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

Constructing morphometric profiles along the human brain cortex using in vivo Magnetic Resonance Imaging (MRI)

The geometry of the brain cortex is comprised of gyri (outward folds) and sulci (inward folds). Several biological properties about the anatomy and physiology of the brain cortex have been measured at the top of the sulci and at the bottom of the gyri; however, no one has yet measured how the values of these properties (called biomarkers) change along the path joining the top of the sulci and the bottom of the gyri. In this work, a methodology to display that information is shown, using different modalities of MRI images as input. There are four main steps to the methodology: the first two consist on obtaining the lines that run on top of the gyri and at the bottom of the sulci, while the next two make use of these lines to create a geodesic path between the top of the gyri and the bottom of the sulci and assigning biomarker values to each point of this geodesic path. The results of this work are composed of the validation of the methodology and three examples of possible applications of the methodology. These applications could be applied in future work to improve the detection and study the neurodevelopment of neurodegenerative diseases.Ingeniería Biomédic

Computational Cortical Surface Analysis for Study of Early Brain Development

The study of morphological attributes of the cerebral cortex and their development is very important in understanding the dynamic and critical early brain development. Comparing with conventional studies in the image space, cortical surface-based analysis provides a better way to display, observe, and quantify the attributes of the cerebral cortex. The goal of this dissertation is to develop novel cortical surface-based methods for better studying the attributes of the cerebral cortex during early brain development. Specifically, this dissertation aims to develop methods for 1) estimating the development of morphological attributes of the cerebral cortex and 2) discovering the major cortical folding patterns. Estimation of the Development of Cortical Attributes. The early development of cortical attributes is highly correlated to the brain cognitive functionality and some neurodevelopmental disorders. Hence, accurately modeling the early development of cortical attributes is crucial for better understanding the mysterious normal and abnormal brain development. This task is very challenging, because infant cortical attributes change dramatically, complicatedly and regionally-heterogeneously during the first year of life. To address these problems, this dissertation proposes a Dynamically-Assembled Regression Forest (DARF). DARF first trains a single decision tree at each vertex on the cortical surface, and then groups nearby decision trees around each vertex as a vertex-specific forest to predict the cortical attribute. Since the vertex-specific forest can better capture regional details than the conventional regression forest trained for the whole brain, the prediction result is more precise. Moreover, because nearby forests share a large portion of decision trees, the prediction result is spatially smooth. On the other hand, missing cortical attribute maps in the longitudinal datasets often lead to insufficient data for unbiased analysis or training of accurate prediction models. To address this issue, a missing data estimation strategy based on DARF is further proposed. Experiments show that DARF outperforms the existing popular regression methods, and the proposed missing data estimation strategy based on DARF can effectively recover the missing cortical attribute maps. Discovery of Major Cortical Folding Patterns. The folding patterns of the cerebral cortex are highly variable across subjects. Exploring major cortical folding patterns in neonates is of great importance in neuroscience. Conventional geometric measurements of the cortex have limited capability in distinguishing major folding patterns. Although the recent sulcal pits-based analysis provides a better way for comparing sulcal patterns across individuals of adults or older children, whether and how sulcal pits are suitable for discovering major sulcal patterns in infants remain unknown. This dissertation adapts a sulcal pits extraction method from adults to infants, and validates the spatial consistency of sulcal pits in infants, so that they can be used as reliable landmarks for exploring major sulcal patterns. This dissertation further proposes a sulcal graph-based method for discovering major sulcal patterns, which is then applied to studying three primary cortical regions in 677 neonatal cortical surfaces. The experiments show that the proposed method is able to identify the previously unreported major sulcal patterns. Finally, this dissertation investigates and verifies that the sulcal pattern information could be utilized to help DARF for better estimating cortical attribute maps.Doctor of Philosoph