Uncovering the synchronization dynamics from correlated neuronal activity quantifies assembly formation

 Synchronous network excitation is believed to play an outstanding role in neuronal information processing. Due to the stochastic nature of the contributing neurons, however, those synchronized states are difficult to detect in electrode recordings. We present a framework and a model for the identification of such network states and of their dynamics in a specific experimental situation. Our approach operationalizes the notion of neuronal groups forming assemblies via synchronization based on experimentally obtained spike trains. The dynamics of such groups is reflected in the sequence of synchronized states, which we describe as a renewal dynamics. We furthermore introduce a rate function which is dependent on the internal network phase that quantifies the activity of neurons contributing to the observed spike train. This constitutes a hidden state model which is formally equivalent to a hidden Markov model, and all its parameters can be accurately determined from the experimental time series using the Baum-Welch algorithm. We apply our method to recordings from the cat visual cortex which exhibit oscillations and synchronizations. The parameters obtained for the hidden state model uncover characteristic properties of the system including synchronization, oscillation, switching, background activity and correlations. In applications involving multielectrode recordings, the extracted models quantify the extent of assembly formation and can be used for a temporally precise localization of system states underlying a specific spike train. Received: 30 March 1993/Accepted in revised form: 16 April 1994     

Identification of nonstationary dynamics in physiological recordings

We present a novel framework for the analysis of time series from dynamical systems that alternate between different operating modes. The method simultaneously segments and identifies the dynamical modes by using predictive models. In extension to previous approaches, it allows an identification of smooth transition between successive modes. The method can be used for analysis, diagnosis, prediction, and control. In an application to EEG and respiratory data recorded from humans during afternoon naps, the obtained segmentations of the data agree with the sleep stage segmentation of a medical expert to a large extent. However, in contrast to the manual segmentation, our method does not require a priori knowledge about physiology. Moreover, it has a high temporal resolution and reveals previously unclassified details of the transitions. In particular, a parameter is found that is potentially helpful for vigilance monitoring. We expect that the method will generally be useful for the analysis of nonstationary dynamical systems, which are abundant in medicine, chemistry, biology and engineering. Received: 5 May 1999 / Accepted in revised form: 28 December 1999     

Selective activation of G alpha i mediated signalling of S1P3 by FTY720-phosphate

The immune modulator FTY720 is phosphorylated in vivo to FTY720 phosphate (FTY-P), which activates four sphingosine 1-phosphate (S1P) receptors including S1P₃. Upon activation with S1P, S1P₃ couples to G_i- and G_q-protein-dependent signalling pathways. Here we show that FTY-P selectively activates the S1P₃-mediated and G_i-coupled inhibition of adenylyl cyclase. Contemporaneously, it antagonizes the S1P-induced activation of G_q via S1P₃ in intracellular calcium flux measurements, GTP-binding experiments, and flow cytometric analyses of activation-induced receptor down-regulation. In contrast to S1P, pre-treatment with FTY-P did not desensitize S1P-induced calcium flux or chemotaxis via S1P₃. The lack of receptor desensitization prevented S1P₃-mediated migration to FTY-P. Human umbilical vein endothelial cells express S1P₁ and S1P₃, and respond to S1P and FTY-P by ERK1/2 phosphorylation and by intracellular calcium release in a pertussis toxin-sensitive manner. But whereas a mixture of S1P and FTY-P was not affecting ERK1/2 phosphorylation, the intracellular calcium flux was hampered with increasing amounts of FTY-P, which points to a cross-talk between S1P₁ and S1P₃. FTY-P is therefore one of the rare ligands which bind to a receptor that couples multiple G-proteins but selectively activates only one signalling pathway.     

Location of inhibitor binding sites in the human inducible prostaglandin E synthase, MPGES1

The inducible microsomal prostaglandin E(2) synthase 1 (MPGES1) is an integral membrane protein coexpressed with and functionally coupled to cyclooxygenase 2 (COX-2) generating the pro-inflammatory molecule PGE(2). The development of effective inhibitors of MPGES1 holds promise as a highly selective route for controlling inflammation. In this paper, we describe the use of backbone amide H/D exchange mass spectrometry to map the binding sites of different types of inhibitors of MPGES1. The results reveal the locations of specific inhibitor binding sites that include the GSH binding site and a hydrophobic cleft in the protein thought to accommodate the prostaglandin H(2) substrate. In the absence of three-dimensional crystal structures of the enzyme-bound inhibitors, the results provide clear physical evidence that three pharmacologically active inhibitors bind in a hydrophobic cleft composed of sections of transmembrane helices Ia, IIb, IIIb, and IVb at the interface of subunits in the trimer. In principle, the H/D exchange behavior of the protein can be used as a preliminary guide for optimization of inhibitor efficacy. Finally, a comparison of the structures and H/D exchange behavior of MPGES1 and the related enzyme MGST1 in the presence of glutathione and the inhibitor glutathione sulfonate confirms the unusual observation that two proteins from the same superfamily harbor GSH binding sites in different locations.     

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.     

Attachment Status Affects Heart Rate Responses to Experimental Ostracism in Inpatients with Depression

Depression is assumed to be both a risk factor for rejection and a result of it, and as such constitutes an important factor in rejection research. Attachment theory has been applied to understand psychological disorders, such as depression, and can explain individual differences in responses to rejection. Research on autonomic nervous system activity to rejection experiences has been contradictory, with opposing strings of argumentation (activating vs. numbing). We investigated autonomic nervous system-mediated peripheral physiological responses (heart rate) to experimentally manipulated ostracism (Cyberball) in 97 depressed patients with organized (n = 52) and disorganized attachment status (n = 45). Controlling for baseline mean heart rate levels, depressed patients with disorganized attachment status responded to ostracism with significantly higher increases in heart rate than depressed patients with organized attachment status (p = .029; ηp² = .051). These results suggest that attachment status may be a useful indicator of autonomic responses to perceived social threat, which in turn may affect the therapeutic process and the patient-therapist relationship.     

Identification of Key Residues Determining Species Differences in Inhibitor Binding of Microsomal Prostaglandin E Synthase-1

Microsomal prostaglandin E synthase-1 (MPGES1) is induced during an inflammatory reaction from low basal levels by pro-inflammatory cytokines and subsequently involved in the production of the important mediator of inflammation, prostaglandin E₂. Nonsteroidal anti-inflammatory drugs prevent prostaglandin E₂ production by inhibiting the upstream enzymes cyclooxygenases 1 and 2. In contrast to these conventional drugs, a new generation of NSAIDs targets the terminal enzyme MPGES1. Some of these compounds potently inhibit human MPGES1 but do not have an effect on the rat orthologue. We investigated this interspecies difference in a rat/human chimeric form of the enzyme as well as in several mutants and identified key residues Thr-131, Leu-135, and Ala-138 in human MPGES1, which play a crucial role as gate keepers for the active site of MPGES1. These residues are situated in transmembrane helix 4, lining the entrance to the cleft between two subunits in the protein trimer, and regulate access of the inhibitor in the rat enzyme. Exchange toward the human residues in rat MPGES1 was accompanied with a gain of inhibitor activity, whereas exchange in human MPGES1 toward the residues found in rat abrogated inhibitor activity. Our data give evidence for the location of the active site at the interface between subunits in the homotrimeric enzyme and suggest a model of how the natural substrate PGH₂, or competitive inhibitors of MPGES1, enter the active site via the phospholipid bilayer of the membrane.     

Dynamics of Dual Prism Adaptation: Relating Novel Experimental Results to a Minimalistic Neural Model

In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo–motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements (‘dual–adaptation’). A fundamental question is how the adaptation schedule determines the acquisition speed of new skills. Given a fixed number of movements in two different environments, will dual–adaptation be faster if switches (‘phase changes’) between the environments occur more frequently? We investigated the dynamics of dual–adaptation under different training schedules in a virtual pointing experiment. Surprisingly, we found that acquisition speed of dual visuo–motor mappings in a pointing task is largely independent of the number of phase changes. Next, we studied the neuronal mechanisms underlying this result and other key phenomena of dual–adaptation by relating model simulations to experimental data. We propose a simple and yet biologically plausible neural model consisting of a spatial mapping from an input layer to a pointing angle which is subjected to a global gain modulation. Adaptation is performed by reinforcement learning on the model parameters. Despite its simplicity, the model provides a unifying account for a broad range of experimental data: It quantitatively reproduced the learning rates in dual–adaptation experiments for both direct effect, i.e. adaptation to prisms, and aftereffect, i.e. behavior after removal of prisms, and their independence on the number of phase changes. Several other phenomena, e.g. initial pointing errors that are far smaller than the induced optical shift, were also captured. Moreover, the underlying mechanisms, a local adaptation of a spatial mapping and a global adaptation of a gain factor, explained asymmetric spatial transfer and generalization of prism adaptation, as observed in other experiments.