Transient Information Flow in a Network of Excitatory and Inhibitory Model Neurons: Role of Noise and Signal Autocorrelation

We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an oscillatory firing regime to a state of asynchronous irregular firing or quiescence depending on the rate of external background spikes. We find that in terms of information buffering the network performs best for a moderate, non-zero, amount of noise. Analogous to the phenomenon of stochastic resonance the performance decreases for higher and lower noise levels. The optimal amount of noise corresponds to the transition zone between a quiescent state and a regime of stochastic dynamics. This provides a potential explanation on the role of non-oscillatory population activity in a simplified model of cortical micro-circuits.Comment: 27 pages, 7 figures, to appear in J. Physiology (Paris) Vol. 9

Oxygen Polarography in the Awake Macaque: Bridging BOLD fMRI and Electrophysiology

Blood oxygen level dependent (BOLD) fMRI is the predominant method for evaluating human brain activity. This technique identifies brain activity by measuring blood oxygen changes associated with neural activity. Although clearly related, the nature of the relationship between BOLD fMRI identified brain activity and electrophysiologically measured neural activity remains unclear. Direct comparison of BOLD fMRI and electrophysiology has been severely limited by the technical challenges of combining the two techniques. Microelectrode electrophysiology in non-human primates is an excellent model for studying neural activity related to high order brain function similar to that commonly studied with BOLD fMRI in humans, i.e. attention, working memory, engagement. This thesis discusses the development of, validation of, and first results obtained using a new multi-site oxygen polarographic recording system in the awake macaques as a surrogate for BOLD fMRI. Oxygen polarography measures tissue oxygen which is coupled to blood oxygen. This tool offers higher resolution than BOLD fMRI and can be more readily combined with electrophysiology. Using this new tool we evaluated local field potential and oxygen responses to an engaging visual stimulus in two distinct brain systems. In area V3, a key region in the visual system and representative of stimulus driven sensory cortex, we show increased tissue oxygen and local field potential power in response to visual stimulus. In area 23 of the posterior cingulate cortex (PCC), a hub of the default-mode network we show decreased oxygen and local field potential in response to the same stimulus. The default-mode network is a set of brain regions identified in humans whose BOLD fMRI activity is higher at rest than during external engagement, arguing that they sub-serve a function that is engaged as the default-mode in humans. Our results provide new evidence of default-mode network activity in the macaque similar to that seen in humans, provide evidence that the BOLD identified default-mode suppression reflects neural suppression and overall support a strong relationship between neural activity and BOLD fMRI. However, we also note that the LFP responses in both regions show substantial nuances that cannot be seen in the oxygen response and suggest response complexity that is invisible with fMRI. Further the nature of the relationship between LFP and oxygen differs between regions. Our multi-site technique also allows us to evaluate inter-regional interaction of ongoing oxygen fluctuations. Inter-regional correlation of BOLD fMRI fluctuations is commonly used as an index of functional connectivity and has provided new insight into behaviorally relevant aspects of the brains organization and its disruption in disease. Here we demonstrate that we can measure the same inter-regional correlation using oxygen polarography. We utilize the increased resolution of our technique to investigate the frequency structure of the signals driving the correlation and find that inter-regional correlation of oxygen fluctuations appears to depend on a rhythmic mechanism operating at ~0.06 Hz

Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations

Graph Signal Processing For Cancer Gene Co-Expression Network Analysis

Cancer heterogeneity arises from complex molecular interactions. Elucidating systems-level properties of gene interaction networks distinguishing cancer from normal cells is critical for understanding disease mechanisms and developing targeted therapies. Previous works focused only on identifying differences in network structures. In this study, we used graph frequency analysis of cancer genetic signals defined on a co-expression network to describe the spectral properties of underlying cancer systems. We demonstrated that cancer cells exhibit distinctive signatures in the graph frequency content of their gene expression signals. Applying graph frequency filtering, graph Fourier transforms, and its inverse to gene expression from different cancer stages resulted in significant improvements in average F-statistics of the genes compared to using their unfiltered expression levels. We propose graph spectral properties of cancer genetic signals defined on gene co-expression networks as cancer hallmarks with potential application for differential co-expression analysis

A new novel synchronization index of brain networks in hyperbolic EEG dynamics

The functional connectivity ofbrain connectivity changes its pattern over time i.e. dynamics, even in the resting state with an infinite number of degrees of freedom with local couplings. Recently, quantifying the level of synchrony has received considerable attention. We hypothesized that time-varying instantaneous phase synchronization over local couplings are defined in hyperbolic space and different brain regions can identify failures, flexibility, and stability in network dynamics.Our goal is to understand the phase synchronization changes of the beta-gamma band, and in addition, to investigate Shannon entropy based on phase synchronization stability. Whole EEG dynamics from local phase synchronizations was used to detect treatment resistance from both hemispheres in OCD patients. Temporal filtering and Hilbert transforms were performed to infer beta-gamma band phase difference activity from the EEG brain dynamics.Then, the response beta-gamma band phase stability was quantified using a new phase synchronization index (PSI). Results indicated significantly changed phase synchronization of the response and non-response to treatment, patients in OCD patients in F7 electrode. Greater phase fluctuations of beta-gamma synchronizations in treatment resistance OCD is claiming phase deficiencies within neural populations.This study first provides experimental and theoretical support for characterizing cycle structure depends on the non-Euclidian dynamics of neural phase synchrony caused by disturbances of underlying neurotransmitter systems, as reflected in different normal and disease states.Publisher's Versio