Multiple Comparison Procedures for Large Correlated Data with Application to Brain Connectivity Analysis

Recent developments in medical imaging and image analysis allow the determination of interregional brain connectivity through diffusion Magnetic Resonance Imaging (MRI) tractography. The brain connectivity is represented by a connection matrix where each cell represents a certain measure of connectivity between two regions of interest of the brain. An important question related to this challenging field of neuroscience is the ability to learn from connection matrices and to perform rigorous statistical analysis to derive new medical results. In particular, we focus on the level of brain regions of interest. The brain connectivity analysis involves the problem of multiplicity correction or the so-called multiple testing or multiple comparisons problem, which is the principal subject of this thesis. The work described in this thesis can be divided into three essential parts. First, the general problem of multiple comparisons. The objective of this part is to develop new multiple comparisons procedures that have optimal behavior. More precisely, we propose a comprehensive family of error rates together with a corresponding family of multiple comparison procedures. The new family generalizes almost all existing error rates. Second, the problem of multiple comparisons for positively dependent data. By supposing that the brain regions of interest that are in the same vicinity or that belong to the same functional subnetwork are positively correlated, we develop new multiple comparison strategies that exploit this additional information in order to increase sensitivity for detecting real effects. Finally, the application of multiple comparisons to brain connectivity matrices. This part is an application of the two previous parts. In addition, we adapt the statistical methods to brain connectivity analysis by estimating the structure of positive dependence. This third part can be seen as a validation of the statistical methods developed in the first and the second part. The thesis represents a complete framework of an adaptive strategy for the statistical analysis of both functional and structural connection matrices ready to be used in single subject analysis or group comparison. The framework is general enough to be used not only in neuroscience but also in many other research domains. In particular, in the brain connectivity context, it will permit an efficient investigation of a large range of pathologies where changes in brain connectivity are a key ingredient of medical interpretation, such as Alzheimer or Schizophrenia.

Comparing connectomes across subjects and populations at different scales

Elda Fischi Gomez, Alessandra Griffa, Patric Hagmann, Djalel Eddine Meskaldji, Stephan Morgenthaler, Jean-Philippe Thiran

Brain connectivity can be represented by a network that enables the comparison of the different patterns of structural and functional connectivity among individuals. In the literature, two levels of statistical analysis have been considered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that summarizes the information of each brain is used in a statistical test; 2) the local analysis where a single test is performed either for each node/connection which implies a multiplicity correction, or for each group of nodes/connections where each subset is summarized by one single test in order to reduce the number of tests to avoid a penalizing multiplicity correction. We comment on the different levels of analysis and present some methods that have been proposed at each scale. We highlight as well the possible factors that could influence the statistical results and the questions that have to be addressed in such an analysis. (C) 2013 Elsevier Inc. All rights reserved.

Elsevier2013

Nonrigid medical image registration

Francisco Javier Sanchez Castro

Over the last two decades medical image processing has become a fundamental tool in medicine, and brain imaging is a paradigm thereof. The fast development of the imaging acquisition techniques allows medical staff to dispose of a variety of data coming from different imaging modalities. However, the analysis of such amount of data based on visual inspection is very difficult and highly time consuming. Often, the information contained in a medical image cannot be entirely seen by a human eye and vice-versa, computers do not have human common sense or the experience acquired by medical experts. Therefore, it is desirable to combine both medical experts and computers in an optimal trade-off to improve the outcome of the wide range of applications where medical imaging is nowadays applied. Among these applications we can mention diagnosis, surgical planning and intra-operative surgical navigation. Image registration is one of the most popular image processing techniques used in clinical environments. It consists of bringing two or more images into a single spatial representation, i.e. finding the optimal geometric transformation between the data. Image registration overcomes the difficulties encountered by clinicians to fuse images mentally since it enables to integrate different images into one representation. The applications of image registration in medicine are numerous and can be grouped in clinical, related to detection and diagnosis, and surgical. Some of these applications are: preprocedural planning and simulation, interventional radiology, diagnostic radiology, minimally invasive procedures, radiation therapy, intraoperative navigation, robot-assisted interventions, etc. During the decade of the 90s a large number of registration algorithms were proposed, linked to a variety of medical applications. Nowadays, rigid registration is considered to be a well-covered domain with efficient solutions for the most common applications. Current research mostly focuses on nonrigid registration methods. However, the complex framework of nonrigid registration makes it a complex problem where there is not a universal solution. This complex framework includes a number of components that interact: the dimensionality of the data involved, the feature space to base the registration on, the kind and domain of the transformation model, the modalities of the data and the subjects involved, the user interaction, the similarity metric to assess the alignment quantitatively, the search space where the optimal solution has to be found, and the search strategy including optimization of the objective function and implementation details. We can summarize the motivation of this thesis in the following sentence: Validation of nonrigid registration algorithms is a very difficult task and rather an application-dependent problem. Therefore, this dissertation is structured to face the problem of brain image registration in three closely correlated parts: algorithms, applications and validation. In the algorithmic context, we propose a control point based nonrigid registration algorithm which aims to improve the performance of classical point based algorithms. In the framework of the implementation of the algorithm we develop a control point detector based on information theory tools and a transformation model based on a type of radial basis functions specially suited for interpolation of scattered and nonuniform distributed data samples. The algorithm is evaluated for typical inter-subject monomodal and multimodal brain image registration. In the context of clinical applications we face a neurosurgical application: the subthalamic nucleus targeting for deep brain stimulation in Parkinson's disease surgery. The extensive study covers from state-of-the-art targeting procedures to automatic targeting methods through different registration algorithms. Moreover, we analyze the influence of surrounding anatomical features on the target location. We also study the feasibility of an atlas of landmarks aiming to improve the targeting accuracy and at the same time to reduce both the computational cost and the preprocessing needed compared to the other automatic targeting methods proposed. All the proposed methods and techniques are statistically cross-validated against state-of-the-art techniques and expert's targeting variability through an application-adapted validation scheme. The results show that automatic subthalamic nucleus localization is possible and as accurate as the methods currently used. We also provide valuable conclusions about the anatomical structures that must be used to infer more accurate targets. In the context of validation of brain image registration, we develop a general (non application-oriented) validation scheme that provides an independent and high dimensional means of assessing the quality of the alignment given by a registration algorithm. This procedure is applied to structural magnetic resonance images and is based on the information carried by the diffusion tensor images. It provides an error measure per voxel allowing an independent evaluation for different regions. Almost 800 registered images are analyzed to provide with strength the conclusions derived from the statistical analysis.

EPFL2008

Advanced image-processing techniques in magnetic resonance imaging for the investigation of brain pathologies and tumour angiogenesis

Alessandro Daducci

Magnetic resonance imaging (MRI) is increasingly being used in medical settings because of its ability to produce, non-invasively, high quality images of the inside of the human body. Since its introduction in early 70’s, more and more complex acquisition techniques have been proposed, raising MRI to be exploited in a wide spectrum of applications. Innovative MRI modalities, such as diffu- sion and functional imaging, require complex analysis techniques and advanced algorithms in order to extract useful information from the acquired data. The aim of the present work has been to develop and optimize state-of-the- art techniques to be applied in the analysis of MRI data both in experimental and clinical settings. During my doctoral program I have been actively involved in several research projects, each time facing many different issues. In this dissertation, however, I will report the results obtained in three most appealing projects I partecipated to. These projects were devoted (i) to the implementation of an innovative experimental protocol for functional MRI in laboratory animals, (ii) to the development of new methods for the analysis of Dynamic Contrast Enhanced MRI data in experimental tumour models and (iii) to the analysis of diffusion MRI data in stroke patients. Particular emphasis will be given to the technical aspects regarding the algorithms and processing methods used in the analysis of data. Apart from a brief introduction on magnetic resonance imaging principles, the dissertation is organised in three parts, each one of them covering a separate topic dealt with during my doctoral studies. Each chapter is self-contained, in that it gives an introduction about the issue faced in the study, describes the methods and the techniques employed, and concludes with a discussion about the results obtained. Chapter 3 illustrates the methodology proposed by our group for an innovative method of fMRI (Activation-Induced Manganese Enhanced MRI, AIM-MRI) in rats. The method is based on in-vivo, absolute quantification of Mn concentration in different brain regions performed by fast T1 mapping and coregistration to a rat brain atlas. This strategy allows to quantify the accumulation of Mn in different regions of the rat brain, which is strictly related to the ac- tivation status of each area. This mechanism is similar to what happens in classical functional MRI studies but, in addition, it might be used for functional experiments performed in awake animals. In chapter 4 we cover the topic of tissues classification from MRI images in some experimental tumour models and present some advanced image-processing techniques we introduced for the analysis of Dynamic Contrast-Enhanced MRI (DCEMRI) data. In particular, we proposed to estimate a set of characteristic features from DCEMRI time profiles which well describe their shape, and then to extract peculiar behaviours which discriminate each tissue inside the tumour by combining cluster analysis and machine learning techniques. We have tested several approaches, each time comparing them with the state-of-the-art techniques of analysis for this kind of data. Finally, the proposed approach has been validated in a real case application in order to assess the efficacy of an anti-cancer therapy. Chapter 5 addresses the study on post-stroke plasticity carried out in collab- oration with the Signal Processing Laboratory of the Swiss Federal Institute of Technology in Lausanne (EPFL). Brain structural connectivity was monitored with Diffusion Spectrum Imaging (DSI), i.e. a high angular resolution diffusion MRI technique, during the functional recovery from the stroke, at hyperacute, acute and chronic stages after the lesion onset. The reproducibility of extracted fibre tracts has been extensively studied on healthy subjects, and the parameter set of fiber-tracking algorithm giving the best results was then used in patient analysis. The extracted fibre bundles between each pair of cortical regions were characterized by means of several connectivity measures.