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PubMed2004

Localization of realistic cortical activity in MEG using current multipoles.

We present a novel approach to MEG source estimation based on a regularized first-order multipole solution. The Gaussian regularizing prior is obtained by calculation of the sample mean and covariance matrix for the equivalent moments of realistic simulated cortical activity. We compare the regularized multipole localization framework to the classical dipole and general multipole source estimation methods by evaluating the ability of all three solutions to localize the centroids of physiologically plausible patches of activity simulated on the surface of a human cerebral cortex. The results, obtained with a realistic sensor configuration, a spherical head model, and given in terms of field and localization error, depict the performance of the dipolar and multipolar models as a function of variable source surface area (50-500 mm(2)), noise conditions (20, 10, and 5 dB SNR), source orientation (0-90 degrees ), and source depth (3-11 cm). We show that as the sources increase in size, they become less accurately modeled as current dipoles. The regularized multipole systematically outperforms the single dipole model, increasingly so as the spatial extent of the sources increases. In addition, our simulations demonstrate that as the orientation of the sources becomes more radial, dipole localization accuracy decreases substantially, while the performance of the regularized multipole model is far less sensitive to orientation and even succeeds in localizing quasi-radial source configurations. Furthermore, our results show that the multipole model is able to localize superficial sources with higher accuracy than the current dipole. These results indicate that the regularized multipole solution may be an attractive alternative to current-dipole-based source estimation methods in MEG.

Algorithms↗
PubMed2004

Statistical parametric mapping for event-related potentials: I. Generic considerations.

In this paper, we frame the strategy and motivations behind developments in statistical parametric mapping (SPM) for the analysis of electroencephalogram (EEG) data. This work deals specifically with SPM procedures for the analysis of event-related potentials (ERP). We place these developments in the larger context of integrating electrophysiological and hemodynamic measurements of evoked brain responses through the fusion of EEG and fMRI data. In this paper, we consider some fundamental issues when selecting an appropriate statistical model that enables diverse questions to be asked of the data and at the same time retains maximum sensitivity. The three key issues addressed in this paper are as follows: (i) should multivariate or mass univariate analyses be adopted, (ii) should time be treated as an experimental factor or as a dimension of the measured response variable, and (iii) how to form appropriate explanatory variables in a hierarchical observation model. We review the relative merits of the different options and explain the rationale for our choices. In brief, we motivate a mass univariate approach in terms of sensitivity to region-specific responses. This involves modeling responses at each voxel or space bin separately. In contradistinction, we treat time as an experimental factor to enable inferences about temporally distributed responses that encompass multiple time bins. In a companion paper, we develop statistical models of ERPs in the time domain that follow from the heuristics established here and illustrate the approach using simulated and real data.

Analysis of Variance↗
PubMed2004

Statistical parametric mapping for event-related potentials (II): a hierarchical temporal model.

In this paper, we describe a temporal model for event-related potentials (ERP) in the context of statistical parametric mapping (SPM). In brief, we project channel data onto a two-dimensional scalp surface or into three-dimensional brain space using some appropriate inverse solution. We then treat the spatiotemporal data in a mass-univariate fashion. This implicitly factorises the model into spatial and temporal components. The key contribution of this paper is the use of observation models that afford an explicit distinction between observation error and variation in the expression of ERPs. This distinction is created by employing a two-level hierarchical model, in which the first level models the ERP effects within-subject and trial type, while the second models differences in ERP expression among trial types and subjects. By bringing the analysis of ERP data into a classical hierarchical (i.e., mixed effects) framework, many apparently disparate approaches (e.g., conventional P300 analyses and time-frequency analyses of stimulus-locked oscillations) can be reconciled within the same estimation and inference procedure. Inference proceeds in the normal way using t or F statistics to test for effects that are localised in peristimulus time or in some time-frequency window. The use of F statistics is an important generalisation of classical approaches, because it allows one to test for effects that lie in a multidimensional subspace (i.e., of unknown but constrained form). We describe the analysis procedures, the underlying theory and compare its performance to established techniques.

Analysis of Variance↗
PubMed2004

Statistical parametric mapping of 5-HT1A receptor binding in temporal lobe epilepsy with hippocampal ictal onset on intracranial EEG.

Experimental data in animals show that 5-HT(1A) receptors are predominantly located in limbic areas and suggest that serotonin, via these receptors, mediates an antiepileptic and anticonvulsant effect. In this PET study, we used an antagonist of the 5-HT(1A) receptor, [(18)F]MPPF, to assess the extent of 5-HT(1A) receptor binding changes in a group of seven temporal lobe epilepsy (TLE) patients with hippocampal ictal onset demonstrated by intracerebral EEG recording. On the basis of MRI-measured hippocampal volumes (HV), patients were classified into "normal HV" or "hippocampal atrophy" (HA). Voxel-based analyses (SPM99) were performed to objectively assess the differences in [(18)F]MPPF binding potential (BP) between patients (taken as a group or as individuals) and a database of 48 controls subjects. In the full group of patients, a significant decreased BP was detected ipsilateral to the epileptogenic zone in the hippocampus, temporal pole, insula, and temporal neocortex. This result was confirmed in the subgroup of patients with HA. In patients with normal HV, the BP decrease was restricted to the temporal pole. TLE patients also demonstrated an increased BP in various regions contralateral to the epileptogenic zone. These data suggest that in TLE patients with hippocampal seizure onset, the decrease in 5-HT(1A) receptor binding partly reflects hippocampal neuronal loss, but is also observed in various regions involved in temporo-limbic epileptogenic networks that appeared normal on MRI. Further studies are warranted to evaluate the clinical usefulness of [(18)F]MPPF-PET as compared to other established PET tracers in drug resistant TLE.

Adult↗
PubMed2004

Separability and cross talk: optimizing dual wavelength combinations for near-infrared spectroscopy of the adult head.

By means of noninvasive near-infrared spectroscopy (NIRS), cerebral concentration changes in oxygenated and deoxygenated hemoglobin ([oxy-Hb] and [deoxy-Hb]) can be determined. The quality of the concentration changes' assessment critically depends on the wavelength combination used. Trying to optimize this combination, two spectroscopic effects must be taken into account: cross talk and separability. Cross talk between [oxy-Hb] and [deoxy-Hb] occurs because the assumption made in the analysis-that there is a homogeneous concentration change-does not hold true for the adult human head. Separability-to be introduced in this paper-is a measure for the degree of physical noise of the measurement that will influence the noise of the concentration changes' assessment. In other words, high separability corresponds to a low noise with respect to the concentration changes assessed. Here, we present analytical expressions for both measures and provide model-based estimates of cross talk and separability for any combination of two wavelengths between 610 and 920 nm. These theoretical considerations allow for two predictions: (a) if both wavelengths used are greater than approximately 780 nm, cross talk is high and separability is low resulting in erroneous and noisy concentration data. (b) If one wavelength is chosen below 720 nm while the other is greater than 730 nm, cross talk is low and separability is high resulting in accurate concentration changes. We show the relevance of these theoretical results for noninvasive NIRS by testing the predictions on experimental data obtained in adults undergoing visual stimulation.

Adult↗
PubMed2004

Modelling functional integration: a comparison of structural equation and dynamic causal models.

The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper, we review two different approaches to modelling effective connectivity from fMRI data, structural equation models (SEMs) and dynamic causal models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM, a distinction is made between the 'neuronal level' and the 'hemodynamic level'. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics, which in turn cause changes in the observed hemodynamics. In SEM, changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.

Algorithms↗
PubMed2004

Advances in functional and structural MR image analysis and implementation as FSL.

The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).

Bayes Theorem↗
PubMed2005

Bayesian fMRI time series analysis with spatial priors.

We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. Further, using a computationally efficient Variational Bayes framework, we are able to let the data determine the optimal amount of smoothing. We assume an arbitrary order Auto-Regressive (AR) model for the errors. Our model generalizes earlier work on voxel-wise estimation of GLM-AR models and inference in GLMs using Posterior Probability Maps (PPMs). Results are shown on simulated data and on data from an event-related fMRI experiment.

Bayes Theorem↗
PubMed2005

An arteriolar compliance model of the cerebral blood flow response to neural stimulus.

Although functional magnetic resonance imaging (fMRI) is a widely used and powerful tool for studying brain function, the quantitative interpretation of fMRI measurements for basic neuroscience and clinical studies can be complicated by inter-subject and inter-session variability arising from modulation of the baseline vascular state by disease, aging, diet, and pharmacological agents. In particular, recent studies have shown that the temporal dynamics of the cerebral blood flow (CBF) and the blood oxygenation level dependent (BOLD) responses to stimulus are modulated by changes in baseline CBF induced by various vasoactive agents and by decreases in vascular compliance associated with aging. These effects are not readily explained using current models of the CBF and BOLD responses. We present here a second-order nonlinear feedback model of the evoked CBF response in which neural activity modulates the compliance of arteriolar smooth muscle. Within this model framework, the baseline vascular state affects the dynamic response by changing the relative contributions of an active smooth muscle component and a passive connective tissue component to the overall vessel compliance. Baseline dependencies of the BOLD signal are studied by coupling the arteriolar compliance model with a previously described balloon model of the venous compartment. Numerical simulations show that the proposed model describes to first order the observed dependence of CBF and BOLD responses on the baseline vascular state.

Aging↗
PubMed2005

Multivariate source prelocalization (MSP): use of functionally informed basis functions for better conditioning the MEG inverse problem.

Spatially characterizing and quantifying the brain electromagnetic response using MEG/EEG data still remains a critical issue since it requires solving an ill-posed inverse problem that does not admit a unique solution. To overcome this lack of uniqueness, inverse methods have to introduce prior information about the solution. Most existing approaches are directly based upon extrinsic anatomical and functional priors and usually attempt at simultaneously localizing and quantifying brain activity. By contrast, this paper deals with a preprocessing tool which aims at better conditioning the source reconstruction process, by relying only upon intrinsic knowledge (a forward model and the MEG/EEG data itself) and focusing on the key issue of localization. Based on a discrete and realistic anatomical description of the cortex, we first define functionally Informed Basis Functions (fIBF) that are subject specific. We then propose a multivariate method which exploits these fIBF to calculate a probability-like coefficient of activation associated with each dipolar source of the model. This estimated distribution of activation coefficients may then be used as an intrinsic functional prior, either by taking these quantities into account in a subsequent inverse method, or by thresholding the set of probabilities in order to reduce the dimension of the solution space. These two ways of constraining the source reconstruction process may naturally be coupled. We successively describe the proposed Multivariate Source Prelocalization (MSP) approach and illustrate its performance on both simulated and real MEG data. Finally, the better conditioning induced by the MSP process in a classical regularization scheme is extensively and quantitatively evaluated.

Algorithms↗
PubMed2005

On the human sensorimotor-cortex beta rhythm: sources and modeling.

Cortical oscillations in the beta band (13-35 Hz) are known to be modulated by the GABAergic agonist benzodiazepine. To investigate the mechanisms generating the approximately 20-Hz oscillations in the human cortex, we administered benzodiazepines to healthy adults and monitored cortical oscillatory activity by means of magnetoencephalography. Benzodiazepine increased the power and decreased the frequency of beta oscillations over rolandic areas. Minimum current estimates indicated the effect to take place around the hand area of the primary sensorimotor cortex. Given that previous research has identified sources of the beta rhythm in the motor cortex, our results suggest that these same motor-cortex beta sources are modulated by benzodiazepine. To explore the mechanisms underlying the increase in beta power with GABAergic inhibition, we simulated a conductance-based neuronal network comprising excitatory and inhibitory neurons. The model accounts for the increase in the beta power, the widening of the spectral peak, and the slowing down of the rhythms with benzodiazepines, implemented as an increase in GABAergic conductance. We found that an increase in IPSCs onto inhibitory neurons was more important for generating neuronal synchronization in the beta band than an increase in IPSCs onto excitatory pyramidal cells.

Adult↗
PubMed2005

Unified segmentation.

A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.

Algorithms↗
PubMed2005

Evidence of vibrotactile input to human auditory cortex.

Low frequency vibrations can be detected by both tactile and auditory systems. The aim of the present study is to find out, by means of whole-scalp magnetoencephalography (MEG), whether vibrotactile stimulation alone would activate human auditory cortical areas. We recorded MEG signals from eleven normal-hearing adults to 200-Hz vibrations (on average 19.5 dB above the individual tactile detection threshold), delivered to right-hand fingertips. All subjects reported a perception of a sound when they touched the vibrating tube, and they reported to perceive nothing when not touching the tube. The vibrotactile stimuli elicited clear and reproducible vibrotactile evoked fields (VTEFs) in ten subjects, whereas no MEG responses were observed when the tube was not touched. First responses to the vibrotactile stimuli, peaking around 60 ms, originated in the primary somatosensory cortex in all subjects. They were followed by activations in the auditory cortices, either bilaterally (N = 5) or unilaterally (N = 5), and by activations in the secondary somatosensory (SII) cortex, either contralaterally (N = 3) or ipsilaterally (N = 4). Both the SII and auditory activations consisted of transient responses at 100-200 ms. Additional auditory sustained activation was identified in nine subjects, either bilaterally (N = 2) or ipsilaterally (N = 7), at 200-700 ms. Our results suggest convergence of vibrotactile input to the auditory cortex in normal-hearing adults, in agreement with results previously obtained in a congenitally deaf adult.

Adult↗
PubMed2005

A distributed parameter model of cerebral blood-tissue exchange with account of capillary transit time distribution.

Quantitative estimates of physiological parameters associated with cerebral blood flow can be derived from the analysis of dynamic contrast-enhanced (DCE) images, using an appropriate model of the underlying tissue impulse residue function. The theoretical formulation of a distributed parameter model of tissue microcirculation, which accounts for the effects of capillary permeability and transit time distribution, is presented here. This model considers a statistical distribution of capillary-tissue units, each described by a distributed parameter model that accounts for convective transport within the capillary and transcapillary axial diffusion. Monte Carlo simulations were performed to study the confidence of the parameter estimates, and the model was used to analyze DCE CT images of patient study cases with metastatic cerebral tumors. The tumors were found to yield significantly higher estimates than normal tissues for the parameters associated with the extravasation of tracer and for the standard deviation of capillary transit times. The proposed model can be used with DCE imaging to study the microcirculatory characteristics of cerebral tumors.

Aged↗
PubMed2006

Mechanisms of evoked and induced responses in MEG/EEG.

Cortical responses, recorded by electroencephalography and magnetoencephalography, can be characterized in the time domain, to study event-related potentials/fields, or in the time-frequency domain, to study oscillatory activity. In the literature, there is a common conception that evoked, induced, and on-going oscillations reflect different neuronal processes and mechanisms. In this work, we consider the relationship between the mechanisms generating neuronal transients and how they are expressed in terms of evoked and induced power. This relationship is addressed using a neuronally realistic model of interacting neuronal subpopulations. Neuronal transients were generated by changing neuronal input (a dynamic mechanism) or by perturbing the systems coupling parameters (a structural mechanism) to produce induced responses. By applying conventional time-frequency analyses, we show that, in contradistinction to common conceptions, induced and evoked oscillations are perhaps more related than previously reported. Specifically, structural mechanisms normally associated with induced responses can be expressed in evoked power. Conversely, dynamic mechanisms posited for evoked responses can induce responses, if there is variation in neuronal input. We conclude, it may be better to consider evoked responses as the results of mixed dynamic and structural effects. We introduce adjusted power to complement induced power. Adjusted power is unaffected by trial-to-trial variations in input and can be attributed to structural perturbations without ambiguity.

Algorithms↗
PubMed2006

Thalamo-cortical connectivity in children born preterm mapped using probabilistic magnetic resonance tractography.

Our aim was to investigate the feasibility of studying white matter tracts and connections between the thalamus and the cortex in 2-year-old infants who were born preterm by probabilistic magnetic resonance (MR) tractography. Using this approach, we were able to visualize and quantify connectivity distributions in a number of white matter tracts, including the corticospinal tracts, optic radiations, fibers of the genu and splenium of the corpus callosum, superior longitudinal fasciculus and inferior fronto-occipital fasciculus, and to map the distribution within thalamus of fibers connecting to specific cortical regions. In eleven infants with no MR evidence of focal cerebral lesions and appropriate neurodevelopment as shown by general quotient (GQ) scores above 100, we mapped cortical connections to the thalamus that appeared similar to those reported in adults. However, in a proof-of-principle experiment, we examined one further child with marked white matter abnormalities and found that the volume and pattern of thalamo-cortical connections were severely disrupted. This technique promises to be a useful tool for assessing connectivity in the developing brain and in infants with lesions.

Cerebral Cortex↗
PubMed2006

A comment on the severity of the effects of non-white noise in fMRI time-series.

Analysis of fMRI time-series data is usually performed within the general linear model (GLM), and conclusions are based on the probability (p) of a false-positive voxel. In its simplest form, this process assumes that temporal noise in the time-series is random (white); if it is not, then an error in the estimation of p will occur. Various methods for correcting the problem have been developed, but they are not always used. Analysis packages vary in their ability to deal with this problem, and there is a lack of consistent advice to non-experts concerning whether this issue is serious or is small enough to be ignored. The variability of expert opinion partly reflects the fact that the magnitude of the error that occurs when estimating p in fMRI data is largely undocumented. In this discussion, aimed more at "neuroscience users" than methods experts, we try to clarify the position by documenting the scale of the error in a simple block-design experiment. Based on a fixed-effects group analysis of null datasets (8 participants scanned while at rest), we show that the magnitude of the problem can be very substantial. Without correction, it was necessary to set a nominal probability threshold of about p=0.000001 in order to achieve an actual probability of a false positive of 0.001. This means that if task-related activations were present in such a dataset, only if they reached the former, very low nominal p value could they be relied upon. With the use of standard correction methods, the error was largely removed. Our results suggest that some form of pre-whitening or correction for effects of non-white noise is essential in single-subject GLM analyses and fixed-effects group analyses of fMRI data. It is less important but probably prudent to apply a correction at the first level of random-effects group analyses. We also evaluate the effect of high-pass filtering. We find that the number of false positives in a fixed-effects analysis increases markedly as the filter cut-off frequency is increased. The sensitivity benefits derived from high-pass filtering may thus be partially offset by a significant increase in the number of false positives.

Adult↗
PubMed2006

Bayesian fMRI data analysis with sparse spatial basis function priors.

In previous work we have described a spatially regularised General Linear Model (GLM) for the analysis of brain functional Magnetic Resonance Imaging (fMRI) data where Posterior Probability Maps (PPMs) are used to characterise regionally specific effects. The spatial regularisation is defined over regression coefficients via a Laplacian kernel matrix and embodies prior knowledge that evoked responses are spatially contiguous and locally homogeneous. In this paper we propose to finesse this Bayesian framework by specifying spatial priors using Sparse Spatial Basis Functions (SSBFs). These are defined via a hierarchical probabilistic model which, when inverted, automatically selects an appropriate subset of basis functions. The method includes non-linear wavelet shrinkage as a special case. As compared to Laplacian spatial priors, SSBFs allow for spatial variations in signal smoothness, are more computationally efficient and are robust to heteroscedastic noise. Results are shown on synthetic data and on data from an event-related fMRI experiment.

Algorithms↗