Compendio de métodos para caracterizar la geometría de los tejidos cerebrales a partir de imágenes de resonancia magnética por difusión del agua.

221 p.FIDMAG Hermanas Hospitalarias Research Foundation; CIBERSAM:Centro de Investigación Biomédica en Re

Converging Medial Frontal Resting State and Diffusion Based Abnormalities in Borderline Personality Disorder

Background The psychological profile of patients with borderline personality disorder (BPD), with impulsivity and emotional dysregulation as core symptoms, has guided the search for abnormalities in specific brain areas such as the hippocampal-amygdala complex and the frontomedial cortex. However, whole-brain imaging studies so far have delivered highly heterogeneous results involving different brain locations. Methods Functional resting-state and diffusion magnetic resonance imaging data were acquired in patients with BPD and in an equal number of matched control subjects (n = 60 for resting and n = 43 for diffusion). While mean diffusivity and fractional anisotropy brain images were generated from diffusion data, amplitude of low-frequency fluctuations and global brain connectivity images were used for the first time to evaluate BPD-related brain abnormalities from resting functional acquisitions. Results Whole-brain analyses using a p = .05 corrected threshold showed a convergence of alterations in BPD patients in genual and perigenual structures, with frontal white matter fractional anisotropy abnormalities partially encircling areas of increased mean diffusivity and global brain connectivity. Additionally, a cluster of enlarged amplitude of low-frequency fluctuations (high resting activity) was found involving part of the lefthippocampus and amygdala. In turn, this cluster showed increased resting functional connectivity with theanterior cingulate. Conclusions With a multimodal approach and without using a priori selected regions, we prove that structural and functional abnormality in BPD involves both temporolimbic and frontomedial structures as well as their connectivity. These structures have been previously related to behavioral and clinical symptoms in patients with BPD

Multivariate brain functional connectivity through regularized estimators

Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic framework of multivariate connectivity implemented here may be easily extended to other types of regularized models

Migración portable y de altas prestaciones de aplicaciones Matlab a C++: deconvolución esférica de datos de resonancia magnética por difusión

En muchos de los campos de la investi gación científica, se ba establecido Matlab como he rramienta de facto para el diseño de aplicaciones. Esta aproximación o&ece mucbaa ventajas como el rápido despliegue de prototipos, alto rendimiento en álge Lrta liu..ml, o:1uL1·o:1 uLrvb. Siu ,,u.uL~"~ hu. ~vli~1,;ivuo:1b desarrolladas son altamente dependientes del motor de ejecución de Matlab, limitando su despliegue en multitud de plataformas de altas prestaciones. En este trabajo presentamos un caso práctico de migración de una aplicación inicialmente basada en Matlab a una aplicación nativa en lenguaje e++. Pa ra ello se presentará la metodología empleada para la migración y las herramientas que facilitan esta tarea. La evaluación llevada a cabo demuestta que la solución implementada ofrece un buen rendimiento sobre dis tintas plataformas y sistemas altamente heterogéneoEste trabajo ha sido financiado por el Proyecto Europeo ICT 644235 RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applicationsy el Ministerio de Economia y Competitividad, bajo el proyecto TIN2013-41350-P Scalable Data Management Techniques for High-End Computing System

Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis

A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps. All possible combinations of algorithms and data features were considered in pairwise classifications of matched samples of healthy controls (N = 127), patients with schizophrenia (N = 128) and patients with bipolar disorder (N = 128). Results show that the selection of feature type is important, with grey matter VBM (without data reduction) delivering the best diagnostic prediction rates (averaging over classifiers: schizophrenia vs. healthy 75%, bipolar disorder vs. healthy 63% and schizophrenia vs. bipolar disorder 62%) whereas algorithms usually yielded very similar results. Indeed, those grey matter VBM accuracy rates were not even improved by combining all feature types in a single prediction model. Further multi-class classifications considering the three groups simultaneously made evident a lack of predictive power for the bipolar group, probably due to its intermediate anatomical features, located between those observed in healthy controls and those found in patients with schizophrenia. Finally, we provide MRIPredict (https://www.nitrc.org/projects/mripredict/), a free tool for SPM, FSL and R, to easily carry out voxelwise predictions based on VBM images

Compendio de métodos para caracterizar la geometría de los tejidos cerebrales a partir de imágenes de resonancia magnética por difusión del agua.

221 p.FIDMAG Hermanas Hospitalarias Research Foundation; CIBERSAM:Centro de Investigación Biomédica en Re

Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal

In magnetic resonance imaging, the application of a strong diffusion weighting suppresses the signal contributions from the less diffusion-restricted constituents of the brain's white matter, thus enabling the estimation of the transverse relaxation time T2 that arises from the more diffusion-restricted constituents such as the axons. However, the presence of cell nuclei and vacuoles can confound the estimation of the axonal T2, as diffusion within those structures is also restricted, causing the corresponding signal to survive the strong diffusion weighting. We devise an estimator of the axonal T2 based on the directional spherical variance of the strongly diffusion-weighted signal. The spherical variance T2 estimates are insensitive to the presence of isotropic contributions to the signal like those provided by cell nuclei and vacuoles. We show that with a strong diffusion weighting these estimates differ from those obtained using the directional spherical mean of the signal which contains both axonal and isotropically-restricted contributions. Our findings hint at the presence of an MRIvisible isotropically-restricted contribution to the signal in the white matter ex vivo fixed tissue (monkey) at 7T, and do not allow us to discard such a possibility also for in vivo human data collected with a clinical 3T system

Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results

Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate's size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework's capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work, along with the simulator developed, pave the way toward more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI