I am new in neuroimage analysis, and new in the whole world of research in general, but I already have experience working with AI by employing a deep learning model (ViT) to analyze static gestures of the American Sign Language.

Now, I am working on a research project that involves using multimodal MRI data from different datasets found online. 

I’m currently in the data collection and planning phase, and I’m exploring the best pipelines to process and analyze MRI data for this kind of research. I’d appreciate guidance on the following:

1. 
   

   Preprocessing:

   
   
   
   
   • What are the best tools and frameworks for preprocessing multimodal MRI data (e.g., SPM, FSL, ANTs)?
   
   
   • Any tips on aligning data from different modalities to create a unified dataset?
   
   
   
   
   
2. 
   

   Feature Extraction:

   
   
   
   
   • What methods are commonly used to extract relevant features from each MRI modality?
   
   
   
   
   
3. 
   

   Pipeline Suggestions:

   
   
   
   
   • Are there established pipelines for combining multimodal MRI data? For instance, would a sequential approach (processing each modality separately before fusion) or a simultaneous fusion approach (multimodal embeddings) work better for this application? I have found a couple of online software that can possibly help me in the pipeline process, but it seems most of these only work with single modal, static sMRI images, in which some are T-1 weight but others are T-2.
   
   
   
   
   
4. 
   

   Challenges:

   
   
   
   
   • Any pitfalls I should watch out for when working with multimodal neuroimaging data, or is it better to change the paradigm into a single modal approach?