Application of mean-shift clustering to Blood oxygen level dependent functional MRI activation detection

BACKGROUND: Functional magnetic resonance imaging (fMRI) analysis is commonly done with cross-correlation analysis (CCA) and the General Linear Model (GLM). Both CCA and GLM techniques, however, typically perform calculations on a per-voxel basis and do not consider relationships neighboring voxels may have. Clustered voxel analyses have then been developed to improve fMRI signal detections by taking advantages of relationships of neighboring voxels. Mean-shift clustering (MSC) is another technique which takes into account properties of neighboring voxels and can be considered for enhancing fMRI activation detection. METHODS: This study examines the adoption of MSC to fMRI analysis. MSC was applied to a Statistical Parameter Image generated with the CCA technique on both simulated and real fMRI data. The MSC technique was then compared with CCA and CCA plus cluster analysis. A range of kernel sizes were used to examine how the technique behaves. RESULTS: Receiver Operating Characteristic curves shows an improvement over CCA and Cluster analysis. False positive rates are lower with the proposed technique. MSC allows the use of a low intensity threshold and also does not require the use of a cluster size threshold, which improves detection of weak activations and highly focused activations. CONCLUSION: The proposed technique shows improved activation detection for both simulated and real Blood Oxygen Level Dependent fMRI data. More detailed studies are required to further develop the proposed technique

Brain segmentation using endogenous contrast mechanism using breath holding fMRI signal for tissue characterization

MRL has fast become the modality of choice for the analysis of the complexity of the human brain. MRJ is a non-invasive method and gives high spatial resolution maps of the brain with soft tissue contrast. Conventional MRI technique modified to be used to image the functionality at high temporal resolution is known as fMRI. In fMRI the BOLD signal we measure is the hemodynamic response to neuronal and vascular changes at rest or in response to a stimulus where the various tissue types will have a different response. While fMRI has been traditionally been used to detect and identify eloquent regions of the cortex corresponding to specific tasks/stimulus, a number of groups have also used tMRI to study cerebrovascular changes and its consequence on the BOLD signal. A number of different perturbation methods including breath holding, hypercapnia, inhalation of various gas mixtures, and injection of acetozolamyde has been used to study spatio-temporal changes in the fMRI signal intensity. Spatiotemporal changes corresponding to changes in cerebral blood flow (CBF), cerebral blood volume (CBV), oxygen extraction fraction (OEF), and other physiological factors are then estimated and differences between diseased regions and healthy regions are then elucidated

Application of resting-state fMRI methods to acute ischemic stroke

Diffusion weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) perfusion-weighted imaging (PWI) are commonly employed in clinical practice and in research to give pathophysiological information for patients with acute ischemic stroke. DWI is thought to roughly reflect the severely damaged infarct core, while DSC-PWI reflects the area of hypoperfusion. The volumetric difference between DWI and DSC-PWI is termed the PWI/DWI-mismatch, and has been suggested as an MRI surrogate of the ischemic penumbra. However, due to the application of a contrast agent, which has potentially severe side-effects (e.g., nephrogenic systemic fibrosis), the DSC-PWI precludes repetitive examinations for monitoring purposes. New approaches are being sought to overcome this shortcoming. BOLD (blood oxygen-level dependent) signal can reflect the metabolism of blood oxygen in the brain and hemodynamics can be assessed with resting-state fMRI. The aim of this thesis was to use resting-state fMRI as a new approach to give similar information as DSC-PWI. This thesis comprises two studies: In the first study (see Chapter 2), two resting-state fMRI methods, local methods which compare low frequency amplitudes between two hemispheres and a k-means clustering approach, were applied to investigate the functional damage of patients with acute ischemic stroke both in the time domain and frequency domain. We found that the lesion areas had lower amplitudes than contralateral homotopic healthy tissues. We also differentiated the lesion areas from healthy tissues using a k-means clustering approach. In the second study (see Chapter 3), time-shift analysis (TSA), which assesses time delays of the spontaneous low frequency fluctuations of the resting-state BOLD signal, was applied to give similar pathophysiological information as DSC-PWI in the acute phase of stroke. We found that areas which showed a pronounced time delay to the respective mean time course were very similar to the hypoperfusion area. In summary, we suggest that the resting-state fMRI methods, especially the time-shift analysis (TSA), may provide comparable information to DSC-PWI and thus serve as a useful diagnostic tool for stroke MRI without the need for the application of a contrast agent

Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

Functional magnetic resonance imaging (fMRI) is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD) in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities

Quality data assessment and improvement in pre-processing pipeline to minimize impact of spurious signals in functional magnetic imaging (fMRI)

In the recent years, the field of quality data assessment and signal denoising in functional magnetic resonance imaging (fMRI) is rapidly evolving and the identification and reduction of spurious signal with pre-processing pipeline is one of the most discussed topic. In particular, subject motion or physiological signals, such as respiratory or/and cardiac pulsatility, were showed to introduce false-positive activations in subsequent statistical analyses. Different measures for the evaluation of the impact of motion related artefacts, such as frame-wise displacement and root mean square of movement parameters, and the reduction of these artefacts with different approaches, such as linear regression of nuisance signals and scrubbing or censoring procedure, were introduced. However, we identify two main drawbacks: i) the different measures used for the evaluation of motion artefacts were based on user-dependent thresholds, and ii) each study described and applied their own pre-processing pipeline. Few studies analysed the effect of these different pipelines on subsequent analyses methods in task-based fMRI.The first aim of the study is to obtain a tool for motion fMRI data assessment, based on auto-calibrated procedures, to detect outlier subjects and outliers volumes, targeted on each investigated sample to ensure homogeneity of data for motion. The second aim is to compare the impact of different pre-processing pipelines on task-based fMRI using GLM based on recent advances in resting state fMRI preprocessing pipelines. Different output measures based on signal variability and task strength were used for the assessment

Multi-parametric Imaging Using Hybrid PET/MR to Investigate the Epileptogenic Brain

Neuroimaging analysis has led to fundamental discoveries about the healthy and pathological human brain. Different imaging modalities allow garnering complementary information about brain metabolism, structure and function. To ensure that the integration of imaging data from these modalities is robust and reliable, it is fundamental to attain deep knowledge of each modality individually. Epilepsy, a neurological condition characterised by recurrent spontaneous seizures, represents a field in which applications of neuroimaging and multi-parametric imaging are particularly promising to guide diagnosis and treatment. In this PhD thesis, I focused on different imaging modalities and investigated advanced denoising and analysis strategies to improve their application to epilepsy. The first project focused on fluorodeoxyglucose (FDG) positron emission tomography (PET), a well-established imaging modality assessing brain metabolism, and aimed to develop a novel, semi-quantitative pipeline to analyse data in children with epilepsy, thus aiding presurgical planning. As pipelines for FDG-PET analysis in children are currently lacking, I developed age-appropriate templates to provide statistical parametric maps identifying epileptogenic areas on patient scans. The second and third projects focused on two magnetic resonance imaging (MRI) modalities: resting-state functional MRI (rs-fMRI) and arterial spin labelling (ASL), respectively. The aim was to i) probe the efficacy of different fMRI denoising pipelines, and ii) formally compare different ASL data acquisition strategies. In the former case, I compared different pre-processing methods and assessed their impact on fMRI signal quality and related functional connectivity analyses. In the latter case, I compared two ASL sequences to investigate their ability to quantify cerebral blood flow and interregional brain connectivity. The final project addressed the combination of rs-fMRI and ASL, and leveraged graph-theoretical analysis tools to i) compare metrics estimated via these two imaging modalities in healthy subjects and ii) assess topological changes captured by these modalities in a sample of temporal lobe epilepsy patients

Identifying Changes of Functional Brain Networks using Graph Theory

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgement