Functional magnetic resonance imaging

During the late 19th century, Angelo Mosso invented the 'human circulation balance', which could non-invasively measure the redistribution of blood during emotional and intellectual activity.[12] However, although briefly mentioned by William James in 1890, the details and precise workings of this balance and the experiments Mosso performed with it remained largely unknown until the recent discovery of the original instrument as well as Mosso's reports by Stefano Sandrone and colleagues.[13] Angelo Mosso investigated several critical variables that are still relevant in modern neuroimaging such as the 'signal-to-noise ratio', the appropriate choice of the experimental paradigm and the need for the simultaneous recording of differing physiological parameters.[13] Mosso's manuscripts do not provide direct evidence that the balance was really able to measure changes in cerebral blood flow due to cognition,[13] however a modern replication performed by David T Field[14] has now demonstrated using modern signal processing techniques unavailable to Mosso that a balance apparatus of this type is able to detect changes in cerebral blood volume related to cognition.

In 1890, Charles Roy and Charles Sherrington first experimentally linked brain function to its blood flow, at Cambridge University.[15] The next step to resolving how to measure blood flow to the brain was Linus Pauling's and Charles Coryell's discovery in 1936 that oxygen-rich blood with Hb was weakly repelled by magnetic fields, while oxygen-depleted blood with dHb was attracted to a magnetic field, though less so than ferromagnetic elements such as iron. Seiji Ogawa at AT&T Bell labs recognized that this could be used to augment MRI, which could study just the static structure of the brain, since the differing magnetic properties of dHb and Hb caused by blood flow to activated brain regions would cause measurable changes in the MRI signal. BOLD is the MRI contrast of dHb, discovered in 1990 by Ogawa. In a seminal 1990 study based on earlier work by Thulborn et al., Ogawa and colleagues scanned rodents in a strong magnetic field (7.0 T) MRI. To manipulate blood oxygen level, they changed the proportion of oxygen the animals breathed. As this proportion fell, a map of blood flow in the brain was seen in the MRI. They verified this by placing test tubes with oxygenated or deoxygenated blood and creating separate images. They also showed that gradient-echo images, which depend on a form of loss of magnetization called T₂^* decay, produced the best images. To show these blood flow changes were related to functional brain activity, they changed the composition of the air breathed by rats, and scanned them while monitoring brain activity with EEG.[16] The first attempt to detect the regional brain activity using MRI was performed by Belliveau and colleagues[17] at Harvard University using the contrast agent Magnevist, a paramagnetic substance remaining in the bloodstream after intravenous injection. However, this method is not popular in human fMRI, because of the inconvenience of the contrast agent injection, and because the agent stays in the blood only for a short time.[18]

Three studies in 1992 were the first to explore using the BOLD contrast in humans. Kenneth Kwong and colleagues, using both gradient-echo and inversion recovery echo-planar imaging (EPI) sequence at a magnetic field strength of 1.5 T published studies showing clear activation of the human visual cortex.[19] The Harvard team thereby showed that both blood flow and blood volume increased locally in activity neural tissue. Ogawa and others conducted a similar study using a higher field (4.0 T) and showed that the BOLD signal depended on T2* loss of magnetization. T2* decay is caused by magnetized nuclei in a volume of space losing magnetic coherence (transverse magnetization) from both bumping into one another and from intentional differences in applied magnetic field strength across locations (field inhomogeneity from a spatial gradient). Bandettini and colleagues used EPI at 1.5 T to show activation in the primary motor cortex, a brain area at the last stage of the circuitry controlling voluntary movements. The magnetic fields, pulse sequences and procedures and techniques used by these early studies are still used in current-day fMRI studies. But today researchers typically collect data from more slices (using stronger magnetic gradients), and preprocess and analyze data using statistical techniques.[20]

The brain does not store glucose, its primary source of energy. When neurons become active, getting them back to their original state of polarization requires actively pumping ions across the neuronal cell membranes, in both directions. The energy for those ion pumps is mainly produced from glucose. More blood flows in to transport more glucose, also bringing in more oxygen in the form of oxygenated hemoglobin molecules in red blood cells. This is from both a higher rate of blood flow and an expansion of blood vessels. The blood-flow change is localized to within 2 or 3 mm of where the neural activity is. Usually the brought-in oxygen is more than the oxygen consumed in burning glucose (it is not yet settled whether most glucose consumption is oxidative), and this causes a net decrease in deoxygenated hemoglobin (dHb) in that brain area's blood vessels. This changes the magnetic property of the blood, making it interfere less with the magnetization and its eventual decay induced by the MRI process.[21]

The cerebral blood flow (CBF) corresponds to the consumed glucose differently in different brain regions. Initial results show there is more inflow than consumption of glucose in regions such as the amygdala, basal ganglia, thalamus and cingulate cortex, all of which are recruited for fast responses. In regions that are more deliberative, such as the lateral frontal and lateral parietal lobes, it seems that incoming flow is less than consumption. This affects BOLD sensitivity.[22]

Hemoglobin differs in how it responds to magnetic fields, depending on whether it has a bound oxygen molecule. The dHb molecule is more attracted to magnetic fields. Hence, it distorts the surrounding magnetic field induced by an MRI scanner, causing the nuclei there to lose magnetization faster via the T₂^* decay. Thus MR pulse sequences sensitive to T₂^* show more MR signal where blood is highly oxygenated and less where it is not. This effect increases with the square of the strength of the magnetic field. The fMRI signal hence needs both a strong magnetic field (1.5 T or higher) and a pulse sequence such as EPI, which is sensitive to T₂^* contrast.[23]

The physiological blood-flow response largely decides the temporal sensitivity, that is how accurately we can measure when neurons are active, in BOLD fMRI. The basic time resolution parameter (sampling time) is designated TR; the TR dictates how often a particular brain slice is excited and allowed to lose its magnetization. TRs could vary from the very short (500 ms) to the very long (3 s). For fMRI specifically, the hemodynamic response lasts over 10 seconds, rising multiplicatively (that is, as a proportion of current value), peaking at 4 to 6 seconds, and then falling multiplicatively. Changes in the blood-flow system, the vascular system, integrate responses to neuronal activity over time. Because this response is a smooth continuous function, sampling with ever-faster TRs does not help; it just gives more points on the response curve obtainable by simple linear interpolation anyway. Experimental paradigms such as staggering when a stimulus is presented at various trials can improve temporal resolution, but reduces the number of effective data points obtained.[24]

Spatial resolution of an fMRI study refers to how well it discriminates between nearby locations. It is measured by the size of voxels, as in MRI. A voxel is a three-dimensional rectangular cuboid, whose dimensions are set by the slice thickness, the area of a slice, and the grid imposed on the slice by the scanning process. Full-brain studies use larger voxels, while those that focus on specific regions of interest typically use smaller sizes. Sizes range from 4 to 5 mm, or with laminar resolution fMRI (lfMRI), to submillimeter.[28] Smaller voxels contain fewer neurons on average, incorporate less blood flow, and hence have less signal than larger voxels. Smaller voxels imply longer scanning times, since scanning time directly rises with the number of voxels per slice and the number of slices. This can lead both to discomfort for the subject inside the scanner and to loss of the magnetization signal. A voxel typically contains a few million neurons and tens of billions of synapses, with the actual number depending on voxel size and the area of the brain being imaged.[29]

The vascular arterial system supplying fresh blood branches into smaller and smaller vessels as it enters the brain surface and within-brain regions, culminating in a connected capillary bed within the brain. The drainage system, similarly, merges into larger and larger veins as it carries away oxygen-depleted blood. The dHb contribution to the fMRI signal is from both the capillaries near the area of activity and larger draining veins that may be farther away. For good spatial resolution, the signal from the large veins needs to be suppressed, since it does not correspond to the area where the neural activity is. This can be achieved either by using strong static magnetic fields or by using spin-echo pulse sequences.[30] With these, fMRI can examine a spatial range from millimeters to centimeters, and can hence identify Brodmann areas (centimers), subcortical nuclei such as the caudate, putamen and thalamus, and hippocampal subfields such as the combined dentate gyrus/CA3, CA1, and subiculum.[31]

Temporal resolution is the smallest time period of neural activity reliably separated out by fMRI. One element deciding this is the sampling time, the TR. Below a TR of 1 or 2 seconds, however, scanning just generates sharper HDR curves, without adding much additional information (e.g. beyond what is alternatively achieved by mathematically interpolating the curve gaps at a lower TR). Temporal resolution can be improved by staggering stimulus presentation across trials. If one-third of data trials are sampled normally, one-third at 1 s, 4 s, 7 s and so on, and the last third at 2 s, 5 s and 8 s, the combined data provide a resolution of 1 s, though with only one-third as many total events.

The time resolution needed depends on brain processing time for various events. An example of the broad range here is given by the visual processing system. What the eye sees is registered on the photoreceptors of the retina within a millisecond or so. These signals get to the primary visual cortex via the thalamus in tens of milliseconds. Neuronal activity related to the act of seeing lasts for more than 100 ms. A fast reaction, such as swerving to avoid a car crash, takes around 200 ms. By about half-a-second, awareness and reflection of the incident sets in. Remembering a similar event may take a few seconds, and emotional or physiological changes such as fear arousal may last minutes or hours. Learned changes, such as recognizing faces or scenes, may last days, months, or years. Most fMRI experiments study brain processes lasting a few seconds, with the study conducted over some tens of minutes. Subjects may move their heads during that time, and this head motion needs to be corrected for. So does drift in the baseline signal over time. Boredom and learning may modify both subject behavior and cognitive processes.[32]

When a person performs two tasks simultaneously or in overlapping fashion, the BOLD response is expected to add linearly. This is a fundamental assumption of many fMRI studies that is based on the principle that continuously differentiable systems can be expected to behave linearly when perturbations are small; they are linear to first order. Linear addition means the only operation allowed on the individual responses before they are combined (added together) is a separate scaling of each. Since scaling is just multiplication by a constant number, this means an event that evokes, say, twice the neural response as another, can be modeled as the first event presented twice simultaneously. The HDR for the doubled-event is then just double that of the single event.

To the extent that the behavior is linear, the time course of the BOLD response to an arbitrary stimulus can be modeled by convolution of that stimulus with the impulse BOLD response. Accurate time course modeling is important in estimating the BOLD response magnitude.[33][34]

This strong assumption was first studied in 1996 by Boynton and colleagues, who checked the effects on the primary visual cortex of patterns flickering 8 times a second and presented for 3 to 24 seconds. Their result showed that when visual contrast of the image was increased, the HDR shape stayed the same but its amplitude increased proportionally. With some exceptions, responses to longer stimuli could also be inferred by adding together the responses for multiple shorter stimuli summing to the same longer duration. In 1997, Dale and Buckner tested whether individual events, rather than blocks of some duration, also summed the same way, and found they did. But they also found deviations from the linear model at time intervals less than 2 seconds.

A source of nonlinearity in the fMRI response is from the refractory period, where brain activity from a presented stimulus suppresses further activity on a subsequent, similar, stimulus. As stimuli become shorter, the refractory period becomes more noticeable. The refractory period does not change with age, nor do the amplitudes of HDRs. The period differs across brain regions. In both the primary motor cortex and the visual cortex, the HDR amplitude scales linearly with duration of a stimulus or response. In the corresponding secondary regions, the supplementary motor cortex, which is involved in planning motor behavior, and the motion-sensitive V5 region, a strong refractory period is seen and the HDR amplitude stays steady across a range of stimulus or response durations. The refractory effect can be used in a way similar to habituation to see what features of a stimulus a person discriminates as new.[35] Further limits to linearity exist because of saturation: with large stimulation levels a maximum BOLD response is reached.

Noise is unwanted changes to the MR signal from elements not of interest to the study. The five main sources of noise in fMRI are thermal noise, system noise, physiological noise, random neural activity and differences in both mental strategies and behavior across people and across tasks within a person. Thermal noise multiplies in line with the static field strength, but physiological noise multiplies as the square of the field strength. Since the signal also multiplies as the square of the field strength, and since physiological noise is a large proportion of total noise, higher field strengths above 3 T do not always produce proportionately better images.

Heat causes electrons to move around and distort the current in the fMRI detector, producing thermal noise. Thermal noise rises with the temperature. It also depends on the range of frequencies detected by the receiver coil and its electrical resistance. It affects all voxels similarly, independent of anatomy.[58]

System noise is from the imaging hardware. One form is scanner drift, caused by the superconducting magnet's field drifting over time. Another form is changes in the current or voltage distribution of the brain itself inducing changes in the receiver coil and reducing its sensitivity. A procedure called impedance matching is used to bypass this inductance effect. There could also be noise from the magnetic field not being uniform. This is often adjusted for by using shimming coils, small magnets physically inserted, say into the subject's mouth, to patch the magnetic field. The nonuniformities are often near brain sinuses such as the ear and plugging the cavity for long periods can be discomfiting. The scanning process acquires the MR signal in k-space, in which overlapping spatial frequencies (that is repeated edges in the sample's volume) are each represented with lines. Transforming this into voxels introduces some loss and distortions.[59]

Physiological noise is from head and brain movement in the scanner from breathing, heart beats, or the subject fidgeting, tensing, or making physical responses such as button presses. Head movements cause the voxel-to-neurons mapping to change while scanning is in progress. Since fMRI is acquired in slices, after movement, a voxel continues to refer to the same absolute location in space while the neurons underneath it would have changed. Another source of physiological noise is the change in the rate of blood flow, blood volume, and use of oxygen over time. This last component contributes to two-thirds of physiological noise, which, in turn, is the main contributor to total noise.[60]

Even with the best experimental design, it is not possible to control and constrain all other background stimuli impinging on a subject—scanner noise, random thoughts, physical sensations, and the like. These produce neural activity independent of the experimental manipulation. These are not amenable to mathematical modeling and have to be controlled by the study design.

A person's strategies to respond or react to a stimulus, and to solve problems, often change over time and over tasks. This generates variations in neural activity from trial to trial within a subject. Across people too neural activity differs for similar reasons. Researchers often conduct pilot studies to see how participants typically perform for the task under consideration. They also often train subjects how to respond or react in a trial training session prior to the scanning one.[61]

The scanner platform generates a 3 D volume of the subject's head every TR. This consists of an array of voxel intensity values, one value per voxel in the scan. The voxels are arranged one after the other, unfolding the three-dimensional structure into a single line. Several such volumes from a session are joined together to form a 4 D volume corresponding to a run, for the time period the subject stayed in the scanner without adjusting head position. This 4 D volume is the starting point for analysis. The first part of that analysis is preprocessing.

The first step in preprocessing is conventionally slice timing correction. The MR scanner acquires different slices within a single brain volume at different times, and hence the slices represent brain activity at different timepoints. Since this complicates later analysis, a timing correction is applied to bring all slices to the same timepoint reference. This is done by assuming the timecourse of a voxel is smooth when plotted as a dotted line. Hence the voxel's intensity value at other times not in the sampled frames can be calculated by filling in the dots to create a continuous curve.

Head motion correction is another common preprocessing step. When the head moves, the neurons under a voxel move and hence its timecourse now represents largely that of some other voxel in the past. Hence the timecourse curve is effectively cut and pasted from one voxel to another. Motion correction tries different ways of undoing this to see which undoing of the cut-and-paste produces the smoothest timecourse for all voxels. The undoing is by applying a rigid-body transform to the volume, by shifting and rotating the whole volume data to account for motion. The transformed volume is compared statistically to the volume at the first timepoint to see how well they match, using a cost function such as correlation or mutual information. The transformation that gives the minimal cost function is chosen as the model for head motion. Since the head can move in a vastly varied number of ways, it is not possible to search for all possible candidates; nor is there right now an algorithm that provides a globally optimal solution independent of the first transformations we try in a chain.

Distortion corrections account for field nonuniformities of the scanner. One method, as described before, is to use shimming coils. Another is to recreate a field map of the main field by acquiring two images with differing echo times. If the field were uniform, the differences between the two images also would be uniform. Note these are not true preprocessing techniques since they are independent of the study itself. Bias field estimation is a real preprocessing technique using mathematical models of the noise from distortion, such as Markov random fields and expectation maximization algorithms, to correct for distortion.

In general, fMRI studies acquire both many functional images with fMRI and a structural image with MRI. The structural image is usually of a higher resolution and depends on a different signal, the T1 magnetic field decay after excitation. To demarcate regions of interest in the functional image, one needs to align it with the structural one. Even when whole-brain analysis is done, to interpret the final results, that is to figure out which regions the active voxels fall in, one has to align the functional image to the structural one. This is done with a coregistration algorithm that works similar to the motion-correction one, except that here the resolutions are different, and the intensity values cannot be directly compared since the generating signal is different.

Typical MRI studies scan a few different subjects. To integrate the results across subjects, one possibility is to use a common brain atlas, and adjust all the brains to align to the atlas, and then analyze them as a single group. The atlases commonly used are the Talairach one, a single brain of an elderly woman created by Jean Talairach, and the Montreal Neurological Institute (MNI) one. The second is a probabilistic map created by combining scans from over a hundred individuals. This normalization to a standard template is done by mathematically checking which combination of stretching, squeezing, and warping reduces the differences between the target and the reference. While this is conceptually similar to motion correction, the changes required are more complex than just translation and rotation, and hence optimization even more likely to depend on the first transformations in the chain that is checked.

Temporal filtering is the removal of frequencies of no interest from the signal. A voxel's intensity change over time can be represented as the sum of a number of different repeating waves with differing periods and heights. A plot with these periods on the x-axis and the heights on the y-axis is called a power spectrum, and this plot is created with the Fourier transform technique. Temporal filtering amounts to removing the periodic waves not of interest to us from the power spectrum, and then summing the waves back again, using the inverse Fourier transform to create a new timecourse for the voxel. A high-pass filter removes the lower frequencies, and the lowest frequency that can be identified with this technique is the reciprocal of twice the TR. A low-pass filter removes the higher frequencies, while a band-pass filter removes all frequencies except the particular range of interest.

Smoothing, or spatial filtering, is the idea of averaging the intensities of nearby voxels to produce a smooth spatial map of intensity change across the brain or region of interest. The averaging is often done by convolution with a Gaussian filter, which, at every spatial point, weights neighboring voxels by their distance, with the weights falling exponentially following the bell curve. If the true spatial extent of activation, that is the spread of the cluster of voxels simultaneously active, matches the width of the filter used, this process improves the signal-to-noise ratio. It also makes the total noise for each voxel follow a bell-curve distribution, since adding together a large number of independent, identical distributions of any kind produces the bell curve as the limit case. But if the presumed spatial extent of activation does not match the filter, signal is reduced.[62]

These fMRI images are from a study showing parts of the brain lighting up on seeing houses and other parts on seeing faces. The 'r' values are correlations, with higher positive or negative values indicating a stronger relationship (i.e., a better match).

One common approach to analysing fMRI data is to consider each voxel separately within the framework of the general linear model. The model assumes, at every time point, that the HDR is equal to the scaled and summed version of the events active at that point. A researcher creates a design matrix specifying which events are active at any timepoint. One common way is to create a matrix with one column per overlapping event, and one row per time point, and to mark it if a particular event, say a stimulus, is active at that time point. One then assumes a specific shape for the HDR, leaving only its amplitude changeable in active voxels. The design matrix and this shape are used to generate a prediction of the exact HDR response of the voxel at every timepoint, using the mathematical procedure of convolution. This prediction does not include the scaling required for every event before summing them.

The basic model assumes the observed HDR is the predicted HDR scaled by the weights for each event and then added, with noise mixed in. This generates a set of linear equations with more equations than unknowns. A linear equation has an exact solution, under most conditions, when equations and unknowns match. Hence one could choose any subset of the equations, with the number equal to the number of variables, and solve them. But, when these solutions are plugged into the left-out equations, there will be a mismatch between the right and left sides, the error. The GLM model attempts to find the scaling weights that minimize the sum of the squares of the error. This method is provably optimal if the error were distributed as a bell curve, and if the scaling-and-summing model were accurate. For a more mathematical description of the GLM model, see generalized linear models.

The GLM model does not take into account the contribution of relationships between multiple voxels. Whereas GLM analysis methods assess whether a voxel or region's signal amplitude is higher or lower for one condition than another, newer statistical models such as multi-voxel pattern analysis (MVPA), utilize the unique contributions of multiple voxels within a voxel-population. In a typical implementation, a classifier or more basic algorithm is trained to distinguish trials for different conditions within a subset of the data. The trained model is then tested by predicting the conditions of the remaining (independent) data. This approach is most typically achieved by training and testing on different scanner sessions or runs. If the classifier is linear, then the training model is a set of weights used to scale the value in each voxel before summing them to generate a single number that determines the condition for each testing set trial. More information on training and testing classifiers is at statistical classification.[63]

If the baseline condition is too close to maximum activation, certain processes may not be represented appropriately.[70] Another limitation on experimental design is head motion, which can lead to artificial intensity changes of the fMRI signal.[70]

In a block design, two or more conditions are alternated by blocks. Each block will have a duration of a certain number of fMRI scans and within each block only one condition is presented. By making the conditions differ in only the cognitive process of interest, the fMRI signal that differentiates the conditions should represent this cognitive process of interest. This is known as the subtraction paradigm.[71] The increase in fMRI signal in response to a stimulus is additive. This means that the amplitude of the hemodynamic response (HDR) increases when multiple stimuli are presented in rapid succession. When each block is alternated with a rest condition in which the HDR has enough time to return to baseline, a maximum amount of variability is introduced in the signal. As such, we conclude that block designs offer considerable statistical power.[72][73] There are however severe drawbacks to this method, as the signal is very sensitive to signal drift, such as head motion, especially when only a few blocks are used. Another limiting factor is a poor choice of baseline, as it may prevent meaningful conclusions from being drawn. There are also problems with many tasks lacking the ability to be repeated. Since within each block only one condition is presented, randomization of stimulus types is not possible within a block. This makes the type of stimulus within each block very predictable. As a consequence, participants may become aware of the order of the events.[72][73]

Event-related designs allow more real world testing, however, the statistical power of event related designs is inherently low, because the signal change in the BOLD fMRI signal following a single stimulus presentation is small.[74][75]

Both block and event-related designs are based on the subtraction paradigm, which assumes that specific cognitive processes can be added selectively in different conditions. Any difference in blood flow (the BOLD signal) between these two conditions is then assumed to reflect the differing cognitive process. In addition, this model assumes that a cognitive process can be selectively added to a set of active cognitive processes without affecting them.[71]

The brain is never completely at rest. It never stops functioning and firing neuronal signals, as well as using oxygen as long as the person in question is alive. In fact, in Stark and Squire's, 2001 study[76] When zero is not zero: The problem of ambiguous baseline conditions in fMRI, activity in the medial temporal lobe (as well as in other brain regions) was substantially higher during rest than during several alternative baseline conditions. The effect of this elevated activity during rest was to reduce, eliminate, or even reverse the sign of the activity during task conditions relevant to memory functions. These results demonstrate that periods of rest are associated with significant cognitive activity and are therefore not an optimal baseline for cognition tasks. In order to discern baseline and activation conditions it is necessary to interpret a lot of information. This includes situations as simple as breathing. Periodic blocks may result in identical data of other variance in the data if the person breathes at a regular rate of 1 breath/5sec, and the blocks occur every 10s, thus impairing the data.

Neuroimaging methods such as fMRI and MRI offer a measure of the activation of certain brain areas in response to cognitive tasks engaged in during the scanning process. Data obtained during this time allow cognitive neuroscientists to gain information regarding the role of particular brain regions in cognitive function.[77] However, an issue arises when certain brain regions are alleged by researchers to identify the activation of previously labeled cognitive processes.[78] Poldrack[79] clearly describes this issue:

The usual kind of inference that is drawn from neuroimaging data is of the form ‘if cognitive process X is engaged, then brain area Z is active.’ Perusal of the discussion sections of a few fMRI articles will quickly reveal, however, an epidemic of reasoning taking the following form:

(1) In the present study, when task comparison A was presented, brain area Z was active.

(2) In other studies, when cognitive process X was putatively engaged, then brain area Z was active.

(3) Thus, the activity of area Z in the present study demonstrates engagement of cognitive process X by task comparison A.

This is a ‘reverse inference’, in that it reasons backwards from the presence of brain activation to the engagement of a particular cognitive function.

Reverse inference demonstrates the logical fallacy of affirming what you just found, although this logic could be supported by instances where a certain outcome is generated solely by a specific occurrence. With regard to the brain and brain function it is seldom that a particular brain region is activated solely by one cognitive process.[79] Some suggestions to improve the legitimacy of reverse inference have included both increasing the selectivity of response in the brain region of interest and increasing the prior probability of the cognitive process in question.[79] However, Poldrack[77] suggests that reverse inference should be used merely as a guide to direct further inquiry rather than a direct means to interpret results.

Forward inference is a data driven method that uses patterns of brain activation to distinguish between competing cognitive theories. It shares characteristics with cognitive psychology's dissociation logic and philosophy's forward chaining. For example, Henson[80] discusses forward inference's contribution to the "single process theory vs. dual process theory" debate with regard to recognition memory. Forward inference supports the dual process theory by demonstrating that there are two qualitatively different brain activation patterns when distinguishing between "remember vs. know judgments". The main issue with forward inference is that it is a correlational method. Therefore, one cannot be completely confident that brain regions activated during cognitive process are completely necessary for that execution of those processes.[77] In fact, there are many known cases that demonstrate just that. For example, the hippocampus has been shown to be activated during classical conditioning,[81] however lesion studies have demonstrated that classical conditioning can occur without the hippocampus.[82]

MRI, in general, has better spatial resolution than EEG and MEG, but not as good a resolution as invasive procedures such as single-unit electrodes. While typical resolutions are in the millimeter range, ultra-high-resolution MRI or MR spectroscopy works at a resolution of tens of micrometers. It uses 7 T fields, small-bore scanners that can fit small animals such as rats, and external contrast agents such as fine iron oxide. Fitting a human requires larger-bore scanners, which make higher fields strengths harder to achieve, especially if the field has to be uniform; it also requires either internal contrast such as BOLD or a non-toxic external contrast agent unlike iron oxide.

Parallel imaging is another technique to improve spatial resolution. This uses multiple coils for excitation and reception. Spatial resolution improves as the square root of the number of coils used. This can be done either with a phased array where the coils are combined in parallel and often sample overlapping areas with gaps in the sampling or with massive coil arrays, which are a much denser set of receivers separate from the excitation coils. These, however, pick up signals better from the brain surface, and less well from deeper structures such as the hippocampus.

Temporal resolution of fMRI is limited by: (1) the feedback mechanism that raises the blood flow operating slowly; (2) having to wait till net magnetization recovers before sampling a slice again; and (3) having to acquire multiple slices to cover the whole brain or region of interest. Advanced techniques to improve temporal resolution address these issues. Using multiple coils speeds up acquisition time in exact proportion to the coils used. Another technique is to decide which parts of the signal matter less and drop those. This could be either those sections of the image that repeat often in a spatial map (that is small clusters dotting the image periodically) or those sections repeating infrequently (larger clusters). The first, a high-pass filter in k-space, has been proposed by Gary H. Glover and colleagues at Stanford. These mechanisms assume the researcher has an idea of the expected shape of the activation image.

Typical gradient-echo EPI uses two gradient coils within a slice, and turns on first one coil and then the other, tracing a set of lines in k-space. Turning on both gradient coils can generate angled lines, which cover the same grid space faster. Both gradient coils can also be turned on in a specific sequence to trace a spiral shape in k-space. This spiral imaging sequence acquires images faster than gradient-echo sequences, but needs more math transformations (and consequent assumptions) since converting back to voxel space requires the data be in grid form (a set of equally spaced points in both horizontal and vertical directions).

BOLD contrast depends on blood flow, which is both sluggish in response to stimulus and subject to noisy influences. Other biomarkers now looked at to provide better contrast include temperature, acidity/alkalinity (pH), calcium-sensitive agents, neuronal magnetic field, and the Lorentz effect. Temperature contrast depends on changes in brain temperature from its activity. The initial burning of glucose raises the temperature, and the subsequent inflow of fresh, cold blood lowers it. These changes alter the magnetic properties of tissue. Since the internal contrast is too difficult to measure, external agents such thulium compounds are used to enhance the effect. Contrast based on pH depends on changes in the acid/alkaline balance of brain cells when they go active. This too often uses an external agent. Calcium-sensitive agents make MRI more sensitive to calcium concentrations, with calcium ions often being the messengers for cellular signalling pathways in active neurons. Neuronal magnetic field contrast measures the magnetic and electric changes from neuronal firing directly. Lorentz-effect imaging tries to measure the physical displacement of active neurons carrying an electric current within the strong static field.[94]

• EMRF/TRTF (Peter A. Rinck, ed.), Magnetic Resonance: A peer-reviewed, critical introduction (A free access online textbook)
• Joseph P. Hornak, The basics of MRI (online)
• Richard B. Buxton, Introduction to functional magnetic resonance imaging: Principles and techniques, Cambridge University Press, 2002, ISBN 0-521-58113-3
• Roberto Cabeza and Alan Kingstone, Editors, Handbook of Functional Neuroimaging of Cognition, Second Edition, MIT Press, 2006, ISBN 0-262-03344-5
• Huettel, S. A.; Song, A. W.; McCarthy, G., Functional Magnetic Resonance Imaging Second Edition, 2009, Massachusetts: Sinauer, ISBN 978-0-87893-286-3