Kevin Whittingstall

Frequency-Band Coupling in Surface EEG Reflects Spiking Activity in Monkey Visual Cortex

Although the electroencephalogram (EEG) is widely used in research and clinical settings, its link to the underlying neural activity during sensory processing remains poorly understood. To investigate this, we made simultaneous recordings of surface EEG, intracortical local field potential, and multiunit activity (MUA) in the alert monkey visual cortex during presentation of natural movies. Using a general linear model, we show that in single trials, EEG power in the gamma band (30-100 Hz) and phase in delta band (2-4 Hz) are significant predictors of the MUA response. Specifically, we found that the MUA response was strongest only when increases in EEG gamma power occurred during the negative-going phase of the delta wave, thus revealing a frequency-band coupling mechanism that can be exploited to infer population spiking activity. This finding may open up a new dimension in the use and interpretation of EEG in normal and pathological conditions.

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

BackgroundInformation theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.ResultsHere we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies.ConclusionThe new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.

Towards quantitative connectivity analysis: reducing tractography biases

Diffusion MRI tractography is often used to estimate structural connections between brain areas and there is a fast-growing interest in quantifying these connections based on their position, shape, size and length. However, a portion of the connections reconstructed with tractography is biased by their position, shape, size and length. Thus, connections reconstructed are not equally distributed in all white matter bundles. Quantitative measures of connectivity based on the streamline distribution in the brain such as streamline count (density), average length and spatial extent (volume) are biased by erroneous streamlines produced by tractography algorithms. In this paper, solutions are proposed to reduce biases in the streamline distribution. First, we propose to optimize tractography parameters in terms of connectivity. Then, we propose to relax the tractography stopping criterion with a novel probabilistic stopping criterion and a particle filtering method, both based on tissue partial volume estimation maps calculated from a T1-weighted image. We show that optimizing tractography parameters, stopping and seeding strategies can reduce the biases in position, shape, size and length of the streamline distribution. These tractography biases are quantitatively reported using in-vivo and synthetic data. This is a critical step towards producing tractography results for quantitative structural connectivity analysis.

Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model

Despite the widespread use of EEGs to measure the large-scale dynamics of the human brain, little is known on how the dynamics of EEGs relates to that of the underlying spike rates of cortical neurons. However, progress was made by recent neurophysiological experiments reporting that EEG delta-band phase and gamma-band amplitude reliably predict some complementary aspects of the time course of spikes of visual cortical neurons. To elucidate the mechanisms behind these findings, here we hypothesize that the EEG delta phase reflects shifts of local cortical excitability arising from slow fluctuations in the network input due to entrainment to sensory stimuli or to fluctuations in ongoing activity, and that the resulting local excitability fluctuations modulate both the spike rate and the engagement of excitatory-inhibitory loops producing gamma-band oscillations. We quantitatively tested these hypotheses by simulating a recurrent network of excitatory and inhibitory neurons stimulated with dynamic inputs presenting temporal regularities similar to that of thalamic responses during naturalistic visual stimulation and during spontaneous activity. The network model reproduced in detail the experimental relationships between spike rate and EEGs, and suggested that the complementariness of the prediction of spike rates obtained from EEG delta phase or gamma amplitude arises from nonlinearities in the engagement of excitatory-inhibitory loops and from temporal modulations in the amplitude of the network input, which respectively limit the predictability of spike rates from gamma amplitude or delta phase alone. The model suggested also ways to improve and extend current algorithms for online prediction of spike rates from EEGs.

Effects of Neural Synchrony on Surface EEG

It has long been assumed that the surface electroencephalography (EEG) signal depends on both the amplitude and spatial synchronization of underlying neural activity, though isolating their respective contribution remains elusive. To address this, we made simultaneous surface EEG measurements along with intracortical recordings of local field potentials (LFPs) in the primary visual cortex of behaving nonhuman primates. We found that trial-by-trial fluctuations in EEG power could be explained by a linear combination of LFP power and interelectrode temporal synchrony. This effect was observed in both stimulus and stimulus-free conditions and was particularly strong in the gamma range (30-100 Hz). Subsequently, we used pharmacological manipulations to show that neural synchrony can produce a positively modulated EEG signal even when the LFP signal is negatively modulated. Taken together, our results demonstrate that neural synchrony can modulate EEG signals independently of amplitude changes in neural activity. This finding has strong implications for the interpretation of EEG in basic and clinical research, and helps reconcile EEG response discrepancies observed in different modalities (e.g., EEG vs. functional magnetic resonance imaging) and different spatial scales (e.g., EEG vs. intracranial EEG).

Real-time multi-peak tractography for instantaneous connectivity display

The computerized process of reconstructing white matter tracts from diffusion MRI (dMRI) data is often referred to as tractography. Tractography is nowadays central in structural connectivity since it is the only non-invasive technique to obtain information about brain wiring. Most publicly available tractography techniques and most studies are based on a fixed set of tractography parameters. However, the scale and curvature of fiber bundles can vary from region to region in the brain. Therefore, depending on the area of interest or subject (e.g., healthy control vs. tumor patient), optimal tracking parameters can be dramatically different. As a result, a slight change in tracking parameters may return different connectivity profiles and complicate the interpretation of the results. Having access to tractography parameters can thus be advantageous, as it will help in better isolating those which are sensitive to certain streamline features and potentially converge on optimal settings which are area-specific. In this work, we propose a real-time fiber tracking (RTT) tool which can instantaneously compute and display streamlines. To achieve such real-time performance, we propose a novel evolution equation based on the upsampled principal directions, also called peaks, extracted at each voxel of the dMRI dataset. The technique runs on a single Computer Processing Unit (CPU) without the need for Graphical Unit Processing (GPU) programming. We qualitatively illustrate and quantitatively evaluate our novel multi-peak RTT technique on phantom and human datasets in comparison with the state of the art offline tractography from MRtrix, which is robust to fiber crossings. Finally, we show how our RTT tool facilitates neurosurgical planning and allows one to find fibers that infiltrate tumor areas, otherwise missing when using the standard default tracking parameters.

Regional Variations in Vascular Density Correlate With Resting-State and Task-Evoked Blood Oxygen Level-Dependent Signal Amplitude

Functional magnetic resonance imaging (fMRI) has become one of the primary tools used for noninvasively measuring brain activity in humans. For the most part, the blood oxygen level‐dependent (BOLD) contrast is used, which reflects the changes in hemodynamics associated with active brain tissue. The main advantage of the BOLD signal is that it is relatively easy to measure and thus is often used as a proxy for comparing brain function across population groups (i.e., control vs. patient). However, it is particularly weighted toward veins whose structural architecture is known to vary considerably across the brain. This makes it difficult to interpret whether differences in BOLD between cortical areas reflect true differences in neural activity or vascular structure. We therefore investigated how regional variations of vascular density (VAD) relate to the amplitude of resting‐state and task‐evoked BOLD signals. To address this issue, we first developed an automated method for segmenting veins in images acquired with susceptibility‐weighted imaging, allowing us to visualize the venous vascular tree across the brain. In 19 healthy subjects, we then applied voxel‐based morphometry (VBM) to T1‐weighted images and computed regional measures of gray matter density (GMD). We found that, independent of spatial scale, regional variations in resting‐state and task‐evoked fMRI amplitudes were better correlated to VAD compared to GMD. Using a general linear model (GLM), it was observed that the bulk of regional variance in resting‐state activity could be modeled by VAD. Cortical areas whose resting‐state activity was most suppressed by VAD correction included Cuneus, Precuneus, Culmen, and BA 9, 10, and 47. Taken together, our results suggest that resting‐state BOLD signals are significantly related to the underlying structure of the brain vascular system. Calibrating resting BOLD activity by venous structure may result in a more accurate interpretation of differences observed between cortical areas and/or individuals. Hum Brain Mapp 35:1906–1920, 2014.

Dopamine-Induced Dissociation of BOLD and Neural Activity in Macaque Visual Cortex

Neuromodulators determine how neural circuits process information during cognitive states such as wakefulness, attention, learning, and memory. fMRI can provide insight into their function and dynamics, but their exact effect on BOLD responses remains unclear, limiting our ability to interpret the effects of changes in behavioral state using fMRI. Here, we investigated the effects of dopamine (DA) injections on neural responses and haemodynamic signals in macaque primary visual cortex (V1) using fMRI (7T) and intracortical electrophysiology. Aside from DAs involvement in diseases such as Parkinsons and schizophrenia, it also plays a role in visual perception. We mimicked DAergic neuromodulation by systemic injection of L-DOPA and Carbidopa (LDC) or by local application of DA in V1 and found that systemic application of LDC increased the signal-to-noise ratio (SNR) and amplitude of the visually evoked neural responses in V1. However, visually induced BOLD responses decreased, whereas cerebral blood flow (CBF) responses increased. This dissociation of BOLD and CBF suggests that dopamine increases energy metabolism by a disproportionate amount relative to the CBF response, causing the reduced BOLD response. Local application of DA in V1 had no effect on neural activity, suggesting that the dopaminergic effects are mediated by long-range interactions. The combination of BOLD-based and CBF-based fMRI can provide a signature of dopaminergic neuromodulation, indicating that the application of multimodal methods can improve our ability to distinguish sensory processing from neuromodulatory effects.

Integration of EEG source imaging and fMRI during continuous viewing of natural movies

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are noninvasive neuroimaging tools which can be used to measure brain activity with excellent temporal and spatial resolution, respectively. By combining the neural and hemodynamic recordings from these modalities, we can gain better insight into how and where the brain processes complex stimuli, which may be especially useful in patients with different neural diseases. However, due to their vastly different spatial and temporal resolutions, the integration of EEG and fMRI recordings is not always straightforward. One fundamental obstacle has been that paradigms used for EEG experiments usually rely on event-related paradigms, while fMRI is not limited in this regard. Therefore, here we ask whether one can reliably localize stimulus-driven EEG activity using the continuously varying feature intensities occurring in natural movie stimuli presented over relatively long periods of time. Specifically, we asked whether stimulus-driven aspects in the EEG signal would be co-localized with the corresponding stimulus-driven BOLD signal during free viewing of a movie. Secondly, we wanted to integrate the EEG signal directly with the BOLD signal, by estimating the underlying impulse response function (IRF) that relates the BOLD signal to the underlying current density in the primary visual area (V1). We made sequential fMRI and 64-channel EEG recordings in seven subjects who passively watched 2-min-long segments of a James Bond movie. To analyze EEG data in this natural setting, we developed a method based on independent component analysis (ICA) to reject EEG artifacts due to blinks, subject movement, etc., in a way unbiased by human judgment. We then calculated the EEG source strength of this artifact-free data at each time point of the movie within the entire brain volume using low-resolution electromagnetic tomography (LORETA). This provided for every voxel in the brain (i.e., in 3D space) an estimate of the current density at every time point. We then carried out a correlation between the time series of visual contrast changes in the movie with that of EEG voxels. We found the most significant correlations in visual area V1, just as seen in previous fMRI studies (Bartels A, Zeki, S, Logothetis NK. Natural vision reveals regional specialization to local motion and to contrast-invariant, global flow in the human brain. Cereb Cortex 2008;18(3):705-717), but on the time scale of milliseconds rather than of seconds. To obtain an estimate of how the EEG signal relates to the BOLD signal, we calculated the IRF between the BOLD signal and the estimated current density in area V1. We found that this IRF was very similar to that observed using combined intracortical recordings and fMRI experiments in nonhuman primates. Taken together, these findings open a new approach to noninvasive mapping of the brain. It allows, firstly, the localization of feature-selective brain areas during natural viewing conditions with the temporal resolution of EEG. Secondly, it provides a tool to assess EEG/BOLD transfer functions during processing of more natural stimuli. This is especially useful in combined EEG/fMRI experiments, where one can now potentially study neural-hemodynamic relationships across the whole brain volume in a noninvasive manner.

Glucose hypometabolism is highly localized, but lower cortical thickness and brain atrophy are widespread in cognitively normal older adults

Several studies have suggested that glucose hypometabolism may be present in specific brain regions in cognitively normal older adults and could contribute to the risk of subsequent cognitive decline. However, certain methodological shortcomings, including a lack of partial volume effect (PVE) correction or insufficient cognitive testing, confound the interpretation of most studies on this topic. We combined [(18)F]fluorodeoxyglucose ([(18)F]FDG) positron emission tomography (PET) and magnetic resonance (MR) imaging to quantify cerebral metabolic rate of glucose (CMRg) as well as cortical volume and thickness in 43 anatomically defined brain regions from a group of cognitively normal younger (25 ± 3 yr old; n = 25) and older adults (71 ± 9 yr old; n = 31). After correcting for PVE, we observed 11-17% lower CMRg in three specific brain regions of the older group: the superior frontal cortex, the caudal middle frontal cortex, and the caudate (P ≤ 0.01 false discovery rate-corrected). In the older group, cortical volumes and cortical thickness were 13-33 and 7-18% lower, respectively, in multiple brain regions (P ≤ 0.01 FDR correction). There were no differences in CMRg between individuals who were or were not prescribed antihypertensive medication. There were no significant correlations between CMRg and cognitive performance or metabolic parameters measured in fasting plasma. We conclude that highly localized glucose hypometabolism and widespread cortical thinning and atrophy can be present in older adults who are cognitively normal, as assessed using age-normed neuropsychological testing measures.