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

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Clinical Functional MRI

Part of the book series: Medical Radiology ((Med Radiol Diagn Imaging))

Abstracts

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, is 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 lie 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) that allows the analysis of 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 gray matter neuronal activity but also enabled insights into the human white matter structure using diffusion-weighted magnetic resonance imaging. With proper measurement and modeling schemes including diffusion tensor imaging (DTI), major long-range fiber tracts can be reconstructed using computational tractography providing important information to guide neurosurgical procedures potentially reducing the risk of lesioning important fiber tracts.

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Notes

  1. 1.

    Note that in a real experiment, one would not just present once the control and main condition as in Fig. 14, but several “on-off” cycles; with too few repetitions, task-related response could not be distinguished from potential low-frequency drifts (see Sect. 3.2.2).

  2. 2.

    In the fMRI literature, the term “general linear model” refers to its univariate version where “univariate” refers to the number of dependent variables (one). In its general form, the general linear model has been defined for multiple dependent variables, that is, it encompasses tests as general as multivariate covariance analysis (MANCOVA).

  3. 3.

    Note that the constant term is treated as a confound and it is not included in contrast vectors, i.e., it is implicitly assumed that b 0 is multiplied by 0 in all contrasts. To include the constant explicitly, each contrast vector must be expanded by one entry at the beginning or end.

  4. 4.

    While functional sequences are T2* weighted, the first functional volume of a run contains the richest anatomical detail because it is T1 weighted. Unfortunately, this data set is often thrown away either by the scanner directly (during “prep scans”) or by transfer of the data to the researcher. We recommend to keep the first functional volume and to use it for visualization and coregistration because of its relative richness in anatomical details.

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Authors and Affiliations

  1. Department of Cognitive Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV, Maastricht, The Netherlands

    Rainer Goebel

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  1. Rainer Goebel
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Correspondence to Rainer Goebel .

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  1. Division of Diagnostic and Interventional Neuroradiology, University Hospitals Basel, Basel, Switzerland

    Christoph Stippich

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Goebel, R. (2015). Revealing Brain Activity and White Matter Structure Using Functional and Diffusion-Weighted Magnetic Resonance Imaging. In: Stippich, C. (eds) Clinical Functional MRI. Medical Radiology(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45123-6_2

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