The contribution to fMRI signals from large draining veins is especially present when single slices, large flip angles, and short TR gradient echo sequences are used (Duyn et al. 1994; Haacke et al. 1994), since these enhance the contribution of the increase in blood flow and blood volume (inflow effect). Gao et al. (1996) have demonstrated that fMRI images more sensitive to the microcirculation in the brain parenchyma may be obtained when the pulse sequence is designed toward minimizing inflow effects and maximizing the BOLD contribution. This can be achieved with multislice, heavily T₂ ^*-weighted single-shot echo-planar images with a long TR (Gao et al. 1996). Therefore, when performing clinical fMRI, the implementation of these imaging parameters should be a prerequisite for maximizing the fMRI signal toward the site of neuronal activity. In doing so, several studies have shown a good correlation between the BOLD fMRI signal and intraoperative mapping, suggesting that the functional-anatomical specificity is adequate for presurgical mapping, provided an optimal acquisition technique is used (Nitschke et al. 1998; Tomczak et al. 2000; Krings 2001b; Stippich et al. 2003; Meier et al. 2013).

The use of higher magnetic field strengths will also decrease the effect of the draining veins (Gati et al. 1997; Moon et al. 2007). The decrease in draining vein influence by increasing field strengths is a result of the field dependence on the alteration in T₂ ^* values due to susceptibility-induced gradients around blood vessels which in turn depend on diffusional averaging. Thus, the change in T₂* is a function of the motion of protons in the brain tissue as well as of the magnitude of the field gradients encountered by the tissue protons. The latter depends on the size of the blood vessels and orientation of these vessels relative to the main magnetic field direction (Ogawa et al. 1993). In this study by Ogawa et al. (1993), it was predicted that the overall effect in susceptibility difference on a T₂ ^*-sensitive imaging technique would be somewhere between linear and quadratic depending on the main magnetic field. In addition, it was reported that at high magnetic fields, the contribution of small vessels is more pronounced than at low magnetic fields (Ogawa et al. 1993; Menon et al. 1995). Thus, with respect to signal intensity changes induced by susceptibility alterations of the blood vessels secondary to neuronal activity, this effect will increase more than linearly with the magnetic field strength, with a larger contribution of the more desired small vessel effect and therefore resulting in advantages toward higher fields (Ugurbil et al. 1999) for distinguishing parenchymal from venous activation. However, higher fields are also more sensitive to other artifacts such as susceptibility-induced image deformations in EPI sequences (see Sect. 4.1), a larger influence of scanner instability (Cohen and DuBois 1999), and physiological effects (Weisskoff 1996) which will be discussed below.

BOLD functional activation maps can also be obtained by using the spin echo (SE)-based T₂-weighted contrast rather than the gradient echo (GRE)-based T₂ ^*-weighted contrast (Duong et al. 2004; Moon et al. 2007). However the observed BOLD effect is much smaller on the T₂-relaxation parameter (SE technique) resulting in a much lower contrast-to-noise ratio (CNR) making this technique less sensitive. On the other hand, the use of the T₂ effect can be preferable if one wants to avoid spurious activation in large veins (Lowe et al. 2000; Uludağ et al. 2009) since SE sequences have a high specificity for microvessels and are therefore less sensitive to susceptibility effects around larger blood vessels in optimized settings (Oja et al. 1999; Uludağ et al. 2009).

Another technique to maximize parenchymal activation compared to venous activation has been demonstrated by Cohen et al. (2004) where hypercapnic normalization of the BOLD activation maps with the aid of CO₂ inhalation has been used to normalize the BOLD-activated areas making them independent of the resting state CBV in the voxels (Cohen et al. 2004). Maximizing the fMRI signal toward the site of neuronal activity can also be achieved by optimizing the mode of stimulation. A study by Rumeur et al. (2000) has shown that different types of sensory stimulation are capable of differentiating primary sensory cortex from the venous draining network (Rumeur et al. 2000). Discontinuous stimulation of a limited skin area elicited activity in the microvasculature of the sensory cortex only, whereas robust continuous stimulation of a larger skin area led to activation not only in functional sites but also in the venous network. The authors claimed that the discontinuous stimulation paradigm induced less effective summation of the hemodynamic response and thus less migration of BOLD contrast to the venous network. As a result resting state fMRI will also be less sensitive to the venous draining network due to the lower mode of activation in the resting state. Also, if the activated region has a rather small surface area (<100 mm²), the oxygenation state in the vein draining the respective area will spread within this vein without dilution only up to 4–5 mm beyond the edge of the activated region (Turner 2002), minimizing the influence of the draining vein signal on presurgical BOLD maps.

Practical tip: In preoperative fMRI, the impact of spurious venous signals is important in order to know which distance to keep between the lesion to be removed and the adjacent functionally active area clear of when removing a lesion. Despite the fact that functionally “safe” resection borders cannot be determined from fMRI studies due to the statistical nature of the activation maps, there are several studies that recommend distances of 1 cm or higher between resection border and functional area as reasonably safe regarding the risk for surgically induced postoperative neurological deficits (see chapters “Task-based presurgical functional MRI in patients with brain tumors” and “Presurgical functional localization possibilities, limitations and validity” for details). This does not apply to the draining venous vessels as they do not represent “real” activated regions, so that here it is merely required to keep clear of the vessels. Practically, it is advised to always acquire anatomical high-resolution images of the patient, preferably contrast enhanced, to depict large blood vessels and to co-register the fMRI activation images with these anatomical images in order to distinguish “activation” in anatomically visible vessels from “activation” within the brain parenchyma. When small very intensely activated patches are observed, it is also recommended to compare these time courses with those of the more diffusely activated parenchymal regions to distinguish both types of activation (see chapter “Task-based presurgical functional MRI in patients with brain tumors”).

Different pathological conditions can attenuate the hemodynamic response which is the source of any fMRI signal. Brain tumor vessels typically lack cerebral autoregulation. Their vasculature is less responsive than that of the surrounding normal tissue. Intracranial space-occupying lesions can alter physiologic conditions; they may induce proliferation of pathologic vessels in the tissue adjacent to the lesion, thus altering the density, size, and topography of the vessels and consequently increase blood volume. The blood-brain barrier may break down within the tumor mass and partially extend into the tissue adjacent to the tumor. The mass effect of the lesion itself and the peritumoral vasogenic edema may change the apparent diffusion coefficient and therefore also cause mechanical vascular compression. If, however, the brain’s ability to autoregulate the flow of blood is completely lost in the brain tissue in the immediate vicinity to the tumor, which may still be functioning, this area may not respond to an increase in neural activity with corresponding increase in blood flow and subsequent BOLD signal (Lüdemann et al. 2006; Chen et al. 2008). The biochemical environment (adenosine 59-triphosphate, pH, glucose, lactate, etc.) and the cortical levels of neurotransmitters in and around gliomas might be altered. Specifically, the release of nitric oxide by reactive astrocytes and macrophages at the normal brain tissue-glioma interface may increase the “resting state” regional cerebral blood flow and thus alter the physiological hemodynamic response (Schreiber et al. 2000; Hund-Georgiadis et al. 2003; Fujiwara et al. 2004; Maravita and Iriki 2004). Similarly, therapeutic drugs administered to the patients may also interact with hemodynamic autoregulation (Seifritz et al. 2000; Braus and Brassen 2005). In regions more distant from the gliomas, where tumor vasculature is not encountered, the release of nitric oxide by reactive astrocytes and macrophages at the normal brain tissue-glioma interface may result in a luxury perfusion and reduced oxygen extraction fraction, resulting and leading to a reduced BOLD contrast enhancement (Schreiber et al. 2000).

It is of utmost importance to realize that the absence of fMRI activity in a particular brain region does not mean that electrical activity within this area does not exist and it is thus safe to surgically remove this brain tissue. We will demonstrate this important point using the following case report: Fig. 1 illustrates fMRI activity during bilateral finger-tapping versus rest in a patient with a left Rolandic tumor (glioma WHO grade II in the postcentral gyrus extending through the central sulcus into the “hand knob” of the precentral gyrus). In the non-lesioned right hemisphere, fMRI activity is observed within the right sensorimotor cortex (SM1; pre- and postcentral gyri), the right premotor cortex (PM), and the right parietal cortex (PP). In contrast, in the lesioned left hemisphere, fMRI activation is only observed anteriorly to the tumor in the left premotor cortex (PM). While this fMRI activation map might be interpreted as an absence of electrical neuronal activity within the left SM1 and PP areas (e.g., due to plastic changes and a takeover of motor function by the ipsilateral non-lesioned hemisphere), the signal time courses clearly show that this is a false conclusion. In the left-sided, tumor-infiltrated SM1 hand representation, the BOLD signals decrease during performance of the motor task and increase during baseline condition (rest), i.e., an inverse BOLD MR signal change as compared to physiological activation. This finding may result from lesion-induced neurovascular uncoupling, where oxygen extraction (cause of the initial dip of the BOLD signal) occurs without an increase in regional cerebral blood flow and volume (rCBF and rCBV), resulting in a steady decrease of fMRI signal despite an increased electrical neuronal activity driving the actual movements. As pointed out in an editorial by Bryan and Kraut (1998), these “negative results” deserve further study (Bryan and Kraut 1998; Sunaert et al. 1998). One may speculate that – depending on the lesion-induced hemodynamic changes in different patients – there could be continuous alterations of BOLD signals from “normal” via “absent” to “inverse.” Possible BOLD alterations should be taken into account when functional lateralization is determined using fMRI as they may lead to an incorrect interpretation of clinical fMRI data.

In some patients with low- or high-grade gliomas, BOLD signal changes can be observed in those gliomas, which in principle should not contain functional tissue. This issue has been investigated in several studies (Skirboll et al. 1996; Schiffbauer et al. 2001; Ganslandt et al. 2004). From these studies using magnetic source imaging methods (which is a combination of EEG and anatomical MRI), it can be concluded that real functional activity might be located within or at the margins of the tumor in a considerable percentage of patients (e.g., 25 % in the study of Schiffbauer et al. (2001), so that only partial resection is possible. The interpretation of this activity at the tumor margin remains ambiguous, either the cortex has been “displaced” by the growing tumor or is near the mark of being invaded by infiltrative tumor. Therefore, activated areas in tumor tissue are not always related to artifacts but could also be “real” functionally active tissue infiltrated by a tumor. If in doubt of the real or artificial nature of the activations, it is advised to combine different techniques (like MEG) to pinpoint the real nature of the BOLD signal change. More information about the combination of these different techniques can be found in chapter “Multimodality in functional neuroimaging”).

Arteriovenous malformations (AVMs) can produce widespread vascular steal effects beyond their nidus that preclude a normal hemodynamic response (for details see chapter “Presurgical functional localization possibilities, limitations and validity”). However, cerebral blood flow reductions do not necessarily cause cerebral dysfunction, as suggested in previous reports. Alterations of the microvascular architecture are prone to exist in the neighborhood of vascular malformations, and neovascularization is characteristic for malignant brain tumors and brain metastases of different pathologies, resulting in a change in the observed BOLD signal in the near neighborhood of the AVM. Also several studies have been published in different patient populations with AVMs reporting a shift of the functionally active regions in the neighborhood of the AVMs (Caramia et al. 2009). Vascular malformations are believed to form during gestation, and the development of these lesions and the associated brain damage due to hemorrhage or ischemia can lead to reorganization not only of the local anatomy but also of the functional cortex (Lehéricy et al. 2002). This can result in a cortical reorganization where the real functionally active region has been shifted into other previously defined functional regions (Maldjian et al. 1996; Fandino et al. 1999). More about brain plasticity has been explained in chapter “Brain plasticity in fMRI &​ DTI”.

The effect of caffeine – a vasoconstrictor – on BOLD signals has been studied by several researchers (Parrish et al. 2001; Laurienti et al. 2002; Chen and Parrish 2009; Rack-Gomer et al. 2009) with different results. In an experiment determining the hemodynamic response function, volunteers were presented a short visual flash to activate primary visual areas and triggering a finger to thumb opposition. They were scanned in two consecutive sessions: before and after drinking three cups of coffee (~250 mg caffeine). The observed hemodynamic response function before and after administration of caffeine demonstrated a large increase (up to 50 %) in BOLD signal after caffeine intake as compared to the control session (Fig. 2a). The explanation for this effect may rely on the different properties of caffeine, i.e., its property to modulate neural activity and neurovascular tone. The influence on neurovascular tone decreases the resting state CBF (Rack-Gomer et al. 2009). As a result the increase in rCBF and respective BOLD signal change during stimulation is higher after intake of caffeine. Caffeine also has an excitatory action on neurons through the blockade of A1 adenosine receptors increasing neural activity following stimulation (Parrish et al. 2001; Laurienti et al. 2002). On the other hand, caffeine can have an influence on the observed resting state connectivity in the brain, which can be a real effect on brain activity or a result of the influence of caffeine on the baseline and reactive cerebral blood flow; therefore, one should be very careful while interpreting resting state fMRI data of patients (Rack-Gomer et al. 2009).

The effect of alcohol on BOLD signals is shown in Fig. 2b. A similar experiment as the one described above has been performed in volunteers before and after the consumption of three glasses of beer (~34 ml of alcohol). The graph demonstrates that alcohol has an adverse effect as compared to caffeine on the hemodynamic response. The effect of alcohol is twofold: First, alcohol is a vasodilator which increases the baseline CBF resulting in a smaller increase in rCBF and hence relative BOLD signal after stimulation (Levin et al. 1998; Seifritz et al. 2000). Second, alcohol also decreases the attention to the task the subjects have to perform and consequently neuronal activation of the stimulated brain areas (Luchtmann et al. 2010).

It is also important to note that various pharmacological agents may potentially alter the BOLD signal. Cocaine, e.g., influences neuronal connectivity (Li et al. 2000; Tomasi et al. 2010), neuronal activation, and cerebral blood flow in the resting state (Breiter et al. 1997; Lee et al. 2003; Lowen et al. 2009). However, the BOLD signal has been reported to remain unaffected after cocaine administration (Gollub et al. 1998). Theophylline has been shown to increase the BOLD signal in the rat primary motor cortex (Morton et al. 2002), a finding which has also been observed in patients with hyposmia where theophylline has been shown to increase odor-induced BOLD responses (Levy et al. 1998). The decrease in cerebral blood flow in the resting condition, due to a vasoconstrictor response, might possibly account for the increased BOLD response after theophylline administration. Furthermore, theophylline has also known neuroexcitatory effects, e.g., as an antagonist of the inhibitory neurotransmitter adenosine. This neuroexcitatory effect of theophylline could increase the number of neurons that are activated in response to a given stimulus, hereby increasing the observed BOLD response (Morton et al. 2002).

On the other hand, nicotine – a drug without effects on local brain hemodynamics as demonstrated in a simple visual task (Jacobsen et al. 2002) – has been demonstrated to increase attention to cognitive tasks both in patients with schizophrenia and in smokers (Kumari et al. 2003; Jacobsen et al. 2004) but also changing the resting state connectivity and possible subsequent stimulus-driven responses (Hahn et al. 2007). It is important to keep in mind that many other pharmacological agents, which have not yet been tested in this respect, may influence the BOLD response and that patients with brain tumors may receive such pharmaceutical products (D’Esposito et al. 2003).

Several studies report on the effect of CO₂ on the resting state BOLD signal and on the BOLD response during activation while inhaling this mixture of gas. CO₂ is a potent vasodilator increasing the BOLD signal. Breathing a mixture of air with 5 % CO₂ increases the measured BOLD signal by 10 % (Kastrup et al. 1998). This effect can have consequences on patient studies as well. A change in the breathing rate during task performance, as a result of excitement and stress at the beginning of the experiment, can occur in some patients. This change in breathing rate alters the oxygen supply to the brain and the CO₂ content in the blood affecting vasodilatation and thus the global BOLD signal in the brain. This can interfere with the task-related BOLD response. When a volunteer is asked to change his breathing rate from normal breathing to hyperventilation and vice versa, the time course of the BOLD signals clearly displays the large effect of hyperventilation on the resting state fMRI signal (Fig. 3). Therefore, in order to minimize effects of anxiousness and the change in breathing rate in clinical fMRI studies, it is very important to instruct and comfort the patients comprehensively and allow them to accommodate themselves to the scanner environment before initiation of the experiment. RsfMRI exams which are not driven by external stimuli (chapter “Presurgical resting state fMRI”) are even more susceptible to changes in breathing rate and cardiac cycle. Thus, careful instruction of the patients, and possibly also the monitoring of functions of the autonomic nervous system (ANS), could improve rsfMRI results (Murphy et al. 2011; Iacovella and Hasson 2011).

Several solutions exist to correct for or to minimize inter-image motion which have been proposed by several researchers. Head movement during the acquisition phase can be restricted by fixation of the head with molds and straps (Fitzsimmons et al. 1997; Edward et al. 2000; Debus et al. 2008), which represents an intermediate level of head fixation. The use of a “bite-bar” (Fig. 7) – a custom-molded dental fixation, regularly used in our institution to restrain head motion while imaging volunteers – provides a highly rigid fixation, but can only be used in a limited number of patients (Dymarkowski et al. 1998). The presence of dental prosthesis interferes, and not all patients tolerate this kind of fixation. Furthermore, safety precautions preclude its use in these patients who are at risk of having epileptic seizures during imaging (Jiang et al. 1995; Freire and Mangin 2001).

Another solution is the use of motion correction algorithms during data post-processing, which are nowadays integrated in most different fMRI analysis software. Motion correction is typically achieved by the rigid realignment of the consecutively acquired images in the data series with the first image (or an arbitrary other image) (Friston et al. 1995, 1996b). If the patient moves with a frequency unrelated to the frequency of the applied stimulation paradigm, this realignment post-processing can successfully separate this motion caused by the patient himself from true activation (Fig. 8). As most motion correction algorithms are intensity based, it is also possible that false motion is observed in the time series which may actually be the result of a large activation at a specific brain region shifting the center of intensity to a certain direction following the activation paradigm (Biswal and Hyde 1997).

However, most of the patient motion is attributed to the applied stimulus (Hill et al. 2000), which is certainly the case if the patient is performing different motor paradigms (e.g., the patient is moving his head at the start of the task for looking at his fingers performing the task). This effect cannot be easily separated from the real functional activation due to their temporal synchrony (Hajnal et al. 1994).

The problem of patient motion could be further reduced in a number of different ways. First, stimulation paradigms can be optimized to minimize head motion, an issue that is currently under investigation. Much attention is given to differences in anatomic-functional information that can be obtained from active versus passive tasks which will inherently reduce motion (Gasser et al. 2004). Also, resting state fMRI does not suffer from task-based patient motion as there is no active task performed.