The Effect of Signaling Latencies and Node Refractory States on the Dynamics of Networks.

We describe the construction and theoretical analysis of a framework derived from canonical neurophysiological principles that model the competing dynamics of incident signals into nodes along directed edges in a network. The framework describes the dynamics between the offset in the latencies of propagating signals, which reflect the geometry of the edges and conduction velocities, and the internal refractory dynamics and processing times of the downstream node receiving the signals. This framework naturally extends to the construction of a perceptron model that takes into account such dynamic geometric considerations. We first describe the model in detail, culminating with the model of a geometric dynamic perceptron. We then derive upper and lower bounds for a notion of optimal efficient signaling between vertex pairs based on the structure of the framework. Efficient signaling in the context of the framework we develop here means that there needs to be a temporal match between the arrival time of the signals relative to how quickly nodes can internally process signals. These bounds reflect numerical constraints on the compensation of the timing of signaling events of upstream nodes attempting to activate downstream nodes they connect into that preserve this notion of efficiency. When a mismatch between signal arrival times and the internal states of activated nodes occurs, it can cause a breakdown in the signaling dynamics of the network. In contrast to essentially all of the current state of the art in machine learning, this work provides a theoretical foundation for machine learning and intelligence architectures based on the timing of node activations and their abilities to respond rather than necessary changes in synaptic weights. At the same time, the theoretical ideas we developed are guiding the discovery of experimentally testable new structure-function principles in the biological brain.

In vivo stimulus-induced vasodilation occurs without IP3 receptor activation and may precede astrocytic calcium increase.

Calcium-dependent release of vasoactive gliotransmitters is widely assumed to trigger vasodilation associated with rapid increases in neuronal activity. Inconsistent with this hypothesis, intact stimulus-induced vasodilation was observed in inositol 1,4,5-triphosphate (IP3) type-2 receptor (R2) knock-out (KO) mice, in which the primary mechanism of astrocytic calcium increase-the release of calcium from intracellular stores following activation of an IP3-dependent pathway-is lacking. Further, our results in wild-type (WT) mice indicate that in vivo onset of astrocytic calcium increase in response to sensory stimulus could be considerably delayed relative to the simultaneously measured onset of arteriolar dilation. Delayed calcium increases in WT mice were observed in both astrocytic cell bodies and perivascular endfeet. Thus, astrocytes may not play a role in the initiation of blood flow response, at least not via calcium-dependent mechanisms. Moreover, an increase in astrocytic intracellular calcium was not required for normal vasodilation in the IP3R2-KO animals.

Neuromodulation: selected approaches and challenges.

The brain operates through complex interactions in the flow of information and signal processing within neural networks. The 'wiring' of such networks, being neuronal or glial, can physically and/or functionally go rogue in various pathological states. Neuromodulation, as a multidisciplinary venture, attempts to correct such faulty nets. In this review, selected approaches and challenges in neuromodulation are discussed. The use of water-dispersible carbon nanotubes has been proven effective in the modulation of neurite outgrowth in culture and in aiding regeneration after spinal cord injury in vivo. Studying neural circuits using computational biology and analytical engineering approaches brings to light geometrical mapping of dynamics within neural networks, much needed information for stimulation interventions in medical practice. Indeed, sophisticated desynchronization approaches used for brain stimulation have been successful in coaxing 'misfiring' neuronal circuits to resume productive firing patterns in various human disorders. Devices have been developed for the real-time measurement of various neurotransmitters as well as electrical activity in the human brain during electrical deep brain stimulation. Such devices can establish the dynamics of electrochemical changes in the brain during stimulation. With increasing application of nanomaterials in devices for electrical and chemical recording and stimulating in the brain, the era of cellular, and even intracellular, precision neuromodulation will soon be upon us.

Identifying steady state in the network dynamics of spiking neural networks.

Analysis of the dynamics of complex networks can provide valuable information. For example, the dynamics can be used to characterize and differentiate between different network inputs and configurations. However, without quantitatively delineating the network's dynamic regimes, analysis of the network's dynamics is based on heuristics and qualitative signatures of transient or steady-state regimes. This is not ideal because interesting phenomena can occur during the transient regime, steady-state regime, or at the transition between the two dynamic regimes. Moreover, for simulated and observed systems, precise knowledge of the network's dynamical regime is imperative when considering metrics on minimal mathematical descriptions of the dynamics, otherwise either too much or too little data is analyzed. Here, we develop quantitative methods to ascertain the starting point and period of steady-state network activity. Using the precise knowledge of the network's dynamic regimes, we build minimal representations of the network dynamics that form the basis for future work. We show applications of our techniques on idealized signals and on the dynamics of a biologically inspired spiking neural network.

A framework for simulating and estimating the state and functional topology of complex dynamic geometric networks.

We introduce a framework for simulating signal propagation in geometric networks (networks that can be mapped to geometric graphs in some space) and developing algorithms that estimate (i.e., map) the state and functional topology of complex dynamic geometric networks. Within the framework, we define the key features typically present in such networks and of particular relevance to biological cellular neural networks: dynamics, signaling, observation, and control. The framework is particularly well suited for estimating functional connectivity in cellular neural networks from experimentally observable data and has been implemented using graphics processing unit high-performance computing. Computationally, the framework can simulate cellular network signaling close to or faster than real time. We further propose a standard test set of networks to measure performance and compare different mapping algorithms.

Computing Temporal Sequences Associated With Dynamic Patterns on the C. elegans Connectome.

Understanding how the structural connectivity and spatial geometry of a network constrains the dynamics it is able to support is an active and open area of research. We simulated the plausible dynamics resulting from the known C. elegans connectome using a recent model and theoretical analysis that computes the dynamics of neurobiological networks by focusing on how local interactions among connected neurons give rise to the global dynamics in an emergent way. We studied the dynamics which resulted from stimulating a chemosensory neuron (ASEL) in a known feeding circuit, both in isolation and embedded in the full connectome. We show that contralateral motorneuron activations in ventral (VB) and dorsal (DB) classes of motorneurons emerged from the simulations, which are qualitatively similar to rhythmic motorneuron firing pattern associated with locomotion of the worm. One interpretation of these results is that there is an inherent-and we propose-purposeful structural wiring to the C. elegans connectome that has evolved to serve specific behavioral functions. To study network signaling pathways responsible for the dynamics we developed an analytic framework that constructs Temporal Sequences (TSeq), time-ordered walks of signals on graphs. We found that only 5% of TSeq are preserved between the isolated feeding network relative to its embedded counterpart. The remaining 95% of signaling pathways computed in the isolated network are not present in the embedded network. This suggests a cautionary note for computational studies of isolated neurobiological circuits and networks.

Generalizable Machine Learning in Neuroscience Using Graph Neural Networks.

Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.

Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks.

Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective-direct and causal-connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.

All-Optical Electrophysiology in hiPSC-Derived Neurons With Synthetic Voltage Sensors.

Voltage imaging and "all-optical electrophysiology" in human induced pluripotent stem cell (hiPSC)-derived neurons have opened unprecedented opportunities for high-throughput phenotyping of activity in neurons possessing unique genetic backgrounds of individual patients. While prior all-optical electrophysiology studies relied on genetically encoded voltage indicators, here, we demonstrate an alternative protocol using a synthetic voltage sensor and genetically encoded optogenetic actuator that generate robust and reproducible results. We demonstrate the functionality of this method by measuring spontaneous and evoked activity in three independent hiPSC-derived neuronal cell lines with distinct genetic backgrounds.

Quantum dot labeling and imaging of glial fibrillary acidic protein intermediate filaments and gliosis in the rat neural retina and dissociated astrocytes.

The use of antibody and peptide functionalized semiconductor quantum dots holds considerable potential for specific labeling of target antigens and high resolution optical imaging of biological preparations. Despite this potential, their use in neuroscience is not yet widespread; a number of technical and methodological challenges must still be overcome in order to produce reliable and reproducible labeling protocols. We have optimized and used anti-GFAP functionalized quantum dots for specific labeling of intermediate filaments in astrocyte and Müller glial cells in sections of intact rat neural sensory retina and dissociated primary spinal cord astrocytes. These techniques produced stable and robust imaging of retinal astrocytes and Müller cells with minimal non-specific background labeling and intense fluorescence resulting in a high signal to noise ratio. This resulted in clear and efficient labeling of normal levels of GFAP in the retina and the ability to differentiate it from pathologically high levels of GFAP associated with reactive gliosis in a laser induced injury model. Labeling and imaging of dissociated astrocytes demonstrated the presence of what appeared to be highly complex organizations of fine intermediate filaments that spanned between cells to form intricate networks of filamentous intercellular bridges. The presence of these structures in situ and in vivo as well as any potential functions remain to be determined, but their identification should be greatly facilitated by quantum dot labeling protocols.

Nanotechnology approaches to crossing the blood-brain barrier and drug delivery to the CNS.

Nanotechnologies are materials and devices that have a functional organization in at least one dimension on the nanometer (one billionth of a meter) scale, ranging from a few to about 100 nanometers. Nanoengineered materials and devices aimed at biologic applications and medicine in general, and neuroscience in particular, are designed fundamentally to interface and interact with cells and their tissues at the molecular level. One particularly important area of nanotechnology application to the central nervous system (CNS) is the development of technologies and approaches for delivering drugs and other small molecules such as genes, oligonucleotides, and contrast agents across the blood brain barrier (BBB). The BBB protects and isolates CNS structures (i.e. the brain and spinal cord) from the rest of the body, and creates a unique biochemical and immunological environment. Clinically, there are a number of scenarios where drugs or other small molecules need to gain access to the CNS following systemic administration, which necessitates being able to cross the BBB. Nanotechnologies can potentially be designed to carry out multiple specific functions at once or in a predefined sequence, an important requirement for the clinically successful delivery and use of drugs and other molecules to the CNS, and as such have a unique advantage over other complimentary technologies and methods. This brief review introduces emerging work in this area and summarizes a number of example applications to CNS cancers, gene therapy, and analgesia.

Automated detection of intercellular signaling in astrocyte networks using the converging squares algorithm.

Intercellular calcium waves in central nervous system astrocyte networks underline the principle mechanism of cell signaling in astrocyte syncsytiums, which putatively contribute to the modulation of neuronal signaling and metabolic regulation. In support of carrying out systems level analyses of astrocyte networks, we have optimized and validated the converging squares image segmentation algorithm to automatically detect the relative spatial locations of all cells in a visible network as a preliminary step towards analyzing the dynamics of astrocyte intracellular calcium transients, which are the signals that mediate intercellular calcium waves. We used the temporal derivatives of pixel intensities as the data source for the algorithm. The method works by converging progressively smaller squares until the signal peak is reached. It is robust to noise and performs comparably to manual cell signal identification, but is much faster and efficient. This is the first reported application of this algorithm to glial networks that we are aware of.

The role of abnormal vitreomacular adhesion in age-related macular degeneration: spectral optical coherence tomography and surgical results.

PURPOSE: To assess the incidence of vitreomacular adhesion and traction in age-related macular degeneration (AMD), and to evaluate surgical treatment in a subset of patients with choroidal neovascularization (CNV) nonresponsive to anti-neovascular growth factor (anti-VEGF) treatment. DESIGN: Retrospective observational case-control and interventional case series. METHODS: Spectral optical coherence tomography, combined with simultaneous scanning laser ophthalmoscope (Spectral OCT/SLO), was performed in 170 eyes of 94 elderly patients, 61 with exudative AMD, 59 with nonexudative AMD, and 50 control eyes. The presence of hyaloid adhesion to the posterior pole, and vitreomacular traction (VMT) were determined. Five patients with VMT underwent surgical hyaloid removal. Best-corrected visual acuity (BCVA) and retinal thickness were evaluated as outcomes. RESULTS: Hyaloid adhesion was present in 17 eyes with exudative AMD (27.8%), 15 eyes with nonexudative AMD (25.4%), and eight control eyes (16%). Significant difference was found among the groups (P = .002). Among the eyes with hyaloid adhesion, VMT was shown in 10 eyes (59%) with exudative AMD, two eyes (13%) with nonexudative AMD, and one control eye (12%). VMT was associated with the severity of AMD (P = .0082). The area of hyaloid adhesion was significantly smaller than and concentric to the area of CNV complex in eyes with exudative AMD. Eyes with VMT that underwent surgery experienced a modest improvement of BCVA and decrease of retinal thickness. CONCLUSIONS: Hyaloid adhesion to the macula is associated with AMD, and frequently causes VMT in eyes with CNV. Tractional forces may antagonize the effect of anti-VEGF treatment, and cause pharmacological resistance in a subpopulation of patients. Future studies are needed to define the role of vitreoretinal surgery in such cases. Spectral OCT/SLO allows careful diagnosis and follow-up.

Stem cell sources and therapeutic approaches for central nervous system and neural retinal disorders.

In the past decades, stem cell biology has made a profound impact on our views of mammalian development as well as opened new avenues in regenerative medicine. The potential of stem cells to differentiate into various cell types of the body is the principal reason they are being explored in treatments for diseases in which there may be dysfunctional cells and/or loss of healthy cells due to disease. In addition, other properties are unique to stem cells; their endogenous trophic support, ability to home to sites of pathological entities, and stability in culture, which allows genetic manipulation, are also being utilized to formulate stem cell-based therapy for central nervous system (CNS) disorders. In this review, the authors will review key characteristics of embryonic and somatic (adult) stem cells, consider therapeutic strategies employed in stem cell therapy, and discuss the recent advances made in stem cell-based therapy for a number of progressive neurodegenerative diseases in the CNS as well as neuronal degeneration secondary to other abnormalities and injuries. Although a great deal of progress has been made in our knowledge of stem cells and their utility in treating CNS disorders, much still needs to be elucidated regarding the biology of the stem cells and the pathogenesis of targeted CNS diseases to maximize therapeutic benefits. Nonetheless, stem cells present tremendous promise in the treatment of a variety of neurodegenerative diseases.

A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence.

A confluence of technological capabilities is creating an opportunity for machine learning and artificial intelligence (AI) to enable "smart" nanoengineered brain machine interfaces (BMI). This new generation of technologies will be able to communicate with the brain in ways that support contextual learning and adaptation to changing functional requirements. This applies to both invasive technologies aimed at restoring neurological function, as in the case of neural prosthesis, as well as non-invasive technologies enabled by signals such as electroencephalograph (EEG). Advances in computation, hardware, and algorithms that learn and adapt in a contextually dependent way will be able to leverage the capabilities that nanoengineering offers the design and functionality of BMI. We explore the enabling capabilities that these devices may exhibit, why they matter, and the state of the technologies necessary to build them. We also discuss a number of open technical challenges and problems that will need to be solved in order to achieve this.

An Optimized Structure-Function Design Principle Underlies Efficient Signaling Dynamics in Neurons.

Dynamic signaling on branching axons is critical for rapid and efficient communication between neurons in the brain. Efficient signaling in axon arbors depends on a trade-off between the time it takes action potentials to reach synaptic terminals (temporal cost) and the amount of cellular material associated with the wiring path length of the neuron's morphology (material cost). However, where the balance between structural and dynamical considerations for achieving signaling efficiency is, and the design principle that neurons optimize to preserve this balance, is still elusive. In this work, we introduce a novel analysis that compares morphology and signaling dynamics in axonal networks to address this open problem. We show that in Basket cell neurons the design principle being optimized is the ratio between the refractory period of the membrane, and action potential latencies between the initial segment and the synaptic terminals. Our results suggest that the convoluted paths taken by axons reflect a design compensation by the neuron to slow down signaling latencies in order to optimize this ratio. Deviations in this ratio may result in a breakdown of signaling efficiency in the cell. These results pave the way to new approaches for investigating more complex neurophysiological phenomena that involve considerations of neuronal structure-function relationships.

Nanotechnology approaches for drug and small molecule delivery across the blood brain barrier.

Nanotechnology involves the design, synthesis, and characterization of materials and devices that have a functional organization in at least one dimension on the nanometer (ie, one billionth of a meter) scale. One area in which nanotechnology may have a significant clinical impact in neuroscience is the selective transport and delivery of drugs and other small molecules across the blood brain barrier that cannot cross otherwise. Using a variety of nanoparticles composed of different chemical compositions, different groups are exploring proof-of-concept approaches for the delivery of different antineoplastic drugs, oligonucleotides, genes, and magnetic resonance imaging contrast agents. This review discusses some of the main technical challenges associated with the development of nanotechnologies for delivery across the blood brain barrier and summarizes ongoing work.

Comparison of standard surface chemistries for culturing mesenchymal stem cells prior to neural differentiation.

A critical element of any stem cell differentiation protocol is the ability to compare its effects relative to an undifferentiated population of the same cells. In an attempt to standardize pre-differentiation conditions of adult derived mesenchymal stem cells prior to neural induction experiments, we asked what is the simplest chemical surface that supports the growth and maintenance of these cells in a pre-differentiation state. Adult bone marrow-derived rat mesenchymal stem cells (BMSCs) were expanded in vitro on Permanox Lab-Tek tissue culture treated plastic (TCP), poly-D-lysine (PDL) coated glass, PDL-laminin-1 coated glass, and untreated glass. TCP provided the best surface for maintaining morphologies generally considered to be undifferentiated, while PDL coated glass and uncoated glass provided the least suitable surfaces. Expansion of BMSCs on PDL-laminin-1 coated glass resulted in expression of nestin, a marker associated with neuronal and other progenitor cells, and therefore may confound experimental results if used as a pre-differentiation surface.

The effect of recombinant human hyaluronidase on dexamethasone penetration into the posterior segment of the eye after sub-Tenon's injection.

PURPOSE: The aim of this study was to investigate the extent if recombinant human hyaluronidase (rhuPH20) can enhance trans-scleral penetration of sub-Tenon's dexamethasone (DM) into the posterior segment of the eye. METHODS: rhuPH20 was purified from conditioned media through a series of ion exchange, hydrophobic interaction, aminophenylboronate, and hydroxyapatite chromatography to greater than 90% purity based upon specific activity. Only the right eye of each rabbit was injected. The first group (n = 16) received an injection of DM and rhuPH20, whereas the second group (n = 16) received DM only. The eyes were enucleated 1, 2, 3, and 6 h after the injection, and the choroid, retina, vitreous, aqueous, and serum were harvested. DM concentration was assessed by mass spectrometry. Histology (n = 2) and immunohistochemistry (n = 2) was performed to detect toxicity and the presence of the rHuPH20, respectively. RESULTS: We observed no histopathologic damage to ocular tissues after sub-Tenon's injection. This enzyme significantly increased DM level in the choroid and the retina 3 h after administration. The rise in levels was transient returning to normal levels by 6 h. CONCLUSIONS: Sub-Tenon's coinjection of rHuPH20 with DM resulted in a general increase in DM levels in ocular tissues and the serum, with significant increase in the choroid and the retina, 3 h after administration.

Assembly and Calcium Binding Properties of Quantum Dot-Calmodulin Calcium Sensor.

We have developed the first nanoengineered quantum dot molecular complex designed to measure changes of calcium ion (Ca2+) concentration at high spatial and temporal resolutions in real time. The sensor is ratiometric and composed of three components: a quantum dot (QD) emitting at 620 nm as a fluorescence donor, an organic dye (Alexa Fluor 647) as a fluorescence acceptor, and a calmodulin-M13 (CaM-M13) protein part as a calcium sensing component. In this work, we have determined the maximal number of CaM-M13 required for saturating a single QD particle to be approximately 16. The dissociation constant, Kd of the QD-based calcium ion sensor was also estimated to be around 30 microM.