Over the years, technology, especially digitalization, has revolutionized the world and changed our lives. In our daily life, happiness is conditioned by different variables, such as relationships with certain groups of individuals, health, security values, expectations, etc. If you look at the bval file, would you expect this image to look similar to or different from the one that you just saw? Why? Think about this as you load the third and fourth images in the time-series, noting the intensity differences and how they correspond to their respective b-values.One of the most important goals of humanity has always been happiness. Now use the arrows next to the Volume field to load the next volume in the time-series. We can verify this by comparing it with the b-value for the first volume in the time-series, which should be 0. The first volume that is displayed, which has a time-series index of 0 (i.e., 0 indicates the first volumes in the time-series, 1 indicates the second volume, and so on), looks like a typical T2-weighted functional image. Note that we are seeing the first volume of a time-series, and that we can flip through the images by clicking on the up or down arrows next to the “Volume” field in order to scroll forward or backward through the volumes. The last pixdim field, pixdim4, is the “size” of the time dimension in other words, the time it takes to the acquire each volume (an interval known as the repetition time, or TR), is 8.7 seconds.īy clicking and dragging the crosshairs, you can examine the whole brain from all three viewpoints. The dimensions of each individual voxel can be found in the pixdim fields, in which we find that each voxel is 2.5x2.5x2.5mm large. The other dim fields specify the x-, y-, and z-dimensions in voxels, which mean that each volume is composed of a three-dimensional cube with 96x96圆0 voxels. We can see that this image is the primary phase-encoded image, since the field dim4, which indicates the number of volumes in the image, is 102 - in other words, there are 102 volumes that have been collected for this dataset. The primary phase-encoding direction will be used to collect the majority of the data, while the opposite phase-encoding image will be used to unwarp the primary phase-encoded image, which we will review in a later chapter. A researcher will often collect two sets of diffusion-weighted images, one in a primary phase-encoding direction, and the other in a secondary and opposite phase-encoding direction. This diffusion-weighted image has the label “AP” affixed to it, which means that the data were acquired in the Anterior-to-Posterior phase-encoding direction. GIMME (Group Iterative Multiple Model Estimation).fASL Tutorial #5: Examining the Results.fASL Tutorial #2: Downloading and Installing.Introduction to the Human Connectome Project.
Introduction to the Human Connectome Project (HCP) Parametric Modulation in SPM, FSL, and AFNI.Functional Connectivity and the CONN Toolbox.fMRI Tutorial #5: Statistics and Modelingįunctional Connectivity with the CONN Toolbox.fMRI Tutorial #2: Overview of The Flanker Task.Unix Tutorial #9: Automating The Analysis.
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