Here I would share some of my learning notes and open materials.
Open materials
Both BeeDicomViewer and Mango work good for viewing MRI raw data.
For further analysis, you could use mricroGL or dcm2nii to get data in the nii format.
When you happen to have data in the BIDS format, a preprocessing pipeline, fMRIPrep, will work very well. Feel free to link my notes for your reference. If you feel more comfortable with matlab, then dpabi would be a good choice. However, these two pipelines only provide an analytic platform, so it would be good for a beginner to combine it with MRI principles to get why those processing steps we should perform.
b) contrast analysis(GLM)I used to run GLM with SPM, but switch to nilearn for my current project. My notes will come out soon:)
There are plenty of toolbox that could plot imaging results. Here I would like to share some amazing toolbox that I am familiar with.
I first know nilearn from Dr.Li Jixing at CityU HK and this package can plot ROI, surface map, stats activations, and whatever you want. But if you would plot some clusters with different highlighted colors, paraview would definitely be a better choice. Feel free to check my note for your reference.
Again,if you feel more comfortable with matlab, you can find mricorGL and BrainNet Viewer very useful. My notes about microGL will come out soon:)
2. Meta-review
First of all, you may not want to miss the youtube channel of Dr. Gilad Feldman. In his channel, you can find all video recordings of his previous workshops about behavioural meta-analysis review and of course the pre-registered report template:).
As for fMRI coordinate-based meta-analysis, you can follow my previous project on uncertainty processing. In this study, we employed ALE algorithm and functional connectivity analysis (incl. MACM and RSFC) to identify both task-based joint activations and synchronized spontaneous neural signals in the absence of an actual task. Here you can find my notes about MACM analysis based on BrainMap dataset.
Besides practical tips, it is always good to learn the goals of meta science and rules of open science before starting a meta-review study.
To publish a high-impact meta research, the contribution should be clear. Interested in how to indicate a clear contribution, you are highly recommended to watch the talk offered by Dr.Rong Su from IOWA. This can also help you have a sense of how to come up with a research gap.
In general, the preprocessing analysis would include the following steps:
a) power line filter and band-pass filter
b) bad channels removal and interpolate
c) ICA components weight computation and bad components rejection
d) Eye blinks artifact removal
e) re-reference
f) downsampling (optional)
I used to use EEGLAB and ERP to run those analyses, and I am getting familiar with mne-python.
(2) ERP analysis
I had been involved in a project using eye-tracking a few years ago. Need some time to sort out my notes that written on the hard paper.
I will sort out my notes from a tutorial offered by a current doctoral student from Institute of Psychology CAS.