Functional magnetic resonance imaging or functional MRI (fMRI) is a functional neuroimaging procedure using MRI technology that measures brain activity by detecting changes associated with blood flow.
This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases.
The primary form of fMRI uses the blood-oxygen-level dependent (BOLD) contrast, discovered by Seiji Ogawa. This is a type of specialized brain and body scan used to map neural activity in the brain or spinal cord of humans or other animals by imaging the change in blood flow (hemodynamic response) related to energy use by brain cells.
Since the early 1990s, fMRI has come to dominate brain mapping research because it does not require people to undergo shots, surgery, or to ingest substances, or be exposed to ionising radiation, etc. Other methods of obtaining contrast are arterial spin labeling and diffusion MRI.
The procedure is similar to MRI but uses the change in magnetization between oxygen-rich and oxygen-poor blood as its basic measure. This measure is frequently corrupted by noise from various sources and hence statistical procedures are used to extract the underlying signal. The resulting brain activation can be presented graphically by color-coding the strength of activation across the brain or the specific region studied.
fMRI is used both in the research world, and to a lesser extent, in the clinical world. It can also be combined and complemented with other measures of brain physiology such as EEG and NIRS.
Newer methods which improve both spatial and time resolution are being researched, and these largely use biomarkers other than the BOLD signal. Some companies have developed commercial products such as lie detectors based on fMRI techniques, but the research is not believed to be mature enough for widespread commercialization.
The fMRI concept builds on the earlier MRI scanning technology and the discovery of properties of oxygen-rich blood. MRI brain scans use a strong, permanent, static magnetic field to align nuclei in the brain region being studied.
Another magnetic field, the gradient field, is then applied to spatially locate different nuclei. Finally, a radiofrequency (RF) pulse is played to kick the nuclei to higher magnetization levels, with the effect now depending on where they are located. When the RF field is removed, the nuclei go back to their original states, and the energy they emit is measured with a coil to recreate the positions of the nuclei.
MRI thus provides a static structural view of brain matter. The central thrust behind fMRI was to extend MRI to capture functional changes in the brain caused by neuronal activity. Differences in magnetic properties between arterial (oxygen-rich) and venous (oxygen-poor) blood provided this link.
Since the 1890s it has been known that changes in blood flow and blood oxygenation in the brain (collectively known as hemodynamics) are closely linked to neural activity. When neurons become active, local blood flow to those brain regions increases, and oxygen-rich (oxygenated) blood displaces oxygen-depleted (deoxygenated) blood around 2 seconds later. This rises to a peak over 4–6 seconds, before falling back to the original level (and typically undershooting slightly).
Oxygen is carried by the hemoglobin molecule in red blood cells. Deoxygenated hemoglobin (dHb) is more magnetic (paramagnetic) than oxygenated hemoglobin (Hb), which is virtually resistant to magnetism (diamagnetic). This difference leads to an improved MR signal since the diamagnetic blood interferes with the magnetic MR signal less. This improvement can be mapped to show which neurons are active at a time.
History of fMRI
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. However, although briefly mentioned by William James in 1890, the details and precise workings of this balance and the experiments Mosso performed with it have remained largely unknown until the recent discovery of the original instrument as well as Mosso’s reports by Stefano Sandrone and colleagues.
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.
Mosso’s manuscripts do not provide direct evidence that the balance was really able to measure changes in cerebral blood flow due to cognition, however a modern replication performed by David T Field 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. 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.
The first attempt to detect the regional brain activity using MRI was performed by Belliveau and others at Harvard University using the contrast agent Magnevist, a ferromagnetic substance remaining in the bloodstream after intravenous injection. However, this method is not popular in human fMRI, because any medically unnecessary injection is to a degree unsafe and uncomfortable, and because the agent stays in the blood only for a short time.
Three studies in 1992 were the first to explore using the BOLD contrast in humans. Kenneth Kwong and colleagues, used a gradient-echo Echo Planar Imaging (EPI) sequence at a magnetic field strength of 1.5 T to study activation in the visual cortex. Ogawa and others conducted the 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.
The brain does not store glucose, the primary source of its energy. When neurons become active, getting them back to their original (polarized) state requires actively pumping ions back and forth across the neuronal cell membranes. 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 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.
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.
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.
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 is the TR, which 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.
BOLD Hemodynamic Response
The change in the MR signal from neuronal activity is called the hemodynamic response (HDR). It lags the neuronal events triggering it by 1 to 2 seconds, since it takes that long for the vascular system to respond to the brain’s need for glucose. From this point it typically rises to a peak at about 5 seconds after the stimulus.
If the neurons keep firing, say from a continuous stimulus, the peak spreads to a flat plateau while the neurons stay active. After activity stops, the BOLD signal falls below the original level, the baseline, a phenomenon called the undershoot. Over time the signal recovers to the baseline. There is some evidence that continuous metabolic requirements in a brain region contribute to the undershoot.
This glutamate affects nearby supporting cells, astrocytes, causing a change in calcium ion concentration. This, in turn, releases nitric oxide at the contact point of astrocytes and intermediate-sized blood vessels, the arterioles. Nitric oxide is a vasodilator causing arterioles to expand and draw in more blood.
A single voxel’s response signal over time is called its timecourse. Typically, the unwanted signal, called the noise, from the scanner, random brain activity and similar elements is as big as the signal itself. To eliminate these, fMRI studies repeat a stimulus presentation multiple times.
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 to 1 mm. Smaller voxels contain fewer neurons on average, incorporate less blood flow, and hence have less signal than larger voxels.
Smaller voxels also take longer to scan, 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.
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.
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.
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.
Matching Neural Activity to the BOLD Signal
Researchers have checked the BOLD signal against both signals from implanted electrodes (mostly in monkeys) and signals of field potentials (that is the electric or magnetic field from the brain’s activity, measured outside the skull) from EEG and MEG.
So the BOLD contrast reflects mainly the inputs to a neuron and the neuron’s integrative processing within its body, and less the output firing of neurons. In humans, electrodes can be implanted only in patients who need surgery as treatment, but evidence suggests a similar relationship at least for the auditory cortex and the primary visual cortex. Activation locations detected by BOLD fMRI in cortical areas (brain surface regions) are known to tally with CBF-based functional maps from PET scans.
Some regions just a few millimeters in size, such as the lateral geniculate nucleus (LGN) of the thalamus, which relays visual inputs from the retina to the visual cortex, have been shown to generate the BOLD signal correctly when presented with visual input.
Nearby regions such as the pulvinar nucleus were not stimulated for this task, indicating millimeter resolution for the spatial extent of the BOLD response, at least in thalamic nuclei. In the rat brain, single-whisker touch has been shown to elicit BOLD signals from the somatosensory cortex.
However, the BOLD signal cannot separate feedback and feedforward active networks in a region; the slowness of the vascular response means the final signal is the summed version of the whole region’s network; blood flow is not discontinuous as the processing proceeds.
Also, both inhibitory and excitatory input to a neuron from other neurons sum and contribute to the BOLD signal. Within a neuron these two inputs might cancel out. The BOLD response can also be affected by a variety of factors, including disease, sedation, anxiety, medications that dilate blood vessels, and attention (neuromodulation).
The amplitude of the BOLD signal does not necessarily affect its shape. A higher-amplitude signal may be seen for stronger neural activity, but peaking at the same place as a weaker signal. Also, the amplitude does not necessarily reflect behavioral performance.
A complex cognitive task may initially trigger high-amplitude signals associated with good performance, but as the subject gets better at it, the amplitude may decrease with performance staying the same. This is expected to be due to increased efficiency in performing the task.
The BOLD response across brain regions cannot be compared directly even for the same task, since the density of neurons and the blood-supply characteristics are not constant across the brain. However, the BOLD response can often be compared across subjects for the same brain region and the same task.
More recent characterization of the BOLD signal has used optogenetic techniques in rodents to precisely control neuronal firing while simultaneously monitoring the BOLD response using high field magnets (a technique sometimes referred to as “optofMRI”).
These techniques suggest that neuronal firing is well correlated with the measured BOLD signal including approximately linear summation of the BOLD signal over closely spaced bursts of neuronal firing. Linear summation is an assumption of commonly used event-related fMRI designs.
Physicians use fMRI to assess how risky brain surgery or similar invasive treatment is for a patient and to learn how a normal, diseased or injured brain is functioning. They map the brain with fMRI to identify regions linked to critical functions such as speaking, moving, sensing, or planning. This is useful to plan for surgery and radiation therapy of the brain.
Clinicians also use fMRI to anatomically map the brain and detect the effects of tumors, stroke, head and brain injury, or diseases such as Alzheimer’s.
Clinical use of fMRI still lags behind research use. Patients with brain pathologies are more difficult to scan with fMRI than are young healthy volunteers, the typical research-subject population. Tumors and lesions can change the blood flow in ways not related to neural activity, masking the neural HDR. Drugs such as antihistamines and even caffeine can affect HDR.
Some patients may be suffering from disorders such as compulsive lying, which makes certain studies impossible. It is harder for those with clinical problems to stay still for long. Using head restraints or bite bars may injure epileptics who have a seizure inside the scanner; bite bars may also discomfort those with dental prostheses.
Despite these difficulties, fMRI has been used clinically to map functional areas, check left-right hemispherical asymmetry in language and memory regions, check the neural correlates of a seizure, study how the brain recovers partially from a stroke, test how well a drug or behavioral therapy works, detect the onset of Alzheimer’s, and note the presence of disorders like depression.
Mapping of functional areas and understanding lateralization of language and memory help surgeons avoid removing critical brain regions when they have to operate and remove brain tissue. This is of particular importance in removing tumors and in patients who have intractable temporal lobe epilepsy.
Lesioning tumors requires pre-surgical planning to ensure no functionally useful tissue is removed needlessly. Recovered depressed patients have shown altered fMRI activity in the cerebellum, and this may indicate a tendency to relapse. Pharmacological fMRI, assaying brain activity after drugs are administered, can be used to check how much a drug penetrates the blood–brain barrier and dose vs effect information of the medication.