Silvia Mangia, Timo Liimatainen, Michael Garwood and Shalom Michaeli.
Rotating frame relaxation during adiabatic pulses vs. conventional spin lock: simulations and experimental results at 4 T. Magnetic Resonance Imaging 27(8):1074–1087, 2009.
Abstract Spin relaxation taking place during radiofrequency (RF) irradiation can be assessed by measuring the longitudinal and transverse rotating frame relaxation rate constants (R1? and R2?). These relaxation parameters can be altered by utilizing different settings of the RF irradiation, thus providing a useful tool to generate contrast in MRI. In this work, we investigate the dependencies of R1? and R2? due to dipolar interactions and anisochronous exchange (i.e., exchange between spins with different chemical shift δ??0) on the properties of conventional spin-lock and adiabatic pulses, with particular emphasis on the latter ones which were not fully described previously. The results of simulations based on relaxation theory provide a foundation for formulating practical considerations for in vivo applications of rotating frame relaxation methods. Rotating frame relaxation measurements obtained from phantoms and from the human brain at 4 T are presented to confirm the theoretical predictions. Spin relaxation taking place during radiofrequency (RF) irradiation can be assessed by measuring the longitudinal and transverse rotating frame relaxation rate constants (R1? and R2?). These relaxation parameters can be altered by utilizing different settings of the RF irradiation, thus providing a useful tool to generate contrast in MRI. In this work, we investigate the dependencies of R1? and R2? due to dipolar interactions and anisochronous exchange (i.e., exchange between spins with different chemical shift δ??0) on the properties of conventional spin-lock and adiabatic pulses, with particular emphasis on the latter ones which were not fully described previously. The results of simulations based on relaxation theory provide a foundation for formulating practical considerations for in vivo applications of rotating frame relaxation methods. Rotating frame relaxation measurements obtained from phantoms and from the human brain at 4 T are presented to confirm the theoretical predictions.
URL, DOI

Marta Bianciardi, Masaki Fukunaga, Peter Gelderen, Silvina G Horovitz, Jacco A Zwart, Karin Shmueli and Jeff H Duyn.
Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. Magnetic Resonance Imaging 27(8):1019–1029, 2009.
Abstract {Signal fluctuations in functional magnetic resonance imaging (fMRI) can result from a number of sources that may have a neuronal, physiologic or instrumental origin. To determine the relative contribution of these sources, we recorded physiological (respiration and cardiac) signals simultaneously with fMRI in human volunteers at rest with their eyes closed. State-of-the-art technology was used including high magnetic field (7 T), a multichannel detector array and high-resolution (3 mm3) echo-planar imaging. We investigated the relative contribution of thermal noise and other sources of variance to the observed fMRI signal fluctuations both in the visual cortex and in the whole brain gray matter. The following sources of variance were evaluated separately: low-frequency drifts due to scanner instability, effects correlated with respiratory and cardiac cycles, effects due to variability in the respiratory flow rate and cardiac rate, and other sources, tentatively attributed to spontaneous neuronal activity. We found that low-frequency drifts are the most significant source of fMRI signal fluctuations (3.0% signal change in the visual cortex
URL, DOI

Fabrizio Esposito, Adriana Aragri, Tommaso Piccoli, Gioacchino Tedeschi, Rainer Goebel and Francesco Di Salle.
Distributed analysis of simultaneous EEG-fMRI time-series: modeling and interpretation issues. Magnetic Resonance Imaging 27(8):1120–1130, 2009.
Abstract Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) represent brain activity in terms of a reliable anatomical localization and a detailed temporal evolution of neural signals. Simultaneous EEG-fMRI recordings offer the possibility to greatly enrich the significance and the interpretation of the single modality results because the same neural processes are observed from the same brain at the same time. Nonetheless, the different physical nature of the measured signals by the two techniques renders the coupling not always straightforward, especially in cognitive experiments where spatially localized and distributed effects coexist and evolve temporally at different temporal scales.The purpose of this article is to illustrate the combination of simultaneously recorded EEG and fMRI signals exploiting the principles of EEG distributed source modeling. We define a common source space for fMRI and EEG signal projection and gather a conceptually unique framework for the spatial and temporal comparative analysis. We illustrate this framework in a graded-load working-memory simultaneous EEG-fMRI experiment based on the n-back task where sustained load-dependent changes in the blood-oxygenation-level-dependent (BOLD) signals during continuous item memorization co-occur with parametric changes in the EEG theta power induced at each single item. In line with previous studies, we demonstrate on two single-subject cases how the presented approach is capable of colocalizing in midline frontal regions two phenomena simultaneously observed at different temporal scales, such as the sustained negative changes in BOLD activity and the parametric EEG theta synchronization. We discuss the presented approach in relation to modeling and interpretation issues typically arising in simultaneous EEG-fMRI studies. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) represent brain activity in terms of a reliable anatomical localization and a detailed temporal evolution of neural signals. Simultaneous EEG-fMRI recordings offer the possibility to greatly enrich the significance and the interpretation of the single modality results because the same neural processes are observed from the same brain at the same time. Nonetheless, the different physical nature of the measured signals by the two techniques renders the coupling not always straightforward, especially in cognitive experiments where spatially localized and distributed effects coexist and evolve temporally at different temporal scales.The purpose of this article is to illustrate the combination of simultaneously recorded EEG and fMRI signals exploiting the principles of EEG distributed source modeling. We define a common source space for fMRI and EEG signal projection and gather a conceptually unique framework for the spatial and temporal comparative analysis. We illustrate this framework in a graded-load working-memory simultaneous EEG-fMRI experiment based on the n-back task where sustained load-dependent changes in the blood-oxygenation-level-dependent (BOLD) signals during continuous item memorization co-occur with parametric changes in the EEG theta power induced at each single item. In line with previous studies, we demonstrate on two single-subject cases how the presented approach is capable of colocalizing in midline frontal regions two phenomena simultaneously observed at different temporal scales, such as the sustained negative changes in BOLD activity and the parametric EEG theta synchronization. We discuss the presented approach in relation to modeling and interpretation issues typically arising in simultaneous EEG-fMRI studies.
URL, DOI

John Ashburner.
Computational anatomy with the SPM software. Magnetic Resonance Imaging 27(8):1163–1174, 2009.
Abstract An overview of computational procedures for examining neuroanatomical variability is presented. The review focuses on approaches that can be applied using the SPM software package, beginning by explaining briefly how statistical parametric mapping is usually applied to functional imaging data. The review then proceeds to discuss volumetry, with an emphasis on voxel-based morphometry, and the pre-processing steps involved using the SPM software. Most volumetric studies involve univariate approaches, with a correction for some global measure, such as total brain volume. In contrast, the overall form of the brain may be more accurately modeled using multivariate approaches. Such models of anatomical variability may prove accurate enough for computer assisted diagnoses. An overview of computational procedures for examining neuroanatomical variability is presented. The review focuses on approaches that can be applied using the SPM software package, beginning by explaining briefly how statistical parametric mapping is usually applied to functional imaging data. The review then proceeds to discuss volumetry, with an emphasis on voxel-based morphometry, and the pre-processing steps involved using the SPM software. Most volumetric studies involve univariate approaches, with a correction for some global measure, such as total brain volume. In contrast, the overall form of the brain may be more accurately modeled using multivariate approaches. Such models of anatomical variability may prove accurate enough for computer assisted diagnoses.
URL, DOI

Roman Rodionov, Marie Chupin, Elaine Williams, Alexander Hammers, Chandrasekharan Kesavadas and Louis Lemieux.
Evaluation of atlas-based segmentation of hippocampi in healthy humans. Magnetic Resonance Imaging 27(8):1104–1109, 2009.
Abstract Introduction and aimRegion of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus. Introduction and aimRegion of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus.
URL, DOI

Wietske Zwaag, José P Marques, Martin Hergt and Rolf Gruetter.
Investigation of high-resolution functional magnetic resonance imaging by means of surface and array radiofrequency coils at 7 T. Magnetic Resonance Imaging 27(8):1011–1018, 2009.
Abstract In this investigation, high-resolution, 1?1?1-mm3 functional magnetic resonance imaging (fMRI) at 7 T is performed using a multichannel array head coil and a surface coil approach. Scan geometry was optimized for each coil separately to exploit the strengths of both coils. Acquisitions with the surface coil focused on partial brain coverage, while whole-brain coverage fMRI experiments were performed with the array head coil. BOLD sensitivity in the occipital lobe was found to be higher with the surface coil than with the head array, suggesting that restriction of signal detection to the area of interest may be beneficial for localized activation studies.Performing independent component analysis (ICA) decomposition of the fMRI data, we consistently detected BOLD signal changes and resting state networks. In the surface coil data, a small negative BOLD response could be detected in these resting state network areas. Also in the data acquired with the surface coil, two distinct components of the positive BOLD signal were consistently observed. These two components were tentatively assigned to tissue and venous signal changes. In this investigation, high-resolution, 1?1?1-mm3 functional magnetic resonance imaging (fMRI) at 7 T is performed using a multichannel array head coil and a surface coil approach. Scan geometry was optimized for each coil separately to exploit the strengths of both coils. Acquisitions with the surface coil focused on partial brain coverage, while whole-brain coverage fMRI experiments were performed with the array head coil. BOLD sensitivity in the occipital lobe was found to be higher with the surface coil than with the head array, suggesting that restriction of signal detection to the area of interest may be beneficial for localized activation studies.Performing independent component analysis (ICA) decomposition of the fMRI data, we consistently detected BOLD signal changes and resting state networks. In the surface coil data, a small negative BOLD response could be detected in these resting state network areas. Also in the data acquired with the surface coil, two distinct components of the positive BOLD signal were consistently observed. These two components were tentatively assigned to tissue and venous signal changes.
URL, DOI

Sandra M Meyers, Cornelia Laule, Irene M Vavasour, Shannon H Kolind, Burkhard Mädler, Roger Tam, Anthony L Traboulsee, Jimmy Lee, David K B Li and Alex L MacKay.
Reproducibility of myelin water fraction analysis: a comparison of region of interest and voxel-based analysis methods. Magnetic Resonance Imaging 27(8):1096–1103, 2009.
Abstract This study compared region of interest (ROI) and voxel-based analysis (VBA) methods to determine the optimal method of myelin water fraction (MWF) analysis. Twenty healthy controls were scanned twice using a multi-echo T2 relaxation sequence and ROIs were drawn in white and grey matter. MWF was defined as the fractional signal from 15 to 40 ms in the T2 distribution. For ROI analysis, the mean intensity of voxels within an ROI was fit using non-negative least squares. For VBA, MWF was obtained for each voxel and the mean and median values within an ROI were calculated. There was a slightly higher correlation between Scan 1 and 2 for the VBA method (R2=0.98) relative to the ROI method (R2=0.95), and the VBA mean square difference between scans was 300% lower, indicating VBA was the most consistent between scans. For the VBA method, mean MWF was found to be more reproducible than median MWF. As the VBA method is more reproducible and gives more options for visualization and analysis of MWF, it is recommended over the ROI method of MWF analysis. This study compared region of interest (ROI) and voxel-based analysis (VBA) methods to determine the optimal method of myelin water fraction (MWF) analysis. Twenty healthy controls were scanned twice using a multi-echo T2 relaxation sequence and ROIs were drawn in white and grey matter. MWF was defined as the fractional signal from 15 to 40 ms in the T2 distribution. For ROI analysis, the mean intensity of voxels within an ROI was fit using non-negative least squares. For VBA, MWF was obtained for each voxel and the mean and median values within an ROI were calculated. There was a slightly higher correlation between Scan 1 and 2 for the VBA method (R2=0.98) relative to the ROI method (R2=0.95), and the VBA mean square difference between scans was 300% lower, indicating VBA was the most consistent between scans. For the VBA method, mean MWF was found to be more reproducible than median MWF. As the VBA method is more reproducible and gives more options for visualization and analysis of MWF, it is recommended over the ROI method of MWF analysis.
URL, DOI

Sepideh Sadaghiani, Kâmil Uğurbil and Kâmil Uludağ.
Neural activity-induced modulation of BOLD poststimulus undershoot independent of the positive signal. Magnetic Resonance Imaging 27(8):1030–1038, 2009.
Abstract Despite intense research on the blood oxygenation level-dependent (BOLD) signal underlying functional magnetic resonance imaging, our understanding of its physiological basis is far from complete. In this study, it was investigated whether the so-called poststimulus BOLD signal undershoot is solely a passive vascular effect or actively induced by neural responses. Prolonged static and flickering black-white checkerboard stimulation with isoluminant grey screen as baseline condition were employed on eight human subjects. Within the same region of interest, the positive BOLD time courses for static and flickering stimuli were identical over the entire stimulus duration. In contrast, the static stimuli exhibited no poststimulus BOLD signal undershoot, whereas the flickering stimuli caused a strong BOLD poststimulus undershoot. To ease the interpretation, we performed an additional study measuring both BOLD signal and cerebral blood flow (CBF) using arterial spin labeling. Also for CBF, a difference in the poststimulus period was found for the two stimuli. Thus, a passive blood volume effect as the only contributor to the poststimulus undershoot comes short in explaining the BOLD poststimulus undershoot phenomenon for this particular experiment. Rather, an additional active neuronal activation or deactivation can strongly modulate the BOLD poststimulus behavior. In summary, the poststimulus time course of BOLD signal could potentially be used to differentiate neuronal activity patterns that are otherwise indistinguishable using the positive evoked response. Despite intense research on the blood oxygenation level-dependent (BOLD) signal underlying functional magnetic resonance imaging, our understanding of its physiological basis is far from complete. In this study, it was investigated whether the so-called poststimulus BOLD signal undershoot is solely a passive vascular effect or actively induced by neural responses. Prolonged static and flickering black-white checkerboard stimulation with isoluminant grey screen as baseline condition were employed on eight human subjects. Within the same region of interest, the positive BOLD time courses for static and flickering stimuli were identical over the entire stimulus duration. In contrast, the static stimuli exhibited no poststimulus BOLD signal undershoot, whereas the flickering stimuli caused a strong BOLD poststimulus undershoot. To ease the interpretation, we performed an additional study measuring both BOLD signal and cerebral blood flow (CBF) using arterial spin labeling. Also for CBF, a difference in the poststimulus period was found for the two stimuli. Thus, a passive blood volume effect as the only contributor to the poststimulus undershoot comes short in explaining the BOLD poststimulus undershoot phenomenon for this particular experiment. Rather, an additional active neuronal activation or deactivation can strongly modulate the BOLD poststimulus behavior. In summary, the poststimulus time course of BOLD signal could potentially be used to differentiate neuronal activity patterns that are otherwise indistinguishable using the positive evoked response.
URL, DOI

Till Nierhaus, Tobias Schön, Robert Becker, Petra Ritter and Arno Villringer.
Background and evoked activity and their interaction in the human brain. Magnetic Resonance Imaging 27(8):1140–1150, 2009.
Abstract Most functional neuroimaging studies have investigated brain activity evoked by certain types of stimulation or tasks. In recent years, resting brain activity and its influence on evoked activity has become accessible for investigation. However, despite numerous studies on background and evoked activities, either observed with vascular (functional magnetic resonance imaging, positron emission tomography, optical) or electrophysiological (electroencephalography, magnetoencephalography) or a combination of both methods, so far, there is no generally accepted view concerning both the precise meaning of background activity and its relationship to evoked activity. In this article, we give an overview of the current knowledge on this issue and we review recent studies examining the influence of ongoing activity on behavioral responses and the relationship between ongoing and evoked activity. Most functional neuroimaging studies have investigated brain activity evoked by certain types of stimulation or tasks. In recent years, resting brain activity and its influence on evoked activity has become accessible for investigation. However, despite numerous studies on background and evoked activities, either observed with vascular (functional magnetic resonance imaging, positron emission tomography, optical) or electrophysiological (electroencephalography, magnetoencephalography) or a combination of both methods, so far, there is no generally accepted view concerning both the precise meaning of background activity and its relationship to evoked activity. In this article, we give an overview of the current knowledge on this issue and we review recent studies examining the influence of ongoing activity on behavioral responses and the relationship between ongoing and evoked activity.
URL, DOI

Robert V Mulkern, Steven J Haker and Stephan E Maier.
On high b diffusion imaging in the human brain: ruminations and experimental insights. Magnetic Resonance Imaging 27(8):1151–1162, 2009.
Abstract Interest in the manner in which brain tissue signal decays with b factor in diffusion imaging schemes has grown in recent years following the observation that the decay curves depart from purely monoexponential decay behavior. Regardless of the model or fitting function proposed for characterizing sufficiently sampled decay curves (vide infra), the departure from monoexponentiality spells increased tissue characterization potential. The degree to which this potential can be harnessed to improve specificity, sensitivity and spatial localization of diseases in brain, and other tissues, largely remains to be explored. Furthermore, the degree to which currently popular diffusion tensor imaging methods, including visually impressive white matter fiber ?tractography? results, have almost completely ignored the nonmonoexponential nature of the basic signal decay with b factor is worthy of communal introspection. Here we limit our attention to a review of the basic experimental features associated with brain water signal diffusion decay curves as measured over extended b-factor ranges, the simple few parameter fitting functions that have been proposed to characterize these decays and the more involved models, e.g.,?ruminations,? which have been proposed to account for the nonmonoexponentiality to date. Interest in the manner in which brain tissue signal decays with b factor in diffusion imaging schemes has grown in recent years following the observation that the decay curves depart from purely monoexponential decay behavior. Regardless of the model or fitting function proposed for characterizing sufficiently sampled decay curves (vide infra), the departure from monoexponentiality spells increased tissue characterization potential. The degree to which this potential can be harnessed to improve specificity, sensitivity and spatial localization of diseases in brain, and other tissues, largely remains to be explored. Furthermore, the degree to which currently popular diffusion tensor imaging methods, including visually impressive white matter fiber ?tractography? results, have almost completely ignored the nonmonoexponential nature of the basic signal decay with b factor is worthy of communal introspection. Here we limit our attention to a review of the basic experimental features associated with brain water signal diffusion decay curves as measured over extended b-factor ranges, the simple few parameter fitting functions that have been proposed to characterize these decays and the more involved models, e.g.,?ruminations,? which have been proposed to account for the nonmonoexponentiality to date.
URL, DOI

Natalia Petridou, Andreas Schäfer, Penny Gowland and Richard Bowtell.
Phase vs. magnitude information in functional magnetic resonance imaging time series: toward understanding the noise. Magnetic Resonance Imaging 27(8):1046–1057, 2009.
Abstract Although it has been shown that the phase of the MR signal from the brain is particularly prone to variation due to respiration, the overall physiological information contained in phase time series is not well understood. Here, we explore the different physiological processes contributing to the phase time series noise, identify their spatiotemporal characteristics and examine their relationship to BOLD-related and non-BOLD-related physiological noise in the magnitude time series. This was performed by manipulating the contribution of physiological noise to the total signal variance by modulating the TE and voxel volume, and using a short TR in order to adequately sample physiological signal fluctuations. The phase and magnitude signals were compared both before and after removal of signal fluctuations at the primary respiratory and cardiac frequencies with RETROICOR. We found that the temporal phase noise increased with TE at a faster rate than predicted by 1/TSNR as a result of physiological noise. As suggested by previous studies, the primary contributor to phase physiological noise was respiration-related effects which were manifested at a large scale (>1 cm). Notably, RETROICOR removed respiration-related large-scale artifacts and this resulted in considerable improvements in the temporal phase stability (7?90%). Physiological noise in the magnitude time series after RETROICOR consisted of low-frequency BOLD-related fluctuations (<0.13 Hz) localized to gray matter and the vasculature, and fluctuations in the vasculature correlated with slow (<0.1 Hz) variations in respiration volume and cardiac rhythm. Physiological noise in the phase signal after RETROICOR also occurred in frequencies below 0.13 Hz and was consistent with (1) residual large-scale magneto-mechanical effects correlated with slow variations in respiration volume and cardiac rhythm over time, and (2) local scale (<1 cm) effects localized in gray matter and vasculature most likely due to vascular dephasing mediated by a BOLD susceptibility change. While BOLD-related magnitude noise exhibited a TE dependence similar to BOLD, the ?BOLD-related? noise in the phase data increased with increasing TE and thus caused the overall phase noise to increase at a faster rate with TE than predicted by 1/TSNR. Interestingly, the spatial specificity of this effect was more evident for the higher resolution phase data, as opposed to the magnitude data, suggesting that at a higher spatial resolution the phase signal may contain more information on physiological processes than the magnitude signal. Although it has been shown that the phase of the MR signal from the brain is particularly prone to variation due to respiration, the overall physiological information contained in phase time series is not well understood. Here, we explore the different physiological processes contributing to the phase time series noise, identify their spatiotemporal characteristics and examine their relationship to BOLD-related and non-BOLD-related physiological noise in the magnitude time series. This was performed by manipulating the contribution of physiological noise to the total signal variance by modulating the TE and voxel volume, and using a short TR in order to adequately sample physiological signal fluctuations. The phase and magnitude signals were compared both before and after removal of signal fluctuations at the primary respiratory and cardiac frequencies with RETROICOR. We found that the temporal phase noise increased with TE at a faster rate than predicted by 1/TSNR as a result of physiological noise. As suggested by previous studies, the primary contributor to phase physiological noise was respiration-related effects which were manifested at a large scale (>1 cm). Notably, RETROICOR removed respiration-related large-scale artifacts and this resulted in considerable improvements in the temporal phase stability (7?90%). Physiological noise in the magnitude time series after RETROICOR consisted of low-frequency BOLD-related fluctuations (<0.13 Hz) localized to gray matter and the vasculature, and fluctuations in the vasculature correlated with slow (<0.1 Hz) variations in respiration volume and cardiac rhythm. Physiological noise in the phase signal after RETROICOR also occurred in frequencies below 0.13 Hz and was consistent with (1) residual large-scale magneto-mechanical effects correlated with slow variations in respiration volume and cardiac rhythm over time, and (2) local scale (<1 cm) effects localized in gray matter and vasculature most likely due to vascular dephasing mediated by a BOLD susceptibility change. While BOLD-related magnitude noise exhibited a TE dependence similar to BOLD, the ?BOLD-related? noise in the phase data increased with increasing TE and thus caused the overall phase noise to increase at a faster rate with TE than predicted by 1/TSNR. Interestingly, the spatial specificity of this effect was more evident for the higher resolution phase data, as opposed to the magnitude data, suggesting that at a higher spatial resolution the phase signal may contain more information on physiological processes than the magnitude signal.
URL, DOI

Christopher I Petkov, Christoph Kayser, Mark Augath and Nikos K Logothetis.
Optimizing the imaging of the monkey auditory cortex: sparse vs. continuous fMRI. Magnetic Resonance Imaging 27(8):1065–1073, 2009.
Abstract The noninvasive imaging of the monkey auditory system with functional magnetic resonance imaging (fMRI) can bridge the gap between electrophysiological studies in monkeys and imaging studies in humans. Some of the recent imaging of monkey auditory cortical and subcortical structures relies on a technique of ?sparse imaging,? which was developed in human studies to sidestep the negative influence of scanner noise by adding periods of silence in between volume acquisition. Among the various aspects that have gone into the ongoing optimization of fMRI of the monkey auditory cortex, replacing the more common continuous-imaging paradigm with sparse imaging seemed to us to make the most obvious difference in the amount of activity that we could reliably obtain from awake or anesthetized animals. Here, we directly compare the sparse- and continuous-imaging paradigms in anesthetized animals. We document a strikingly greater auditory response with sparse imaging, both quantitatively and qualitatively, which includes a more expansive and robust tonotopic organization. There were instances where continuous imaging could better reveal organizational properties that sparse imaging missed, such as aspects of the hierarchical organization of auditory cortex. We consider the choice of imaging paradigm as a key component in optimizing the fMRI of the monkey auditory cortex. The noninvasive imaging of the monkey auditory system with functional magnetic resonance imaging (fMRI) can bridge the gap between electrophysiological studies in monkeys and imaging studies in humans. Some of the recent imaging of monkey auditory cortical and subcortical structures relies on a technique of ?sparse imaging,? which was developed in human studies to sidestep the negative influence of scanner noise by adding periods of silence in between volume acquisition. Among the various aspects that have gone into the ongoing optimization of fMRI of the monkey auditory cortex, replacing the more common continuous-imaging paradigm with sparse imaging seemed to us to make the most obvious difference in the amount of activity that we could reliably obtain from awake or anesthetized animals. Here, we directly compare the sparse- and continuous-imaging paradigms in anesthetized animals. We document a strikingly greater auditory response with sparse imaging, both quantitatively and qualitatively, which includes a more expansive and robust tonotopic organization. There were instances where continuous imaging could better reveal organizational properties that sparse imaging missed, such as aspects of the hierarchical organization of auditory cortex. We consider the choice of imaging paradigm as a key component in optimizing the fMRI of the monkey auditory cortex.
URL, DOI

Giancarlo Valente, Federico De Martino, Giuseppe Filosa, Marco Balsi and Elia Formisano.
Optimizing ICA in fMRI using information on spatial regularities of the sources. Magnetic Resonance Imaging 27(8):1110–1119, 2009.
Abstract Spatial independent component analysis (ICA) is a well-established technique for multivariate analysis of functional magnetic resonance imaging (fMRI) data. It blindly extracts spatiotemporal patterns of neural activity from functional measurements by seeking for sources that are maximally independent. Additional information on one or more sources (e.g., spatial regularity) is often available; however, it is not considered while looking for independent components. In the present work, we propose a new ICA algorithm based on the optimization of an objective function that accounts for both independence and other information on the sources or on the mixing model in a very general fashion. In particular, we apply this approach to fMRI data analysis and illustrate, by means of simulations, how inclusion of a spatial regularity term helps to recover the sources more effectively than with conventional ICA. The improvement is especially evident in high noise situations. Furthermore we employ the same approach on data sets from a complex mental imagery experiment, showing that consistency and physiological plausibility of relatively weak components are improved. Spatial independent component analysis (ICA) is a well-established technique for multivariate analysis of functional magnetic resonance imaging (fMRI) data. It blindly extracts spatiotemporal patterns of neural activity from functional measurements by seeking for sources that are maximally independent. Additional information on one or more sources (e.g., spatial regularity) is often available; however, it is not considered while looking for independent components. In the present work, we propose a new ICA algorithm based on the optimization of an objective function that accounts for both independence and other information on the sources or on the mixing model in a very general fashion. In particular, we apply this approach to fMRI data analysis and illustrate, by means of simulations, how inclusion of a spatial regularity term helps to recover the sources more effectively than with conventional ICA. The improvement is especially evident in high noise situations. Furthermore we employ the same approach on data sets from a complex mental imagery experiment, showing that consistency and physiological plausibility of relatively weak components are improved.
URL, DOI

Mustafa Çavuşoğlu, Josef Pfeuffer, Kâmil Uğurbil and Kâmil Uludağ.
Comparison of pulsed arterial spin labeling encoding schemes and absolute perfusion quantification. Magnetic Resonance Imaging 27(8):1039–1045, 2009.
Abstract Arterial spin labeling (ASL) using magnetic resonance imaging (MRI) is a powerful noninvasive technique to investigate the physiological status of brain tissue by measuring cerebral blood flow (CBF). ASL assesses the inflow of magnetically labeled arterial blood into an imaging voxel. In the last 2 decades, various ASL sequences have been proposed which differ in their ease of implementation and their sensitivity to artifacts. In addition, several quantification methods have been developed to determine the absolute value of CBF from ASL magnetization difference images. In this study, we evaluated three pulsed ASL sequences and three absolute quantification schemes. It was found that FAIR-QUIPSSII implementation of ASL yields 10?20% higher signal-to-noise ratio (SNR) and 18% higher CBF as compared with PICORE-Q2TIPS (with FOCI pulses) and PICORE-QUIPSSII (with BASSI pulses). In addition, quantification schemes employed can give rise to up to a 35% difference in CBF values. We conclude that, although all quantitative ASL sequences and CBF calibration methods should in principle result in the similar CBF values and image quality, substantial differences in CBF values and SNR were found. Thus, comparing studies using different ASL sequences and analysis algorithms is likely to result in erroneous intra- and intergroup differences. Therefore, (i) the same quantification schemes should consistently be used, and (ii) quantification using local tissue proton density should yield the most accurate CBF values because, although still requiring definitive demonstration in future studies, the proton density of blood is assumed to be very similar to the value of gray matter. Arterial spin labeling (ASL) using magnetic resonance imaging (MRI) is a powerful noninvasive technique to investigate the physiological status of brain tissue by measuring cerebral blood flow (CBF). ASL assesses the inflow of magnetically labeled arterial blood into an imaging voxel. In the last 2 decades, various ASL sequences have been proposed which differ in their ease of implementation and their sensitivity to artifacts. In addition, several quantification methods have been developed to determine the absolute value of CBF from ASL magnetization difference images. In this study, we evaluated three pulsed ASL sequences and three absolute quantification schemes. It was found that FAIR-QUIPSSII implementation of ASL yields 10?20% higher signal-to-noise ratio (SNR) and 18% higher CBF as compared with PICORE-Q2TIPS (with FOCI pulses) and PICORE-QUIPSSII (with BASSI pulses). In addition, quantification schemes employed can give rise to up to a 35% difference in CBF values. We conclude that, although all quantitative ASL sequences and CBF calibration methods should in principle result in the similar CBF values and image quality, substantial differences in CBF values and SNR were found. Thus, comparing studies using different ASL sequences and analysis algorithms is likely to result in erroneous intra- and intergroup differences. Therefore, (i) the same quantification schemes should consistently be used, and (ii) quantification using local tissue proton density should yield the most accurate CBF values because, although still requiring definitive demonstration in future studies, the proton density of blood is assumed to be very similar to the value of gray matter.
URL, DOI

Antonino Mario Cassará, Bruno Maraviglia, Stefan Hartwig, Lutz Trahms and Martin Burghoff.
Neuronal current detection with low-field magnetic resonance: simulations and methods. Magnetic Resonance Imaging 27(8):1131–1139, 2009.
Abstract The noninvasive detection of neuronal currents in active brain networks [or direct neuronal imaging (DNI)] by means of nuclear magnetic resonance (NMR) remains a scientific challenge. Many different attempts using NMR scanners with magnetic fields >1 T (high-field NMR) have been made in the past years to detect phase shifts or magnitude changes in the NMR signals. However, the many physiological (i.e., the contemporarily BOLD effect, the weakness of the neuronal-induced magnetic field, etc.) and technical limitations (e.g., the spatial resolution) in observing the weak signals have led to some contradicting results. In contrast, only a few attempts have been made using low-field NMR techniques. As such, this paper was aimed at reviewing two recent developments in this front. The detection schemes discussed in this manuscript, the resonant mechanism (RM) and the DC method, are specific to NMR instrumentations with main fields below the earth magnetic field (50 ?T), while some even below a few microteslas (ULF-NMR). However, the experimental validation for both techniques, with differentiating sensitivity to the various neuronal activities at specific temporal and spatial resolutions, is still in progress and requires carefully designed magnetic field sensor technology. Additional care should be taken to ensure a stringent magnetic shield from the ambient magnetic field fluctuations. In this review, we discuss the characteristics and prospect of these two methods in detecting neuronal currents, along with the technical requirements on the instrumentation. The noninvasive detection of neuronal currents in active brain networks [or direct neuronal imaging (DNI)] by means of nuclear magnetic resonance (NMR) remains a scientific challenge. Many different attempts using NMR scanners with magnetic fields >1 T (high-field NMR) have been made in the past years to detect phase shifts or magnitude changes in the NMR signals. However, the many physiological (i.e., the contemporarily BOLD effect, the weakness of the neuronal-induced magnetic field, etc.) and technical limitations (e.g., the spatial resolution) in observing the weak signals have led to some contradicting results. In contrast, only a few attempts have been made using low-field NMR techniques. As such, this paper was aimed at reviewing two recent developments in this front. The detection schemes discussed in this manuscript, the resonant mechanism (RM) and the DC method, are specific to NMR instrumentations with main fields below the earth magnetic field (50 ?T), while some even below a few microteslas (ULF-NMR). However, the experimental validation for both techniques, with differentiating sensitivity to the various neuronal activities at specific temporal and spatial resolutions, is still in progress and requires carefully designed magnetic field sensor technology. Additional care should be taken to ensure a stringent magnetic shield from the ambient magnetic field fluctuations. In this review, we discuss the characteristics and prospect of these two methods in detecting neuronal currents, along with the technical requirements on the instrumentation.
URL, DOI

Federico Giove, Tommaso Gili, Vittorio Iacovella, Emiliano Macaluso and Bruno Maraviglia.
Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic Resonance Imaging 27(8):1058–1064, 2009.
Abstract Correlated fluctuations of low-frequency fMRI signal have been suggested to reflect functional connectivity among the involved regions. However, large-scale correlations are especially prone to spurious global modulations induced by coherent physiological noise. Cardiac and respiratory rhythms are the most offending component, and a tailored preprocessing is needed in order to reduce their impact. Several approaches have been proposed in the literature, generally based on the use of physiological recordings acquired during the functional scans, or on the extraction of the relevant information directly from the images. In this paper, the performances of the denoising approach based on general linear fitting of global signals of noninterest extracted from the functional scans were assessed. Results suggested that this approach is sufficiently accurate for the preprocessing of functional connectivity data. Correlated fluctuations of low-frequency fMRI signal have been suggested to reflect functional connectivity among the involved regions. However, large-scale correlations are especially prone to spurious global modulations induced by coherent physiological noise. Cardiac and respiratory rhythms are the most offending component, and a tailored preprocessing is needed in order to reduce their impact. Several approaches have been proposed in the literature, generally based on the use of physiological recordings acquired during the functional scans, or on the extraction of the relevant information directly from the images. In this paper, the performances of the denoising approach based on general linear fitting of global signals of noninterest extracted from the functional scans were assessed. Results suggested that this approach is sufficiently accurate for the preprocessing of functional connectivity data.
URL, DOI

Francesca Benuzzi, Fausta Lui, Davide Duzzi, Paolo F Nichelli and Carlo Adolfo Porro.
Brain networks responsive to aversive visual stimuli in humans. Magnetic Resonance Imaging 27(8):1088–1095, 2009.
Abstract The neural mechanisms subserving recognition of noxious stimuli and empathy for pain appear to involve at least in part the cortical regions associated with the processing of pain affect. An important issue concerns the specificity of brain networks associated with observing and representing painful conditions, in comparison with other unpleasant stimuli. Recently, we found both similarities and differences between the brain patterns of activity related to the observation of noxious or disgusting stimuli delivered to one hand or foot. Overlap regions included the perigenual anterior cingulate (pACC), whose activity was related to the perceived unpleasantness. We aimed here at revealing how pACC functional connectivity changes in relationship to the different experimental conditions, using a psychophysiological interaction model. Activity in pACC during the observation of painful stimuli was specifically and positively related to regions in the right hemisphere, including portions of the prefrontal, midcingulate and insular cortex. On the other hand, positive changes in pACC connectivity during the vision of disgusting stimuli were present in the right basal ganglia. These data suggest that pACC activity is part of different networks involved in the recognition of painful or disgusting stimuli. The neural mechanisms subserving recognition of noxious stimuli and empathy for pain appear to involve at least in part the cortical regions associated with the processing of pain affect. An important issue concerns the specificity of brain networks associated with observing and representing painful conditions, in comparison with other unpleasant stimuli. Recently, we found both similarities and differences between the brain patterns of activity related to the observation of noxious or disgusting stimuli delivered to one hand or foot. Overlap regions included the perigenual anterior cingulate (pACC), whose activity was related to the perceived unpleasantness. We aimed here at revealing how pACC functional connectivity changes in relationship to the different experimental conditions, using a psychophysiological interaction model. Activity in pACC during the observation of painful stimuli was specifically and positively related to regions in the right hemisphere, including portions of the prefrontal, midcingulate and insular cortex. On the other hand, positive changes in pACC connectivity during the vision of disgusting stimuli were present in the right basal ganglia. These data suggest that pACC activity is part of different networks involved in the recognition of painful or disgusting stimuli.
URL, DOI