Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

Complex brain networks: graph theoretical analysis of structural and functional systems

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Additive manufacturing, otherwise known as three-dimensional (3D) printing, is driving major innovations in many areas, such as engineering, manufacturing, art, education and medicine. Recent advances have enabled 3D printing of biocompatible materials, cells and supporting components into complex 3D functional living tissues. 3D bioprinting is being applied to regenerative medicine to address the need for tissues and organs suitable for transplantation. Compared with non-biological printing, 3D bioprinting involves additional complexities, such as the choice of materials, cell types, growth and differentiation factors, and technical challenges related to the sensitivities of living cells and the construction of tissues. Addressing these complexities requires the integration of technologies from the fields of engineering, biomaterials science, cell biology, physics and medicine. 3D bioprinting has already been used for the generation and transplantation of several tissues, including multilayered skin, bone, vascular grafts, tracheal splints, heart tissue and cartilaginous structures. Other applications include developing high-throughput 3D-bioprinted tissue models for research, drug discovery and toxicology.

3D bioprinting of tissues and organs

About 3,000 individuals in the United States are awaiting a donor heart; worldwide, 22 million individuals are living with heart failure. A bioartificial heart is a theoretical alternative to transplantation or mechanical left ventricular support. Generating a bioartificial heart requires engineering of cardiac architecture, appropriate cellular constituents and pump function. We decellularized hearts by coronary perfusion with detergents, preserved the underlying extracellular matrix, and produced an acellular, perfusable vascular architecture, competent acellular valves and intact chamber geometry. To mimic cardiac cell composition, we reseeded these constructs with cardiac or endothelial cells. To establish function, we maintained eight constructs for up to 28 d by coronary perfusion in a bioreactor that simulated cardiac physiology. By day 4, we observed macroscopic contractions. By day 8, under physiological load and electrical stimulation, constructs could generate pump function (equivalent to about 2% of adult or 25% of 16-week fetal heart function) in a modified working heart preparation.

/pdf/perfusion-decellularized-matrix-using-nature-s-platform-to-5am6bi5v1u.pdf

Perfusion-decellularized matrix: using nature's platform to engineer a bioartificial heart

Summary
Purpose: We propose a patient-specific algorithm for seizure prediction using multiple features of spectral power from electroencephalogram (EEG) and support vector machine (SVM) classification.

Methods:  The proposed patient-specific algorithm consists of preprocessing, feature extraction, SVM classification, and postprocessing. Preprocessing removes artifacts of intracranial EEG recordings and they are further preprocessed in bipolar and/or time-differential methods. Features of spectral power of raw, or bipolar and/or time-differential intracranial EEG (iEEG) recordings in nine bands are extracted from a sliding 20-s–long and half-overlapped window. Nine bands are selected based on standard EEG frequency bands, but the wide gamma bands are split into four. Cost-sensitive SVMs are used for classification of preictal and interictal samples, and double cross-validation is used to achieve in-sample optimization and out-of-sample testing. We postprocess SVM classification outputs using the Kalman Filter and it removes sporadic and isolated false alarms. The algorithm has been tested on iEEG of 18 patients of 20 available in the Freiburg EEG database who had three or more seizure events. To investigate the discriminability of the features between preictal and interictal, we use the Kernel Fisher Discriminant analysis.

Key findings:  The proposed patient-specific algorithm for seizure prediction has achieved high sensitivity of 97.5% with total 80 seizure events and a low false alarm rate of 0.27 per hour and total false prediction times of 13.0% over a total of 433.2 interictal hours by bipolar preprocessing (92.5% sensitivity, a false positive rate of 0.20 per hour, and false prediction times of 9.5% by time-differential preprocessing). This high prediction rate demonstrates that seizures can be predicted by the patient-specific approach using linear features of spectral power and nonlinear classifiers. Bipolar and/or time-differential preprocessing significantly improves sensitivity and specificity. Spectral powers in high gamma bands are the most discriminating features between preictal and interictal.

Significance:  High sensitivity and specificity are achieved by nonlinear classification of linear features of spectral power. Power changes in certain frequency bands already demonstrated their possibilities for seizure prediction indicators, but we have demonstrated that combining those spectral power features and classifying them in a multivariate approach led to much higher prediction rates. Employing only linear features is advantageous, especially when it comes to an implantable device, because they can be computed rapidly with low power consumption.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1528-1167.2011.03138.x

Seizure prediction with spectral power of EEG using cost-sensitive support vector machines

Most sensory stimuli are actively sampled, yet the role of sampling behavior in shaping sensory codes is poorly understood. Mammals sample odors by sniffing, a complex behavior that controls odorant access to receptor neurons. Whether sniffing shapes the neural code for odors remains unclear. We addressed this question by imaging receptor input to the olfactory bulb of awake rats performing odor discriminations that elicited different sniffing behaviors. High-frequency sniffing of an odorant attenuated inputs encoding that odorant, whereas lower sniff frequencies caused little attenuation. Odorants encountered later in a sniff bout were encoded as the combination of that odorant and the background odorant during low-frequency sniffing, but were encoded as the difference between the two odorants during high-frequency sniffing. Thus, sniffing controls an adaptive filter for detecting changes in the odor landscape. These data suggest an unexpected functional role for sniffing and show that sensory codes can be transformed by sampling behavior alone.

Sniffing controls an adaptive filter of sensory input to the olfactory bulb

Synchronization between CA1 pyramidal neurons was studied using dual-cell patch-clamp techniques simultaneous with an extracellular measurement of network activity. We explored various linear and nonlinear methods to detect weak synchronization in this network, using cross-correlation, mutual information in one and two dimensions, and phase correlation in both broad and narrow band. The linear and nonlinear methods demonstrated different patterns of sensitivity to detect synchrony in this network, depending on the dynamical state of the network. Bursts in 4-amino-pyridine (4AP) were highly synchronous events. Unexpectedly, seizure-like events in 4AP were desynchronous events, both in comparison with interictal periods preceding the seizure without bursts (cut Schaffer collateral tract) and in comparison with bursts preceding the seizures (intact Schaffer collateral tract). The finding that seizure-like events are associated with desynchronization in such networks is consistent with recent theoretical work, suggesting that asynchrony is necessary to maintain a high level of activity in neuronal networks for sustained periods of time and that synchrony may disrupt such activity.

/pdf/decreased-neuronal-synchronization-during-experimental-1pq6ykhci5.pdf

Decreased neuronal synchronization during experimental seizures.

Stochastic resonance, a nonlinear phenomenon in which random noise optimizes a system’s response to a signal, has been postulated to provide a role for noise in information processing in the brain. In these experiments, a time varying electric field was used to deliver both signal and noise directly to a network of neurons from mammalian brain. As the magnitude of the stochastic component of the field was increased, resonance was observed in the response of the neuronal network to a weak periodic signal. This is the first demonstration of stochastic resonance in neuronal networks from the brain. [S0031-9007(96)01583-9]

/pdf/stochastic-resonance-in-a-neuronal-network-from-mammalian-3e29wyryt2.pdf

Theoden I. Netoff

Papers

Perfusion-decellularized matrix: using nature's platform to engineer a bioartificial heart

Seizure prediction with spectral power of EEG using cost-sensitive support vector machines

Sniffing controls an adaptive filter of sensory input to the olfactory bulb

Decreased neuronal synchronization during experimental seizures.

Stochastic Resonance in a Neuronal Network from Mammalian Brain