Evaluation of second-level inference in fMRI analysis

We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic

Contribution of Exploratory Methods to the Investigation of Extended Large-Scale Brain Networks in Functional MRI: Methodologies, Results, and Challenges

A large-scale brain network can be defined as a set of segregated and integrated regions, that is, distant regions that share strong anatomical connections and functional interactions. Data-driven investigation of such networks has recently received a great deal of attention in blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). We here review the rationale for such an investigation, the methods used, the results obtained, and also discuss some issues that have to be faced for an efficient exploration

Spatiotemporal nonlinearity in resting-state fMRI of the human brain

In this work, the spatiotemporal nonlinearity in resting-state fMRI data of the human brain was detected by nonlinear dynamics methods. Nine human subjects during resting state were imaged using single-shot gradient echo planar imaging on a 1.5T scanner. Eigenvalue spectra for the covariance matrix, correlation dimensions and Spatiotemporal Lyapunov Exponents were calculated to detect the spatiotemporal nonlinearity in resting-state fMRI data. By simulating, adjusting, and comparing the eigenvalue spectra of pure correlated noise with the corresponding real fMRI data, the intrinsic dimensionality was estimated. The intrinsic dimensionality was used to extract the first few principal components from the real fMRI data using Principal Component Analysis, which will preserve the correct phase dynamics, while reducing both computational load and noise level of the data. Then the phase-space was reconstructed using the time-delay embedding method for their principal components and the correlation dimension was estimated by the Grassberger-Procaccia algorithm of multiple variable series. The Spatiotemporal Lyapunov Exponents were calculated by using the method based on coupled map lattices. Through nonlinearity testing, there are significant differences of correlation dimensions and Spatiotemporal Lyapunov Exponents between fMRI data and their surrogate data. The fractal dimension and the positive Spatiotemporal Lyapunov Exponents characterize the spatiotemporal nonlinear dynamics property of resting-state fMRI data. Therefore, the results suggest that fluctuations presented in resting state may be an inherent model of basal neural activation of human brain, cannot be fully attributed to noise

Analysing datafied life

Our life is being increasingly quantified by data. To obtain information from quantitative data, we need to develop various analysis methods, which can be drawn from diverse fields, such as computer science, information theory and statistics. This thesis focuses on investigating methods for analysing data generated for medical research. Its focus is on the purpose of using various data to quantify patients for personalized treatment. From the perspective of data type, this thesis proposes analysis methods for the data from the fields of Bioinformatics and medical imaging. We will discuss the need of using data from molecular level to pathway level and also incorporating medical imaging data. Different preprocessing methods should be developed for different data types, while some post-processing steps for various data types, such as classification and network analysis, can be done by a generalized approach. From the perspective of research questions, this thesis studies methods for answering five typical questions from simple to complex. These questions are detecting associations, identifying groups, constructing classifiers, deriving connectivity and building dynamic models. Each research question is studied in a specific field. For example, detecting associations is investigated for fMRI signals. However, the proposed methods can be naturally extended to solve questions in other fields. This thesis has successfully demonstrated that applying a method traditionally used in one field to a new field can bring lots of new insights. Five main research contributions for different research questions have been made in this thesis. First, to detect active brain regions associated to tasks using fMRI signals, a new significance index, CR-value, has been proposed. It is originated from the idea of using sparse modelling in gene association study. Secondly, in quantitative Proteomics analysis, a clustering based method has been developed to extract more information from large scale datasets than traditional methods. Clustering methods, which are usually used in finding subgroups of samples or features, are used to match similar identities across samples. Thirdly, a pipeline originally proposed in the field of Bioinformatics has been adapted to multivariate analysis of fMRI signals. Fourthly, the concept of elastic computing in computer science has been used to develop a new method for generating functional connectivity from fMRI data. Finally, sparse signal recovery methods from the domain of signal processing are suggested to solve the underdetermined problem of network model inference.Open Acces