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. 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. Three studies in 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.
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Ogawa and others conducted a similar study using a higher field 4. Bandettini and colleagues used EPI at 1. 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, 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. 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. 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.
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The dHb molecule is more attracted to magnetic fields. This effect increases with the square of the strength of the magnetic field. The fMRI signal hence needs both a strong magnetic field 1. 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. 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. It lags the neuronal events triggering it by a couple of seconds, since it takes a while for the vascular system to respond to the brain's need for glucose.
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.
The mechanism by which the neural system provides feedback to the vascular system of its need for more glucose is partly the release of glutamate as part of neuron firing.
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. 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. 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. 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. Temporal resolution can be improved by staggering stimulus presentation across trials.
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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. 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.
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  .. 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 , Dale and Buckner tested whether individual events, rather than blocks of some duration, also summed the same way, and found they did.
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.
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As stimuli become shorter, the refractory period becomes more noticeable. The refractory period does not change with age, nor do the amplitudes of HDRs [ citation needed ]. 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. 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. The local field potential, which includes both post-neuron-synaptic activity and internal neuron processing, better predicts the BOLD signal.
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. 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 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. 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".
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 , and developmental disabilities such as Autism etc.. Clinical use of fMRI still lags behind research use. 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. 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. Research is primarily performed in non-human primates such as the rhesus macaque. These studies can be used both to check or predict human results and to validate the fMRI technique itself. But the studies are difficult because it is hard to motivate an animal to stay still and typical inducements such as juice trigger head movement while the animal swallows it.
It is also expensive to maintain a colony of larger animals such as the macaque. The goal of fMRI data analysis is to detect correlations between brain activation and a task the subject performs during the scan. It also aims to discover correlations with the specific cognitive states, such as memory and recognition, induced in the subject. This means that a series of processing steps must be performed on the acquired images before the actual statistical search for task-related activation can begin.
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. 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. 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. 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. 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.
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. 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. 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 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.
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.
Then, appropriate study participants can be recruited. Before the scan, subjects must first be screened for MRI safety, and any participants with MRI counter-indications, like the presence of a cardiac pacemaking device, must be excluded. Next, the nature of the experiment and the functional task directions should be reviewed, as subject performance is critical for robust results. In the scanner room, hearing protection should be provided before placing the head coil with padding around the head to reduce motion. The stimulus presentation equipment also needs to be set up.
Goggle or projector systems are often used for visual presentation, but other types of stimulus delivery equipment exist. Once the subject is comfortable, the scanner bed is sent into the magnet bore. Then, the imaging sequences are set up, including a high-resolution anatomical scan to reregister to the functional scans.
The subject should be reminded of the task instructions, and the functional acquisition needs to be synchronized with the start of the task paradigm. This is critical, as the task timing needs to be matched with image acquisition timing for accurate BOLD measurements. The subject should be monitored during the scan, and additional functional runs performed if necessary. Finally, the subject is helped out of the scanner and off the scanner bed.
The specific image processing method and software package used will vary depending on the experiment. In this video, we will go over common BOLD task based processing methods. First, fMR data should be pre-processed to remove image artifacts and prepare it for statistical analysis. This involves slice time correction and motion correction, as well as co-registration to the anatomical image.
For group studies, normalization to a standard template space is often performed as well, so the brain areas and spatial coordinates can be compared across subjects. Once data is prepped, statistical analysis is performed to locate regions with significant MR signal correlated with the stimulus or cognitive function that was tested. The general linear model is typically used to analyze task-based experiments. This model assumes that a BOLD signal was obtained that matches the expected hemodynamic response function, and convolves this function with the stimulus design.
Further analysis can be performed as needed. Although these are seemingly basic functions, there is still much to be learned about these and many other cognitive processes. In addition, fMRI can be used to investigate brain function in diseased brain states and psychological disorders. There are many active areas of research such as anxiety disorders, posttraumatic stress disorder, autism, and dementia.
There are also resting state fMRI analysis techniques that can be used to investigate functional connectivity, such as independent component analysis and cross correlation analysis. Thanks for watching, good luck with your experiments, and remember that MRI safety always comes first! A subscription to J o VE is required to view this content. You will only be able to see the first 20 seconds. To learn more about our GDPR policies click here.
If you want more info regarding data storage, please contact gdpr jove. This is a sample clip. To watch the full video start a free trial today! Add to Favorites Embed Share. Summary Functional magnetic resonance imaging fMRI is a non-invasive neuroimaging technique used to investigate human brain function and cognition in both healthy individuals and populations with abnormal brain states. Already have an Account? Glimcher 3,4,5. Kana 1 , Donna L. Murdaugh 1 , Lauren E. Libero 1 , Mark R. Pennick 1 , Heather M. Wadsworth 1 , Rishi Deshpande 1 , Christi P.
Zatorre 1. General Laboratory Techniques. Introduction to Fluorescence Microscopy. Introduction to Light Microscopy. An Introduction to Neuroanatomy. An Introduction to Behavioral Neuroscience. An Introduction to Neurophysiology.