Robin I. Goldman

Simultaneous EEG-fMRI Reveals Temporal Evolution of Coupling between Supramodal Cortical Attention Networks and the Brainstem

Cortical and subcortical networks have been identified that are commonly associated with attention and task engagement, along with theories regarding their functional interaction. However, a link between these systems has not yet been demonstrated in healthy humans, primarily because of data acquisition and analysis limitations. We recorded simultaneous EEG–fMRI while subjects performed auditory and visual oddball tasks and used these data to investigate the BOLD correlates of single-trial EEG variability at latencies spanning the trial. We focused on variability along task-relevant dimensions in the EEG for identical stimuli and then combined auditory and visual data at the subject level to spatially and temporally localize brain regions involved in endogenous attentional modulations. Specifically, we found that anterior cingulate cortex (ACC) correlates strongly with both early and late EEG components, whereas brainstem, right middle frontal gyrus (rMFG), and right orbitofrontal cortex (rOFC) correlate significantly only with late components. By orthogonalizing with respect to event-related activity, we found that variability in insula and temporoparietal junction is reflected in reaction time variability, rOFC and brainstem correlate with residual EEG variability, and ACC and rMFG are significantly correlated with both. To investigate interactions between these correlates of temporally specific EEG variability, we performed dynamic causal modeling (DCM) on the fMRI data. We found strong evidence for reciprocal effective connections between the brainstem and cortical regions. Our results support the adaptive gain theory of locus ceruleus–norepinephrine (LC–NE) function and the proposed functional relationship between the LC–NE system, right-hemisphere ventral attention network, and P300 EEG response.

Simultaneous EEG and fMRI made easy

It is attractive to consider an approach similar to the ballistocadiogram suppression in removal of the gradient artifacts. However, small timing shifts of the digital sampling with respect to the gradient activity will result in incomplete removal of the artifact. With a sampling frequency fs and an artifact frequency (e.g., due to gradients) of f0, sampling errors of = 2πf0/fs are to be expected. With as the residual artifact (the difference between the original and phase shifted signal) it is easy to show that:

Simultaneous EEG-fMRI reveals a temporal cascade of task-related and default-mode activations during a simple target detection task.

Focused attention continuously and inevitably fluctuates, and to completely understand the mechanisms responsible for these modulations it is necessary to localize the brain regions involved. During a simple visual oddball task, neural responses measured by electroencephalography (EEG) modulate primarily with attention, but source localization of the correlates is a challenge. In this study we use single-trial analysis of simultaneously-acquired scalp EEG and functional magnetic resonance image (fMRI) data to investigate the blood oxygen level dependent (BOLD) correlates of modulations in task-related attention, and we unravel the temporal cascade of these transient activations. We hypothesize that activity in brain regions associated with various task-related cognitive processes modulates with attention, and that their involvements occur transiently in a specific order. We analyze the fMRI BOLD signal by first regressing out the variance linked to observed stimulus and behavioral events. We then correlate the residual variance with the trial-to-trial variation of EEG discriminating components for identical stimuli, estimated at a sequence of times during a trial. Post-stimulus and early in the trial, we find activations in right-lateralized frontal regions and lateral occipital cortex, areas that are often linked to task-dependent processes, such as attentional orienting, and decision certainty. After the behavioral response we see correlates in areas often associated with the default-mode network and introspective processing, including precuneus, angular gyri, and posterior cingulate cortex. Our results demonstrate that during simple tasks both task-dependent and default-mode networks are transiently engaged, with a distinct temporal ordering and millisecond timescale.

Prospective active marker motion correction improves statistical power in BOLD fMRI

Group level statistical maps of blood oxygenation level dependent (BOLD) signals acquired using functional magnetic resonance imaging (fMRI) have become a basic measurement for much of systems, cognitive and social neuroscience. A challenge in making inferences from these statistical maps is the noise and potential confounds that arise from the head motion that occurs within and between acquisition volumes. This motion results in the scan plane being misaligned during acquisition, ultimately leading to reduced statistical power when maps are constructed at the group level. In most cases, an attempt is made to correct for this motion through the use of retrospective analysis methods. In this paper, we use a prospective active marker motion correction (PRAMMO) system that uses radio frequency markers for real-time tracking of motion, enabling on-line slice plane correction. We show that the statistical power of the activation maps is substantially increased using PRAMMO compared to conventional retrospective correction. Analysis of our results indicates that the PRAMMO acquisition reduces the variance without decreasing the signal component of the BOLD (beta). Using PRAMMO could thus improve the overall statistical power of fMRI based BOLD measurements, leading to stronger inferences of the nature of processing in the human brain.

Removal of BCG Artifacts Using a Non-Kirchhoffian Overcomplete Representation

We present a nonlinear unmixing approach for extracting the ballistocardiogram (BCG) from EEG recorded in an MR scanner during simultaneous acquisition of functional MRI (fMRI). First, an overcomplete basis is identified in the EEG based on a custom multipath EEG electrode cap. Next, the overcomplete basis is used to infer non-Kirchhoffian latent variables that are not consistent with a conservative electric field. Neural activity is strictly Kirchhoffian while the BCG artifact is not, and the representation can hence be used to remove the artifacts from the data in a way that does not attenuate the neural signals needed for optimal single-trial classification performance. We compare our method to more standard methods for BCG removal, namely independent component analysis and optimal basis sets, by looking at single-trial classification performance for an auditory oddball experiment. We show that our overcomplete representation method for removing BCG artifacts results in better single-trial classification performance compared to the conventional approaches, indicating that the derived neural activity in this representation retains the complex information in the trial-to-trial variability.

A System for Single-trial Analysis of Simultaneously Acquired EEG and fMRI

In this paper we describe a system for simultaneously acquiring EEG and fMRI and evaluate it in terms of discriminating, single-trial, task-related neural components in the EEG. Using an auditory oddball stimulus paradigm, we acquire EEG data both inside and outside a 1.5T MR scanner and compare both power spectra and single-trial discrimination performance for both conditions. We find that EEG activity acquired inside the MR scanner during echo planer image acquisition is of high enough quality to enable single-trial discrimination performance that is 95 % of that acquired outside the scanner. We conclude that EEG acquired simultaneously with fMRI is of high enough fidelity to permit single-trial analysis.

Adaptive Thresholding for Improving Sensitivity in Single-Trial Simultaneous EEG/fMRI.

A common approach used to fuse simultaneously recorded EEG and fMRI is to correlate trial-by-trial variability in the EEG, or variability of components derived therefrom, with the blood oxygenation level dependent response. When this correlation is done using the conventional univariate approach, for example with the general linear model, there is the usual problem of correcting the statistics for multiple comparisons. Cluster thresholding is often used as the correction of choice, though in many cases it is utilized in an ad hoc way, for example by employing the same cluster thresholds for both traditional regressors (stimulus or behaviorally derived) and EEG-derived regressors. In this paper we describe a resampling procedure that takes into account the a priori statistics of the trial-to-trial variability of the EEG-derived regressors in a way that trades off cluster size and maximum voxel Z-score to properly correct for multiple comparisons. We show that this data adaptive procedure improves sensitivity for smaller clusters of activation, without sacrificing the specificity of the results. Our results suggest that extra care is needed in correcting statistics when the regressor model is derived from noisy and/or uncertain measurements, as is the case for regressors constructed from single-trial variations in the EEG.

Signal Processing and Machine Learning for Single-trial Analysis of Simultaneously Acquired EEG and fMRI

Publisher Summary The simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a potentially powerful multimodal imaging technique for measuring the functional activity of the human brain. Given that EEG measures the electrical activity of neural populations while fMRI measures hemodynamics via a blood oxygenation-level-dependent (BOLD) signal related to neuronal activity, simultaneous EEG/fMRI (hereafter referred to as EEG/fMRI) offers a modality to investigate the relationship between these two phenomena within the context of noninvasive neuroimaging. Though fMRI is widely used to study cognitive and perceptual function, there is still substantial debate regarding the relation- ship between local neuronal activity and hemodynamic changes. Another rationale for EEG/fMRI is that, despite the fact that the individual modalities measure markedly different physiological phenomena, in terms of spatial and temporal resolution they are quite complementary. EEG offers millisecond temporal resolution; however, the spatial sampling density and ill-posed nature of the inverse model problem limit its spatial resolution. On the other hand, fMRI provides millimeter spatial resolution, but because of scanning rates and the low-pass nature of the BOLD response, the temporal resolution is limited. One approach that has been adopted to take advantage of this complementarity is to use fMRI activations to seed EEG source localization.

A multimodal encoding model applied to imaging decision-related neural cascades in the human brain

ABSTRACT Perception and cognition in the brain are naturally characterized as spatiotemporal processes. Decision‐making, for example, depends on coordinated patterns of neural activity cascading across the brain, running in time from stimulus to response and in space from primary sensory regions to the frontal lobe. Measuring this cascade is key to developing an understanding of brain function. Here we report on a novel methodology that employs multi‐modal imaging for inferring this cascade in humans at unprecedented spatiotemporal resolution. Specifically, we develop an encoding model to link simultaneously measured electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals to infer high‐resolution spatiotemporal brain dynamics during a perceptual decision. After demonstrating replication of results from the literature, we report previously unobserved sequential reactivation of a substantial fraction of the pre‐response network whose magnitude correlates with a proxy for decision confidence. Our encoding model, which temporally tags BOLD activations using time localized EEG variability, identifies a coordinated and spatially distributed neural cascade that is associated with a perceptual decision. In general the methodology illuminates complex brain dynamics that would otherwise be unobservable using fMRI or EEG acquired separately. HIGHLIGHTSAn encoding model method is proposed that links simultaneously measured EEG and fMRI.The method temporally tags BOLD activations using time localized EEG variability.The method is applied to EEG/fMRI acquired during a perceptual decision making task.Results include a previously unobserved reactivation of a pre‐response network.The magnitude of the reactivation correlates with a proxy for decision confidence.

Neurobiological correlates of impulsivity in healthy adults: Lower prefrontal gray matter volume and spontaneous eye-blink rate but greater resting-state functional connectivity in basal ganglia-thalamo-cortical circuitry

&NA; Studies consistently implicate aberrance of the brains reward‐processing and decision‐making networks in disorders featuring high levels of impulsivity, such as attention‐deficit hyperactivity disorder, substance use disorder, and psychopathy. However, less is known about the neurobiological determinants of individual differences in impulsivity in the general population. In this study of 105 healthy adults, we examined relationships between impulsivity and three neurobiological metrics – gray matter volume, resting‐state functional connectivity, and spontaneous eye‐blink rate, a physiological indicator of central dopaminergic activity. Impulsivity was measured both by performance on a task of behavioral inhibition (go/no‐go task) and by self‐ratings of attentional, motor, and non‐planning impulsivity using the Barratt Impulsiveness Scale (BIS‐11). Overall, we found that less gray matter in medial orbitofrontal cortex and paracingulate gyrus, greater resting‐state functional connectivity between nodes of the basal ganglia‐thalamo‐cortical network, and lower spontaneous eye‐blink rate were associated with greater impulsivity. Specifically, less prefrontal gray matter was associated with higher BIS‐11 motor and non‐planning impulsivity scores, but was not related to task performance; greater correlated resting‐state functional connectivity between the basal ganglia and thalamus, motor cortices, and prefrontal cortex was associated with worse no‐go trial accuracy on the task and with higher BIS‐11 motor impulsivity scores; lower spontaneous eye‐blink rate was associated with worse no‐go trial accuracy and with higher BIS‐11 motor impulsivity scores. These data provide evidence that individual differences in impulsivity in the general population are related to variability in multiple neurobiological metrics in the brains reward‐processing and decision‐making networks. HighlightsDifferences in impulsivity are linked to variability in multiple metrics.Greater impulsivity is associated with less prefrontal gray matter volume.Greater impulsivity is associated with increased functional connectivity.Greater impulsivity is associated with lower spontaneous eye‐blink rate.