The amplitude and timing of the BOLD signal reflects the relationship between local field potential power at different frequencies.

There is growing evidence that several components of the mass neural activity contributing to the local field potential (LFP) can be partly separated by decomposing the LFP into nonoverlapping frequency bands. Although the blood oxygen level-dependent (BOLD) signal has been found to correlate preferentially with specific frequency bands of the LFP, it is still unclear whether the BOLD signal relates to the activity expressed by each LFP band independently of the others or if, instead, it also reflects specific relationships among different bands. We investigated these issues by recording, simultaneously and with high spatiotemporal resolution, BOLD signal and LFP during spontaneous activity in early visual cortices of anesthetized monkeys (Macaca mulatta). We used information theory to characterize the statistical dependency between BOLD and LFP. We found that the alpha (8-12 Hz), beta (18-30 Hz), and gamma (40-100 Hz) LFP bands were informative about the BOLD signal. In agreement with previous studies, gamma was the most informative band. Both increases and decreases in BOLD signal reliably followed increases and decreases in gamma power. However, both alpha and beta power signals carried information about BOLD that was largely complementary to that carried by gamma power. In particular, the relationship between alpha and gamma power was reflected in the amplitude of the BOLD signal, while the relationship between beta and gamma bands was reflected in the latency of BOLD with respect to significant changes in gamma power. These results lay the basis for identifying contributions of different neural pathways to cortical processing using fMRI.

Noradrenergic neurons of the locus coeruleus are phase locked to cortical up-down states during sleep.

Nonrapid eye movement (NREM) sleep is characterized by periodic changes in cortical excitability that are reflected in the electroencephalography (EEG) as high-amplitude slow oscillations, indicative of cortical Up/Down states. These slow oscillations are thought to be involved in NREM sleep-dependent memory consolidation. Although the locus coeruleus (LC) noradrenergic system is known to play a role in off-line memory consolidation (that may occur during NREM sleep), cortico-coerulear interactions during NREM sleep have not yet been studied in detail. Here, we investigated the timing of LC spikes as a function of sleep-associated slow oscillations. Cortical EEG was monitored, along with activity of LC neurons recorded extracellularly, in nonanesthetized naturally sleeping rats. LC spike-triggered averaging of EEG, together with phase-locking analysis, revealed preferential firing of LC neurons along the ascending edge of the EEG slow oscillation, correlating with Down-to-Up state transition. LC neurons were locked best when spikes were shifted forward ∼50 ms in time with respect to the EEG slow oscillation. These results suggest that during NREM sleep, firing of LC neurons may contribute to the rising phase of the EEG slow wave by providing a neuromodulatory input that increases cortical excitability, thereby promoting plasticity within these circuits.

Optimal band separation of extracellular field potentials.

Local Field Potentials (LFPs) exhibit a broadband spectral structure that is traditionally partitioned into distinct frequency bands which are thought to originate from different types of neural events triggered by different processing pathways. However, the exact frequency boundaries of these processes are not known and, as a result, the frequency bands are often selected based on intuition, previous literature or visual inspection of the data. Here, we address these problems by developing a rigorous method for defining LFP frequency bands and their boundaries. The criterion introduced for determining the boundaries delimiting the bands is to maximize the information about an external correlate carried jointly by all bands in the partition. The method first partitions the LFP frequency range into two bands and then successively increases the number of bands in the partition. We applied the partitioning method to LFPs recorded from primary visual cortex of anaesthetized macaques, and we determined the optimal band partitioning that describes the encoding of naturalistic visual stimuli. The first optimal boundary partitioned the LFP response at 60 Hz into low and high frequencies, which had been previously found to convey independent information about the natural movie correlate. The second optimal boundary divided the high-frequency range at approximately 100 Hz into gamma and high-gamma frequencies, consistent with recent reports that these two bands reflect partly distinct neural processes. A third important boundary was at 25 Hz and it split the LFP range below 50 Hz into a stimulus-informative and a stimulus-independent band.

The laminar and temporal structure of stimulus information in the phase of field potentials of auditory cortex.

Recent studies have shown that the phase of low-frequency local field potentials (LFPs) in sensory cortices carries a significant amount of information about complex naturalistic stimuli, yet the laminar circuit mechanisms and the aspects of stimulus dynamics responsible for generating this phase information remain essentially unknown. Here we investigated these issues by means of an information theoretic analysis of LFPs and current source densities (CSDs) recorded with laminar multi-electrode arrays in the primary auditory area of anesthetized rats during complex acoustic stimulation (music and broadband 1/f stimuli). We found that most LFP phase information originated from discrete "CSD events" consisting of granular-superficial layer dipoles of short duration and large amplitude, which we hypothesize to be triggered by transient thalamocortical activation. These CSD events occurred at rates of 2-4 Hz during both stimulation with complex sounds and silence. During stimulation with complex sounds, these events reliably reset the LFP phases at specific times during the stimulation history. These facts suggest that the informativeness of LFP phase in rat auditory cortex is the result of transient, large-amplitude events, of the "evoked" or "driving" type, reflecting strong depolarization in thalamo-recipient layers of cortex. Finally, the CSD events were characterized by a small number of discrete types of infragranular activation. The extent to which infragranular regions were activated was stimulus dependent. These patterns of infragranular activations may reflect a categorical evaluation of stimulus episodes by the local circuit to determine whether to pass on stimulus information through the output layers.

Investigating static nonlinearities in neurovascular coupling.

Many statistical models of coupling between time changes of the band-limited power of neural signals and functional magnetic resonance imaging Blood Oxygenation Level Dependent (BOLD) signal time changes rely on linear convolution. The effect of nonlinear behaviors in single-trial relationships between neural signals and BOLD responses is rarely tested and included in models. Here we investigate whether using a static nonlinearity improves the prediction of single-trial BOLD responses from neural signals. A static nonlinearity is a nonlinear transformation of the convolution of neural responses which is implemented by the same nonlinear function for all time points. We evaluated this approach by applying it to simultaneous recordings of functional magnetic resonance imaging BOLD and band-limited neural signals (Local Field Potentials and Multi Unit Activity) from primary visual cortex of anaesthetized macaques. We found that using a simple polynomial static nonlinearity was sufficient to obtain highly significant improvements of the accuracy of single-trial BOLD prediction over the accuracy obtained with linear convolution. This suggests that static nonlinearities may be a useful tool for a compact and accurate statistical description of neurovascular coupling.

Sensory information in local field potentials and spikes from visual and auditory cortices: time scales and frequency bands.

Studies analyzing sensory cortical processing or trying to decode brain activity often rely on a combination of different electrophysiological signals, such as local field potentials (LFPs) and spiking activity. Understanding the relation between these signals and sensory stimuli and between different components of these signals is hence of great interest. We here provide an analysis of LFPs and spiking activity recorded from visual and auditory cortex during stimulation with natural stimuli. In particular, we focus on the time scales on which different components of these signals are informative about the stimulus, and on the dependencies between different components of these signals. Addressing the first question, we find that stimulus information in low frequency bands (<12 Hz) is high, regardless of whether their energy is computed at the scale of milliseconds or seconds. Stimulus information in higher bands (>50 Hz), in contrast, is scale dependent, and is larger when the energy is averaged over several hundreds of milliseconds. Indeed, combined analysis of signal reliability and information revealed that the energy of slow LFP fluctuations is well related to the stimulus even when considering individual or few cycles, while the energy of fast LFP oscillations carries information only when averaged over many cycles. Addressing the second question, we find that stimulus information in different LFP bands, and in different LFP bands and spiking activity, is largely independent regardless of time scale or sensory system. Taken together, these findings suggest that different LFP bands represent dynamic natural stimuli on distinct time scales and together provide a potentially rich source of information for sensory processing or decoding brain activity.

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

BACKGROUND: Information 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. RESULTS: Here 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. CONCLUSION: The 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.

On the use of information theory for the analysis of the relationship between neural and imaging signals.

Functional magnetic resonance imaging (fMRI) is a widely used method for studying the neural basis of cognition and of sensory function. A potential problem in the interpretation of fMRI data is that fMRI measures neural activity only indirectly, as a local change of deoxyhemoglobin concentration due to the metabolic demands of neural function. To build correct sensory and cognitive maps in the human brain, it is thus crucial to understand whether fMRI and neural activity convey the same type of information about external correlates. While a substantial experimental effort has been devoted to the simultaneous recordings of hemodynamic and neural signals, so far, the development of analysis methods that elucidate how neural and hemodynamic signals represent sensory information has received less attention. In this article, we critically review why the analytical framework of information theory, the mathematical theory of communication, is ideally suited to this purpose. We review the principles of information theory and explain how they could be applied to the analysis of fMRI and neural signals. We show that a critical advantage of information theory over more traditional analysis paradigms commonly used in the fMRI literature is that it can elucidate, within a single framework, whether an empirically observed correlation between neural and fMRI signals reflects either a similar stimulus tuning or a common source of variability unrelated to the external stimuli. In addition, information theory determines the extent to which these shared sources of stimulus signal and of variability lead fMRI and neural signals to convey similar information about external correlates. We then illustrate the formalism by applying it to the analysis of the information carried by different bands of the local field potential. We conclude by discussing the current methodological challenges that need to be addressed to make the information-theoretic approach more robustly applicable to the simultaneous recordings of neural and imaging data.

Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information.

Local field potentials (LFPs) reflect subthreshold integrative processes that complement spike train measures. However, little is yet known about the differences between how LFPs and spikes encode rich naturalistic sensory stimuli. We addressed this question by recording LFPs and spikes from the primary visual cortex of anesthetized macaques while presenting a color movie. We then determined how the power of LFPs and spikes at different frequencies represents the visual features in the movie. We found that the most informative LFP frequency ranges were 1-8 and 60-100 Hz. LFPs in the range of 12-40 Hz carried little information about the stimulus, and may primarily reflect neuromodulatory inputs. Spike power was informative only at frequencies <12 Hz. We further quantified "signal correlations" (correlations in the trial-averaged power response to different stimuli) and "noise correlations" (trial-by-trial correlations in the fluctuations around the average) of LFPs and spikes recorded from the same electrode. We found positive signal correlation between high-gamma LFPs (60-100 Hz) and spikes, as well as strong positive signal correlation within high-gamma LFPs, suggesting that high-gamma LFPs and spikes are generated within the same network. LFPs <24 Hz shared strong positive noise correlations, indicating that they are influenced by a common source, such as a diffuse neuromodulatory input. LFPs <40 Hz showed very little signal and noise correlations with LFPs >40 Hz and with spikes, suggesting that low-frequency LFPs reflect neural processes that in natural conditions are fully decoupled from those giving rise to spikes and to high-gamma LFPs.