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Recurrence microstates can be used to analyze many properties of stationary states of stochastic and deterministic time series, including the level of correlation of stochastic signals. Here, we show how artificially inserted data (data that does not belong to a original stationary signal) may be detected using recurrence microstates statistics. We show that the method is sensitive enough to detect the breaking of the stationary signal even when the corrupted inserted data span into the same domain of the original data. Examples of our analyses are applied to two numerically generated time series of dynamical systems, namely the logistic map, and the Lorenz equations. Finally to show results applied to experimental time series, we analyze a digital audio signal of a human speech.

The partial phase synchronization (sometimes called cooperation) of neurons is fundamental for the understanding of the complex behavior of the brain. The lack or the excess of synchronization can generate brain disorders like Parkinson's disease and epilepsy. The phase synchronization phenomenon is strongly related to the regular or chaotic dynamics of individual neurons. The individual dynamics themselves are a function of the ion channel conductances, turning the conductances into important players in the process of neuron synchronized health depolarization/repolarization processes. It is well known that many diseases are related to alterations of the ion-channel conductance properties. To normalize their functioning, drugs are used to block or activate specific channels, changing their conductances. We investigate the synchronization process of a Hodgkin-Huxley-type neural network as a function of the values of the individual neuron conductances, showing the dynamics of the neurons must be taken into account in the synchronization process. Particular sets of conductances lead to non-chaotic individual neuron dynamics allowing synchronization states for very weak coupling and resulting in a non-monotonic transition to synchronized states, as the coupling strength among neurons is varied. On the other hand, a monotonic transition to synchronized states is observed for individual chaotic dynamics of the neurons. We conclude the analysis of the individual dynamics of isolated neurons allows the prediction of the synchronization process of the network. We provide alternative ways to achieve the desired network state (phase synchronized or desynchronized) without any changes in the synaptic current of neurons but making just small changes in the neuron ion-channel conductances. The mechanism behind the control is the close relation between ion-channel conductance and the regular or chaotic dynamics of neurons. Finally, we show that by changing at least two conductances simultaneously the control may be much more efficient since the second conductance makes the synchronization possible just by performing a small change in the first. The study presented here may have an impact on new drug development research.

We show how the maximum of recurrence-mi\-cro\-states entropy configures a new way to properly compute appropriate recurrence vicinity parameter and time sampling to perform recurrence analysis of continuous data. For experimental data, we show the same procedure may be used to find the optimum sampling or to perform down-sampling of the data, preserving recurrence meaning and adjusting the ideal sampling. The new method retrieves results obtained using traditional methods with the advantage of being independent of any free parameter, since all parameter dependencies are automatically set. Our results are also less sensitive to noise when experimental data is used. Due to the automatized way to capture suitable recurrence parameters, the method is adequate for using in autonomous numerical algorithms, allowing the recovery of relevant recurrence information embedded in time series (including over-sampled data), rationalizing the process of data acquisition and allowing only relevant data to be collected.

Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal correlations. Here we propose a new technique to reliably address both problems. Our approach follows two steps: first, we train an artificial neural network (ANN) with flicker (colored) noise to predict the value of the parameter, $$\alpha$$ α , that determines the strength of the correlation of the noise. To predict $$\alpha$$ α the ANN input features are a set of probabilities that are extracted from the time series by using symbolic ordinal analysis. Then, we input to the trained ANN the probabilities extracted from the time series of interest, and analyze the ANN output. We find that the $$\alpha$$ α value returned by the ANN is informative of the temporal correlations present in the time series. To distinguish between stochastic and chaotic signals, we exploit the fact that the difference between the permutation entropy (PE) of a given time series and the PE of flicker noise with the same $$\alpha$$ α parameter is small when the time series is stochastic, but it is large when the time series is chaotic. We validate our technique by analysing synthetic and empirical time series whose nature is well established. We also demonstrate the robustness of our approach with respect to the length of the time series and to the level of noise. We expect that our algorithm, which is freely available, will be very useful to the community.

Time series analysis comprises a wide repertoire of methods for extracting information from data sets. Despite great advances in time series analysis, identifying and quantifying the strength of nonlinear temporal correlations remain a challenge. We have recently proposed a new method based on training a machine learning algorithm to predict the temporal correlation parameter, α, of flicker noise (FN) time series. The algorithm is trained using as input features the probabilities of ordinal patterns computed from FN time series, xαFN(t), generated with different values of α. Then, the ordinal probabilities computed from the time series of interest, x(t), are used as input features to the trained algorithm and that returns a value, αe, that contains meaningful information about the temporal correlations present in x(t). We have also shown that the difference, Ω, of the permutation entropy (PE) of the time series of interest, x(t), and the PE of a FN time series generated with α=αe, xαeFN(t), allows the identification of the underlying determinism in x(t). Here, we apply our methodology to different datasets and analyze how αe and Ω correlate with well-known quantifiers of chaos and complexity. We also discuss the limitations for identifying determinism in highly chaotic time series and in periodic time series contaminated by noise. The open source algorithm is available on Github.

We investigate the dynamical properties of two coupled neural networks with 2,048 identical Hodgkin--Huxley type bursting neurons. The internal connection architecture of each network follows a small-world topology and the external connection scheme is based on the local mean field potential, where one network receives the signal from the other. To analyze the system, we use Kuramoto order parameter computed over each neuron data, and recurrence quantification analyses, particularly the ratio of recurrent points belonging to diagonal lines of the recurrence plot RP, namely the determinism, computed over the local mean field potential of the networks, an easier experimentally accessible data. We analyze the complex synchronization scenario depicted by the network as a function of internal and external coupling parameters. Particularly, we identify regions of non-monotonic dependence of the synchronization level as a function of the coupling strength; coupling induced phase desynchronization (PD), where the synchronization levels are similar to those expected for randomly distributed phases; almost complete spike synchronization (SS) for which even spikes composing a burst are synchronized. This regime occurs as a product of the fast modulated signal imposed by the coupling between networks and weak internal coupling; finally, bursting synchronization (BS) regions are associated with slow modulated internal coupling.

We simulate a small-world neural network composed of 2000 thermally sensitive identical Hodgkin–Huxley type neurons investigating the synchronization characteristics as a function of the coupling strength and the temperature of the neurons. The Kuramoto order parameter computed over individual neuron membrane potential signals, and recurrence analysis evaluated from the mean field of the network are used to identify the non-monotonous behavior of the synchronization level as a function of the coupling parameter. We show that moderated high temperatures induce a low variability of the inter-burst intervals of neurons leading to phase synchronization and further increases of temperature result in a low variability of inter-spike intervals leading the network to display spike synchronization.

We investigate the role of bistability in the synchronization of a network of identical bursting neurons coupled through an generic electrical mean-field scheme. These neurons can exhibit distinct multistable states and, in particular, bistable behavior is observed when their sodium conductance is varied. With this, we consider three different initialization compositions: (i) the whole network is in the same periodic state; (ii) half of the network periodic, half chaotic; (iii) half periodic, and half in a different periodic state. We show that (i) and (ii) reach phase synchronization (PS) for all coupling strengths, while for (iii) small coupling regimes do not induce PS, and instead, there is a coexistence of different frequencies. For stronger coupling, case (iii) synchronizes, but after (i) and (ii). Since PS requires all neurons being in the same state (same frequencies), these different behaviors are governed by transitions between the states. We find that, during these transitions, (ii) and (iii) have transient chimera states and that (iii) has breathing chimeras. By studying the stability of each state, we explain the observed transitions. Therefore, bistability of neurons can play a major role in the synchronization of generic networks, with the simple initialization of the system being capable of drastically changing its asymptotic space.

We investigate the synchronization features of a network of spiking neurons under a distance-dependent coupling following a power-law model. The interplay between topology and coupling strength leads to the existence of different spatiotemporal patterns, corresponding to either non-synchronized or phase-synchronized states. Particularly interesting is what we call synchronization malleability, in which the system depicts significantly different phase synchronization degrees for the same parameters as a consequence of a different ordering of neural inputs. We analyze the functional connectivity of the network by calculating the mutual information between neuronal spike trains, allowing us to characterize the structures of synchronization in the network. We show that these structures are dependent on the ordering of the inputs for the parameter regions where the network presents synchronization malleability and we suggest that this is due to a complex interplay between coupling, connection architecture, and individual neural inputs., 13 pages, 10 figures

The recurrence analysis of dynamic systems has been studied since Poincare’s seminal work. Since then, several approaches have been developed to study recurrence properties in nonlinear dynamical systems. In this work, we study the recently developed entropy of recurrence microstates. We propose a new quantifier, the maximum entropy ( S max). The new concept uses the diversity of microstates of the recurrence plot and is able to set automatically the optimum recurrence neighborhood ( ϵ—vicinity), turning the analysis free of the vicinity parameter. In addition, ϵ turns out to be a novel quantifier of dynamical properties itself. We apply S max and the optimum ϵ to deterministic and stochastic systems. The S max quantifier has a higher correlation with the Lyapunov exponent and, since it is a parameter-free measure, a more useful recurrence quantifier of time series.

The connection architecture plays an important role in the synchronization of networks, where the presence of local and nonlocal connection structures are found in many systems, such as the neural ones. Here, we consider a network composed of chaotic bursting oscillators coupled through a Watts-Strogatz-small-world topology. The influence of coupling strength and rewiring of connections is studied when the network topology is varied from regular to small-world to random. In this scenario, we show two distinct nonstationary transitions to phase synchronization: one induced by the increase in coupling strength and another resulting from the change from local connections to nonlocal ones. Besides this, there are regions in the parameter space where the network depicts a coexistence of different bursting frequencies where nonstationary zig-zag fronts are observed. Regarding the analyses, we consider two distinct methodological approaches: one based on the phase association to the bursting activity where the Kuramoto order parameter is used and another based on recurrence quantification analysis where just a time series of the network mean field is required.

Neurons modeled by the Rulkov map display a variety of dynamic regimes that include tonic spikes and chaotic bursting. Here we study an ensemble of bursting neurons coupled with the Watts-Strogatz small-world topology. We characterize the sequences of bursts using the symbolic method of time-series analysis known as ordinal analysis, which detects nonlinear temporal correlations. We show that the probabilities of the different symbols distinguish different dynamical regimes, which depend on the coupling strength and the network topology. These regimes have different spatio-temporal properties that can be visualized with raster plots.

Alpha-thalassemia is highly prevalent in the plural society of Brazil and is a public health problem. There is limited knowledge on its accurate frequency and distribution in the Amazon region. Knowing the frequency of thalassemia and the prevalence of responsible mutations is, therefore, an important step in the understanding and control program. Hematological and molecular data, in addition to serum iron and serum ferritin, from 989 unrelated first-time blood donors from Amazonas Hemotherapy and Hematology Foundation (FHEMOAM) were collected. In this study, the subjects were screened for −α3.7/4.2/20.5, −SEA, −FIL, and −MED deletions. Alpha-thalassemia screening was carried out between 2016 and 2017 among 714 (72.1%) male and 275 (27.9%) female donors. The aims of this analysis were to describe the distribution of various alpha-thalassemia alleles by gender, along with their genotypic interactions, and to illustrate the hematological changes associated with each phenotype. Amongst the patients, 5.35% (n = 53) were diagnosed with deletion –α−3.7 and only one donor with α−4.2 deletion. From the individuals with –α−3.7, 85.8% (n = 46) were heterozygous and 14.20% (n = 7) were homozygous. The frequency of the –α−3.7 deletion was higher in male (5.89%) than in female (4.0%). There is no significant difference in the distribution of –α−3.7 by gender (p=0.217). The –α20.5, −SEA, and −MED deletions were not found. All subjects were analyzed for serum iron and serum ferritin, with 1.04% being iron deficient (n = 5) and none with very high levels of stored iron (>220 µg/dL). Alpha-thalassemia-23.7kb deletion was the most common allele detected in Manaus blood donors, which is a consistent result, once it is the most common type of α-thalassemia found throughout the world. As expected, the mean of hematological data was significantly lower in alpha-thalassemia carriers (p<0.001), mainly homozygous genotype. Leukocytes and platelet count did not differ significantly. Due to the small number of individuals with iron deficiency found among blood donors, the differential diagnosis between the two types of anemia was not possible, even because minor changes were found among hematological parameters with iron deficiency and α-thalassemia. Despite this, the study showed the values of hematological parameters, especially MCV and MCH, are lower in donors with iron deficiency, especially when associated with α-thalassemia, and therefore, it may be useful to discriminate different types of microcytic anaemia. In conclusion, we believed screening for thalassemia trait should be included as part of a standard blood testing before blood donation. It should be noted that this was the first study to perform the screening for alpha deletions in blood donors from the Manaus region, and further studies are required to look at the effects of donated thalassemic blood.

The transition to phase synchronized states of neural networks with bursting dynamics may have nonstationary characteristics, as well as sensitivity to initial conditions. Here, we analyze the paradigmatic network composed of neurons of Rulkov to investigate dynamic properties of the transitions to phase synchronization displayed by networks under two different topologies of the connection matrices, namely, small-world and random ones. Our analyses of both connection architectures reveal that neural networks under small-world topology display higher sensibility to initial conditions, and contrarily to the random connection case, depict a nonstationary transition to phase synchronization through the presence of a two-state on–off intermittency. The analyses are based on the recurrence quantifier determinism calculated by using only the local (mean) field potential (LFP) of the network, an experimentally easy accessible data. The use of LFP data offers advantages in the quantification of the nonstationary dynamics at the transition to phase synchronized states, since the more traditional Kuramoto order parameter must be computed over the individual signals of the neurons.

Here we investigate the mechanism for explosive synchronization (ES) of a complex neural network composed of nonidentical neurons and coupled by Newman-Watts small-world matrices. We find a range of nonlocal connection probabilities for which the network displays an abrupt transition to phase synchronization, characterizing ES. The mechanism behind the ES is the following: As the coupling parameter is varied in a network of distinct neurons, ES is likely to occur due to a bistable regime, namely a chaotic nonsynchronized and a regular phase-synchronized state in the phase space. In this case, even small coupling changes make possible a transition between them. The onset of ES occurs via a saddle-node bifurcation of a periodic orbit that leads the network dynamics to display a locally stable phase-synchronized state. The presence of this regime is accompanied by a hysteresis loop on the network dynamics as the coupling parameter is adiabatically increased and decreased. The end of the hysteresis loop is marked by a frontier crisis of the chaotic attractor which also determines the end of the coupling strength interval where ES is possible.

It is known that neural networks under small-world topology can present anomalous synchronization and nonstationary behavior for weak coupling regimes. Here, we propose methods to suppress the anomalous synchronization and also to diminish the nonstationary behavior occurring in weakly coupled neural network under small-world topology. We consider a network of 2000 thermally sensitive identical neurons, based on the model of Hodgkin–Huxley in a small-world topology, with the probability of adding non local connection equal to p = 0 . 001 . Based on experimental protocols to suppress anomalous synchronization, as well as nonstationary behavior of the neural network dynamics, we make use of (i) external stimulus (pulsed current); (ii) biologic parameters changing (neuron membrane conductance changes); and (iii) body temperature changes. Quantification analysis to evaluate phase synchronization makes use of the Kuramoto’s order parameter, while recurrence quantification analysis, particularly the determinism, computed over the easily accessible mean field of network, the local field potential (LFP), is used to evaluate nonstationary states. We show that the methods proposed can control the anomalous synchronization and nonstationarity occurring for weak coupling parameter without any effect on the individual neuron dynamics, neither in the expected asymptotic synchronized states occurring for large values of the coupling parameter.

In this work, we study the phase synchronization of a neural network and explore how the heterogeneity in the neurons' dynamics can lead their phases to intermittently phase-lock and unlock. The neurons are connected through chemical excitatory connections in a sparse random topology, feel no noise or external inputs, and have identical parameters except for different in-degrees. They follow a modification of the Hodgkin-Huxley model, which adds details like temperature dependence, and can burst either periodically or chaotically when uncoupled. Coupling makes them chaotic in all cases but each individual mode leads to different transitions to phase synchronization in the networks due to increasing synaptic strength. In almost all cases, neurons' inter-burst intervals differ among themselves, which indicates their dynamical heterogeneity and leads to their intermittent phase-locking. We argue then that this behavior occurs here because of their chaotic dynamics and their differing initial conditions. We also investigate how this intermittency affects the formation of clusters of neurons in the network and show that the clusters' compositions change at a rate following the degree of intermittency. Finally, we discuss how these results relate to studies in the neuroscience literature, especially regarding metastability.

Recurrence entropy ( S ) is a novel time series quantifier based on recurrence microstates. Here we show for the first time that max ( S ) can be used as a parameter-free quantifier of short and long-term memories (time-correlations) of stochastic and chaotic signals. max ( S ) can also evaluate properties of the distribution function of the data set that are not related to time correlation. To illustrate this fact, we show that even shuffled versions of distinct time correlated stochastic signals lead to different, but coherently varying, values of max ( S ) . Such a property of max ( S ) is associated to its ability to quantify in how many ways distinct short recurrence sequences can be found in time series. Applied to a deterministic dissipative system, the method brings evidence about the attractor properties and the degree of chaoticity. We conclude that the development of a new parameter-free quantifier of stochastic and chaotic time series can open new perspectives to stochastic data and deterministic time series analyses and may find applications in many areas of science.

The study of synchronization in complex networks is useful for understanding a variety of systems, including neural systems. However, the properties of the transition to synchronization are still not well known. In this work, we analyze the details of the transition to synchronization in complex networks composed of bursting oscillators under small-world and scale-free topologies using recurrence quantification analysis, specifically the determinism. We demonstrate the existence of non-stationarity states in the transition region. In the small-world network, the transition region denounces the existence of two-state intermittency.

The synchronization of neurons is fundamental for the functioning of the brain since its lack or excess may be related to neurological disorders, such as autism, Parkinson&rsquo, s and neuropathies such as epilepsy. In this way, the study of synchronization, as well as its suppression in coupled neurons systems, consists of an important multidisciplinary research field where there are still questions to be answered. Here, through mathematical modeling and numerical approach, we simulated a neural network composed of 5000 bursting neurons in a scale-free connection scheme where non-trivial synchronization phenomenon is observed. We proposed two different protocols to the suppression of phase synchronization, which is related to deep brain stimulation and delayed feedback control. Through an optimization process, it is possible to suppression the abnormal synchronization in the neural network.

Animal synchrony is found in phylogenetically distant animal groups, indicating behavioral adaptations to different selective pressures and in different signaling modalities. A notable example of synchronous display is found in fiddler crabs in that males wave their single enlarged claw during courtship. They present species-specific signals, which are composed of distinctive movement signatures. Given that synchronous waving has been reported for several fiddler crab species, the display pattern could influence the ability of a given species to sufficiently adjust wave timing to allow for synchrony. In this study, we quantified the wave displays of fiddler crabs to predict their synchronous behavior. We combined this information with the group's phylogenetic relationships to trace the evolution of display synchrony in an animal taxon. We found no phylogenetic signal in interspecific variation in predicted wave synchrony, which mirrors the general nonphylogenetic pattern of synchrony across animal taxa. Interestingly, our analyses show that the phenomenon of synchronization stems from the peculiarities of display pattern, mating systems, and the complexity of microhabitats. This is the first study to combine mathematical simulations and phylogenetic comparative methods to reveal how ecological factors and the mechanics of animal signals affect the evolution of the synchronous phenomena.

We study the dynamical proprieties of phase synchronization and intermittent behavior of neural systems using a network of networks structure based on an experimentally obtained human connectome for healthy and Alzheimer-affected brains. We consider a network composed of 78 neural subareas (subnetworks) coupled with a mean-field potential scheme. Each subnetwork is characterized by a small-world topology, composed of 250 bursting neurons simulated through a Rulkov model. Using the Kuramoto order parameter we demonstrate that healthy and Alzheimer-affected brains display distinct phase synchronization and intermittence properties as a function of internal and external coupling strengths. In general, for the healthy case, each subnetwork develops a substantial level of internal synchronization before a global stable phase-synchronization state has been established. For the unhealthy case, despite the similar internal subnetwork synchronization levels, we identify higher levels of global phase synchronization occurring even for relatively small internal and external coupling. Using recurrence quantification analysis, namely the determinism of the mean-field potential, we identify regions where the healthy and unhealthy networks depict nonstationary behavior, but the results denounce the presence of a larger region or intermittent dynamics for the case of Alzheimer-affected networks. A possible theoretical explanation based on two locally stable but globally unstable states is discussed.

Anomalous phase synchronization describes a synchronization phenomenon occurring even for the weakly coupled network and characterized by a non-monotonous dependence of the synchronization strength on the coupling strength. Its existence may support a theoretical framework to some neurological diseases, such as Parkinson’s and some episodes of seizure behavior generated by epilepsy. Despite the success of controlling or suppressing the anomalous phase synchronization in neural networks applying external perturbations or inducing ambient changes, the origin of the anomalous phase synchronization as well as the mechanisms behind the suppression is not completely known. Here, we consider networks composed of N = 2000 coupled neurons in a small-world topology for two well known neuron models, namely, the Hodgkin-Huxley-like and the Hindmarsh-Rose models, both displaying the anomalous phase synchronization regime. We show that the anomalous phase synchronization may be related to the individual behavior of the coupled neurons; particularly, we identify a strong correlation between the behavior of the inter-bursting-intervals of the neurons, what we call neuron variability, to the ability of the network to depict anomalous phase synchronization. We corroborate the ideas showing that external perturbations or ambient parameter changes that eliminate anomalous phase synchronization and at the same time promote small changes in the individual dynamics of the neurons, such that an increasing individual variability of neurons implies a decrease of anomalous phase synchronization. Finally, we demonstrate that this effect can be quantified using a well known recurrence quantifier, the “determinism.” Moreover, the results obtained by the determinism are based on only the mean field potential of the network, turning these measures more suitable to be used in experimental situations.

Barkhausen effect is a paradigmatic case of crackling noise and it offers insights on the complex microscopic magnetization process associated to the jerky motion of magnetic domain walls in ferromagnetic materials. The study of Barkhausen noise in magnetic films shows that the domain wall dynamics strongly depends on the structural characteristics of the material and film thickness. In fact, this phenomenon presents a dimensional transition that takes place for the thickness in the range from 100 to 50 nm in both cases: amorphous and polycrystalline films. Here we explore the entropy in the dynamics of magnetic domain walls using an entropy quantifier S based on the recurrence analysis. From the Barkhausen noise time series recorded in amorphous and polycrystalline films with distinct thicknesses, we address the entropy far from and at the dimensional crossover in the domain wall dynamics. Our findings reveal S presents a maximum at the dimensional transition; the distribution of S follows a Gaussian distribution for three-dimensional films far from the border of the dimensional crossover; the S distribution at the imminence of the dimensional crossover is not Gaussian, but strongly asymmetric, and despite the results are influenced by the film thickness, they seem to be insensitive to the structural characteristics of the materials.

The purpose of this work is to present convergence properties of regular and chaotic conservative trajectories under small dissipation. It is known that when subjected to dissipation, stable periodic points become sinks attracting the surrounding trajectories which belong to rational/irrational tori, while chaotic trajectories converge to a chaotic attractor, if it exists. Using the standard map and a mixed plot we show that this simple scenario can be rather complicated and strongly depends on the dissipation intensity. For small dissipations the huge amount of attractors of the phase-space generates a complicated and intricate dynamics where trajectories are steered to their attractors based on the local (non)hyperbolicity, measured by the Lyapunov vectors. Dissipation creates holes (or attracting channels) on the torus from the conservative limit and allows trajectories to penetrate it. These holes are regions of large local hyperbolicity and are related to sticky channels reported recently. For stronger dissipation sinks tend to attract all trajectories, prevailing over the chaotic attractor.

The circadian rhythm in mammals is determined by the output of biological clock cells, e.g. in the brain suprachiasmatic nucleus. Each biological clock cell has its own period and can respond to photic stimulation, such that the output rhythm is given both by the coupling among cells and the external forcing. We propose a model for the coupling among biological clock cells of the suprachiasmatic nucleus where the coupling is mediated by the local concentration of a diffusing neurotransmitter which is both secreted and absorbed by the cells, influencing their individual rhythms. Such a coupling is non-local because it considers all the cells in the assembly, and the interaction strength decays exponentially with the spatial distance. We investigate the synchronization properties of this network with respect to the coupling strength and the inverse characteristic length of the coupling, which varies from zero (a global, all-to-all, coupling) to infinity (a local, nearest-neighbor, coupling). Quantitative diagnostics of phase and frequency synchronization are applied to describe the transition from a non-synchronized to a partially synchronized behavior as the coupling parameters are changed.

Background: Sickle cell disease (SCD) includes a group of inherited red blood cell disorders. SCD patients vary widely from person to person. We describe β S and β C haplotypes and α-thalassemia 3.7 kb genotypes from SCD patients Fundacao Hospitalar de Hematologia e Hemoterapia do Amazonas, Manaus, AM. Methods: Our survey included 139 HbSS and 11 HbSC patients. Molecular genotypes have been identified by PCR-RFLP and α-thalassemia 3.7 kb deletion by ASO-PCR. Male/female distribution was 42.3%/57.7%. Results: The average age at enrolment was 19.1 years for HbSS and 25.85 years for HbSC. Average fetal hemoglobin was 11.27% for HbSS and 7.86% for HbSC. Anemia in HbSS patients was more severe and hemolysis twice as stronger as HbSC individuals. The frequency distribution of the most common β-globin haplotypes among HbSS patients was 52.5% CAR/CAR, 23.7% CAR/Ben and 18% Ben/Ben. For the HbSC group, the haplotype distribution was 36.3% CAR/CI, 27.3% Benin/CI, 18.2% CAR/CII, 9.1% CAR/CIII and 9.1% Benin/CII. Of the HbSS patients, 13.7% and 2.8% were heterozygous and homozygous for the α-thalassemia 3.7 kb deletion, respectively. No HbSC patients presented the deletion. Conclusions: Here we present the distribution of haplotypes β S and β C and α-thalassemia deletion 3.7 kb in a population sample with SCD from the Occidental Brazilian Amazon. Results have been analyzed in the context of hematological and biochemical profiles. J Hematol. 2016;5(4):123-128 doi: https://doi.org/10.14740/jh310w