Mircea I. Chelaru

Correlated variability in laminar cortical circuits.

Despite the fact that strong trial-to-trial correlated variability in responses has been reported in many cortical areas, recent evidence suggests that neuronal correlations are much lower than previously thought. Here, we used multicontact laminar probes to revisit the issue of correlated variability in primary visual (V1) cortical circuits. We found that correlations between neurons depend strongly on local network context--whereas neurons in the input (granular) layers showed virtually no correlated variability, neurons in the output layers (supragranular and infragranular) exhibited strong correlations. The laminar dependence of noise correlations is consistent with recurrent models in which neurons in the granular layer receive intracortical inputs from nearby cells, whereas supragranular and infragranular layer neurons receive inputs over larger distances. Contrary to expectation that the output cortical layers encode stimulus information most accurately, we found that the input network offers superior discrimination performance compared to the output networks.

Efficient coding in heterogeneous neuronal populations

A ubiquitous feature of neuronal responses within a cortical area is their high degree of inhomogeneity. Even cells within the same functional column are known to have highly heterogeneous response properties when the same stimulus is presented. Whether the wide diversity of neuronal responses is an epiphenomenon or plays a role for cortical function is unknown. Here, we examined the relationship between the heterogeneity of neuronal responses and population coding. Contrary to our expectation, we found that the high variability of intrinsic response properties of individual cells changes the structure of neuronal correlations to improve the information encoded in the population activity. Thus, the heterogeneity of neuronal responses is in fact beneficial for sensory coding when stimuli are decoded from the population response.

Sex differences in the behavioral response to methylphenidate in three adolescent rat strains (WKY, SHR, SD)

Methylphenidate (MPD) is the most widely used drug in the treatment of attention-deficit hyperactivity disorder (ADHD). ADHD has a high incidence in children and can persist in adolescence and adulthood. The relation between sex and the effects of acute and chronic MPD treatment was examined using adolescent male and female rats from three genetically different strains: spontaneously hyperactive rat (SHR), Wistar-Kyoto (WKY) and Sprague-Dawley (SD). Rats from each strain and sex were randomly divided into a control group that received saline injections and three MPD groups that received either 0.6 or 2.5 or 10mg/kg MPD injections. All rats received saline on experimental day 1 (ED1). On ED2 to ED7 and ED11, the rats were injected either with saline or MPD and received no treatment on ED8-ED10. The open field assay was used to assess the dose-response of acute and chronic MPD administration. Significant sex differences were found. Female SHR and SD rats were significantly more active after MPD injections than their male counterparts, while the female WKY rats were less active than the male WKY rats. Dose dependent behavioral sensitization or tolerance to MPD treatment was not observed for SHR or SD rats, but tolerance to MPD was found in WKY rats for the 10mg/kg MPD dose. The use of dose-response protocol and evaluating different locomotor indices provides the means to identify differences between the sexes and the genetic strain in adolescent rats. In addition these differences suggest that the differences to MPD treatment between the sexes are not due to the reproductive hormones.

Negative Correlations in Visual Cortical Networks

The amount of information encoded by cortical circuits depends critically on the capacity of nearby neurons to exhibit trial-to-trial (noise) correlations in their responses. Depending on their sign and relationship to signal correlations, noise correlations can either increase or decrease the population code accuracy relative to uncorrelated neuronal firing. Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive. We addressed this issue by performing multiple-electrode recording in the superficial layers of the primary visual cortex (V1) of alert monkey. Despite the fact that positive noise correlations decayed exponentially with the difference in the orientation preference between cells, negative correlations were uniformly distributed across the population. Using a statistical model for Fisher Information estimation, we found that a mild increase in negative correlations causes a sharp increase in network accuracy even when mean correlations were held constant. To examine the variables controlling the strength of negative correlations, we implemented a recurrent spiking network model of V1. We found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy. Altogether, these results contribute to our understanding of the neuronal mechanism involved in the generation of negative correlations and their beneficial impact on cortical circuit function.

Reactivation of visual-evoked activity in human cortical networks

In the absence of sensory input, neuronal networks are far from being silent. Whether spontaneous changes in ongoing activity reflect previous sensory experience or stochastic fluctuations in brain activity is not well understood. Here we demonstrate reactivation of stimulus-evoked activity that is distributed across large areas in the human brain. We performed simultaneous electrocorticography recordings from occipital, parietal, temporal, and frontal areas in awake humans in the presence and absence of sensory stimulation. We found that, in the absence of visual input, repeated exposure to brief natural movies induces robust stimulus-specific reactivation at individual recording sites. The reactivation sites were characterized by greater global connectivity compared with those sites that did not exhibit reactivation. Our results indicate a surprising degree of short-term plasticity across multiple networks in the human brain as a result of repeated exposure to unattended information.

F125. Brain connectivity related to sleep-wake state: An intracranial EEG study

Introduction Understanding brain connectivity in health and disease is a major challenge for basic and translational neuroscience. Direct EEG recordings from the brain surface (electrocorticography; ECoG) in epilepsy provide unique opportunity for studying human neuro-electric connectivity with reference to the wake, sleep and epileptic states. A major conundrum is reconciling the views of sleep being a disconnected state (Massimini et al., 2005) with the hypersynchronicity of sleep that favors seizure occurrence in the partial epilepsies, implying a heightened connectivity. Using spectral analysis and graph-theoretic measures (Bullmore and Sporns, 2009) applied to ECoG recordings in 6 patients undergoing continuous monitoring, we demonstrate how a reconciliation between these two scenarios is possible. Methods ECoG data in average reference format from six patients with refractory focal epilepsy undergoing prolonged pre-surgical video-EEG telemetry with subdural grid electrodes was analyzed. Segments of wakefulness and sleep (25–30 s long) were concatenated into contiguous epochs and normalized to zero mean and unit variance. Power spectra were computed by standard methods and the amplitude spectra curve-fitted empirically with a three-parameter function. The analysis was repeated over sliding windows of 30 s duration across the whole epoch. A fuzzy C-means method was used to cluster each parameter triplet into an activity score between [0–1], with 0 representing deep sleep, and 1 being alert wakefulness. The epoch time series were then filtered into the canonical Berger δ, θ, α, β and γ EEG bands. For each band, standard graph-theoretic measures were computed over sliding window segments across whole epochs to correspond with the activity score computations. Pearson correlations between the activity score and concurrently computed graph-theoretic connectivity metrics were calculated, and statistically significant correlations following Bonferroni correction (by a factor of 30) retained. Results We found that the coherence network modularity in the beta bands – relevant to high-frequency seizure-onset rhythms – correlated positively with wakefulness, while delta, theta and alpha modularity correlated negatively. An approximately reverse relationship was observed with respect to clustering coefficient. Conclusion Our results complement those obtained by resting state fMRI (Cox et al., 2014) and cortico-cortical evoked potentials (Usami et al., 2015) of sleep. We suggest that ECoG-based brain connectivity metrics are both state (sleep-wake) dependent and time-scale (waveform frequency) dependent. It is possible for the cortex to be ‘disconnected’ with respect to frequencies ostensibly underlying conscious wakefulness in the sleep state, yet ‘hyperconnected’ on time scales relevant to the transmission of epileptic seizures.

A unified statistical model for the human electrocorticogram

OBJECTIVE Extracellular field potentials (ECFs) generated in the cerebral cortex span a vast range of spatiotemporal scales. The process(es) leading to this large dynamic range remain debatable. Here we propose a novel statistical description of the amplitude spectrum of the human electrocorticogram (ECoG). METHODS Spectral analysis was performed on long-term recordings from epilepsy patients undergoing pre-surgical evaluation with intracranial electrodes. Amplitude spectra were fit with a multi-component Gaussian model on semi-logarithmic axes. RESULTS The Gaussian formulation provided excellent fits to the data. It also suggested how the changes accompanying the sleep-wake cycle and certain epileptiform transitions could be understood by variation in the parameters of the model. CONCLUSIONS The proposed continuum model synthesizes several previous observations regarding the statistical structure of the resting human ECoG. It offers a conceptual platform for understanding the EEG changes accompanying the sleep-wake cycle and pathologically hypersynchronous behaviour. SIGNIFICANCE Statistical characterisation of the spectral distribution of field potentials yield insight into the cortico-cortical interactions that underlie the summated cortical ECFs comprising the ECoG. Such insight is relevant for a synoptic understanding of major state changes in the brain that are diagnosed in clinical practice by visual inspection of the ECoG.