-
[show abstract]
[hide abstract]
ABSTRACT: We investigate the properties of the Phase Locking Value (PLV) and the Phase Lag Index (PLI) as metrics for quantifying interactions in bivariate local field potential (LFP), electroencephalography (EEG) and magnetoencephalography (MEG) data. In particular we describe the relationship between nonparametric estimates of PLV and PLI and the parameters of two distributions that can both be used to model phase interactions. The first of these is the von Mises distribution, for which the sample PLV is a maximum likelihood estimator. The second is the relative phase distribution associated with bivariate circularly symmetric complex Gaussian data. We derive an explicit expression for the PLV for this distribution and show that it is a function of the cross-correlation between the two signals. We compare the bias and variance of the sample PLV and the PLV computed from the cross-correlation. We also show that both the von Mises and Gaussian models are suitable for representing relative phase in application to LFP data from a visually-cued motor study in macaque. We then compare results using the two different PLV estimators and conclude that, for this data, the sample PLV provides equivalent information to the cross-correlation of the two complex time series.
NeuroImage 02/2013; · 5.89 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: We describe the use of non-parametric permutation tests to detect activation in cortically-constrained maps of current density computed from MEG data. The methods are applicable to any inverse imaging method that maps event-related MEG to a coregistered cortical surface. To determine an appropriate threshold to apply to statistics computed from these maps, it is important to control for the multiple testing problem associated with testing 10's of thousands of hypotheses (one per surface element). By randomly permuting pre- and post-stimuius data from the collection of individual epochs in an event related study, we develop thresholds that control the familywise (type 1) error rate. These thresholds are based on the distribution of the maximum intensity, which implicitly accounts for spatial and temporal correlation in the cortical maps. We demonstrate the method in application to simulated data and experimental data from a somatosensory evoked response study.
Lecture Notes in Computer Science 02/2013; 18:512-23.
-
[show abstract]
[hide abstract]
ABSTRACT: Modularity-based partitioning methods divide networks into modules by comparing their structure against random networks conditioned to have the same number of nodes, edges, and degree distribution. We propose a novel way to measure modularity and divide graphs, based on conditional probabilities of the edge strength of random networks. We provide closed-form solutions for the expected strength of an edge when it is conditioned on the degrees of the two neighboring nodes, or alternatively on the degrees of all nodes comprising the network. We analytically compute the expected network under the assumptions of Gaussian and Bernoulli distributions. When the Gaussian distribution assumption is violated, we prove that our expression is the best linear unbiased estimator. Finally, we investigate the performance of our conditional expected model in partitioning simulated and real-world networks.
Physical Review E 01/2012; 85(1 Pt 2):016109. · 2.26 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The talking face affords multiple types of information. To isolate cortical sites with responsibility for integrating linguistically relevant visual speech cues, speech and nonspeech face gestures were presented in natural video and point-light displays during fMRI scanning at 3.0T. Participants with normal hearing viewed the stimuli and also viewed localizers for the fusiform face area (FFA), the lateral occipital complex (LOC), and the visual motion (V5/MT) regions of interest (ROIs). The FFA, the LOC, and V5/MT were significantly less activated for speech relative to nonspeech and control stimuli. Distinct activation of the posterior superior temporal sulcus and the adjacent middle temporal gyrus to speech, independent of media, was obtained in group analyses. Individual analyses showed that speech and nonspeech stimuli were associated with adjacent but different activations, with the speech activations more anterior. We suggest that the speech activation area is the temporal visual speech area (TVSA), and that it can be localized with the combination of stimuli used in this study.
Human Brain Mapping 10/2011; 32(10):1660-76. · 5.88 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Although it is well established that multiple frontal, parietal, and occipital regions in humans are involved in anticipatory deployment of visual spatial attention, less is known about the electrophysiological signals in each region across multiple subsecond periods of attentional deployment. We used MEG measures of cortical stimulus-locked, signal-averaged (event-related field) activity during a task in which a symbolic cue directed covert attention to the relevant location on each trial. Direction-specific attention effects occurred in different cortical regions for each of multiple time periods during the delay between the cue and imperative stimulus. A sequence of activation from V1/V2 to extrastriate, parietal, and frontal regions occurred within 110 ms after cue, possibly related to extraction of cue meaning. Direction-specific activations ∼300 ms after cue in frontal eye field (FEF), lateral intraparietal area (LIP), and cuneus support early covert targeting of the cued location. This was followed by coactivation of a frontal-parietal system [superior frontal gyrus (SFG), middle frontal gyrus (MFG), LIP, anterior intraparietal sulcus (IPSa)] that may coordinate the transition from targeting the cued location to sustained deployment of attention to both space and feature in the last period. The last period involved direction-specific activity in parietal regions and both dorsal and ventral sensory regions [LIP, IPSa, ventral IPS, lateral occipital region, and fusiform gyrus], which was accompanied by activation that was not direction specific in right hemisphere frontal regions (FEF, SFG, MFG). Behavioral performance corresponded with the magnitude of attention-related activity in different brain regions at each time period during deployment. The results add to the emerging electrophysiological characterization of different cortical networks that operate during anticipatory deployment of visual spatial attention.
Journal of Neuroscience 09/2011; 31(39):13880-9. · 7.11 Impact Factor
-
Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
-
Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
-
[show abstract]
[hide abstract]
ABSTRACT: Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).
Computational Intelligence and Neuroscience 01/2011; 2011:879716.
-
[show abstract]
[hide abstract]
ABSTRACT: Flat mapping based cortical surface registration constrained by manually traced sulcal curves has been widely used for inter subject comparisons of neuroanatomical data. Even for an experienced neuroanatomist, manual sulcal tracing can be quite time consuming, with the cost increasing with the number of sulcal curves used for registration. We present a method for estimation of an optimal subset of size N(C) from N possible candidate sulcal curves that minimizes a mean squared error metric over all combinations of N(C) curves. The resulting procedure allows us to estimate a subset with a reduced number of curves to be traced as part of the registration procedure leading to optimal use of manual labeling effort for registration. To minimize the error metric we analyze the correlation structure of the errors in the sulcal curves by modeling them as a multivariate Gaussian distribution. For a given subset of sulci used as constraints in surface registration, the proposed model estimates registration error based on the correlation structure of the sulcal errors. The optimal subset of constraint curves consists of the N(C) sulci that jointly minimize the estimated error variance for the subset of unconstrained curves conditioned on the N(C) constraint curves. The optimal subsets of sulci are presented and the estimated and actual registration errors for these subsets are computed.
NeuroImage 04/2010; 50(3):950-9. · 5.89 Impact Factor
-
Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands, 14-17 April, 2010; 01/2010
-
Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands, 14-17 April, 2010; 01/2010
-
[show abstract]
[hide abstract]
ABSTRACT: Detection of small lesions in fluorodeoxyglucose (FDG) positron emission tomography (PET) is limited by image resolution and low signal to noise ratio. We have previously described a matched subspace detection method that uses the time activity curve to distinguish tumors from background in dynamic FDG PET. Applying this algorithm on a voxel by voxel basis throughout the dynamic image produces a test statistic image or "map" which on thresholding indicates the potential locations of secondary or metastatic tumors. In this paper, we describe a thresholding method that controls familywise error rate (FWER) for the matched subspace detection statistical map. The method involves three steps. First, the PET image is segmented into several homogeneous regions. Then, the statistical map is normalized to a zero mean unit variance Gaussian random field. Finally, the images are thresholded at a fixed FWER by estimating their spatial smoothness and applying a random field theory maximum statistic approach. We evaluate this thresholding method using digital phantoms generated from clinical dynamic images. We also present an application of the proposed approach to clinical PET data from a breast cancer patient with metastatic disease.
IEEE transactions on medical imaging. 10/2009; 28(10):1623-31.
-
[show abstract]
[hide abstract]
ABSTRACT: In the last decade, large arrays of sensors for magnetoencephalography (MEG) (and electroencephalography (EEG)) have become more commonplace, allowing new opportunities for the application of beamforming techniques to the joint problems of signal estimation and noise reduction. We introduce a new approach to noise cancellation, the generalized sidelobe canceller (GSC), itself an alternative to the linearly constrained minimum variance (LCMV) algorithm. The GSC framework naturally fits within the other noise reduction techniques that employ real or virtual reference arrays. Using expository human subject data with strong environmental and biological artifacts, we demonstrate a straightforward sequence of steps for practical noise filtering, applicable to any large array sensor design.
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 08/2009; 2009:149-152.
-
[show abstract]
[hide abstract]
ABSTRACT: Manually labeled landmark sets are often required as inputs for landmark-based image registration. Identifying an optimal subset of landmarks from a training dataset may be useful in reducing the labor intensive task of manual labeling. In this paper, we present a new problem and a method to solve it: given a set of N landmarks, find the k(< N) best landmarks such that aligning these k landmarks that produce the best overall alignment of all N landmarks. The resulting procedure allows us to select a reduced number of landmarks to be labeled as a part of the registration procedure. We apply this methodology to the problem of registering cerebral cortical surfaces extracted from MRI data. We use manually traced sulcal curves as landmarks in performing inter-subject registration of these surfaces. To minimize the error metric, we analyze the correlation structure of the sulcal errors in the landmark points by modeling them as a multivariate Gaussian process. Selection of the optimal subset of sulcal curves is performed by computing the error variance for the subset of unconstrained landmarks conditioned on the constrained set. We show that the registration error predicted by our method closely matches the actual registration error. The method determines optimal curve subsets of any given size with minimal registration error.
Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 06/2009; 20-25:699-706.
-
[show abstract]
[hide abstract]
ABSTRACT: We describe a method to detect brain activation in cortically constrained maps of current density computed from magnetoencephalography (MEG) data using multivariate statistical inference. We apply time-frequency (wavelet) analysis to individual epochs to produce dynamic images of brain signal power on the cerebral cortex in multiple time-frequency bands. We form vector observations by concatenating the power in each frequency band, and fit them into separate multivariate linear models for each time band and cortical location with experimental conditions as predictor variables. The resulting Roy's maximum root statistic maps are thresholded for significance using permutation tests and the maximum statistic approach. A source is considered significant if it exceeds a statistical threshold, which is chosen to control the familywise error rate, or the probability of at least one false positive, across the cortical surface. We compare and evaluate the multivariate approach with existing univariate approaches to time-frequency MEG signal analysis, both on simulated data and experimental data from an MEG visuomotor task study. Our results indicate that the multivariate method is more powerful than the univariate approach in detecting experimental effects when correlations exist between power across frequency bands. We further describe protected F-tests and linear discriminant analysis to identify individual frequencies that contribute significantly to experimental effects.
Human Brain Mapping 05/2009; 30(6):1922-34. · 5.88 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Sulcal and gyral landmarks on the human cerebral cortex are required for various studies of the human brain. Whether used directly to examine sulcal geometry, or indirectly to drive cortical surface registration methods, the accuracy of these landmarks is essential. While several methods have been developed to automatically identify sulci and gyri, their accuracy may be insufficient for certain neuroanatomical studies. We describe a semi-automated procedure that delineates a sulcus or gyrus given a limited number of user-selected points. The method uses a graph theory approach to identify the lowest-cost path between the points, where the cost is a combination of local curvature features and the distance between vertices on the surface representation. We implemented the algorithm in an interface that guides the user through a cortical surface delineation protocol, and we incorporated this tool into our BrainSuite software. We performed a study to compare the results produced using our method with results produced using Display, a popular tool that has been used extensively for manual delineation of sulcal landmarks. Six raters were trained on the delineation protocol. They performed delineations on 12 brains using both software packages. We performed a statistical analysis of 3 aspects of the delineation task: time required to delineate the surface, registration accuracy achieved compared to an expert-delineated gold-standard, and variation among raters. Our new method was shown to be faster to use, to provide reduced inter-rater variability, and to provide results that were at least as accurate as those produced using Display.
Journal of neuroscience methods 01/2009; 178(2):385-92. · 2.30 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Statistical inference from MEG-based distributed activation maps is well suited to the general linear modeling framework, a standard approach to the analysis of fMRI and PET neuroimaging studies. However, there are important differences from the other neuroimaging modalities related to how observations are created and fitted in GLM models, as well as how subsequent statistical inference is performed. In this paper, we demonstrate how MEG oscillatory components can be analyzed in this framework based on a custom ANCOVA model that takes into account baseline and inter-hemispheric effects, rather than a simpler ANOVA design. We present the methodology using as an example an MEG study of visual spatial attention, since the model design depends on the specific experiment and neuroscience hypotheses being tested. However, the techniques presented here can be readily adapted to accommodate other experimental paradigms. We create statistics that estimate the temporal evolution of attention effects on alpha power in several cortical regions. We present evidence for direction-specific attention effects on alpha activity in occipital and parietal regions and demonstrate the sub-second timing of these effects in each region. The results support a mechanism for anticipatory attentional deployment that dynamically modulates the local alpha synchrony in a network of parietal control and occipital sensory regions.
NeuroImage 08/2008; 44(1):164-74. · 5.89 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The spiking activity of single neurons in the primate motor cortex is correlated with various limb movement parameters, including velocity. Recent findings obtained using local field potentials suggest that hand speed may also be encoded in the summed activity of neuronal populations. At this macroscopic level, the motor cortex has also been shown to display synchronized rhythmic activity modulated by motor behavior. Yet whether and how neural oscillations might be related to limb speed control is still poorly understood. Here, we applied magnetoencephalography (MEG) source imaging to the ongoing brain activity in subjects performing a continuous visuomotor (VM) task. We used coherence and phase synchronization to investigate the coupling between the estimated activity throughout the brain and the simultaneously recorded instantaneous hand speed. We found significant phase locking between slow (2- to 5-Hz) oscillatory activity in the contralateral primary motor cortex and time-varying hand speed. In addition, we report long-range task-related coupling between primary motor cortex and multiple brain regions in the same frequency band. The detected large-scale VM network spans several cortical and subcortical areas, including structures of the frontoparietal circuit and the cerebello-thalamo-cortical pathway. These findings suggest a role for slow coherent oscillations in mediating neural representations of hand kinematics in humans and provide further support for the putative role of long-range neural synchronization in large-scale VM integration. Our findings are discussed in the context of corticomotor communication, distributed motor encoding, and possible implications for brain-machine interfaces.
Proceedings of the National Academy of Sciences 06/2007; 104(18):7676-81. · 9.68 Impact Factor
-
Proceedings of the 2007 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Washington, DC, USA, April 12-16, 2007; 01/2007
-
[show abstract]
[hide abstract]
ABSTRACT: Magnetoencephalography (MEG) provides unique insights into the spatio-temporal dynamics of neural activation in the human brain. Unfortunately, the accuracy with which neural sources can be localized is limited by the highly illposed nature of the inverse problem. A large number of inverse methods have been proposed that deal with this illposedness using a range of different modeling and regularization procedures. Here we describe an objective task-based framework for comparing different inverse methods. Using the free-response receiver operating characteristic (FROC) we compare the performance of matched filters, cortically constrained dipole scanning, and minimum norm imaging methods for the task of detecting focal cortical activation. Our results indicate that the scanning methods outperform matched filters and minimum norm imaging for the case of one and two 2 cm2 patches of cortical activity when the dynamics of the two patches are both strongly and weakly correlated and irrespective of the spacing of the two activated regions.
IEEE Transactions on Biomedical Engineering 10/2006; 53(9):1783-93. · 2.28 Impact Factor
