Functional magnetic resonance imaging or functional MRI ( fMRI ) measures brain activity by detecting changes associated with blood flow. This technique depends on the fact that cerebral blood flow and neuronal activation are combined. When the area of ​​the brain is used, the flow of blood to the region also increases.
The main form of fMRI uses blood-oxygen-level dependent (BOLD) contrast, which is found by Seiji Ogawa. It is a special type of brain and body scan used to map neural or spinal cord activity in humans or other animals by imaging changes in blood flow (hemodynamic response) associated with energy use by brain cells. Since the early 1990s, fMRI has dominated brain mapping research because it does not require people to do shooting or surgery, to ingest substances, or to be exposed to ionizing radiation. This measure is often undermined by sounds from various sources; therefore, statistical procedures are used to extract the underlying signal. The resulting brain activation can be graphically represented by a color code of activation force throughout the brain or specific area studied. This technique can localize activity into millimeters but, using standard techniques, is no better than in windows a few seconds. Another method of obtaining contrast is the arterial spin labeling and MRI diffusion. The latter procedure is similar to BOLD fMRI but gives contrast based on the magnitude of the diffusion of water molecules in the brain.
fMRI is used both in the world of research, and to a lesser extent, in the clinical world. It can also be combined and complemented by other measures of brain physiology such as EEG and NIRS. Newer methods that improve both spatial resolution and time are being researched, and these mostly use biomarkers other than BOLD signals. Some companies have developed commercial products such as lie detectors based on fMRI techniques, but this research is not believed to be mature enough for broad commercialization.
Video Functional magnetic resonance imaging
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The fMRI concept builds on previous MRI scanning technologies and the discovery of oxygen-rich blood properties. MRI brain scans use a strong and permanent static magnetic field to align the nucleus in the area of ​​the brain under study. The other magnetic field, the gradient plane, is then applied spatially to find different nuclei. Finally, radio frequency (RF) pulses are played to kick the nucleus to a higher level of magnetization, with the effect now depending on where they are located. When the RF field is removed, the atomic nucleus returns to its original state, and the energy it emits is measured with a coil to re-create the atomic nucleus position. Thus MRI provides a static structural view of brain matter. The central impetus behind fMRI is to extend the MRI to capture functional changes in the brain caused by neuronal activity. The difference in magnetic properties between arteries (oxygen-rich) and venous (oxygen-poor) blood provide this connection.
Since the 1890s it has been known that changes in blood flow and blood oxygenation in the brain (collectively known as hemodynamics) are closely related to neural activity. When the neuron becomes active, the local blood flow to the brain area increases, and oxygen-rich blood (oxygen) displaces oxygen-deficient blood about 2 seconds later. It rises to a peak of more than 4-6 seconds, before falling back to its original level (and usually slightly worsening). 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 magnetic resistant (diamagnetic). This difference causes an increase in MR signal because diamagnetic blood interferes with less magnetic MR signal. These fixes can be mapped to indicate which neurons are active at a time.
History
During the late 19th century, Angelo Mosso discovered 'the balance of human circulation', which could non-invasively measure the redistribution of blood during emotional and intellectual activity. However, although briefly mentioned by William James in 1890, the exact details and workings of this equilibrium and Mosso's experiments with it remain largely unknown until the recent discovery of the original instrument as well as Mosso's report by Stefano Sandrone and his colleagues. Angelo Mosso investigated some critical variables that are still relevant in modern neuroimaging such as 'signal-to-noise ratio', the exact choice of the experimental paradigm and the need for simultaneous recording of different physiological parameters. The Mosso manuscript does not provide direct evidence that the balance is really capable of measuring changes in cerebral blood flow due to cognition, but modern replication by David T Field has now been demonstrated using modern signal processing techniques not available for Mosso which is a tool of equilibrium. this type is able to detect changes in blood volume of the brain associated with cognition.
In 1890, Charles Roy and Charles Sherrington first connected brain function with blood flow, at Cambridge University. The next step to solving the blood flow to brain is the discovery of Linus Pauling and Charles Coryell in 1936 that oxygen-rich blood with weak Hb is rejected by magnetic fields, while oxygen-deficient blood with dHb is attracted to magnetic fields, though less so than ferromagnetic such as iron. Seiji Ogawa in AT & amp; T Bell realizes that this can be used to add MRI, which can study the static structure of the brain, because the different magnetic properties of dHb and Hb caused by blood flow to the activated brain region will cause measurable changes. in MRI signal. BOLD is a contrast MRI dHb, discovered in 1990 by Ogawa. In a seminal 1990 study based on earlier work by Thulborn et al., Ogawa and colleagues scanned mice in a strong magnetic field (7.0Ã, T) MRI. To manipulate blood oxygen levels, they change the proportion of oxygen inhaled by animals. When this proportion falls, blood flow maps in the brain are seen in MRI. They verify this by placing the test tube with oxygenated or deoxygenated blood and creating a separate image. They also show that the image of the gradients, which relies on a form of magnetization loss called decay T 2 * , produces the best image. To show this change in blood flow associated with functional brain activity, they changed the composition of air inhaled by mice, and examined it while monitoring brain activity with EEG. The first attempt to detect regional brain activity using MRI was done by Belliveau and others at Harvard University using the Magnevist contrast agent, the ferromagnetic substance left in the bloodstream after intravenous injection. However, this method is not popular in human fMRI, because any medically unnecessary injection is unsafe and uncomfortable, and because the agent remains in the blood for only a short time.
Three studies in 1992 were the first to explore using BOLD contrast in humans. Kenneth Kwong and colleagues, using Echo Planar Imaging (EPI) gradient sequences on a magnetic field strength of 1.5 T to study activation in the visual cortex. Ogawa and others did research using a higher field (4.0 T) and showed that the BOLD signal relied on the loss of T2 * magnetization. T2 * decay is caused by magnetic cores in the volume of space losing magnetic coherence (transverse magnetization) of both bumps into each other and from intentional differences in magnetic field strength applied across locations (inhomogeneity field of spatial gradient). Bandettini and colleagues used EPI at 1.5 T to show activation in the main motor cortex, the area of ​​the brain at the last stage of the circuit that controls voluntary movements. The magnetic fields, pulse sequences and procedures and techniques used by this preliminary study are still used in current fMRI studies. But today researchers usually collect data from more slices (using stronger magnetic gradients), and preprocess and analyze data using statistical techniques.
Physiology
The brain does not store glucose, its main energy source. When the neurons become active, getting them back to their original state requires active polarization of pumping ions across the nerve cell membrane, in both directions. The energy for the ion pump is mainly produced from glucose. More blood flows to transport more glucose, it also carries more oxygen in the form of an oxygenated hemoglobin molecule in red blood cells. It comes from higher blood flow rates and blood vessel expansion. Changes in blood flow are localized into 2 or 3 mm in which the neural activity. Oxygen is usually carried over oxygen consumed in glucose combustion (it is not known if most of the glucose consumption is oxidative), and this causes a net reduction in deoxygenated hemoglobin (dHb) in the blood vessels in the brain area. This changes the magnetic properties of the blood, making it less disturbing to the magnetization and ultimately decay caused by the MRI process.
Brain blood flow (CBF) corresponds to glucose consumed differently in different brain regions. Preliminary results suggest there is more inflow than glucose consumption in areas such as the amygdala, basal ganglia, thalamus and cingulate cortex, all of which are recruited for rapid response. In more deliberative areas, such as the lateral frontal and lateral parietal lobes, there appears to be less inflows than consumption. This affects the sensitivity of BOLD.
Hemoglobin differs in how it responds to the magnetic field, depending on whether it has a bonded oxygen molecule. The dHb molecule is more attracted to the magnetic field. Therefore, it distorts the surrounding magnetic field induced by the MRI scanner, causing the nucleus there to lose magnetization faster through the decay of T 2 * . Thus the sequence of sensitive MR pulses against T 2 * shows more MR signals in which the blood is strongly oxygenated and less where it is not. This effect increases with the square of the strength of the magnetic field. The fMRI signal requires a strong magnetic field (1.5 Â ° T or higher) and a pulse sequence such as EPI, which is sensitive to contrast T 2 * .
The response of physiological blood flow largely resolves the temporal sensitivity, ie how accurately we can measure when the neurons are active, in the fMRI BOLD. The basic time resolution parameter (sampling time) is set TR; TR determines how often certain brain slices are excited and left to lose their magnetism. TR can vary from a very short (500 ms) to very long (3 seconds). For fMRI in particular, the hemodynamic response lasts more than 10 seconds, multiplicially increases (ie, as the current value proportion), peaks at 4 to 6 seconds, and then falls multiplicatively. Changes in the bloodstream system, the vascular system, integrate responses to neuronal activity over time. Since this response is a smooth continuous function, sampling with an ever-faster TR does not help; it only gives more points on the response curve that can be obtained with simple linear interpolation. Such an experimental paradigm is surprising when the stimulus presented at various trials can increase the temporal resolution, but reduces the number of effective data points obtained.
Maps Functional magnetic resonance imaging
BOLD hemodynamic response
The change in MR signal from neuronal activity is called the hemodynamic response (HDR). This slows down a neuronal event that triggers it for a few seconds, as it may take a while for the vascular system to respond to the brain's need for glucose. From this point it usually rises to the top in about 5 seconds after the stimulus. If the neuron continues to shoot, say from continuous stimulus, the peak spreads to the flat plain while the neuron remains active. After the activity stops, the BOLD signal falls below the initial level, the baseline, a phenomenon called undershoot. Over time, the signal recovers to the baseline. There is some evidence that the need for continuous metabolism in brain regions contributes to undershoot.
The mechanism by which the nervous system provides feedback to the vascular system from its need for more partial glucose is the release of glutamate as part of the firing of neurons. This glutamate affects nearby support cells, astrocytes, causing changes in the concentration of calcium ions. This, in turn, releases nitric oxide at the point of contact of astrocytes and middle-sized blood vessels, arterioles. Nitric oxide is a vasodilator that causes arterioles to expand and attract more blood.
Single voxel response signal from time to time is called timecourse. Typically, unwanted signals, called noise, from the scanner, random brain activity and similar elements equal to the signal itself. To eliminate this, the fMRI study repeated the stimulus presentation several times.
Spatial resolution
The spatial resolution of an fMRI study refers to how well it distinguishes between nearby locations. This is measured by voxel size, as in MRI. Voxel is a three-dimensional rectangular cube, whose dimensions are determined by the thickness of the slice, the area of ​​the incision, and the grid imposed on the slice by the scanning process. Full brain studies use larger voxels, whereas those focusing on particular areas of interest typically use smaller sizes. Sizes range from 4 to 5 mm to 1 mm. Smaller voxels contain fewer average neurons, incorporate less blood flow, and therefore have fewer signals than larger voxels. Smaller voxels imply a longer scan time, because the scan time is directly increased by the number of voxels per slice and the number of slices. This can cause discomfort to the subject within the scanner and the loss of magnetization signal. Voxel usually contains several million neurons and tens of billions of synapses, with the actual number depending on the size of the voxel and the imaged brain area.
The vascular artery system supplies fresh blood to smaller and smaller blood vessels when it enters the surface of the brain and inside the brain, which culminates in the capillary bed connected in the brain. The drainage system, too, joins into a larger and larger vein as it carries blood that runs out of oxygen. The dHb contribution to the fMRI signal comes from the capillaries near the activity area and the larger jetting vein that may be further away. For good spatial resolution, signals from large veins need to be suppressed, as they do not correspond to areas where neural activity is. This can be achieved either by using a strong static magnetic field or by using a sequence of spin-echo pulses. With this, fMRI can examine spatial ranges from millimeters to centimeters, and therefore can identify Brodmann (centimers) areas, subcortical nuclei such as caudatus, putamen and thalamus, and hippocampal subcompacts such as combined dentate gyrus/CA3, CA1, and subiculum.
Temporary resolution
Temporal resolution is the smallest time period of neural activity that is believed to be separated by fMRI. One element decides this is the sampling time, TR. Under TR 1 or 2 sec, however, scanning only produces a sharper HDR curve, without adding much additional information (eg beyond what the alternative achieves by mathematically interpolating the curve gap on the lower TR). Temporal resolution can be enhanced by the presentation of a surprising stimulus throughout the experiment. If one-third of the sample data were sampled normally, one-third at 1, 4, 7, and so on, and the last third at 2 s, 5 s and 8 s, the combined data gave a resolution of 1 d, even though only one-third of the total event.
The time resolution required depends on the brain processing time for various events. A wide range of examples here is given by the visual processing system. What the eye sees is registered on the retinal photoreceptors in milliseconds or more. These signals reach the primary visual cortex through the thalamus within tens of milliseconds. Neuronal activity associated with viewing action lasts for more than 100 ms. Quick reaction, like swerving to avoid car accidents, takes about 200 ms. About half a second, the awareness and reflection of the incident arose. Remembering a similar event may take a few seconds, and emotional or physiological changes such as fear can last a few minutes or hours. Changes learned, such as recognizing a face or a scene, may take several days, months, or years. Most fMRI experiments studied brain processes lasting a few seconds, with research done for several decades. Subjects can move their heads during that time, and this head movement needs to be corrected. So also with the baseline signal from time to time. Boredom and learning can change the behavior of subjects and cognitive processes.
Added linear from multiple activations
When a person performs two tasks simultaneously or in overlapping mode, the BOLD response is expected to add linearly. This is a fundamental assumption of many fMRI studies. Linear addition means the only operation allowed on individual responses before being combined (added together) is a separate scale of each. Since scaling is only multiplicity with a constant number, it means events that generate, say, twice the neural response as another, can be modeled as the first event presented twice simultaneously. HDR for multiple events is only twice that of a single event.
This strong assumption was first studied in 1996 by Boynton and colleagues, who examined the effect on the main visual cortex of flashing patterns 8 times per second and presented for 3 to 24 seconds. The results show that when the visual contrast of the image increases, the shape of HDR remains the same but the amplitude increases proportionately. With some exceptions, responses to longer stimuli can also be inferred by adding together responses for some short stimuli summing to the same duration longer. In 1997, Dale and Buckner tested whether individual events, rather than blocks of some duration, were also summed in the same way, and found them doing so. But they also find deviations from the linear model at intervals of less than 2 seconds.
The source of the nonlinearity in the fMRI response comes from the refractory period, in which the brain activity of the presented stimulus suppresses further activity in a similar subsequent stimulus. When the stimulus becomes shorter, the refractory period becomes more visible. The refractory period does not change with age, so does the amplitude of HDR. Periods differ across brain regions. Both in the primary motor cortex and the visual cortex, the HDR amplitude scale is linear with the duration of the stimulus or response. In a suitable secondary area, an additional cortical motor, involved in motor behavior planning, and a motion sensitive V5 region, a strong refractory period is seen and the HDR amplitude remains stable in various stimuli or response periods. Refractory effects can be used in a manner similar to habituation to see what stimulus features are discriminated against as new.
Matching nerve activity to BOLD signal
Researchers have examined the BOLD signal to both signals from embedded electrodes (mostly on monkeys) and potential field signals (ie electric or magnetic fields of brain activity, measured outside the skull) of EEG and MEG. Local terrain potentials, which include post-neuron-synaptic activity and internal neuron processes, better predict BOLD signals. Thus BOLD contrast reflects mainly the input to neurons and the integrative processing of neurons in the body, and less of the firing output of neurons. In humans, electrodes can only be implanted in patients requiring surgery for treatment, but evidence suggests a similar relationship at least for the auditory cortex and the primary visual cortex. The activation location detected by the fMRI BOLD in the cortical area (the surface area of ​​the brain) is known to calculate with a CBF-based functional map of the PET scan. Some areas are only a few millimeters in size, such as the lateral geniculate nucleus (LGN) of the thalamus, which relays visual input from the retina to the visual cortex, has been shown to produce a true BOLD signal when presented with visual input. The immediate areas such as the pulvinar core are not stimulated for this task, indicating a millimeter resolution for the spatial rate of BOLD response, at least in the nucleus of the thalamus. In the rat brain, a single mustache touch has been shown to obtain a BOLD signal from the somatosensory cortex.
However, BOLD signals can not separate feedback and feed into active networks in a region; delay in vascular response means the end signal is the full version of the whole area network; uninterrupted blood flow during the process. Also, both the inhibitory and excitatory input into neurons from the number of other neurons and contribute to the BOLD signal. In the second neuron this input may be lost. BOLD responses can also be influenced by a variety of factors, including illness, sedation, anxiety, vascular dilation drugs, and attention (neuromodulation).
The amplitude of the BOLD signal does not always affect its shape. Higher amplitude signals can be seen for stronger neural activity, but peak at the same place as weaker signals. Also, amplitude does not necessarily reflect behavioral performance. A complicated cognitive task can initially trigger high amplitude signals associated with good performance, but when the subject gets better, its amplitude can decrease with performance that remains the same. This is expected because of increased efficiency in performing tasks. BOLD responses across brain regions can not be compared directly even for the same task, because neuronal density and blood supply characteristics are not constant in the brain. However, the BOLD response can often be compared between subjects for the same brain region and the same task.
More recent characterization of BOLD signals has used optogenetic techniques in rodents to precisely control nerve shootings while simultaneously monitoring BOLD responses using high-field magnets (a technique sometimes referred to as "optofMRI"). These techniques indicate that neuronal firing correlates well with measured BOLD signals including a linearly approximately summation of BOLD signals over near-neuronal blast bursts. Linear summation is the fMRI design assumption related to commonly used events.
Medical use
Doctors use fMRI to assess how brain surgery is at risk or similar invasive treatment for patients and to study how normal brain, pain or injury works. They mapped the brain with fMRI to identify areas associated with important functions such as talking, moving, feeling, or planning. This is useful for planning surgery and brain radiation therapy. Doctors also use fMRI to anatomically map the brain and detect the effects of tumor, 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 the use of research. Patients with brain pathology are more difficult to scan with fMRI than healthy young volunteers, a distinctive population of research subjects. Tumors and lesions can alter blood flow in a way unrelated to neural activity, covering the neural HDR. Drugs such as antihistamines and even caffeine can affect HDR. Some patients may suffer from disorders such as lying compulsively, which makes certain research impossible. More difficult for those who have clinical problems for long stay. Using head restraints or biting may injure epileptic seizures within the scanner; bar bites can also be troublesome to those who have dental prostheses.
In spite of these difficulties, fMRI has been used clinically to map functional areas, examining left-right hemisphere assymetry in language and memory areas, examining the neural correlation of seizures, studying how the brain recovers part of the stroke, testing how well the drug or behavioral therapy works, detecting onset of Alzheimer's, and noted the presence of disorders such as depression. Mapping of functional areas and understanding of language and memory lateralization helps surgeons avoid removing critical brain areas when they have to operate and remove brain tissue. This is very important in removing the tumor and in patients who have severe lobe epilepsy epilepsy. Tumor lesioning requires pre-surgical planning to ensure no functional functioning tissue is lost without need. Recovered depression patients have shown a change in fMRI activity in the cerebellum, and this may indicate a tendency to recur. Pharmacological FMRI, testing of brain activity after administration of the drug, can be used to check how many drugs pass through the blood brain barrier and dose vs. drug information effects.
Animal research
Research is primarily conducted on non-human primates such as rhesus macaque. These studies can be used either to check or predict human outcomes and to validate the fMRI technique itself. But the study is difficult because it is difficult to motivate animals to remain silent and typical persuasion as the juice triggers head movement while the animal swallows it. It is also expensive to maintain larger animal colonies such as apes.
Analyzing data
The purpose of fMRI data analysis is to detect the correlation between brain activation and the tasks the subjects performed during the scan. It also aims to find correlations with specific cognitive states, such as memory and recognition, which are induced in the subject. BOLD activation marks are relatively weak, so other sound sources in the data obtained must be carefully controlled. This means that a series of processing steps should be performed on the images obtained before the actual search statistics for activation-related tasks can begin. However, it is possible to predict, for example, that a person's emotions experience solely from their fMRI, with a high degree of accuracy.
Sound source
Noise is an undesirable change in the MR signal of elements that are not interesting for research. The five main sources of noise in fMRI are thermal noise, system noise, physiological noise, random neural activity and differences in mental and behavioral strategies among people and across tasks within a person. The thermal noise multiplies in line with the strength of the static field, but the physiological sound multiplies as the square of the field strength. Since the signal also multiplies as the square of the field strength, and since the physiological sound is a large proportion of total noise, a higher field strength above 3 ° T does not always produce a better proportional image.
The heat causes the electrons to move and distort the current in the fMRI detector, producing a heat sound. Thermal noise increases with temperature. It also depends on the frequency range detected by the receiving coil and its electrical resistance. It affects all the same voxel, independent anatomy.
Noise system is from imaging hardware. One such form is scanner aberrations, caused by superconducting magnetic fields that drift over time. Another form is a change in the distribution of the current or voltage of the brain itself that drives changes to the receiving coil and reduces its sensitivity. The procedure called impedance matching is used to bypass this inductance effect. There may also be a sound from a non-uniform magnetic field. This is often adjusted by using a shimming coil, a small magnet that is physically inserted, say into the subject's mouth, to patch the magnetic field. Unusualness often near the brain sinuses like the ears and inserting the cavity for a long time can be discomfiting. The scanning process obtains an MR signal in k-space, where the overlapping spatial frequency (which is the repeating edge in the sample volume) is represented by the line respectively. Turning these into voxels introduces some loss and distortion.
Physiological sounds are from head and brain movements in the scanner from breathing, heartbeat, or subjects who are anxious, tense, or making a physical response such as button presses. Head movement causes the voxel-to-neuron mapping to change while the scan is in progress. Because fMRI is obtained in slices, after movement, voxel continues to refer to the same absolute location in space while the neurons below it will change. Other sources of physiological noise are changes in blood flow rate, blood volume, and oxygen use over time. This latter component contributes to two-thirds of the physiological sound, which, in turn, is a major contributor to total noise.
Even with the best experimental design, it is impossible to control and limit all other background stimuli that affect the subject - scanner noise, random thoughts, physical sensations, and the like. This results in independent neural activity from experimental manipulation. This is not suitable for mathematical modeling and should be controlled by the research design.
A person's strategy to respond or react to stimuli, and to solve problems, often changes over time and over tasks. This results in a variation of neural activity from trial to trial in the subject. Among people, neural activity is also different for similar reasons. Researchers often conduct pilot studies to see how participants typically perform the task under consideration. They also often train subjects on how to respond or react in a trial training session before scanning.
Preprocessing
The scanner platform produces volume 3Ã, D of subject head every TR. It consists of an array of voxel intensity values, one value per voxel in scanning. Voxel is arranged one by one, opening the three-dimensional structure into a single line. Some such volumes of a session are combined together to form a 4a, D volume associated with a run, for a period of time the subject lives on the scanner without adjusting the head position. Volume 4Ã, D is the starting point for the analysis. The first part of the analysis is preprocessing.
The first step in preprocessing is the conventional slice timing correction. The MR scanner gets different incisions in one brain volume at different times, and therefore the slices represent brain activity at different time points. Because this complicates later analysis, time correction is applied to bring all the slices to the same time point reference. This is done by assuming a timecourse of fine voxel when plotted as a dashed line. Therefore the value of voxel intensity at other times not in the sample frame can be calculated by filling the dots to create a continuous curve.
Head movement correction is another common preprocessing step. As the head moves, the neurons below the voxel move and hence the timecourse now represent most of the other voxels in the past. Therefore the time curve is effectively cut and pasted from one voxel to another voxel. Motion corrections try different ways to undo this to see which cut-and-paste cancel produces smooth timecourse for all voxels. Failure is to apply a rigid body transformation to the volume, by sliding and rotating the entire volume data to account for movement. The changed volume is compared statistically with the volume at the first point in time to see how well they fit, using cost functions such as correlation or mutual information. Transformations that provide minimal cost functions are selected as models for head movement. Because the head can move in a variety of highly variable ways, it is impossible to find all possible candidates; nor are there any algorithms that provide globally optimal solutions independent of the first transformations we try in the chain.
Account distortion correction for field uniformity of the scanner. One method, as described earlier, is to use shimming coils. Another is to recreate the field map of the main field by obtaining two images with different echo time. If the field is uniform, the difference between the two images will also be uniform. Note that this is not a true preprocessing technique because they do not rely on the study itself. Field estimation Bias is a real preprocessing technique using mathematical models of noise from distortion, such as Markov random field and expectation expectation algorithm, to correct distortion.
In general, fMRI studies acquire many functional images with fMRI and structural images with MRI. Structural images usually have higher resolutions and depend on different signals, decay of T1 magnetic field after excitation. To demarcate the area of ​​interest in functional imagery, one needs to align it with the structural one. Even when the whole brain analysis is done, to interpret the final result, ie to know which region of active voxel falls, one must align functional images with the structural ones. This is done with a registrar algorithm that works similar to that of motion correction, except that here the resolutions are different, and the intensity values ​​can not be compared directly because the generating signals are different.
A typical MRI study scans several different subjects. To integrate 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 it as a group. The commonly used atlas is Talairach one, the elderly single female brain created by Jean Talairach, and the Montreal Neurological Institute (MNI) one. The second is a probabilistic map created by combining scans of more than a hundred individuals. This normalization to the standard template is done by mathematically examining the combination of stretching, squeezing, and curling reducing the difference between target and reference. While this is conceptually similar to motion correction, the necessary changes are more complex than just translation and rotation, and therefore optimization is even more likely to depend on the first transformation in the unchecked chain.
Temporary filtering is the removal of the no-interest frequency of the signal. The intensity of voxel changes over time can be represented as the sum of different repetition waves with different periods and heights. The plot with this period on the x-axis and the height on the y-axis is called the power spectrum, and this plot is made by Fourier transform techniques. Temporary filtering to eliminate periodic waves that do not appeal to us from the power spectrum, and then summing the waves back, using the inverse Fourier transform to create a new timecourse for voxel. The high-pass filter eliminates the lower frequency, and the lowest frequency that can be identified by this technique is the opposite of the TR twice. The low pass filter eliminates the higher frequencies, while the band-pass filter eliminates all frequencies except for certain interest ranges.
Smoothing, or spatial filtering, is the idea of ​​the average intensity of nearby voxels to produce a smooth spatial map of intensity changes across the brain or region of interest. The average is often done by convolution with Gaussian filters, which, at each spatial point, weigh neighboring voxels by their distances, with the weight falling exponentially following the bell curve. If the actual spatial activation rate, ie the cluster voxels cluster simultaneously active, matches the width of the filter used, this process increases the signal-to-noise ratio. It also makes total noise for each voxel following the curve-bell distribution, since adding together a large number of independent and identical distributions in whatever form produces a bell curve as the case boundary. But if the estimated spatial distance from the activation does not match the filter, the signal is reduced.
Statistical analysis
One common approach for analyzing fMRI data is to consider each voxel separately within the framework of a general linear model. The model assumes, at any point in time, that the HDR is the same as the scale version and the summary of the active event at that point. A researcher creates a design matrix that determines which events are active at any point in time. One common way is to create a matrix with one column per event overlap, and one line per time point, and to mark it if a particular event, say stimulus, is active at that point in time. One then assumes a special shape for HDR, leaving only the amplitude changed in active voxels. This design and shape matrix is ​​used to generate precise HDR response predictions from voxels at any point of time, using convolution mathematical procedures. This prediction does not include the scale required for each event before adding it up.
The basic model assumes the observed HDR is the predicted HDR by the weights for each event and then added, with mixed noise. This produces a set of linear equations with more than unknown equations. The linear equation has the right solution, in most conditions, when the equation and unknown match. Therefore one can select a subset of equations, with an amount equal to the number of variables, and solve them. But, when this solution is plugged into the left equation, there will be a mismatch between the right and left sides, the error. The GLM model tries to look for scale weights that minimize the sum of squares of errors. This method proves to be optimal if the error is distributed as a bell curve, and if the scaling and summing model is accurate. For a more mathematical description of the GLM model, see general linear model.
The GLM model does not take into account the contribution of the relationship between multiple voxels. While the GLM analysis method assessed whether the amplitude of the voxel signal or region is higher or lower for one condition than the other, newer statistical models such as multi-voxel pattern analysis (MVPA), utilize the unique contributions of some voxels in the voxel population. In typical implementations, a classifier or basic algorithm is trained to distinguish experiments for various conditions within a subset of data. The trained model is then tested by predicting the remaining data condition (independent). This approach is most often achieved by training and testing at different scoping sessions or running. If the classifier is linear, then the training model is a set of weights that are used to scale the values ​​in each voxel before summing them to produce a single number that specifies the conditions for each test run. More information on training and testers is on statistical classification.
Combine with other methods
It is common to combine the acquisition of fMRI signals with response tracking and reaction time of participants. Physiological measures such as heart rate, breathing, skin conductance (sweating rate), and eye movement are sometimes captured together with fMRI. This method can also be combined with other brain imaging techniques such as transcranial stimulation, direct cortical stimulation, and especially, EEG. The fMRI procedure can also be combined with near-infrared spectroscopy (NIRS) to obtain additional information about oxyhemoglobin and deoxyhemoglobin.
The fMRI technique can complement or complement other techniques because of its unique strength and gap. It can record non-invasive brain signals without the risk of ionizing radiation attached to other scanning methods, such as CT or PET scans. It can also record signals from all regions of the brain, unlike EEG/MEG, which are biased to the cortical surface. But fMRI temporal resolution is worse than EEG because HDR takes tens of seconds to climb to its peak. Combining EEG with fMRI is therefore potentially powerful as both have complementary strengths - the EEG has high temporal resolution, and high fMRI spatial resolution. But simultaneous acquisitions need to take into account the EEG signals from the various blood streams triggered by the fMRI gradient plane, and the EEG signals from the static field. For details, see EEG vs fMRI.
While fMRI stands out for its potential to capture the neurological processes associated with health and disease, brain stimulation techniques such as Transcranial Magnetic Stimulation (TMS) have the power to change these neural processes. Therefore, a combination of both is necessary to investigate the mechanism of action of TMS treatment and on the other hand introduces causality to pure correlational observation. The current state of the art setup for this concurrent TMS/fMRI experiment consists of a large volume head coil, usually a bird cage coil, with a MR-compatible TMS coil mounted inside the bird cage coil. It's applied in many experiments that study local and network interactions. However, the classical arrangement with TMS coils housed within the birdcage-type MR head coil is characterized by a poor signal to noise ratio compared to the multi-channel receiving array used in clinical neuroimaging today. In addition, the presence of a TMS coil inside the MR birdcage coil causes an artifact under the TMS coil, which is on the stimulation target. For this reason, the new MR coil arrangement is currently being developed for concurrent TMS/fMRI experiments
Problems in FMRI
Design
If the baseline condition is too close to maximum activation, certain processes may not be properly represented. Another limitation of the experimental design is the head movement, which can cause changes in the artificial intensity of the fMRI signal.
In block design, two or more conditions alternate within blocks. Each block will have a duration number of fMRI scans and in each block only one condition is presented. By creating different conditions only in interesting cognitive processes, fMRI signals that differentiate conditions must represent this interesting cognitive process. This is known as the reduction paradigm. Increased fMRI signal in response to additive stimulus. This means that the amplitude of the hemodynamic response (HDR) increases when some stimuli are presented in rapid succession. When each block alternates with a resting condition where HDR has enough time to return to the baseline, the maximum amount of variability is introduced in the signal. Thus, we conclude that the block design offers considerable statistical power. However there is a severe disadvantage in this method, because signals are very sensitive to floating signals, such as head movements, especially when only a few blocks are used. Another limiting factor is a poor basic choice, as it can prevent meaningful conclusions from being withdrawn. There are also problems with many tasks that lack the ability to be repeated. Because within each block only one condition is presented, randomisation of the stimulus type is not possible in a block. This makes the type of stimulus in every block very predictable. As a consequence, participants can be aware of the sequence of events.
The event-related design allows more real-world testing, however, the statistical power of the event-related design is basically low, since the signal changes in the fMRI signal BOLD follow a small single stimulus presentation.
Both block and event-related designs are based on a reduction paradigm, which assumes that certain cognitive processes can be added selectively under different conditions. Any difference in blood flow (BOLD signal) between these two conditions is then considered to reflect different cognitive processes. In addition, this model assumes that cognitive processes can be selectively added to a set of active cognitive processes without affecting them.
Baseline versus activity conditions
The brain never really rests. It never ceases to function and fires neuronal signals, and uses oxygen as long as the person is alive. In fact, in the study of Stark and Squire, 2001 When zero is not zero: The ambient baseline condition problem in fMRI, activity in the medial temporal lobe (as well as in other brain regions) is much higher during rest than over several alternative baselines. condition. The effect of increased activity during the rest is to reduce, eliminate, or even reverse alerts of activity during task conditions relevant to memory functions. These results suggest that rest periods are associated with significant cognitive activity and therefore are not an optimal baseline for cognitive tasks. To distinguish between basic conditions and activation, need to interpret a lot of information. This includes simple situations such as breathing. The periodic block can generate identical data from other variance in the data if one breathes at a regular rate of 1 breath/5 s, and blocks occur every 10 seconds, thus destroying the data.
Inverted Inference
Neuroimaging methods such as fMRI offer a measure of activation of certain brain areas in response to the cognitive tasks involved during the scanning process. The data obtained during this time allows the cognitive neurologist to obtain information about the role of specific brain regions in cognitive function. However, problems arise when certain brain regions are accused by researchers to identify the activation of cognitive processes labeled earlier. Poldrack clearly illustrates this problem:
- The usual inference types taken from the neuroimaging data are of the form 'if the cognitive process of X is involved, then the Z brain area is active.' A reading of parts of the discussion of some fMRI articles will soon reveal, however, the epidemic of reasoning takes the following form:
- (1) In this study, when comparison of task A is presented, the area of ​​Z brain is active.
- (2) In another study, when the cognitive process of X is forcibly involved, the brain area of ​​Z is active.
- (3) Thus, the activity of area Z in this study shows the involvement of cognitive processes X by comparison of task A.
- This is 'inverted inference', for that reason backs off from the presence of brain activation to the involvement of certain cognitive functions.
Reversed inference shows a logical error in confirming what you have just discovered, although this logic can be supported by an event in which a particular result is generated solely by a particular event. With regard to the brain and brain function it is rare that a particular region of the brain is activated only by a cognitive process. Some suggestions for improving the legitimacy of inverted inference include increasing the selectivity of responses in interesting areas of the brain and increasing the prior probability of the cognitive processes involved. However, Poldrack points out that inverted inference should be used only as a guide to direct further investigation rather than a direct way to interpret results.
Inference forward
Future inference is a data driven method that uses brain activation patterns to distinguish between competing cognitive theories. It shares characteristics with the logic of dissociation of cognitive psychology and the chain of philosophy forward. For example, Henson discusses the contribution of forward inference to the debate of "single process theory vs. dual process theory" with respect to memory recognition. Future inference supports multiple process theory by showing that there are two different patterns of activation of the brain qualitatively when distinguishing between "remember vs. know judgment". The main problem with forward inference is that this is a correlational method. Therefore, one can not be entirely sure that the brain region activated during the cognitive process is really necessary for the execution of the process. In fact, there are many known cases that show it. For example, the hippocampus has been shown to be activated during classical conditioning, but lesion studies have shown that classical conditioning can occur without the hippocampus.
Risk
The most common risk for participants in the fMRI study is claustrophobia and there are reported risks for pregnant women to go through the scanning process. The scanning session also leads participants to the high-pitched sound of Lorentz forces induced in the gradient coil by a fast switching current in a strong static field. Gradient shifts can also induce currents inside the body that cause nerve tingling. Embedded medical devices such as pacemakers may be damaged by this current. The radio frequency field of the excitation coil can heat the body, and this should be monitored more carefully in those with fevers, diabetes, and those with circulatory problems. Local combustion of metal necklaces and other jewelry is also a risk.
Strong static magnetic fields can cause damage by pulling heavy metal objects nearby that turn them into projectiles.
There is no known risk of biological hazards even from very strong static magnetic fields. However, the genotoxic effects (ie, potentially carcinogenic) of MRI scans have been demonstrated in vivo and in vitro, leading a recent review to recommend "the need for further study and prudent use to avoid unnecessary, caution ". In comparison to the genotoxic effects of MRI compared with CT scans, Knuuti et al. reported that although detectable DNA damage after MRI was at levels comparable to that generated by scanning using ionizing radiation (low-dose coronary CT angiography, nuclear imaging, and X-ray angiography), differences in mechanisms that cause this damage. Places suggest that the risk of MRI , if any, is unknown.
Advanced methods
The first fMRI study validates techniques for known brain activity, from other techniques, to be correlated with tasks. In early 2000, fMRI studies began to find a new correlation. Still their technical losses have prompted researchers to try more advanced ways to improve the strengths of both clinical and research studies.
Better spatial resolution
MRI, in general, has better spatial resolution than EEG and MEG, but is not as good as resolutions such as invasive procedures such as single-unit electrodes. While the typical resolution in the millimeter range, ultra-high-resolution MRI or MR spectroscopy works at the resolution of tens of micrometers. It uses a 7 Â ° T field, a small-bore scanner that can hold small animals like mice, and external contrast agents such as fine iron oxide. Human installation requires a larger-bore scanner, which makes the higher field strength more difficult to achieve, especially if the field must be uniform; it also requires either internal contrast such as BOLD or non-toxic external contrast agents such as iron oxide.
Parallel imaging is another technique for improving spatial resolution. It uses multiple scrolls for excitation and reception. Spatial resolution increases as the square root of the number of reels used. This can be done either with a phased array in which the coils are combined in parallel and often take samples of overlapping areas with gaps in sampling or with large loops, which are a much denser set of receivers separate from excitation coils. This, however, picks up better signals from the surface of the brain, and less well from deeper structures such as the hippocampus.
Better temporal resolution
The temporary resolution of fMRI is limited by: (1) feedback mechanisms that increase blood flow slowly; (2) must wait until net magnetization is recovered before sampling another piece; and (3) should get some slices to cover the entire brain or region of interest. Sophisticated techniques to improve temporal resolution overcome this problem. Using multiple coils speeds up the acquisition time in the proper proportion to the coil used. Another technique is deciding which part of the signal is less important and dropping it. These may be parts of the image that are often repeated in a spatial map (ie a small group adorns the image periodically) or rarely repeat parts (larger groups). The first, a high-pass filter in k-space, has been proposed by Gary H. Glover and his colleagues at Stanford. This mechanism assumes the researcher has an idea of ​​the expected shape of the activation image.
EPI typical gradients use two gradient coils in a wedge, and turn on the first coil and then another, tracing a series of lines in k-space. Activating the two gradient coils can produce slashes, which cover the same lattice space faster. The two gradient coils can also be activated in a specific order to trace the spiral shape in k-space. This spiral imaging sequence obtains images faster than the sequence of gradients, but requires more mathematical transformations (and consequent assumptions) because converting back to voxel space requires data in the form of grid (a set of points equally spaced in horizontal and vertical directions ).
The new contrast mechanism
BOLD contrast depends on the blood flow, which is slowly changing and subject to the effects of noise. Other biomarkers are now looking to provide better contrast including temperature, acidity/alkalinity (pH), calcium-sensitive agents, neuronal magnetic fields, and Lorentz effects. The temperature contrast depends on the change in brain temperature of its activity. The initial burning of glucose raises the temperature, and the subsequent infusion of fresh cold blood lowers it. This change changes the magnetic properties of the network. Because the internal contrast is too difficult to measure, external agents such as thulium compounds are used to enhance the effect. Contrast based on pH depends on changing the acid/alkaline balance of brain cells when they are active. This too often uses an external agent. Calcium-sensitive agents make MRIs more sensitive to calcium concentration, with calcium ions often being carriers for cellular signal pathways in active neurons. Neuronal magnetic field contrast measures magnetic and electrical changes from neuronal firing directly. Lorentz's effect imaging attempts to measure the physical displacement of active neurons that carry an electric current in a strong static field.
Commercial use
Some experiments show a neural correlation of people's brand preference. Samuel M. McClure used fMRI to show the dorsolateral prefrontal cortex, the hippocampus and the midbrain were more active when people deliberately drank Coca-Cola as their opponent when they drank Coke without a label. Other studies have shown brain activity that characterizes men's preference for sports cars, and even the difference between Democrats and Republicans in their reactions to campaign advertising with 9/11 attacks. Neuromarketing companies have used the study as a better tool for finding user preferences than conventional survey techniques. One such company is BrightHouse, now closed. Another is Oxford, UK-based Neurosense, which tells clients how they can potentially use fMRI as part of their marketing business activities. A third is Sales Brain in California.
At least two companies have been formed to use fMRI in lie detection: No Lie MRI and Cephos Corporation. No Lie MRI costs almost $ 5000 for its services. These companies rely on such evidence from a study by Joshua Greene at Harvard University suggesting that the prefrontal cortex is more active in those who think of lying.
However, there is still considerable controversy as to whether this technique is reliable enough to be used in legal settings. Several studies have shown that while there is a positive overall correlation, there are many variations between the findings and in some cases considerable difficulties in replicating the findings. A federal judge in Tennessee banned fMRI evidence to support the claim of the defendant telling the truth, arguing that the scan did not conform to the legal standards of scientific evidence. Most researchers agree that fMRI's ability to detect fraud in real-life settings has not been established.
The use of fMRI has been abandoned from legal debates throughout its history. Use of this technology has not been permitted due to holes in evidence supporting fMRI. First, most of the evidence supporting fMRI accuracy is done in the laboratory under controlled circumstances with strong facts. This type of testing is not related to real life. Real life scenarios can be much more complicated by many other influencing factors. It has been proven that many other factors influence BOLD in addition to the usual lies. There are tests performed showing that drug use alters the blood flow in the brain, which drastically affects the BOLD test results. Furthermore, individuals with diseases or disorders such as schizophrenia or compulsive lying can cause abnormal results as well. Finally, there is an ethical question related to fMRI scanning. This BOLD test has caused controversy over whether fMRI is a privacy violation. Being able to scan and interpret what people think may be considered immoral and controversy still continues.
Due to these factors and more, fMRI evidence has been excluded from all forms of the legal system. The test is too uncontrolled and unpredictable. Therefore, it has been stated that fMRI has more testing to be performed before it can be considered appropriate in the eyes of the legal system.
Criticism
Some experts have criticized the fMRI studies for problematic statistical analysis, often based on small, low-sample studies. Other fMRI researchers have defended their work as valid.
Source of the article : Wikipedia
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