A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

Breakthrough advances in informatics over the last decade have thoroughly influenced the field of immunology. The intermingling of machine learning with wet lab applications and clinical results has hatched the newly defined immunoinformatics society. Immunoinformatics of the central neural system, referred to as neuroimmunoinformatics (NII), investigates symmetrical and asymmetrical interactions of the brain-immune interface. This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and symmetrical and asymmetrical protein–protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. The new classification of microglia, the brain’s innate immune cells, was an NII achievement. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII approaches complex tasks in the brain-immune interface, recognizes patterns and allows for hypothesis-free predictions with ultimate targeted individualized treatment strategies, and personalizes disease prognosis and treatment response.

Astrocyte-induced positive integrated information in neuron-astrocyte ensembles

Integrated information is a quantitative measure from information theory of how tightly all parts of a system are interconnected in terms of information exchange. In this study we show that astrocytes, playing an important role in regulation of information transmission between neurons, may contribute to a generation of positive integrated information in neuronal ensembles. Analytically and numerically we show that the presence of astrocytic regulation of neurotransmission may be essential for this information attribute in neuroastrocytic ensembles. Moreover, the proposed "spiking-bursting" mechanism of generating positive integrated information is shown to be generic and not limited to neuron-astrocyte networks and is given a complete analytic description.

Empirical Bayesian significance measure of neuronal spike response

Background

Functional connectivity analyses of multiple neurons provide a powerful bottom-up approach to reveal functions of local neuronal circuits by using simultaneous recording of neuronal activity. A statistical methodology, generalized linear modeling (GLM) of the spike response function, is one of the most promising methodologies to reduce false link discoveries arising from pseudo-correlation based on common inputs. Although recent advancement of fluorescent imaging techniques has increased the number of simultaneously recoded neurons up to the hundreds or thousands, the amount of information per pair of neurons has not correspondingly increased, partly because of the instruments’ limitations, and partly because the number of neuron pairs increase in a quadratic manner. Consequently, the estimation of GLM suffers from large statistical uncertainty caused by the shortage in effective information.

Results

In this study, we propose a new combination of GLM and empirical Bayesian testing for the estimation of spike response functions that enables both conservative false discovery control and powerful functional connectivity detection. We compared our proposed method’s performance with those of sparse estimation of GLM and classical Granger causality testing. Our method achieved high detection performance of functional connectivity with conservative estimation of false discovery rate and q values in case of information shortage due to short observation time. We also showed that empirical Bayesian testing on arbitrary statistics in place of likelihood-ratio statistics reduce the computational cost without decreasing the detection performance. When our proposed method was applied to a functional multi-neuron calcium imaging dataset from the rat hippocampal region, we found significant functional connections that are possibly mediated by AMPA and NMDA receptors.

Conclusions

The proposed empirical Bayesian testing framework with GLM is promising especially when the amount of information per a neuron pair is small because of growing size of observed network.

Electronic supplementary material

The online version of this article (doi:10.1186/s12868-016-0255-x) contains supplementary material, which is available to authorized users.

Accurate detection of low signal-to-noise ratio neuronal calcium transient waves using a matched filter

BACKGROUND

Calcium imaging has become a fundamental modality for studying neuronal circuit dynamics both in vitro and in vivo. However, identifying calcium events (CEs) from spectral data remains laborious and difficult, especially since the signal-to-noise ratio (SNR) often falls below 2. Existing automated signal detection methods are generally applied at high SNRs, leaving a large need for an automated algorithm that can accurately extract CEs from fluorescence intensity data of SNR 2 and below.

NEW METHOD

In this work we develop a Matched filter for Multi-unit Calcium Event (MMiCE) detection to extract CEs from fluorescence intensity traces of simulated and experimentally recorded neuronal calcium imaging data.

RESULTS

MMiCE reached perfect performance on simulated data with SNR ≥ 2 and a true positive (TP) rate of 98.27% (± 1.38% with a 95% confidence interval), and a false positive(FP) rate of 6.59% (± 2.56%) on simulated data with SNR 0.2. On real data, verified by patch-clamp recording, MMiCE performed with a TP rate of 100.00% (± 0.00) and a FP rate of 2.04% (± 4.10).

COMPARISON WITH EXISTING METHOD(S)

This high level of performance exceeds existing methods at SNRs as low as 0.2, which are well below those used in previous studies (SNR ≃ 5-10).

CONCLUSION

Overall, the MMiCE detector performed exceptionally well on both simulated data, and experimentally recorded neuronal calcium imaging data. The MMiCE detector is accurate, reliable, well suited for wide-spread use, and freely available at sites.uci.edu/aggies or from the corresponding author.

Mathematical model of brain tumour with glia-neuron interactions and chemotherapy treatment

In recent years, it became clear that a better understanding of the interactions among the main elements involved in the cancer network is necessary for the treatment of cancer and the suppression of cancer growth. In this work we propose a system of coupled differential equations that model brain tumour under treatment by chemotherapy, which considers interactions among the glial cells, the glioma, the neurons, and the chemotherapeutic agents. We study the conditions for the glioma growth to be eliminated, and identify values of the parameters for which the inhibition of the glioma growth is obtained with a minimal loss of healthy cells.