A four-dimensional probabilistic atlas of the human brain

The authors describe the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on structure and function of the human brain in persons between the ages of 18 and 90 years. Given the presumed large but previously unquantified degree of structural and functional variance among normal persons in the human population, the basis for this atlas and reference system is probabilistic. Through the efforts of the International Consortium for Brain Mapping (ICBM), 7,000 subjects will be included in the initial phase of database and atlas development. For each subject, detailed demographic, clinical, behavioral, and imaging information is being collected. In addition, 5,800 subjects will contribute DNA for the purpose of determining genotype-phenotype-behavioral correlations. The process of developing the strategies, algorithms, data collection methods, validation approaches, database structures, and distribution of results is described in this report. Examples of applications of the approach are described for the normal brain in both adults and children as well as in patients with schizophrenia. This project should provide new insights into the relationship between microscopic and macroscopic structure and function in the human brain and should have important implications in basic neuroscience, clinical diagnostics, and cerebral disorders

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

Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

pre-printAutomated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has show its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple altases alongside label fusion techniques has been introduced using a set of individual "atlases" that encompasses the expected variability in the studied population

Visual Exploration And Information Analytics Of High-Dimensional Medical Images

Data visualization has transformed how we analyze increasingly large and complex data sets. Advanced visual tools logically represent data in a way that communicates the most important information inherent within it and culminate the analysis with an insightful conclusion. Automated analysis disciplines - such as data mining, machine learning, and statistics - have traditionally been the most dominant fields for data analysis. It has been complemented with a near-ubiquitous adoption of specialized hardware and software environments that handle the storage, retrieval, and pre- and postprocessing of digital data. The addition of interactive visualization tools allows an active human participant in the model creation process. The advantage is a data-driven approach where the constraints and assumptions of the model can be explored and chosen based on human insight and confirmed on demand by the analytic system. This translates to a better understanding of data and a more effective knowledge discovery. This trend has become very popular across various domains, not limited to machine learning, simulation, computer vision, genetics, stock market, data mining, and geography. In this dissertation, we highlight the role of visualization within the context of medical image analysis in the field of neuroimaging. The analysis of brain images has uncovered amazing traits about its underlying dynamics. Multiple image modalities capture qualitatively different internal brain mechanisms and abstract it within the information space of that modality. Computational studies based on these modalities help correlate the high-level brain function measurements with abnormal human behavior. These functional maps are easily projected in the physical space through accurate 3-D brain reconstructions and visualized in excellent detail from different anatomical vantage points. Statistical models built for comparative analysis across subject groups test for significant variance within the features and localize abnormal behaviors contextualizing the high-level brain activity. Currently, the task of identifying the features is based on empirical evidence, and preparing data for testing is time-consuming. Correlations among features are usually ignored due to lack of insight. With a multitude of features available and with new emerging modalities appearing, the process of identifying the salient features and their interdependencies becomes more difficult to perceive. This limits the analysis only to certain discernible features, thus limiting human judgments regarding the most important process that governs the symptom and hinders prediction. These shortcomings can be addressed using an analytical system that leverages data-driven techniques for guiding the user toward discovering relevant hypotheses. The research contributions within this dissertation encompass multidisciplinary fields of study not limited to geometry processing, computer vision, and 3-D visualization. However, the principal achievement of this research is the design and development of an interactive system for multimodality integration of medical images. The research proceeds in various stages, which are important to reach the desired goal. The different stages are briefly described as follows: First, we develop a rigorous geometry computation framework for brain surface matching. The brain is a highly convoluted structure of closed topology. Surface parameterization explicitly captures the non-Euclidean geometry of the cortical surface and helps derive a more accurate registration of brain surfaces. We describe a technique based on conformal parameterization that creates a bijective mapping to the canonical domain, where surface operations can be performed with improved efficiency and feasibility. Subdividing the brain into a finite set of anatomical elements provides the structural basis for a categorical division of anatomical view points and a spatial context for statistical analysis. We present statistically significant results of our analysis into functional and morphological features for a variety of brain disorders. Second, we design and develop an intelligent and interactive system for visual analysis of brain disorders by utilizing the complete feature space across all modalities. Each subdivided anatomical unit is specialized by a vector of features that overlap within that element. The analytical framework provides the necessary interactivity for exploration of salient features and discovering relevant hypotheses. It provides visualization tools for confirming model results and an easy-to-use interface for manipulating parameters for feature selection and filtering. It provides coordinated display views for visualizing multiple features across multiple subject groups, visual representations for highlighting interdependencies and correlations between features, and an efficient data-management solution for maintaining provenance and issuing formal data queries to the back end

Tracking dynamic interactions between structural and functional connectivity : a TMS/EEG-dMRI study

Transcranial magnetic stimulation (TMS) in combination with neuroimaging techniques allows to measure the effects of a direct perturbation of the brain. When coupled with high-density electroencephalography (TMS/hd-EEG), TMS pulses revealed electrophysiological signatures of different cortical modules in health and disease. However, the neural underpinnings of these signatures remain unclear. Here, by applying multimodal analyses of cortical response to TMS recordings and diffusion magnetic resonance imaging (dMRI) tractography, we investigated the relationship between functional and structural features of different cortical modules in a cohort of awake healthy volunteers. For each subject, we computed directed functional connectivity interactions between cortical areas from the source-reconstructed TMS/hd-EEG recordings and correlated them with the correspondent structural connectivity matrix extracted from dMRI tractography, in three different frequency bands (alpha, beta, gamma) and two sites of stimulation (left precuneus and left premotor). Each stimulated area appeared to mainly respond to TMS by being functionally elicited in specific frequency bands, that is, beta for precuneus and gamma for premotor. We also observed a temporary decrease in the whole-brain correlation between directed functional connectivity and structural connectivity after TMS in all frequency bands. Notably, when focusing on the stimulated areas only, we found that the structure-function correlation significantly increases over time in the premotor area controlateral to TMS. Our study points out the importance of taking into account the major role played by different cortical oscillations when investigating the mechanisms for integration and segregation of information in the human brain

Advanced MRI techniques in the study of cerebellar cortex

The cerebellum (from the Latin "little brain") is the dorsal portion of the metencephalon and is located in the posterior cranial fossa. Although representing only 10% of the total brain volume, it contains more than 50% of the total number of neurons of the central nervous system (CNS). Its organization resembles the one found in the telencephalon, with the presence of a superficial mantle of gray matter (GM) known as the cerebellar cortex, covering the cerebellar white matter (WM) in which three pairs of deep cerebellar GM nuclei are embedded. The number of studies dedicated to the study of the cerebellum and its function has significantly increased during the last years. Nevertheless, although many theories on the cerebellar function have been proposed, to date we still are not able to answer the question about the exact function of this structure. Indeed, the classical theories focused on the role of the cerebellum in fine-tuning for muscle control has been widely reconsidered during the last years, with new hypotheses that have been advanced. These include its role as sensory acquisition device, extending beyond a pure role in motor control and learning, as well as a pivotal role in cognition, with a recognized cerebellar participation in a variety of cognitive functions, ranging from mood control to language, memory, attention and spatial data management. A huge contribution to our understanding of how the cerebellum participates in all these different aspects of motor and non-motor behavior comes from the application of advanced imaging techniques. In particular, Magnetic Resonance Imaging (MRI) can provide a non-invasive evaluation of anatomical integrity, as well as information about functional connections with other brain regions. This thesis is organized as follows: - In Chapter 1 is presented a general introduction to the cerebellar anatomy and functions, with particular reference to the anatomical organization of cerebellar cortex and its connections with the telencephalon - Chapter 2 will contain a general overview about some of the major advanced MRI methods that can be applied to investigate the anatomical integrity and functional status of the cerebellar cortex - In Chapter 3 will be presented a new method to evaluate the anatomy and integrity of cerebellar cortex using ultra-high field MRI scanners - Chapters 4, 5 and 6 will contain data obtained from the application of some of the previously described advanced imaging techniques to the study of cerebellar cortex in neurodegenerative and neuroinflammatory disorders affecting the CNS

Statistical shape analysis in neuroimaging : methods, challenges, validation : applications to the study of brain asymmetries in schizophrenia

The study of brain shape and its patterns of variations can provide insights into the understanding of normal and pathological brain development and brain degenerative processes. This thesis focuses on the in vivo analysis of human brain shape as extracted from three-dimensional magnetic resonance images. Major automatic methods for the analysis of brain shape are discussed particularly focusing on the computation of shape metrics, the subsequent inference procedures, and their applications to the study of brain asymmetries in schizophrenia. Methodological challenges as well as possible biological factors that complicate the analysis of brain shape, and its validation, are also discussed. The contributions of this research work are as it follows. First, a novel automatic method for the statistical shape analysis of local interhemispheric asymmetries is presented and applied to the study of cerebral structural asymmetries in schizophrenia. The method extracts and analyzes smooth surface representations approximating the gross shape of the outlines of cerebral hemispheres. Second, a novel and fully automatic image processing framework for the validation of measures of brain asymmetry is proposed. The framework is based on the synthesis of realistic three-dimensional magnetic resonance images with a known asymmetry pattern. It employs a parametric model emulating the normal interhemispheric bending of the human brain while retaining other subject-specific features of brain anatomy. The framework is applied for the quantitative validation of measures of asymmetry in brain tissues' composition as computed by voxel-based morphometry. Particularly, the framework is used to investigate the dependence of voxel-based measures of brain asymmetry on the spatial normalization scheme, template space, and amount of spatial smoothing applied. The developed automatic framework is made available as open-source software. Third, a novel Simplified Reeb Graph based descriptor of the human striatum is proposed. The effectiveness of such a descriptor is demonstrated for the purposes of automatic registration, decomposition, and comparison of striatal shapes in schizophrenia patients and matched normal controls. In conclusion, this thesis proposes novel methods for shape representation and analysis within three-dimensional magnetic resonance brain images, an original way for validating these methods, and applies the methods for the study of brain asymmetries in schizophrenia. The impact of this research lies in its potential implications for the development of biomarkers aiming to a better understanding of the brain in normal and pathological conditions, early diagnosis of a number of brain diseases, and development of novel therapeutic strategies for improving the quality of life of affected individuals. In addition, the distribution of simulated data and automatic tools for validation of morphometric measures of brain asymmetry is expected to have a great impact in enabling systematic validation of novel and existing methods for the analysis of brain asymmetries, quantitatively comparing them, and possibly clarifying contradicting findings in the neuroimaging literature of brain lateralizations