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Martinez-Montes, E.

Publications and source records attributed to Martinez-Montes, E..

9 recordsLinked to original sources

DNMT3AR882H Is Not Required for Disease Maintenance in Primary Human AML, but Is Associated With Increased Leukemia Stem Cell Frequency

Genetic mutations are being thoroughly mapped in human cancers, yet a fundamental question in cancer biology is whether such mutations are functionally required for cancer initiation, maintenance of established cancer, or both. Here, we study this question in the context of human acute myeloid leukemia (AML), where DNMT3AR882 missense mutations often arise early, in pre-leukemic clonal hematopoiesis, and corrupt the DNA methylation landscape to initiate leukemia. We developed CRISPR-based methods to directly correct DNMT3AR882 mutations in leukemic cells obtained from patients. Surprisingly, DNMT3AR882 mutations were largely dispensable for disease maintenance. Replacing DNMT3AR882 mutants with wild-type DNMT3A did not impair the ability of AML cells to engraft in vivo, and minimally altered DNA methylation. Taken together, DNMT3AR882 mutations are initially necessary for AML initiation, but are largely dispensable for disease maintenance. The notion that initiating oncogenes differ from those that maintain cancer has important implications for cancer evolution and therapy. STATEMENT OF SIGNIFICANCEUnderstanding which driver mutations are required for cancer initiation, maintenance, or both phases remains poorly understood. Here, we uncover that highly prevalent pre-leukemic DNMT3A mutations are only required during disease initiation, but become dispensable after leukemic transformation, uncovering the context-specific role of this driver mutation with important therapeutic implications.

cancer biology↗

DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML

Disruption of the epigenetic landscape is of particular interest in acute myeloid leukemia (AML) due to its relatively low mutational burden and frequent occurrence of mutations in epigenetic regulators. Here, we applied an information-theoretic analysis of methylation potential energy landscapes, capturing changes in mean methylation level and methylation entropy, to comprehensively analyze DNA methylation stochasticity in subtypes of AML defined by mutually exclusive genetic mutations. We identified AML subtypes with CEBPA double mutation and those with IDH mutations as distinctly high-entropy subtypes, marked by methylation disruption over a convergent set of genes. We found a core program of epigenetic landscape disruption across all AML subtypes, with discordant methylation stochasticity and transcriptional dysregulation converging on functionally important leukemic signatures, suggesting a genotype-independent role of stochastic disruption of the epigenetic landscape in mediating leukemogenesis. We further established a relationship between methylation entropy and gene expression variability, connecting the disruption of the epigenetic landscape to transcription in AML. This approach identified a convergent program of epigenetic dysregulation in leukemia, clarifying the contribution of specific genetic mutations to stochastic disruption of the epigenetic and transcriptional landscapes of AML.

cancer biology↗

Modeling neural activity in neurodegenerative diseases through a neural field model with variable density of neurons.

In recent years, a vertiginous advance has occurred within the Neural Field Theory with the development of the so-called Next Generation Neural Field models. Unlike the phenomenological models, these models manage to describe neuronal activity, macroscopically, from the thermodynamic limit of microscopic laws under the assumption of a homogeneous density of neurons. The study of neural activity during neurodegenerative processes associated to Alzheimers, Parkinsons or Glioblastomas, should include a variable density of neurons. In this work, we propose an update of the Next Generation Neural Field model, extracted from the thermodynamic limit of the quadratic integration-and-fire model with realistic synaptic coupling and a variable density of neurons at the microscopic level. The thermodynamic limit of the system will allow us to study the patterns of synchronized neural activity that appear as the result of different spatial distribution of neurodegeneration. In particular, we demonstrate that during neurodegenerative processes, the relationship established between the thermodynamic states of the Neural Field and the Kuramoto order parameter (Measure of Neural Synchronization) differs from the classic results of the Next Generation Neural Field literature. Instead, the variation in neuron density directly modifies the Kuramoto order parameter. This might help us explain the diverse patterns of activity that can be found in different neurodegenerative processes and that could become experimental biomarkers of such pathologies.

neuroscience↗

LIONirs: flexible Matlab toolbox for fNIRS data analysis.

BackgroundFunctional near-infrared spectroscopy (fNIRS) is a suitable tool for recording brain function in pediatric or challenging populations. As with other neuroimaging techniques, the scientific community is engaged in an evolving debate regarding the most adequate methods for performing fNIRS data analyses. New methodWe introduce LIONirs, a neuroinformatics toolbox for fNIRS data analysis, designed to follow two main goals: (1) flexibility, to explore several methods in parallel and verify results using 3D visualization; (2) simplicity, to apply a defined processing pipeline to a large dataset of subjects by using the MATLAB Batch System. ResultsWithin the graphical user interfaces (DisplayGUI), the user can reject noisy intervals and correct artifacts, while visualizing the topographical projection of the data onto the 3D head representation. Data decomposition methods are available for the identification of relevant signatures, such as brain responses or artifacts. Multimodal data recorded simultaneously to fNIRS, such as physiology, electroencephalography or audio-video, can be visualized using the DisplayGUI. The toolbox includes several functions that allow one to read, preprocess, and analyze fNIRS data, including task-based and functional connectivity measures. Comparison with existing methodsSeveral good neuroinformatics tools for fNIRS data analysis are currently available. None of them emphasize multimodal visualization of the data throughout the preprocessing steps and multidimensional decomposition, which are essential for understanding challenging data. Furthermore, LIONirs provides compatibility and complementarity with other existing tools by supporting common data format. ConclusionsLIONirs offers a flexible platform for basic and advanced fNIRS data analysis, shown through real experimental examples. HighlightsO_LIThe LIONirs toolbox is designed for fNIRS data inspection and visualization. C_LIO_LIMethods are integrated for isolation of relevant activity and correction of artifacts. C_LIO_LIMultimodal auxiliary, EEG or audio-video are visualized alongside the fNIRS data. C_LIO_LITask-based and functional connectivity measure analysis tools are available. C_LIO_LIThe code structure allows to automated and standardized analysis of large data set. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/257634v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@9cced8org.highwire.dtl.DTLVardef@cfdcc3org.highwire.dtl.DTLVardef@a694fcorg.highwire.dtl.DTLVardef@7f35f4_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

Parallel Factor Analysis (PARAFAC) for multidimensional decomposition of fNIRS data - A validation study

SignificanceCurrent techniques for data analysis in functional near-infrared spectroscopy (fNIRS), such as artifact correction, do not allow to integrate the information originating from both wavelengths, considering only temporal and spatial dimensions of the signals structure. Parallel factor analysis (PARAFAC) has previously been validated as a multidimensional decomposition technique in other neuroimaging fields. AimWe aimed to introduce and validate the use of PARAFAC for the analysis of fNIRS data, which is inherently multidimensional (time, space, wavelength). ApproachWe used data acquired in 17 healthy adults during a verbal fluency task to compare the efficacy of PARAFAC for motion artifact correction to traditional 2D decomposition techniques, i.e. target principal (tPCA) and independent component analysis (ICA). Correction performance was further evaluated under controlled conditions with simulated artifacts and hemodynamic response functions. ResultsPARAFAC achieved significantly higher improvement in data quality as compared to tPCA and ICA. Correction in several simulated signals further validated its use and promoted it as a robust method independent of the artifacts characteristics. ConclusionsThis study describes the first implementation of PARAFAC in fNIRS and provides validation for its use to correct artifacts. PARAFAC is a promising data-driven alternative for multidimensional data analyses in fNIRS and this study paves the way for further applications.

neuroscience↗

Neural signals relating future cooperation from prisoner’s dilemma feedback are outcome-contingent

Electing whether to cooperate with someone else is well typified in the iterated prisoners dilemma (iPD) game, although the neural processes that unfold after its distinct outcomes have been only partly described. Recent theoretical models emphasize the ubiquity of intuitive cooperation, raising questions on the neural timelines involved. We studied the outcome stage of an iPD with electroencephalography (EEG) methods. Results showed that neural signals that are modulated by the iPD outcomes can also be indicative of future choice, in an outcome-dependent manner: (i) after zero-gain suckers payoffs (unreciprocated cooperation), a participants decision thereafter may be indicated by changes to the feedback-related negativity (FRN); (ii) after one-sided non-cooperation (participant gain), by the P3; (iii) after mutual cooperation, by late frontal delta-band modulations. Critically, faster choices to reciprocate cooperation were predicted, on a single-trial basis, by P3 and frontal delta modulations at the immediately preceding trial. Delta band signaling is considered in relation to homeostatic regulation processing in the literature. The findings relate feedback to decisional processes in the iPD, providing a first neural account of the brief timelines implied in heuristic modes of cooperation.

neuroscience↗

Chirp analyzer for estimating amplitude and latency of steady-state auditory envelope following responses

The envelope following response (EFR) is a scalp-recorded evoked potential elicited by carrier tones or noise, modulated in amplitude with a continuous sweep of modulation frequencies. This non-stationary response reflects the phase-locked neural activity of the auditory pathway to the temporal envelope of sounds and has been commonly assessed by fixed-frequency methods based on the discrete Fourier transform, such as the Fourier Analyzer (FA). In this work, we study the estimation of the EFR with the use of explicit time-frequency methods, which offer more information about the energy distribution of the recorded signal, such as the Short-Term Fourier Transform (STFT) and the Morlet Continuous Wavelet Transform (CWT). We further introduce the Chirp Analyzer (CA), which is similar to FA, but using as basis function the same linear chirp that amplitude-modulates the carrier stimulus. In a direct comparison using controlled simulated responses, the CA showed to be able to estimate the correct EFR amplitudes, without the typical bias offered by the estimation using STFT (equivalent to FA) and more robust to noise than the CWT method, although with higher sensitivity to the presence of a delay in the response with respect to the stimulus. For addressing the latter issue, we also propose here a novel methodology for estimating the apparent latency of the response. This method proved to be reliable when using the STFT and the CA methods, as assessed using simulated responses. The estimation of the EFR amplitude with any of the methods, but especially with CA, should be corrected by using the estimated delay when possible. An illustrative application of these methods to small datasets of a rat and a human newborn, suggested that all time-frequency methods can be used to study the EFR amplitudes in a wide range of modulation frequencies, but they should be interpreted in the light of the limitations shown in the simulation studies.

neuroscience↗

Estimation of auditory steady-state responses based on the averaging of independent EEG epochs

The amplitude of the auditory steady-state responses (ASSRs) generated in the brainstem exponentially decreases over the averaging of subsequent EEG epochs. This behavior is partially due to the adaptation of the auditory response to the continuous and monotonous stimulation. We analyzed the potential clinical relevance of the ASSR adaptation. Specifically, we compare the ASSR amplitude computed in two conditions: (1) when the auditory responses -embedded in the EEG epochs that are averaged in the estimation procedure- are influenced by the previous stimulation; and (2) when they are independent of the previous stimulation. ASSR were elicited in eight anesthetized adult rats by 8-kHz tones, modulated in amplitude at 115 Hz. ASSR amplitudes were computed using three averaging methods (standard, weighted and sorted averaging). We evaluated the ASSR amplitude as a function of sub-set of epochs selected for the averaging and the improvement in the ASSR detection resulting from averaging independent epochs. Due to adaptation, the ASSR amplitude computed by averaging dependent EEG epochs relied upon the averaging method. Lower ASSR amplitudes were obtained as EEG segments containing unadapted responses were systematically excluded from the averaging. In the absence of EEG artifacts, the ASSR amplitudes did not depend on the averaging method when they were computed from independent EEG epochs. The amplitude of independent ASSRs were up to 35% higher than those obtained by processing dependent EEG segments. Extracting the ASSR amplitude from independent epochs halved the number of EEG segments needed to be averaged to achieve the maximum detection rate of the response. Acquisition paradigm based on a discrete acoustic stimulation (in which segments of AM-sounds of several seconds in length are presented after a given inter stimulus interval), in combination with appropriated averaging methods might increase the accuracy of audiological tests based on ASSRs.

neuroscience↗

Populational Super-Resolution Sparse M/EEG Sources and Connectivity Estimation

In this paper, we describe a novel methodology, BC-VARETA, for estimating the Inverse Solution (sources activity) and its Precision Matrix (connectivity parameters) in the frequency domain representation of Stationary Time Series. The aims of this method are three. First: Joint estimation of Source Activity and Connectivity as a frequency domain linear dynamical system identification approach. Second: Achieve super high resolution in the connectivity estimation through Sparse Hermitian Sources Graphical Model. Third: To be a populational approach, preventing the Inverse Solution and Connectivity statistical analysis across subjects as a postprocessing, by modeling population features of Source Activity and Connectivity. Our claims are supported by a wide simulation framework using realistic head models, realistic Sources Setup, and Inverse Crime effects evaluation. Also, a fair quantitative analysis is performed, based on a diversification of quality measures on which state of the art Inverse Solvers were tested.

neuroscience↗