between the fMRI BOLD signal and neuronal activity.
Neuroscience, Issue 42, fMRI, electrophysiology, rat, BOLD, brain, resting state
1901
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