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Metastable brain waves.
Traveling patterns of neuronal activity-brain waves-have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.
Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity.
Fluctuations in functional interactions between brain regions typically occur at the millisecond time scale. Conventional connectivity metrics are not adequately time-resolved to detect such fast fluctuations in functional connectivity. At the same time, attempts to use conventional metrics in a time-resolved manner usually come with the selection of sliding windows of fixed arbitrary length. In the current work, we evaluated the use of high temporal resolution metrics of functional connectivity in conjunction with non-negative tensor factorisation to detect fast fluctuations in connectivity and temporally evolving subnetworks. To this end, we used the phase difference derivative, wavelet coherence, and we also introduced a new metric, the instantaneous amplitude correlation. In order to deal with the inherently noisy nature of magnetoencephalography data and large datasets, we make use of recurrence plots and we used pair-wise orthogonalisation to avoid spurious estimates of functional connectivity due to signal leakage. Firstly, metrics were evaluated in the context of dynamically coupled neural mass models in the presence and absence of delays and also compared to conventional static metrics with fixed sliding windows. Simulations showed that these high temporal resolution metrics outperformed conventional static connectivity metrics. Secondly, the sensitivity of the metrics to fluctuations in connectivity was analysed in post-movement beta rebound magnetoencephalography data, which showed time locked sensorimotor subnetworks that modulated with the post-movement beta rebound. Finally, sensitivity of the metrics was evaluated in resting-state magnetoencephalography, showing similar spatial patterns across metrics, thereby indicating the robustness of the current analysis. The current methods can be applied in cognitive experiments that involve fast modulations in connectivity in relation to cognition. In addition, these methods could also be used as input to temporal graph analysis to further characterise the rapid fluctuation in brain network topology.
Non-linear Parameter Estimates from Non-stationary MEG Data.
We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do this we use an empirical Bayesian scheme to estimate the cortical current distribution due to a range of laterally shifted head-models. We compare different methods of approaching this problem from the division of M/EEG data into stationary sections and performing separate source inversions, to explaining all of the M/EEG data with a single inversion. We demonstrate this through estimation of head position in both simulated and empirical resting state MEG data collected using a head-cast.
Memory load modulates graded changes in distracter filtering.
Our ability to maintain small amounts of information in mind is critical for successful performance on a wide range of tasks. However, it remains unclear exactly how this maintenance is achieved. One possibility is that it is brought about using mechanisms that overlap with those used for attentional control. That is, the same mechanisms that we use to regulate and optimize our sensory processing may be recruited when we maintain information in visual short-term memory (VSTM). We aimed to test this hypothesis by exploring how distracter filtering is modified by concurrent VSTM load. We presented participants with sequences of target items, the order and location of which had to be maintained in VSTM. We also presented distracter items alongside the targets, and these distracters were graded such that they could be either very similar or dissimilar to the targets. We analyzed scalp potentials using a novel multiple regression approach, which enabled us to explore the neural mechanisms by which the participants accommodated these variable distracters on a trial-to-trial basis. Critically, the effect of distracter filtering interacted with VSTM load; the same graded changes in perceptual similarity exerted effects of a different magnitude depending upon how many items participants were already maintaining in VSTM. These data provide compelling evidence that maintaining information in VSTM recruits an overlapping set of attentional control mechanisms that are otherwise used for distracter filtering.
Probabilistic non-linear registration with spatially adaptive regularisation.
This paper introduces a novel method for inferring spatially varying regularisation in non-linear registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on the transformation parameters is parameterised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a traditional globally defined regularisation penalty, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The strength of the prior may be reduced in areas where the data better support deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce unwanted impacts of regularisation on the inferred transformation. This is especially important for applications where the deformation field itself is of interest, such as tensor based morphometry. The proposed approach is demonstrated using synthetic images, and with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results indicate that using the proposed spatially adaptive prior leads to sparser deformations, which provide better localisation of regional volume change. Additionally, the proposed regularisation model leads to more data driven and localised maps of registration uncertainty. This paper also demonstrates for the first time the use of Bayesian model comparison for selecting different types of regularisation.
How reliable are MEG resting-state connectivity metrics?
MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.
Fusion of magnetometer and gradiometer sensors of MEG in the presence of multiplicative error.
Novel neuroimaging techniques have provided unprecedented information on the structure and function of the living human brain. Multimodal fusion of data from different sensors promises to radically improve this understanding, yet optimal methods have not been developed. Here, we demonstrate a novel method for combining multichannel signals. We show how this method can be used to fuse signals from the magnetometer and gradiometer sensors used in magnetoencephalography (MEG), and through extensive experiments using simulation, head phantom and real MEG data, show that it is both robust and accurate. This new approach works by assuming that the lead fields have multiplicative error. The criterion to estimate the error is given within a spatial filter framework such that the estimated power is minimized in the worst case scenario. The method is compared to, and found better than, existing approaches. The closed-form solution and the conditions under which the multiplicative error can be optimally estimated are provided. This novel approach can also be employed for multimodal fusion of other multichannel signals such as MEG and EEG. Although the multiplicative error is estimated based on beamforming, other methods for source analysis can equally be used after the lead-field modification.
Longitudinal brain MRI analysis with uncertain registration.
In this paper we propose a novel approach for incorporating measures of spatial uncertainty, which are derived from non-rigid registration, into spatially normalised statistics. Current approaches to spatially normalised statistical analysis use point-estimates of the registration parameters. This is limiting as the registration will rarely be completely accurate, and therefore data smoothing is often used to compensate for the uncertainty of the mapping. We derive localised measurements of spatial uncertainty from a probabilistic registration framework, which provides a principled approach to image smoothing. We evaluate our method using longitudinal deformation features from a set of MR brain images acquired from the Alzheimer's Disease Neuroimaging Initiative. These images are spatially normalised using our probabilistic registration algorithm. The spatially normalised longitudinal features are adaptively smoothed according to the registration uncertainty. The proposed adaptive smoothing shows improved classification results, (84% correct Alzheimer's Disease vs. controls), over either not smoothing (79.6%), or using a Gaussian filter with sigma = 2mm (78.8%).
Using Gaussian-process regression for meta-analytic neuroimaging inference based on sparse observations.
The purpose of neuroimaging meta-analysis is to localize the brain regions that are activated consistently in response to a certain intervention. As a commonly used technique, current coordinate-based meta-analyses (CBMA) of neuroimaging studies utilize relatively sparse information from published studies, typically only using (x,y,z) coordinates of the activation peaks. Such CBMA methods have several limitations. First, there is no way to jointly incorporate deactivation information when available, which has been shown to result in an inaccurate statistic image when assessing a difference contrast. Second, the scale of a kernel reflecting spatial uncertainty must be set without taking the effect size (e.g., Z-stat) into account. To address these problems, we employ Gaussian-process regression (GPR), explicitly estimating the unobserved statistic image given the sparse peak activation "coordinate" and "standardized effect-size estimate" data. In particular, our model allows estimation of effect size at each voxel, something existing CBMA methods cannot produce. Our results show that GPR outperforms existing CBMA techniques and is capable of more accurately reproducing the (usually unavailable) full-image analysis results.
Vessel-encoded dynamic magnetic resonance angiography using arterial spin labeling.
A new noninvasive MRI method for vessel selective angiography is presented. The technique combines vessel-encoded pseudocontinuous arterial spin labeling with a two-dimensional dynamic angiographic readout and was used to image the cerebral arteries in healthy volunteers. Time-of-flight angiograms were also acquired prior to vessel-selective dynamic angiography acquisitions in axial, coronal, and/or sagittal planes, using a 3-T MRI scanner. The latter consisted of a vessel-encoded pseudocontinuous arterial spin labeling pulse train of 300 or 1000 ms followed by a two-dimensional thick-slab flow-compensated fast low angle shot readout combined with a segmented Look-Locker sampling strategy (temporal resolution = 55 ms). Selective labeling was performed at the level of the neck to generate individual angiograms for both right and left internal carotid and vertebral arteries. Individual vessel angiograms were reconstructed using a bayesian inference method. The vessel-selective dynamic angiograms obtained were consistent with the time-of-flight images, and the longer of the two vessel-encoded pseudocontinuous arterial spin labeling pulse train durations tested (1000 ms) was found to give better distal vessel visibility. This technique provides highly selective angiograms quickly and noninvasively that could potentially be used in place of intra-arterial x-ray angiography for larger vessels.
Functional brain reorganization for hand movement in patients with multiple sclerosis: defining distinct effects of injury and disability.
Previous work has demonstrated potentially adaptive cortical plasticity that increases with brain injury in patients with multiple sclerosis. However, animal studies showing use-dependent changes in motor cortex organization suggest that functional changes also may occur in response to disability. We therefore wished to test whether brain injury and disability lead to distinguishable patterns of activation with hand movement in patients with multiple sclerosis. By employing a passive as well as an active movement task, we also wished to test whether these changes were independent of voluntary recruitment and thus more likely to reflect true functional reorganization. Fourteen patients [Extended Disability Status Score (EDSS) 0-7.5] with relapsing-remitting multiple sclerosis were selected on the basis of pathology load and hand functional impairment for three study groups: group 1, low diffuse central brain injury (DCBI) as assessed from relative N-acetylaspartate concentration (a marker of axonal integrity) and normal hand function (n = 6); group 2, greater DCBI and normal hand function (n = 4); and group 3, greater DCBI and impaired hand function (n = 4). Functional MRI (fMRI) was used to map brain activation with a four-finger and both one-finger passive and active flexion-extension movement tasks for the three groups. Considering all the patients, we found increased activity in ipsilateral premotor and ipsilateral motor cortex (IMC) and in the ipsilateral inferior parietal lobule with increasing global disability (as assessed from the EDSS score). These changes appear to define true functional reorganization, as fMRI activations in IMC (r = 0.87, P < 0.001) and in the contralateral motor cortex (r = 0.67, P < 0.007) were highly correlated between active and passive single finger movements. We attempted to disambiguate any distinct effects of disability and brain injury by direct contrasts between patients differing predominantly in one or the other. To make these contrasts as powerful as possible, we used impairment of finger tapping as a measure of disability specific to the hand tested. A direct contrast of patients matched for DCBI, but differing in hand disability (group 3 - group 2) showed greater bilateral primary and secondary somatosensory cortex activation with greater disability alone. A contrast matched for hand disability, but differing in DCBI (group 2 - group 1) showed a different pattern of changes with relative ipsilateral premotor cortex and bilateral supplementary motor area activity. We conclude that the pattern of brain activity with finger movements changes both with increasing DCBI and with hand disability in patients with multiple sclerosis, and that these changes are distinct. Those related directly to disability may reflect responses to altered patterns of use. As injury- and disability-related activation changes are found even with passive finger movements, they may reflect true brain reorganization.
Temporal autocorrelation in univariate linear modeling of FMRI data.
In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or "coloring," attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by imposing known autocorrelation via temporal filtering. Removing the autocorrelation, or "prewhitening," gives the best linear unbiased estimator, assuming that the autocorrelation is accurately known. For single-event designs, the efficiency of the estimator is considerably higher for prewhitening compared with coloring. However, it has been suggested that sufficiently accurate estimates of the autocorrelation are currently not available to give prewhitening acceptable bias. To overcome this, we consider different ways to estimate the autocorrelation for use in prewhitening. After high-pass filtering is performed, a Tukey taper (set to smooth the spectral density more than would normally be used in spectral density estimation) performs best. Importantly, estimation is further improved by using nonlinear spatial filtering to smooth the estimated autocorrelation, but only within tissue type. Using this approach when prewhitening reduced bias to close to zero at probability levels as low as 1 x 10(-5).
A general framework for the analysis of vessel encoded arterial spin labeling for vascular territory mapping.
Vessel encoded arterial spin labeling provides a way to perform non-invasive vascular territory imaging. By uniquely encoding the blood within feeding arteries over a number of images, the territories of each can be identified. Here, a new approach for the analysis of vessel encoded arterial spin labeling data is presented. The method includes a full description of how the geometry of arteries and spatial label modulation affects the measured signal. It also incorporates an artery-based classification that considers multiple arteries in each class, explicitly permitting a voxel to be supplied by multiple arteries. The developed framework is cast within a Bayesian inference procedure allowing both flow contributions and the locations of the arteries in the labeling plane to be inferred. By using simulated data, the method was shown to provide more accurate estimates of blood contribution in areas of mixed supply, such as would be found in watershed regions, than conventional methods. It was also able to estimate the location of arteries within the labeling plane, accounting for motion between sequence prescription and acquisition. Similar performance was found for data acquired using a pseudo-continuous labeling scheme both in the neck and above the Circle of Willis.
Characterization and propagation of uncertainty in diffusion-weighted MR imaging.
A fully probabilistic framework is presented for estimating local probability density functions on parameters of interest in a model of diffusion. This technique is applied to the estimation of parameters in the diffusion tensor model, and also to a simple partial volume model of diffusion. In both cases the parameters of interest include parameters defining local fiber direction. A technique is then presented for using these density functions to estimate global connectivity (i.e., the probability of the existence of a connection through the data field, between any two distant points), allowing for the quantification of belief in tractography results. This technique is then applied to the estimation of the cortical connectivity of the human thalamus. The resulting connectivity distributions correspond well with predictions from invasive tracer methods in nonhuman primate.
Bayesian inference of hemodynamic changes in functional arterial spin labeling data.
The study of brain function using MRI relies on acquisition techniques that are sensitive to different aspects of the hemodynamic response contiguous to areas of neuronal activity. For this purpose different contrasts such as arterial spin labeling (ASL) and blood oxygenation level dependent (BOLD) functional MRI techniques have been developed to investigate cerebral blood flow (CBF) and blood oxygenation, respectively. Analysis of such data typically proceeds by separate, linear modeling of the appropriate CBF or BOLD time courses. In this work an approach is developed that provides simultaneous inference on hemodynamic changes via a nonlinear physiological model of ASL data acquired at multiple echo times. Importantly, this includes a significant contribution by changes in the static magnetization, M, to the ASL signal. Inference is carried out in a Bayesian framework. This is able to extract, from dual-echo ASL data, probabilistic estimates of percentage changes of CBF, R(2) (*), and the static magnetization, M. This approach provides increased sensitivity in inferring CBF changes and reduced contamination in inferring BOLD changes when compared with general linear model approaches on single-echo ASL data. We also consider how the static magnetization, M, might be related to changes in CBV by assuming the same mechanism for water exchange as in vascular space occupancy.
Variational Bayes inference of spatial mixture models for segmentation.
Mixture models are commonly used in the statistical segmentation of images. For example, they can be used for the segmentation of structural medical images into different matter types, or of statistical parametric maps into activating and nonactivating brain regions in functional imaging. Spatial mixture models have been developed to augment histogram information with spatial regularization using Markov random fields (MRFs). In previous work, an approximate model was developed to allow adaptive determination of the parameter controlling the strength of spatial regularization. Inference was performed using Markov Chain Monte Carlo (MCMC) sampling. However, this approach is prohibitively slow for large datasets. In this work, a more efficient inference approach is presented. This combines a variational Bayes approximation with a second-order Taylor expansion of the components of the posterior distribution, which would otherwise be intractable to Variational Bayes. This provides inference on fully adaptive spatial mixture models an order of magnitude faster than MCMC. We examine the behavior of this approach when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging statistical parametric maps.
Motor practice promotes increased activity in brain regions structurally disconnected after subcortical stroke.
BACKGROUND: Motor practice is an important component of neurorehabilitation. Imaging studies in healthy individuals show that dynamic brain activation changes with practice. Defining patterns of functional brain plasticity associated with motor practice following stroke could guide rehabilitation. OBJECTIVE: The authors aimed to test whether practice-related changes in brain activity differ after stroke and to explore spatial relationships between activity changes and patterns of structural degeneration. METHODS: They studied 10 patients at least 6 months after left-hemisphere subcortical strokes and 18 healthy controls. Diffusion-weighted magnetic resonance imaging (MRI) was acquired at baseline, and functional MRI (fMRI) was acquired during performance of a visuomotor tracking task before and after a 15-day period of practice of the same task. RESULTS: Smaller short-term practice effects at baseline correlated with lower fractional anisotropy in the posterior limbs of the internal capsule (PLIC) bilaterally in patients (t > 3; cluster P < .05). After 15 days of motor practice a Group × Time interaction (z > 2.3; cluster P < .05) was found in the basal ganglia, thalamus, inferior frontal gyrus, superior temporal gyrus, and insula. In these regions, healthy controls showed decreases and patients showed increases in activity with practice. Some regions of interest had a loss of white matter connectivity at baseline. CONCLUSIONS: Performance gains with motor practice can be associated with increased activity in regions that have been either directly or indirectly impaired by loss of connectivity. These results suggest that neurorehabilitation interventions may be associated with compensatory adaptation of intact brain regions as well as enhanced activity in regions with impaired structural connectivity.