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Trial of the cerebral perfusion response to sodium nitrite infusion in patients with acute subarachnoid haemorrhage using arterial spin labelling MRI.
Aneurysmal subarachnoid haemorrhage (SAH) is a devastating subset of stroke. One of the major determinants of outcome is an evolving multifactorial injury occurring in the first 72 hours, known as early brain injury. Reduced nitric oxide (NO) bioavailability and an associated disruption to cerebral perfusion is believed to play an important role in this process. We sought to explore this relationship, by examining the effect on cerebral perfusion of the in vivo manipulation of NO levels using an exogenous NO donor (sodium nitrite). We performed a double blind placebo controlled randomised experimental medicine study of the cerebral perfusion response to sodium nitrite infusion during the early brain injury period in 15 low grade (World Federation of Neurosurgeons grade 1-2) SAH patients. Patients were randomly assigned to receive sodium nitrite at 10 mcg/kg/min or saline placebo. Assessment occurred following endovascular aneurysm occlusion, mean time after ictus 66h (range 34-90h). Cerebral perfusion was quantified before infusion commencement and after 3 hours, using multi-post labelling delay (multi-PLD) vessel encoded pseudocontinuous arterial spin labelling (VEPCASL) magnetic resonance imaging (MRI). Administration of sodium nitrite was associated with a significant increase in average grey matter cerebral perfusion. Group level voxelwise analysis identified that increased perfusion occurred within regions of the brain known to exhibit enhanced vulnerability to injury. These findings highlight the role of impaired NO bioavailability in the pathophysiology of early brain injury.
Accelerated 3D multi-channel B 1 + mapping at 7 T for the brain and heart.
PURPOSE: To acquire accurate volumetric multi-channel B 1 + $$ {\mathrm{B}}_1^{+} $$ maps in under 14 s whole-brain or 23 heartbeats whole-heart for parallel transmit (pTx) applications at 7 T. THEORY AND METHODS: We evaluate the combination of three recently proposed techniques. The acquisition of multi-channel transmit array B 1 + $$ {\mathrm{B}}_1^{+} $$ maps is accelerated using transmit low rank (TxLR) with absolute B 1 + $$ {\mathrm{B}}_1^{+} $$ mapping (Sandwich) acquired in a B 1 + $$ {\mathrm{B}}_1^{+} $$ time-interleaved acquisition of modes (B1TIAMO) fashion. Simulations using synthetic body images derived from Sim4Life were used to test the achievable acceleration for small scan matrices of 24 × 24. Next, we evaluated the method by retrospectively undersampling a fully sampled B 1 + $$ {\mathrm{B}}_1^{+} $$ library of nine subjects in the brain. Finally, Cartesian undersampled phantom and in vivo images were acquired in both the brain of three subjects (8Tx/32 receive [Rx]) and the heart of another three subjects (8Tx/8Rx) at 7 T. RESULTS: Simulation and in vivo results show that volumetric multi-channel B 1 + $$ {\mathrm{B}}_1^{+} $$ maps can be acquired using acceleration factors of 4 in the body, reducing the acquisition time to within 23 heartbeats, which was previously not possible. In silico heart simulations demonstrated a RMS error to the fully sampled native resolution ground truth of 4.2° when combined in first-order circularly polarized mode (mean flip angle 66°) at an acceleration factor of 4. The 14 s 3D B 1 + $$ {\mathrm{B}}_1^{+} $$ maps acquired in the brain have a RMS error of 1.9° to the fully sampled (mean flip angle 86°). CONCLUSION: The proposed method is demonstrated as a fast pTx calibration technique in the brain and a promising method for pTx calibration in the body.
Do baseline patient reported outcome measures predict changes in self-reported function, following a chronic pain rehabilitation programme?
BACKGROUND: Interdisciplinary pain management programmes, based on cognitive-behavioural principles, aim to improve physical and psychological functioning and enhance self-management in people living with chronic pain. Currently there is insufficient evidence about whether psychological, biological or social factors are predictive of positive outcomes following pain rehabilitation. This study aims to evaluate predictors of change in Brief Pain Inventory - pain interference score (BPI) in a clinical data set to determine whether age, sex and baseline outcome measures are predictive of improvement in pain interference following pain rehabilitation. METHODS: A retrospective, pragmatic observational analysis of routinely collected clinical data in two pain rehabilitation programmes, Balanced Life Programme (BLP) and Get Back Active (GBA) was conducted. Standard regression and hierarchical regression analyses were used to identify predictors of change to assess temporal changes in BPI. Responder analysis was also conducted. RESULTS: Standard regression analyses of 208 (BLP) and 310 (GBA) patients showed that higher baseline BPI and better physical performance measures predicted better improvement in BPI across both programmes. Hierarchical regression showed that age and sex accounted for 2.7% (BLP) and 0.002% (GBA) of the variance in change in BPI. After controlling for age and sex, the other measures explained an additional 23% (BLP) and 19% (GBA) of the variance, p = < .001 where BPI and physical performance measures were consistently statistically significant predictors, p < .05. Responder analysis also showed that pain interference and physical performance were significantly associated with improvement (p = < .0005). CONCLUSIONS: The combination of high self-reported pain interference and better physical performance measures may be a useful indicator of who would benefit from interdisciplinary rehabilitation. Further validation of the results is required.
MMORF—FSL’s MultiMOdal Registration Framework
Abstract We present MMORF—FSL’s MultiMOdal Registration Framework—a newly released nonlinear image registration tool designed primarily for application to magnetic resonance imaging (MRI) images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations within a single registration framework by leveraging rich information from multiple scalar and tensor modalities. The regularisation employed in MMORF promotes local rigidity in the deformation, and we have previously demonstrated how this effectively controls both shape and size distortion, leading to more biologically plausible warps. The performance of MMORF is benchmarked against three established nonlinear registration methods—FNIRT, ANTs, and DR-TAMAS—across four domains: FreeSurfer label overlap, diffusion tensor imaging (DTI) similarity, task-fMRI cluster mass, and distortion. The evaluation is based on 100 unrelated subjects from the Human Connectome Project (HCP) dataset registered to the Oxford-MultiModal-1 (OMM-1) multimodal template via either the T1w contrast alone or in combination with a DTI/DTI-derived contrast. Results show that MMORF is the most consistently high-performing method across all domains—both in terms of accuracy and levels of distortion. MMORF is available as part of FSL, and its inputs and outputs are fully compatible with existing workflows. We believe that MMORF will be a valuable tool for the neuroimaging community, regardless of the domain of any downstream analysis, providing state-of-the-art registration performance that integrates into the rich and widely adopted suite of analysis tools in FSL.
Rehabilitating homonymous visual field deficits: white matter markers of recovery-stage 1 registered report.
Damage to the primary visual cortex (V1) or its afferent white matter tracts results in loss of vision in the contralateral visual field that can present as homonymous visual field deficits. Recent evidence suggests that visual training in the blind field can partially reverse blindness at trained locations. However, the efficacy of visual training to improve vision is highly variable across subjects, and the reasons for this are poorly understood. It is likely that variance in residual functional or structural neural circuitry following the insult may underlie the variation among patients. Many patients with visual field deficits retain residual visual processing in their blind field, termed 'blindsight', despite a lack of awareness. Previous research indicates that an intact structural and functional connection between the dorsal lateral geniculate nucleus (dLGN) and the human extrastriate visual motion-processing area (hMT+) is necessary for blindsight to occur. We therefore predict that changes in this white matter pathway will underlie improvements in motion discrimination training. Twenty stroke survivors with unilateral, homonymous field defects from retro-geniculate brain lesions will complete 6 months of motion discrimination training at home. Visual training will involve performing two daily sessions of a motion discrimination task, at two non-overlapping locations in the blind field, at least 5 days per week. Motion discrimination and integration thresholds, Humphrey perimetry and structural and diffusion-weighted MRI will be collected pre- and post-training. Changes in fractional anisotropy will be analysed in two visual tracts: (i) between the ipsilesional dLGN and hMT+ and (ii) between the ipsilesional dLGN and V1. The (non-visual) tract between the ventral posterior lateral nucleus of the thalamus (VPL) and the primary somatosensory cortex (S1) will be analysed as a control. Tractographic changes will be compared to improvements in motion discrimination and Humphrey perimetry-derived metrics. We predict that (i) improved motion discrimination performance will be directly related to increased fractional anisotropy in the pathway between ipsilesional dLGN and hMT+ and (ii) improvements in Humphrey perimetry will be related to increased fractional anisotropy in the dLGN-V1 pathway. There should be no relationship between behavioural measures and changes in fractional anisotropy in the VPL-S1 pathway. This study has the potential to lead to greater understanding of the white matter microstructure of pathways underlying the behavioural outcomes resulting from visual training in retro-geniculate strokes. Understanding the neural mechanisms that underlie visual rehabilitation is fundamental to the development of more targeted and thus effective treatments for this underserved patient population.
Rehabilitating homonymous visual field deficits: white matter markers of recovery-stage 2 registered report.
Damage to the primary visual cortex or its afferent white matter tracts results in loss of vision in the contralateral visual field that can present as homonymous visual field deficits. Evidence suggests that visual training in the blind field can partially reverse blindness at trained locations. However, the efficacy of visual training is highly variable across participants, and the reasons for this are poorly understood. It is likely that variance in residual neural circuitry following the insult may underlie the variation among patients. Many stroke survivors with visual field deficits retain residual visual processing in their blind field despite a lack of awareness. Previous research indicates that intact structural and functional connections between the dorsal lateral geniculate nucleus and the human extrastriate visual motion-processing area hMT+ are necessary for blindsight to occur. We therefore hypothesized that changes in this white matter pathway may underlie improvements resulting from motion discrimination training. Eighteen stroke survivors with long-standing, unilateral, homonymous field defects from retro-geniculate brain lesions completed 6 months of visual training at home. This involved performing daily sessions of a motion discrimination task, at two non-overlapping locations in the blind field, at least 5 days per week. Motion discrimination and integration thresholds, Humphrey perimetry and structural and diffusion-weighted MRI were collected pre- and post-training. Changes in fractional anisotropy (FA) were analysed in visual tracts connecting the ipsilesional dorsal lateral geniculate nucleus and hMT+, and the ipsilesional dorsal lateral geniculate nucleus and primary visual cortex. The (non-visual) tract connecting the ventral posterior lateral nucleus of the thalamus and the primary somatosensory cortex was analysed as a control. Changes in white matter integrity were correlated with improvements in motion discrimination and Humphrey perimetry. We found that the magnitude of behavioural improvement was not directly related to changes in FA in the pathway between the dorsal lateral geniculate nucleus and hMT+ or dorsal lateral geniculate nucleus and primary visual cortex. Baseline FA in either tract also failed to predict improvements in training. However, an exploratory analysis showed a significant increase in FA in the distal part of the tract connecting the dorsal lateral geniculate nucleus and hMT+, suggesting that 6 months of visual training in chronic, retro-geniculate strokes may enhance white matter microstructural integrity of residual geniculo-extrastriate pathways.
Optimising network modelling methods for fMRI.
A major goal of neuroimaging studies is to develop predictive models to analyze the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e.g., behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of ∼14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets (∼1000 subjects) to evaluate the generalisability of proposed network modelling methods.
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge.
The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.
Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images.
White matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. In the loss functions the network uses anatomical information regarding WMH spatial distribution in loss functions, to improve the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts, and we also submitted our method to MWSC 2017 to be evaluated on the unseen test datasets. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning methods of MWSC 2017.
Balance between competing spectral states in subthalamic nucleus is linked to motor impairment in Parkinson's disease.
Exaggerated local field potential bursts of activity at frequencies in the low beta band are a well-established phenomenon in the subthalamic nucleus of patients with Parkinson's disease. However, such activity is only moderately correlated with motor impairment. Here we test the hypothesis that beta bursts are just one of several dynamic states in the subthalamic nucleus local field potential in Parkinson's disease, and that together these different states predict motor impairment with high fidelity. Local field potentials were recorded in 32 patients (64 hemispheres) undergoing deep brain stimulation surgery targeting the subthalamic nucleus. Recordings were performed following overnight withdrawal of anti-parkinsonian medication, and after administration of levodopa. Local field potentials were analysed using hidden Markov modelling to identify transient spectral states with frequencies under 40 Hz. Findings in the low beta frequency band were similar to those previously reported; levodopa reduced occurrence rate and duration of low beta states, and the greater the reductions, the greater the improvement in motor impairment. However, additional local field potential states were distinguished in the theta, alpha and high beta bands, and these behaved in an opposite manner. They were increased in occurrence rate and duration by levodopa, and the greater the increases, the greater the improvement in motor impairment. In addition, levodopa favoured the transition of low beta states to other spectral states. When all local field potential states and corresponding features were considered in a multivariate model it was possible to predict 50% of the variance in patients' hemibody impairment OFF medication, and in the change in hemibody impairment following levodopa. This only improved slightly if signal amplitude or gamma band features were also included in the multivariate model. In addition, it compares with a prediction of only 16% of the variance when using beta bursts alone. We conclude that multiple spectral states in the subthalamic nucleus local field potential have a bearing on motor impairment, and that levodopa-induced shifts in the balance between these states can predict clinical change with high fidelity. This is important in suggesting that some states might be upregulated to improve parkinsonism and in suggesting how local field potential feedback can be made more informative in closed-loop deep brain stimulation systems.
The psychological correlates of distinct neural states occurring during wakeful rest.
When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants' experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of intrusive thoughts about the past. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brain's response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.
Behavioural relevance of spontaneous, transient brain network interactions in fMRI.
How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.