fMRI: an Introduction (Seminars’s report)

Yesterday I was attending at a seminar by Nancy Kanwisher from MIT. She is acognitive psychologist who is investigating visual activities of brain using fMRI. She is supposed to give 8 lectures about her works, but probably I won’t attend in all of them except those first two one as it is not that much relevant to my work and also I don’t have a necessary neuroscientistic background to understand all of her talk. Anyway, these kinds of stuff can be inspiring.
First, she started introducing fMRI. fMRI which is stands for functional MRI, is a method to measure brain activity. In contrast to what I had thought before, it doesn’t record brain’s electrical activity (due to neuronal activity), but measures the amount it uses Oxygen and convert it to CO2. More brain activity implies more need for O2, and thus more CO2. Accumulation of CO2 changes magnetic properties of that part of the brain and thus it is detectable by MRI. That’s it! That kind of signal is called BOLD (Blood Oxygenation …).
Now, let’s report some of my notes during session:

-Low areas’ BOLD signaling is rather linear in stimuli number. It means that if you have five different stimuli, its activation level is rather the same as adding five BOLD signal of a one stimulus (considering time shift of BOLD response that can be up to 5 seconds). It is shown that this linearity is not the case in higher areas. I am not sure about the meaning of “higher” and “lower” areas. If it is somehow related to {high, low}-level activity, then it is plausible for me. This is my idea about its reason: Lower-lever ones do their job faster and would be free for other tasks sooner than higher-level areas. When there is an another task in the list, the lower-level part is probably free and can process it, so it needs more O2. But in opposition, higher-level wouldn’t get interrupted (due to increase behavioral coherence of the whole system) and so adding new tasks to the list do not need more O2 as it does not work on it. It can be useful for my behavior-based design by considering different modules with different time-scales which some of them is not interruptible (or any-time interruptible), though this is not a very new idea.

-In order to investigate brain, it is better to analyze only one part of it and not the whole. Is there any need to increase data in order to become sure of statistical significance of a higher-dimension hypothesis? It seems plausible but I cannot remember any theorem or clue for it (empirical means and probabilistic bounds!).

-How to make an average over many samples which are not exactly on the same part of the brain in different persons? (e.g. V1 part of the brain is not in the same place for every guy as the brain is not the same for everybody). Spherical coordination of brain seems a good solution to it. Isn’t there any better method? Spherical? Hmmm …

-Why can’t we use a regressioner like NN (or any other general function approximator) to fit measured data and make a simulated version of the brain (or at least, that part of the brain with a priori specified IO)? An important question: Do all of these stuff to get what? Beside that, we can analyze hidden layer of that NN and see what kind of features is clustered and processed together. For instance, if we see that part A1, A5, A17 are activated in the same time (e.g. when the person is given a face picture) and activation level of A3 is decreased below average activity, then it can be concluded that there is some kind of relation between those in face recognition. For example, A1, A5, A17 are active processes and A3 must be inhibited in face recognition. This can be done by observing neural weight of hidden neurons: w(A1) = 0.4, w(A2)=0.0012, … w(A5) = 0.8, … , A(3) = -0.9.

– All of these is somehow similar to system identification. I become curious to know whether neuroscientist have ever used that kind of modeling stuff or not?

– To design an experiment, we can change 1-stimulus or 2-task. They may be some internal clue that some tasks cannot be changes and sometimes it is reverse.

– Angular map between sight-part (in the focus region or far from it) and V1 of visual cortex.