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 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. Radiographic images were obtained for animal samples including the widely used animal models zebrafish (Danio rerio) and the fruit-fly (Drosophila melanogaster), as well as other small animal samples. The use of phosphotungstic acid (PTA) as a contrast agent was also studied. Radiographic images with resolutions of up to (7±0.6)µm were obtained, making this system comparable to commercial ones. This work constitutes a starting point for the development of more complex systems such as X-ray attenuation micro-tomography systems based on low-cost off-the-shelf technology. It will also bring the possibility to expand the studies that can be carried out with small animal models at many institutions (mostly those working on tight budgets), particularly those on the effects of ionizing radiation and absorption of heavy metal contaminants in animal tissues.

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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. The algorithm is trained using a database of wild-type zebrafish (Danio rerio) at different stages of development to minimise the mean square error for a fixed number of iterations. Using a cross-validation scheme, we compare the results to other reconstruction methods, such as filtered backprojection, compressed sensing and a direct deep learning method where the pseudo-inverse solution is corrected by a U-Net. The proposed method performs equally well or better than the alternatives. For a highly reduced number of projections, only the U-Net method provides images comparable to those obtained with ToMoDL. However, ToMoDL has a much better performance if the amount of data available for training is limited, given that the number of network trainable parameters is smaller.

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RESUMEN Introducción: Las técnicas de inteligencia artificial han demostrado tener un gran potencial en el área de la cardiología, especialmente para identificar patrones imperceptibles para el ser humano. En este sentido, dichas técnicas parecen ser las adecuadas para identificar patrones en la textura del miocardio con el objetivo de identificar y cuantificar la fibrosis. Objetivos: Proponer un nuevo método de inteligencia artificial para identificar fibrosis en imágenes cine de resonancia cardíaca. Materiales y métodos: Se realizó un estudio retrospectivo observacional en 75 sujetos del Sanatorio San Carlos de Bariloche. El método propuesto analiza la textura del miocardio en las imágenes cine CMR (resonancia magnética cardíaca) mediante el uso de una red neuronal convolucional que determinar el daño local del tejido miocárdico. Resultados: Se observó una precisión del 89% para cuantificar el daño tisular local en el conjunto de datos de validación y de un 70% para el conjunto de prueba. Además, el análisis cualitativo realizado muestra una alta correlación espacial en la localización de la lesión. Conclusiones: El método propuesto permite identificar espacialmente la fibrosis únicamente utilizando la información de los estudios de cine de resonancia magnética nuclear, mostrando el potencial de la técnica propuesta para cuantificar la viabilidad miocárdica en un futuro o estudiar la etiología de las lesiones.

ABSTRACT Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these techniques seem to be adequate to identify patterns in the myocardial texture which could lead to characterize and quantify fibrosis. Purpose: The aim of this study was to postulate a new artificial intelligence method to identify fibrosis in cine cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational study was carried out in a population of 75 subjects from a clinical center of San Carlos de Bariloche. The proposed method analyzes the myocardial texture in cine CMR images using a convolutional neural network to determine local myocardial tissue damage. Results: An accuracy of 89% for quantifying local tissue damage was observed for the validation data set and 70% for the test set. In addition, the qualitative analysis showed a high spatial correlation in lesion location. Conclusions: The postulated method enables to spatially identify fibrosis using only the information from cine nuclear magnetic resonance studies, demonstrating the potential of this technique to quantify myocardial viability in the future or to study the etiology of lesions.

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RESUMEN Introducción: Las técnicas de inteligencia artificial han demostrado tener un gran potencial en el área de la cardiología, especialmente para cuantificar la función cardíaca de ambos ventrículos, volumen, masa y fracción de eyección (FE). Sin embargo, su aplicación en la clínica no es directa, entre otros motivos por la poca reproducibilidad frente a casos de la práctica diaria. Objetivos: Propuesta y evaluación de una nueva herramienta de inteligencia artificial para cuantificar la función cardíaca de ambos ventrículos (volumen, masa y FE). Estudiar su robustez para su uso en la clínica y analizar los tiempos de cómputo respecto a los métodos convencionales. Materiales y métodos: Se analizaron en total 189 pacientes, 89 de un centro regional y 100 de un centro público. El método propuesto utiliza dos redes convolucionales incorporando información anatómica del corazón para reducir los errores de clasificación. Resultados: Se observa una alta concordancia (coeficiente de Pearson) entre la cuantificación manual y la propuesta para cuantificar la función cardíaca (0,98, 0,92, 0,96 y 0,8 para los volúmenes y para la FE de ambos ventrículos) en tiempos cercanos a los 5 seg. por estudio. Conclusiones: El método propuesto permite cuantificar los volúmenes y función de ambos ventrículos en segundos con una precisión comparable a la de un especialista.

ABSTRACT Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.

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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. Here, we combine electrophysiological recordings in mouse brain slices, modern pharmacological tools, dynamic clamp, and computational modeling to study the ionic mechanisms that generate and modulate TC neuron resonance. We confirm findings of pioneering studies showing that most TC neurons display resonance that results from the interaction of the slow inactivation of the low-threshold calcium current IT with the passive properties of the membrane. We also show that the hyperpolarization-activated cationic current Ih is not involved in the generation of resonance; instead it plays a minor role in the stabilization of TC neuron impedance magnitude due to its large contribution to the steady conductance. More importantly, we also demonstrate that TC neuron resonance is amplified by the inward rectifier potassium current IKir by a mechanism that hinges on its strong voltage-dependent inward rectification (i.e., a negative slope conductance region). Accumulating evidence indicate that the ion channels that control the oscillatory behavior of TC neurons participate in pathophysiological processes. Results presented here points to IKir as a new potential target for therapeutic intervention.NEW & NOTEWORTHY Our study expands the repertoire of ionic mechanisms known to be involved in the generation and control of resonance and provides the first experimental proof of previous theoretical predictions on resonance amplification mediated by regenerative hyperpolarizing currents. In thalamocortical neurons, we confirmed that the calcium current IT generates resonance, determined that the large steady conductance of the cationic current Ih curtails resonance, and demonstrated that the inward rectifier potassium current IKir amplifies resonance.

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PURPOSE: This paper presents CardIAc, an open-source application designed as an alternative to commercial software for left ventricle myocardial strain quantification in short-axis cardiac magnetic resonance images. The aim is to provide a useful extension for myocardial strain analysis that can be easily adapted to incorporate different strategies of motion tracking to improve the strain accuracy. In this way, users with programming skills can easily modify the code and adjust the program's performance according to their own scientific or clinical requirements. The software is intended for research and clinical use is not advised. METHODS: CardIAc was developed as a 3D Slicer extension for an easy installation and usability. The main contribution of this article is to provide a general workflow, going from data and segmentation loading, 3D heart modeling, analysis and several options for visualization of the myocardial strain. RESULTS: CardIAc strain feature was evaluated on a public dataset (Cardiac Motion Analysis Challenge-STACOM 2011) of 15 volunteers, and a synthetic one generated from this real dataset. Results on the real dataset show that cardIAc achieves suitable accuracy for myocardial motion estimation with a median error of 3.66 mm. In particular, global strain curves show strong correlation with the bibliography for healthy patients and similar approaches. On the other hand, results on the synthetic dataset show a mean global error of 4.07%, 7.76% and 8.18% for circumferential, radial and longitudinal strain. CONCLUSION: This paper introduces a new open-source application for strain analysis distributed under a BSD-style open-source license. Results demonstrate the capability and merits of the proposed application for strain analysis.

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We propose a method for segmentation of the left ventricle in magnetic resonance cardiac images. The framework consists of an initial Bayesian segmentation of the central slice of the volume. This segmentation is used to locate a shape prior for the LV myocardial tissue. This shape prior is determined using the fact that the myocardium is approximately annular as seen in the short-axis. Then a second Bayesian segmentation is performed to obtain the final result. This procedure is repeated for the rest of the slices. An extrapolation of the area of the LV is used to determine a stopping criterion. The method was evaluated on the databases of the Cardiac Atlas project. Our results demonstrate a suitable accuracy for myocardial segmentation (≈0.8 Dice's coefficient). For the endocardium and the epicardium the Dice's coefficients are 0.94 and 0.9 respectively. The accuracy was also evaluated in terms of the Hausdorff distance and the average distance. For the myocardium we obtain 8 mm and 2 mm respectively. Our results demonstrate the capability and merits of the proposed method to estimate the structure of the LV. The method requires minimal user input and generates results with quality comparable to more complex approaches. This paper suggests a new efficient approach for automatic LV quantification based on a Bayesian technique with shape priors with errors comparable to state-of-the-art techniques.

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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. Specialized signal processing techniques are proposed to address the complex interplay between spectral harmonicity and different types of CFC, not restricted only to PAC. For this, we provide an in-depth characterization of the time locked index (TLI) as a tool aimed to efficiently quantify the harmonic content of noisy time series. It is shown that the proposed TLI measure is more robust and outperforms traditional phase coherence metrics (e.g., phase locking value, pairwise phase consistency) in several aspects. We found that a nonlinear oscillator under the effect of additive noise can produce spurious CFC with low spectral harmonic content. On the other hand, two coupled oscillatory dynamics with independent fundamental frequencies can produce true CFC with high spectral harmonic content via a rectification mechanism or other post-interaction nonlinear processing mechanisms. These results reveal a complex interplay between CFC and harmonicity emerging in the dynamics of biologically plausible neural network models and more generic nonlinear and parametric oscillators. We show that, contrary to what is usually assumed in the literature, the high harmonic content observed in nonsinusoidal oscillatory dynamics is neither a sufficient nor necessary condition to interpret the associated CFC patterns as epiphenomenal. There is mounting evidence suggesting that the combination of multimodal recordings, specialized signal processing techniques, and theoretical modeling is becoming a required step to completely understand CFC patterns observed in oscillatory rich dynamics of physical and biophysical systems.

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Phase-amplitude cross frequency coupling (PAC) is a rather ubiquitous phenomenon that has been observed in a variety of physical domains; however, the mechanisms underlying the emergence of PAC and its functional significance in the context of neural processes are open issues under debate. In this work we analytically demonstrate that PAC phenomenon naturally emerges in mean-field models of biologically plausible networks, as a signature of specific bifurcation structures. The proposed analysis, based on bifurcation theory, allows the identification of the mechanisms underlying oscillatory dynamics that are essentially different in the context of PAC. Specifically, we found that two PAC classes can coexist in the complex dynamics of the analyzed networks: 1) harmonic PAC which is an epiphenomenon of the nonsinusoidal waveform shape characterized by the linear superposition of harmonically related spectral components, and 2) nonharmonic PAC associated with "true" coupled oscillatory dynamics with independent frequencies elicited by a secondary Hopf bifurcation and mechanisms involving periodic excitation/inhibition (PEI) of a network population. Importantly, these two PAC types have been experimentally observed in a variety of neural architectures confounding traditional parametric and nonparametric PAC metrics, like those based on linear filtering or the waveform shape analysis, due to the fact that these methods operate on a single one-dimensional projection of an intrinsically multidimensional system dynamics. We exploit the proposed tools to study the functional significance of the PAC phenomenon in the context of Parkinson's disease (PD). Our results show that pathological slow oscillations (e.g. ß band) and nonharmonic PAC patterns emerge from dissimilar underlying mechanisms (bifurcations) and are associated to the competition of different BG-thalamocortical loops. Thus, this study provides theoretical arguments that demonstrate that nonharmonic PAC is not an epiphenomenon related to the pathological ß band oscillations, thus supporting the experimental evidence about the relevance of PAC as a potential biomarker of PD.

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Slow repetitive burst firing by hyperpolarized thalamocortical (TC) neurons correlates with global slow rhythms (<4 Hz), which are the physiological oscillations during non-rapid eye movement sleep or pathological oscillations during idiopathic epilepsy. The pacemaker activity of TC neurons depends on the expression of several subthreshold conductances, which are modulated in a behaviorally dependent manner. Here we show that upregulation of the small and neglected inward rectifier potassium current IKir induces repetitive burst firing at slow and delta frequency bands. We demonstrate this in mouse TC neurons in brain slices by manipulating the Kir maximum conductance with dynamic clamp. We also performed a thorough theoretical analysis that explains how the unique properties of IKir enable this current to induce slow periodic bursting in TC neurons. We describe a new ionic mechanism based on the voltage- and time-dependent interaction of IKir and hyperpolarization-activated cationic current Ih that endows TC neurons with the ability to oscillate spontaneously at very low frequencies, even below 0.5 Hz. Bifurcation analysis of conductance-based models of increasing complexity demonstrates that IKir induces bistability of the membrane potential at the same time that it induces sustained oscillations in combination with Ih and increases the robustness of low threshold-activated calcium current IT-mediated oscillations. NEW & NOTEWORTHY The strong inwardly rectifying potassium current IKir of thalamocortical neurons displays a region of negative slope conductance in the current-voltage relationship that generates potassium currents activated by hyperpolarization. Bifurcation analysis shows that IKir induces bistability of the membrane potential; generates sustained subthreshold oscillations by interacting with the hyperpolarization-activated cationic current Ih; and increases the robustness of oscillations mediated by the low threshold-activated calcium current IT. Upregulation of IKir in thalamocortical neurons induces repetitive burst firing at slow and delta frequency bands (<4 Hz).

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Deep brain stimulation (DBS) has become a widely used technique for treating advanced stages of neurological and psychiatric illness. In the case of motor disorders related to basal ganglia (BG) dysfunction, several mechanisms of action for the DBS therapy have been identified which might be involved simultaneously or in sequence. However, the identification of a common key mechanism underlying the clinical relevant DBS configurations has remained elusive due to the inherent complexity related to the interaction between the electrical stimulation and the neural tissue, and the intricate circuital structure of the BG-thalamocortical network. In this work, it is shown that the clinically relevant range for both, the frequency and intensity of the electrical stimulation pattern, is an emergent property of the BG anatomy at the system-level that can be addressed using mean-field descriptive models of the BG network. Moreover, it is shown that the activity resetting mechanism elicited by electrical stimulation provides a natural explanation to the ineffectiveness of irregular (i.e., aperiodic) stimulation patterns, which has been commonly observed in previously reported pathophysiology models of Parkinson's disease. Using analytical and numerical techniques, these results have been reproduced in both cases: 1) a reduced mean-field model that can be thought as an elementary building block capable to capture the underlying fundamentals of the relevant loops constituting the BG-thalamocortical network, and 2) a detailed model constituted by the direct and hyperdirect loops including one-dimensional spatial structure of the BG nuclei. We found that the optimal ranges for the essential parameters of the stimulation patterns can be understood without taking into account biophysical details of the relevant structures.

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Orientation selectivity is ubiquitous in the primary visual cortex (V1) of mammals. In cats and monkeys, V1 displays spatially ordered maps of orientation preference. Instead, in mice, squirrels, and rats, orientation selective neurons in V1 are not spatially organized, giving rise to a seemingly random pattern usually referred to as a salt-and-pepper layout. The fact that such different organizations can sharpen orientation tuning leads to question the structural role of the intracortical connections; specifically the influence of plasticity and the generation of functional connectivity. In this work, we analyze the effect of plasticity processes on orientation selectivity for both scenarios. We study a computational model of layer 2/3 and a reduced one-dimensional model of orientation selective neurons, both in the balanced state. We analyze two plasticity mechanisms. The first one involves spike-timing dependent plasticity (STDP), while the second one considers the reconnection of the interactions according to the preferred orientations of the neurons. We find that under certain conditions STDP can indeed improve selectivity but it works in a somehow unexpected way, that is, effectively decreasing the modulated part of the intracortical connectivity as compared to the non-modulated part of it. For the reconnection mechanism we find that increasing functional connectivity leads, in fact, to a decrease in orientation selectivity if the network is in a stable balanced state. Both counterintuitive results are a consequence of the dynamics of the balanced state. We also find that selectivity can increase due to a reconnection process if the resulting connections give rise to an unstable balanced state. We compare these findings with recent experimental results.

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Thalamocortical neurons are involved in the generation and maintenance of brain rhythms associated with global functional states. The repetitive burst firing of TC neurons at delta frequencies (1-4 Hz) has been linked to the oscillations recorded during deep sleep and during episodes of absence seizures. To get insight into the biophysical properties that are the basis for intrinsic delta oscillations in these neurons, we performed a bifurcation analysis of a minimal conductance-based thalamocortical neuron model including only the IT channel and the sodium and potassium leak channels. This analysis unveils the dynamics of repetitive burst firing of TC neurons, and describes how the interplay between the amplifying variable mT and the recovering variable hT of the calcium channel IT is sufficient to generate low threshold oscillations in the delta band. We also explored the role of the hyperpolarization activated cationic current Ih in this reduced model and determine that, albeit not required, Ih amplifies and stabilizes the oscillation.

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Balanced states in large networks are a usual hypothesis for explaining the variability of neural activity in cortical systems. In this regime the statistics of the inputs is characterized by static and dynamic fluctuations. The dynamic fluctuations have a Gaussian distribution. Such statistics allows to use reverse correlation methods, by recording synaptic inputs and the spike trains of ongoing spontaneous activity without any additional input. By using this method, properties of the single neuron dynamics that are masked by the balanced state can be quantified. To show the feasibility of this approach we apply it to large networks of conductance based neurons. The networks are classified as Type I or Type II according to the bifurcations which neurons of the different populations undergo near the firing onset. We also analyze mixed networks, in which each population has a mixture of different neuronal types. We determine under which conditions the intrinsic noise generated by the network can be used to apply reverse correlation methods. We find that under realistic conditions we can ascertain with low error the types of neurons present in the network. We also find that data from neurons with similar firing rates can be combined to perform covariance analysis. We compare the results of these methods (that do not requite any external input) to the standard procedure (that requires the injection of Gaussian noise into a single neuron). We find a good agreement between the two procedures.

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Neurons in the nervous system exhibit an outstanding variety of morphological and physiological properties. However, close to threshold, this remarkable richness may be grouped succinctly into two basic types of excitability, often referred to as type I and type II. The dynamical traits of these two neuron types have been extensively characterized. It would be interesting, however, to understand the information-processing consequences of their dynamical properties. To that end, here we determine the differences between the stimulus features inducing firing in type I and type II neurons. We work with both realistic conductance-based models and minimal normal forms. We conclude that type I neurons fire in response to scale-free depolarizing stimuli. Type II neurons, instead, are most efficiently driven by input stimuli containing both depolarizing and hyperpolarizing phases, with significant power in the frequency band corresponding to the intrinsic frequencies of the cell.

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