A novel deep learning based method for myocardial strain quantification.

This paper introduces a deep learning method for myocardial strain analysis while also evaluating the efficacy of the method across a public and a private dataset for cardiac pathology discrimination.We measure the global and regional myocardial strain in cSAX CMR images by first identifying a ROI centered in the LV, obtaining the cardiac structures (LV, RV and Myo) and estimating the motion of the myocardium. Finally, we compute the strain for the heart coordinate system and report the global and regional strain.

High-resolution X-Ray imaging of small animal samples based on Commercial-Off-The-Shelf CMOS image sensors.

 An automated system for acquiring microscopic-resolution radiographic images of biological samples was developed. Mass-produced, low-cost, and easily automated components were used, such as Commercial-Off-The-Self CMOS image sensors (CIS), stepper motors, and control boards based on Arduino and RaspberryPi. System configuration, imaging protocols, and Image processing (filtering and stitching) were defined to obtain high-resolution images and for successful computational image reconstruction.

Model-based deep learning framework for accelerated optical projection tomography.

In this work, we propose a model-based deep learning reconstruction algorithm for optical projection tomography (ToMoDL), to greatly reduce acquisition and reconstruction times. The proposed method iterates over a data consistency step and an image domain artefact removal step achieved by a convolutional neural network. A preprocessing stage is also included to avoid potential misalignments between the sample center of rotation and the detector.

Differential contribution of the subthreshold operating currents , , and to the resonance of thalamocortical neurons.

Membrane potential oscillations of thalamocortical (TC) neurons are believed to be involved in the generation and maintenance of brain rhythms that underlie global physiological and pathological brain states. These membrane potential oscillations depend on the synaptic interactions of TC neurons and their intrinsic electrical properties. These oscillations may be also shaped by increased output responses at a preferred frequency, known as intrinsic neuronal resonance.

Complex interplay between spectral harmonicity and different types of cross-frequency couplings in nonlinear oscillators and biologically plausible neural network models.

Cross-frequency coupling (CFC) refers to the nonlinear interaction between oscillations in different frequency bands, and it is a rather ubiquitous phenomenon that has been observed in a variety of physical and biophysical systems. In particular, the coupling between the phase of slow oscillations and the amplitude of fast oscillations, referred as phase-amplitude coupling (PAC), has been intensively explored in the brain activity recorded from animals and humans. However, the interpretation of these CFC patterns remains challenging since harmonic spectral correlations characterizing nonsinusoidal oscillatory dynamics can act as a confounding factor.