Hyperbaric oxygen can induce neuroplasticity and improve cognitive functions of patients suffering from anoxic brain damage.

PURPOSE: Cognitive impairment may occur in 42-50% of cardiac arrest survivors. Hyperbaric oxygen therapy (HBO2) has recently been shown to have neurotherapeutic effects in patients suffering from chronic cognitive impairments (CCI) consequent to stroke and mild traumatic brain injury.The objective of this study was to assess the neurotherapeutic effect of HBO2 in patients suffering from CCI due to cardiac arrest. METHODS: Retrospective analysis of patients with CCI caused by cardiac arrest, treated with 60 daily sessions of HBO2. Evaluation included objective computerized cognitive tests (NeuroTrax), Activity of Daily Living (ADL) and Quality of life questionnaires. The results of these tests were compared with changes in brain activity as assessed by single photon emission computed tomography (SPECT) brain imaging. RESULTS: The study included 11 cases of CCI patients. Patients were treated with HBO2, 0.5-7.5 years (mean 2.6 ± 0.6 years) after the cardiac arrest. HBO2 was found to induce modest, but statistically significant improvement in memory, attention and executive function (mean scores) of 12% , 20% and 24% respectively. The clinical improvements were found to be well correlated with increased brain activity in relevant brain areas as assessed by computerized analysis of the SPECT imaging. CONCLUSIONS: Although further research is needed, the results demonstrate the beneficial effects of HBO2 on CCI in patients after cardiac arrest, even months to years after the acute event.

Time-frequency characterization of electrocorticographic recordings of epileptic patients using frequency-entropy similarity: a comparison to other bi-variate measures.

Expert evaluation of electrocorticographic (ECoG) recordings forms the linchpin of seizure onset zone localization in the evaluation of epileptic patients for surgical resection. Numerous methods have been developed to analyze these complex recordings, including uni-variate (characterizing single channels), bi-variate (comparing channel pairs) and multivariate measures. Developing reliable algorithms may be helpful in clinical tasks such as localization of epileptogenic zones and seizure anticipation, as well as enabling better understanding of neuronal function and dynamics. Recently we have developed the frequency-entropy (F-E) similarity measure, and have tested its capability in mapping the epileptogenic zones. The F-E similarity measure compares time-frequency characterizations of two recordings. In this study, we examine the method's principles and utility and compare it to previously described bi-variate correspondence measures such as correlation, coherence, mean phase coherence and spectral comparison methods. Specially designed synthetic signals were used for illuminating theoretical differences between the measures. Intracranial recordings of four epileptic patients were then used for the measures' comparative analysis by creating a mean inter-electrode matrix for each of the correspondence measures and comparing the structure of these matrices during the inter-ictal and ictal periods. We found that the F-E similarity measure is able to discover spectral and temporal features in data which are hidden for the other measures and are important for foci localization.

Engineered neuronal circuits shaped and interfaced with carbon nanotube microelectrode arrays.

Standard micro-fabrication techniques which were originally developed to fabricate semi-conducting electronic devices were inadvertently found to be adequate for bio-chip fabrication suited for applications such as stimulation and recording from neurons in-vitro as well as in-vivo. However, cell adhesion to conventional micro-chips is poor and chemical treatments are needed to facilitate the interaction between the device surface and the cells. Here we present novel carbon nanotube-based electrode arrays composed of cell-alluring carbon nanotube (CNT) islands. These play a double role of anchoring neurons directly and only onto the electrode sites (with no need for chemical treatments) and facilitating high fidelity electrical interfacing-recording and stimulation. This method presents an important step towards building nano-based neurochips of precisely engineered networks. These neurochips can provide unique platform for studying the activity patterns of ordered networks as well as for testing the effects of network damage and methods of network repair.

Coemergence of regularity and complexity during neural network development.

With the growing recognition that rhythmic and oscillatory patterns are widespread in the brain and play important roles in all aspects of the function of our nervous system, there has been a resurgence of interest in neuronal synchronized bursting activity. Here, we were interested in understanding the development of synchronized bursts as information-bearing neuronal activity patterns. For that, we have monitored the morphological organization and spontaneous activity of neuronal networks cultured on multielectrode-arrays during their self-executed evolvement from a mixture of dissociated cells into an active network. Complex collective network electrical activity evolved from sporadic firing patterns of the single neurons. On the system (network) level, the activity was marked by bursting events with interneuronal synchronization and nonarbitrary temporal ordering. We quantified these individual-to-collective activity transitions using newly-developed system level quantitative measures of time series regularity and complexity. We found that individual neuronal activity before synchronization was characterized by high regularity and low complexity. During neuronal wiring, there was a transient period of reorganization marked by low regularity, which then leads to coemergence of elevated regularity and functional (nonstochastic) complexity. We further investigated the morphology-activity interplay by modeling artificial neuronal networks with different topological organizations and connectivity schemes. The simulations support our experimental results by showing increased levels of complexity of neuronal activity patterns when neurons are wired up and organized in clusters (similar to mature real networks), as well as network-level activity regulation once collective activity forms.

The function of intersegmental connections in determining temporal characteristics of the spinal cord rhythmic output.

Recent renewed interest in the study of rhythmic behaviors and pattern-generating circuits has been inspired by the currently well-established role of oscillating neuronal networks in all aspects of the function of our nervous system: from sensory integration to central processing, and of course motor control. An integrative rather than reductionist approach in the study of pattern-generating circuits is in accordance with current developments. The lamprey spinal cord, a relatively simple and much-studied preparation, is a useful model for such a study. It is an example of a chain of coupled oscillatory units that is characterized by its ability to demonstrate robust coordinated rhythmic output when isolated in vitro. The preparation allows maximum control over the chemical (neuromodulators and hormones) as well as neuronal environment (sensory and descending inputs) of the single oscillatory unit: the pattern-generating circuit. The current study made use of recently developed tools for nonlinear analysis of time-series, specifically neurophysiological signals. These tools allow us to reveal and characterize biological-functional complexity and information capacity of the neuronal output recorded from the lamprey model network. We focused on the importance of different types of inputs to an oscillatory network and their effect on the network's functional output. We show that the basic circuit, when isolated from short- and long-range neuronal inputs, demonstrates its full potential of information capacity: maximal variation quantities and elevated functional complexity. Morphological and functional constraints result in the network exhibiting only a limited range of the above. This constitutes an important substrate for plasticity in neuronal network function.

Compact self-wiring in cultured neural networks.

We present a novel approach for patterning cultured neural networks in which a particular geometry is achieved via anchoring of cell clusters (tens of cells/each) at specific positions. In addition, compact connections among pairs of clusters occur spontaneously through a single non-adherent straight bundle composed of axons and dendrites. The anchors that stabilize the cell clusters are either poly-D-lysine, a strong adhesive substrate, or carbon nanotubes. Square, triangular and circular structures of connectivity were successfully realized. Monitoring the dynamics of the forming networks in real time revealed that the self-assembly process is mainly driven by the ability of the neuronal cell clusters to move away from each other while continuously stretching a neurite bundle in between. Using the presented technique, we achieved networks with wiring regions which are made exclusively of neuronal processes unbound to the surface. The resulted network patterns are very stable and can be maintained for as long as 11 weeks. The approach can be used to build advanced neuro-chips for bio-sensing applications (e.g. drug and toxin detection) where the structure, stability and reproducibility of the networks are of great relevance.

Observations and modeling of synchronized bursting in two-dimensional neural networks.

We present long-term (approximately hours) measurements of the spontaneous activity of two-dimensional cortical cell neural networks placed on multielectrode arrays. We compare histograms of single neuron interspike intervals and the network intersynchronized bursting events intervals. In addition, the effect of Ca concentration on the network activity is being studied. At 1 mM Ca concentration, the network exhibits periodic synchronized bursting that fades away after about 20 min. We present a feedback-regulated integrate and fire model to account for the observations. In the model we include two additional features: dynamical threshold and synapse fatigue.

Atomic force pulling: probing the local elasticity of the cell membrane.

We present a novel approach, based on atomic force microscopy, for exploring the local elastic properties of the membrane-skeleton complex in living cells. Three major elements constitute the basis for the proposed method: (1) pulling the cell membrane by increasing the adhesion of the tip to the cell surface provided via appropriate tip modification; (2) measuring force-distance curves with emphasis on selecting the appropriate withdrawal regions for analysis; (3) fitting of the theoretical model for axisymmetric bending of an annular thick plate to the experimental curve in the withdrawal region, prior to the detachment point of the tip from the cell membrane. This approach, applied to human erythrocytes, suggests a complimentary technique to the commonly used methods. The local use of this methodology for determining the bending modulus of the cell membrane of the human erythrocyte yields a value of (2.07+/-0.32) x 10(-19) J.

Detection and sorting of neural spikes using wavelet packets.

We propose a novel method for the detection and sorting of recorded neural spikes using wavelet packets. We employ the best basis via the Shannon's information cost function and local discriminant basis using mutual information. We demonstrate the efficiency of the method on data recorded in vitro from 2D neural networks. We show that our method is superior both in separation from noise and in identifying superimposed spikes.

Generic modeling of chemotactic based self-wiring of neural networks.

The proper functioning of the nervous system depends critically on the intricate network of synaptic connections that are generated during the system development. During the network formation, the growth cones migrate through the embryonic environment to their targets using chemical communication. A major obstacle in the elucidation of fundamental principles underlying this self-wiring is the complexity of the system being analyzed. Hence much effort is devoted to in vitro experiments of simpler (two-dimensional) 2D model systems. In these experiments neurons are placed on Poly-L-Lysine (PLL) surfaces, so it is easier to monitor their self-wiring. We developed a model to reproduce the salient features of the 2D systems, inspired by the study of the growth of bacterial colonies and the aggregation of amoebae. We represent the neurons (each composed of cell's soma, neurites and growth cones) by active elements that capture the generic features of the real neurons. The model also incorporates stationary units representing the cells' soma and communicating walkers representing the growth cones. The stationary units send neurites one at a time, and respond to chemical signaling. The walkers migrate in response to chemotaxis substances emitted by the soma and communicate with each other and with the soma by means of chemotactic "feedback". The interplay between the chemo-repulsive and chemo-attractive responses is determined by the dynamics of the walker's internal energy which is controlled by the soma. These features enable the neurons to perform the complex task of self-wiring. We present numerical experiments of the model to demonstrate its ability to form fine structures in simple networks of few neurons. Our results raise two fundamental issues: (1) one needs to develop characterization methods (beyond number of connections per neuron) to distinguish the various possible networks; (2) what are the relations between the network organization and its computational properties and efficiency?

Paenibacillus dendritiformis sp. nov., proposal for a new pattern-forming species and its localization within a phylogenetic cluster.

A new strain capable of forming distinctive patterns during colony development was identified by using a combination of phenotypic characterization, fatty acid analysis and analysis of the 16S rRNA gene sequence. The strain formed either a branched, tip-splitting colony morphology (referred to as the T morphotype) or a chiral pattern exhibiting thinner branches with distinctive curling patterns (referred to as the C morphotype). Isolates of the T morphotype exhibited sequence identities greater than 97% to Paenibacillus thiaminolyticus JCM 7540. Phylogenetic analysis placed the T morphotype within the Paenibacillus cluster on a phylogenetic tree. On the basis of unique colony morphology and distinctive phenotypic characteristics, it is proposed that the pattern-forming isolates should be placed within a new species of Paenibacillus, Paenibacillus dendritiformis sp. nov., the type strain of which is T168T (= 30A1T).

Lubricating bacteria model for branching growth of bacterial colonies.

Various bacterial strains (e.g., strains belonging to the genera Bacillus, Paenibacillus, Serratia, and Salmonella) exhibit colonial branching patterns during growth on poor semisolid substrates. These patterns reflect the bacterial cooperative self-organization. A central part of the cooperation is the collective formation of a lubricant on top of the agar which enables the bacteria to swim. Hence it provides the colony means to advance towards the food. One method of modeling the colonial development is via coupled reaction-diffusion equations which describe the time evolution of the bacterial density and the concentrations of the relevant chemical fields. This idea has been pursued by a number of groups. Here we present an additional model which specifically includes an evolution equation for the lubricant excreted by the bacteria. We show that when the diffusion of the fluid is governed by a nonlinear diffusion coefficient, branching patterns evolve. We study the effect of the rates of emission and decomposition of the lubricant fluid on the observed patterns. The results are compared with experimental observations. We also include fields of chemotactic agents and food chemotaxis and conclude that these features are needed in order to explain the observations.

Cooperative organization of bacterial colonies: from genotype to morphotype.

In nature, bacteria must often cope with difficult environmental conditions. To do so they have developed sophisticated cooperative behavior and intricate communication pathways. Utilizing these elements, motile microbial colonies frequently develop complex patterns in response to adverse growth conditions on hard surfaces under conditions of energy limitation. We employ the term morphotype to refer to specific properties of colonial development. The morphologies we discuss include a tip-splitting (T) morphotype, chiral (C) morphotype, and vortex (V) morphotype. A generic modeling approach was developed by combining a detailed study of the cellular behavior and dynamics during colonial development and invoking concepts derived from the study of pattern formation in nonliving systems. Analysis of patterning behavior of the models suggests bacterial processes whereby communication leads to self-organization by using cooperative cellular interactions. New features emerging from the model include various models of cell-cell signaling, such as long-range chemorepulsion, short-range chemoattraction, and, in the case of the V morphotype, rotational chemotaxis. In this regard, pattern formation in microorganisms can be viewed as the result of the exchange of information between the micro-level (the individual cells) and the macro-level (the colony).