In functional magnetic resonance imaging (FMRI) statistical analysis there are problems with acco... more In functional magnetic resonance imaging (FMRI) statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilised temporal ltering strategies to either shape these autocorrelations, or remove them. Shaping, or "colouring", attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by imposing known autocorrelation via temporal filtering. Removing
The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 22, 2015
In human participants, the intensive practice of particular cognitive activities can induce susta... more In human participants, the intensive practice of particular cognitive activities can induce sustained improvements in cognitive performance, which in some cases transfer to benefits on untrained activities. Despite the growing body of research examining the behavioral effects of cognitive training in children, no studies have explored directly the neural basis of these training effects in a systematic, controlled fashion. Therefore, the impact of training on brain neurophysiology in childhood, and the mechanisms by which benefits may be achieved, are unknown. Here, we apply new methods to examine dynamic neurophysiological connectivity in the context of a randomized trial of adaptive working memory training undertaken in children. After training, connectivity between frontoparietal networks and both lateral occipital complex and inferior temporal cortex was altered. Furthermore, improvements in working memory after training were associated with increased strength of neural connectiv...
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
Page 1. A Fast Solution to Robust Minimum Variance Beamformer and Application to Simultaneous MEG... more Page 1. A Fast Solution to Robust Minimum Variance Beamformer and Application to Simultaneous MEG and Local Field Potential Hamid R. Mohseni ∗§ , Morten L. Kringelbach †§ , Mark W. Woolrich ‡ , Penny Probert Smith ∗ and Tipu Z. Aziz § ...
Resting state networks (RSNs) are of fundamental importance in human systems neuroscience with ev... more Resting state networks (RSNs) are of fundamental importance in human systems neuroscience with evidence suggesting that they are integral to healthy brain function and perturbed in pathology. Despite rapid progress in this area, the temporal dynamics governing the functional connectivities that underlie RSN structure remain poorly understood. Here, we present a framework to help further our understanding of RSN dynamics. We describe a methodology which exploits the direct nature and high temporal resolution of magnetoencephalography (MEG). This technique, which builds on previous work, extends from solving fundamental confounds in MEG (source leakage) to multivariate modelling of transient connectivity. The resulting processing pipeline facilitates direct (electrophysiological) measurement of dynamic functional networks. Our results show that, when functional connectivity is assessed in small time windows, the canonical sensorimotor network can be decomposed into a number of transiently synchronising sub-networks, recruitment of which depends on current mental state. These rapidly changing sub-networks are spatially focal with, for example, bilateral primary sensory and motor areas resolved into two separate sub-networks. The likely interpretation is that the larger canonical sensorimotor network most often seen in neuroimaging studies reflects only a temporal aggregate of these transient sub-networks. Our approach opens new frontiers to study RSN dynamics, showing that MEG is capable of revealing the spatial, temporal and spectral signature of the human connectome in health and disease.
In the absence of cognitive tasks and external stimuli, strong rhythmic fluctuations with a frequ... more In the absence of cognitive tasks and external stimuli, strong rhythmic fluctuations with a frequency ≈ 10 Hz emerge from posterior regions of human neocortex. These posterior α-oscillations can be recorded throughout the visual cortex and are particularly strong in the calcarine sulcus, where the primary visual cortex is located. The mechanisms and anatomical pathways through which local \alpha-oscillations are coordinated however, are not fully understood. In this study, we used a combination of magnetoencephalography (MEG), diffusion tensor imaging (DTI), and biophysical modeling to assess the role of white-matter pathways in coordinating cortical α-oscillations. Our findings suggest that primary visual cortex plays a special role in coordinating α-oscillations in higher-order visual regions. Specifically, the amplitudes of α-sources throughout visual cortex could be explained by propagation of α-oscillations from primary visual cortex through white-matter pathways. In particular, α-amplitudes within visual cortex correlated with both the anatomical and functional connection strengths to primary visual cortex. These findings reinforce the notion of posterior α-oscillations as intrinsic oscillations of the visual system. We speculate that they might reflect a default-mode of the visual system during which higher-order visual regions are rhythmically primed for expected visual stimuli by α-oscillations in primary visual cortex.
In recent years, one of the most important findings in systems neuroscience has been the identifi... more In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalography (MEG) data. Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to ~10h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms.
This paper introduces a novel method for inferring spatially varying regularisation in non-rigid ... more This paper introduces a novel method for inferring spatially varying regularisation in non-rigid registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on transformations is parametrised 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 more traditional global regularisation scheme, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The importance of the prior may be reduced in areas where the data better supports deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce the unwanted impact of regularisation on the inferred deformation field. This is especially important for applications such as tensor based morphometry, where the features of interest are directly derived from the deformation field. The proposed approach is demonstrated with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results show that using the proposed spatially adaptive prior leads to deformation fields that have a substantially lower average complexity, but which also provide more accurate localisation of statistical group differences.
In functional magnetic resonance imaging (FMRI) statistical analysis there are problems with acco... more In functional magnetic resonance imaging (FMRI) statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilised temporal ltering strategies to either shape these autocorrelations, or remove them. Shaping, or "colouring", attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by imposing known autocorrelation via temporal filtering. Removing
The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 22, 2015
In human participants, the intensive practice of particular cognitive activities can induce susta... more In human participants, the intensive practice of particular cognitive activities can induce sustained improvements in cognitive performance, which in some cases transfer to benefits on untrained activities. Despite the growing body of research examining the behavioral effects of cognitive training in children, no studies have explored directly the neural basis of these training effects in a systematic, controlled fashion. Therefore, the impact of training on brain neurophysiology in childhood, and the mechanisms by which benefits may be achieved, are unknown. Here, we apply new methods to examine dynamic neurophysiological connectivity in the context of a randomized trial of adaptive working memory training undertaken in children. After training, connectivity between frontoparietal networks and both lateral occipital complex and inferior temporal cortex was altered. Furthermore, improvements in working memory after training were associated with increased strength of neural connectiv...
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
Page 1. A Fast Solution to Robust Minimum Variance Beamformer and Application to Simultaneous MEG... more Page 1. A Fast Solution to Robust Minimum Variance Beamformer and Application to Simultaneous MEG and Local Field Potential Hamid R. Mohseni ∗§ , Morten L. Kringelbach †§ , Mark W. Woolrich ‡ , Penny Probert Smith ∗ and Tipu Z. Aziz § ...
Resting state networks (RSNs) are of fundamental importance in human systems neuroscience with ev... more Resting state networks (RSNs) are of fundamental importance in human systems neuroscience with evidence suggesting that they are integral to healthy brain function and perturbed in pathology. Despite rapid progress in this area, the temporal dynamics governing the functional connectivities that underlie RSN structure remain poorly understood. Here, we present a framework to help further our understanding of RSN dynamics. We describe a methodology which exploits the direct nature and high temporal resolution of magnetoencephalography (MEG). This technique, which builds on previous work, extends from solving fundamental confounds in MEG (source leakage) to multivariate modelling of transient connectivity. The resulting processing pipeline facilitates direct (electrophysiological) measurement of dynamic functional networks. Our results show that, when functional connectivity is assessed in small time windows, the canonical sensorimotor network can be decomposed into a number of transiently synchronising sub-networks, recruitment of which depends on current mental state. These rapidly changing sub-networks are spatially focal with, for example, bilateral primary sensory and motor areas resolved into two separate sub-networks. The likely interpretation is that the larger canonical sensorimotor network most often seen in neuroimaging studies reflects only a temporal aggregate of these transient sub-networks. Our approach opens new frontiers to study RSN dynamics, showing that MEG is capable of revealing the spatial, temporal and spectral signature of the human connectome in health and disease.
In the absence of cognitive tasks and external stimuli, strong rhythmic fluctuations with a frequ... more In the absence of cognitive tasks and external stimuli, strong rhythmic fluctuations with a frequency ≈ 10 Hz emerge from posterior regions of human neocortex. These posterior α-oscillations can be recorded throughout the visual cortex and are particularly strong in the calcarine sulcus, where the primary visual cortex is located. The mechanisms and anatomical pathways through which local \alpha-oscillations are coordinated however, are not fully understood. In this study, we used a combination of magnetoencephalography (MEG), diffusion tensor imaging (DTI), and biophysical modeling to assess the role of white-matter pathways in coordinating cortical α-oscillations. Our findings suggest that primary visual cortex plays a special role in coordinating α-oscillations in higher-order visual regions. Specifically, the amplitudes of α-sources throughout visual cortex could be explained by propagation of α-oscillations from primary visual cortex through white-matter pathways. In particular, α-amplitudes within visual cortex correlated with both the anatomical and functional connection strengths to primary visual cortex. These findings reinforce the notion of posterior α-oscillations as intrinsic oscillations of the visual system. We speculate that they might reflect a default-mode of the visual system during which higher-order visual regions are rhythmically primed for expected visual stimuli by α-oscillations in primary visual cortex.
In recent years, one of the most important findings in systems neuroscience has been the identifi... more In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalography (MEG) data. Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to ~10h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms.
This paper introduces a novel method for inferring spatially varying regularisation in non-rigid ... more This paper introduces a novel method for inferring spatially varying regularisation in non-rigid registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on transformations is parametrised 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 more traditional global regularisation scheme, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The importance of the prior may be reduced in areas where the data better supports deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce the unwanted impact of regularisation on the inferred deformation field. This is especially important for applications such as tensor based morphometry, where the features of interest are directly derived from the deformation field. The proposed approach is demonstrated with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results show that using the proposed spatially adaptive prior leads to deformation fields that have a substantially lower average complexity, but which also provide more accurate localisation of statistical group differences.
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Papers by Mark Woolrich