Searching for plasticity in dissociated cortical cultures on multi-electrode arrays

We attempted to induce functional plasticity in dense cultures of cortical cells using stimulation through extracellular electrodes embedded in the culture dish substrate (multi-electrode arrays, or MEAs). We looked for plasticity expressed in changes in spontaneous burst patterns, and in array-wide response patterns to electrical stimuli, following several induction protocols related to those used in the literature, as well as some novel ones. Experiments were performed with spontaneous culture-wide bursting suppressed by either distributed electrical stimulation or by elevated extracellular magnesium concentrations as well as with spontaneous bursting untreated. Changes concomitant with induction were no larger in magnitude than changes that occurred spontaneously, except in one novel protocol in which spontaneous bursts were quieted using distributed electrical stimulation

Shaping Embodied Neural Networks for Adaptive Goal-directed Behavior

The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves

On the Dynamics of the Spontaneous Activity in Neuronal Networks

Most neuronal networks, even in the absence of external stimuli, produce spontaneous bursts of spikes separated by periods of reduced activity. The origin and functional role of these neuronal events are still unclear. The present work shows that the spontaneous activity of two very different networks, intact leech ganglia and dissociated cultures of rat hippocampal neurons, share several features. Indeed, in both networks: i) the inter-spike intervals distribution of the spontaneous firing of single neurons is either regular or periodic or bursting, with the fraction of bursting neurons depending on the network activity; ii) bursts of spontaneous spikes have the same broad distributions of size and duration; iii) the degree of correlated activity increases with the bin width, and the power spectrum of the network firing rate has a 1/f behavior at low frequencies, indicating the existence of long-range temporal correlations; iv) the activity of excitatory synaptic pathways mediated by NMDA receptors is necessary for the onset of the long-range correlations and for the presence of large bursts; v) blockage of inhibitory synaptic pathways mediated by GABA(A) receptors causes instead an increase in the correlation among neurons and leads to a burst distribution composed only of very small and very large bursts. These results suggest that the spontaneous electrical activity in neuronal networks with different architectures and functions can have very similar properties and common dynamics

Functional identification of biological neural networks using reservoir adaptation for point processes

The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity. We present a reservoir computing approach for functionally identifying a biological neural network, i.e. for building an artificial system that is functionally equivalent to the reference biological network. Employing feed-forward and recurrent networks with fading memory, i.e. reservoirs, we propose a point process based learning algorithm to train the internal parameters of the reservoir and the connectivity between the reservoir and the memoryless readout neurons. Specifically, the model is an Echo State Network (ESN) with leaky integrator neurons, whose individual leakage time constants are also adapted. The proposed ESN algorithm learns a predictive model of stimulus-response relations in in vitro and simulated networks, i.e. it models their response dynamics. Receiver Operating Characteristic (ROC) curve analysis indicates that these ESNs can imitate the response signal of a reference biological network. Reservoir adaptation improved the performance of an ESN over readout-only training methods in many cases. This also held for adaptive feed-forward reservoirs, which had no recurrent dynamics. We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks

A NF-κB signature predicts low-grade glioma prognosis: a precision medicine approach based on patient-derived stem cells

Background: While recent genome wide association studies have suggested novel low-grade glioma (LGG) stratification models based on a molecular classification, we explored the potential clinical utility of patient-derived cells. Specifically, we assayed glioma-associated stem cells (GASC) that are patient-derived stem cells representative of the glioma microenvironment. Methods: By next generation sequencing, we analyzed the transcriptional profile of GASC derived from patients that underwent anaplastic transformation either within 48 months (GASC-BAD) or ≥7 years (GASC-GOOD) after surgery. Gene set enrichment and pathway enrichment analyses were applied. The prognostic role of an NF-κB signature derived from GASC-BAD was tested in 530 newly diagnosed diffuse LGG patients comprised within The Cancer Genome Atlas (TCGA) database. The prognostic value of the GASC upstream regulator p65 NF-κB was assessed, by univariate and multivariate Cox analyses, in a single center case study, including 146 grade II LGG. Results: The key elements differentiating the transcriptome of GASC isolated from LGG with different prognosis were mostly related to hallmarks of cancer (e.g. inflammatory/immune process, and NF-κB activation). Consistently, the NF-κB signature extrapolated from the GASC study was prognostic in the TCGA dataset. Finally, the nuclear expression of the NF-kB-p65 protein, assessed using an inexpensive immunohistochemical method, was an independent predictor of both overall survival and malignant progression free survival in 146 grade II LGG. Conclusion: This study demonstrates for the first time the independent prognostic role of NF-kB activation in LGG, and it outlines the role of patient-based stem cell models as a tool for precision medicine approaches

Glioma-associated stem cells: a novel class of tumor-supporting cells able to predict prognosis of human low-grade gliomas

Background: Translational medicine aims at transferring advances in basic science research into new approaches for diagnosis and treatment of diseases. Low-grade gliomas (LGG) have a heterogeneous clinical behavior that can be only partially predicted employing current state-of-theart markers, hindering the decision-making process. To deepen our comprehension on tumor heterogeneity, we dissected the mechanism of interaction between tumor cells and relevant components of the neoplastic environment, isolating, from LGG and high-grade gliomas (HGG), proliferating stem cell lines from both the glioma stroma and, where possible, the neoplasm. Methods and Findings: We isolated glioma-associated stem cells (GASC) from LGG (n540) and HGG (n573). GASC showed stem cell features, anchorage-independent growth, and supported the malignant properties of both A172 cells and human glioma-stem cells, mainly through the release of exosomes. Finally, starting from GASC obtained from HGG (n513) and LGG (n512) we defined a score, based on the expression of 9 GASC surface markers, whose prognostic value was assayed on 40 subsequent LGG-patients. At the multivariate Cox analysis, the GASCbased score was the only independent predictor of overall survival and malignant progression-free survival. Conclusions: The microenvironment of both LGG and HGG hosts non-tumorigenic multipotent stem cells that can increase in vitro the biological aggressiveness of glioma initiating cells through the release of exosomes. The clinical importance of this finding is supported by the strong prognostic value associated with the characteristics of GASC. This patient-based approach can provide a groundbreaking method to predict prognosis and to exploit novel strategies that target the tumor stroma