Stefano Panzeri, Cesare Magri and Nikos K Logothetis.
On the use of information theory for the analysis of the relationship between neural and imaging signals. Magnetic Resonance Imaging 26(7):1015–1025, 2008.
Abstract Functional magnetic resonance imaging (fMRI) is a widely used method for studying the neural basis of cognition and of sensory function. A potential problem in the interpretation of fMRI data is that fMRI measures neural activity only indirectly, as a local change of deoxyhemoglobin concentration due to the metabolic demands of neural function. To build correct sensory and cognitive maps in the human brain, it is thus crucial to understand whether fMRI and neural activity convey the same type of information about external correlates. While a substantial experimental effort has been devoted to the simultaneous recordings of hemodynamic and neural signals, so far, the development of analysis methods that elucidate how neural and hemodynamic signals represent sensory information has received less attention. In this article, we critically review why the analytical framework of information theory, the mathematical theory of communication, is ideally suited to this purpose. We review the principles of information theory and explain how they could be applied to the analysis of fMRI and neural signals. We show that a critical advantage of information theory over more traditional analysis paradigms commonly used in the fMRI literature is that it can elucidate, within a single framework, whether an empirically observed correlation between neural and fMRI signals reflects either a similar stimulus tuning or a common source of variability unrelated to the external stimuli. In addition, information theory determines the extent to which these shared sources of stimulus signal and of variability lead fMRI and neural signals to convey similar information about external correlates. We then illustrate the formalism by applying it to the analysis of the information carried by different bands of the local field potential. We conclude by discussing the current methodological challenges that need to be addressed to make the information-theoretic approach more robustly applicable to the simultaneous recordings of neural and imaging data. Functional magnetic resonance imaging (fMRI) is a widely used method for studying the neural basis of cognition and of sensory function. A potential problem in the interpretation of fMRI data is that fMRI measures neural activity only indirectly, as a local change of deoxyhemoglobin concentration due to the metabolic demands of neural function. To build correct sensory and cognitive maps in the human brain, it is thus crucial to understand whether fMRI and neural activity convey the same type of information about external correlates. While a substantial experimental effort has been devoted to the simultaneous recordings of hemodynamic and neural signals, so far, the development of analysis methods that elucidate how neural and hemodynamic signals represent sensory information has received less attention. In this article, we critically review why the analytical framework of information theory, the mathematical theory of communication, is ideally suited to this purpose. We review the principles of information theory and explain how they could be applied to the analysis of fMRI and neural signals. We show that a critical advantage of information theory over more traditional analysis paradigms commonly used in the fMRI literature is that it can elucidate, within a single framework, whether an empirically observed correlation between neural and fMRI signals reflects either a similar stimulus tuning or a common source of variability unrelated to the external stimuli. In addition, information theory determines the extent to which these shared sources of stimulus signal and of variability lead fMRI and neural signals to convey similar information about external correlates. We then illustrate the formalism by applying it to the analysis of the information carried by different bands of the local field potential. We conclude by discussing the current methodological challenges that need to be addressed to make the information-theoretic approach more robustly applicable to the simultaneous recordings of neural and imaging data.
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Anne C Zappe, Kâmil Uludağ and Nikos K Logothetis.
Direct measurement of oxygen extraction with fMRI using 6% CO₂ inhalation. Magnetic Resonance Imaging 26(7):961–967, 2008.
Abstract The blood-oxygenation-level-dependent (BOLD) signal is an indirect hemodynamic signal that is sensitive to cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral metabolic rate of oxygen. Therefore, the BOLD signal amplitude and dynamics cannot be interpreted unambiguously without additional physiological measurements, and thus, there remains a need for a functional magnetic resonance imaging (fMRI) signal, which is more closely related to the underlying neuronal activity. In this study, we measured CBF with continuous arterial spin labeling, CBV with an exogenous contrast agent and BOLD combined with intracortical electrophysiological recording in the primary visual cortex of the anesthetized monkey. During inhalation of 6% CO2, it was observed that CBF and CBV are not further increased by a visual stimulus, although baseline CBF for 6% CO2 is below the maximal value of CBF. In contrast, the electrophysiological response to the stimulation was found to be preserved during hypercapnia. As a consequence, the simultaneously measured BOLD signal responds negatively to a visual stimulation for 6% CO2 inhalation in the same voxels responding positively during normocapnia. These observations suggest that the fMRI response to a sensory stimulus for 6% CO2 inhalation occurs in the absence of a hemodynamic response, and it therefore directly reflects oxygen extraction into the tissue. The blood-oxygenation-level-dependent (BOLD) signal is an indirect hemodynamic signal that is sensitive to cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral metabolic rate of oxygen. Therefore, the BOLD signal amplitude and dynamics cannot be interpreted unambiguously without additional physiological measurements, and thus, there remains a need for a functional magnetic resonance imaging (fMRI) signal, which is more closely related to the underlying neuronal activity. In this study, we measured CBF with continuous arterial spin labeling, CBV with an exogenous contrast agent and BOLD combined with intracortical electrophysiological recording in the primary visual cortex of the anesthetized monkey. During inhalation of 6% CO2, it was observed that CBF and CBV are not further increased by a visual stimulus, although baseline CBF for 6% CO2 is below the maximal value of CBF. In contrast, the electrophysiological response to the stimulation was found to be preserved during hypercapnia. As a consequence, the simultaneously measured BOLD signal responds negatively to a visual stimulation for 6% CO2 inhalation in the same voxels responding positively during normocapnia. These observations suggest that the fMRI response to a sensory stimulus for 6% CO2 inhalation occurs in the absence of a hemodynamic response, and it therefore directly reflects oxygen extraction into the tissue.
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Santiago Canals, Michael Beyerlein, Yusuke Murayama and Nikos K Logothetis.
Electric stimulation fMRI of the perforant pathway to the rat hippocampus. Magnetic Resonance Imaging 26(7):978–986, 2008.
Abstract The hippocampal formation is a brain system that is implicated in learning and memory. The major input to the hippocampus arrives from the entorhinal cortex (EC) to the dentate gyrus (DG) through the perforant path. In the present work, we have investigated the functional properties of this connection by concomitantly applying electrophysiological techniques, deep-brain electric microstimulation and functional magnetic resonance imaging in anesthetized rats. We systematically delivered different current intensities at diverse stimulation frequencies to the perforant path while recording electrophysiological and blood-oxygenation-level-dependent (BOLD) signals. We observed a linear relationship between the current intensity used to stimulate the hippocampal formation and the amplitude and extension of the induced BOLD response. In addition, we found a frequency-dependent spatial pattern of activation. With stimulation protocols and train frequencies used for kindling, the activity strongly spreads ipsilaterally through the hippocampus, DG, subiculum and EC. The hippocampal formation is a brain system that is implicated in learning and memory. The major input to the hippocampus arrives from the entorhinal cortex (EC) to the dentate gyrus (DG) through the perforant path. In the present work, we have investigated the functional properties of this connection by concomitantly applying electrophysiological techniques, deep-brain electric microstimulation and functional magnetic resonance imaging in anesthetized rats. We systematically delivered different current intensities at diverse stimulation frequencies to the perforant path while recording electrophysiological and blood-oxygenation-level-dependent (BOLD) signals. We observed a linear relationship between the current intensity used to stimulate the hippocampal formation and the amplitude and extension of the induced BOLD response. In addition, we found a frequency-dependent spatial pattern of activation. With stimulation protocols and train frequencies used for kindling, the activity strongly spreads ipsilaterally through the hippocampus, DG, subiculum and EC.
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Alessandro Gozzi, Adam Schwarz, Valerio Crestan and Angelo Bifone.
Drug–anaesthetic interaction in phMRI: the case of the psychotomimetic agent phencyclidine. Magnetic Resonance Imaging 26(7):999–1006, 2008.
Abstract Pharmacological magnetic resonance imaging (phMRI) provides a powerful means to map the effects of drugs on brain activity, with important applications in pharmacological research. However, phMRI studies in preclinical species are often conducted under general anaesthesia as a means to avoid head motion and to minimise the stress induced by the procedure. Under these conditions, the phMRI response to the drug of interest may be affected by interactions with the anaesthetic agent, with consequences for the interpretation of the data. Here, we have investigated the phMRI response to phencyclidine (PCP), an NMDA receptor blocker, in the halothane-anaesthetised rat for varying levels of anaesthesia and different PCP challenge doses. PCP induces psychotic-like symptoms in humans and laboratory animals and is widely applied as a pharmacological model of schizophrenia. However, PCP possesses anaesthetic properties per se, and its interactions with halothane might result in significant effects on the phMRI activation patterns. We observed two qualitatively different patterns of phMRI response. At 0.5 mg/kg iv PCP and 0.8% halothane maintenance anaesthesia, the lowest doses explored, an activation of discrete cortico-limbo-thalamic structures was observed, consistent with neuroimaging studies in humans and 2-deoxyglucose functional mapping in conscious animal models. However, higher anaesthetic concentrations or higher PCP challenge doses resulted in complete abolition of the positive response and in a widespread cortical deactivation (negative response). In the intermediate regime, we observed a dichotomic behaviour, with individual subjects showing one pattern or the other. These findings indicate a dose-dependent drug?anaesthetic interaction, with a complete reversal of the effects of PCP at higher challenge doses or HT concentrations. Pharmacological magnetic resonance imaging (phMRI) provides a powerful means to map the effects of drugs on brain activity, with important applications in pharmacological research. However, phMRI studies in preclinical species are often conducted under general anaesthesia as a means to avoid head motion and to minimise the stress induced by the procedure. Under these conditions, the phMRI response to the drug of interest may be affected by interactions with the anaesthetic agent, with consequences for the interpretation of the data. Here, we have investigated the phMRI response to phencyclidine (PCP), an NMDA receptor blocker, in the halothane-anaesthetised rat for varying levels of anaesthesia and different PCP challenge doses. PCP induces psychotic-like symptoms in humans and laboratory animals and is widely applied as a pharmacological model of schizophrenia. However, PCP possesses anaesthetic properties per se, and its interactions with halothane might result in significant effects on the phMRI activation patterns. We observed two qualitatively different patterns of phMRI response. At 0.5 mg/kg iv PCP and 0.8% halothane maintenance anaesthesia, the lowest doses explored, an activation of discrete cortico-limbo-thalamic structures was observed, consistent with neuroimaging studies in humans and 2-deoxyglucose functional mapping in conscious animal models. However, higher anaesthetic concentrations or higher PCP challenge doses resulted in complete abolition of the positive response and in a widespread cortical deactivation (negative response). In the intermediate regime, we observed a dichotomic behaviour, with individual subjects showing one pattern or the other. These findings indicate a dose-dependent drug?anaesthetic interaction, with a complete reversal of the effects of PCP at higher challenge doses or HT concentrations.
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Elia Formisano, Federico De Martino and Giancarlo Valente.
Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning. Magnetic Resonance Imaging 26(7):921–934, 2008.
Abstract Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms ?learn? a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set (?brain reading?). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis. Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms ?learn? a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set (?brain reading?). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.
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Simon J P Meara, Karl V Embleton and Geoffrey J M Parker.
Distortion correction for a double inversion-recovery sequence with an echo-planar imaging readout. Magnetic Resonance Imaging 26(7):943–953, 2008.
Abstract A double inversion-recovery (DIR) sequence with an echo-planar imaging (EPI) readout can be used to image selectively the grey matter of the brain, and this has previously been applied to improve the sensitivity of the statistical analysis of functional magnetic resonance imaging (fMRI) data. If a procedure were to be implemented to remove the distortions that are inherent in the EPI-based fMRI data set, then a similar technique would have to be applied to the DIR-EPI image also to ensure that it matches the geometry of the functional data. A comparison of candidate methodologies for correcting distortions in DIR-EPI images, based on the reversed-gradient method, is presented. A corrected image could be calculated from two DIR-EPI images acquired with k-space traversal in opposite directions, but that method was not able to cope with the large regions of low signal intensity corresponding to the nulled white matter. It was found that the optimal procedure to apply the reversed-gradient method to DIR-EPI images was to acquire two additional EPI images (without the two inversion pulses) with opposite-direction k-space traversal; the distortion-correction information calculated from those EPI images was then applied to the DIR-EPI data. A double inversion-recovery (DIR) sequence with an echo-planar imaging (EPI) readout can be used to image selectively the grey matter of the brain, and this has previously been applied to improve the sensitivity of the statistical analysis of functional magnetic resonance imaging (fMRI) data. If a procedure were to be implemented to remove the distortions that are inherent in the EPI-based fMRI data set, then a similar technique would have to be applied to the DIR-EPI image also to ensure that it matches the geometry of the functional data. A comparison of candidate methodologies for correcting distortions in DIR-EPI images, based on the reversed-gradient method, is presented. A corrected image could be calculated from two DIR-EPI images acquired with k-space traversal in opposite directions, but that method was not able to cope with the large regions of low signal intensity corresponding to the nulled white matter. It was found that the optimal procedure to apply the reversed-gradient method to DIR-EPI images was to acquire two additional EPI images (without the two inversion pulses) with opposite-direction k-space traversal; the distortion-correction information calculated from those EPI images was then applied to the DIR-EPI data.
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Shih-pi Ku, Arthur Gretton, Jakob Macke and Nikos K Logothetis.
Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys. Magnetic Resonance Imaging 26(7):1007–1014, 2008.
Abstract Pattern recognition methods have shown that functional magnetic resonance imaging (fMRI) data can reveal significant information about brain activity. For example, in the debate of how object categories are represented in the brain, multivariate analysis has been used to provide evidence of a distributed encoding scheme [Science 293:5539 (2001) 2425?2430]. Many follow-up studies have employed different methods to analyze human fMRI data with varying degrees of success [Nature reviews 7:7 (2006) 523?534]. In this study, we compare four popular pattern recognition methods: correlation analysis, support-vector machines (SVM), linear discriminant analysis (LDA) and Gaussian naïve Bayes (GNB), using data collected at high field (7 Tesla) with higher resolution than usual fMRI studies. We investigate prediction performance on single trials and for averages across varying numbers of stimulus presentations. The performance of the various algorithms depends on the nature of the brain activity being categorized: for several tasks, many of the methods work well, whereas for others, no method performs above chance level. An important factor in overall classification performance is careful preprocessing of the data, including dimensionality reduction, voxel selection and outlier elimination. Pattern recognition methods have shown that functional magnetic resonance imaging (fMRI) data can reveal significant information about brain activity. For example, in the debate of how object categories are represented in the brain, multivariate analysis has been used to provide evidence of a distributed encoding scheme [Science 293:5539 (2001) 2425?2430]. Many follow-up studies have employed different methods to analyze human fMRI data with varying degrees of success [Nature reviews 7:7 (2006) 523?534]. In this study, we compare four popular pattern recognition methods: correlation analysis, support-vector machines (SVM), linear discriminant analysis (LDA) and Gaussian naïve Bayes (GNB), using data collected at high field (7 Tesla) with higher resolution than usual fMRI studies. We investigate prediction performance on single trials and for averages across varying numbers of stimulus presentations. The performance of the various algorithms depends on the nature of the brain activity being categorized: for several tasks, many of the methods work well, whereas for others, no method performs above chance level. An important factor in overall classification performance is careful preprocessing of the data, including dimensionality reduction, voxel selection and outlier elimination.
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Silvia Capuani, Paola Porcari, Fabrizio Fasano, Renzo Campanella and Bruno Maraviglia.
¹⁰B-editing ¹H-detection and ¹⁹F MRI strategies to optimize boron neutron capture therapy. Magnetic Resonance Imaging 26(7):987–993, 2008.
Abstract Boron neutron capture therapy (BNCT) is a binary radiation therapy used to treat malignant brain tumours. It is based on the nuclear reaction (10B + nth ? [11B*] ? α + 7Li + 2.79 MeV) that occurs when 10B captures a thermal neutron to yield α particles and recoiling 7Li nuclei, both responsible of tumour cells destruction by short range and high ionization energy release. The clinical success of the therapy depends on the selective accumulation of the 10B carriers in the tumour and on the high thermal neutron capture cross-section of 10B. Magnetic resonance imaging (MRI) methods provide the possibility of monitoring, through 10B nuclei, the metabolic and physiological processes suitable to optimize the BNCT procedure. In this study, spatial distribution mapping of borocaptate (BSH) and 4-borono-phenylalanine (BPA), the two boron carriers used in clinical trials, has been obtained. The BSH map in excised rat brain and the 19F-BPA image in vivo rat brain, representative of BPA spatial distribution, were reported. The BSH image was obtained by means of double-resonance 10B-editing 1H-detection sequence, named M-Bend, exploiting the J-coupling interaction between 10B and 1H nuclei. Conversely, the BPA map was obtained by 19F-BPA using 19F-MRI. Both images were obtained at 7 T, in C6 glioma-bearing rat brain. Our results demonstrate the powerful of non conventional MRI techniques to optimize the BNCT procedure. Boron neutron capture therapy (BNCT) is a binary radiation therapy used to treat malignant brain tumours. It is based on the nuclear reaction (10B + nth ? [11B*] ? α + 7Li + 2.79 MeV) that occurs when 10B captures a thermal neutron to yield α particles and recoiling 7Li nuclei, both responsible of tumour cells destruction by short range and high ionization energy release. The clinical success of the therapy depends on the selective accumulation of the 10B carriers in the tumour and on the high thermal neutron capture cross-section of 10B. Magnetic resonance imaging (MRI) methods provide the possibility of monitoring, through 10B nuclei, the metabolic and physiological processes suitable to optimize the BNCT procedure. In this study, spatial distribution mapping of borocaptate (BSH) and 4-borono-phenylalanine (BPA), the two boron carriers used in clinical trials, has been obtained. The BSH map in excised rat brain and the 19F-BPA image in vivo rat brain, representative of BPA spatial distribution, were reported. The BSH image was obtained by means of double-resonance 10B-editing 1H-detection sequence, named M-Bend, exploiting the J-coupling interaction between 10B and 1H nuclei. Conversely, the BPA map was obtained by 19F-BPA using 19F-MRI. Both images were obtained at 7 T, in C6 glioma-bearing rat brain. Our results demonstrate the powerful of non conventional MRI techniques to optimize the BNCT procedure.
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Adam J Schwarz, Alessandro Gozzi and Angelo Bifone.
Community structure and modularity in networks of correlated brain activity. Magnetic Resonance Imaging 26(7):914–920, 2008.
Abstract Functional connectivity patterns derived from neuroimaging data may be represented as graphs or networks, with individual image voxels or anatomically-defined structures representing the nodes, and a measure of correlation between the responses in each pair of nodes determining the edges. This explicit network representation allows network-analysis approaches to be applied to the characterization of functional connections within the brain. Much recent research in complex networks has focused on methods to identify community structure, i.e. cohesive clusters of strongly interconnected nodes. One class of such algorithms determines a partition of a network into ?sub-networks' based on the optimization of a modularity parameter, thus also providing a measure of the degree of segregation versus integration in the full network. Here, we demonstrate that a community structure algorithm based on the maximization of modularity, applied to a functional connectivity network calculated from the responses to acute fluoxetine challenge in the rat, can identify communities whose distributions correspond to anatomically meaningful structures and include compelling functional subdivisions in the brain. We also discuss the biological interpretation of the modularity parameter in terms of segregation and integration of brain function. Functional connectivity patterns derived from neuroimaging data may be represented as graphs or networks, with individual image voxels or anatomically-defined structures representing the nodes, and a measure of correlation between the responses in each pair of nodes determining the edges. This explicit network representation allows network-analysis approaches to be applied to the characterization of functional connections within the brain. Much recent research in complex networks has focused on methods to identify community structure, i.e. cohesive clusters of strongly interconnected nodes. One class of such algorithms determines a partition of a network into ?sub-networks' based on the optimization of a modularity parameter, thus also providing a measure of the degree of segregation versus integration in the full network. Here, we demonstrate that a community structure algorithm based on the maximization of modularity, applied to a functional connectivity network calculated from the responses to acute fluoxetine challenge in the rat, can identify communities whose distributions correspond to anatomically meaningful structures and include compelling functional subdivisions in the brain. We also discuss the biological interpretation of the modularity parameter in terms of segregation and integration of brain function.
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Gisela E Hagberg, Marta Bianciardi, Valentina Brainovich, Antonino Mario Cassarà and Bruno Maraviglia.
The effect of physiological noise in phase functional magnetic resonance imaging: from blood oxygen level-dependent effects to direct detection of neuronal currents. Magnetic Resonance Imaging 26(7):1026–1040, 2008.
Abstract Recently, the possibility to use both magnitude and phase image sets for the statistical evaluation of fMRI has been proposed, with the prospective of increasing both statistical power and the spatial specificity. In the present work, several issues that affect the spatial and temporal stability in fMRI phase time series in the presence of physiologic noise processes are reviewed, discussed and illustrated by experiments performed at 3 T. The observed phase value is a fingerprint of the underlying voxel averaged magnetic field variations. Those related to physiological processes can be considered static or dynamic in relation to the temporal scale of a 2D acquisition and will play out on different spatial scales as well: globally across the entire images slice, and locally depending on the constituents and their relative fractions inside the MRI voxel. The ?static? respiration-induced effects lead to magneto-mechanic scan-to-scan variations in the global magnetic field but may also contribute to local BOLD fluctuations due to respiration-related variations in arterial carbon dioxide. Likewise, the ?dynamic? cardiac-related effects will lead to global susceptibility effects caused by pulsatile motion of the brain as well as local blood pressure-related changes in BOLD and changes in blood flow velocity. Finally, subject motion may lead to variations in both local and global tissue susceptibility that will be especially pronounced close to air cavities. Since dissimilar manifestations of physiological processes can be expected in phase and in magnitude images, a direct relationship between phase and magnitude scan-to-scan fluctuations cannot be assumed a priori. Therefore three different models were defined for the phase stability, each dependent on the relation between phase and magnitude variations and the best will depend on the underlying noise processes. By experiments on healthy volunteers at rest, we showed that phase stability depends on the type of post-processing and can be improved by reducing the low-frequency respiration-induced mechano-magnetic effects. Although the manifestations of physiological noise were in general more pronounced in phase than in magnitude images, due to phase wraps and global Bo effects, we suggest that a phase stability similar to that found in magnitude could theoretically be achieved by adequate correction methods. Moreover, as suggested by our experimental data regarding BOLD-related phase effects, phase stability could even supersede magnitude stability in voxels covering dense microvascular networks with BOLD-related fluctuations as the dominant noise contributor. In the interest of the quality of both BOLD-based and nc-MRI methods, future studies are required to find alternative methods that can improve phase stability, designed to match the temporal and spatial scale of the underlying neuronal activity. Recently, the possibility to use both magnitude and phase image sets for the statistical evaluation of fMRI has been proposed, with the prospective of increasing both statistical power and the spatial specificity. In the present work, several issues that affect the spatial and temporal stability in fMRI phase time series in the presence of physiologic noise processes are reviewed, discussed and illustrated by experiments performed at 3 T. The observed phase value is a fingerprint of the underlying voxel averaged magnetic field variations. Those related to physiological processes can be considered static or dynamic in relation to the temporal scale of a 2D acquisition and will play out on different spatial scales as well: globally across the entire images slice, and locally depending on the constituents and their relative fractions inside the MRI voxel. The ?static? respiration-induced effects lead to magneto-mechanic scan-to-scan variations in the global magnetic field but may also contribute to local BOLD fluctuations due to respiration-related variations in arterial carbon dioxide. Likewise, the ?dynamic? cardiac-related effects will lead to global susceptibility effects caused by pulsatile motion of the brain as well as local blood pressure-related changes in BOLD and changes in blood flow velocity. Finally, subject motion may lead to variations in both local and global tissue susceptibility that will be especially pronounced close to air cavities. Since dissimilar manifestations of physiological processes can be expected in phase and in magnitude images, a direct relationship between phase and magnitude scan-to-scan fluctuations cannot be assumed a priori. Therefore three different models were defined for the phase stability, each dependent on the relation between phase and magnitude variations and the best will depend on the underlying noise processes. By experiments on healthy volunteers at rest, we showed that phase stability depends on the type of post-processing and can be improved by reducing the low-frequency respiration-induced mechano-magnetic effects. Although the manifestations of physiological noise were in general more pronounced in phase than in magnitude images, due to phase wraps and global Bo effects, we suggest that a phase stability similar to that found in magnitude could theoretically be achieved by adequate correction methods. Moreover, as suggested by our experimental data regarding BOLD-related phase effects, phase stability could even supersede magnitude stability in voxels covering dense microvascular networks with BOLD-related fluctuations as the dominant noise contributor. In the interest of the quality of both BOLD-based and nc-MRI methods, future studies are required to find alternative methods that can improve phase stability, designed to match the temporal and spatial scale of the underlying neuronal activity.
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Robert Turner, Ana-Maria Oros-Peusquens, Sandro Romanzetti, Karl Zilles and Jon N Shah.
Optimised in vivo visualisation of cortical structures in the human brain at 3 T using IR-TSE. Magnetic Resonance Imaging 26(7):935–942, 2008.
Abstract The primary visual cortex in humans can be identified using magnetic resonance imaging (MRI) in vivo by detection of the stria of Gennari. To fully characterize this area, high spatial resolution is essential, including the use of very thin image slices to avoid loss of definition due to partial volume effects. A three-dimensional magnetization-prepared turbo spin-echo sequence, with appropriate parameter optimization, provided high-resolution imaging (0.4?0.4?0.5 mm3) on a clinical 3-T scanner with adequate contrast to noise ratio. These images allowed visualisation of the stria of Gennari in every slice of a volume covering most of the occipital cortex, in each of six healthy volunteers. The effective longitudinal relaxation time was measured with the isotropic resolution turbo spin echo sequence and found to be substantially shorter than values measured with a dedicated relaxometric sequence. The shortening was attributed to magnetization transfer effects, as supported by the investigation of its slab and turbo-factor dependence. The primary visual cortex in humans can be identified using magnetic resonance imaging (MRI) in vivo by detection of the stria of Gennari. To fully characterize this area, high spatial resolution is essential, including the use of very thin image slices to avoid loss of definition due to partial volume effects. A three-dimensional magnetization-prepared turbo spin-echo sequence, with appropriate parameter optimization, provided high-resolution imaging (0.4?0.4?0.5 mm3) on a clinical 3-T scanner with adequate contrast to noise ratio. These images allowed visualisation of the stria of Gennari in every slice of a volume covering most of the occipital cortex, in each of six healthy volunteers. The effective longitudinal relaxation time was measured with the isotropic resolution turbo spin echo sequence and found to be substantially shorter than values measured with a dedicated relaxometric sequence. The shortening was attributed to magnetization transfer effects, as supported by the investigation of its slab and turbo-factor dependence.
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Marco Bozzali, Mara Cercignani and Carlo Caltagirone.
Brain volumetrics to investigate aging and the principal forms of degenerative cognitive decline: a brief review. Magnetic Resonance Imaging 26(7):1065–1070, 2008.
Abstract The volume of the brain and of some of its structures can provide insight into the pathological process of several diseases. For this reason, in the recent years we saw a tremendous progress in the development of automated techniques for gaining information about global and regional atrophy. This paper reviews the main methods of analysis to quantify brain volume, and their application to the study of normal aging and the principal forms of degenerative dementias. The volume of the brain and of some of its structures can provide insight into the pathological process of several diseases. For this reason, in the recent years we saw a tremendous progress in the development of automated techniques for gaining information about global and regional atrophy. This paper reviews the main methods of analysis to quantify brain volume, and their application to the study of normal aging and the principal forms of degenerative dementias.
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A Mouraux and G D Iannetti.
Across-trial averaging of event-related EEG responses and beyond. Magnetic Resonance Imaging 26(7):1041–1054, 2008.
Abstract Internally and externally triggered sensory, motor and cognitive events elicit a number of transient changes in the ongoing electroencephalogram (EEG): event-related brain potentials (ERPs), event-related synchronization and desynchronization (ERS/ERD), and event-related phase resetting (ERPR). To increase the signal-to-noise ratio of event-related brain responses, most studies rely on across-trial averaging in the time domain, a procedure that is, however, blind to a significant fraction of the elicited cortical activity. Here, we outline the key concepts underlying the limitations of time-domain averaging and consider three alternative methodological approaches that have received increasing interest: time-frequency decomposition of the EEG (using the continuous wavelet transform), blind source separation of the EEG (using Independent Component Analysis) and the analysis of event-related brain responses at the level of single trials. In addition, we provide practical guidelines on the implementation of these methods and on the interpretation of the results they produce. Internally and externally triggered sensory, motor and cognitive events elicit a number of transient changes in the ongoing electroencephalogram (EEG): event-related brain potentials (ERPs), event-related synchronization and desynchronization (ERS/ERD), and event-related phase resetting (ERPR). To increase the signal-to-noise ratio of event-related brain responses, most studies rely on across-trial averaging in the time domain, a procedure that is, however, blind to a significant fraction of the elicited cortical activity. Here, we outline the key concepts underlying the limitations of time-domain averaging and consider three alternative methodological approaches that have received increasing interest: time-frequency decomposition of the EEG (using the continuous wavelet transform), blind source separation of the EEG (using Independent Component Analysis) and the analysis of event-related brain responses at the level of single trials. In addition, we provide practical guidelines on the implementation of these methods and on the interpretation of the results they produce.
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Karen Mullinger, Matthew Brookes, Claire Stevenson, Paul Morgan and Richard Bowtell.
Exploring the feasibility of simultaneous electroencephalography/functional magnetic resonance imaging at 7 T. Magnetic Resonance Imaging 26(7):968–977, 2008.
Abstract The increased blood oxygenation level-dependent contrast available at high field makes the implementation of combined EEG/fMRI experiments at 7 T highly worthwhile from the point of view of fMRI data quality, but the higher field poses greater technical challenges for achieving good quality EEG data. A study of the feasibility of recording EEG signals from human subjects at 7 T using a commercially available, MR-compatible EEG system has therefore been carried out. This involved systematic measurement of the sources of noise in EEG recordings made in the 7 T scanner and measurement of RF heating effects on a gel phantom in the presence of a 32-electrode EEG cap. Having found no significant safety concerns and identified a set-up (involving switching off the magnet's cryo-cooler pumps and mounting the EEG amplifier on a cantilever) that limited scanner-induced noise, combined EEG/fMRI experiments employing visual stimulation were then successfully carried out on two human subjects. With the use of beamformer-based analysis of the EEG data, driven responses and alpha-band, event-related desynchronisation were identified in both subjects. The increased blood oxygenation level-dependent contrast available at high field makes the implementation of combined EEG/fMRI experiments at 7 T highly worthwhile from the point of view of fMRI data quality, but the higher field poses greater technical challenges for achieving good quality EEG data. A study of the feasibility of recording EEG signals from human subjects at 7 T using a commercially available, MR-compatible EEG system has therefore been carried out. This involved systematic measurement of the sources of noise in EEG recordings made in the 7 T scanner and measurement of RF heating effects on a gel phantom in the presence of a 32-electrode EEG cap. Having found no significant safety concerns and identified a set-up (involving switching off the magnet's cryo-cooler pumps and mounting the EEG amplifier on a cantilever) that limited scanner-induced noise, combined EEG/fMRI experiments employing visual stimulation were then successfully carried out on two human subjects. With the use of beamformer-based analysis of the EEG data, driven responses and alpha-band, event-related desynchronisation were identified in both subjects.
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Burkhard Mädler, Sylvia A Drabycz, Shannon H Kolind, Kenneth P Whittall and Alexander L MacKay.
Is diffusion anisotropy an accurate monitor of myelination?. Magnetic Resonance Imaging 26(7):874–888, 2008.
Abstract We compare T2-relaxation and diffusion tensor data from normal human brain. The relationships between myelin-water fraction (MWF) and various diffusion tensor measures [e.g., fractional anisotropy (FA), perpendicular diffusivity (ADC?) and mean diffusivity ] in white matter (WM) and gray matter (GM) structures in the brain were examined in 16 normal volunteers at 1.5 T and 6 normal subjects at 3.0 T and mean diffusivity. We found some degree of linear correlation between these measurements, but by using region of interest (ROI)-based analysis, we also observed several structures which seemed to deviate significantly from a linear relationship. From all investigated relationships between various diffusion tensor measures and myelin-water content, FA and ADC? yielded the highest correlation coefficients with MWF. However, diffusion anisotropy was also significantly influenced by factors other than myelin-water content. The less operator-dependent voxel-based analysis (VBA) between myelin-water and diffusional anisotropy measures is proposed as an innovative alternative to ROI-based analysis. We confirmed that WM structures, in general, have higher diffusional anisotropy than GM structures and also have higher myelin-water content. However, our findings suggest that in the highly organized fibre arrangement of compact WM structures such as the genu of the corpus callosum, elevated degrees of diffusional anisotropies are measured, which do not necessarily correspond to an elevated myelin content but more likely reflect the highly organized directionality of fibre bundles in these areas (low microscopic and macroscopic tortuosity) as well as strongly restricted diffusion in the interstitial space between the myelinated axons. Conversely, in structures with disorganized fibre bundles and multiple fibre crossings, such as the minor and major forceps, low FA values were measured, which does not necessarily reflect a decrease myelin-water content. We compare T2-relaxation and diffusion tensor data from normal human brain. The relationships between myelin-water fraction (MWF) and various diffusion tensor measures [e.g., fractional anisotropy (FA), perpendicular diffusivity (ADC?) and mean diffusivity ] in white matter (WM) and gray matter (GM) structures in the brain were examined in 16 normal volunteers at 1.5 T and 6 normal subjects at 3.0 T and mean diffusivity. We found some degree of linear correlation between these measurements, but by using region of interest (ROI)-based analysis, we also observed several structures which seemed to deviate significantly from a linear relationship. From all investigated relationships between various diffusion tensor measures and myelin-water content, FA and ADC? yielded the highest correlation coefficients with MWF. However, diffusion anisotropy was also significantly influenced by factors other than myelin-water content. The less operator-dependent voxel-based analysis (VBA) between myelin-water and diffusional anisotropy measures is proposed as an innovative alternative to ROI-based analysis. We confirmed that WM structures, in general, have higher diffusional anisotropy than GM structures and also have higher myelin-water content. However, our findings suggest that in the highly organized fibre arrangement of compact WM structures such as the genu of the corpus callosum, elevated degrees of diffusional anisotropies are measured, which do not necessarily correspond to an elevated myelin content but more likely reflect the highly organized directionality of fibre bundles in these areas (low microscopic and macroscopic tortuosity) as well as strongly restricted diffusion in the interstitial space between the myelinated axons. Conversely, in structures with disorganized fibre bundles and multiple fibre crossings, such as the minor and major forceps, low FA values were measured, which does not necessarily reflect a decrease myelin-water content.
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Kâmil Uludağ.
Transient and sustained BOLD responses to sustained visual stimulation. Magnetic Resonance Imaging 26(7):863–869, 2008.
Abstract Examining the transients of the blood-oxygenation-level-dependent (BOLD) signal using functional magnetic resonance imaging is a tool to probe basic brain physiology. In addition to the so-called initial dip and poststimulus undershoot of the BOLD signal, occasionally, overshoot at the beginning and at the end of stimulation and stimulus onset and offset (?phasic?) responses are observed. Hemifield visual stimulation was used in human subjects to study the latter transients. As expected, sustained (?tonic?) stimulus-correlated contralateral activation in the visual cortex and LGN was observed. Interestingly, bilateral phasic responses were observed, which only partly overlapped with the tonic network and which would have been missed using a standard analysis. A biomechanical model of the BOLD signal (?balloon model?) indicated that, in addition to phasic neuronal activity, vascular uncoupling can also give rise to phasic BOLD signals. Thus, additional physiological information (i.e., cerebral blood flow) and examination of spatial distribution of the activity might help to assess the BOLD signal transients correctly. In the current study, although vascular uncoupled responses cannot be ruled out as an explanation of the observed phasic BOLD network, the spatial distribution argues that sustained hemifield visual stimulation evokes both bilateral phasic and contralateral sustained neuronal responses. As a consequence, in rapid event-related experimental designs, both the phasic and tonic networks cannot be separated, possibly confounding the interpretation of BOLD signal data. Furthermore, a combination of phasic and tonic responses in the same region of interest might also mimic a BOLD response typically observed in adaptation experiments. Examining the transients of the blood-oxygenation-level-dependent (BOLD) signal using functional magnetic resonance imaging is a tool to probe basic brain physiology. In addition to the so-called initial dip and poststimulus undershoot of the BOLD signal, occasionally, overshoot at the beginning and at the end of stimulation and stimulus onset and offset (?phasic?) responses are observed. Hemifield visual stimulation was used in human subjects to study the latter transients. As expected, sustained (?tonic?) stimulus-correlated contralateral activation in the visual cortex and LGN was observed. Interestingly, bilateral phasic responses were observed, which only partly overlapped with the tonic network and which would have been missed using a standard analysis. A biomechanical model of the BOLD signal (?balloon model?) indicated that, in addition to phasic neuronal activity, vascular uncoupling can also give rise to phasic BOLD signals. Thus, additional physiological information (i.e., cerebral blood flow) and examination of spatial distribution of the activity might help to assess the BOLD signal transients correctly. In the current study, although vascular uncoupled responses cannot be ruled out as an explanation of the observed phasic BOLD network, the spatial distribution argues that sustained hemifield visual stimulation evokes both bilateral phasic and contralateral sustained neuronal responses. As a consequence, in rapid event-related experimental designs, both the phasic and tonic networks cannot be separated, possibly confounding the interpretation of BOLD signal data. Furthermore, a combination of phasic and tonic responses in the same region of interest might also mimic a BOLD response typically observed in adaptation experiments.
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Stephan E Maier and Robert V Mulkern.
Biexponential analysis of diffusion-related signal decay in normal human cortical and deep gray matter. Magnetic Resonance Imaging 26(7):897–904, 2008.
Abstract Diffusion imaging with high-b factors, high spatial resolution and cerebrospinal fluid signal suppression was performed in order to characterize the biexponential nature of the diffusion-related signal decay with b-factor in normal cortical gray and deep gray matter (GM). Integration of inversion pulses with a line scan diffusion imaging sequence resulted in 91% cerebrospinal fluid signal suppression, permitting accurate measurement of the fast diffusion coefficient in cortical GM (1.142±0.106 ?m2/ms) and revealing a marked similarity with that found in frontal white matter (WM) (1.155±0.046 ?m2/ms). The reversal of contrast between GM and WM at low vs high b-factors is shown to be due to a significantly faster slow diffusion coefficient in cortical GM (0.338±0.027 ?m2/ms) than in frontal WM (0.125±0.014 ?m2/ms). The same characteristic diffusion differences between GM and WM are observed in other brain tissue structures. The relative component size showed nonsignificant differences among all tissues investigated. Cellular architecture in GM and WM are fundamentally different and may explain the two- to threefold higher slow diffusion coefficient in GM. Diffusion imaging with high-b factors, high spatial resolution and cerebrospinal fluid signal suppression was performed in order to characterize the biexponential nature of the diffusion-related signal decay with b-factor in normal cortical gray and deep gray matter (GM). Integration of inversion pulses with a line scan diffusion imaging sequence resulted in 91% cerebrospinal fluid signal suppression, permitting accurate measurement of the fast diffusion coefficient in cortical GM (1.142±0.106 ?m2/ms) and revealing a marked similarity with that found in frontal white matter (WM) (1.155±0.046 ?m2/ms). The reversal of contrast between GM and WM at low vs high b-factors is shown to be due to a significantly faster slow diffusion coefficient in cortical GM (0.338±0.027 ?m2/ms) than in frontal WM (0.125±0.014 ?m2/ms). The same characteristic diffusion differences between GM and WM are observed in other brain tissue structures. The relative component size showed nonsignificant differences among all tissues investigated. Cellular architecture in GM and WM are fundamentally different and may explain the two- to threefold higher slow diffusion coefficient in GM.
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Bariş Yeşilyurt, Kâmil Uğurbil and Kâmil Uludağ.
Dynamics and nonlinearities of the BOLD response at very short stimulus durations. Magnetic Resonance Imaging 26(7):853–862, 2008.
Abstract In designing a functional imaging experiment or analyzing data, it is typically assumed that task duration and hemodynamic response are linearly related to each other. However, numerous human and animal studies have previously reported a deviation from linearity for short stimulus durations (<4 s). Here, we investigated nonlinearities of blood-oxygenation-level-dependent (BOLD) signals following visual stimulation of 5 to 1000 ms duration at two different luminance levels in human subjects. It was found that (a) a BOLD response to stimulus durations as short as 5 ms can be reliably detected; this stimulus duration is shorter than employed in any previous study investigating BOLD signal time courses; (b) the responses are more nonlinear than in any other previous study: the BOLD response to 1000 ms stimulation is only twice as large as the BOLD response to 5 ms stimulation although 200 times more photons were projected onto the retina; (c) the degree of nonlinearity depends on stimulus intensity; that is, nonlinearities have to be characterized not only by stimulus duration but also by stimulus features like luminance. These findings are especially of most practical importance in rapid event-related functional magnetic resonance imaging (fMRI) experimental designs. In addition, an ?initial dip? response ? thought to be generated by a rapid increase in cerebral metabolic rate of oxygen metabolism (CMRO2) relative to cerebral blood flow ? was observed and shown to colocalize well with the positive BOLD response. Highly intense stimulation, better tolerated by human subjects for short stimulus durations, causes early CMRO2 increase, and thus, the experimental design utilized in this study is better for detecting the initial dip than standard fMRI designs. These results and those from other groups suggest that short stimulation combined with appropriate experimental designs allows neuronal events and interactions to be examined by BOLD signal analysis, despite its slow evolution. In designing a functional imaging experiment or analyzing data, it is typically assumed that task duration and hemodynamic response are linearly related to each other. However, numerous human and animal studies have previously reported a deviation from linearity for short stimulus durations (<4 s). Here, we investigated nonlinearities of blood-oxygenation-level-dependent (BOLD) signals following visual stimulation of 5 to 1000 ms duration at two different luminance levels in human subjects. It was found that (a) a BOLD response to stimulus durations as short as 5 ms can be reliably detected; this stimulus duration is shorter than employed in any previous study investigating BOLD signal time courses; (b) the responses are more nonlinear than in any other previous study: the BOLD response to 1000 ms stimulation is only twice as large as the BOLD response to 5 ms stimulation although 200 times more photons were projected onto the retina; (c) the degree of nonlinearity depends on stimulus intensity; that is, nonlinearities have to be characterized not only by stimulus duration but also by stimulus features like luminance. These findings are especially of most practical importance in rapid event-related functional magnetic resonance imaging (fMRI) experimental designs. In addition, an ?initial dip? response ? thought to be generated by a rapid increase in cerebral metabolic rate of oxygen metabolism (CMRO2) relative to cerebral blood flow ? was observed and shown to colocalize well with the positive BOLD response. Highly intense stimulation, better tolerated by human subjects for short stimulus durations, causes early CMRO2 increase, and thus, the experimental design utilized in this study is better for detecting the initial dip than standard fMRI designs. These results and those from other groups suggest that short stimulation combined with appropriate experimental designs allows neuronal events and interactions to be examined by BOLD signal analysis, despite its slow evolution.
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Fabrizio Esposito, Adriana Aragri, Ilaria Pesaresi, Sossio Cirillo, Gioacchino Tedeschi, Elio Marciano, Rainer Goebel and Francesco Di Salle.
Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI. Magnetic Resonance Imaging 26(7):905–913, 2008.
Abstract Resting-state functional magnetic resonance imaging (RS-fMRI) is a technique used to investigate the spontaneous correlations of blood-oxygen-level-dependent signals across different regions of the brain. Using functional connectivity tools, it is possible to investigate a specific RS-fMRI network, referred to as ?default-mode? (DM) network, that involves cortical regions deactivated in fMRI experiments with cognitive tasks. Previous works have reported a significant effect of aging on DM regions activity.Independent component analysis (ICA) is often used for generating spatially distributed DM functional connectivity patterns from RS-fMRI data without the need for a reference region. This aspect and the relatively easy setup of an RS-fMRI experiment even in clinical trials have boosted the combined use of RS-fMRI and ICA-based DM analysis for noninvasive research of brain disorders.In this work, we considered different strategies for combining ICA results from individual-level and population-level analyses and used them to evaluate and predict the effect of aging on the DM component.Using RS-fMRI data from 20 normal subjects and a previously developed group-level ICA methodology, we generated group DM maps and showed that the overall ICA?DM connectivity is negatively correlated with age. A negative correlation of the ICA voxel weights with age existed in all DM regions at a variable degree.As an alternative approach, we generated a distributed DM spatial template and evaluated the correlation of each individual DM component fit to this template with age. Using a ?leave-one-out? procedure, we discuss the importance of removing the bias from the DM template-generation process. Resting-state functional magnetic resonance imaging (RS-fMRI) is a technique used to investigate the spontaneous correlations of blood-oxygen-level-dependent signals across different regions of the brain. Using functional connectivity tools, it is possible to investigate a specific RS-fMRI network, referred to as ?default-mode? (DM) network, that involves cortical regions deactivated in fMRI experiments with cognitive tasks. Previous works have reported a significant effect of aging on DM regions activity.Independent component analysis (ICA) is often used for generating spatially distributed DM functional connectivity patterns from RS-fMRI data without the need for a reference region. This aspect and the relatively easy setup of an RS-fMRI experiment even in clinical trials have boosted the combined use of RS-fMRI and ICA-based DM analysis for noninvasive research of brain disorders.In this work, we considered different strategies for combining ICA results from individual-level and population-level analyses and used them to evaluate and predict the effect of aging on the DM component.Using RS-fMRI data from 20 normal subjects and a previously developed group-level ICA methodology, we generated group DM maps and showed that the overall ICA?DM connectivity is negatively correlated with age. A negative correlation of the ICA voxel weights with age existed in all DM regions at a variable degree.As an alternative approach, we generated a distributed DM spatial template and evaluated the correlation of each individual DM component fit to this template with age. Using a ?leave-one-out? procedure, we discuss the importance of removing the bias from the DM template-generation process.
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Yuri Matsumoto and Alan Jasanoff.
T₂ relaxation induced by clusters of superparamagnetic nanoparticles: Monte Carlo simulations. Magnetic Resonance Imaging 26(7):994–998, 2008.
Abstract Clustering strongly affects the transverse (T2) relaxation induced by superparamagnetic nanoparticles in magnetic resonance experiments. In this study, we used Monte Carlo simulations to investigate systematically the relationship between T2 values and the geometric parameters of nanoparticle clusters. We computed relaxation as a function of particle size, number of particles per cluster, interparticle distance, and cluster shape (compact vs. linear). We found that compact clusters induced relaxation equivalent to similarly sized single particles. For small particles, the shape and density of clusters had a significant effect on T2. In contrast, for larger particles, T2 relaxation was relatively independent of cluster geometry until interparticle distances within a cluster exceeded ten times the particle diameter. Results from our simulations suggest principles for the design of nanoparticle aggregation-based sensors for MRI. Clustering strongly affects the transverse (T2) relaxation induced by superparamagnetic nanoparticles in magnetic resonance experiments. In this study, we used Monte Carlo simulations to investigate systematically the relationship between T2 values and the geometric parameters of nanoparticle clusters. We computed relaxation as a function of particle size, number of particles per cluster, interparticle distance, and cluster shape (compact vs. linear). We found that compact clusters induced relaxation equivalent to similarly sized single particles. For small particles, the shape and density of clusters had a significant effect on T2. In contrast, for larger particles, T2 relaxation was relatively independent of cluster geometry until interparticle distances within a cluster exceeded ten times the particle diameter. Results from our simulations suggest principles for the design of nanoparticle aggregation-based sensors for MRI.
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Essa Yacoub, Kâmil Uludağ, Kâmil Uğurbil and Noam Harel.
Decreases in ADC observed in tissue areas during activation in the cat visual cortex at 9.4 T using high diffusion sensitization. Magnetic Resonance Imaging 26(7):889–896, 2008.
Abstract Recent studies in the human visual cortex using diffusion-weighted functional magnetic resonance imaging (fMRI) have suggested that the apparent diffusion coefficient (ADC) decreases, in contrast to earlier studies that consistently reported ADC increases during neuronal activation. The changes, in either case, are hypothesized to provide the ability to improve the spatial specificity of fMRI over conventional blood-oxygenation-level-dependent (BOLD) methods. Most recently, the ADC decreases have been suggested as originating from transient cell swelling caused by either shrinkage of the extracellular space or some intracellular neuronal process that precedes the hemodynamic response. All of these studies have been conducted in humans and at lower magnetic fields, which can be limited by the signal-to-noise ratio (SNR). The low SNR can lead to significant partial-volume effects because of the lower spatial resolutions required to attain sufficient SNR in diffusion-weighted images. Human studies also have the potential confound of motion. At high magnetic fields and in animal model studies, these limitations are alleviated. At high fields, SNR increases, tissue signals are enhanced and signal changes inside the blood are significantly reduced compared to lower fields. In this work, we were able to measure a small but significant ADC decrease in tissue areas, in conjunction with brain activation in the cat visual cortex at 9.4 T when using highly diffusion-weighted images (b>1200 s/mm2) where intravascular effects are minimal. When using low b-values, delayed increases in the tissue ADC during activation were observed. No significant changes in ADC were observed in surface vessels for any diffusion weighting. Furthermore, we did not observe any temporal differences in the highly diffusion-weighted data compared to BOLD; however, although the changes may likely be vascular in nature, they are highly localized to the tissue areas. Recent studies in the human visual cortex using diffusion-weighted functional magnetic resonance imaging (fMRI) have suggested that the apparent diffusion coefficient (ADC) decreases, in contrast to earlier studies that consistently reported ADC increases during neuronal activation. The changes, in either case, are hypothesized to provide the ability to improve the spatial specificity of fMRI over conventional blood-oxygenation-level-dependent (BOLD) methods. Most recently, the ADC decreases have been suggested as originating from transient cell swelling caused by either shrinkage of the extracellular space or some intracellular neuronal process that precedes the hemodynamic response. All of these studies have been conducted in humans and at lower magnetic fields, which can be limited by the signal-to-noise ratio (SNR). The low SNR can lead to significant partial-volume effects because of the lower spatial resolutions required to attain sufficient SNR in diffusion-weighted images. Human studies also have the potential confound of motion. At high magnetic fields and in animal model studies, these limitations are alleviated. At high fields, SNR increases, tissue signals are enhanced and signal changes inside the blood are significantly reduced compared to lower fields. In this work, we were able to measure a small but significant ADC decrease in tissue areas, in conjunction with brain activation in the cat visual cortex at 9.4 T when using highly diffusion-weighted images (b>1200 s/mm2) where intravascular effects are minimal. When using low b-values, delayed increases in the tissue ADC during activation were observed. No significant changes in ADC were observed in surface vessels for any diffusion weighting. Furthermore, we did not observe any temporal differences in the highly diffusion-weighted data compared to BOLD; however, although the changes may likely be vascular in nature, they are highly localized to the tissue areas.
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Hongxia Lei, Vladimir Mlynárik, Nathalie Just and Rolf Gruetter.
Snapshot gradient-recalled echo-planar images of rat brains at long echo time at 9.4 T. Magnetic Resonance Imaging 26(7):954–960, 2008.
Abstract With improved B0 homogeneity along with satisfactory gradient performance at high magnetic fields, snapshot gradient-recalled echo-planar imaging (GRE-EPI) would perform at long echo times (TEs) on the order of T2*, which intrinsically allows obtaining strongly T2*-weighted images with embedded substantial anatomical details in ultrashort time. The aim of this study was to investigate the feasibility and quality of long TE snapshot GRE-EPI images of rat brain at 9.4 T. When compensating for B0 inhomogeneities, especially second-order shim terms, a 200?200 ?m2 in-plane resolution image was reproducibly obtained at long TE (>25 ms). The resulting coronal images at 30 ms had diminished geometric distortions and, thus, embedded substantial anatomical details. Concurrently with the very consistent stability, such GRE-EPI images should permit to resolve functional data not only with high specificity but also with substantial anatomical details, therefore allowing coregistration of the acquired functional data on the same image data set. With improved B0 homogeneity along with satisfactory gradient performance at high magnetic fields, snapshot gradient-recalled echo-planar imaging (GRE-EPI) would perform at long echo times (TEs) on the order of T2*, which intrinsically allows obtaining strongly T2*-weighted images with embedded substantial anatomical details in ultrashort time. The aim of this study was to investigate the feasibility and quality of long TE snapshot GRE-EPI images of rat brain at 9.4 T. When compensating for B0 inhomogeneities, especially second-order shim terms, a 200?200 ?m2 in-plane resolution image was reproducibly obtained at long TE (>25 ms). The resulting coronal images at 30 ms had diminished geometric distortions and, thus, embedded substantial anatomical details. Concurrently with the very consistent stability, such GRE-EPI images should permit to resolve functional data not only with high specificity but also with substantial anatomical details, therefore allowing coregistration of the acquired functional data on the same image data set.
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David W Carmichael, Khalid Hamandi, Helmut Laufs, John S Duncan, David L Thomas and Louis Lemieux.
An investigation of the relationship between BOLD and perfusion signal changes during epileptic generalised spike wave activity. Magnetic Resonance Imaging 26(7):870–873, 2008.
Abstract In pathological conditions interpretation of functional magnetic resonance imaging (fMRI) results can be difficult. This is due to a reliance on the assumed coupling between neuronal activity and changes in cerebral blood flow (CBF) and oxygenation. We wanted to investigate the coupling between blood oxygen level dependant contrast (BOLD) and CBF time courses in epilepsy patients with generalised spike wave activity (GSW) to better understand the underlying mechanisms behind the EEG-fMRI signal changes observed, especially in regions of negative BOLD response (NBR). Four patients with frequent GSW were scanned with simultaneous electroencephalographic (EEG)-fMRI with BOLD and arterial spin labeling (ASL) sequences. We examined the relationship between simultaneous CBF and BOLD measurements by looking at the correlation of the two signals in terms of percentage signal change on a voxel-by-voxel basis. This method is not reliant on coincident activation. BOLD and CBF were positively correlated in patients with epilepsy during background EEG activity and GSW. The subject average value of the ?CBF/?BOLD slope lay between +19 and +36 and also showed spatial variation which could indicate areas with altered vascular response. There was not a significant difference between ?CBF/?BOLD during GSW, suggesting that neurovascular coupling to BOLD signal is generally maintained between states and, in particular, within areas of NBR. In pathological conditions interpretation of functional magnetic resonance imaging (fMRI) results can be difficult. This is due to a reliance on the assumed coupling between neuronal activity and changes in cerebral blood flow (CBF) and oxygenation. We wanted to investigate the coupling between blood oxygen level dependant contrast (BOLD) and CBF time courses in epilepsy patients with generalised spike wave activity (GSW) to better understand the underlying mechanisms behind the EEG-fMRI signal changes observed, especially in regions of negative BOLD response (NBR). Four patients with frequent GSW were scanned with simultaneous electroencephalographic (EEG)-fMRI with BOLD and arterial spin labeling (ASL) sequences. We examined the relationship between simultaneous CBF and BOLD measurements by looking at the correlation of the two signals in terms of percentage signal change on a voxel-by-voxel basis. This method is not reliant on coincident activation. BOLD and CBF were positively correlated in patients with epilepsy during background EEG activity and GSW. The subject average value of the ?CBF/?BOLD slope lay between +19 and +36 and also showed spatial variation which could indicate areas with altered vascular response. There was not a significant difference between ?CBF/?BOLD during GSW, suggesting that neurovascular coupling to BOLD signal is generally maintained between states and, in particular, within areas of NBR.
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