•Neurovascular Coupling•Draining Veins•Linearity•Pre-undershoot•Post-undershoot•Long duration•Mental Chronometry•Negative Signal Changes•Resting state source•Dead fish activation•Voodoo correlations•Global signal regression•Motion artifacts•The decoding signal•non-neuronal BOLD•relationship to other measures•contrast: spin-echo vs gradient-echo•contrast: SEEP contrast•contrast: diffusion changes•contrast: neuronal currents•contrast: NMR phase imaging•lie detection•correlation ≠ connection•clustering conundrum•reproducibility•dynamic connectivity changes
This will be a chapter in my upcoming book “Functional MRI” in the MIT Press Essential Knowledge Series
Functional MRI is unique in that, in spite of being almost 30 years old as a method, it continues to progress in terms of sophistication of acquisition, hardware, processing, in our understanding of the signal itself. There has been no plateau in any of these areas. In fact, by looking at the literature, one gets the impression that this advancement is accelerating. Every new advance opens the potential range where it might have an impact, allowing new questions about the brain to be addressed.
In spite of its success – perhaps as a result of its success – it has had its share of controversies coincident with methods advancements, new observations, and novel applications. Some controversies have been more contentious than others. Over the years, I’ve been following these controversies and have at times performed research to resolve them or at least better understand them. A good controversy can help to move the field forward as it can focus and motivate groups of people to work on the issue itself, shedding a broader light on the field as these are overcome.
While a few of the controversies or issues of contention have been fully resolved, most remain to some degree unresolved. Understanding fMRI through its controversies allows a deeper appreciation for how the field advances as a whole – and how science really works – the false starts, the corrections, and the various claims made by those with potentially useful pulse sequences, processing methods, or applications. Below is the list of twenty-six major controversies in fMRI – in approximately chronological order.
#1: The Neurovascular coupling debate.
Since the late 1800’s, the general consensus, hypothesized by Roy and Sherrington in 1890 (1), was that activation-induced cerebral flow changes were driven by local changes in metabolic demand. In 1986, a publication by Fox et al. (2)challenged that view, demonstrating that with activation, localized blood flow seemingly increased beyond oxidative metabolic demand, suggesting an “uncoupling” of the hemodynamic response from metabolic demand during activation. Many, including Louis Sokolof, a well-established neurophysiologist at the National Institutes of Health, strongly debated the results. Fox nicely describes this period in history from his perspective in “The coupling controversy” (3).
I remember well, in the early days of fMRI, Dr. Sokolof standing up from the audience to debate Fox on several circumstances, arguing that the flow response should match the metabolic need and there should be no change in oxygenation. He argued that what we are seeing in fMRI is something other than an oxygenation change.
In the pre-fMRI days, I recall not knowing what direction the signal should go – as when I first laid eyes on the impactful video presented by Tom Brady during his plenary lecture on the future of MRI at the Society for Magnetic Resonance (SMR) Meeting in August of 1991, it was not clear from these time series movies of subtracted images the direction he performed the subtraction operation. Was it task minus rest or rest minus task? Did the signal go up or down with activation? I also remember very well, analyzing my first fMRI experiments, expecting to see a decrease in BOLD signal – as Ogawa, in an earlier paper(4), hypothesized that metabolic rate increases would lead to a decrease blood oxygenation thus a darkening of the BOLD signal during brain activation. Instead, all I saw were signal increases. It was Fox’s work that helped me to understand why the BOLD signal should increasewith activation. Flow goes up and oxygen delivery exceeds metabolic need, leading to an increase in blood oxygenation.
While models of neurovascular coupling have improved, we still do not understand the precise need for flow increases. First, we had the “watering the whole garden to feed one thirsty flower” hypothesis which suggested that flow increases matched metabolic need for one area but since vascular control was coarse, the abundant oxygenation was delivered to a wider area than was needed, causing the increase in oxygenation. We also had the “diffusion limited” model, where it was hypothesized that in order to deliver enough oxygen to the furthest neurons from the oxygen supplying vessels, an overabundance of oxygen was needed at the vessel itself since the decrease of oxygen as it diffused from the vessel to the furthest cell was described as an exponential. This theory has fallen a bit from favor as the increases in CMRO2or the degree to which the diffusion of oxygen to tissue from blood is limited tend to be higher than physiologic measures. The alternative to the metabolic need hypothesis involves neurogenic “feed-forward” hypotheses – which still doesn’t get at the “why” of the flow response.
Currently, this is where the field stands. We know that the flow response is robust and consistent. We know that in active areas, oxygenation in healthy brains always increases, however we just don’t understand specifically why it’s necessary. Is it a neurogenic, metabolic, or some other mechanism to satisfy some evolutionary critical need that extends beyond simple need for more oxygen? We are still figuring that out. Nevertheless, it can be said that whatever the reason for this increase in flow, it is fundamentally important, as the BOLD response is stunningly consistent.
#2: The Draining Vein Effect
The question: “what about the draining veins?” I think was first posited at an early fMRI conference by Kamil Ugurbil of the University of Minnesota. Here and for the next several years he alerted the community to the observation that, especially at low field, draining veins are a problem as they smear and distort the fMRI signal change such that it’s hard to know specifically where the underlying neuronal activation is with a high degree of certainty. In the “20 years of fMRI: the science and the stories” special issue of NeuroImage, Ravi Menon writes a thorough narrative of the “Great brain vs vein” debate (5). When the first fMRI papers were published, only one, by Ogawa et al. (6), was at high field – 4 Tesla – and relatively high resolution. In Ogawa’s paper, it was shown that there was a differentiation between veins (very hot spots) and capillaries (more diffuse weaker activation in grey matter). Ravi followed this up with another paper (7)using multi-echo imaging, to show that blood in veins had an intrinsically shorter T2* decay than gray matter at 4T and appeared as dark dots in T2* weighted structural images yet appeared as bright functional hot spots in the 4T functional images. Because of the low SNR and CNR at 1.5T, allowing only the strongest BOLD effects to be seen, and because models suggested that at low field strengths, large vessels contributed the most to the signal, the field worried that all fMRI was looking at was veins – at least at 1.5T.
The problem of large vein effects is prevalent using standard gradient-echo EPI – even at high fields. Simply put, the BOLD signal is directly proportional to the venous blood volume contribution in each voxel. If the venous blood volume is high – as with the case of a vein filling a voxel – then the potential for high BOLD changes is high if there is a blood oxygenation change in the area. At high field, indeed, there is not much intravascular signal left in T2* weighted Gradient-echo sequences, however, the extravascular effect of large vessels still exists. Spin-echo sequences (sensitive to small compartments) still are sensitive to the susceptibility effects around intravascular red blood cells within large vessels – even at 7T where intravascular contribution is reduced. Even with some vein sensitivity, promising high resolution orientation column results have been produced at 7T using gradient-echo and spin-echo sequences (8). The use of arterial spin labeling has potential as a method insensitive to large veins, although the temporal efficiency, intrinsic sensitivity, and brain coverage limitations blunt its utility. Vascular Space Occupancy (VASO), a method sensitive to blood volume changes, has been shown to be exquisitely sensitive to blood volume changes in small vessels and capillaries. Preliminary results have shown clear layer dependent activation using VASO where other approaches have provided less clear delineation(9).
Methods have arisen to identify and mask large vein effects – including thresholding based on percent signal change (large veins tend to fill voxels and thus exhibit a larger fractional signal change), as well as temporal fluctuations (large veins are pulsatile thus exhibit more magnitude and phase noise). While these seem to be workable solutions, they have not been picked up and used extensively. With regard to using the phase variations as a regressor to eliminate pulsatile blood and tissue, perhaps the primary reason for this not being adopted is because standard scanners do not produce these images readily, thus users do not have easy access to this information.
The draining vein issue is least problematic at voxel sizes larger than 2mm, as at these resolutions, mostly the region of activation- as defined as >1 cm “blob” is used and interpreted. Other than enhancing the magnitude of activation, vein effects do not distort these “blobs” thus are typically of no concern for low resolution studies. In fact, the presence of veins helps to amplify the signal in these cases. Spatial smoothing and multi-subject averaging – still commonly practiced – also ameliorate vein effects as they tend to be averaged out as each subject has a spatially variant macroscopic venous structure.
The draining vein problem is most significant where details of high resolution fMRI maps need to be interpreted for understanding small and tortuous activation patterns in the context of layer and column level mapping. So far no fully effective method works at this resolution as the goal is not to mask the voxels containing veins since there may be useful capillary and therefore neuronal effects still within the voxel. We need to eliminate vein effects more effectively on the acquisition side.
#3: The linearity of the BOLD response
The BOLD response is complex and not fully understood. In the early days of fMRI, it was found that BOLD contrast was both linear and nonlinear. The hemodynamic response tends to overestimate activation at very brief (<3 seconds) stimulus durations (10)or at very low stimulus intensities (11). With stimuli that were of duration of 2 seconds or less, the response was much larger than predicted by a linear system. The reasons for these nonlinearities are still not fully understood, however for interpreting transient or weak activations relative to longer duration activation, a clear understanding of the nonlinearity of neurovascular coupling across all activation intensities and durations needs to be well established.
#4: The pre-undershoot
The fMRI pre-undershoot was first observed in the late 90’s. With activation, it was sometimes observed that the fMRI signal, in the first 0.5 second of stimulation, first deflected slightly downwards before it increased (12, 13). Only a few groups were able to replicate this finding in fMRI however it appears to be ubiquitous in optical imaging work on animal models. The hypothesized mechanism is that before the flow response has a chance to start, a more rapid increase in oxidative metabolic rate causes the blood to become transiently less oxygenated. This transient effect is then washed away by the large increase in flow that follows.
Animal studies have demonstrated that this pre-undershoot could be enhanced by decreasing blood pressure. Of the groups that have seen the effect in humans, a handful claim that it is more closely localized to the locations of “true” neuronal activation. These studies were all in the distant past (>15 years ago) and since then, very few papers have come out revisiting the study of the elusive pre-undershoot. It certainly may be that it exists, but the precise characteristics of the stimuli and physiologic state of the subject may be critically important to produce it.
While simulations of the hemodynamic response can readily reproduce this effect (14), the ability to robustly reproduce and modulate this effect experimentally in healthy humans has proven elusive. Until these experiments are possible, this effect remains incompletely understood and not fully characterized.
#5: The post-undershoot
In contrast to the pre-undershoot, the post-undershoot is ubiquitous and has been studied extensively (15). Similar to the pre-undershoot, its origins are still widely debated. The basic observation is that following brain activation, the BOLD signal decreases and then passes below baseline for up to 40 seconds. The hypothesized reasons for this include: 1) a perseveration of elevated blood volume causing the amount of deoxyhemoglobin to remain elevated even though oxygenation and flow are back to baseline levels, 2) a perseveration of an elevated oxidative metabolic rate causing the blood oxygenation to decrease below baseline levels as the flow and total blood volume have returned to baseline states, 3) a post stimulus decrease in flow below baseline levels. A decrease in flow with steady state blood volume and oxidative metabolic rate would cause a decrease in blood oxygenation. Papers have been published arguing for, and showing evidence suggesting each of these three hypotheses, so the mechanism of the post undershoot, as common as it is, stays unresolved. It has also been suggested that if it is perhaps due to a decrease in flow, then this decrease might indicate a refractory period where neuronal inhibition is taking place (16). For now, the physiologic underpinnings of the post-undershoot remain a mystery.
#6: Long duration stimulation effects
This is a controversy that has long been resolved (17). In the early days of fMRI, investigators were performing the basic tests to determine if the response was a reliable indicator of neuronal activity and one of the tests was to determine if, with long duration steady state neuronal activation, the BOLD response remains elevated. A study by Kruger et al came out suggesting that the BOLD response habituated after 5 minutes (18). A counter study came out showing that with a flashing checkerboard on for 25 minutes, the BOLD response and flow response (measured simultaneously) remained elevated (19). It was later concluded that the stimuli in the first study was leading to some degree of attentional and neuronal habituation and not a long duration change in the relationship between the level of BOLD and the level of neuronal activity. Therefore, it is now accepted that as long as neurons are firing, and as long as the brain is in a normal physiologic state, the fMRI signal will remain elevated for the entire duration of activation.
#7: Mental Chronometry with fMRI.
A major topic of study over the entire history of fMRI has been how much temporal resolution can be extracted from the fMRI signal. The fMRI response is relatively slow, taking about two seconds to start to increase and, with sustained activation, takes about 10 seconds to reach a steady-state “on” condition. On cessation, it takes a bit longer to return to baseline – about 10 to 12 seconds, and has a long post-stimulus undershoot lasting up to 40 seconds. In addition to this slow response, it has been shown that the spatial distribution in delay is up to four seconds due to spatial variations in the brain vasculature.
Given this sluggishness and spatial variability of the hemodynamic response, it may initially seem that there wouldn’t be any hope in sub-second temporal resolution. However, the situation is more promising than one would expect. Basic simulations demonstrate that, assuming no spatial variability in the hemodynamic response, and given a typical BOLD response magnitude of 5% and a typical temporal standard deviation of about 1%, after 11 runs of 5 minutes each, a relative delay of 50 to 100ms could be discerned from one area to the next. However, the spatial variation in the hemodynamic response, is plus or minus 2 seconds depending on where one is looking in the brain and depends mostly on what aspect of the underlying vasculature is captured with each voxel. Large veins tend to have longer delays.
Several approaches have attempted to bypass or to calibrate the hemodynamic response. One way of bypassing the slow variable hemodynamic response problem is to modulate the timing of experiment, so the relative onset delays with task timing delays can be observed. This is using the understanding that in each voxel, the hemodynamic response is extremely well behaved and repeatable. Using this approach, relative delays of 500ms to 50 ms have been discerned (20-23). While these measures are not absolute, they are useful in discriminating which areas show delay modulations with specific tasks. This approach has been applied – to 500ms accuracy – to uncover the underlying dynamics and the relative timings of specific regions involved with word rotation and word recognition (23). Multivariate decoding approaches have been able to robustly resolve sub second (and sub-TR) relative delays in the hemodynamic response(24). By slowing down the paradigm itself, Formisano et al. (25)have been able to resolve absolute timing of mental operations down to the order of a second. The fastest brain activation on-off rate that has been able to be resolved has recently been published by Lewis et al.(26)and is in the range of 0.75 Hz. While this is not mental chronometry in the strict sense of the term, it does indicate an upper limit at which high-speed changes in neuronal activation may be extracted.
#8: Negative signal changes.
Negative BOLD signal changes were mostly ignored for the first several years of fMRI as researchers did not know precisely how to interpret them. After several years, with a growing number of negative signal change observations, the issue arose in the field, spawning several hypotheses to explain them. One hypothesis invoked a “steal” effect, where active regions received an increase in blood flow at the expense of adjacent areas which would hypothetically experience a decrease in flow. If flow decreases from adjacent areas, these areas would exhibit a decrease in BOLD signal but not actually be “deactivated.” Another hypothesis was that these were areas that were more active during rest, thus becoming “deactivated” during a task as neuronal activity was re-allocated to other regions of the brain. A third was that they represented regions that were actively inhibited by the task. While in normal healthy subjects, the evidence for the steal effects is scant, the other hypotheses are clear possibilities. In fact, the default mode network was first reported as a network that showed consistent deactivation during most cognitive tasks(27). This network deactivation was also seen in the PET literature(28). A convincing demonstration of neuronal suppression associated with negative BOLD changes was carried out by Shmuel et al (29)showing simultaneous decreased neuronal spiking and decreased fMRI signal in a robustly deactivated ring of visual cortex surrounding activation to a ring annulus. These observations seem to point to the idea that the entire brain is tonically active and able to be inhibited by activity in other areas through several mechanisms. This inhibition is manifest as a decrease in BOLD.
#9: Sources of resting state signal fluctuations
Since the discovery that the resting state signal showed temporal correlations across functionally related regions in the brain, there has been an effort to determine their precise origin as well as their evolutionary purpose. The predominant frequency of these fluctuations is in the range of 0.1 Hz, which was eye opening to the neuroscience community since, previously, most had not considered that neuronally-meaningful fluctuations in brain activity occurred on such a slow time scale. The most popular model for the source of resting state fluctuations is that spontaneously activated regions induce fluctuations in the signal. As measured with EEG, MEG, or ECoG, these spontaneous spikes or local field potentials occur across a wide range of frequencies. When this rapidly changing signal is convolved with a hemodynamic response, the resulting fluctuations approximate the power spectrum of a typical BOLD time series. Direct measures using implanted electrodes combined with BOLD imaging show a temporal correspondence of BOLD fluctuations with spiking activity(30). Recent work with simultaneous calcium imaging – a more direct measure of neuronal activation – has also shown a close correspondence both spatially and temporally with BOLD fluctuations(31), thus strongly suggesting that these spatially and temporally correlated fluctuations are in fact, neuronal. Mention of these studies is only the tip of a very large iceberg of converging evidence that resting state fluctuations are in fact related to ongoing, synchronize, spontaneous neuronal activity.
While the basic neurovascular mechanisms behind resting state fluctuations may be understood to some degree, the mystery of the origins and purpose of these fluctuations remains. To complicate the question further, it appears that there are different types of correlated resting state fluctuations. Some are related to the task being performed. Some may be related to brain “state” or vigilance. Some are completely insensitive to task or vigilance state. It has been hypothesized that the spatially broad, global fluctuations may relate more closely to changes in vigilance or arousal. Some are perhaps specific to a subject, and relatively stable across task, brain state, or vigilance state – reflecting characteristics of an individual’s brain that may change only very slowly over time or with disease. A recent study suggests that resting state networks, as compared in extremely large data sets of many subjects, reveal clear correlations to demographics, life style, and behavior (32).
Regarding the source or purpose of the fluctuations, some models simply state it’s an epiphenomenon of a network at a state of criticality – ready to go into action. The networks have to be spontaneously active to be able to transition easily into engagement. The areas that are typically most engaged together are resultantly fluctuating together during “rest.” In a sense, resting state may be the brain continually priming itself in a readiness state for future engagement. Aside from this issue there is the issue of whether or not there are central “regulators” of resting state fluctuations. Do the resting state fluctuations arise from individual nodes of circuits simply firing on their own or is there a central hub that sends out spontaneous signals to these networks to fire. There has also been growing evidence suggesting that this activity represents more than just a subconscious priming. The default mode network, for instance, has been shown to be central to cognitive activity as rumination and self-directed attention.
Work is ongoing in trying to determine if specific circuits have signature frequency profiles that might help to differentiate them. Work is also ongoing to determine what modulates resting state fluctuations. So far, it’s clear that brain activation tasks, vigilance state, lifestyle, demographics, disease and immediately previous task performance can have an effect. There are currently no definitive conclusions as to the deep origin and purpose of resting state fluctuations.
#10: Dead Fish (false positive) Activation.
In about 2006, a poster at the Organization for Human Brain Mapping was published by Craig Bennett, mostly as a joke (I think) – but with the intent to illustrate the shaky statistical ground that fMRI was standing on with regard to the multiple comparisons problem. This study was picked up by the popular media and went a bit viral. The study showed BOLD activation in a dead salmon’s brain. Here, it was clearly suggested that fMRI based on BOLD contrast has some problems if it shows activation where there should be none. In fact, it was a clear indication of false positives that can happen by chance even if the appropriate statistical tests are not used. The basic problem in creation of statistical maps is that the maps not appropriately normalized to “multiple comparisons.” It’s known, that purely by chance, if enough comparison is made (in this case it’s one comparison every voxel), then some voxels will appear to have significantly changed in signal intensity. Bonferroni corrections and false discovery rate corrections are almost always used and are all available in current statistical packages. Bonferroni is likely too conservative as each voxel is not fully independent. False discovery rate is perhaps closer to the appropriate test. When using these, false activations are minimized, however, they can still occur for other reasons. The structure of the brain and skull, having edges which can enhance any small motion or system instability, resulting in false positives. While this poster brought out a good point and was perhaps among the most cited fMRI works in the popular literature and blogs, it failed to convey a more nuanced and important message, that no matter what statistical test is used, the reality is that the signal and the noise are not fully understood, therefore all are actually approximations of truth, subject to errors. That said, a well-designed study with clear criteria and models of activation as well as appropriately conservative statistical tests, will minimize this false positive effect. In fact, it is likely that we are missing much of what is really going on by using over-simplified models of what we should expect of the fMRI signal.
A recent paper by Gonzalez-Castillo et al (33)showed that with a more open model of what to expect from brain activation, and 9 hours of averaging, nearly all grey matter becomes “active” in some manner. Does this mean that there is no null hypothesis? Likely not, but the true nature of the signal is still not fully understood, and both false positives and false negatives permeate the literature.
#11: Voodoo correlations and double dipping.
In about 2009, Vul et al published a paper that caused a small commotion in the field in that it identified a clear error in neuroimaging data analysis. It listed the papers – some quite high profile – that used this erroneous procedure that resulted in elevated correlations. The basic problem that was identified was that studies were performing circular analysis rather than pure selective analysis(34). A circular analysis is a form of selective analysis in which biases involved with the selection are not taken into account. Analysis is “selective” when a subset of data is first selected before performing secondary analysis on the selected data. This is otherwise known as “double dipping.” Because data always contain noise, the selected subset will never be determined by true effects only. Even if the data have no true effects at all, the selected data will show tendencies that they were selected for.
So, a simple solution to this problem is to analyze the selected regions using independent data (not data that were used to select the regions). Therefore, effects and not noise will replicate. Thus, the results will reflect actual effects without bias due to the influence of noise on the selection.
This was an example of an adroit group of statisticians helping to correct a problem as it was forming in fMRI. Since this paper, the number of papers published with this erroneous approach has sharply diminished.
#12: Global signal regression for time series cleanup
The global signal is obtained simply by averaging up the MRI signal intensity in every voxel of the brain for each time point. For resting state correlation analysis, global variations of the fMRI signal are often considered nuisance effects and are commonly removed(35, 36)by regression of the global signal against the fMRI time series. However the removal of global signal has been shown artifactually cause anticorrelated resting state networks in functional connectivity analyses (37). Before this was known, papers were published showing large anticorrelated networks in the brain and interpreted as large networks that were actively inhibited by the activation of another network. If global regression was not performed, these so called “anti-correlated” networks simply showed minimal correlation with – positive or negative – the spontaneously active network. Studies have shown that the removing the global signal not only induces negative correlation, but distorts positive correlations – leading to errors in interpretation (38). Since then the field has mostly moved away from global signal regression. However, some groups use it as it does clean up the artifactual signal to some degree.
The global signal has been studied directly. Work has shown that it is a direct measure of vigilance as assessed by EEG (39). The monitoring of the global signal may be an effective way to ensure that when the resting state data is collected, subjects are in a similar vigilance state – which can have a strong influence on brain connectivity (40). In general, the neural correlates of the global signal change fluctuations are still not fully understood, however, it appears that the field has reached a consensus that, as a pre-processing step, the global signal should not be removed. Simply removing the global signal will not only induce temporal artifacts that look like interesting effects but will remove potentially useful and neuronally relevant signal.
#13: Motion Artifacts
Functional MRI is extremely sensitive to motion, particularly in voxels that have a large difference in signal intensity relative to adjacent voxels. Typical areas that manifest motion effects are edges, sinuses and ear canals where susceptibility dropout is common. In these areas, even a motion of a fraction of a voxel can induce a large fractional signal change, leading to incorrect results. Motion can be categorized into task correlated, slow, and pulsatile. Work has been performed over the past 27 years to develop methods to avoid or eliminate in acquisition or post processing, motion induced signal changes. In spite of this effort, motion is still a major challenge today. The most difficult kind of motion to eliminate is task-correlated motion that occurs when a subject tenses up or moves during a task or strains to see a display. Other types of motion include slow settling of the head during a scan, rotation of the head, swallowing, pulsation of blood and csf, and breathing induced motion-like effects.
Typical correction for motion is carried out by the use of motion regressors that are obtained by most image registration software. An a hoc method for dealing with motion – that can be quite effective – include visual inspection of the functional images and manually choosing time series signals that clearly contain motion effects that can then be regressed out or “orthogonalized.” Other approaches include image registration and time series “scrubbing.” Scrubbing involves automated detection of “outlier” images and eliminating them from analysis. Other ways of working around motion have included paradigm designs that involve brief tasks such that any motion from the task itself is able to be identified as a rapid change whereas a change in the hemodynamic response is slow, thus allowing the signals to be separable by their temporal signatures.
In recent years, an effort has been made to proactively reduce motion effects by tracking optical sensors positioned on the head, and then feeding the position information back to the imaging gradients such that the gradients themselves are slightly adjusted to maintain a constant head position through changing the location or orientation of the imaging volume. The primary company selling such capability is KinetiCor.
A more direct strategy for dealing with motion is the implementation of more effective methods to keep the head rigid and motionless. This approach has included bite bars and plastic moldable head casts. These have some effectiveness in some subjects but run the risk of being uncomfortable – resulting in abbreviated scanning times or worse, more motion due to active repositioning during the scan due to discomfort of the subject.
Aside from the problem of motion of the head, motion of the abdomen, without any concomitant head motion, can have an effect on the MRI signal. With each breath, the lungs fill with air, altering the difference in susceptibility between the signal in the chest cavity and the outside air. This alteration has an effect on the main magnetic field that can extend all the way into the brain, leading to breathing induced image distortions and signal dropout. This problem is increased at higher fields where the effects of susceptibility differences between tissue are enhanced. Possible solutions to this problem are direct measurement of the magnetic field in the proximity to the head using a “field camera” such as the one sold by the Swiss company called Skope, and then perhaps using these dynamically measured field perturbations as regressors in post processing or by feeding this signal to the gradients and shims prior to data collection in an attempt to compensate.
In resting state fMRI, motion is even more difficult to identify and remove as slow motion or breathing artifacts may have similar temporal signatures. Also, if there are systematic differences between intrinsic motion in specific groups such as children or those with Attention Deficit Disorder (ADD), then interpretation of group differences in resting state fMRI results is particularly problematic as the degree of motion can vary with the degree to which individuals suffer from these disorders.
In high resolution studies, specifically looking at small structures at the base of the brain, motion is a major confound as with each cardiac cycle, the base of the brain physically moves. Solutions to this have included cardiac gating and simple averaging. Gating is promising, however signal changes associated with the inevitably varying TR, which would vary with the cardiac cycle length, need to be accounted for by so far imperfect post processing approaches.
A novel approach to motion elimination has been with the use of multi-echo EPI, allowing the user to differentiate BOLD effects from non-BOLD effects based on the signal fit to a T2* change model.
Another motion artifact is that which arises from through plane movement. If the motion is such that a slightly different head position causes a slightly different slice to be excited (with an RF pulse), then some protons will not experience that RF pulse and will have magnetization that is no longer in equilibrium (achieved by a constant TR). If this happens, even if the slice position is corrected, there will be some residual non-equilibrium signal remaining that will take a few TR’s to get back into equilibrium. Methods have been put forward to correct for this, modeling the T1 effects of the tissue, however these can also eliminate the BOLD signal changes, so this problem still remains.
Lastly, apparent motion can also be caused by scanner drift. As the scanner is running, gradients and gradient amplifiers and RF coils can heat up, causing a drift in the magnetic field as well as the resonance frequency of the RF coils, causing a slow shifting of the image location and quality of image reconstruction. Most vendors have implemented software to account for this, but it is not an ideal solution. It would be better to have a scanner that does not have this instability to begin with.
In general, motion is still a problem in the field and one which every user still struggles with, however it is becoming better managed as we gain greater understanding of the sources of motion, the spatial and temporal signatures of motion artifacts, and the temporal and spatial properties of the BOLD signal itself.
There’s a payoff waiting once complete elimination of motion and non-neuronal physiologic fluctuations in solved. The upper temporal signal to noise of an fMRI time series is no higher than about 120/1 due to physiologic noise setting an upper limit. If this noise source were to be eliminated, then the temporal signal to noise ratio would only be limited by coil sensitivity and field strength, perhaps allowing fMRI time series SNR values to approach 1000/1. This improvement would transform the field.
#14: Basis of the decoding signal
Processing approaches have advanced beyond univariate processing that involves comparison of all signal changes against temporal model with the assumption that there is no spatial relationship between voxels or even blobs of activation. Instead, fine-grained voxel wise patterns of activation in fMRI have been shown to carry previously unappreciated information regarding specific brain activity associated with a region. Using multivariate models which compare the voxel wise pattern of activity associated with each stimulus, detailed information related to such discriminations as visual angle and face identity, as well as object category have been differentiated(41). A model to explain these voxel-specific signal change patterns hypothesizes that while each voxel is not small enough to capture the unique pool of neurons that are selectively activated by specific stimuli, the relative population of neurons that are active in a voxel causes a modulation in the signal that, considered with the array of other uniquely activated vowels, makes a pattern that, while having limited meaningful macroscopic topographic information, conveys information as to what activity the functional area is performing. The proposed mechanism of these multi-voxel signal changes of sub voxel activations, taken as a pattern, convey useful and unique functional information. Another proposed mechanism for these changes is that there remain subtle macroscopic changes that occur.
One study has attempted to answer the question of whether the source of pattern effect mapping is multi-voxel or sub-voxel by testing the performance of pattern effect mapping as the activation maps were spatially smoothed (42). The hypothesis was that if the information were sub-voxel and varied from voxel to voxel, then the performance of the algorithm would diminish with spatial smoothing. If the information was distributed at a macroscopic level, smoothing would improve detection. This study showed that both voxel-wise and multi-voxel information contributed in different areas to the multivariate decoding success, thus while the decoding signal is robust, the origins, are, as with many other fMRI signals, complicated and varying.
#15: Signal change but no neuronal activity?
Several years ago, Sirotin and Das reported that they observed a hemodynamic response where no neuronal activity was present. They recorded simultaneously hemodynamic and electrical signals in an animal model during repeated and expected periodic stimuli. When the stimulus was removed when the animal was expecting it, then there was no neuronal firing but a hemodynamic response remained (43). Following this controversial observation, there were some papers that disputed their claim, suggesting that, in fact, there was, in fact, very subtle electrical activity still present. The hemodynamic response is known to consistently over-estimate neuronal activity for very low level or very brief stimuli. This study appears to be an example of just such an effect, despite what the authors claimed. While there was no clear conclusion to this controversy, the study was not replicated. In general, a claim such as this is extremely difficult to make as it is nearly impossible to show that that something doesn’t exist when one is simply unable to detect it.
#16: Curious relationships to other measures
Over the years the hemodynamic response magnitude has been compared with other neuronal measures such as EEG, PET, and MEG. These measures show comparable effects, yet at times, there have been puzzling discrepancies reported. One example is the following paper (44), comparing the visual checkerboard flashing rate dependence of BOLD and of MEG. At high spatialstimulus frequencies, the flashing visual checkerboard showed similar monotonic relationships between BOLD and MEG. At low spatial frequency, the difference between BOLD and MEG was profound – showing a much stronger BOLD signal than MEG signal. The reasons for this discrepancy are still not understood yet studies like these are important for revealing differences where it is otherwise easy to assume that they are measuring very similar effects.
#17: Contrast mechanisms: spin-echo vs gradient-echo.
Spin-echo sequences are known to be sensitive to susceptibility effects from small compartments and gradient-echo sequences are known to be sensitive to susceptibility effects from compartments of all sizes(45). From this observation it has been incorrectly inferred that spin-echo sequences are be sensitive to capillaries rather than large draining veins. The mistake in this inferences is that the blood within draining veins is not taken into account. Within draining veins are small compartments – red blood cells. So, while spin-echo sequences may be less sensitive to large vessel extravascular effects, they are still sensitive to the intravascular signal in vessels where the blood signal is present.
There have been methods proposed for removing the intravascular signal, such as the application of diffusion gradients that null out any rapidly moving spins. However, spin-echo sequences have lower BOLD contrast than gradient-echo by at least a factor of 2 to 4, and with the addition of diffusion weighting, the contrast almost completely disappears.
Another misunderstanding is that spin-echo EPI is truly a spin-echo. EPI takes time to form an image, having a long “readout” window – at least 20 ms whereas with spin-echo sequences there is only a moment when the echo forms. All other times, the signal is being refocused by gradient-echoes – as with T2* sensitive gradient echo imaging. Therefore, it is nearly impossible in EPI sequences to obtain a perfect spin-echo contrast. Most of spin-echo EPI contrast is actually T2* weighted. “Pure” spin-echo contrast is where the readout window is isolated to just the echo – only obtainable with multi-shot sequences. However, even at high field strengths, spin-echo sequences are considerably less sensitive to BOLD contrast than gradient-echo sequences.
There is hope for “pure” spin-echo sequences at very high field might be effective in eliminating large vessel effects as, at high field, blood T2 rapidly shortens, and therefore the intravascular signal contributes minimally to the functional contrast. Spin-echo sequence have been used at 7T to visualize extremely fine functional activation structures such as ocular dominance and orientation columns (46, 47).
For these reasons, spin-echo sequences have not caught on for all but a small handful of fMRI studies at very high field. If high field is used and a short readout window is employed, then spin-echo sequences may be one of sequences of choice, along with the blood volume sensitive sequence, VASO – for spatial localization of activation.
#18: Contrast mechanisms: SEEP contrast.
Over the years, after tens of thousands of observations of the fMRI response and the testing of various pulse sequence parameters, some investigators have claimed that the signal does not always behave in a manner that is BOLD-like. One interesting example appeared about 15 years ago. Here Stroman et al.(48)was investigating the spinal cord and failed to find a clear BOLD signal. On using a T2-weighted sequence they claimed to see a signal change that did not show a TE dependence and was therefore not T2-based. Rather they claimed that it was based proton density changes – but not perfusion. It was also curiously most prevalent in the spinal cord.
The origin of this signal has not been completely untangled and SEEP contrast has disappeared from the current literature. Those investigating spinal cord activation have been quite successful using standard BOLD contrast.
#19: Contrast Mechanisms: Activation-Induced Diffusion Changes.
Denis LeBihan is a pioneer in fMRI and diffusion imaging. In the early 1990’s, he was an originator of diffusion tensor mapping(49). Later, he advanced the concept of intra-voxel incoherent motion (IVIM)(50, 51). The idea behind IVIM is that with extremely low levels of diffusion weighting, a pulse sequence may become selectively sensitized to randomly, slowly flowing blood rather than free water diffusion. The pseudo-random capillary network supports blood flow patterns that may resemble, imperfectly, rapid diffusion, and thus could be imaged as rapid diffusion using gradients sensitized to this high diffusion rate of random flow rather than pure diffusion. This concept was advanced in the late 1980’s and excited the imaging community in that it suggested that, if MRI were sensitive to capillary perfusion, it could be sensitive to activation-induced changes in perfusion. This contrast mechanism, while theoretically sound, was unable to be clearly demonstrated in practice as relative blood volume is only 2% and sensitization of diffusion weighting to capillary perfusion also, unfortunately, sensitizes the sequences to CSF pulsation and motion in the brain.
Le Bihan emerged several years later with yet another potential functional contrast that is sensitive not to hemodynamics but, hypothetically to activation-induced cell swelling. He claimed that diffusion weighting revealed that on activation, measurable decreases in diffusion coefficient occur in the brain. The proposed mechanism is that with neuronal activation, active neurons swell, thus increasing the intracellular water content. High levels of diffusion weighting are used to detect subtle activation-induced shifts in neuronal water content. A shift in water distribution from extracellular space which has slightly higher diffusion coefficient, to intracellular space which slightly lower diffusion coefficient, would cause an increase in signal in a highly diffusion weighted sequences.
While LeBihan has published these findings(52, 53), the idea has not achieved wide acceptance. First, the effect itself is relatively small and noisy, if present at all. Secondly, there have been papers published demonstrating that the mechanism behind this signal is vascular rather than neuronal (54). Lastly, and perhaps most importantly, many groups, including my own, have tried this approach, coming up with null results. If the technique gives noisy and inconsistent results, it is likely not going to compete with BOLD, regardless of how selective it is to neuronal effects. Of course, it’s always worthwhile to work on advancing such methodology!
#20: Contrast mechanisms: Neuronal Current Imaging.
In the late 90’s it was proposed that MRI is theoretically directly sensitive to electric currents produced by neuronal activity. A current carried by a wire is known to set up magnetic fields around the wire. These magnetic fields when superimposed on the primary magnetic field of the scanner can cause NMR phase shifts or, if the wires are very small and randomly distributed, an NMR phase dispersion – or a signal attenuation. In the brain, dendrites and white matter tracts behave as wires that carry current. Basic models have calculated that the field in the vicinity of these fibers can be as high as 0.2 nT(55). MEG in fact, does detect these subtle magnetic fields as they fall off near the skull. At the skull surface the magnetic fields produced are on the order of 100 fT, inferring that at the source, they are on the order of 0.1 nT.
Over the last 15 years, work has continued around the world toward the goal of using MRI to detect neuronal currents. This work includes the attempts to observe rapid activation-induced phase shifts and magnitude shifts in susceptibility weighted sequences as the superimposed field distortion would cause either a phase shift or dephasing, depending on the geometry. Using these methods, no conclusive results have been reported in vivo. Other attempted methods include “Lorentz” imaging(56). Here the hypothesis is that when current is passing through a nerve within a magnetic field, a net torque is produced, causing the nerve to move just a small amount – potentially detectable by well-timed diffusion weighting. Again, no clear results have emerged. More recent approaches are based on the hypothesis that the longitudinal relaxation properties of spins may be affected if in resonance with intrinsic resonances in the brain such as alpha (10Hz) frequencies(57, 58). Spin-locking approaches that involve adiabatic pulses at these specific frequencies aim to observe neuronal activity based changes in the resonance of the NMR tissue. In such manner, maps of predominant oscillating frequency may be made. Again, such attempts have resulted in suggestive but not conclusive results.
Many are working on methods to detect neuronal currents directly, however, by most calculations, the effect is likely an order of magnitude to small. Coupled with the fact that physiologic noise and even BOLD contrast would tend to overwhelm neuronal current effects, the challenge remains daunting. Such approaches would require extreme acquisition speed (to detect transient effects that cascade over 200 ms throughout the brain), insensitivity to BOLD effects and physiologic noise, yet likely an order of magnitude higher sensitivity overall.
#21: Contrast mechanisms: NMR phase imaging.
The idea of observing phase changes rather than magnitude changes induced by oxygenation changes is not a new one. Back in the early 90’s it was shown that large vessels show a clear NMR phase change with activation. It was suggested that this approach may allow the clean separation of large vessel from tissue effects when interpreting BOLD. In general, it is known that large, relative to a voxel size, changes in susceptibility induce a net phase shift as well.
The concept of observing phase changes has been revisited. Studies have suggested that large activated regions, while mostly showing magnitude changes, may act as a single large susceptibility perturber and induce a large shift in phase. These studies suggest that use of both phase and magnitude information would boost both sensitivity and specificity. This approach has not yet caught on in the field. Perhaps one reason for it not catching on is that vendors typically only provide magnitude reconstruction of the scanner data. Most users simply don’t have access to NMR phase information. It may also turn out that any gains are only very small, thus making the extra effort in obtaining double the data and spending twice the time on pre and post processing not worth the effort.
#22: fMRI for Lie Detection
Over the past 20 years, fMRI has provided evidence that the brain of someone who is lying is active in distinctly different ways than that which is telling the truth. There is additional prefrontal and parietal activity with story fabrication(59). There have been papers that have demonstrated this effect in many different ways. In fact, there exist studies that show not only that lie detection is possible but extraction of the hidden knowledge of the truth is also possible using fMRI(60).
Because of this success, companies touting MRI-based lie detection services have cropped up (e.g. No Lie MRI— http://noliemri.com/and CEPHOS—http://www.cephoscorp.com/). The problem with this is that lie detection has never been fully tested in motivated and at times psychopathic criminals and negative results are more common than not. Inconclusive fMRI based lie detection results, if allowed in court, could potentially bias favor on the defense because, a negative or inconclusive result would provide suggestion that the individual is innocent.
The difference between research success and success or readiness for implementation in public use illustrates much of what the rest of fMRI faces. In real world applications, there are many variables which make fMRI nearly uninterpretable. Generalizing from group studies or highly controlled studies on motivated volunteers to individual studies in patients or criminals is extremely challenging to say the least.
Nevertheless, in spite of scientific, legal, ethical, operational, and social hurdles, machine learning and classification methods may ultimately prove capable in interpreting individual subject activation in association with lie detection with actual criminals or other real-world situations. There may in fact be regions of the brain or patterns of activity that tell truth from lies regardless of whether the person is a psychopathic hardened criminal or a motivated college student volunteer. No one has done those comparison studies yet.
#23: Does Correlation Imply Connectivity?
In resting state and sometimes task based fMRI, the correlation between voxels or regions is calculated. A growing trend is to replace the word correlation with connectivity. The assumption is that a high temporal correlation between disparate regions of the brain directly implies a high level of functional connectivity. It’s also assumed that any changein correlation between these regions implies that there is a corresponding change in connectivity. As a first approximation, these statements may be considered true, however there are many situations in which they are not true.
First, other processes can lead to a high correlation between disparate regions. Bulk movement, cardiac pulsation, and respiration are three primary sources of artifactual correlation. For the most part these are well dealt with – as motion is relatively easily identified and cardiac pulsation is at a distinct frequency (1Hz) that is fortunately far from resting state correlation frequencies (0.1 Hz). However aliased cardiac frequencies and respiration-induced correlations present a bit more of a challenge to remove. Respiration also can create signal changes that show T2* changes, so multi-echo sequences optimized to separate BOLD from non-BOLD effects would be less effective in removing respiration effects. Respiration is however identifiable by the fact that it is more spatially diffuse than correlations between distinct regions. This separation based on spatial pattern and spatial diffusivity is still extremely difficult to perform robustly and without error.
Modulations in correlation can occur for a number of reasons. First, if, when looking at a pair of regions that show correlation, if both signals contain noise (as they all do), an increase in the amplitude of one signal will naturally increase the correlation value, but no “real” increase in connectivity likely occurred. Likewise, if the noise in one or both of the signals is reduced, then, again, there will be a measurable reduction in correlation but likely no change in actual functional connectivity. If the relative latency or shape of one of the responses changes, then a change in correlation will occur without a change in connectivity perhaps. Let’s say an additional frequency was added from another oscillating signal that has nothing to do with the signal driving the “connectivity” between the two regions. If this happens, then again, the correlation between the two signals will be reduced without the connectivity between the regions being altered.
All of the above issues have not been addressed in any systematic manner as we are still in the early days of just figuring out the best ways to cleanly and efficiently extract correlation data. In the future, in order to make more meaningful interpretations of these signals, we will need to control for a the potentially confounding effects mentioned above.
#24: The clustering conundrum.
In 2016, a bombshell paper by Eklund et al (61)identified an error in most of the statistical fMRI packages, including SPM, FSL, and AFNI. Quoting a part of the abstract of that paper:
“In theory, we should find 5% false positives (for a significance threshold of 5%), but instead we found that the most common software packages for fMRI analysis (SPM, FSL, AFNI) can result in false-positive rates of up to 70%. These results question the validity of a number of fMRI studies and may have a large impact on the interpretation of weakly significant neuroimaging results.”
The results showed that the packages correctly inferred statistical significance when considering independent voxels, however, when considering clusters of voxels as a single activation – as most activations are considered “clusters” or spatially correlated activations – the estimations of cluster sizes, smoothness, or the statistical threshold for what constitutes a “significantly activated” cluster were incorrect, leading to incorrectly large clusters.
The implication of their paper was that for the past 15 years, these commonly used packages have overestimated activation extent. For well-designed studies with highly significant results, this would have virtually no effect on how results are interpreted. The same conclusions would be likely have been made. Most studies’ conclusions do not rely on absolute cluster size for their conclusions. Instead they draw conclusions based on the center of mass of activation, or whether a region was activated or not. Again, these studies would not be significantly affected.
Perhaps the greatest implications might be for pooled data in large data sets. If incorrectly large clusters are averaged across thousands of studies, then perhaps errors in interpretation of the extent of activation my crop up.
Unfortunately, the paper also went on to make a highly emotive statement: “These results question the validity of some 40,000 fMRI studies and may have a large impact on the interpretation of neuroimaging results.” Later in a correction, they removed the figure of 40,000 papers. Unfortunately, the media picked up on this figure with the sensational statement that most fMRI studies are “wrong,” which in itself is completely untrue by most definitions of the word “wrong.” The studies may have simply slightly overestimated the size of activation. If a paper relied on this error to reach a conclusion, it was naturally treading on thin statistical ice in the first place and would likely be in question anyway. Most activation (and most of the voxels in these clusters found) are highly significant.
After many popular articles raised concern in the public, the issue eventually died down. Most of the scientists in the field who understood the issue were completely unfazed as the slightly larger clusters did not have any influence on the conclusions drawn by their papers or papers that they relied on for guiding their work.
This brings up an issue related to statistical tests. All statistical tests involve estimates on the nature of the signal and noise, so, by definition are always somewhat “wrong.” They are close enough however. So much more goes into the proper interpretation of results. Most seasoned fMRI practitioners have gained experience in not over interpreting aspects of the results that are not extremely robust.
While the Eklund paper has performed a service to the field by alerting it to a highly propagated error, it also raised false concerns about the high-level of robustness and reproducibility of fMRI in drawing inferences about brain activation. Yes, brain activation extent has been slightly overestimated, but no, most of the papers produced using these analyses need to change in any manner, their conclusions.
#25: The issue of reproducibility
While fMRI is a robust and reproducible method, there is room for improvement. In science in general, it’s been suggested that up to 70% of published results have failed to be reproduced. The reasons for this high fraction may be in part related to what we mean by “successfully reproduced” as well as pressure to publish findings that push the limits of what may be a reasonable level of interpretation. Some might argue that this number is an indication of the health of the scientific process by which a study’s result either lives or dies based on whether it is replicated. If a study is not able to be replicated, the conclusions generally fade away from acceptance.
One researcher, Russ Poldrack, of Stanford is spearheading an effort to increase transparency and reproducibility of fMRI data as he heads the Stanford Center for Reproducible Science. The goal is to increase further the reproducibility of fMRI and therefore move the field towards a more full realization of its potential and less wasted work in the long run. He specifically is encouraging more replication papers, more shared data and code, more “data papers” contributing a valuable data set that may be re-analyzed, and an increased number of “registered studies” where the hypotheses and methods are stated up front before any data are collected. All of these approaches will make the entire process of doing science with fMRI much more transparent and able to be better leveraged by a growing number of researchers as they develop better processing methods or generate more penetrating hypotheses.
#26: Dynamic Connectivity Changes
The most recent “controversy” in the field of fMRI revolves around the interpretation of resting state scans. Typically, in the past, maps of resting state connectivity were created from entire time series lasting up to 30 minutes in length. The assumption was that brain connectivity remained somewhat constant over these long time periods. There is evidence that this assumption is not correct. In fact, it’s been shown that the connectivity profile of the brain changes fluidly over time. Today, fMRI practitioners now regard individual fMRI scans as rich 4D datasets with meaningful functional connectivity dynamics (dFC) requiring updated models able to accommodate this additional time-varying dimension. For example, individual scans today are often described in terms of a limited set of recurring, short-duration (tens of seconds), whole-brain FC configurations named FC states. Metrics describing their dwell times, ordering and frequency of transitions can then be used to quantify different aspects of empirically observed dFC. Many questions remain both about the etiology of empirically observed FC dynamics; as well as regarding the ability of models, such as FC states, to accurately capture behavioral, cognitive and clinically relevant dynamic phenomena.
Despite reports of dFC in resting humans, macaques and rodents, a consensus does not exist regarding the underlying meaning or significance of dFC while at rest. Those who hypothesize it to be neuronally relevant have explored resting dFC in the context of consciousness, development and clinical disorders. These studies have shown how the complexity of dFC decreases as consciousness levels decline; how dynamic inter-regional interactions can be used to predict brain maturity, and how dFC derivatives (e.g., dwell times) can be diagnostically informative for conditions such as schizophrenia, mild cognitive impairment, and autism; to name a few.
Yet, many others have raised valid concerns regarding the ability of current dFC estimation methods to capture neuronally relevant dFC at rest. These concerns include lack of appropriate null models to discern real dynamics from sampling variability, improper pre-processing leading to spurious dynamics, and excessive temporal smoothing (a real concern for sliding window techniques used to estimate FC states) that hinder our ability to capture sharp and rapid transitions of interest (62). Finally, some have even stated that resting dFC is primarily a manifestation of sampling variability, residual head motion artifacts, and fluctuations in sleep state; and as such, mostly irrelevant.
One reason causing such discrepant views is that it is challenging to demonstrate the potential cognitive correlates of resting dFC, given the unconstrained cognitive nature of rest and scarcity of methods to infer the cognitive correlates of whole-brain FC patterns. When subjects are instructed to quietly rest, retrospective reports demonstrate that subjects often engage in a succession of self-paced cognitive processes including inner speech, musical experience, visual imagery, episodic memory recall, future planning, mental manipulation of numbers, and periods of heightened somatosensory sensations. Reconfigurations of FC patterns during rest could, to some extent, be a manifestation of this flow of covert self-paced cognition; even if other factors (e.g., random exploration of cognitive architectures, fluctuations in autonomic system activity and arousal levels, etc.) also contribute.
Research is ongoing in fMRI to determine the neural correlates of dynamic connectivity changes, to determine the best methods for extracting this rich data, as well as to determine how this information may be used to better understand ongoing cognition in healthy and clinical populations and individuals.
In conclusion, other interesting controversies also come to mind – such as BOLD activation in white matter, BOLD tensor mapping, the problem of reverse inference from fMRI data, the difference between mapping connectivity (ahem..correlation) vs mapping fMRI magnitude, inferring brain region causation from different brain regions during activation using fMRI, and other non-BOLD contrast mechanisms such as temperature or elasticity. Challenges also come to mind – including the robust extraction of individual similarities and differences from fMRI data, the problem of how to best parcellate brains, the creation of fMRI-based “biomarkers,” and potential utility of gradient coils.
The field of fMRI has advanced and further defined itself through many of these controversies. In my opinion, these indicate that the field is still growing and is quite robust and dynamic. Also, in the end, a consensus is usually reached – thus pushing the understanding forward. Controversies are healthy and welcomed. Let’s keep them coming!
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6 thoughts on “Twenty-Six Controversies and Challenges in fMRI”
We use Spect and FMRI Scans in addiction treatment, and mental health assessments. I would love to do a guest post on your website.
I think another interesting aspect to add in Part 2 is in regards to the function of astrocytes. While the predominant focus in terms of brain activity is neuronal activation, astrocytes seem entirely capable of inducing fMRI BOLD response on their own… https://www.docdroid.net/PglDCMK/takata2018.pdf
Just something to think about.
Thanks! You bring up a great point. I will definitely include this in part 2, or even use this as a topic in itself in a future posting, as definitely the diverse possible influences on BOLD have not been fully sorted out yet.
You’re welcome! Another key measurement is OREF as well. Metabolism doesnt seem to always be altered by changes in CBF.
This is great, thank you.
Thank you for this review. Really helpful as I immerse myself in our imaging data, and encounter discrepancies in associations between neuralactivstion & behavior