3,758 research outputs found
Reconstructing Neuronal Connectivity From Calcium Imaging Data Using Generalized Transfer Entropy
The reconstruction of neuronal connectivity is a very interesting and important topic in neuroscience as it helps with understanding neuronal circuit and function. With the advancement of calcium fluorescence imaging technique, we can now observe the dynamical activity of hundreds of neurons in vivo in one setting, which provides a foundation for inferring connectivity within a community or network. Poor signal-to-noise ratio and low frame rate with respect to neurons’ actual firing rate are challenges that come with calcium imaging data. Here we review several methods that can be applied to calcium imaging data, without the direct need for converting the data to spike trains which is the more traditional and popular way of connectivity analysis. We then apply generalized transfer entropy to three different sets of calcium imaging data obtained from mice visual cortex, and infer the directed functional connectivity network, in which a directed edge implies a direct causal influence by source neuron to sink neuron. The transfer entropy causal influence measure is time-dependent but requires no prior statistical assumptions on neuron firing patterns and network topology, hence model-free and applicable in face of aforementioned challenges. The performance of this measure has previously been tested on simulated data, and its performance applied to real data, as is the case in this project, is assessed using randomization. We found using properties of randomized networks compared with properties of our reconstructed network that transfer entropy was able to identify significant non-random features of the imaging data. Therefore, the inferred connectivity can provide information on the functional organization of the neuronal networks
From Correlation to Causation: Estimation of Effective Connectivity from Continuous Brain Signals based on Zero-Lag Covariance
Knowing brain connectivity is of great importance both in basic research and
for clinical applications. We are proposing a method to infer directed
connectivity from zero-lag covariances of neuronal activity recorded at
multiple sites. This allows us to identify causal relations that are reflected
in neuronal population activity. To derive our strategy, we assume a generic
linear model of interacting continuous variables, the components of which
represent the activity of local neuronal populations. The suggested method for
inferring connectivity from recorded signals exploits the fact that the
covariance matrix derived from the observed activity contains information about
the existence, the direction and the sign of connections. Assuming a sparsely
coupled network, we disambiguate the underlying causal structure via
-minimization. In general, this method is suited to infer effective
connectivity from resting state data of various types. We show that our method
is applicable over a broad range of structural parameters regarding network
size and connection probability of the network. We also explored parameters
affecting its activity dynamics, like the eigenvalue spectrum. Also, based on
the simulation of suitable Ornstein-Uhlenbeck processes to model BOLD dynamics,
we show that with our method it is possible to estimate directed connectivity
from zero-lag covariances derived from such signals. In this study, we consider
measurement noise and unobserved nodes as additional confounding factors.
Furthermore, we investigate the amount of data required for a reliable
estimate. Additionally, we apply the proposed method on a fMRI dataset. The
resulting network exhibits a tendency for close-by areas being connected as
well as inter-hemispheric connections between corresponding areas. Also, we
found that a large fraction of identified connections were inhibitory.Comment: 18 pages, 10 figure
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
Model-free reconstruction of neuronal network connectivity from calcium imaging signals
A systematic assessment of global neural network connectivity through direct
electrophysiological assays has remained technically unfeasible even in
dissociated neuronal cultures. We introduce an improved algorithmic approach
based on Transfer Entropy to reconstruct approximations to network structural
connectivities from network activity monitored through calcium fluorescence
imaging. Based on information theory, our method requires no prior assumptions
on the statistics of neuronal firing and neuronal connections. The performance
of our algorithm is benchmarked on surrogate time-series of calcium
fluorescence generated by the simulated dynamics of a network with known
ground-truth topology. We find that the effective network topology revealed by
Transfer Entropy depends qualitatively on the time-dependent dynamic state of
the network (e.g., bursting or non-bursting). We thus demonstrate how
conditioning with respect to the global mean activity improves the performance
of our method. [...] Compared to other reconstruction strategies such as
cross-correlation or Granger Causality methods, our method based on improved
Transfer Entropy is remarkably more accurate. In particular, it provides a good
reconstruction of the network clustering coefficient, allowing to discriminate
between weakly or strongly clustered topologies, whereas on the other hand an
approach based on cross-correlations would invariantly detect artificially high
levels of clustering. Finally, we present the applicability of our method to
real recordings of in vitro cortical cultures. We demonstrate that these
networks are characterized by an elevated level of clustering compared to a
random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted
for publicatio
Revealing networks from dynamics: an introduction
What can we learn from the collective dynamics of a complex network about its
interaction topology? Taking the perspective from nonlinear dynamics, we
briefly review recent progress on how to infer structural connectivity (direct
interactions) from accessing the dynamics of the units. Potential applications
range from interaction networks in physics, to chemical and metabolic
reactions, protein and gene regulatory networks as well as neural circuits in
biology and electric power grids or wireless sensor networks in engineering.
Moreover, we briefly mention some standard ways of inferring effective or
functional connectivity.Comment: Topical review, 48 pages, 7 figure
Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches
Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient
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