There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with hi... more There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with high temporal resolution opens up new avenues to study the in vivo functioning of the human brain with functional magnetic resonance imaging. Because the speed of sampling and the signal level are intrinsically connected in magnetic resonance imaging via the T1 relaxation time, optimization efforts usually must make a trade-off to increase the temporal sampling rate at the cost of the signal level. We present a method, which combines a sparse event-related stimulus paradigm with subsequent data reshuffling to achieve high temporal resolution while maintaining high signal levels (HiHi). The proof-of-principle is presented by separately measuring the single-voxel time course of the BOLD response in both the primary visual and primary motor cortices with 100-ms temporal resolution.
Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devic... more Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higherorder brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online 'winner-takes-all approach' with quadrantspecific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, 'cognitive BCIs' access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury.
This article is published in The Journal of Neuroscience, Rapid Communications Section, which pub... more This article is published in The Journal of Neuroscience, Rapid Communications Section, which publishes brief, peer-reviewed papers online, not in print. Rapid Communications are posted online approximately one month earlier than they would appear if printed. They are listed in the Table of Contents of the next open issue of JNeurosci. Cite this article as: JNeurosci, 2000, 20:RC93 (1-6). The publication date is the date of posting online at www.jneurosci.org.
h i g h l i g h t s • A Toolbox to integrate preprocessing of physiological data and fMRI noise m... more h i g h l i g h t s • A Toolbox to integrate preprocessing of physiological data and fMRI noise modeling. • Robust preprocessing via iterative peak detection, shown for noisy data and patients. • Flexible support of peripheral data formats and noise models (RETROICOR, RVHRCOR). • Fully automated noise correction and performance assessment for group studies. • Integration in fMRI pre-processing pipelines as SPM Toolbox (Batch Editor GUI).
Neuroimaging holds the promise that it may one day aid the clinical assessment of individual psyc... more Neuroimaging holds the promise that it may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups, which do not permit accurate inferences at the level of the individual. We examined the potential of structural Magnetic Resonance Imaging (MRI) data for making accurate quantitative predictions about symptom progression in individuals at ultra-high risk for developing psychosis. Forty people at ultra-high risk for psychosis were scanned using structural MRI at first clinical presentation and assessed over a period of 2 years using the Positive and Negative Syndrome Scale. Using a multivariate machine learning method known as relevance vector regression (RVR), we examined the relationship between brain structure at first clinical presentation, characterized in terms of gray matter (GM) volume and cortical thickness (CT), and symptom progression at 2-year follow-up. The application of RVR to whole-brain CT MRI data allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation = 0.34, p = 0.026; Mean Squared-Error = 249.63, p = 0.024). This prediction was informed by regions traditionally associated with schizophrenia, namely the right lateral and medial temporal cortex and the left insular cortex. In contrast, the application of RVR to GM volume did not allow prediction of symptom progression with statistically significant accuracy.These results provide proof-of-concept that it could be possible to use structural MRI to inform quantitative prediction of symptom progression in individuals at ultra-high risk of developing psychosis. This would enable clinicians to target those individuals at greatest need of preventative interventions thereby resulting in a more efficient use of health care resources.
tual awareness (Figure 1). We generated perceptual hysteresis by slowly increasing the contrast o... more tual awareness (Figure 1). We generated perceptual hysteresis by slowly increasing the contrast of an initially hidden visual stimulus until the percept "popped out" and then reducing it again until the percept "dropped out." Hysteresis refers to the fact that during contrast
Aging is ubiquitous to the human condition. The MRI correlates of healthy aging have been extensi... more Aging is ubiquitous to the human condition. The MRI correlates of healthy aging have been extensively investigated using a range of modalities, including volumetric MRI, quantitative MRI (qMRI), and diffusion tensor imaging. Despite this, the reported brainstem related changes remain sparse. This is, in part, due to the technical and methodological limitations in quantitatively assessing and statistically analyzing this region. By utilizing a new method of brainstem segmentation, a large cohort of 100 healthy adults were assessed in this study for the effects of aging within the human brainstem in vivo. Using qMRI, tensor-based morphometry (TBM), and voxel-based quantification (VBQ), the volumetric and quantitative changes across healthy adults between 19 and 75 years were characterized. In addition to the increased R2* in substantia nigra corresponding to increasing iron deposition with age, several novel findings were reported in the current study. These include selective volumetr...
Perception depends on the interplay of ongoing spontaneous activity and stimulus-evoked activity ... more Perception depends on the interplay of ongoing spontaneous activity and stimulus-evoked activity in sensory cortices. This raises the possibility that training ongoing spontaneous activity alone might be sufficient for enhancing perceptual sensitivity. To test this, we trained human participants to control ongoing spontaneous activity in circumscribed regions of retinotopic visual cortex using real-time functional MRI-based neurofeedback. After training, we tested participants using a new and previously untrained visual detection task that was presented at the visual field location corresponding to the trained region of visual cortex. Perceptual sensitivity was significantly enhanced only when participants who had previously learned control over ongoing activity were now exercising control and only for that region of visual cortex. Our new approach allows us to non-invasively and non-pharmacologically manipulate regionally specific brain activity and thus provide "brain training" to deliver particular perceptual enhancements.
The sensitivity of diffusion tensor imaging (DTI) for detecting microstructural white matter alte... more The sensitivity of diffusion tensor imaging (DTI) for detecting microstructural white matter alterations has motivated the application of voxel-based statistics (VBS) to fractional anisotropy (FA) images (FA-VBS). However, detected group differences may depend on the spatial registration method used. The objective of this study was to investigate the influence of spatial registration on detecting cerebral asymmetries in FA-VBS analyses with reference to data obtained using Tract-Based Spatial Statistics (TBSS). In the first part of this study we performed FA-VBS analyses using three single-contrast and one multi-contrast registration: (i) whole-brain registration based on T2 contrast, (ii) whole-brain registration based on FA contrast, (iii) individual-hemisphere registration based on FA contrast, and (iv) a combination of (i) and (iii). We then compared the FA-VBS results with those obtained from TBSS. We found that the FA-VBS results depended strongly on the employed registration approach, with the best correspondence between FA-VBS and TBSS results when approach (iv), the ''multi-contrast individual-hemisphere'' method was employed. In the second part of the study, we investigated the spatial distribution of residual misregistration for each registration approach and the effect on FA-VBS results. For the FA-VBS analyses using the three single-contrast registration methods, we identified FA asymmetries that were (a) located in regions prone to misregistrations, (b) not detected by TBSS, and (c) specific to the applied registration approach. These asymmetries were considered candidates for apparent FA asymmetries due to systematic misregistrations associated with the FA-VBS approach. Finally, we demonstrated that the ''multi-contrast individual-hemisphere'' approach showed the least residual spatial misregistrations and thus might be most appropriate for cerebral FA-VBS analyses.
In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time ... more In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time series as a function of image SNR (SNR 0). This model has been used to study physiological noise in fMRI, to optimize fMRI acquisition parameters, and to estimate maximum attainable tSNR for a given set of MR image acquisition and processing parameters. In its current form, this noise model requires the accurate estimation of image SNR. For multi-channel receiver coils, this is not straightforward because it requires export and reconstruction of large amounts of k-space raw data and detailed, custommade image reconstruction methods. Here we present a simple extension to the model that allows characterization of the temporal noise properties of EPI time series acquired with multi-channel receiver coils, and reconstructed with standard root-sum-of-squares combination, without the need for raw data or custom-made image reconstruction. The proposed extended model includes an additional parameter k which reflects the impact of noise correlations between receiver channels on the data and scales an apparent image SNR (SNR9 0) measured directly from root-sum-of-squares reconstructed magnitude images so that k = SNR9 0 /SNR 0 (under the condition of SNR 0 .50 and number of channels #32). Using Monte Carlo simulations we show that the extended model parameters can be estimated with high accuracy. The estimation of the parameter k was validated using an independent measure of the actual SNR 0 for non-accelerated phantom data acquired at 3T with a 32-channel receiver coil. We also demonstrate that compared to the original model the extended model results in an improved fit to human task-free non-accelerated fMRI data acquired at 7T with a 24-channel receiver coil. In particular, the extended model improves the prediction of low to medium tSNR values and so can play an important role in the optimization of high-resolution fMRI experiments at lower SNR levels.
Background: Traumatic spinal cord injury (SCI) leads to disruption of axons and macroscopic tissu... more Background: Traumatic spinal cord injury (SCI) leads to disruption of axons and macroscopic tissue loss. Using diffusion tensor imaging (DTI), we assessed degeneration of the corticospinal tract (CST) in the cervical cord above a traumatic lesion and explored its relationship with cervical atrophy, remote axonal changes within the cranial CST and upper limb function. Methods: Nine cervical injured volunteers with bilateral motor and sensory impairment and ten controls were studied. DTI of the cervical cord and brain provided measurements of fractional anisotropy (FA), while anatomical MRI assessed crosssectional spinal cord area (i.e. cord atrophy). Spinal and central regions of interest (ROI) included the bilateral CST in the cervical cord and brain. Regression analysis identified correlations between spinal FA and cranial FA in the CST and disability. Results: In individuals with SCI, FA was significantly lower in both CSTs throughout the cervical cord and brain when compared with controls (p#0.05). Reduced FA of the cervical cord in patients with SCI was associated with smaller cord area (p = 0.002) and a lower FA of the cranial CST at the internal capsule level (p = 0.001). Lower FA in the cervical CST also correlated with impaired upper limb function, independent of cord area (p = 0.03). Conclusion: Axonal degeneration of the CST in the atrophic cervical cord, proximal to the site of injury, parallels cranial CST degeneration and is associated with disability. This DTI protocol can be used in longitudinal assessment of microstructural changes immediately following injury and may be utilised to predict progression and monitor interventions aimed at promoting spinal cord repair.
Quantitative mapping of the longitudinal relaxation rate (R1 = 1/T1) in the human brain enables t... more Quantitative mapping of the longitudinal relaxation rate (R1 = 1/T1) in the human brain enables the investigation of tissue microstructure and macroscopic morphology which are becoming increasingly important for clinical and neuroimaging applications. R1 maps are now commonly estimated from two fast high-resolution 3D FLASH acquisitions with variable excitation flip angles, because this approach is fast and does not rely on special acquisition techniques. However, these R1 maps need to be corrected for bias due to RF transmit field (B1 +) inhomogeneities, requiring additional B1 + mapping which is usually time consuming and difficult to implement. We propose a technique that simultaneously estimates the B1 + inhomogeneities and R1 values from the uncorrected R1 maps in the human brain without need for B1 + mapping. It employs a probabilistic framework for unified segmentation based correction of R1 maps for B1 + inhomogeneities (UNICORT). The framework incorporates a physically informed generative model of smooth B1 + inhomogeneities and their multiplicative effect on R1 estimates. Extensive cross-validation with the established standard using measured B1 + maps shows that UNICORT yields accurate B1 + and R1 maps with a mean deviation from the standard of less than 4.3% and 5%, respectively. The results of different groups of subjects with a wide age range and different levels of atypical brain anatomy further suggest that the method is robust and generalizes well to wider populations. UNICORT is easy to apply, as it is computationally efficient and its basic framework is implemented as part of the tissue segmentation in SPM8.
T1-weighted anatomical brain scans are routinely used in neuroimaging studies, for example, as an... more T1-weighted anatomical brain scans are routinely used in neuroimaging studies, for example, as anatomical reference for functional data and in brain morphometry studies. Subject motion can degrade the quality of these images. An additional problem is the occurrence of signal dropouts in the case of long echo times and low receiver bandwidths. These problems are addressed in two different studies. In the first study, it is shown that the high scalp signal, which results from the low T1 value of fat, may cause a typical ringing artefact in the presence of head motion. This problem may be enhanced if phased array coils are used for signal reception due to their increased sensitivity in the peripheral head regions. It is shown that this artefact can be avoided by combining certain fat suppression techniques that reduce the scalp signal. In the second study, it is shown that signal dropout affects mainly the orbitofrontal cortex and the temporal lobes, and that a bandwidth of 100 Hz/pixel should be chosen for the investigation of these areas to avoid signal losses while maintaining an acceptable signal-to-noise ratio. Experimental results are based on the MDEFT sequence but can be applied to other T1-weighted sequences like FLASH and MP-RAGE. Furthermore, the presented methods for improving the image quality can be combined with other artefact reduction techniques.
Most functional magnetic resonance imaging (fMRI) studies record the blood oxygen level-dependent... more Most functional magnetic resonance imaging (fMRI) studies record the blood oxygen level-dependent (BOLD) signal using fast gradient-echo echo-planar imaging (GE EPI). However, GE EPI can suffer from substantial signal dropout caused by inhomogeneities in the static magnetic field. These field inhomogeneities occur near air/tissue interfaces, because they are generated by variations in magnetic susceptibilities. Thus, fMRI studies are often limited by a reduced BOLD sensitivity (BS) in inferior brain regions. Recently, a method has been developed which allows for optimizing the BS in dropout regions by specifically adjusting the slice tilt, the direction of the phase-encoding (PE), and the z-shim moment. However, optimal imaging parameters were only reported for the orbitofrontal cortex (OFC) and inferior temporal lobes. The present study determines the optimal slice tilt, PE direction, and z-shim moment at 3 Tand 1.5 T, otherwise using standard fMRI acquisition parameters. Results are reported for all brain regions, yielding a whole-brain atlas of optimal parameters. At both field strengths, optimal parameters increase the BS by more than 60% in many voxels in the OFC and by at least 30% in the other dropout regions. BS gains are shown to be more widespread at 3 T, suggesting an increased benefit from the dropout compensation at higher fields. Even the mean BS of a large brain region, e.g., encompassing the medial OFC, can be increased by more than 15%. The maps of optimal parameters allow for assessing the feasibility and improving fMRI of brain regions affected by susceptibility-induced BS losses.
Normal ageing is associated with characteristic changes in brain microstructure. Although in vivo... more Normal ageing is associated with characteristic changes in brain microstructure. Although in vivo neuroimaging captures spatial and temporal patterns of age-related changes of anatomy at the macroscopic scale, our knowledge of the underlying (patho)physiological processes at cellular and molecular levels is still limited. The aim of this study is to explore brain tissue properties in normal ageing using quantitative magnetic resonance imaging (MRI) alongside conventional morphological assessment. Using a whole-brain approach in a cohort of 26 adults, aged 18-85 years, we performed voxel-based morphometric (VBM) analysis and voxel-based quantification (VBQ) of diffusion tensor, magnetization transfer (MT), R1, and R2* relaxation parameters. We found age-related reductions in cortical and subcortical grey matter volume paralleled by changes in fractional anisotropy (FA), mean diffusivity (MD), MT and R2*. The latter were regionally specific depending on their differential sensitivity to microscopic tissue properties. VBQ of white matter revealed distinct anatomical patterns of age-related change in microstructure. Widespread and profound reduction in MT contrasted with local FA decreases paralleled by MD increases. R1 reductions and R2* increases were observed to a smaller extent in overlapping occipito-parietal white matter regions. We interpret our findings, based on current biophysical models, as a fingerprint of age-dependent brain atrophy and underlying microstructural changes in myelin, iron deposits and water. The VBQ approach we present allows for systematic unbiased exploration of the interaction between imaging parameters and extends current methods for detection of neurodegenerative processes in the brain. The demonstrated parameter-specific distribution patterns offer insights into age-related brain structure changes in vivo and provide essential baseline data for studying disease against a background of healthy ageing.
... Maria Luisa Gorno-Tempini , Chloe Hutton 1 , Oliver Josephs , Ralf Deichmann , Cathy Price an... more ... Maria Luisa Gorno-Tempini , Chloe Hutton 1 , Oliver Josephs , Ralf Deichmann , Cathy Price and Robert Turner. ... References. 1. PA Bandettini, EC Wong, A. Jesmanowicz, RS Hinks and JS Hyde, Spin-echo and gradient-echo EPI of human brain activation using BOLD contrast: A ...
Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal ... more Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal sensitivity to small BOLD signal changes. To increase the sensitivity, higher field strengths are often employed, since they provide an increased image signal-to-noise ratio (SNR). However, as image SNR increases, the relative contribution of physiological noise to the total time series noise will be greater compared to that from thermal noise. At 7 T, we studied how the physiological noise contribution can be best reduced for EPI time series acquired at three different spatial resolutions (1.1 mm × 1.1 mm × 1.8 mm, 2 mm × 2 mm × 2 mm and 3 mm × 3 mm × 3 mm). Applying optimal physiological noise correction methods improved temporal SNR (tSNR) and increased the numbers of significantly activated voxels in fMRI visual activation studies for all sets of acquisition parameters. The most dramatic results were achieved for the lowest spatial resolution, an acquisition parameter combination commonly used in cognitive neuroimaging which requires high functional sensitivity and temporal resolution (i.e. 3 mm isotropic resolution and whole brain image repetition time of 2 s). For this data, physiological noise models based on cardio-respiratory information improved tSNR by approximately 25% in the visual cortex and 35% sub-cortically. When the time series were additionally corrected for the residual effects of head motion after retrospective realignment, the tSNR was increased by around 58% in the visual cortex and 71% sub-cortically, exceeding tSNR~140. In conclusion, optimal physiological noise correction at 7 T increases tSNR significantly, resulting in the highest tSNR per unit time published so far. This tSNR improvement translates into a significant increase in BOLD sensitivity, facilitating the study of even subtle BOLD responses.
In functional MRI, magnetic field inhomogeneities due to air-tissue susceptibility differences ma... more In functional MRI, magnetic field inhomogeneities due to air-tissue susceptibility differences may lead to severe signal dropouts and geometric distortions in echo-planar images. Therefore, the inhomogeneities in the field are routinely minimized by shimming prior to imaging. However in fMRI, the Blood Oxygen Level Dependent (BOLD) effect is the measure of interest, so the BOLD sensitivity (BS) should be optimized rather than the magnetic field homogeneity. The analytical expression for an estimate of the BOLD sensitivity has been recently developed, allowing for the computation of BOLD sensitivity maps from echo-planar images and field maps. This report describes a novel shimming procedure that optimizes the local BOLD sensitivity over a region of interest. The method is applied in vivo and compared to a standard global shimming procedure. A breath-holding experiment was carried out and demonstrated that the BS-based shimming significantly improved the detection of activation in a target region of interest, the medial orbitofrontal cortex.
Diffusion tensor imaging is widely used in research and clinical applications, but this modality ... more Diffusion tensor imaging is widely used in research and clinical applications, but this modality is highly sensitive to artefacts. We developed an easy-to-implement extension of the original diffusion tensor model to account for physiological noise in diffusion tensor imaging using measures of peripheral physiology (pulse and respiration), the so-called extended tensor model. Within the framework of the extended tensor model two types of regressors, which respectively modeled small (linear) and strong (nonlinear) variations in the diffusion signal, were derived from peripheral measures. We tested the performance of four extended tensor models with different physiological noise regressors on nongated and gated diffusion tensor imaging data, and compared it to an established data-driven robust fitting method. In the brainstem and cerebellum the extended tensor models reduced the noise in the tensor-fit by up to 23% in accordance with previous studies on physiological noise. The extended tensor model addresses both large-amplitude outliers and small-amplitude signal-changes. The framework of the extended tensor model also facilitates further investigation into physiological noise in diffusion tensor imaging. The proposed extended tensor model can be readily combined with other artefact correction methods such as robust fitting and eddy current correction.
Diffusion tensor imaging is widely used in research and clinical applications, but still suffers ... more Diffusion tensor imaging is widely used in research and clinical applications, but still suffers from substantial artifacts. Here, we focus on vibrations induced by strong diffusion gradients in diffusion tensor imaging, causing an echo shift in k-space and consequential signal-loss. We refined the model of vibration-induced echo shifts, showing that asymmetric k-space coverage in widely used Partial Fourier acquisitions results in locally differing signal loss in images acquired with reversed phase encoding direction (blip-up/blip-down). We implemented a correction of vibration artifacts in diffusion tensor imaging using phase-encoding reversal (COVIPER) by combining blip-up and blip-down images, each weighted by a function of its local tensor-fit error. COVIPER was validated against low vibration reference data, resulting in an error reduction of about 72% in fractional anisotropy maps. COV-IPER can be combined with other corrections based on phase encoding reversal, providing a comprehensive correction for eddy currents, susceptibility-related distortions and vibration artifact reduction.
There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with hi... more There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with high temporal resolution opens up new avenues to study the in vivo functioning of the human brain with functional magnetic resonance imaging. Because the speed of sampling and the signal level are intrinsically connected in magnetic resonance imaging via the T1 relaxation time, optimization efforts usually must make a trade-off to increase the temporal sampling rate at the cost of the signal level. We present a method, which combines a sparse event-related stimulus paradigm with subsequent data reshuffling to achieve high temporal resolution while maintaining high signal levels (HiHi). The proof-of-principle is presented by separately measuring the single-voxel time course of the BOLD response in both the primary visual and primary motor cortices with 100-ms temporal resolution.
Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devic... more Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higherorder brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online 'winner-takes-all approach' with quadrantspecific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, 'cognitive BCIs' access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury.
This article is published in The Journal of Neuroscience, Rapid Communications Section, which pub... more This article is published in The Journal of Neuroscience, Rapid Communications Section, which publishes brief, peer-reviewed papers online, not in print. Rapid Communications are posted online approximately one month earlier than they would appear if printed. They are listed in the Table of Contents of the next open issue of JNeurosci. Cite this article as: JNeurosci, 2000, 20:RC93 (1-6). The publication date is the date of posting online at www.jneurosci.org.
h i g h l i g h t s • A Toolbox to integrate preprocessing of physiological data and fMRI noise m... more h i g h l i g h t s • A Toolbox to integrate preprocessing of physiological data and fMRI noise modeling. • Robust preprocessing via iterative peak detection, shown for noisy data and patients. • Flexible support of peripheral data formats and noise models (RETROICOR, RVHRCOR). • Fully automated noise correction and performance assessment for group studies. • Integration in fMRI pre-processing pipelines as SPM Toolbox (Batch Editor GUI).
Neuroimaging holds the promise that it may one day aid the clinical assessment of individual psyc... more Neuroimaging holds the promise that it may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups, which do not permit accurate inferences at the level of the individual. We examined the potential of structural Magnetic Resonance Imaging (MRI) data for making accurate quantitative predictions about symptom progression in individuals at ultra-high risk for developing psychosis. Forty people at ultra-high risk for psychosis were scanned using structural MRI at first clinical presentation and assessed over a period of 2 years using the Positive and Negative Syndrome Scale. Using a multivariate machine learning method known as relevance vector regression (RVR), we examined the relationship between brain structure at first clinical presentation, characterized in terms of gray matter (GM) volume and cortical thickness (CT), and symptom progression at 2-year follow-up. The application of RVR to whole-brain CT MRI data allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation = 0.34, p = 0.026; Mean Squared-Error = 249.63, p = 0.024). This prediction was informed by regions traditionally associated with schizophrenia, namely the right lateral and medial temporal cortex and the left insular cortex. In contrast, the application of RVR to GM volume did not allow prediction of symptom progression with statistically significant accuracy.These results provide proof-of-concept that it could be possible to use structural MRI to inform quantitative prediction of symptom progression in individuals at ultra-high risk of developing psychosis. This would enable clinicians to target those individuals at greatest need of preventative interventions thereby resulting in a more efficient use of health care resources.
tual awareness (Figure 1). We generated perceptual hysteresis by slowly increasing the contrast o... more tual awareness (Figure 1). We generated perceptual hysteresis by slowly increasing the contrast of an initially hidden visual stimulus until the percept "popped out" and then reducing it again until the percept "dropped out." Hysteresis refers to the fact that during contrast
Aging is ubiquitous to the human condition. The MRI correlates of healthy aging have been extensi... more Aging is ubiquitous to the human condition. The MRI correlates of healthy aging have been extensively investigated using a range of modalities, including volumetric MRI, quantitative MRI (qMRI), and diffusion tensor imaging. Despite this, the reported brainstem related changes remain sparse. This is, in part, due to the technical and methodological limitations in quantitatively assessing and statistically analyzing this region. By utilizing a new method of brainstem segmentation, a large cohort of 100 healthy adults were assessed in this study for the effects of aging within the human brainstem in vivo. Using qMRI, tensor-based morphometry (TBM), and voxel-based quantification (VBQ), the volumetric and quantitative changes across healthy adults between 19 and 75 years were characterized. In addition to the increased R2* in substantia nigra corresponding to increasing iron deposition with age, several novel findings were reported in the current study. These include selective volumetr...
Perception depends on the interplay of ongoing spontaneous activity and stimulus-evoked activity ... more Perception depends on the interplay of ongoing spontaneous activity and stimulus-evoked activity in sensory cortices. This raises the possibility that training ongoing spontaneous activity alone might be sufficient for enhancing perceptual sensitivity. To test this, we trained human participants to control ongoing spontaneous activity in circumscribed regions of retinotopic visual cortex using real-time functional MRI-based neurofeedback. After training, we tested participants using a new and previously untrained visual detection task that was presented at the visual field location corresponding to the trained region of visual cortex. Perceptual sensitivity was significantly enhanced only when participants who had previously learned control over ongoing activity were now exercising control and only for that region of visual cortex. Our new approach allows us to non-invasively and non-pharmacologically manipulate regionally specific brain activity and thus provide "brain training" to deliver particular perceptual enhancements.
The sensitivity of diffusion tensor imaging (DTI) for detecting microstructural white matter alte... more The sensitivity of diffusion tensor imaging (DTI) for detecting microstructural white matter alterations has motivated the application of voxel-based statistics (VBS) to fractional anisotropy (FA) images (FA-VBS). However, detected group differences may depend on the spatial registration method used. The objective of this study was to investigate the influence of spatial registration on detecting cerebral asymmetries in FA-VBS analyses with reference to data obtained using Tract-Based Spatial Statistics (TBSS). In the first part of this study we performed FA-VBS analyses using three single-contrast and one multi-contrast registration: (i) whole-brain registration based on T2 contrast, (ii) whole-brain registration based on FA contrast, (iii) individual-hemisphere registration based on FA contrast, and (iv) a combination of (i) and (iii). We then compared the FA-VBS results with those obtained from TBSS. We found that the FA-VBS results depended strongly on the employed registration approach, with the best correspondence between FA-VBS and TBSS results when approach (iv), the ''multi-contrast individual-hemisphere'' method was employed. In the second part of the study, we investigated the spatial distribution of residual misregistration for each registration approach and the effect on FA-VBS results. For the FA-VBS analyses using the three single-contrast registration methods, we identified FA asymmetries that were (a) located in regions prone to misregistrations, (b) not detected by TBSS, and (c) specific to the applied registration approach. These asymmetries were considered candidates for apparent FA asymmetries due to systematic misregistrations associated with the FA-VBS approach. Finally, we demonstrated that the ''multi-contrast individual-hemisphere'' approach showed the least residual spatial misregistrations and thus might be most appropriate for cerebral FA-VBS analyses.
In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time ... more In 2001, Krueger and Glover introduced a model describing the temporal SNR (tSNR) of an EPI time series as a function of image SNR (SNR 0). This model has been used to study physiological noise in fMRI, to optimize fMRI acquisition parameters, and to estimate maximum attainable tSNR for a given set of MR image acquisition and processing parameters. In its current form, this noise model requires the accurate estimation of image SNR. For multi-channel receiver coils, this is not straightforward because it requires export and reconstruction of large amounts of k-space raw data and detailed, custommade image reconstruction methods. Here we present a simple extension to the model that allows characterization of the temporal noise properties of EPI time series acquired with multi-channel receiver coils, and reconstructed with standard root-sum-of-squares combination, without the need for raw data or custom-made image reconstruction. The proposed extended model includes an additional parameter k which reflects the impact of noise correlations between receiver channels on the data and scales an apparent image SNR (SNR9 0) measured directly from root-sum-of-squares reconstructed magnitude images so that k = SNR9 0 /SNR 0 (under the condition of SNR 0 .50 and number of channels #32). Using Monte Carlo simulations we show that the extended model parameters can be estimated with high accuracy. The estimation of the parameter k was validated using an independent measure of the actual SNR 0 for non-accelerated phantom data acquired at 3T with a 32-channel receiver coil. We also demonstrate that compared to the original model the extended model results in an improved fit to human task-free non-accelerated fMRI data acquired at 7T with a 24-channel receiver coil. In particular, the extended model improves the prediction of low to medium tSNR values and so can play an important role in the optimization of high-resolution fMRI experiments at lower SNR levels.
Background: Traumatic spinal cord injury (SCI) leads to disruption of axons and macroscopic tissu... more Background: Traumatic spinal cord injury (SCI) leads to disruption of axons and macroscopic tissue loss. Using diffusion tensor imaging (DTI), we assessed degeneration of the corticospinal tract (CST) in the cervical cord above a traumatic lesion and explored its relationship with cervical atrophy, remote axonal changes within the cranial CST and upper limb function. Methods: Nine cervical injured volunteers with bilateral motor and sensory impairment and ten controls were studied. DTI of the cervical cord and brain provided measurements of fractional anisotropy (FA), while anatomical MRI assessed crosssectional spinal cord area (i.e. cord atrophy). Spinal and central regions of interest (ROI) included the bilateral CST in the cervical cord and brain. Regression analysis identified correlations between spinal FA and cranial FA in the CST and disability. Results: In individuals with SCI, FA was significantly lower in both CSTs throughout the cervical cord and brain when compared with controls (p#0.05). Reduced FA of the cervical cord in patients with SCI was associated with smaller cord area (p = 0.002) and a lower FA of the cranial CST at the internal capsule level (p = 0.001). Lower FA in the cervical CST also correlated with impaired upper limb function, independent of cord area (p = 0.03). Conclusion: Axonal degeneration of the CST in the atrophic cervical cord, proximal to the site of injury, parallels cranial CST degeneration and is associated with disability. This DTI protocol can be used in longitudinal assessment of microstructural changes immediately following injury and may be utilised to predict progression and monitor interventions aimed at promoting spinal cord repair.
Quantitative mapping of the longitudinal relaxation rate (R1 = 1/T1) in the human brain enables t... more Quantitative mapping of the longitudinal relaxation rate (R1 = 1/T1) in the human brain enables the investigation of tissue microstructure and macroscopic morphology which are becoming increasingly important for clinical and neuroimaging applications. R1 maps are now commonly estimated from two fast high-resolution 3D FLASH acquisitions with variable excitation flip angles, because this approach is fast and does not rely on special acquisition techniques. However, these R1 maps need to be corrected for bias due to RF transmit field (B1 +) inhomogeneities, requiring additional B1 + mapping which is usually time consuming and difficult to implement. We propose a technique that simultaneously estimates the B1 + inhomogeneities and R1 values from the uncorrected R1 maps in the human brain without need for B1 + mapping. It employs a probabilistic framework for unified segmentation based correction of R1 maps for B1 + inhomogeneities (UNICORT). The framework incorporates a physically informed generative model of smooth B1 + inhomogeneities and their multiplicative effect on R1 estimates. Extensive cross-validation with the established standard using measured B1 + maps shows that UNICORT yields accurate B1 + and R1 maps with a mean deviation from the standard of less than 4.3% and 5%, respectively. The results of different groups of subjects with a wide age range and different levels of atypical brain anatomy further suggest that the method is robust and generalizes well to wider populations. UNICORT is easy to apply, as it is computationally efficient and its basic framework is implemented as part of the tissue segmentation in SPM8.
T1-weighted anatomical brain scans are routinely used in neuroimaging studies, for example, as an... more T1-weighted anatomical brain scans are routinely used in neuroimaging studies, for example, as anatomical reference for functional data and in brain morphometry studies. Subject motion can degrade the quality of these images. An additional problem is the occurrence of signal dropouts in the case of long echo times and low receiver bandwidths. These problems are addressed in two different studies. In the first study, it is shown that the high scalp signal, which results from the low T1 value of fat, may cause a typical ringing artefact in the presence of head motion. This problem may be enhanced if phased array coils are used for signal reception due to their increased sensitivity in the peripheral head regions. It is shown that this artefact can be avoided by combining certain fat suppression techniques that reduce the scalp signal. In the second study, it is shown that signal dropout affects mainly the orbitofrontal cortex and the temporal lobes, and that a bandwidth of 100 Hz/pixel should be chosen for the investigation of these areas to avoid signal losses while maintaining an acceptable signal-to-noise ratio. Experimental results are based on the MDEFT sequence but can be applied to other T1-weighted sequences like FLASH and MP-RAGE. Furthermore, the presented methods for improving the image quality can be combined with other artefact reduction techniques.
Most functional magnetic resonance imaging (fMRI) studies record the blood oxygen level-dependent... more Most functional magnetic resonance imaging (fMRI) studies record the blood oxygen level-dependent (BOLD) signal using fast gradient-echo echo-planar imaging (GE EPI). However, GE EPI can suffer from substantial signal dropout caused by inhomogeneities in the static magnetic field. These field inhomogeneities occur near air/tissue interfaces, because they are generated by variations in magnetic susceptibilities. Thus, fMRI studies are often limited by a reduced BOLD sensitivity (BS) in inferior brain regions. Recently, a method has been developed which allows for optimizing the BS in dropout regions by specifically adjusting the slice tilt, the direction of the phase-encoding (PE), and the z-shim moment. However, optimal imaging parameters were only reported for the orbitofrontal cortex (OFC) and inferior temporal lobes. The present study determines the optimal slice tilt, PE direction, and z-shim moment at 3 Tand 1.5 T, otherwise using standard fMRI acquisition parameters. Results are reported for all brain regions, yielding a whole-brain atlas of optimal parameters. At both field strengths, optimal parameters increase the BS by more than 60% in many voxels in the OFC and by at least 30% in the other dropout regions. BS gains are shown to be more widespread at 3 T, suggesting an increased benefit from the dropout compensation at higher fields. Even the mean BS of a large brain region, e.g., encompassing the medial OFC, can be increased by more than 15%. The maps of optimal parameters allow for assessing the feasibility and improving fMRI of brain regions affected by susceptibility-induced BS losses.
Normal ageing is associated with characteristic changes in brain microstructure. Although in vivo... more Normal ageing is associated with characteristic changes in brain microstructure. Although in vivo neuroimaging captures spatial and temporal patterns of age-related changes of anatomy at the macroscopic scale, our knowledge of the underlying (patho)physiological processes at cellular and molecular levels is still limited. The aim of this study is to explore brain tissue properties in normal ageing using quantitative magnetic resonance imaging (MRI) alongside conventional morphological assessment. Using a whole-brain approach in a cohort of 26 adults, aged 18-85 years, we performed voxel-based morphometric (VBM) analysis and voxel-based quantification (VBQ) of diffusion tensor, magnetization transfer (MT), R1, and R2* relaxation parameters. We found age-related reductions in cortical and subcortical grey matter volume paralleled by changes in fractional anisotropy (FA), mean diffusivity (MD), MT and R2*. The latter were regionally specific depending on their differential sensitivity to microscopic tissue properties. VBQ of white matter revealed distinct anatomical patterns of age-related change in microstructure. Widespread and profound reduction in MT contrasted with local FA decreases paralleled by MD increases. R1 reductions and R2* increases were observed to a smaller extent in overlapping occipito-parietal white matter regions. We interpret our findings, based on current biophysical models, as a fingerprint of age-dependent brain atrophy and underlying microstructural changes in myelin, iron deposits and water. The VBQ approach we present allows for systematic unbiased exploration of the interaction between imaging parameters and extends current methods for detection of neurodegenerative processes in the brain. The demonstrated parameter-specific distribution patterns offer insights into age-related brain structure changes in vivo and provide essential baseline data for studying disease against a background of healthy ageing.
... Maria Luisa Gorno-Tempini , Chloe Hutton 1 , Oliver Josephs , Ralf Deichmann , Cathy Price an... more ... Maria Luisa Gorno-Tempini , Chloe Hutton 1 , Oliver Josephs , Ralf Deichmann , Cathy Price and Robert Turner. ... References. 1. PA Bandettini, EC Wong, A. Jesmanowicz, RS Hinks and JS Hyde, Spin-echo and gradient-echo EPI of human brain activation using BOLD contrast: A ...
Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal ... more Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal sensitivity to small BOLD signal changes. To increase the sensitivity, higher field strengths are often employed, since they provide an increased image signal-to-noise ratio (SNR). However, as image SNR increases, the relative contribution of physiological noise to the total time series noise will be greater compared to that from thermal noise. At 7 T, we studied how the physiological noise contribution can be best reduced for EPI time series acquired at three different spatial resolutions (1.1 mm × 1.1 mm × 1.8 mm, 2 mm × 2 mm × 2 mm and 3 mm × 3 mm × 3 mm). Applying optimal physiological noise correction methods improved temporal SNR (tSNR) and increased the numbers of significantly activated voxels in fMRI visual activation studies for all sets of acquisition parameters. The most dramatic results were achieved for the lowest spatial resolution, an acquisition parameter combination commonly used in cognitive neuroimaging which requires high functional sensitivity and temporal resolution (i.e. 3 mm isotropic resolution and whole brain image repetition time of 2 s). For this data, physiological noise models based on cardio-respiratory information improved tSNR by approximately 25% in the visual cortex and 35% sub-cortically. When the time series were additionally corrected for the residual effects of head motion after retrospective realignment, the tSNR was increased by around 58% in the visual cortex and 71% sub-cortically, exceeding tSNR~140. In conclusion, optimal physiological noise correction at 7 T increases tSNR significantly, resulting in the highest tSNR per unit time published so far. This tSNR improvement translates into a significant increase in BOLD sensitivity, facilitating the study of even subtle BOLD responses.
In functional MRI, magnetic field inhomogeneities due to air-tissue susceptibility differences ma... more In functional MRI, magnetic field inhomogeneities due to air-tissue susceptibility differences may lead to severe signal dropouts and geometric distortions in echo-planar images. Therefore, the inhomogeneities in the field are routinely minimized by shimming prior to imaging. However in fMRI, the Blood Oxygen Level Dependent (BOLD) effect is the measure of interest, so the BOLD sensitivity (BS) should be optimized rather than the magnetic field homogeneity. The analytical expression for an estimate of the BOLD sensitivity has been recently developed, allowing for the computation of BOLD sensitivity maps from echo-planar images and field maps. This report describes a novel shimming procedure that optimizes the local BOLD sensitivity over a region of interest. The method is applied in vivo and compared to a standard global shimming procedure. A breath-holding experiment was carried out and demonstrated that the BS-based shimming significantly improved the detection of activation in a target region of interest, the medial orbitofrontal cortex.
Diffusion tensor imaging is widely used in research and clinical applications, but this modality ... more Diffusion tensor imaging is widely used in research and clinical applications, but this modality is highly sensitive to artefacts. We developed an easy-to-implement extension of the original diffusion tensor model to account for physiological noise in diffusion tensor imaging using measures of peripheral physiology (pulse and respiration), the so-called extended tensor model. Within the framework of the extended tensor model two types of regressors, which respectively modeled small (linear) and strong (nonlinear) variations in the diffusion signal, were derived from peripheral measures. We tested the performance of four extended tensor models with different physiological noise regressors on nongated and gated diffusion tensor imaging data, and compared it to an established data-driven robust fitting method. In the brainstem and cerebellum the extended tensor models reduced the noise in the tensor-fit by up to 23% in accordance with previous studies on physiological noise. The extended tensor model addresses both large-amplitude outliers and small-amplitude signal-changes. The framework of the extended tensor model also facilitates further investigation into physiological noise in diffusion tensor imaging. The proposed extended tensor model can be readily combined with other artefact correction methods such as robust fitting and eddy current correction.
Diffusion tensor imaging is widely used in research and clinical applications, but still suffers ... more Diffusion tensor imaging is widely used in research and clinical applications, but still suffers from substantial artifacts. Here, we focus on vibrations induced by strong diffusion gradients in diffusion tensor imaging, causing an echo shift in k-space and consequential signal-loss. We refined the model of vibration-induced echo shifts, showing that asymmetric k-space coverage in widely used Partial Fourier acquisitions results in locally differing signal loss in images acquired with reversed phase encoding direction (blip-up/blip-down). We implemented a correction of vibration artifacts in diffusion tensor imaging using phase-encoding reversal (COVIPER) by combining blip-up and blip-down images, each weighted by a function of its local tensor-fit error. COVIPER was validated against low vibration reference data, resulting in an error reduction of about 72% in fractional anisotropy maps. COV-IPER can be combined with other corrections based on phase encoding reversal, providing a comprehensive correction for eddy currents, susceptibility-related distortions and vibration artifact reduction.
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Papers by Chloe Hutton