Nuevos Métodos Electrocardiográficos: Un Estudio Basado en Electrodos Bipolares Precordiales / NEW ELECTROCARDIOGRAPHIC METHODS: A NEW STUDY BASED IN PRECORDIAL BIPOLAR ELECTRODES

En este artículo se presentan varios métodos nuevos de exploración electrocardiográfica. Todos ellos se basan en la modificación del procesamiento de la información que rutinariamente recoge el ECG standard de 12 canales, sin electrodos o cables adicionales. Se basan en derivaciones bipolares precordiales que miden el desfase en tiempo y amplitud entre los electrodos que van de V1 a V6, y muestran, en forma magnificada y con mucho más detalle, las anormalidades en el eje derecha a izquierda (como lo hace DI). El ECG se realiza rutinariamente y sus resultados son los trazados habituales. No obstante, usando los datos ya recogidos, se obtienen derivaciones precordiales bipolares, coordenadas polares, subderivaciones y derivaciones ponderadas nuevas. Los nuevos trazados permiten apreciar con claridad fenómenos difíciles de apreciar en los trazados de rutina del ECG de 12 canales.

Several new methods of electrocardiographic exploration are presented in this work. All are based on processing the information already obtained when a standard 12-channel ECG is performed. They need no additional electrodes or cables. They are based on precordial bipolar leads measuring time - amplitude mismatch among V1 to V6 unipolar electrodes, and present, with much more detail and magnified, abnormalities on the right to left axis. ECG is performed in the usual way producing the classic 12 tracings. However, using the data already collected, bipolar precordial leads, polar coordinates, sub derivations and new weighted derivations can be obtained. The new methods allow seeing clearly some phenomena difficult to appreciate in the routine tracings of the 12-channel ECG.

Repeating or spacing learning sessions are strategies for memory improvement with shared molecular and neuronal components.

Intellectual disability is a common feature in genetic disorders with enhanced RAS-ERK1/2 signaling, including neurofibromatosis type 1 (NF1) and Noonan syndrome (NS). Additional training trials and additional spacing between trials, respectively, restores memory deficits in animal models of NF1 and NS. However, the relationship between the underlying mechanisms in these strategies remain obscure. Here, we developed an approach to examine the effect of adding training trials or spacing to a weak training protocol and used genetic and behavioral manipulations in Drosophila to explore such question. We found that repetition and spacing effects are highly related, being equally effective to improve memory in control flies and sharing mechanistic bases, including the requirement of RAS activity in mushroom body neurons and protein synthesis dependence. After spacing or repeating learning trials, memory improvement depends on the formation of long-term memory (LTM). Moreover, a disease-related gain-of-function RasV152G allele impaired LTM. Using minimal training protocols, we established that both learning strategies were also equally effective for memory rescue in the RasV152G mutant and showed non-additive interaction of the spacing and repetition effects. Memory improvement was never detected after Ras inhibition. We conclude that memory improvement by spacing or repeating training trials are two ways of using the same molecular resources, including RAS-ERK1/2-dependent signaling. This evidence supports the concept that learning problems in RAS-related disorders depend on the impaired ability to exploit the repetition and the spacing effect required for long-term memory induction.

The Phosphatase CSW Controls Life Span by Insulin Signaling and Metabolism Throughout Adult Life in Drosophila.

Noonan syndrome and related disorders are caused by mutations in genes encoding for proteins of the RAS-ERK1/2 signaling pathway, which affect development by enhanced ERK1/2 activity. However, the mutations' effects throughout adult life are unclear. In this study, we identify that the protein most commonly affected in Noonan syndrome, the phosphatase SHP2, known in Drosophila as corkscrew (CSW), controls life span, triglyceride levels, and metabolism without affecting ERK signaling pathway. We found that CSW loss-of-function mutations extended life span by interacting with components of the insulin signaling pathway and impairing AKT activity in adult flies. By expressing csw-RNAi in different organs, we determined that CSW extended life span by acting in organs that regulate energy availability, including gut, fat body and neurons. In contrast to that in control animals, loss of CSW leads to reduced homeostasis in metabolic rate during activity. Clinically relevant gain-of-function csw allele reduced life span, when expressed in fat body, but not in other tissues. However, overexpression of a wild-type allele did not affect life span, showing a specific effect of the gain-of-function allele independently of a gene dosage effect. We concluded that CSW normally regulates life span and that mutations in SHP2 are expected to have critical effects throughout life by insulin-dependent mechanisms in addition to the well-known RAS-ERK1/2-dependent developmental alterations.

Seizure localization using pre ictal phase-amplitude coupling in intracranial electroencephalography.

Understanding changes in brain rhythms provides useful information to predict the onset of a seizure and to localize its onset zone in epileptic patients. Brain rhythms dynamics in general, and phase-amplitude coupling in particular, are known to be drastically altered during epileptic seizures. However, the neural processes that take place before a seizure are not well understood. We analysed the phase-amplitude coupling dynamics of stereoelectroencephalography recordings (30 seizures, 5 patients) before and after seizure onset. Electrodes near the seizure onset zone showed higher phase-amplitude coupling. Immediately before the beginning of the seizure, phase-amplitude coupling dropped to values similar to the observed in electrodes far from the seizure onset zone. Thus, our results bring accurate information to detect epileptic events during pre-ictal periods and to delimit the zone of seizure onset in patients undergoing epilepsy surgery.

Ultrasound Axicon: Systematic Approach to Optimize Focusing Resolution through Human Skull Bone.

The use of axicon lenses is useful in many high-resolution-focused ultrasound applications, such as mapping, detection, and have recently been extended to ultrasonic brain therapies. However, in order to achieve high spatial resolution with an axicon lens, it is necessary to adjust the separation, called stand-off (δ), between a conventional transducer and the lens attached to it. Comprehensive ultrasound simulations, using the open-source k-Wave toolbox, were performed for an axicon lens attached to a piezo-disc type transducer with a radius of 14 mm, and a frequency of about 0.5 MHz, that is within the range of optimal frequencies for transcranial transmission. The materials properties were measured, and the lens geometry was modelled. Hydrophone measurements were performed through a human skull phantom. We obtained an initial easygoing design model for the lens angle and optimal stand-off using relatively simple formulas. The skull is not an obstacle for focusing of ultrasound with optimized axicon lenses that achieve an identical resolution to spherical transducers, but with the advantage that the focusing distance is shortened. An adequate stand-off improves the lateral resolution of the acoustic beam by approximately 50%. The approach proposed provides an effective way of designing polydimethylsiloxane (PDMS)-based axicon lenses equipped transducers.

Consistent Gradient of Performance and Decoding of Stimulus Type and Valence From Local and Network Activity.

The individual differences approach focuses on the variation of behavioral and neural signatures across subjects. In this context, we searched for intracranial neural markers of performance in three individuals with distinct behavioral patterns (efficient, borderline, and inefficient) in a dual-valence task assessing facial and lexical emotion recognition. First, we performed a preliminary study to replicate well-established evoked responses in relevant brain regions. Then, we examined time series data and network connectivity, combined with multivariate pattern analyses and machine learning, to explore electrophysiological differences in resting-state versus task-related activity across subjects. Next, using the same methodological approach, we assessed the neural decoding of performance for different dimensions of the task. The classification of time series data mirrored the behavioral gradient across subjects for stimulus type but not for valence. However, network-based measures reflected the subjects' hierarchical profiles for both stimulus types and valence. Therefore, this measure serves as a sensitive marker for capturing distributed processes such as emotional valence discrimination, which relies on an extended set of regions. Network measures combined with classification methods may offer useful insights to study single subjects and understand inter-individual performance variability. Promisingly, this approach could eventually be extrapolated to other neuroscientific techniques.

High mutual cooperation rates in rats learning reciprocal altruism: The role of payoff matrix.

Cooperation is one of the most studied paradigms for the understanding of social interactions. Reciprocal altruism -a special type of cooperation that is taught by means of the iterated prisoner dilemma game (iPD)- has been shown to emerge in different species with different success rates. When playing iPD against a reciprocal opponent, the larger theoretical long-term reward is delivered when both players cooperate mutually. In this work, we trained rats in iPD against an opponent playing a Tit for Tat strategy, using a payoff matrix with positive and negative reinforcements, that is food and timeout respectively. We showed for the first time, that experimental rats were able to learn reciprocal altruism with a high average cooperation rate, where the most probable state was mutual cooperation (85%). Although when subjects defected, the most probable behavior was to go back to mutual cooperation. When we modified the matrix by increasing temptation rewards (T) or by increasing cooperation rewards (R), the cooperation rate decreased. In conclusion, we observe that an iPD matrix with large positive reward improves less cooperation than one with small rewards, shown that satisfying the relationship among iPD reinforcement was not enough to achieve high mutual cooperation behavior. Therefore, using positive and negative reinforcements and an appropriate contrast between rewards, rats have cognitive capacity to learn reciprocal altruism. This finding allows to infer that the learning of reciprocal altruism has early appeared in evolution.

Putative dopamine neurons in the ventral tegmental area enhance information coding in the prefrontal cortex.

It has been proposed that neuronal populations in the prefrontal cortex (PFC) robustly encode task-relevant information through an interplay with the ventral tegmental area (VTA). Yet, the precise computation underlying such functional interaction remains elusive. Here, we conducted simultaneous recordings of single-unit activity in PFC and VTA of rats performing a GO/NoGO task. We found that mutual information between stimuli and neural activity increases in the PFC as soon as stimuli are presented. Notably, it is the activity of putative dopamine neurons in the VTA that contributes critically to enhance information coding in the PFC. The higher the activity of these VTA neurons, the better the conditioned stimuli are encoded in the PFC.

A spherical treadmill system to train head-fixed adult rats.

BACKGROUND: While spherical treadmills are widely used in mouse models, there are only a few experimental setups suitable for adult rats, and none of them include head-fixation. NEW METHOD: We introduce a novel spherical treadmill apparatus for head-fixed rats that allows a wide repertory of natural responses. The rat is secured to a frame and placed on a freely rotating sphere. While being head-fixed, it can walk in any direction and perform different motor tasks. COMPARISON WITH EXISTING METHODS: Instead of being air-lifted, which is acceptable for light animals, the treadmill is sustained by three spherical bearings ensuring a smooth rotation in any direction. Movement detection is accomplished using a video camera that registers a dot pattern plotted on the sphere. RESULTS: Long Evans rats were trained to perform an auditory discrimination task in a Go/No-Go (walking/not-walking) paradigm. Animals were able to successfully discriminate between a 1 kHz and a 8 kHz auditory stimulus and execute the correct response, reaching the learning criterion (80% of correct responses) in approximately 20 training sessions. CONCLUSIONS: Our system broadens the possibilities of head-fixation experiments in adult rats making them compatible with spatial navigation on a spherical treadmill.

Efficient enhancement of information in the prefrontal cortex during the presence of reward predicting stimuli.

The prefrontal cortex (PFC) is a key brain structure for decision making, behavioural flexibility and working memory. Neurons in PFC encode relevant stimuli through changes in their firing rate, although the metabolic cost of spiking activity puts strong constrains to neural codes based on firing rate modulation. Thus, how PFC neural populations code relevant information in an efficient way is not clearly understood. To address this issue we made single unit recordings in the PFC of rats performing a GO/NOGO discrimination task and analysed how entropy between pairs of neurons changes during cue presentation. We found that entropy rises only during reward-predicting cues. Moreover, this change in entropy occurred along an increase in the efficiency of the whole process. We studied possible mechanisms behind the efficient gain in entropy by means of a two neuron leaky integrate-and-fire model, and found that a precise relationship between synaptic efficacy and firing rate is required to explain the experimentally observed results.

Cortical response states for enhanced sensory discrimination.

Brain activity during wakefulness is characterized by rapid fluctuations in neuronal responses. Whether these fluctuations play any role in modulating the accuracy of behavioral responses is poorly understood. Here, we investigated whether and how trial changes in the population response impact sensory coding in monkey V1 and perceptual performance. Although the responses of individual neurons varied widely across trials, many cells tended to covary with the local population. When population activity was in a 'low' state, neurons had lower evoked responses and correlated variability, yet higher probability to predict perceptual accuracy. The impact of firing rate fluctuations on network and perceptual accuracy was strongest 200 ms before stimulus presentation, and it greatly diminished when the number of cells used to measure the state of the population was decreased. These findings indicate that enhanced perceptual discrimination occurs when population activity is in a 'silent' response mode in which neurons increase information extraction.

Spontaneous Fluctuations in Visual Cortical Responses Influence Population Coding Accuracy.

Information processing in the cerebral cortex depends not only on the nature of incoming stimuli, but also on the state of neuronal networks at the time of stimulation. That is, the same stimulus will be processed differently depending on the neuronal context in which it is received. A major factor that could influence neuronal context is the background, or ongoing neuronal activity before stimulation. In visual cortex, ongoing activity is known to play a critical role in the development of local circuits, yet whether it influences the coding of visual features in adult cortex is unclear. Here, we investigate whether and how the information encoded by individual neurons and populations in primary visual cortex (V1) depends on the ongoing activity before stimulus presentation. We report that when individual neurons are in a "low" prestimulus state, they have a higher capacity to discriminate stimulus features, such as orientation, despite their reduction in evoked responses. By measuring the distribution of prestimulus activity across a population of neurons, we found that network discrimination accuracy is improved in the low prestimulus state. Thus, the distribution of ongoing activity states across the network creates an "internal context" that dynamically filters incoming stimuli to modulate the accuracy of sensory coding. The modulation of stimulus coding by ongoing activity state is consistent with recurrent network models in which ongoing activity dynamically controls the balanced background excitation and inhibition to individual neurons.

Development of a multilocus sequence typing scheme for the study of Anaplasma marginale population structure over space and time.

Bovine Anaplasmosis caused by Anaplasma marginale is a worldwide disease prevalent in tropical and subtropical regions where Rhipicephalus microplus is considered the most significant biological vector. Molecular markers previously applied for A. marginale typing are efficient for isolate discrimination but they are not a suitable tool for studying population structure and dynamics. Here we report the development of an MLST scheme based on the study of seven genes: dnaA, ftsZ, groEl, lipA, recA, secY and sucB. Five annotated genomes (Saint Maries, Florida, Mississippi, Puerto Rico and Virginia) and 53 bovine blood samples from different world regions were analyzed. High nucleotide diversity and a large proportion of synonymous substitutions, indicative of negative selection resulted from DnaSP 5.00.02 package application. Recombination events were detected in almost all genes, this evidence together with the coexistence of more than one A. marginale strain in the same sample might suggest the superinfection phenomena as a potential source of variation. The allelic profile analysis performed through GoeBURST shown two main CC that did not support geography. In addition, the AMOVA test confirmed the occurrence of at least two main genetically divergent groups. The composition of the emergent groups reflected the impact of both historical and environmental traits on A. marginale population structure. Finally, a web-based platform "Galaxy MLST-Pipeline" was developed to automate DNA sequence editing and data analysis that together with the Data Base are freely available to users. The A. marginale MLST scheme developed here is a valuable tool with a high discrimination power, besides PCR based strategies are still the better choice for epidemiological intracellular pathogens studies. Finally, the allelic profile describe herein would contribute to uncover the mechanisms in how intracellular pathogens challenge virulence paradigm.

Dopamine modulation of GABAergic function enables network stability and input selectivity for sustaining working memory in a computational model of the prefrontal cortex.

Dopamine modulation of GABAergic transmission in the prefrontal cortex (PFC) is thought to be critical for sustaining cognitive processes such as working memory and decision-making. Here, we developed a neurocomputational model of the PFC that includes physiological features of the facilitatory action of dopamine on fast-spiking interneurons to assess how a GABAergic dysregulation impacts on the prefrontal network stability and working memory. We found that a particular non-linear relationship between dopamine transmission and GABA function is required to enable input selectivity in the PFC for the formation and retention of working memory. Either degradation of the dopamine signal or the GABAergic function is sufficient to elicit hyperexcitability in pyramidal neurons and working memory impairments. The simulations also revealed an inverted U-shape relationship between working memory and dopamine, a function that is maintained even at high levels of GABA degradation. In fact, the working memory deficits resulting from reduced GABAergic transmission can be rescued by increasing dopamine tone and vice versa. We also examined the role of this dopamine-GABA interaction for the termination of working memory and found that the extent of GABAergic excitation needed to reset the PFC network begins to occur when the activity of fast-spiking interneurons surpasses 40 Hz. Together, these results indicate that the capability of the PFC to sustain working memory and network stability depends on a robust interplay of compensatory mechanisms between dopamine tone and the activity of local GABAergic interneurons.

State-dependent changes of connectivity patterns and functional brain network topology in autism spectrum disorder.

Anatomical and functional brain studies have converged to the hypothesis that autism spectrum disorders (ASD) are associated with atypical connectivity. Using a modified resting-state paradigm to drive subjects' attention, we provide evidence of a very marked interaction between ASD brain functional connectivity and cognitive state. We show that functional connectivity changes in opposite ways in ASD and typicals as attention shifts from external world towards one's body generated information. Furthermore, ASD subject alter more markedly than typicals their connectivity across cognitive states. Using differences in brain connectivity across conditions, we ranked brain regions according to their classification power. Anterior insula and dorsal-anterior cingulate cortex were the regions that better characterize ASD differences with typical subjects across conditions, and this effect was modulated by ASD severity. These results pave the path for diagnosis of mental pathologies based on functional brain networks obtained from a library of mental states.

A computational theory for the learning of equivalence relations.

Equivalence relations (ERs) are logical entities that emerge concurrently with the development of language capabilities. In this work we propose a computational model that learns to build ERs by learning simple conditional rules. The model includes visual areas, dopaminergic, and noradrenergic structures as well as prefrontal and motor areas, each of them modeled as a group of continuous valued units that simulate clusters of real neurons. In the model, lateral interaction between neurons of visual structures and top-down modulation of prefrontal/premotor structures over the activity of neurons in visual structures are necessary conditions for learning the paradigm. In terms of the number of neurons and their interaction, we show that a minimal structural complexity is required for learning ERs among conditioned stimuli. Paradoxically, the emergence of the ER drives a reduction in the number of neurons needed to maintain those previously specific stimulus-response learned rules, allowing an efficient use of neuronal resources.

A big-world network in ASD: dynamical connectivity analysis reflects a deficit in long-range connections and an excess of short-range connections.

Over the last years, increasing evidence has fuelled the hypothesis that Autism Spectrum Disorder (ASD) is a condition of altered brain functional connectivity. The great majority of these empirical studies relies on functional magnetic resonance imaging (fMRI) which has a relatively poor temporal resolution. Only a handful of studies has examined networks emerging from dynamic coherence at the millisecond resolution and there are no investigations of coherence at the lowest frequencies in the power spectrum-which has recently been shown to reflect long-range cortico-cortical connections. Here we used electroencephalography (EEG) to assess dynamic brain connectivity in ASD focusing in the low-frequency (delta) range. We found that connectivity patterns were distinct in ASD and control populations and reflected a double dissociation: ASD subjects lacked long-range connections, with a most prominent deficit in fronto-occipital connections. Conversely, individuals with ASD showed increased short-range connections in lateral-frontal electrodes. This effect between categories showed a consistent parametric dependency: as ASD severity increased, short-range coherence was more pronounced and long-range coherence decreased. Theoretical arguments have been proposed arguing that distinct patterns of connectivity may result in networks with different efficiency in transmission of information. We show that the networks in ASD subjects have less Clustering coefficient, greater Characteristic Path Length than controls - indicating that the topology of the network departs from small-world behaviour - and greater modularity. Together these results show that delta-band coherence reveal qualitative and quantitative aspects associated with ASD pathology.

Plasticity in the rat prefrontal cortex: linking gene expression and an operant learning with a computational theory.

The plasticity in the medial Prefrontal Cortex (mPFC) of rodents or lateral prefrontal cortex in non human primates (lPFC), plays a key role neural circuits involved in learning and memory. Several genes, like brain-derived neurotrophic factor (BDNF), cAMP response element binding (CREB), Synapsin I, Calcium/calmodulin-dependent protein kinase II (CamKII), activity-regulated cytoskeleton-associated protein (Arc), c-jun and c-fos have been related to plasticity processes. We analysed differential expression of related plasticity genes and immediate early genes in the mPFC of rats during learning an operant conditioning task. Incompletely and completely trained animals were studied because of the distinct events predicted by our computational model at different learning stages. During learning an operant conditioning task, we measured changes in the mRNA levels by Real-Time RT-PCR during learning; expression of these markers associated to plasticity was incremented while learning and such increments began to decline when the task was learned. The plasticity changes in the lPFC during learning predicted by the model matched up with those of the representative gene BDNF. Herein, we showed for the first time that plasticity in the mPFC in rats during learning of an operant conditioning is higher while learning than when the task is learned, using an integrative approach of a computational model and gene expression.

Differential representation of sunflower ESTs in enriched organ-specific cDNA libraries in a small scale sequencing project.

BACKGROUND: Subtractive hybridization methods are valuable tools for identifying differentially regulated genes in a given tissue avoiding redundant sequencing of clones representing the same expressed genes, maximizing detection of low abundant transcripts and thus, affecting the efficiency and cost effectiveness of small scale cDNA sequencing projects aimed to the specific identification of useful genes for breeding purposes. The objective of this work is to evaluate alternative strategies to high-throughput sequencing projects for the identification of novel genes differentially expressed in sunflower as a source of organ-specific genetic markers that can be functionally associated to important traits. RESULTS: Differential organ-specific ESTs were generated from leaf, stem, root and flower bud at two developmental stages (R1 and R4). The use of different sources of RNA as tester and driver cDNA for the construction of differential libraries was evaluated as a tool for detection of rare or low abundant transcripts. Organ-specificity ranged from 75 to 100% of non-redundant sequences in the different cDNA libraries. Sequence redundancy varied according to the target and driver cDNA used in each case. The R4 flower cDNA library was the less redundant library with 62% of unique sequences. Out of a total of 919 sequences that were edited and annotated, 318 were non-redundant sequences. Comparison against sequences in public databases showed that 60% of non-redundant sequences showed significant similarity to known sequences. The number of predicted novel genes varied among the different cDNA libraries, ranging from 56% in the R4 flower to 16 % in the R1 flower bud library. Comparison with sunflower ESTs on public databases showed that 197 of non-redundant sequences (60%) did not exhibit significant similarity to previously reported sunflower ESTs. This approach helped to successfully isolate a significant number of new reported sequences putatively related to responses to important agronomic traits and key regulatory and physiological genes. CONCLUSIONS: The application of suppressed subtracted hybridization technology not only enabled the cost effective isolation of differentially expressed sequences but it also allowed the identification of novel sequences in sunflower from a relative small number of analyzed sequences when compared to major sequencing projects.