Revealing Brain Activity and White Matter Structure Using Functional and Diffusion-Weighted Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is based on the magnetic excitation of body tissue and the reception of returned electromagnetic signals from the body. Excitation induces phase-locked precession of protons with a frequency proportional to the strength of the surrounding magnetic field as described by the Larmor equation. This fact can be exploited for spatial encoding by applying magnetic field gradients along spatial dimensions on top of the strong static magnetic field of the scanner. The obtained frequency-encoded information for each slice is accumulated in two-dimensional k space. The k space data can be transformed into image space by Fourier analysis. Functional MRI (fMRI) allows localizing brain function since increased local neuronal activity leads to a surprisingly strong increase in local blood flow, which itself results in measurable increases in local magnetic field homogeneity. Increased local blood flow delivers chemical energy (glucose and oxygen) to the neurons. The temporary increase and decrease of local blood flow, triggered by increased neuronal activity, are called the hemodynamic response starting 2–4 s after stimulus onset. Increased local blood flow results in an oversupply of oxygenated hemoglobin in the vicinity of increased neuronal activity. The oversupply flushes deoxygenated hemoglobin from the capillaries and the downstream venules. Deoxygenated hemoglobin is paramagnetic reducing the homogeneity of the local magnetic field resulting in a weaker MRI signal than would be measurable without it. Oxygenated hemoglobin is diamagnetic and does not strongly reduce field homogeneity. Since the increased local blood flow replaces deoxygenated hemoglobin with oxygenated hemoglobin, local field homogeneity increases, leading to a stronger MRI signal as compared to a nonactivated state. Measured functional brain images thus reflect neuronal activity changes as blood oxygenation level-dependent (BOLD) contrast. Functional images are acquired using the fast echo planar imaging (EPI) pulse sequence allowing acquisition of a 64 × 64 image matrix in less than 100 ms. To sample signal changes over time, a set of slices typically covering the whole brain is measured repeatedly. Activation of neurons results in a BOLD signal increase of only about 1–5% and it lies buried within strong physical and physiological noise fluctuations of similar size. Proper preprocessing steps, including 3D motion correction and removal of drifts, reduce the effect of artifacts increasing the signal-to-noise ratio (SNR). In order to reliably detect stimulus-related effects, proper statistical data analysis is performed. In order to estimate response profiles condition-related time course episodes may be averaged in various regions of interest (ROIs). The core statistical tool in fMRI data analysis is the general linear model (GLM) allowing to analyze blocked and event-related experimental designs. To run a GLM, a design matrix (model) has to be constructed containing reference functions (predictors, model time courses) for all effects of interest (conditions) as well as confounds. The GLM fits the created model to the data independently for each voxel’s data (time course) providing a set of beta values estimating the effects of each condition. These beta values are compared with each other using contrasts resulting in a statistical value at each voxel. The statistical values of all voxels form a three-dimensional statistical map. To protect against wrongly declaring voxels as significant, statistical maps are thresholded properly by taking into account the multiple comparison problem. This problem is caused by the large number of independently performed statistical tests (one for each voxel). In recent years, parallel imaging techniques have been developed, which allow acquiring MRI data simultaneously with two or more receiver coils. Parallel imaging can be used to increase temporal or spatial resolution. It also helps to reduce EPI imaging artifacts, such as geometrical distortions and signal dropouts in regions of different neighboring tissue types. MRI has not only revolutionized functional brain imaging targeting grey matter neuronal activity but also enabled insights into human white matter structure using diffusion-weighted magnetic resonance imaging. With proper measurement and modeling schemes including diffusion tensor imaging (DTI), major fiber tracts can be reconstructed using computational tractography providing important information to guide neurosurgical procedures potentially reducing the risk of lesioning important fiber bundles. Since its invention in the early 1990s, functional magnetic resonance imaging (fMRI) has rapidly assumed a leading role among the techniques used to localize brain activity. The spatial and temporal resolution provided by state-of-the-art MR technology and its noninvasive character, which allows multiple studies of the same subject, are some of the main advantages of fMRI over the other functional neuroimaging techniques that are based on changes in blood flow and cortical metabolism (e.g., positron-emission tomography, PET). fMRI is based on the discovery of Ogawa et al. (1990) that magnetic resonance imaging (MRI, also called nuclear magnetic resonance imaging) can be used in a way that allows obtaining signals depending on the level of blood oxygenation. The measured signal is therefore also called “BOLD” signal (BOLD = blood oxygenation level-dependent). Since locally increased neuronal activity leads to increased local blood flow, which again changes local blood oxygenation, fMRI allows indirect measurements of neuronal activity changes. With appropriate data analysis and visualization methods, these BOLD measurements allow drawing conclusions about the localization and dynamics of brain function. This chapter describes the basic principles and methodology of functional and diffusion-weighted MRI. After a description of the physical principles of MRI at a conceptual level, the physiology of the blood oxygenation level-dependent (BOLD) contrast mechanism is described. The subsequent, major part of the chapter provides an introduction to the current strategies of statistical image analysis techniques with a focus on the analysis of single-subject data because of its relevance for presurgical mapping of human brain function. This is followed by a description of functional connectivity focusing on the analysis of resting-state fMRI data. Finally, principles of diffusion-weighted MRI measurements are described including diffusion tensor imaging, which is the most common acquisition and modeling approach in clinical MRI.