skills

Analysis of task-based and resting-state fMRI data

I had a great experience working with t-fMRI data while studying the neural correlates of vicarious facilitation of pain. In this study, I used fMRIprep for the preprocessing, for which I needed to convert the dataset into BIDS format using Python. Next, I used FSL feat for further preprocessing and extracting trial-by-trial beta series of BOLD response to painful stimuli for each subject. Finally, I used Mediation Toolbox to investigate which brain regions mediate the increased pain rating in response to emotional faces. For learning the concepts and how-to of task-based functional neuroimaging, I have used several resources including Russ Poldrack’s “Handbook of Functional MRI Data Analysis”, Jennette Mumford’s youtube channel, and FSL Course.

I have no research experience with rs-fMRI data yet. However, I have read the book “Introduction to Resting State fMRI Functional Connectivity” and completed its exercises which involved using FSL in analyzing rs-fMRI data.

Analysis of structural MRI data

I have experience working with different sturctural neuroimaging packages, including FSL FIRST, SPM-CAT, and FreeSurfer, as part of my research or by training.

Neuroimaging and clinical systematic review and meta-analysis

I was first introduced to systematic reviews and meta-analyses by Dr. Ramin Sadeghi, who was the systematic review expert in my medical school. With practice, and by performing a handful of published/unpublished meta-analyses, I gained experience in clinical systematic reviews and meta-analyses. Next, I learned about neuroimaging meta-analyses in a workshop taught by Prof. Simon Eickhoff. After this workshop, I started my very first neuroimaging project as an activation likelihood estimation (ALE) meta-analysis on late-life depression, from which I learned a lot. Now, I think I am fully capable of planning and performing coordinate-based neuroimaging meta-analyses.

Computational neuroscience

I am familiar with several advance computational topics such as model selection and fitting, dimensionality reduction, Bayesian modeling principles, Markov decision processes, optimal control, reinforcement learning, dynamic causal modeling, models of neurons and neuronal networks, and I’m looking forward to using these methods in my future research.

Programming

I am fully confident with using Python in analyzing data, as well as building web and desktop applications. You can see some of my published Python projects in my github account. Currently I am learning how to use Nipype and Nilearn to analyze neuroimaging data with Python. I also have some limited experience with R (particularly with clinical meta-analyses) and MATLAB.

Statistical analysis

I am familiar with many of the common descriptive and analytical statistical procedures, and can perform and interpret them using Python (with Pandas, NumPy, SciPy and Statsmodels) and SPSS.

Machine learning

I have attended several courses on machine learning and I’m familiar with most of the basic concepts. I can use Tensorflow, PyTorch and Scikit-learn to do simple machine learning procedures, and I’m looking forward to become more experienced in the field.