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Ed T Bullmore

Publications and source records attributed to Ed T Bullmore.

5 recordsLinked to original sources
PubMed2006

The multiscale character of evoked cortical activity.

Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional "region of interest" analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.

Adult↗
PubMed2005

Formal characterization and extension of the linearized diffusion tensor model.

We analyzed the properties of the logarithm of the Rician distribution leading to a full characterization of the probability law of the errors in the linearized diffusion tensor model. An almost complete lack of bias, a simple relation between the variance and the signal-to-noise ratio in the original complex data, and a close approximation to normality facilitated estimation of the tensor components by an iterative weighted least squares algorithm. The theory of the linear model has also been used to derive the distribution of mean diffusivity, to develop an informative statistical test for relative lack of fit of the ellipsoidal (or spherical) model compared to an unrestricted linear model in which no specific shape is assumed for the diffusion process, and to estimate the signal-to-noise ratios in the original data. The false discovery rate (FDR) has been used to control thresholds for statistical significance in the context of multiple comparisons at voxel level. The methods are illustrated by application to three diffusion tensor imaging (DTI) datasets of clinical interest: a healthy volunteer, a patient with acute brain injury, and a patient with hydrocephalus. Interestingly, some salient features, such as a region normally comprising the basal ganglia and internal capsule, and areas of edema in patients with brain injury and hydrocephalus, had patterns of error largely independent from their mean diffusivities. These observations were made in brain regions with sufficiently large signal-to-noise ratios (>2) to justify the assumptions of the log Rician probability model. The combination of diffusivity and its error may provide added value in diagnostic DTI of acute pathologic expansion of the extracellular fluid compartment in brain parenchymal tissue.

Adult↗
PubMed2004

Spatiotemporal wavelet resampling for functional neuroimaging data.

The study of dynamic interdependences between brain regions is currently a very active research field. For any connectivity study, it is important to determine whether correlations between two selected brain regions are statistically significant or only chance effects due to non-specific correlations present throughout the data. In this report, we present a wavelet-based non-parametric technique for testing the null hypothesis that the correlations are typical of the data set and not unique to the regions of interest. This is achieved through spatiotemporal resampling of the data in the wavelet domain. Two functional MRI data sets were analysed: (1) Data from 8 healthy human subjects viewing a checkerboard image, and (2) "Null" data obtained from 3 healthy human subjects, resting with eyes closed. It was demonstrated that constrained resampling of the data in the wavelet domain allows construction of bootstrapped data with four essential properties: (1) Spatial and temporal correlations within and between slices are preserved, (2) The irregular geometry of the intracranial images is maintained, (3) There is adequate type I error control, and (4) Expected experiment-induced correlations are identified. The limitations and possible extensions of the proposed technique are discussed.

Adult↗
PubMed2004

Differential neural responses to overt and covert presentations of facial expressions of fear and disgust.

There is debate in cognitive neuroscience whether conscious versus unconscious processing represents a categorical or a quantitative distinction. The purpose of the study was to explore this matter using functional magnetic resonance imaging (fMRI). We first established objective thresholds of the critical temporal parameters for overt and covert presentations of fear and disgust. Next we applied these stimulus parameters in an fMRI experiment to determine whether non-consciously perceived (covert) facial expressions of fear and disgust show the same double dissociation (amygdala response to fear, insula to disgust) observed with consciously perceived (overt) stimuli. A backward masking paradigm was used. In the psychophysics experiment, the following parameters were established: 30-ms target duration for the covert condition, and 170-ms target duration for the overt condition. Results of the block-design fMRI study indicated substantial differences underlying the perception of fearful and disgusted facial expressions, with significant effects of both emotion and target duration. Findings for the overt condition (170 ms) confirm previous evidence of amygdala activation to fearful faces, and insula activation to disgusted faces, and a double dissociation between these two emotions. In the covert condition (30 ms), the amygdala was not activated to fear, nor was the insula activated to disgust. Overall, findings demonstrate significant differences between the neural responses to fear and to disgust, and between the covert presentations of these two emotions. These results therefore suggest distinct neural correlates of conscious and unconscious emotion perception.

Adult↗