Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal ...to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually.
The study of spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord. Two ultra-high field functional magnetic ...resonance imaging (fMRI) studies in humans have provided evidence for reproducible resting-state connectivity between the dorsal horns as well as between the ventral horns, and a study in non-human primates has shown that these resting-state signals are impacted by spinal cord injury. As these studies were carried out at ultra-high field strengths using region-of-interest (ROI) based analyses, we investigated whether such resting-state signals could also be observed at the clinically more prevalent field strength of 3T. In a reanalysis of a sample of 20 healthy human participants who underwent a resting-state fMRI acquisition of the cervical spinal cord, we were able to observe significant dorsal horn connectivity as well as ventral horn connectivity, but no consistent effects for connectivity between dorsal and ventral horns, thus replicating the human 7T results. These effects were not only observable when averaging along the acquired length of the spinal cord, but also when we examined each of the acquired spinal segments separately, which showed similar patterns of connectivity. Finally, we investigated the robustness of these resting-state signals against variations in the analysis pipeline by varying the type of ROI creation, temporal filtering, nuisance regression and connectivity metric. We observed that – apart from the effects of band-pass filtering – ventral horn connectivity showed excellent robustness, whereas dorsal horn connectivity showed moderate robustness. Together, our results provide evidence that spinal cord resting-state connectivity is a robust and spatially consistent phenomenon that could be a valuable tool for investigating the effects of pathology, disease progression, and treatment response in neurological conditions with a spinal component, such as spinal cord injury.
White matter (WM) plasticity supports skill learning and memory. Up- and downregulation of brain activity in animal models lead to WM alterations. But can bidirectional brain-activity manipulation ...change WM structure in the adult human brain? We employ fMRI neurofeedback to endogenously and directionally modulate activity in the sensorimotor cortices. Diffusion tensor imaging is acquired before and after two separate conditions, involving regulating sensorimotor activity either up or down using real or sham neurofeedback (n = 20 participants × 4 scans). We report rapid opposing changes in corpus callosum microstructure that depend on the direction of activity modulation. Our findings show that fMRI neurofeedback can be used to endogenously and directionally alter not only brain-activity patterns but also WM pathways connecting the targeted brain areas. The level of associated brain activity in connected areas is therefore a possible mediator of previously described learning-related changes in WM.
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•fMRI neurofeedback is used to bidirectionally modulate sensorimotor activity•Brain-activity self-regulation results in bidirectional white matter alterations•White matter structural changes relate to the direction of the activity regulation•Neurofeedback can be used to endogenously and directionally alter brain structure
Up- and downregulation of neuronal activity alter myelin and white matter in animal models. Sampaio-Baptista et al. demonstrate using fMRI neurofeedback that bidirectional regulation of sensorimotor activity results in bidirectional changes in white matter in adult humans. fMRI neurofeedback can be used to directionally modulate brain structure.
Abstract
The brain operates at a critical point that is balanced between order and disorder. Even during rest, unstable periods of random behavior are interspersed with stable periods of balanced ...activity patterns that support optimal information processing. Being born preterm may cause deviations from this normal pattern of development. We compared 33 extremely preterm (EPT) children born at < 27 weeks of gestation and 28 full-term controls. Two approaches were adopted in both groups, when they were 10 years of age, using structural and functional brain magnetic resonance imaging data. The first was using a novel intrinsic ignition analysis to study the ability of the areas of the brain to propagate neural activity. The second was a whole-brain Hopf model, to define the level of stability, desynchronization, or criticality of the brain. EPT-born children exhibited fewer intrinsic ignition events than controls; nodes were related to less sophisticated aspects of cognitive control, and there was a different hierarchy pattern in the propagation of information and suboptimal synchronicity and criticality. The largest differences were found in brain nodes belonging to the rich-club architecture. These results provide important insights into the neural substrates underlying brain reorganization and neurodevelopmental impairments related to prematurity.
A long-standing issue in non-rigid image registration is the choice of the level of regularisation. Regularisation is necessary to preserve the smoothness of the registration and penalise against ...unnecessary complexity. The vast majority of existing registration methods use a fixed level of regularisation, which is typically hand-tuned by a user to provide “nice" results. However, the optimal level of regularisation will depend on the data which is being processed; lower signal-to-noise ratios require higher regularisation to avoid registering image noise as well as features, and different pairs of images require registrations of varying complexity depending on their anatomical similarity. In this paper we present a probabilistic registration framework that infers the level of regularisation from the data. An additional benefit of this proposed probabilistic framework is that estimates of the registration uncertainty are obtained. This framework has been implemented using a free-form deformation transformation model, although it would be generically applicable to a range of transformation models. We demonstrate our registration framework on the application of inter-subject brain registration of healthy control subjects from the NIREP database. In our results we show that our framework appropriately adapts the level of regularisation in the presence of noise, and that inferring regularisation on an individual basis leads to a reduction in model over-fitting as measured by image folding while providing a similar level of overlap.
► Infers the level of regularisation in non-rigid registration using Bayes. ► Adapts regularisation to signal-to-noise ratios and anatomical variability. ► Provides a spatial map of the uncertainty in the registration.
Fractional anisotropy and the mean diffusion coefficient were measured in the cerebral volume in 20 schizophrenic and 24 healthy subjects, men and women, using diffusion tensor imaging. In addition, ...3D SPGR was used for segmentation of brain tissue into grey and white matter and cerebrospinal fluid. In schizophrenic patients, fractional anisotropy was reduced in the splenium of the corpus callosum and in adjacent occipital white matter. The segmentation revealed no tissue deficits in the volume of reduced fractional anisotropy. The mean diffusion was increased in the total white and grey matter volume of the schizophrenic patients compared with the healthy subjects. The findings support the view that global and regional white matter abnormalities occur in chronic schizophrenia.
We compared the course and cortical projections of white matter fibers passing through the extreme capsule in humans and macaques. Previous comparisons of this tract have suggested a uniquely human ...posterior projection, but these studies have always employed different techniques in the different species. Here we used the same technique, diffusion MRI, in both species to avoid attributing differences in techniques to differences in species. Diffusion MRI-based probabilistic tractography was performed from a seed area in the extreme capsule in both human and macaques. We compared in vivo data of humans and macaques as well as one high-resolution ex vivo macaque dataset. Tractography in the macaque was able to replicate most results known from macaque tracer studies, including selective innervation of frontal cortical areas and targets in the superior temporal cortex. In addition, however, we also observed some tracts that are not commonly reported in macaque tracer studies and that are more reminiscent of results previously only reported in the human. In humans, we show that the ventrolateral prefrontal cortex innervations are broadly similar to those in the macaque. These results suggest that evolutionary changes in the human extreme capsule fiber complex are likely more gradual than punctuated. Further, they demonstrate both the potential and limitations of diffusion MRI tractography.
The aim of this study was to describe the development of morphologic and diffusion tensor imaging sequences of peripheral nerves at 7 T, using carpal tunnel syndrome (CTS) as a model system of focal ...nerve injury.
Morphologic images were acquired at 7 T using a balanced steady-state free precession sequence. Diffusion tensor imaging was performed using single-shot echo-planar imaging and readout-segmented echo-planar imaging sequences. Different acquisition and postprocessing methods were compared to describe the optimal analysis pipeline. Magnetic resonance imaging parameters including cross-sectional areas, signal intensity, fractional anisotropy (FA), as well as mean, axial, and radial diffusivity were compared between patients with CTS (n = 8) and healthy controls (n = 6) using analyses of covariance corrected for age (significance set at P < 0.05). Pearson correlations with Bonferroni correction were used to determine association of magnetic resonance imaging parameters with clinical measures (significance set at P < 0.01).
The 7 T acquisitions with high in-plane resolution (0.2 × 0.2mm) afforded detailed morphologic resolution of peripheral nerve fascicles. For diffusion tensor imaging, single-shot echo-planar imaging was more efficient than readout-segmented echo-planar imaging in terms of signal-to-noise ratio per unit scan time. Distortion artifacts were pronounced, but could be corrected during postprocessing. Registration of FA maps to the morphologic images was successful. The developed imaging and analysis pipeline identified lower median nerve FA (pisiform bone, 0.37 SD 0.10) and higher radial diffusivity (1.08 0.20) in patients with CTS compared with healthy controls (0.53 0.06 and 0.78 0.11, respectively, P < 0.047). Fractional anisotropy and radial diffusivity strongly correlated with patients' symptoms (r = -0.866 and 0.866, respectively, P = 0.005).
Our data demonstrate the feasibility of morphologic and diffusion peripheral nerve imaging at 7 T. Fractional anisotropy and radial diffusivity were found to be correlates of symptom severity.
This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalized analysis. This is achieved by incorporating registration uncertainty into an ...ensemble learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalized feature data, e.g., grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an ensemble learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel-based morphometry and deformation tensor-based morphometry over bootstrap aggregating, a common ensemble learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important.
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