fMRI decoding in block design

Software Support
1

Hi everyone,

I’m a beginner in fMRI decoding analysis and would really appreciate some advice.

In my experiment, the TR is 1.35 s, and the design is block-based.

  • Main task: 4 conditions × 5 runs
  • Localiser task: 2 conditions (U and N letters) × 2 runs, used as the training dataset for decoding
    I’m mainly interested in the neural representations in V1 and V2.

My question is about how to prepare the input data for decoding.
Should I:

  1. Model each trial as a separate regressor in the first-level GLM (i.e., a beta-series approach) and use those beta estimates as decoding inputs,
    or
  2. Use a different method more suitable for block design?

I’ve noticed that many studies use the first (single-trial beta) approach, but when I tried it, the results didn’t seem appropriate for my block-design data.

So I’m wondering — what is the recommended way to extract decoding features for a block design like mine?
Should I average the time series within each block, or perform some other preprocessing before decoding?

Any suggestions, references, or example workflows would be really appreciated!

Thanks in advance,
Shanshan