Recovery of Left Ventricular Function and Long-Term Outcomes in Patients With Takotsubo Syndrome.

Background: Takotsubo syndrome (TTS) is a form of transient left ventricular (LV) dysfunction that usually resolves within days to weeks.

Objectives: We aimed to assess the predictors and prognostic impact of time-to-LV recovery after TTS.

Methods: Prospective serial imaging data from the nationwide, multicenter RETAKO (REgistry on TAKOtsubo Syndrome) were comprehensively reviewed to assess the timing of LV recovery.

Emergence of a small-world functional network in cultured neurons.

The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity.

Multiscale evolving complex network model of functional connectivity in neuronal cultures.

Cultures of cortical neurons grown on multielectrode arrays exhibit spontaneous, robust, and recurrent patterns of highly synchronous activity called bursts. These bursts play a crucial role in the development and topological self-organization of neuronal networks. Thus, understanding the evolution of synchrony within these bursts could give insight into network growth and the functional processes involved in learning and memory.

Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models.

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail.

An efficient parameterization of dynamic neural networks for nonlinear system identification.

Dynamic neural networks (DNNs), which are also known as recurrent neural networks, are often used for nonlinear system identification. The main contribution of this letter is the introduction of an efficient parameterization of a class of DNNs. Having to adjust less parameters simplifies the training problem and leads to more parsimonious models.