Functional magnetic resonance imaging

Transcription

1 University of Ljubljana Faculty of Mathematics and Physics Department of Physics Seminar I b - 2nd year, Second cycle degree Functional magnetic resonance imaging Author: Patricia Cotič Supervisor: Assoc. prof. Igor Serša Ljubljana, May 2015 Abstract The seminar gives an introduction to functional magnetic resonance imaging (fmri) - a noninvasive technique that uses magnetic resonance imaging (MRI) to study brain activity. In the first part, a brief introduction to nuclear magnetic resonance as well as MRI principles is given. A more detail explanation is devoted to nuclear magnetic relaxation and basic pulse sequences as it is of great importance in fmri. Next, fmri based on blood oxygen level dependent signal (BOLD-fMRI) is presented. The focus is on the physiological basis of BOLD s response to neural activity and on echo planar imaging as an ultra-fast imaging technique used in fmri. Challenges that are currently being tackled in fmri are highlighted in the conclusions.

2 Contents 1 Introduction 2 2 The basis of NMR and MRI Nuclear magnetic resonance effect - RF absorption and emission Nuclear magnetic relaxation Basic pulse sequences in MRI The concept of spatial encoding in MRI and the MRI hardware Functional magnetic resonance imaging Image contrast in fmri - the BOLD contrast Brain physiology related to BOLD Echo planar imaging as an ultra-fast imaging technique Conclusion 11 References 12 1 Introduction Functional magnetic resonance imaging (fmri) is a noninvasive technique that uses magnetic resonance imaging (MRI) to study brain activity. Such as positron emission tomography (PET), fmri is based on the assumption that neuronal activity is linked to coupled cerebral metabolic and haemodynamic changes, i.e. changes in cellular energy production through glucose utilization, as well as changes in cerebral blood flow, blood volume and metabolic rate of oxygen [1]. Although relative changes between normal and stimulated brain activity may be captured from independent measure of these features (such as perfusion fmri with arterial spin labeling [2]), the most widely used fmri technique is based on the detection of BOLD (blood oxygen level dependent) signal. BOLD measures the relative concentration of oxygenated and deoxygenated blood on the basis of their different magnetic susceptibilities. As it is a complex measure of haemodynamic changes, BOLD-fMRI serves as an indirect measure of neural activity. The basis for BOLD-fMRI was founded by Ogawa et al. in 1990, following an experiment on rats which demonstrated that an in vivo change of blood oxygenation could be detected with MRI [3]. Their work was inspired by earlier findings of Pauling and Coryell in the 1930-ies, which showed that magnetic properties of haemoglobin depend on the binding of oxygen. Oxygenated haemoglobin (HbO 2 ) was found to be diamagnetic, whereas deoxygenated haemoglobin (Hb) 1 was strongly paramagnetic (spin S = 2 of the heme iron) and hence has a nonzero magnetic moment [4]. The introduction of BOLD signal in 1990 was followed by three pioneering works in 1992 using BOLD-fMRI on humans with Hb as an endogeneous MRI contrast agent (Bandettini et al., Kwong et al. and Ogawa et al.). Along with BOLD-fMRI, perfusion fmri was first demonstrated by Belliveau et al. using an exogeneous contrast agent injected into the blood stream (for a more detail history on fmri, see e.g. [2, 5]). Nowadays, BOLD-fMRI is widely used in clinical applications, such as preoperative functional mapping of brain regions to guide surgeons in preserving vital brain areas in e.g. epilepsy surgery, to detect brain lesions caused by diseases (e.g. epilepsy, multiple sclerosis, Alzheimer s disease), stroke, turmors, injury [6]. Basic research spans areas such as brain functional mapping to produce brain atlases [7], study of neurophysiological correlates to the BOLD effect [8], as well as the very recent study of brain functional connectivity [9]. 1 Throughout the text, Hb refers to deoxygenated haemoglobin. 2

3 2 The basis of NMR and MRI Despite the complexity of fmri alone, the method could not be made clear without the introduction of nuclear magnetic resonance (NMR) and MRI principles. They were set by Nobel Laureates Bloch and Purcell for their independent discovery of NMR principles in 1946, Lauterbur and Mansfield for the elucidation of image generation in MRI in 1973, as well as Ernst for the development of multi-dimensional NMR spectroscopy technique based on Fourier transform in 1975 [5, 8]. The following chapter aims in giving a very brief overview of those principles. 2.1 Nuclear magnetic resonance effect - RF absorption and emission A nucleus with a nonzero spin I is defined with a magnetic moment µ = γ I, where γ is the gyromagnetic ratio [10, 11]. In MRI and fmri, the nucleus being imaged is proton ( 1 H) present in water molecules of the tissue. 1 When the nucleus is placed in a stationary magnetic field ( B 0 = B 0 ê z ) the Zeeman interaction between the magnetic moment and magnetic field causes the energy of nuclear spin states to split according to m Ĥ m E m = = γ B 0 m I z m = γ B 0 m, where Ĥ = γ B 0I z refers to Hamiltonian describing Zeeman interaction. For proton with m = ±1/2, the corresponding energy states are E ± = 1 2 γ B 0 with an energy difference E = γ B 0 (here, subscripts + and - refer to m = 1/2 and m = 1/2, respectively). In thermal equilibrium, the population of energy states n + and n follows Boltzmann distribution with a small population bias towards the lower energy state, i.e. n + /n = e E/kT. This gives rise to a net macroscopic magnetization which is parallel to B 0 and of magnitude [2, 11] M = µ(n + n ) n + n 1 E kt B 0 T. The observation of M is a clear indicator of local proton density in the tissue. However, since only a time dependent change in magnetization produces a signal, M has to be perturbed from its equilibrium to experience a torque and start to precess about B 0 according to [10, 11] d M dt = M γ B 0. (2.1) The above equation describes the precession of M about the z-axis at a frequency called Larmor frequency, given by ω 0 = γb 0. According to quantum mechanics, the energy that is required to tilt M from equilibrium has to match the spin excitation energy E = ω 0 (the magnetic resonance effect). As Larmor frequency for proton is ω 0 /2π = 42.6 MHz (at B 0 = 1 T), spin excitation is performed with radio frequency (RF) pulses. In MRI, RF pulses are generated using an RF excitation coil that is oriented along x-direction and driven at ω 0 with a sinusoidal voltage. This generates a linearly 1 Other biological relevant nuclei such as 12 C and 16 O have zero spin, whereas their isotopes 13 C and 17 O, as well as 31 P with a nonzero zero spin constitute only a small percent of the human body mass. Moreover, the gyromagnetic ratio that defines the magnitude of the NMR signal is the largest for proton (γ = /Ts). 3

4 polarized time varying magnetic field B 1 (t) = 2B 1 cos ω 0 t ê x [10]. To visualize the precession of M in the (x, y) plane, lets consider B 1 as a sum of two circularly polarised magnetic field components (in opposite direction) and further take into account that only the component that is rotating in the same direction as M will have an effect on the spins, i.e. B 1 (t) = B 1 cos ω 0 t ê x B 1 sin ω 0 t ê y. Considering now B = (B 1 cos ω 0 t, B 1 sin ω 0 t, B 0 ) and a starting condition M(0) = M 0 ê z, the solution of Eq. (2.1) is [12] where ω 1 = γb 1. M(t) = (M x (t), M y (t), M z (t)) = M 0 (sin ω 1 t sin ω 0 t, sin ω 1 t cos ω 0 t, cos ω 1 t), (2.2) According to Eq. (2.1), M starts to rotate simultaneously both about B 0 at ω 0 and B 1 at ω 1. The angle by which M rotates about B 1 is defined by the duration t of the RF pulse, i.e. θ = γb 1 t. It should be noted that the precession of magnetization is often interpreted in a rotating frame (x, y, z ), which rotates about the z-axis at ω 0 and where B 1 appears to be static in the x direction. Hence, M rotates about x -axis at ω 1. The magnitude of the transverse component M xy is being measured by the generated signal in the receiver coil. After transforming the signal to the rotating frame using phase sensitive detection, the signal has the form [12] S(t) = S 0 e t/t 2 cos ω 0 t. The signal called free induction decay (FID) is oscillating at ω 0 and decays away under an exponential envelope due to the spin-spin relaxation effect introduced in the following section. By tuning the RF excitation/receiver coil to proton s Larmor frequency, only protons can undergo an RF absorption and emission (magnetic resonance) that gives a signal. Note however, that the detection of resonant frequencies from different nuclei (e.g. 1 H, 13 C, 31 P and 23 Na) is the basis for MR spectroscopy. 2.2 Nuclear magnetic relaxation After M has been perturbed, it s equilibrium state is gradually being restored by two relaxation processes - a T1 and T2 relaxation process. T1 relaxation is responsible for the recovery of the longitudinal magnetization M z to equilibrium according to [8] dm z dt = M z M 0. T 1 The process is accompanied with the change in protons spin energy states due to spin interaction with randomly fluctuating magnetic moments of neighbouring molecules (spin-lattice interaction). As the interactions will be stronger when the rate of molecular motion is close to the Larmor frequency, T1 depends greatly on the used magnetic field strength and tissue composition (T1 is large for the relatively free water in cerebrospinal fluid (CSF) and much smaller for water in white and grey matter as its rate of motion is closer to Larmor frequency at conventional magnetic fields used in MRI - see Tab. 2.1). T2 relaxation process is responsible for the decay of transverse magnetization M xy according 4

5 to [8] dm xy dt = M xy T 2. The process termed spin-spin interaction occurs due to nuclear dipolar interactions between randomly fluctuating proton magnetic moments and is much less dependent on the magnetic field strength than T1 relaxation. 2 Typical T1 and T2 relaxation times of brain grey and white matter, as well as CSF are given in Tab However, besides the spin-spin interactions, inhomogeneities in the static magnetic field produced by local changes in magnetic susceptibility also contribute to the decay of M xy and are of primary importance in fmri. Variations in magnetic susceptibility can be considered as macroscopic, affecting several voxels (proximity of air/tissue interfaces near air cavities in the human head), and microscopic, affecting individual voxels (proximity of blood vessels with paramagnetic Hb). The later is the basis for signal detection in BOLD-fMRI. All effects that contribute to the decay of M xy are taken into account in the T2* relaxation according to [8] 1 T 2 = 1 T 2 + γ B mi + γ B ma, where B mi and B ma refer to magnetic field inhomogeneities due to microscopic and macroscopic variations in magnetic susceptibility, respectively. 2.3 Basic pulse sequences in MRI As will be later described, the use of magnetic field gradients is essential to allow for signal spatial encoding. However, due to gradients the signal starts to dephase after the application of an RF excitation pulse. To reform the signal, two basic pulse sequences are used - gradient echo (GE) and spin echo (SE). In GE, signal refocusing is performed by changing the polarity of the gradient. After the RF excitation pulse, the initially negative gradient that causes spins to dephase, is reversed. This results in spin rephasing and the generation of a signal echo at time TE (time to echo) after the RF pulse. Although GE removes the gradient effects, it cannot rephase the magnetic susceptibility effects expressed in the T2* relaxation time. Thus, the signal of GE decays as e T E/T 2 [2, 8]. In SE the signal is rather refocused with the use of a second 180 RF pulse that follows the initial 90 pulse. In addition to eliminating the gradient effects, it also completely eliminates the magnetic susceptibility effects at the centre of the echo but leaves the random appearing T2 relaxation. Hence, the signal decay is expressed by T2. 2 Apart from nuclear dipolar interactions, nuclear interactions with neighbouring electrons (chemical shift) also modulate the proton s Larmor frequency defined by Zeeman interaction with the static magnetic field. However, those changes are on the order of only ppm and are thus ignored in conventional MRI and fmri applications, but provide the basis for MR spectroscopy. Table T [8]. Typical T1 and T2 relaxation times of brain grey and white matter, as well as CSF at Grey matter White matter CSF T1 (ms) T2 (ms)

6 It should be noted that for GE, the initial RF pulse is typically θ < 90 to yield higher signalto-noise ratio, whereas SE employs a 90 pulse [8]. Moreover, when the GE or SE pulse sequence is repeated with time TR (time to repetition), the signal additionally decays by factor 1 e T R/T 1. The difference in tissue relaxation properties can be expressed in the image contrast by varying pulse sequence parameters that affect the excitation and observation of spin signal. In particular, three parameters are of importance: energy per initial RF pulse (expressed in the flip angle of M), TR and TE. By manipulating TR, contrast in T1 is expressed in a T1-weighted image, whereas the change of TE in SE and GE pulse sequences generates T2- and T2*-weighted images, respectively. As can be seen from Fig. 2.1, the largest signal is obtained when using TR and TE close to the tissue T1 and T2 (T2*) relaxation times. 2.4 The concept of spatial encoding in MRI and the MRI hardware A typical clinical MRI hardware that is used nowadays is comprised by the following main components: a superconducting magnet (generates a static magnetic field strength of T), three gradient coils (25 40 mt/m), RF excitation and receiver coil (Fig. 2.2). The use of three magnetic field gradients (G x, G y, G z ), where the magnetic field strength vary linearly along one direction, serves for spatial encoding in MRI [2]. It is based on the idea that the generation of local variations in the magnetic field, and thus in the Larmor frequency of spins, can serve for spatial localization [8]. The gradient in z-direction (constructed as a Maxwell pair of coils) serves for slice selection. As Figure 2.1 Contrast in T1, T2 and T2* relaxation times for water protons in different brain tissue (white and grey matter and CSF) at 3 T. T1-weighted image (SE with TE = 18 ms, (a) TR = 500 ms, (b) TR = 1000 ms), T2-weighted image (SE with (c) TE = 180 ms and (d) TE = 60 ms, TR = 2000 ms), (e) T2*-weighted image (GE with TE = 30 ms, information about flip angle and TR not given) [8]. 6

7 Figure 2.2 Schematic diagram of MRI hardware (a) and presentation of slice selection with RF excitation pulse and gradient in z-direction G z [8]. can be seen from Fig. 2.2, the application of an RF excitation pulse with narrow bandwidth ( ω) in the presence of G z will only excite protons within a certain slice. Here, the centre position of the slice is adjusted by the carrier frequency of the pulse, whereas the slice thickness is determined by the duration of the pulse envelope and the gradient field strength [8]. For further localization within the slice, a readout gradient G x is used to produce variation in Larmor frequency along the x-axis, whereas a phase gradient G y is used to further separate the signals from different positions along the y-axis [2]. By an appropriate sequence of pulses, a 2D image is generated by means of the Fourier imaging method with data acquisition in the k-space [8]. 3 This is accomplished by taking into account the spatial variation in Larmor frequency due to a gradient G = (G x, G y ) ω( r) = γb 0 + γ G r (2.3) By neglecting the contribution of static magnetic field, the signal from spins at position r (with constant gradient) follows [8, 11] S(t) = ρ( r)e iγg rt d r = ρ( r)e i k r d r = S( k), (2.4) where k γ Gt and ρ( r) refers to density of spins at location r. 4 From Eq. (2.4) it is evident that the signal being measured is exactly the inverse Fourier transform of the spin density. Hence, once the data is acquired in the k-space, the spin density can be easily reconstructed using Fourier transform. To navigate through each point in the k-space, a particular pulse sequence is employed, where the magnitude and duration of the gradients are being manipulated with G x and G y, respectively. This is illustrated from the pulse sequence diagram of the standard GE (FLASH) method in Fig Here, G y is responsible for phase encoding, since the generated phase is locked during data acquisition along the k x line. On the contrary, G x is responsible for frequency encoding as the phase is changing during acquisition [2]. The time between the acquisition of two successive lines in the k-space refers to TR. 3 In the first imaging scheme introduced by Lauterbur in 1973, a 2D image was generated by collecting 1D signals projected on several rotating x-axes and further applying a back-projection technique. The method was much more prone to large signal artefact than Fourier imaging. 4 For a general gradient, k takes the form k γ t 0 G(t )dt. 7

8 Figure 2.3 Pulse sequence diagram and k-space representation of the GE-FLASH pulse sequence. Gradients G x and G y are responsible for collecting data along k x and k y, respectively. Note from the figure that a negative gradient G z has to be applied after spin excitation to refocus the spins to zero phase [8]. 3 Functional magnetic resonance imaging 3.1 Image contrast in fmri - the BOLD contrast In MRI, image intensity can be generated by various contrast mechanism: proton density, as well as T1, T2 and T2* relaxation and diffusion rate of tissue water, where the change in T2* relaxation of tissue water is of most importance in fmri. In perfusion fmri where the blood perfusion is measured, this change is generated with the use of contrast agents such as paramagnetic gadolinium ion that is injected into the blood stream, whereas in the more sensitive BOLD-fMRI, paramagnetic Hb serves as an endogenous contrast agent and generates intravoxel magnetic field inhomogeneities [8]. However, as explained later, BOLD-fMRI cannot assess the blood flow, but rather blood oxygenation. As the seminar focuses on BOLD-fMRI, the generation of BOLD contrast is described in more detail. Between stimulated and resting brain, a BOLD contrast appears due to a changed ratio [HbO 2 ]/[Hb] in the venous system. As previously described, it is predominantly generated through the T2* relaxation due to magnetic susceptibility changes across the tissue in the proximity of blood vessels, but can also be observed from T2-weighted images. The origin of T2 effect are both field inhomogeneities within blood vessels (intravascular water component) as well as in the surrounding tissue (extravascular water component) [8]. In the intravascular system, the field gradients around blood cells with Hb are very small compared to water diffusion distances across and around the cell membrane which averages the T2* relaxations and leaves only the random appearing T2 effect. Similar averaging effect due to the diffusion rate appears also for the extravascular BOLD component in the proximity of small blood vessels [13]. Thus, the T2* relaxation effect from the extravascular system originates from large blood vessels. Although both BOLD effects increase with the increase in magnetic field strength, the increase in T2 is linear, whereas increase in T2* is exponential [14]. Thus, performing a GE pulse sequence in combination with higher fields is desired and typically, fields of 1.5 T and 3 T are used in fmri clinical applications. 8

9 3.2 Brain physiology related to BOLD Coupling of haemodynamics to metabolism - haemodynamic response function Arterial blood consists mostly of the diamagnetic HbO 2 that is being deoxygenated upon passing the capillary bed. Thus, the concentration of Hb increases in the venous system and generally dominates. However, upon neuronal activation from a large population of neurons (a single voxel in a typical fmri experiment (100 mm 3 ) may contain 10 7 neurons or more [8]), the local venous concentration of HbO 2 increases compared to Hb, which gives the BOLD contrast [14]. To understand this phenomenon it should be taken into account that BOLD signal (measure of oxygenated blood) is associated with changes in cerebral metabolism (glucose utilization) and haemodynamics (blood flow, blood volume, oxygen utilization). Already from PET studies, those changes have proved to be well linked to neuronal activation [5]. Since neuronal activation is an energy-requiring process [2, 15] and the brain does not store energy, glucose and oxygen have to be delivered to produce cellular energy (ATP) from glucose metabolism which manifests in an increase in blood flow [6, 8]. However, the increase in blood flow and glucose metabolism by far exceed the utilization of oxygen. Hence, as a result to brain activation, the relative concentration of Hb in venules and veins decreases leading to an increase in T2* and T2 and thus the BOLD signal. In reality, BOLD signal exhibits a far more complex dependence than described above. A typical time course of the signal that follows an instantaneous stimulus is called the haemodynamic response function (HRF) and is presented in Fig At high field strengths there is an initial decrease in signal intensity (initial dip) due to rapid deoxygenation of capillary blood and increased blood volume of draining veins [2, 8]. The initial dip is followed by a positive BOLD Figure 3.1 A typical haemodynamic response function of BOLD signal following a stimulus [1]. 9

10 response that lasts for about 5 10 s, when deoxygenation and increase in blood flow stop. For the remaining time (up to 30 s), there is a signal undershoot due to more slowly resolving blood volume after activation [1, 16]. This explanation that couples haemodynamics to neuronal activity is however still questionable [16] and try to be elucidated by coupled electroencephalography (EEG) and fmri studies [17, 18]. Nonetheless, it is believed that metabolic mechanisms have the primary effect on the typical BOLD signal and the effects from other neighbouring nuclei are believed to be negligible. Coupling of haemodynamics to neuronal activation The coupling between changes in neural activity, metabolism and haemodynamics has been described above. However, the mechanisms that link those changes are still unclear. Thus, several studies try to answer questions such as Which aspect of neural activity drives the BOLD signal? [15]. This is particularly important when trying to link results from simultaneous EEG-fMRI measurements and will be therefore briefly discussed. Neural activity is characterized by two electrical potentials - synaptic potential and action potential - and each is accompanied by a particular metabolism. Synaptic potential appears at dendrites (input of neuron) and can be measured as local field potentials (LFP) with EEG, whereas action potential is a brief (1 ms) change in transmembrane potential (ouput of neuron) that can be captured with single-unit (SUA) and multi-unit activity (MUA) measurements. Several studies show that BOLD signal better correlates with synaptic activity (see Fig. 3.2), which proposes that metabolic mechanisms involved in neuronal input signalling drive vascular changes with predominantly linear coupling [2, 15, 17]. Figure 3.2 Correlation between BOLD signal and synaptic activity (LFP), as well as action potential activity (SUA, MUA) after 24 s of visual stimulus in monkeys [17]. 3.3 Echo planar imaging as an ultra-fast imaging technique The goal of fmri is to capture BOLD response after short stimuli. Given the specific time scale of HRF, it is clear that ultra-fast imaging is necessary. As BOLD contrast is primarily expressed through the T2* relaxation process, GE pulse sequences are mostly used in BOLD-fMRI. To account for the time consuming FLASH method (time for one slice is N *TR, where N refers to the number of horizontal lines in k-space), echo-planar imaging (EPI) that was proposed by Mansfield in 1977 is usually used. Here, only one RF pulse is applied per slice, which is then followed by a series of echoes produced by successive switching of G x and G y gradients as shown in Fig However, the fast decaying signal due to T2* relaxation limits the spatial resolution of the EPI image (typical resolution is or compared to the resolution of or obtained with FLASH). Nonetheless, this is compensated by the high temporal speed in EPI (a or EPI image is acquired in 30 to 50 ms, whereas 10

11 Figure 3.3 Pulse sequence and corresponding k-representation for GE-EPI. Between successive inversions of readout gradient G x, short G y pulses (blips) are applied to move along k y -direction. Note that the EPI typical zigzag acquistion in k-space may lead to image acquisition artefacts [1, 8]. the acquisition of a FLASH image lasts up to 10 s.). In BOLD-fMRI, the acquisition parameters have to be selected as a compromise between spatial and temporal resolution. As spatial resolution actually defines the magnitude of T2* (magnetic field inhomogeneity effects scale with voxel size), small voxel size is desirable, which is even more pronounced at large magnetic fields. However, to meet the required temporal resolution of HRF, whole volume scans with EPI are typically obtained with a 3 4 mm in-plane resolution in combination with a slice thickness of 5 10 mm and TR = 2 4 s [8]. On the other hand, the choice of TE defines the BOLD contrast and the optimal TE is found to be at TE = T2* (TE = 30 ms at 3 T and TE = 50 ms at 1.5 T) [1]. The limitation of EPI is its susceptibility to artefacts due to macroscopic field inhomogeneities around air cavities in the head, chemical shift due to signal from fat protons (resonance frequency from fat proton is displaced by 3.44 ppm (220 Hz at 1.5 T)), as well as alteration in polarity of the readout gradient that can shift the echo position and lead to so called Nyquist ghost [8]. Moreover, in addition to the limited gradient switching speed (due to both technological and physiological limits the slew rate is typically mt/m/s [8]), the predominant contribution of large draining veins to BOLD signal largely restricts high spatial resolution for EPI. Apart from multi-slice EPI that was describes above, 3D EPI or spin echo EPI (SE-EPI) can be used. In 3D EPI, the slice selection gradient is replaced with a second phase encoding gradient, which enables whole volume excitation by a single RF pulse. Thus, faster scanning can be achieved but at the same time also additional image blurring is introduced [8]. SE-EPI works similarly as GE-EPI with the main difference that an additional 180 pulse is applied prior to the gradient echo train. Thus, the signal follows a T2 relaxation decay, which eliminates the predominant extravascular BOLD component from large draining veins. Thus, SE-EPI on one hand increases spatial resolution, but on the other hand has much smaller BOLD sensitivity compared to GE-EPI at typical T2* values. This restricts the use of SE-EPI to cases where T2* is very low due to large field inhomogeneities, or to cases where the static magnetic field is large enough (at least 3 or 4 T [8]) to generate a BOLD signal. 4 Conclusion In BOLD-fMRI, neural activity is measured with a signal that is a complex function of local proton density, T1 and T2 relaxation properties of the tissue, blood oxygenation, blood flow 11

12 and blood volume. Typically, it is measured using GE-EPI pulse technique that is sensitive to T2* relaxation effects including BOLD changes. However, the technique is prone to several artefacts. To overcome this, as well as to increase spatial resolution, advanced fmri methods are being studied such as sensitivity encoding (SENSE) with the use of multiple RF surface coils and UNFOLD that tends to reduce the k-space coverage [8]. Taking into account BOLD time course (haemodynamic response function) and the size of the vascular system that contributes to the signal, it is almost unbelievable that BOLD-fMRI can capture neural activity. Recent studies on simultaneous EEG-fMRI also show that fmri takes an important role in decoding brain functional connectivity [19], i.e. the generation of our thoughts. Understanding what contributes to changes in brain functional connectivity as well as neurovascular coupling between normal and diseased brain is a leading driving force of BOLDfMRI studies [15, 20, 21]. In recent years, the number of simultaneous EEG-fMRI studies is greatly increasing. The approach of combining EEG with fmri is particularly appealing as the methods have complementary temporal and spatial resolution [1]. On one hand, fmri can contribute to the EEG source localization problem, whereas on the other hand, EEG can serve in the interpretation of neurovascular coupling [22, 23]. Although, the EEG artefacts generated by MRI present a large problem and require efficient data analysis, combined EEG-fMRI studies have already shown the capability to offer answers to many questions in neuroscience [1]. To achieve both a high spatial and temporal resolution of functional brain mapping, some studies tried to use MRI at fields > 1 T to directly map magnetic field changes caused by neuronal currents [24]. Although it was confronted with strong scepticism [25], light is now shed on direct neuronal detection at low fields with SQUID sensors to suppress large BOLD effects and other susceptibility variations [26]. However, the currently reached signal sensitivity is far below the sensitivity reached from BOLD-fMRI. References [1] C. Mulert, L. Lemieux (Eds.), EEG-fMRI Physiological Basis, Technique and Applications, Berlin Heidelberg: Springer-Verlag, [2] R. B. Buxton, Introduction to functional magnetic resonance imaging: principles and techniques, New York: Cambridge University Press, [3] S. Ogawa, T. M. Lee, A. K. Kay, D. W. Tank, Proc. Nayl. Acad. Sci. (USA) 87 (1990), [4] L. Pauling, C. D. Coryell, Proc. Nayl. Acad. Sci. (USA) 22 (1936), [5] M. E. Raichle, Trends in Neurosciences 32(2) (2009), [6] D. G. Nair, Brain Research Reviews 50 (2005), [7] magnetic resonance imaging (acquired ). [8] P. Jezzard, P. M. Matthews, S. M. Smith (Eds.), Functional MRI - An Introduction to Methods, New York: Oxford University Press, [9] M. P. van den Heuvel, H. E. Hulshoff Pol, European Neuropsychopharmacology 20 (2010), [10] J. Dolinšek, Spektroskopija trdne snovi, povzetek predavanj, 2014/2015. [11] I. Serša, Slikanje z magnetno resonanco, zapiski, 2014/2015. [12] S. Clare, Functional MRI: Methods and Applications, doctoral thesis, University of Nottingham, [13] C. Stippich (Ed.), Clinical Functional MRI: Presurgical Functional Neuroimaging, New York, Dordrecht, London: Springer-Verlag Berlin Heidelberg, [14] S. Ogawa, R. S. Menon, D. W. Tank, S. G. Kim, H. Merkle, J. M. Ellermann, K. Ugurbil, Biophysical Journal 64(3) (1993), [15] B. N. Pasley, R. D. Freeman, Scholarpedia 3(3) (2008), [16] R. B. Buxton, K. Uludag, D. J. Dubowitz, T. T. Liu, NeuroImage 23 (2004), S220 S233. [17] N. K. Logothetis, J. Pauls, M. Augath, T. Trinath, A. Oeltermann, Nature 412 (2001), [18] H. Laufs, A. Kleinschmidt, A. Beyerle, E. Eger, A. Salek-Haddadi, C. Preibisch, K. Krakow, NeuroImage 19(4) (2003), [19] D. Van de Ville, J. Britz, C. M. Michel, PNAS 107(42) (2010), [20] M. D Esposito, L. Y. Deouell, A. Gazzaley, Nature reviews 4 (2003), [21] C. Iadecola, Nature reviews 5 (2004), [22] P. Ritter, A. Villringer, Neuroscience and Biobehavioral Reviews 30 (2006), [23] D. Mantini, M. G. Perrucci, C. Del Gratta, G. L. Romani, M. Corbetta, PNAS 104(32) (2007), [24] G. E. Hagberg, M. Bianciardi, B. Maraviglia, Magnetic Resonance Imaging 24 (2006), [25] K. J. Friston, Science 326 (2009), [26] A. M. Cassara, B. Maraviglia, S. Hartwig, L. Trahms, M. Burghoff, Magnetic Resonance Imaging 27(8) (2009),