79 research outputs found
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
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
Cortical Surface Registration and Shape Analysis
A population analysis of human cortical morphometry is critical for insights into brain development or degeneration. Such an analysis allows for investigating sulcal and gyral folding patterns. In general, such a population analysis requires both a well-established cortical correspondence and a well-defined quantification of the cortical morphometry. The highly folded and convoluted structures render a reliable and consistent population analysis challenging. Three key challenges have been identified for such an analysis: 1) consistent sulcal landmark extraction from the cortical surface to guide better cortical correspondence, 2) a correspondence establishment for a reliable and stable population analysis, and 3) quantification of the cortical folding in a more reliable and biologically meaningful fashion. The main focus of this dissertation is to develop a fully automatic pipeline that supports a population analysis of local cortical folding changes. My proposed pipeline consists of three novel components I developed to overcome the challenges in the population analysis: 1) automatic sulcal curve extraction for stable/reliable anatomical landmark selection, 2) group-wise registration for establishing cortical shape correspondence across a population with no template selection bias, and 3) quantification of local cortical folding using a novel cortical-shape-adaptive kernel. To evaluate my methodological contributions, I applied all of them in an application to early postnatal brain development. I studied the human cortical morphological development using the proposed quantification of local cortical folding from neonate age to 1 year and 2 years of age, with quantitative developmental assessments. This study revealed a novel pattern of associations between the cortical gyrification and cognitive development.Doctor of Philosoph
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Automated Sulcal Depth Measurement on Cortical Surface Reflecting Geometrical Properties of Sulci
Sulcal depth that is one of the quantitative measures of cerebral cortex has been widely used as an important marker for brain morphological studies. Several studies have employed Euclidean (EUD) or geodesic (GED) algorithms to measure sulcal depth, which have limitations that ignore sulcal geometry in highly convoluted regions and result in under or overestimated depth. In this study, we proposed an automated measurement for sulcal depth on cortical surface reflecting geometrical properties of sulci, which named the adaptive distance transform (ADT). We first defined the volume region of cerebrospinal fluid between the 3D convex hull and the cortical surface, and constructed local coordinates for that restricted region. Dijkstra’s algorithm was then used to compute the shortest paths from the convex hull to the vertices of the cortical surface based on the local coordinates, which may be the most proper approach for defining sulcal depth. We applied our algorithm to both a clinical dataset including patients with mild Alzheimer’s disease (AD) and 25 normal controls and a simulated dataset whose shape was similar to a single sulcus. The mean sulcal depth in the mild AD group was significantly lower than controls (p = 0.007, normal [mean±SD]: 7.29±0.23 mm, AD: 7.11±0.29) and the area under the receiver operating characteristic curve was relatively high, showing the value of 0.818. Results from clinical dataset that were consistent with former studies using EUD or GED demonstrated that ADT was sensitive to cortical atrophy. The robustness against inter-individual variability of ADT was highlighted through simulation dataset. ADT showed a low and constant normalized difference between the depth of the simulated data and the calculated depth, whereas EUD and GED had high and variable differences. We suggest that ADT is more robust than EUD or GED and might be a useful alternative algorithm for measuring sulcal depth
Surface-Based tools for Characterizing the Human Brain Cortical Morphology
Tesis por compendio de publicacionesThe cortex of the human brain is highly convoluted. These characteristic convolutions
present advantages over lissencephalic brains. For instance, gyrification allows an expansion
of cortical surface area without significantly increasing the cranial volume, thus
facilitating the pass of the head through the birth channel. Studying the human brain’s
cortical morphology and the processes leading to the cortical folds has been critical for an
increased understanding of the pathological processes driving psychiatric disorders such
as schizophrenia, bipolar disorders, autism, or major depression. Furthermore, charting
the normal developmental changes in cortical morphology during adolescence or aging
can be of great importance for detecting deviances that may be precursors for pathology.
However, the exact mechanisms that push cortical folding remain largely unknown.
The accurate characterization of the neurodevelopment processes is challenging. Multiple
mechanisms co-occur at a molecular or cellular level and can only be studied through
the analysis of ex-vivo samples, usually of animal models. Magnetic Resonance Imaging
can partially fill the breach, allowing the portrayal of the macroscopic processes surfacing
on in-vivo samples.
Different metrics have been defined to measure cortical structure to describe the brain’s
morphological changes and infer the associated microstructural events. Metrics such as
cortical thickness, surface area, or cortical volume help establish a relation between the
measured voxels on a magnetic resonance image and the underlying biological processes.
However, the existing methods present limitations or room for improvement.
Methods extracting the lines representing the gyral and sulcal morphology tend to
over- or underestimate the total length. These lines can provide important information
about how sulcal and gyral regions function differently due to their distinctive ontogenesis.
Nevertheless, some methods label every small fold on the cortical surface as a sulcal
fundus, thus losing the perspective of lines that travel through the deeper zones of a sulcal
basin. On the other hand, some methods are too restrictive, labeling sulcal fundi only for
a bunch of primary folds.
To overcome this issue, we have proposed a Laplacian-collapse-based algorithm that
can delineate the lines traversing the top regions of the gyri and the fundi of the sulci
avoiding anastomotic sulci. For this, the cortex, represented as a 3D surface, is segmented
into gyral and sulcal surfaces attending to the curvature and depth at every point
of the mesh. Each resulting surface is spatially filtered, smoothing the boundaries. Then,
a Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the
morphology of each structure. These thin curves are processed to detect where the extremities
or endpoints lie. Finally, sulcal fundi and gyral crown lines are obtained by
eroding the surfaces while preserving the structure topology and connectivity between
the endpoints. The assessment of the presented algorithm showed that the labeled sulcal lines were close to the proposed ground truth length values while crossing through the
deeper (and more curved) regions. The tool also obtained reproducibility scores better or
similar to those of previous algorithms.
A second limitation of the existing metrics concerns the measurement of sulcal width.
This metric, understood as the physical distance between the points on opposite sulcal
banks, can come in handy in detecting cortical flattening or complementing the information
provided by cortical thickness, gyrification index, or such features. Nevertheless,
existing methods only provided averaged measurements for different predefined sulcal
regions, greatly restricting the possibilities of sulcal width and ignoring the intra-region
variability.
Regarding this, we developed a method that estimates the distance from each sulcal
point in the cortex to its corresponding opposite, thus providing a per-vertex map of the
physical sulcal distances. For this, the cortical surface is sampled at different depth levels,
detecting the points where the sulcal banks change. The points corresponding to each sulcal
wall are matched with the closest point on a different one. The distance between those
points is the sulcal width. The algorithm was validated against a simulated sulcus that
resembles a simple fold. Then the tool was used on a real dataset and compared against
two widely-used sulcal width estimation methods, averaging the proposed algorithm’s
values into the same region definition those reference tools use. The resulting values were
similar for the proposed and the reference methods, thus demonstrating the algorithm’s
accuracy.
Finally, both algorithms were tested on a real aging population dataset to prove the
methods’ potential in a use-case scenario. The main idea was to elucidate fine-grained
morphological changes in the human cortex with aging by conducting three analyses: a
comparison of the age-dependencies of cortical thickness in gyral and sulcal lines, an
analysis of how the sulcal and gyral length changes with age, and a vertex-wise study of
sulcal width and cortical thickness.
These analyses showed a general flattening of the cortex with aging, with interesting
findings such as a differential age-dependency of thickness thinning in the sulcal and
gyral regions. By demonstrating that our method can detect this difference, our results
can pave the way for future in vivo studies focusing on macro- and microscopic changes
specific to gyri or sulci. Our method can generate new brain-based biomarkers specific
to sulci and gyri, and these can be used on large samples to establish normative models
to which patients can be compared. In parallel, the vertex-wise analyses show that sulcal
width is very sensitive to changes during aging, independent of cortical thickness. This
corroborates the concept of sulcal width as a metric that explains, in the least, the unique
variance of morphology not fully captured by existing metrics. Our method allows for
sulcal width vertex-wise analyses that were not possible previously, potentially changing
our understanding of how changes in sulcal width shape cortical morphology.
In conclusion, this thesis presents two new tools, open source and publicly available, for estimating cortical surface-based morphometrics. The methods have been validated
and assessed against existing algorithms. They have also been tested on a real dataset,
providing new, exciting insights into cortical morphology and showing their potential for
defining innovative biomarkers.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidente: Juan Domingo Gispert López.- Secretario: Norberto Malpica González de Vega.- Vocal: Gemma Cristina Monté Rubi
Consistent sulcal parcellation of longitudinal cortical surfaces
Automated accurate and consistent sulcal parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains, since longitudinal cortical changes are normally very subtle, especially in aging brains. However, applying the existing methods (which were typically developed for cortical sulcal parcellation of a single cortical surface) independently to longitudinal cortical surfaces might generate longitudinally-inconsistent results. To overcome this limitation, this paper presents a novel energy function based method for accurate and consistent sulcal parcellation of longitudinal cortical surfaces. Specifically, both spatial and temporal smoothness are imposed in the energy function to obtain consistent longitudinal sulcal parcellation results. The energy function is efficiently minimized by a graph cuts method. The proposed method has been successfully applied to sulcal parcellation of both real and simulated longitudinal inner cortical surfaces of human brain MR images. Both qualitative and quantitative evaluation results demonstrate the validity of the proposed method
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
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Cortical structure: Linking MRI and cytoarchitecture
MRI provides a powerful tool to investigate brain structure in living humans. However a major challenge is interpreting the biological underpinnings of changes at this scale. This dissertation describes investigations into the problem of linking microscale post mortem cortical cytoarchitecture with millimeter-scale measures of cortical anatomy accessible through in vivo MRI.
Chapter 1 introduces the problem and previous work done to address it. The following two chapters apply classical atlases of cortical cytoarchitecture to understanding morphological changes both in health (Chapter 2) and in disease (Chapter 3). Chapter 2 demonstrates that sensory processing hierarchies exhibit increasing gradients of cortical thickness, related to changes in cortical cytoarchitecture. In Chapter 3, cytoarchitectonically described differences in gyral and sulcal laminar structure were used to create markers of laminar change from MRI changes in schizophrenia.
Classical measurements of histology have limitations; they are observer dependent, two-dimensional with limited coverage of the cortex. To address these issues, Chapters 4-6 document work carried on BigBrain, a 3D 20m resolution histological dataset. I created a high-resolution 3D atlas of laminar cytoarchitecture, which was mapped to MRI-compatible cortical surface reconstructions. Chapter 4 records the development of an automated 1D profile-based approach to laminar analysis, revealing basic principles of cortical cytoarchitecture. In Chapter 5 this approach was extended to identify 6 cortical layers throughout the isocortex. These tools can be used to segment 1D cortical intensity profiles derived from any modality. In Chapter 6, the analysis of cortical gradients initially identified using MRI cortical thickness in Chapter 2 was replicated and extended using novel histological data. First histological cortical thicknesses were tested for the same patterns organization measured on in vivo MRI in Chapter 2. These analyses were extended to test which layers contributed most to overall thickness. High-resolution, complete maps of cortical cytoarchitecture mapped to MRI-template cortical surface reconstructions, are a powerful tool and dataset for the neuroimaging community. They offer new possibilities for linking cortical microstructure to in vivo neuroimaging
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