All of this seems really confused to me, or so simplified that it no longer makes any sense.
The simplest thing you can do in a scanner is have them sit there doing nothing while you acquire scans. In the end, you just have a 4d matrix of unsigned integers. For each voxel you can acquire an average over the scan and check whether it is significantly above zero using a t-test. Given enough data everything will be significantly greater than zero, including parts outside the head. Or you can compute a global mean to center all the voxles, and check which parts of the brain are significantly above the average, or significantly below the average. Extremely simple analyses (and so yes, there are lots more you could do).
In task fMRI, you have them do a task, and you use events in your task design as predictors of the BOLD, and then display a voxel map of either the beta values, or, more commonly, the T-values of those regression betas (or a contrast of those regression betas). In this case, you really aren't looking at activity. You are looking at correlations.
Those islands of activity in whole-brain analysis images in figures in papers happen because the result images are thresholded, e.g. at p < .05 false-discovery-rate correction for multiple comparisons. Personally, I think unthresholded images are better because they are more informative.
Let's take a concrete example. You have a subject do a task where they have to choose between two gambles varying in risk and reward. Then, for each voxel, you predict the BOLD time course using a series of events (time of presentation of gamble options) with magnitude equal to the coefficient of variation between the two gambles. So now, for each voxel you have a beta value showing how CoV predicts BOLD. You notice that anterior insula on both cases has the highest beta values. You threshold at conventional statistical signficance, after correcting for multiple comparions, and all the spurious, or less important, correlations drop out of the image, and you are left with two bright spots on a map pin-pointed on the left and right anterior insula. See: in this anaylysis, not all "psychologically and biolotically meaninful activity" is being examined or looked at. For example, button presses events show up localized in the motor areas too, but they weren't looking at those. But they could have, if we were interested.
The simplest thing you can do in a scanner is have them sit there doing nothing while you acquire scans. In the end, you just have a 4d matrix of unsigned integers. For each voxel you can acquire an average over the scan and check whether it is significantly above zero using a t-test. Given enough data everything will be significantly greater than zero, including parts outside the head. Or you can compute a global mean to center all the voxles, and check which parts of the brain are significantly above the average, or significantly below the average. Extremely simple analyses (and so yes, there are lots more you could do).
In task fMRI, you have them do a task, and you use events in your task design as predictors of the BOLD, and then display a voxel map of either the beta values, or, more commonly, the T-values of those regression betas (or a contrast of those regression betas). In this case, you really aren't looking at activity. You are looking at correlations.
Those islands of activity in whole-brain analysis images in figures in papers happen because the result images are thresholded, e.g. at p < .05 false-discovery-rate correction for multiple comparisons. Personally, I think unthresholded images are better because they are more informative.
Let's take a concrete example. You have a subject do a task where they have to choose between two gambles varying in risk and reward. Then, for each voxel, you predict the BOLD time course using a series of events (time of presentation of gamble options) with magnitude equal to the coefficient of variation between the two gambles. So now, for each voxel you have a beta value showing how CoV predicts BOLD. You notice that anterior insula on both cases has the highest beta values. You threshold at conventional statistical signficance, after correcting for multiple comparions, and all the spurious, or less important, correlations drop out of the image, and you are left with two bright spots on a map pin-pointed on the left and right anterior insula. See: in this anaylysis, not all "psychologically and biolotically meaninful activity" is being examined or looked at. For example, button presses events show up localized in the motor areas too, but they weren't looking at those. But they could have, if we were interested.