Aim: I am very interested to see whether certian human behaviour e.g. drinking/smoking habbits, cognitive functions etc. or even psychiatric disorders like depression can be predicted by functional MRI (fMRI).
Data: I have got LOTS of BIG DATA we can play around with including hundreds of behaviourial/demogrphic measures and fMRI for hundreds of subjects.
Plan: I will first explain what sort of data are in my hand, and what exactly is fMRI and how can it be used. But I haven't explored many of the behaviourial/demogrphic measures I have got, so there is no particular behaviour in my mind that can/should be predicted. So the next thing I think is that we will look through the data see which measures are predictable (very correlated with fMRI). Once we select the object we want to predict, then we will need to build certain models to do the prediction.
Chanllenges: 1. Dimension reduction: Omg the data are of very high dimensionality. I have known some dimension reduction techniques that I could share and will be very happy to learn others.
2. Feature selection: Omg there are so many feature selection methods... We should have really good discussion on this. I am hoping to apply some DEEP LEARNING technique to this step but I know littel about.
3. Model fitting: Omg there are also so many models that can do the prediction (all kind of linear and non-linear). We will see where we end up with.
I know this may sound like a big project. But there are many possiblities that this 7-hour WARP can end up-with:
1. We may end-up with discovering what behaviours/demogrphics that are most related/predictable to fMRI.
2. We may end-up with learning/discussing all kinds of demension reduction/feature selection techniques, and do a little comparison/summary on them.
3. We may end-up with comparing the advantages/disadvantages of linear vs. non-linear models.
4. Or we may end-up with just making an outline of this project in a more detailed plan. e.g. apart from the chanlleges I listed above, what are other components that should be included in prediction. How can we systemize a predicting procedure that could improve the reproducibility and minimise the variance.
.............
Here are some related papers:
Using connectome-based predictive modeling to predict individual behavior from brain connectivity
DEEP LEARNING FOR ROBUST FEATURE GENERATION IN AUDIOVISUAL EMOTION RECOGNITION