Percolation in living neural networks

We study living neural networks by measuring the neurons' response to a global electrical stimulation. Neural connectivity is lowered by reducing the synaptic strength, chemically blocking neurotransmitter receptors. We use a graph-theoretic approach to show that the connectivity undergoes a percolation transition. This occurs as the giant component disintegrates, characterized by a power law with critical exponent $\beta \simeq 0.65$ is independent of the balance between excitatory and inhibitory neurons and indicates that the degree distribution is gaussian rather than scale freeComment: PACS numbers: 87.18.Sn, 87.19.La, 64.60.Ak http://www.weizmann.ac.il/complex/tlusty/papers/PhysRevLett2006.pd

Percolation in Living Neural Networks

We study living neural networks by measuring the neurons' response to a global electrical stimulation. Neural connectivity is lowered by reducing the synaptic strength, chemically blocking neurotransmitter receptors. We use a graph-theoretic approach to show that the connectivity undergoes a percolation transition. This occurs as the giant component disintegrates, characterized by a power law with an exponent β ≃ 0.65 . β is independent of the balance between excitatory and inhibitory neurons and indicates that the degree distribution is Gaussian rather than scale free

Cerebral Autoregulation Real-Time Monitoring.

Cerebral autoregulation is a mechanism which maintains constant cerebral blood flow (CBF) despite changes in mean arterial pressure (MAP). Assessing whether this mechanism is intact or impaired and determining its boundaries is important in many clinical settings, where primary or secondary injuries to the brain may occur. Herein we describe the development of a new ultrasound tagged near infra red light monitor which tracks CBF trends, in parallel, it continuously measures blood pressure and correlates them to produce a real time autoregulation index. Its performance is validated in both in-vitro experiment and a pre-clinical case study. Results suggest that using such a tool, autoregulation boundaries as well as its impairment or functioning can be identified and assessed. It may therefore assist in individualized MAP management to ensure adequate organ perfusion and reduce the risk of postoperative complications, and might play an important role in patient care

optical and acoustic properties of the phantom and the tissue[32, 33].

<p>optical and acoustic properties of the phantom and the tissue[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161907#pone.0161907.ref032" target="_blank">32</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161907#pone.0161907.ref033" target="_blank">33</a>].</p

Boxplot for autoregulation index values calculated for the two to cFLOW-AR sensors.

<p>Pink astrics represent averaged ARI for each condition. A distinct separation between the conditions is apparent.</p

Cerebral Autoregulation Real-Time Monitoring - Fig 5

<p>Left—MAP and CFI data over time throughout the study. MAP was increased to 140mmHg followed by a return to baseline and a decrease to 40mmHg. Dashed green lines represent initial injections of Phnylephrine and Nitroprusside respectively. Blue points represent all MAP values. Red points are associated with periods in which the algorithm identified a significant MAP change and a correlation index (ARI) can be calculated. Right—Scatter plot of CFI versus MAP revealing two distinct slopes obtained for values under of over 100mmHg. This point was defined as the upper limit of autoregulation (ULA).</p