I'm a Postdoctoral Research Fellow at the Martinos Center for Biomedical Imaging (MGH) & Harvard Medical School, bridging the gap between clinical neuroscience and computational analysis. With a background as a Medical Doctor (MD), my passion lies in using neuroimaging techniques and software development to understand the brain and explore solutions for neurological and psychiatric conditions.

• MD from University of Sao Paulo with expertise in neuroanatomy and clinical research principles.
• Focused on multimodal neuroimaging: fMRI (task-based & resting-state), PET-MR (neuroinflammation, receptor mapping), DTI (tractography, structural connectivity), and MRS (metabolite analysis).
• Developing and applying computational pipelines for neuroimaging data analysis.

• Languages: Python, MATLAB and Shell Scripting
• Python Ecosystem:
  • Neuroimaging: nibabel, nilearn, nipype
  • Data Science: Pandas, NumPy, SciPy, Scikit-learn
  • Visualization: Matplotlib, Seaborn, Plotly
  • Machine Learning: (Learning PyTorch, TensorFlow)
  • Graph Analysis: NetworkX (Learning StellarGraph, PyTorch Geometric)
• Neuroimaging Software: FreeSurfer, FSL (FEAT, TBSS, etc.), SPM12, CAT12, CONN Toolbox, MRtrix3, AFNI, ANTS, Lead-DBS, TrackVis
• Data Formats: DICOM, NIfTI, BIDS
• DevOps & Workflow: Git, Docker, Singularity, High-Performance Computing (HPC) environments
• Other: 3D Data Manipulation & Visualization

My primary goal is to integrate multimodal neuroimaging data to build comprehensive models of brain structure, function, and metabolism. Currently exploring:

• Mechanisms of neuroinflammation in conditions like Long-COVID using PET-MR (e.g., [11C]PBR28) and MRS.
• Brain plasticity and inflammation following Spinal Cord Injury (SCI) using rsfMRI, PET-MR, and advanced dMRI (Connectome 2.0).
• Developing robust, reproducible analysis pipelines for longitudinal and multi-site neuroimaging studies.
• The application of graph theory and network neuroscience to understand connectome alterations.

I'm continuously expanding my skillset, currently diving deeper into:

• Advanced Machine Learning techniques for neuroimaging (CNNs, GCNs).
• Graph Convolutional Networks for analyzing brain connectivity data.
• Transformer models and attention mechanisms applied to neurological time-series or imaging data.

• Email: mmurakamijunior@mgh.harvard.edu
• LinkedIn: https://www.linkedin.com/in/mario-minor-murakami-junior-9b6380144/