This thesis describes the development, implementation and application of various quantitative functional magnetic resonance imaging (fMRI) approaches at ultra-high magnetic field including the assessment with regards to applicability and reproducibility. Functional MRI (fMRI) commonly uses the blood oxygenation level dependent (BOLD) contrast to detect functionally induced changes in the oxy-deoxyhaemoglobin composition of blood which reflect cerebral neural activity. As these blood oxygenation changes do not only occur at the activation site but also downstream in the draining veins, the spatial specificity of the BOLD signal is limited. Therefore, the focus has moved towards more quantitative fMRI approaches such as arterial spin labelling (ASL), vascular space occupancy (VASO) or calibrated fMRI which measure quantifiable physiologically and physically relevant parameters such as cerebral blood flow (CBF), cerebral blood volume (CBV) or cerebral metabolic rate of oxygen (CMRO2), respectively. In this thesis a novel MRI technique was introduced which allowed the simultaneous acquisition of multiple physiological parameters in order to beneficially utilise their spatial and temporal characteristics. The advantages of ultra-high magnetic field were utilised to achieve higher signal-to-noise and contrast-to-noise ratios compared to lower field strengths. This technique was successfully used to study the spatial and temporal characteristics of CBV, CBF and BOLD in the visual cortex. This technique is the first one that allows simultaneous acquisition of CBV, CBF and BOLD weighted fMRI signals in the human brain at 7 Tesla. Additionally, this thesis presented a calibrated fMRI technique which allowed the quantitative estimation of changes in cerebral oxygen metabolism at ultra-high field. CMRO2 reflects the amount of thermodynamic work due to neural activity and is therefore a significant physical measure in neuroscience. The calibrated fMRI approach presented in this thesis was optimised for the use at ultra-high field by adjusting the MRI parameters as well as implementing a specifically designed radio-frequency (RF) pulse. A biophysical model was used to calibrate the fMRI data based on the simultaneous acquisition of BOLD and CBF weighted MRI signals during a gas-breathing challenge. The reproducibility was assessed across multiple brain regions and compared to that of various physiologically relevant parameters. The results indicate that the degree of intra-subject variation for calibrated fMRI is lower than for the classic BOLD contrast or ASL. Consequently, calibrated fMRI is a viable alternative to classic fMRI contrasts with regards to spatial specificity as well as functional reproducibility. This calibrated fMRI approach was also compared to a novel direct calibration technique which relies on complete venous oxygenation saturation during the calibration scan via a gas-breathing challenge. This thesis introduced several reliable quantitative fMRI approaches at 7 Tesla and the results presented are a step forward to the wider application of quantitative fMRI.:1 Introduction 3 2 Background to Functional Magnetic Resonance Imaging 7 2.1 Magnetic Resonance 7 2.1.1 Quantum Mechanics 7 2.1.2 The Classical Point of View 10 2.1.3 Radio Frequency Pulses 12 2.1.4 Relaxation Effects 13 2.1.5 The Bloch Equations 15 2.2 Magnetic Resonance Imaging 16 2.2.1 Data Acquisition 16 2.2.2 Image Formation 17 2.2.2.1 Slice Selection 17 2.2.2.2 Frequency Encoding 18 2.2.2.3 Phase Encoding 19 2.2.2.4 Mathematics of Image Formation 20 2.2.2.5 Signal Formation 22 2.3 Advanced Imaging Methods 24 2.3.1 Echo-Planar Imaging (EPI) 24 2.3.2 Partial Fourier Acquisition 25 2.3.3 Generalised Autocalibrating Partially Parallel Acquisition (GRAPPA) 25 2.3.4 Inversion Recovery (IR) 26 2.3.5 Adiabatic Inversion 26 2.3.5.1 Hyperbolic Secant (HS) RF pulses 28 2.3.5.2 Time Resampled Frequency Offset Corrected Inversion (tr-FOCI) RF Pulses 28 2.4 Physiological Background 29 2.4.1 Neuronal Activity 30 2.4.2 Energy Metabolism 31 2.4.3 Physiological Changes During Brain Activation 32 2.4.4 The BOLD Contrast 34 2.4.5 Disadvantages of the BOLD Contrast 35 2.5 Arterial Spin Labelling (ASL) 35 2.5.1 Pulsed Arterial Spin Labelling 37 2.5.2 Arterial Spin Labelling at Ultra-High Field 41 2.6 Vascular Space Occupancy (VASO) 42 2.6.1 VASO at Ultra-High Field 44 2.6.2 Slice-Saturation Slab-Inversion (SS-SI) VASO 45 2.7 Calibrated Functional Magnetic Resonance Imaging 47 2.7.1 The Davis Model 47 2.7.2 The Chiarelli Model 50 2.7.3 The Generalised Calibration Model (GCM) 52 3 Materials and Methods 53 3.1 Scanner Setup 53 3.2 Gas Delivery and Physiological Monitoring System 53 3.3 MRI Sequence Developments 55 3.3.1 Tr-FOCI Adiabatic Inversion 55 3.3.2 Optimisation of the PASL FAIR QUIPSSII Sequence Parameters 60 3.3.3 Multi-TE Multi-TI EPI 64 4 Experiment I: Comparison of Direct and Modelled fMRI Calibration 68 4.1 Background Information 68 4.2 Methods 69 4.2.1 Experimental Design 69 4.2.2 Visuo-Motor Task 70 4.2.3 Gas Manipulations 71 4.2.4 Scanning Parameters 71 4.2.5 Data Analysis 72 4.2.6 M-value Modelling 72 4.2.7 Direct M-Value Estimation 73 4.3 Results 74 4.4 Discussion 79 4.4.1 M-value Estimation 79 4.4.2 BOLD Time Courses 82 4.4.3 M-Maps and Single Subject Analysis 82 4.4.4 Effects on CMRO2 Estimation 83 4.4.5 Technical Limitations and Implications for Calibrated fMRI 84 4.5 Conclusion 89 5 Experiment II: Reproducibility of BOLD, ASL and Calibrated fMRI 90 5.1 Background Information 90 5.2 Methods 91 5.2.1 Experimental Design 91 5.2.2 Data Analysis 91 5.2.3 Reproducibility 93 5.2.4 Learning and Habituation Effects 95 5.3 Results 95 5.4 Discussion 101 5.4.1 Breathing Manipulations 102 5.4.2 Functional Reproducibility 107 5.4.3 Habituation Effects on Reproducibility 109 5.4.4 Technical Considerations for Calibrated fMRI 110 5.5 Conclusion 112 6 Experiment III: Simultaneous Acquisition of BOLD, ASL and VASO Signals 113 6.1 Background Information 113 6.2 Methods 114 6.2.1 SS-SI VASO Signal Acquisition 114 6.2.2 ASL and BOLD Signal Acquisition 114 6.2.3 Experimental Design 114 6.2.4 Data Analysis 115 6.3 Results 115 6.4 Discussion 116 6.5 Conclusion 120 7 Conclusion and Outlook 121