Advanced morphometric techniques applied to the study of human brain anatomy

El desarrollo de las técnicas de imágenes por resonancia magnética han permitido el estudio y cuantificación, in vivo, de los cambios que ocurren en la morfología cerebral ligados a procesos tales como el neurodesarrollo, el envejecimiento, el aprendizaje o la enfermedad. Un gran número de métodos de morfometría han sido desarrollados con el fin de extraer la información contenida en estas imágenes y traducirla en indicadores de forma o tamaño, tales como el volumen o el grosor cortical; marcadores que son posteriormente empleados para encontrar diferencias estadísticas entre poblaciones de sujetos o realizar correlaciones entre la morfología cerebral y, por ejemplo, la edad o la severidad de determinada enfermedad. A pesar de la amplia variedad de biomarcadores y metodologías de morfometría, muchos estudios sesgan sus hipótesis, y con ello los resultados experimentales, al empleo de un número reducido de biomarcadores o a al uso de una única metodología de procesamiento. Con el presente trabajo se pretende demostrar la importancia del empleo de diversos métodos de morfometría para lograr una mejor caracterización del proceso que se desea estudiar. En el mismo se emplea el análisis de forma para detectar diferencias, tanto globales como locales, en la morfología del tálamo entre pacientes adolescentes con episodios tempranos de psicosis y adolescentes sanos. Los resultados obtenidos demuestran que la diferencia de volumen talámico entre ambas poblaciones de sujetos, previamente descrita en la literatura, se debe a una reducción del volumen de la región anterior-mediodorsal y del núcleo pulvinar del tálamo de los pacientes respecto a los sujetos sanos. Además, se describe el desarrollo de un estudio longitudinal, en sujetos sanos, que emplea simultáneamente distintos biomarcadores para la caracterización y cuantificación de los cambios que ocurren en la morfología de la corteza cerebral durante la adolescencia. A través de este estudio se revela que el proceso de “alisado” que experimenta la corteza cerebral durante la adolescencia es consecuencia de una disminución de la profundidad, ligada a un incremento en el ancho, de los surcos corticales. Finalmente, esta metodología es aplicada, en un diseño transversal, para el estudio de las causas que provocan el decrecimiento tanto del grosor cortical como del índice de girificación en adolescentes con episodios tempranos de psicosis. ABSTRACT The ever evolving sophistication of magnetic resonance image techniques continue to provide new tools to characterize and quantify, in vivo, brain morphologic changes related to neurodevelopment, senescence, learning or disease. The majority of morphometric methods extract shape or size descriptors such as volume, surface area, and cortical thickness from the MRI image. These morphological measurements are commonly entered in statistical analytic approaches for testing between-group differences or for correlations between the morphological measurement and other variables such as age, sex, or disease severity. A wide variety of morphological biomarkers are reported in the literature. Despite this wide range of potentially useful biomarkers and available morphometric methods, the hypotheses and findings of the grand majority of morphological studies are biased because reports assess only one morphometric feature and usually use only one image processing method. Throughout this dissertation biomarkers and image processing strategies are combined to provide innovative and useful morphometric tools for examining brain changes during neurodevelopment. Specifically, a shape analysis technique allowing for a fine-grained assessment of regional thalamic volume in early-onset psychosis patients and healthy comparison subjects is implemented. Results show that disease-related reductions in global thalamic volume, as previously described by other authors, could be particularly driven by a deficit in the anterior-mediodorsal and pulvinar thalamic regions in patients relative to healthy subjects. Furthermore, in healthy adolescents different cortical features are extracted and combined and their interdependency is assessed over time. This study attempts to extend current knowledge of normal brain development, specifically the largely unexplored relationship between changes of distinct cortical morphological measurements during adolescence. This study demonstrates that cortical flattening, present during adolescence, is produced by a combination of age-related increase in sulcal width and decrease in sulcal depth. Finally, this methodology is applied to a cross-sectional study, investigating the mechanisms underlying the decrease in cortical thickness and gyrification observed in psychotic patients with a disease onset during adolescence

Depthgram: Visualizing outliers in high-dimensional functional data with application to fMRI data exploration.

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that facilitates the study of brain activity by measuring changes in blood flow. Brain activity signals can be recorded during the alternate performance of given tasks, that is, task fMRI (tfMRI), or during resting-state, that is, resting-state fMRI (rsfMRI), as a measure of baseline brain activity. This contributes to the understanding of how the human brain is organized in functionally distinct subdivisions. fMRI experiments from high-resolution scans provide hundred of thousands of longitudinal signals for each individual, corresponding to brain activity measurements over each voxel of the brain along the duration of the experiment. In this context, we propose novel visualization techniques for high-dimensional functional data relying on depth-based notions that enable computationally efficient 2-dim representations of fMRI data, which elucidate sample composition, outlier presence, and individual variability. We believe that this previous step is crucial to any inferential approach willing to identify neuroscientific patterns across individuals, tasks, and brain regions. We present the proposed technique via an extensive simulation study, and demonstrate its application on a motor and language tfMRI experiment.Agencia Estatal de Investigación, Spain, Grant/Award Number: PID2019-109196GB-I00; Ministerio de Economía y Competitividad, Spain, Grant/Award Numbers: ECO2015-66593-P, MTM2014-56535-R.S

Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data

ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse.

The archetypical folded shape of the human cortex has been a long-standing topic for neuroscientific research. Nevertheless, the accurate neuroanatomical segmentation of sulci remains a challenge. Part of the problem is the uncertainty of where a sulcus transitions into a gyrus and vice versa. This problem can be avoided by focusing on sulcal fundi and gyral crowns, which represent the topological opposites of cortical folding. We present Automated Brain Lines Extraction (ABLE), a method based on Laplacian surface collapse to reliably segment sulcal fundi and gyral crown lines. ABLE is built to work on standard FreeSurfer outputs and eludes the delineation of anastomotic sulci while maintaining sulcal fundi lines that traverse the regions with the highest depth and curvature. First, it segments the cortex into gyral and sulcal surfaces; then, each surface is spatially filtered. A Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the surfaces. This surface is then used for careful detection of the endpoints of the lines. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the connectivity between the endpoints. The method is validated by comparing ABLE with three other sulcal extraction methods using the Human Connectome Project (HCP) test-retest database to assess the reproducibility of the different tools. The results confirm ABLE as a reliable method for obtaining sulcal lines with an accurate representation of the sulcal topology while ignoring anastomotic branches and the overestimation of the sulcal fundi lines. ABLE is publicly available via https://github.com/HGGM-LIM/ABLE .This work was supported by the project exAScale ProgramIng models for extreme Data procEssing (ASPIDE), that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 801091. This work has received funding from “la Caixa” Foundation under the project code LCF/PR/HR19/52160001. Susanna Carmona funded by Instituto de Salud Carlos III, co-funded by European Social Fund “Investing in your future” (Miguel Servet Type I research contract CP16/00096). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovación (MCIN) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). Yasser Alemán-Gómez is supported by the Swiss National Science Foundation (185897) and the National Center of Competence in Research (NCCR) SYNAPSY - The Synaptic Bases of Mental Diseases, funded as well by the Swiss National Science Foundation (51AU40-1257).S

Partial‐volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial-volume-based over probabilistic-based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex-matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel-wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub-region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect

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

A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)

Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation

Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge

Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples. To mitigate the recurrent bottleneck of voxel-wise label creation, we investigate the use of weak labels: these are oversized annotations which are considerably faster to create. We present a deep learning algorithm for aneurysm detection that exploits weak labels during training. In addition, our model leverages prior anatomical knowledge by focusing only on plausible locations for aneurysm occurrence. We created a retrospective dataset of 284 TOF-MRA subjects (170 females) out of which 157 are patients (with 198 aneurysms), and 127 are controls. Our open TOF-MRA dataset, the largest in the community, is released on OpenNEURO. To assess model generalizability, we participated in a challenge for aneurysm detection with TOF-MRA data (93 patients, 20 controls, 125 aneurysms). Weak labels were 4 times faster to generate than their voxel-wise counterparts. When using prior anatomical knowledge, our network achieved a sensitivity of 80% on the in-house data, with False Positive (FP) rate of 1.2 per patient. On the public challenge, sensitivity was 68% (FP rate = 2.5), ranking 4th/18 on the open leaderboard. We found no significant difference in sensitivity between aneurysm risk-of-rupture groups (p = 0.75), locations (p = 0.72), or sizes (p = 0.15). Our code is made available for reproducibility.Comment: Paper submitted to a Journa

Individual differences in the dominance of interhemispheric connections predict cognitive ability beyond sex and brain size

Global structural brain connectivity has been reported to be sex-dependent with women having increased interhemispheric connectivity (InterHc) and men having greater intrahemispheric connectivity (IntraHc). However, (a) smaller brains show greater InterHc, (b) larger brains show greater IntraHc, and (c) women have, on average, smaller brains than men. Therefore, sex differences in brain size may modulate sex differences in global brain connectivity. At the behavioural level, sex-dependent differences in connectivity are thought to contribute to men-women differences in spatial and verbal abilities. But this has never been tested at the individual level. The current study assessed whether individual differences in global structural connectome measures (InterHc, IntraHc and the ratio of InterHc relative to IntraHc) predict spatial and verbal ability while accounting for the effect of sex and brain size. The sample included forty men and forty women, who did neither differ in age nor in verbal and spatial latent components defined by a broad battery of tests and tasks. High-resolution T1-weighted and diffusion-weighted images were obtained for computing brain size and reconstructing the structural connectome. Results showed that men had higher IntraHc than women, while women had an increased ratio InterHc/IntraHc. However, these sex differences were modulated by brain size. Increased InterHc relative to IntraHc predicted higher spatial and verbal ability irrespective of sex and brain size. The positive correlations between the ratio InterHc/IntraHc and the spatial and verbal abilities were confirmed in 1000 random samples generated by bootstrapping. Therefore, sex differences in global structural connectome connectivity were modulated by brain size and did not underlie sex differences in verbal and spatial abilities. Rather, the level of dominance of InterHc over IntraHc may be associated with individual differences in verbal and spatial abilities in both men and women.Sin financiación5.426 JCR (2017) Q1, 36/261 Neurosciences, 1/14 Neuroimaging, 13/128 Radiology, Nuclear Medicine and Medical ImagingUE

Atypical age-dependency of executive function and white matter microstructure in children and adolescents with autism spectrum disorders

Executive function (EF) performance is associated with measurements of white matter microstructure (WMS) in typical individuals. Impaired EF is a hallmark symptom of autism spectrum disorders (ASD) but it is unclear how impaired EF relates to variability in WMS. Twenty-one male youth (8–18 years) with ASD and without intellectual disability and twenty-one typical male participants (TP) matched for age, intelligence quotient, handedness, race and parental socioeconomic status were recruited. Five EF domains were assessed and several DTI-based measurements of WMS [fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD)] were estimated for eighteen white matter tracts. The ASD group had lower scores for attention (F = 8.37, p = 0.006) and response inhibition (F = 13.09, p = 0.001). Age-dependent changes of EF performance and WMS measurements were present in TP but attenuated in the ASD group. The strongest diagnosis-by-age effect was found for forceps minor, left anterior thalamic radiation and left cingulum angular bundle (all p’s ≤ 0.002). In these tracts subjects with ASD tended to have equal or increased FA and/or reduced MD and/or RD at younger ages while controls had increased FA and/or reduced MD and/or RD thereafter. Only for TP individuals, increased FA in the left anterior thalamic radiation was associated with better response inhibition, while reduced RD in forceps minor and left cingulum angular bundle was related to better problem solving and working memory performance respectively. These findings provide novel insight into the age-dependency of EF performance and WMS in ASD, which can be instructive to cognitive training programs.Spanish Ministry of Economy and Competitiveness, Instituto de Salud Carlos III, CIBERSAM, the Ramon y Cajal Program, the CDTI under the CENIT Program (AMIT Project), Madrid Regional Government (S2010/BMD-2422 AGES), the ERA-NET NEURON (Network of European Funding for Neuroscience Research) (PIM2010ERN-00642), Fundación Alicia Koplowitz (FAK2012, FAK2013), Fundación Mutua Madrileña (FMM2009), and Caja Navarra3.553 JCR (2017) Q1, 9/124 Pediatrics; Q2, 43/142 PsychiatryUE