Showing papers by "Richard M. Leahy published in 2011"

Brainstorm: a user-friendly application for MEG/EEG analysis

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).

PET Image Reconstruction Using Information Theoretic Anatomical Priors

Abstract: We describe a nonparametric framework for incorporating information from co-registered anatomical images into positron emission tomographic (PET) image reconstruction through priors based on information theoretic similarity measures. We compare and evaluate the use of mutual information (MI) and joint entropy (JE) between feature vectors extracted from the anatomical and PET images as priors in PET reconstruction. Scale-space theory provides a framework for the analysis of images at different levels of detail, and we use this approach to define feature vectors that emphasize prominent boundaries in the anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. Through simulations that model the best case scenario of perfect agreement between the anatomical and functional images, and a more realistic situation with a real magnetic resonance image and a PET phantom that has partial volumes and a smooth variation of intensities, we evaluate the performance of MI and JE based priors in comparison to a Gaussian quadratic prior, which does not use any anatomical information. We also apply this method to clinical brain scan data using Fallypride, a tracer that binds to dopamine receptors and therefore localizes mainly in the striatum. We present an efficient method of computing these priors and their derivatives based on fast Fourier transforms that reduce the complexity of their convolution-like expressions. Our results indicate that while sensitive to initialization and choice of hyperparameters, information theoretic priors can reconstruct images with higher contrast and superior quantitation than quadratic priors.

Inferring hand movement kinematics from MEG, EEG and intracranial EEG: From brain-machine interfaces to motor rehabilitation

Abstract: The ability to use electrophysiological brain signals to decode various parameters of voluntary movement is a central question in Brain Machine Interface (BMI) research. Invasive BMI systems can successfully decode movement trajectories from the spiking activity of neurons in primary motor cortex and posterior parietal cortex. It has long been assumed that non-invasive techniques do not provide sufficient signal resolution to decode the kinematics of complex time-varying movements. This view stems from the hypothesis that movement parameters such as direction, position, velocity, or acceleration are primarily encoded by neuronal firing in motor cortex. Consequently, the fact that such signals cannot be detected using non-invasive techniques such as Electroencephalography (EEG) or Magnetoencephalography (MEG) has led to the claim that fine movement properties cannot be decoded with non-invasive methods. However, this view has been proven wrong by numerous studies in recent years. First, a growing body of research over the last decade has shown that the local field potential (LFP) signal, which represents the summed activity of a neuronal population, can encode movement parameters at a level comparable to unit recordings. These findings were confirmed in humans by the successful use of electrocorticography (ECoG) to achieve continuous movement decoding via invasive human BMI approaches. Very recently, a number of non-invasive studies were able to provide striking evidence that even surface-level MEG or EEG data can contain sufficient information on hand movement in order to infer movement direction and hand kinematics from brain signals recorded using non-invasive methods. Here we provide a brief review of this recent literature and discuss its importance on the future of BMI research and its implications on the development of novel motor rehabilitation strategies.

Dynamic Activation of Frontal, Parietal, and Sensory Regions Underlying Anticipatory Visual Spatial Attention

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.

Optimal Rebinning of Time-of-Flight PET Data

Abstract: Time-of-flight (TOF) positron emission tomography (PET) scanners offer the potential for significantly improved signal-to-noise ratio (SNR) and lesion detectability in clinical PET. However, fully 3D TOF PET image reconstruction is a challenging task due to the huge data size. One solution to this problem is to rebin TOF data into a lower dimensional format. We have recently developed Fourier rebinning methods for mapping TOF data into non-TOF formats that retain substantial SNR advantages relative to sinograms acquired without TOF information. However, mappings for rebinning into non-TOF formats are not unique and optimization of rebinning methods has not been widely investigated. In this paper we address the question of optimal rebinning in order to make full use of TOF information. We focus on FORET-3D, which approximately rebins 3D TOF data into 3D non-TOF sinogram formats without requiring a Fourier transform in the axial direction. We optimize the weighting for FORET-3D to minimize the variance, resulting in H2-weighted FORET-3D, which turns out to be the best linear unbiased estimator (BLUE) under reasonable approximations and furthermore the uniformly minimum variance unbiased (UMVU) estimator under Gaussian noise assumptions. This implies that any information loss due to optimal rebinning is as a result only of the approximations used in deriving the rebinning equation and developing the optimal weighting. We demonstrate using simulated and real phantom TOF data that the optimal rebinning method achieves variance reduction and contrast recovery improvement compared to nonoptimized rebinning weightings. In our preliminary study using a simplified simulation setup, the performance of the optimal rebinning method was comparable to that of fully 3D TOF MAP.

Partitioning directed graphs based on modularity and information flow

Abstract: Although models of the behavior of individual neurons and synapses are now well established, understanding the way in which they cooperate in large ensembles remains a major scientific challenge. We present two novel graph theory methods to study cortical interactions and image the highly organized structure of large scale networks. First, we present a new method to partition directed graphs into modules, based on modularity and an expected network conditioned on the in- and out-degrees of all nodes. We also propose a method to segment graphs based on information flow. These methods are combined to study the community structure of brain networks and information flow within the modules.

Brainstorm: A User-Friendly Application for MEG/EEG Analysis

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).

A nonlocal averaging technique for kinetic parameter estimation from dynamic PET data

Abstract: Due to the high noise levels in frame-by-frame reconstructed dynamic PET images, voxel-wise estimation of kinetic parameters remains a challenge Most traditional denoising schemes compute the time activity curve for a given voxel by averaging voxels in its immediate local neighborhood We recognize the fact that similarities between time activity curves are not necessarily local due to the distributed nature of the tracer uptake properties of similar tissue types We, therefore, present a denoising scheme based on the nonlocal mean (NLM) that allows us to compute voxel-wise estimates of kinetic parameters We validate our method using a simulation study and compare the results of our approach with that of the traditional Gaussian filtering technique The results demonstrate that nonlocal averaging generates lower bias as well as lower variance in the denoised time series for different regions of interest We show that this method can thereby be used to generate better estimates of kinetic parameters than those obtained using a Gaussian filter To enable application to real data sets, we have accelerated the developed NLM denoising framework using a prior clustering step The compound approach was applied to preclinical [18F] FDG PET data from a mouse study Significant qualitative improvement was observed in Patlak parametric images computed after NLM denoising

Viability of sharing MEG data using minimum-norm imaging

Abstract: Cortical activation maps estimated from MEG data fall prey to variability across subjects, trials, runs and potentially MEG centers. To combine MEG results across sites, we must demonstrate that inter-site variability in activation maps is not considerably higher than other sources of variability. By demonstrating relatively low inter-site variability with respect to inter-run variability, we establish a statistical foundation for sharing MEG data across sites for more powerful group studies or clinical trials of pathology. In this work, we analyze whether pooling MEG data across sites is more variable than aggregating MEG data across runs when estimating significant cortical activity. We use data from left median nerve stimulation experiments on four subjects at each of three sites on two runs occurring on consecutive days for each site. We estimate cortical current densities via minimum-norm imaging. We then compare maps across machines and across runs using two metrics: the Simpson coefficient, which admits equality if one map is equal in location to the other, and the Dice coefficient, which admits equality if one map is equal in location and size to the other. We find that sharing MEG data across sites does not noticeably affect group localization accuracy unless one set of data has abnormally low signal power.

Structural analysis of the cerebral cortex using blind source separation

Abstract: Blind Source Separation (BSS) methods have become ubiquitous, but their performance varies greatly depending on how well their assumptions are satisfied by the data. Cortical thickness and sulcal folding patterns are ideal datasets for BSS analysis because there is limited prior knowledge on how they are affected by brain development and pathologies of the central nervous system. However, to date there are no studies exploring these datasets with BSS methods. We propose a novel spatial BSS method based on the Second Order Blind Identification (SOBI) method, but tailored for data on the cerebral cortex. Simulations show our method outperforms the regular SOBI and popular FastICA methods. Experimental data reveal underlying patterns in cortical maps of curvature variance.