Variance and Autocorrelation of the Spontaneous Slow Brain Activity

Slow (<0.1 Hz) oscillatory activity in the human brain, as measured by functional magnetic imaging, has been used to identify neural networks and their dysfunction in specific brain diseases. Its intrinsic properties may also be useful to investigate brain functions. We investigated the two functional maps: variance and first order autocorrelation coefficient (r1). These two maps had distinct spatial distributions and the values were significantly different among the subdivisions of the precuneus and posterior cingulate cortex that were identified in functional connectivity (FC) studies. The results reinforce the functional segregation of these subdivisions and indicate that the intrinsic properties of the slow brain activity have physiological relevance. Further, we propose a sample size (degree of freedom) correction when assessing the statistical significance of FC strength with r1 values, which enables a better understanding of the network changes related to various brain diseases

Limitations of the isolated GP-STN network

An in vitro mouse slice preparation from control and MPTP-treated mice in which functional reciprocal GP-STN connectivity is maintained, does not produce oscillatory bursting or synchronous activity neuronal activity. Pharmacological interventions that produce bursting activity do so without concomitant neuronal synchrony, or a requirement for glutamate or GABA transmission. Pre-treatment with MPTP did not alter this behaviour. Thus, we have no evidence that the functionally connected, but isolated, GP — STN network can act as a pacemaker for synchronous correlated activity in the basal ganglia and must conclude that other inputs such as those from cortex and/or striatum are required

Bidirectional Coupling between Astrocytes and Neurons Mediates Learning and Dynamic Coordination in the Brain: A Multiple Modeling Approach

In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model) which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a “learning signal” to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity), and the modeling strategy may be extended to coordination among remote neuron clusters

Perception of Apparent Motion is Related to the Magnetic Response From the Human Extrastriate Cortex

this article. 2 Methods 2.1 Subjects Five healthy volunteers (all men, aged 26-39) participated in this study. All subjects gave informed consent prior to participation in this study. All of them were right handed but a subject (S3). All subjects had normal visual acuity or with optical lenses, if necessar