Determining Significant Connectivity by 4D Spatiotemporal Wavelet Packet Resampling of Functional Neuroimaging Data

An active area of neuroimaging research involves examining functional relationships between spatially remote brain regions. When determining whether two brain regions exhibit significant correlation due to true functional connectivity, one must account for the background spatial correlation inherent in neuroimaging data. We define background correlation as spatiotemporal correlation in the data caused by factors other than neurophysiologically based functional associations such as scanner induced correlations and image preprocessing. We develop a 4D spatiotemporal wavelet packet resampling method which generates surrogate data that preserves only the average background spatial correlation within an axial slice, across axial slices, and through each voxel time series, while excluding the specific correlations due to true functional relationships. We also extend an amplitude adjustment algorithm which adjusts our surrogate data to closely match the amplitude distribution of the original data. Our method improves upon existing wavelet-based methods and extends them to 4D. We apply our resampling technique to determine significant functional connectivity from resting state and motor task fMRI datasets

A Spectral Method for Generating Surrogate Graph Signals

The increasing availability of network data is leading to a growing interest in processing of signals on graphs. One notable tool for extending conventional signal-processing operations to networks is the graph Fourier transform that can be obtained as the eigendecomposition of the graph Laplacian. In this letter, we used the graph Fourier transform to define a new method for generating surrogate graph signals. The approach is based on sign-randomization of the graph Fourier coefficients and, therefore, the correlation structure of the surrogate graph signals (i.e., smoothness on the graph topology) is imposed by the measured data. The proposed method of surrogate data generation can be widely applied for nonparametric statistical hypothesis testing. Here, we showed a proof-of-concept with a high-density electroencephalography dataset

Improving fMRI Analysis and MR Reconstruction with the Incorporation of MR Relaxivities and Correlation Effect Examination

Functional magnetic resonance imaging (fMRI) and functional connectivity MRI (fcMRI) use the physical principles of nuclear MR to provide high resolution representations of brain activity and connectivity. As the fMRI and fcMRI signals are detected from the excited hydrogen atoms in a magnetic field, the acquired data is determined by the underlying physical processes, such as the MR relaxivities. In fMRI and fcMRI, the Fourier encoded frequency space measurements are reconstructed into brain images, then spatiotemporal processing operations are applied before computing the brain activation and connectivity statistics. This dissertation seeks to utilize the magnetic resonance (MR) relaxivities at different stages of the fMRI pipeline, and aims to observe the statistical implications of the spatiotemporal processing operators on the fMRI and fcMRI data. We first develop a new statistical complex-valued nonlinear fMRI activation model that incorporates the MR relaxivities of gray matter into the brain activation statistics by utilizing the physical MR magnetization equation and the first scans of the fMRI data. We provide both theoretical and experimental comparison between the proposed model with the conventional linear magnitude-only and complex-valued fMRI activation models. Our statistical analysis results show that the new model provides better accuracy in computing brain activation statistics while theoretically eliminating false positives in non-gray matter areas. We then develop a linear Fourier reconstruction operator that incorporates the MR relaxivities into the image reconstruction process to account for their effects. The utilization of a linear system makes it achievable to theoretically compute the statistical implications of the use of the proposed operator. By focusing on longitudinal relaxation time, T1, to include into the image reconstruction, we show that the application of the proposed Fourier reconstruction operator provides better image contrast in the reconstructed images by recovering the information of the tissue characteristics that exist prior to T1 equilibrium. We finally examine the effects of time series preprocessing on computed functional correlations through the use of linear operators and provide ways of accounting for such effects in computing functional activity and connectivity statistics. Using both theoretical and experimentally acquired functional connectivity data, we examine the correlations induced by commonly used spatial and temporal processing operations. Furthermore, we provide the expansion of the statistical fcMRI and fMRI models to incorporate the quantified processing induced correlations in computing brain activity and connectivity statistics

Determining significant connectivity by 4D spatiotemporal wavelet packet resampling of functional neuroimaging data

An active area of neuroimaging research involves examining functional relationships between spatially remote brain regions. When determining whether two brain regions exhibit significant correlation due to true functional connectivity, one must account for the background spatial correlation inherent in neuroimaging data. We define background correlation as spatiotemporal correlation in the data caused by factors other than neurophysiologically based functional associations such as scanner induced correlations and image preprocessing. We develop a 4D spatiotemporal wavelet packet resampling method which generates surrogate data that preserves only the average background spatial correlation within an axial slice, across axial slices, and through each voxel time series, while excluding the specific correlations due to true functional relationships. We also extend an amplitude adjustment algorithm which adjusts our surrogate data to closely match the amplitude distribution of the original data. Our method improves upon existing wavelet-based methods and extends them to 4D. We apply our resampling technique to determine significant functional connectivity from resting state and motor task fMRI datasets

Development of novel methods for obtaining robust dynamic susceptibility contrast magnetic resonance imaging biomarkers from diseased brain in children

Dynamic susceptibility contrast (DSC-) MRI is an important imaging technique from which estimates of perfusion measures including cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) can be calculated. These perfusion measures can be used to indicate health in a range of diseases. However, acquisition protocol varies from centre-to-centre, which leads to variability in data quality between centres and limits the clinical applicability of DSC-MRI. Currently, the recommended process for assessing data quality is by eye, which is very time consuming and subjective between reviewers. In this work an automated processing pipeline for DSC-MRI was produced. Work to develop the pipeline demonstrated that data quality of DSC-MRI data can be assessed using machine learning classifiers, which were trained using metrics calculated from the data and the results of qualitative review. It also showed that it was possible to denoise the data using singular value decomposition (SVD) based methods, which were validated on a simulator and confirmed in patient data. The pipeline created was applied to a multicentre patient dataset where it demonstrated the importance of denoising DSC-MRI data in improving data quality and how data quality can vary with acquisition protocol. It was also applied to a single centre study of patients receiving differing treatments for brain tumours and suggested there are no significant changes in relative CBV (rCBV) in non-tumour brain between differing treatment groups. The pipeline developed during this work has wider applications in other imaging modalities and could be adapted to be applied to other perfusion imaging methods, such as dynamic contrast enhanced (DCE-) MRI, or any other imaging modality that involves analysis of a signal variation with time, such as computed tomography (CT) perfusion imaging or positron emission tomography (PET)

Quantification en imagerie optique diffuse cérébrale : analyse du signal et étude du problème direct

Physiologie -- Physiologie cérébrale humaine -- Couplage neurovasculaire -- Les vaisseaux sanguins cérébraux : les principaux sinus -- Imagerie optique diffuse et analyse du signal -- Définition du problème -- Problème direct -- Problème inverse -- Méthode d'analyse en imagerie optique diffuse -- Neuronavigation IRM-IOD -- IRM fonctionnelle -- Physique de l'IRM -- Neuronavigation -- Équipement de neuronavigation et de visualisation -- Analyse de la sensibilité -- Inverted responses in diffuse optical imaging and their correlation with negative BOLD signal -- Materials and methods -- Results -- Quantification -- Problème direct en imagerie optique diffuse -- Segmentation des tissus à partir de données IRMa -- Simulation Monte Carlo -- Formulation du problème -- Discrétisation et mise en oeuvre numérique -- Hybrid boundary element method applied to volumetric diffuse optical tomography -- Definition of the problem -- Born approximation -- Numerical discretization -- Results

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective