Connectivity inference from neural recording data: Challenges, mathematical bases and research directions

Yoshimoto, Junichiro; Doya, Kenji

doi:10.1016/j.neunet.2018.02.016

Cited by 61 publications

(52 citation statements)

References 112 publications

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“…One common approach to obtain information from in vitro neuronal networks is to record their activity via multi-electrode array (MEA) or calcium fluorescence imaging and then use network activity features to describe their physiology. One main limitation, however, is that these highdimensional data, which report about the information representation in the network, do not translate into a clear understanding of how this representation was produced and how it emerged based on neuronal connectivity [25]. The synchronization of spontaneous spike trains among different MEA sites or neurons, also referred to as network bursting, is an example of observed neural behaviors widely reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Only model-based approaches [25,28] have been proposed for inference of effective connectivity. Among them, dynamic causal modeling (DCM) [29] and structural equation modeling [30] variants have shown best performances ( Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods estimate the effective connectivity of a measured neuronal network by explicitly modeling the data generation process, i.e. only the connectivity of a simulated network model is inferred without any theoretical guarantee about its accuracy and its ability to correctly estimate the connectivity of the biological network [25,26].…”

Section: Introductionmentioning

confidence: 99%

“…Because of this limitation, descriptive, model-free approaches are usually preferred as they are easy to implement, rely on a limited number of assumptions that are directly related to the investigated neuronal network, and can be easily tested [25,28]. A number of model-free methods proposed for reconstructing the connectivity of in vitro neuronal networks [31] have been previously reviewed [32] and tested [26] (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, as the partial correlation matrix is symmetric, this method is not useful for detecting the causal direction of neuronal links. It also does not attempt to infer self-connections [25]. A combination of correlation and network deconvolution was used by Magrans and Nowe [49] to infer a network of undirected connections with elimination of arbitrary path lengths caused by indirect effects.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Puppo

PrÐ

Bang

et al. 2020

Preprint

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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 iPSC-derived neuronal cultures by reconstructing their effective connectivity, thus facilitating future efforts to generate predictive models for neurological disorders, drug testing and neuronal network modeling.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Puppo

PrÐ

Bang

et al. 2020

Preprint

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Wave digital model of calcium‐imaging‐based neuronal activity of mice

Jenderny

Ochs

2022

Int J Numerical Modelling

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The modeling of real neuronal networks by close‐to‐biology circuits can contribute to a better understanding of these networks from a circuit theoretical point of view. This can potentially lead to novel design principles for electrical circuits. This modeling process involves the mimicking of real measured neuronal activity, which is often only available as calcium imaging recordings. In this work, we develop a wave digital model as a circuit emulator capable of mimicking the calcium‐imaging‐based neuronal activity of mouse neurons. For this purpose, we design an electrical reference circuit of a mouse neuron based on an extended Morris–Lecar model in combination with an RC circuit. The circuit parameters are determined by a parameter scaling without requiring any parameter optimization. The input signal of the resulting wave digital model is designed based on estimating spike times with the spike inference software MLspike. Wave digital emulations show the successful mimicking of calcium‐based neuronal activity for mouse neurons located in the visual, motor and somatosensory cortex as well the possibility to predict ion currents occurring during this activity. This proves the suitability of the reference circuit as a model for the considered types of mouse neurons. It also implies that the circuit can act as an observer for electrical signals of mouse neurons. This is achieved despite the lack of electrophysiological measurements in most cases. Hence, the proposed wave digital model can be seen as an important step toward being able to verify close‐to‐biology electrical circuits as a model of real neuronal networks without electrophysiology.

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Statistical Analysis of Pairwise Connectivity

Krempl

Kottke

Pham

2021

Lecture Notes in Computer Science

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Analysing correlations between streams of events is an important problem. It arises for example in Neurosciences, when the connectivity of neurons should be inferred from spike trains that record neurons' individual spiking activity. While recently some approaches for inferring delayed synaptic connections have been proposed, they are limited in the types of connectivities and delays they are able to handle, or require computation-intensive procedures. This paper proposes a faster and more flexible approach for analysing such delayed correlated activity: a statistical Analysis of the Connectivity of spiking Events (ACE), based on the idea of hypothesis testing. It first computes for any pair of a source and a target neuron the inter-spike delays between subsequent source-and target-spikes. Then, it derives a null model for the distribution of inter-spike delays for uncorrelated neurons. Finally, it compares the observed distribution of inter-spike delays to this null model and infers pairwise connectivity based on the Pearson's χ 2 test statistic. Thus, ACE is capable to detect connections with a priori unknown, non-discrete (and potentially large) inter-spike delays, which might vary between pairs of neurons. Since ACE works incrementally, it has potential for being used in online processing. In an experimental evaluation, ACE is faster and performs comparable or better than four baseline approaches, in terms of AUPRC (reported here), F1, and AUROC (reported on our website), for the majority of the 11 evaluated scenarios.

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Connectivity inference from neural recording data: Challenges, mathematical bases and research directions

Cited by 61 publications

References 112 publications

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Wave digital model of calcium‐imaging‐based neuronal activity of mice

Statistical Analysis of Pairwise Connectivity

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