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MiTfAT: A Python-based Analysis Tool for Molecular fMRI Experiments
{Functional Magnetic Resonance Imaging, fMRI, is a technique used in neuroscience to measurebrain activity based on any signal that can be measured in an MRI scanner. Normally, fMRI isused to detect changes associated with blood flow, but it can also be used to detect changes inconcentrations of molecules with different magnetic properties that might be directly injectedinto the brain of a subject.Regardless of the signal that is measured in fMRI recordings, from a computational pointof view, fMRI recordings will result in a number of time-series. And then those time-seriesshould be analyzed to find the answers to various questions of interest. The length of thetime-series depends on the number of time-steps in which we have measured the signals, andtheir number depends on how-many voxels we have measured (a voxel is a 3-dimensional pixelor the unit of volume in which each fMRI signal is measured). The size of each of these voxelsdepends on the magnetic flux density of the MRI scanner, measured in Tesla (T). The higherthe magnetic flux density, the smaller the voxels can be, and the higher is the spatial resolutionof the measurements. Hence, we end up with one time-series for each of the voxels arrangedin a 3-dimensional structure. One characteristic of the fMRI measurements is that while theycan have a high spatial resolution, and while they allow us to measure brain activity not onlyin the cortex but also in deeper regions of the brain, their temporal resolution is not normallyhigh. And that can provide challenges for researchers who need to analyze the fMRI data.}
@article{item_3288731, title = {{MiTfAT: A Python-based Analysis Tool for Molecular fMRI Experiments}}, journal = {{The Journal of Open Source Software}}, abstract = {{Functional Magnetic Resonance Imaging, fMRI, is a technique used in neuroscience to measurebrain activity based on any signal that can be measured in an MRI scanner. Normally, fMRI isused to detect changes associated with blood flow, but it can also be used to detect changes inconcentrations of molecules with different magnetic properties that might be directly injectedinto the brain of a subject.Regardless of the signal that is measured in fMRI recordings, from a computational pointof view, fMRI recordings will result in a number of time-series. And then those time-seriesshould be analyzed to find the answers to various questions of interest. The length of thetime-series depends on the number of time-steps in which we have measured the signals, andtheir number depends on how-many voxels we have measured (a voxel is a 3-dimensional pixelor the unit of volume in which each fMRI signal is measured). The size of each of these voxelsdepends on the magnetic flux density of the MRI scanner, measured in Tesla (T). The higherthe magnetic flux density, the smaller the voxels can be, and the higher is the spatial resolutionof the measurements. Hence, we end up with one time-series for each of the voxels arrangedin a 3-dimensional structure. One characteristic of the fMRI measurements is that while theycan have a high spatial resolution, and while they allow us to measure brain activity not onlyin the cortex but also in deeper regions of the brain, their temporal resolution is not normallyhigh. And that can provide challenges for researchers who need to analyze the fMRI data.}}, volume = {6}, number = {58}, pages = {1--3}, year = {2021}, slug = {item_3288731}, author = {Bokharaie, VS} }